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 .
Status of Claims
This office action for the 18/784092 application is in response to the communications filed July 25, 2024.
Claims 1-10 were initially submitted July 25, 2024.
Claims 8-10 were amended July 25, 2024.
Claims 1-10 are currently pending and considered below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a data processing unit configured for”, “a correlation degree analysis unit configured for”, “a first prediction model generation unit configured for”, and “a second prediction model generation unit configured for” in claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 3 and 4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 3,
The claim recites the limitation of “the chi-square test”. This limitation lacks antecedent basis and is therefore considered to be indefinite. For the purposes of examination, the Examiner will interpret this limitation as “a chi-square test”.
As per claim 4,
The claim recites the limitation of “the Kaplan-Meier analysis method”. This limitation lacks antecedent basis and is therefore considered to be indefinite. For the purposes of examination, the Examiner will interpret this limitation as “a Kaplan-Meier analysis method”.
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 1-10 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.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method for prognostic survival stage prediction, comprising: acquiring patients' original information data within a previous preset time period and integrating the patients' original information data so as to obtain a first data set without recurrence time and a second data set with recurrence time, wherein each data set comprises preoperative information, postoperative information and survival status of a corresponding patient; performing analysis based on the preoperative information, the postoperative information and the survival status of each corresponding patient, so as to obtain a correlation degree among the preoperative information, the postoperative information and the survival status; obtain a postoperative survival probability prediction model; and obtain a survival time period prediction model if judging that a survival probability of a target patient is less than or equal to a preset value according to the postoperative survival probability prediction model. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“based on machine learning”, “training in the first data set based on the correlation degree among the preoperative information, the postoperative information and the survival status, so as to”, and “training in the second data set so as to” which corresponds to merely using a computer as a tool to perform an abstract idea. Page 12 Line 12 of the as-filed specification describes that the hardware that implements the steps of the abstract idea amounts to nothing more than a general-purpose processor, i.e. a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 1 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein after performing analysis to obtain the correlation degree among the preoperative information, the postoperative information and the survival status, the method further comprises: analyzing a degree of influence of a variety of the preoperative information and a variety of the postoperative information on the survival status, in order to obtain influence degree results corresponding to multiple influence factors; and sequencing the influence factors based on the influence degree results.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 2 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein analyzing the degree of influence of a variety of the preoperative information and a variety of the postoperative information on the survival status specifically comprises: analyzing the degree of influence of a variety of the preoperative information and a variety of the postoperative information on the survival status by use of a chi-square test, F-test, information gain, Pearson correlation, Spearman correlation and decision tree algorithm.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 2 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein performing analysis to obtain the correlation degree among the preoperative information, the postoperative information and the survival status specifically comprises: performing analysis by use of a Kaplan-Meier analysis method so as to obtain the correlation degree among the preoperative information, the postoperative information and the survival status.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein integrating the patients' original information data specifically comprises: dividing the preoperative information, the postoperative information and the survival status into multiple necessary features; traversing the patients' original information data and deleting data which does not contain all the necessary features; and preprocessing remaining data after the deleting and dividing the preprocessed data into a training set and a validation set.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 5 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein preprocessing the remaining data after the deleting specifically comprises: performing one-hot encoding and normalization processing on the remaining data by use of staging features and distant metastasis features, so as to obtain the training set and the validation set.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein a data ratio of the training set to the validation set is 9:1.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 is substantially similar to claim 1. Accordingly, claim 8 is rejected for the same reasons as claim 1.
As per claim 9,
Claim 9 is substantially similar to claim 1. Accordingly, claim 9 is rejected for the same reasons as claim 1.
As per claim 10,
Claim 10 is substantially similar to claim 1. Accordingly, claim 10 is rejected for the same reasons as claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(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.
Claims 1, 2, 4, 5, and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Teverovskiy et al. (US 2007/0099219; herein referred to as Teverovskiy) in view of Madabhushi et al. (US 2017/0193657; herein referred to as Madabhushi).
As per claim 1,
Teverovskiy teaches a method for prognostic survival stage prediction based on machine learning:
(Paragraph [0099] of Teverovskiy. The teaching describes that a cohort of 539 patients who underwent radical prostatectomy was studied incorporating high-density tissue microarrays (TMAs) constructed from prostatectomy specimens. Morphometric studies were performed using hematoxylin and eosin (H&E) stained tissue sections and molecular biological determinants were assessed with immunohistochemistry (IHC). A predictive model for both PSA recurrence and overall survival was derived from a selected set of features through supervised multivariate learning.)
Teverovskiy further teaches acquiring patients' original information data within a previous preset time period, wherein each data set comprises preoperative information, postoperative information and survival status of a corresponding patient:
(Paragraphs [0091] and [0093] of Teverovskiy. The teaching describes that measurable prostate specific antigen (PSA) after the operation was used to define prostate cancer recurrence (also referred to as a biochemical recurrence (BCR)). Patients were followed post-operatively. Their recurrence status at their last visit, as well as their follow-up time, was recorded, which generated a set of right-censored data. Gleason scores were measured both pre-operatively from the biopsy specimen and post-operatively using the excised prostate gland. The four specific clinical measures, or features, considered in this study were (1) the biopsy Gleason grade, (2) the biopsy Gleason score, (3) the post-operative Gleason grade, and (4) the post-operative Gleason score. Because this cohort of patients had right-censored outcome data, survival analysis models had to be built for the prediction of recurrence.)
Teverovskiy further teaches performing analysis based on the preoperative information, the postoperative information and the survival status of each corresponding patient, so as to obtain a correlation degree among the preoperative information, the postoperative information and the survival status:
(Paragraph [0100] of Teverovskiy. The teaching describes a cohort of 132 patients, 41 features (including 17 clinical, 14 molecular, and 10 morphometric) were selected which predicted PSA recurrence with 88% accuracy. In a cohort of 268 patients, 10 features (3 clinical, 1 molecular, and 6 morphometric) were found to be predictive of PSA recurrence with 87% accuracy; additionally, 14 features (2 clinical, 1 molecular, and 11 morphometric) were found to be predictive of overall survival with 80% accuracy. Using the log-rank test, significant differences in tumor recurrence and death were observed between risk groups (p<0.0001).)
Teverovskiy further teaches training in the first data set based on the correlation degree among the preoperative information, the postoperative information and the survival status, so as to obtain a postoperative survival probability prediction model and obtain a survival time period prediction model if judging that a survival probability of a target patient is less than or equal to a preset value according to the postoperative survival probability prediction mode:
(Paragraphs [0058], [0065], [0091], [0093], [0095] and [0111] of Teverovskiy. The teaching describes using an objective function substantially in accordance with an approximation (e.g., derivative) of the concordance index (CI) to train an associated model (NNci). When the difference between the outputs of a pair in Ω is larger than the margin y, this pair of samples will stop contributing to the objective function. This mechanism effectively overcomes over-fitting of the data during training of the model and makes the optimization preferably focus on only moving more pairs of samples in Ω to satisfy {circumflex over (t)}i−{circumflex over (t)}j>γ. The influence of the training samples is adaptively adjusted according to the pair-wise comparisons during training. Note that the positive margin γ in R is preferable for improved generalization performance. In other words, the parameters of the neural network are adjusted during training by calculating the CI after all the patient data has been entered. measurable prostate specific antigen (PSA) after the operation was used to define prostate cancer recurrence (also referred to as a biochemical recurrence (BCR)). Patients were followed post-operatively. Their recurrence status at their last visit, as well as their follow-up time, was recorded, which generated a set of right-censored data. Gleason scores were measured both pre-operatively from the biopsy specimen and post-operatively using the excised prostate gland. The four specific clinical measures, or features, considered in this study were (1) the biopsy Gleason grade, (2) the biopsy Gleason score, (3) the post-operative Gleason grade, and (4) the post-operative Gleason score. Because this cohort of patients had right-censored outcome data, survival analysis models had to be built for the prediction of recurrence. It was also determined that the morphometric feature of compactness of epithelial nuclei correlated with cancer progression, where compactness was calculated by the Definiens Cellenger software as the ratio of the length and width product of the epithelial nuclei to the epithelial nuclei area. This may be because epithelial nuclei invasion into stroma increases as cancer progresses (i.e., tissue with advanced cancer typically includes an abundance of epithelial nuclei). The background-based morphometric feature that was determined to correlate with outcome in this example measured the actual size of the tissue core used in the analysis. Predictive accuracy of a model was evaluated using the concordance index (CI). In dealing with censored outcomes this is often the metric of choice. The concordance index is based on pairwise comparisons between the prognostic scores of two randomly selected patients who meet any one of the following criteria: both patients experienced the event and the event time of the first patient is shorter than that of the second patient or only the first patient experienced the event and his event time is shorter than the second patient's follow-up time. The CI estimates the probability that a patient with the higher prognostic score from the model will experience the event within a shorter time than a patient with a lower score and is tightly associated with the area under the ROC curve (AUC).)
Teverovskiy does not explicitly teach integrating the patients' original information data so as to obtain a first data set without recurrence time and a second data set with recurrence time, or training in the second data set so as to obtain a survival time period prediction model if judging that a survival probability of a target patient is less than or equal to a preset value according to the postoperative survival probability prediction model.
However, Madabhushi teaches integrating the patients' original information data so as to obtain a first data set without recurrence time and a second data set with recurrence time, and training in data set so as to:
(Paragraphs [0012]-[0016] of Madabhushi. The teaching describes NSCLC recurrence is predicted based on TIL density features or TIL spatial architecture features extracted from an H&E stained image of a region of tissue demonstrating NSCLC pathology. In this embodiment, a first set of 70 digitized H&E stained images of lung biopsy specimens from patients known to have experienced NSCLC recurrence or NSCLC non-recurrence is used to train a machine learning classifier to predict NSCLC recurrence. Training the classifier may include determining an optimal threshold risk score that separates low and high recurrence rates based on the training set. The training set may be divided into two groups: those who experience recurrence (Rc+) and those that did not experience recurrence (Rc−). In one embodiment, the optimal threshold may be determined such that 7% of the Rc+ patients have a risk score below the threshold. Thus, a risk score less than the threshold indicates low risk of recurrence, suggesting a recommended treatment of no adjuvant chemotherapy after surgical resection, while risk scores above the threshold indicate a high risk of recurrence, suggesting a recommended treatment of adjuvant chemotherapy after resection.)
It would have been obvious one of ordinary skill in the art before the time of filing to modify the data set in the teaching of Teverovskiy, to include the recurrent and non-recurrent population data sets in determining cancer prognosis as taught by Madabhushi. Paragraph [0024] of Madabhushi teaches that the disclosed methods improve the ability to improved prediction methods for understanding the nature of cancer. One of ordinary skill in the art in possession of the cancer predictive methods of Teverovskiy would have looked to Madabhushi to achieve such an improvement. One of ordinary skill in the art would have added to the teaching of Teverovskiy, the teaching of Madabhushi based on this incentive without yielding unexpected results.
The combined teaching of Teverovskiy and Madabhushi would have then taught training in the second data set so as to obtain a survival time period prediction model if judging that a survival probability of a target patient is less than or equal to a preset value according to the postoperative survival probability prediction model:
(Paragraphs [0058], [0065], [0091], [0093], [0095] and [0111] of Teverovskiy. The teaching describes using an objective function substantially in accordance with an approximation (e.g., derivative) of the concordance index (CI) to train an associated model (NNci). When the difference between the outputs of a pair in Ω is larger than the margin y, this pair of samples will stop contributing to the objective function. This mechanism effectively overcomes over-fitting of the data during training of the model and makes the optimization preferably focus on only moving more pairs of samples in Ω to satisfy {circumflex over (t)}i−{circumflex over (t)}j>γ. The influence of the training samples is adaptively adjusted according to the pair-wise comparisons during training. Note that the positive margin γ in R is preferable for improved generalization performance. In other words, the parameters of the neural network are adjusted during training by calculating the CI after all the patient data has been entered. measurable prostate specific antigen (PSA) after the operation was used to define prostate cancer recurrence (also referred to as a biochemical recurrence (BCR)). Patients were followed post-operatively. Their recurrence status at their last visit, as well as their follow-up time, was recorded, which generated a set of right-censored data. Gleason scores were measured both pre-operatively from the biopsy specimen and post-operatively using the excised prostate gland. The four specific clinical measures, or features, considered in this study were (1) the biopsy Gleason grade, (2) the biopsy Gleason score, (3) the post-operative Gleason grade, and (4) the post-operative Gleason score. Because this cohort of patients had right-censored outcome data, survival analysis models had to be built for the prediction of recurrence. It was also determined that the morphometric feature of compactness of epithelial nuclei correlated with cancer progression, where compactness was calculated by the Definiens Cellenger software as the ratio of the length and width product of the epithelial nuclei to the epithelial nuclei area. This may be because epithelial nuclei invasion into stroma increases as cancer progresses (i.e., tissue with advanced cancer typically includes an abundance of epithelial nuclei). The background-based morphometric feature that was determined to correlate with outcome in this example measured the actual size of the tissue core used in the analysis. Predictive accuracy of a model was evaluated using the concordance index (CI). In dealing with censored outcomes this is often the metric of choice. The concordance index is based on pairwise comparisons between the prognostic scores of two randomly selected patients who meet any one of the following criteria: both patients experienced the event and the event time of the first patient is shorter than that of the second patient or only the first patient experienced the event and his event time is shorter than the second patient's follow-up time. The CI estimates the probability that a patient with the higher prognostic score from the model will experience the event within a shorter time than a patient with a lower score and is tightly associated with the area under the ROC curve (AUC).)
(Paragraphs [0012]-[0016] of Madabhushi. The teaching describes NSCLC recurrence is predicted based on TIL density features or TIL spatial architecture features extracted from an H&E stained image of a region of tissue demonstrating NSCLC pathology. In this embodiment, a first set of 70 digitized H&E stained images of lung biopsy specimens from patients known to have experienced NSCLC recurrence or NSCLC non-recurrence is used to train a machine learning classifier to predict NSCLC recurrence. Training the classifier may include determining an optimal threshold risk score that separates low and high recurrence rates based on the training set. The training set may be divided into two groups: those who experience recurrence (Rc+) and those that did not experience recurrence (Rc−). In one embodiment, the optimal threshold may be determined such that 7% of the Rc+ patients have a risk score below the threshold. Thus, a risk score less than the threshold indicates low risk of recurrence, suggesting a recommended treatment of no adjuvant chemotherapy after surgical resection, while risk scores above the threshold indicate a high risk of recurrence, suggesting a recommended treatment of adjuvant chemotherapy after resection.)
As per claim 2,
The combined teaching of Teverovskiy and Madabhushi teaches the limitations of claim 1.
Teverovskiy further teaches wherein after performing analysis to obtain the correlation degree among the preoperative information, the postoperative information and the survival status, the method further comprises: analyzing a degree of influence of a variety of the preoperative information and a variety of the postoperative information on the survival status, in order to obtain influence degree results corresponding to multiple influence factors; and sequencing the influence factors based on the influence degree results:
(Paragraphs [0058], [0065], [0091], [0093], [0095], [0111] and [0136] of Teverovskiy. The teaching describes using an objective function substantially in accordance with an approximation (e.g., derivative) of the concordance index (CI) to train an associated model (NNci). When the difference between the outputs of a pair in Ω is larger than the margin y, this pair of samples will stop contributing to the objective function. This mechanism effectively overcomes over-fitting of the data during training of the model and makes the optimization preferably focus on only moving more pairs of samples in Ω to satisfy {circumflex over (t)}i−{circumflex over (t)}j>γ. The influence of the training samples is adaptively adjusted according to the pair-wise comparisons during training. Note that the positive margin γ in R is preferable for improved generalization performance. In other words, the parameters of the neural network are adjusted during training by calculating the CI after all the patient data has been entered. measurable prostate specific antigen (PSA) after the operation was used to define prostate cancer recurrence (also referred to as a biochemical recurrence (BCR)). Patients were followed post-operatively. Their recurrence status at their last visit, as well as their follow-up time, was recorded, which generated a set of right-censored data. Gleason scores were measured both pre-operatively from the biopsy specimen and post-operatively using the excised prostate gland. The four specific clinical measures, or features, considered in this study were (1) the biopsy Gleason grade, (2) the biopsy Gleason score, (3) the post-operative Gleason grade, and (4) the post-operative Gleason score. Because this cohort of patients had right-censored outcome data, survival analysis models had to be built for the prediction of recurrence. It was also determined that the morphometric feature of compactness of epithelial nuclei correlated with cancer progression, where compactness was calculated by the Definiens Cellenger software as the ratio of the length and width product of the epithelial nuclei to the epithelial nuclei area. This may be because epithelial nuclei invasion into stroma increases as cancer progresses (i.e., tissue with advanced cancer typically includes an abundance of epithelial nuclei). The background-based morphometric feature that was determined to correlate with outcome in this example measured the actual size of the tissue core used in the analysis. Predictive accuracy of a model was evaluated using the concordance index (CI). In dealing with censored outcomes this is often the metric of choice. The concordance index is based on pairwise comparisons between the prognostic scores of two randomly selected patients who meet any one of the following criteria: both patients experienced the event and the event time of the first patient is shorter than that of the second patient or only the first patient experienced the event and his event time is shorter than the second patient's follow-up time. The CI estimates the probability that a patient with the higher prognostic score from the model will experience the event within a shorter time than a patient with a lower score and is tightly associated with the area under the ROC curve (AUC). The resulting output of the NNci and the SVRc models can be interpreted as a relative risk estimate of PSA recurrence for an individual patient. Using the quartiles of this score (≦25%, >25%-75%, >75%), risk groups of patients were created; the Kaplan-Meier estimates of recurrence for each risk group according to the NNci model are presented in FIG. 8. The groups showed a statistically significant difference in time to PSA recurrence (log-rank test, p-value<0.0001).)
As per claim 4,
The combined teaching of Teverovskiy and Madabhushi teaches the limitations of claim 2.
wherein performing analysis to obtain the correlation degree among the preoperative information, the postoperative information and the survival status specifically comprises: performing analysis by use of a Kaplan-Meier analysis method so as to obtain the correlation degree among the preoperative information, the postoperative information and the survival status:
(Paragraphs [0058], [0065], [0091], [0093], [0095], [0111] and [0136] of Teverovskiy. The teaching describes using an objective function substantially in accordance with an approximation (e.g., derivative) of the concordance index (CI) to train an associated model (NNci). When the difference between the outputs of a pair in Ω is larger than the margin y, this pair of samples will stop contributing to the objective function. This mechanism effectively overcomes over-fitting of the data during training of the model and makes the optimization preferably focus on only moving more pairs of samples in Ω to satisfy {circumflex over (t)}i−{circumflex over (t)}j>γ. The influence of the training samples is adaptively adjusted according to the pair-wise comparisons during training. Note that the positive margin γ in R is preferable for improved generalization performance. In other words, the parameters of the neural network are adjusted during training by calculating the CI after all the patient data has been entered. measurable prostate specific antigen (PSA) after the operation was used to define prostate cancer recurrence (also referred to as a biochemical recurrence (BCR)). Patients were followed post-operatively. Their recurrence status at their last visit, as well as their follow-up time, was recorded, which generated a set of right-censored data. Gleason scores were measured both pre-operatively from the biopsy specimen and post-operatively using the excised prostate gland. The four specific clinical measures, or features, considered in this study were (1) the biopsy Gleason grade, (2) the biopsy Gleason score, (3) the post-operative Gleason grade, and (4) the post-operative Gleason score. Because this cohort of patients had right-censored outcome data, survival analysis models had to be built for the prediction of recurrence. It was also determined that the morphometric feature of compactness of epithelial nuclei correlated with cancer progression, where compactness was calculated by the Definiens Cellenger software as the ratio of the length and width product of the epithelial nuclei to the epithelial nuclei area. This may be because epithelial nuclei invasion into stroma increases as cancer progresses (i.e., tissue with advanced cancer typically includes an abundance of epithelial nuclei). The background-based morphometric feature that was determined to correlate with outcome in this example measured the actual size of the tissue core used in the analysis. Predictive accuracy of a model was evaluated using the concordance index (CI). In dealing with censored outcomes this is often the metric of choice. The concordance index is based on pairwise comparisons between the prognostic scores of two randomly selected patients who meet any one of the following criteria: both patients experienced the event and the event time of the first patient is shorter than that of the second patient or only the first patient experienced the event and his event time is shorter than the second patient's follow-up time. The CI estimates the probability that a patient with the higher prognostic score from the model will experience the event within a shorter time than a patient with a lower score and is tightly associated with the area under the ROC curve (AUC). The resulting output of the NNci and the SVRc models can be interpreted as a relative risk estimate of PSA recurrence for an individual patient. Using the quartiles of this score (≦25%, >25%-75%, >75%), risk groups of patients were created; the Kaplan-Meier estimates of recurrence for each risk group according to the NNci model are presented in FIG. 8. The groups showed a statistically significant difference in time to PSA recurrence (log-rank test, p-value<0.0001).)
As per claim 5,
The combined teaching of Teverovskiy and Madabhushi teaches the limitations of claim 1.
wherein integrating the patients' original information data specifically comprises: dividing the preoperative information, the postoperative information and the survival status into multiple necessary features; traversing the patients' original information data and deleting data which does not contain all the necessary features; and preprocessing remaining data after the deleting and dividing the preprocessed data into a training set and a validation set:
(Paragraphs [0075] and [0093] of Teverovskiy. The teaching describes that in order to reduce the number of feature space dimensions, feature selection may be performed on the training set using two different classifiers: the Bayesian classifier and the k nearest neighbor classifier [12]. The leave-one-out method [13] may be used for cross-validation, and the sequential forward search algorithm may be used to choose the best features. The concordance index estimated using 5-fold cross validation was used to measure the models' predictive accuracy.)
As per claim 8,
Claim 8 is substantially similar to claim 1. Accordingly, claim 8 is rejected for the same reasons as claim 1.
As per claim 9,
Claim 9 is substantially similar to claim 1. Accordingly, claim 9 is rejected for the same reasons as claim 1.
As per claim 10,
Claim 10 is substantially similar to claim 1. Accordingly, claim 10 is rejected for the same reasons as claim 1.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Teverovskiy and Madabhushi in further view of Dissanayake et al. (Dissanayake, Kaushalya, and Md Gapar Md Johar. “Comparative study on heart disease prediction using feature selection techniques on classification algorithms.” Applied Computational Intelligence and Soft Computing, vol. 2021, 1 Nov. 2021, pp. 1–17, https://doi.org/10.1155/2021/5581806; herein referred to as Dissanayanke)
As per claim 3,
The combined teaching of Teverovskiy and Madabhushi teaches the limitations of claim 2.
Teverovskiy further teaches wherein analyzing the degree of influence of a variety of the preoperative information and a variety of the postoperative information on the survival status specifically comprises: analyzing the degree of influence of a variety of the preoperative information and a variety of the postoperative information on the survival status by use of F-test:
(Paragraphs [0058], [0065], [0091], [0093], [0095], [0111], [0136] and [0178] of Teverovskiy. The teaching describes using an objective function substantially in accordance with an approximation (e.g., derivative) of the concordance index (CI) to train an associated model (NNci). When the difference between the outputs of a pair in Ω is larger than the margin y, this pair of samples will stop contributing to the objective function. This mechanism effectively overcomes over-fitting of the data during training of the model and makes the optimization preferably focus on only moving more pairs of samples in Ω to satisfy {circumflex over (t)}i−{circumflex over (t)}j>γ. The influence of the training samples is adaptively adjusted according to the pair-wise comparisons during training. Note that the positive margin γ in R is preferable for improved generalization performance. In other words, the parameters of the neural network are adjusted during training by calculating the CI after all the patient data has been entered. measurable prostate specific antigen (PSA) after the operation was used to define prostate cancer recurrence (also referred to as a biochemical recurrence (BCR)). Patients were followed post-operatively. Their recurrence status at their last visit, as well as their follow-up time, was recorded, which generated a set of right-censored data. Gleason scores were measured both pre-operatively from the biopsy specimen and post-operatively using the excised prostate gland. The four specific clinical measures, or features, considered in this study were (1) the biopsy Gleason grade, (2) the biopsy Gleason score, (3) the post-operative Gleason grade, and (4) the post-operative Gleason score. Because this cohort of patients had right-censored outcome data, survival analysis models had to be built for the prediction of recurrence. It was also determined that the morphometric feature of compactness of epithelial nuclei correlated with cancer progression, where compactness was calculated by the Definiens Cellenger software as the ratio of the length and width product of the epithelial nuclei to the epithelial nuclei area. This may be because epithelial nuclei invasion into stroma increases as cancer progresses (i.e., tissue with advanced cancer typically includes an abundance of epithelial nuclei). The background-based morphometric feature that was determined to correlate with outcome in this example measured the actual size of the tissue core used in the analysis. Predictive accuracy of a model was evaluated using the concordance index (CI). In dealing with censored outcomes this is often the metric of choice. The concordance index is based on pairwise comparisons between the prognostic scores of two randomly selected patients who meet any one of the following criteria: both patients experienced the event and the event time of the first patient is shorter than that of the second patient or only the first patient experienced the event and his event time is shorter than the second patient's follow-up time. The CI estimates the probability that a patient with the higher prognostic score from the model will experience the event within a shorter time than a patient with a lower score and is tightly associated with the area under the ROC curve (AUC). The resulting output of the NNci and the SVRc models can be interpreted as a relative risk estimate of PSA recurrence for an individual patient. Using the quartiles of this score (≦25%, >25%-75%, >75%), risk groups of patients were created; the Kaplan-Meier estimates of recurrence for each risk group according to the NNci model are presented in FIG. 8. The groups showed a statistically significant difference in time to PSA recurrence (log-rank test, p-value<0.0001). Features were chosen to enter or leave the model according to the significance level of an F-test from an analysis of covariance, where the features already chosen act as the covariates and the feature under consideration is the dependent variable.)
The combined teaching of Teverovskiy and Madabhushi does not explicitly teach wherein analyzing the degree of influence of a variety of the preoperative information and a variety of the postoperative information on the survival status specifically comprises: analyzing the degree of influence of a variety of the preoperative information and a variety of the postoperative information on the survival status by use of a chi-square test, F-test, information gain, Pearson correlation, Spearman correlation and decision tree algorithm.
However, Dissanayake teaches the use of chi-square test, information gain, Pearson correlation, Spearman correlation and decision tree algorithm in conjunction with F-test to determine medical correlation information:
(Abstract and Pages 2 and 7 of Dissanayake. The teaching describes that for results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The ANOVA tests include a formula to calculate the one-way ANOVA F-test. The filter technique can employ various feature selection criteria, including correlation coefficient (Pearson, Spearman, and Kendall Tau), Relief, Chi-square, information gain, and Fisher score.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the mathematical techniques of F-test in the combined teaching of Teverovskiy and Madabhushi, the mathematical techniques of Dissanayake. The Abstract of Dissanayake teaches that the methods used for health prediction improve prediction accuracy. One of ordinary skill in the art would have added to the combined teaching of Teverovskiy and Madabhushi, the teaching of Dissanayake based on this incentive without yielding unexpected results.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Teverovskiy and Madabhushi in further view of Alizadeh et al. (US 2021/0033608; herein referred to as Alizadeh).
As per claim 6,
The combined teaching of Teverovskiy and Madabhushi teaches the limitations of claim 5.
The combined teaching of Teverovskiy and Madabhushi does not explicitly teach wherein preprocessing the remaining data after the deleting specifically comprises: performing one-hot encoding and normalization processing on the remaining data by use of staging features and distant metastasis features, so as to obtain the training set and the validation set.
However, Alizadeh teaches performing one-hot encoding and normalization processing on the remaining data by use of staging features and distant features, so as to obtain the training set and the validation set:
(Paragraphs [0035], [0036] and [0056] of Alizadeh. The teaching describes that FIG. 8 provides training and evaluation scheme of MARIA, as a new machine learning framework for more accurate prediction of HLA-II ligands, utilized in accordance with various embodiments. FIG. 9 provides an example of how variable length amino acid sequences (8-26AA) to be one-hot encoded for machine learning purposes, utilized in accordance with various embodiments. FIG. 31 provides training, validation, test of MARIA models for HLA-DQ2.2 presentation, generated in accordance with various embodiments. To train the MARIA DQ2.2 model, 5845 peptides shared between HLA-DQ2.2 and HLA-DQ2.5, and 2529 peptide unique to HLA-DQ2.2 were used as the positive examples; 8374 length-matched peptides were used as negative examples. Peptide sequences were assigned into training, validation, and test set. No peptides in validation and test set were substring of a training peptide, vice versa.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the machine learning models of the combined teaching of Teverovskiy and Madabhushi, the machine learning techniques of Alizadeh. Paragraph [0143] of Alizadeh teaches that MARIA allows robust and more accurate HLA-II prediction, and that its performance gains are achieved by combining these improved training data with a new supervised machine learning model using a multimodal recurrent neural network (RNN). This suggests that the features such as the one-hot encoded machine learning methods contributed to this improvement. One of ordinary skill in the art would have added to the combined teaching of Teverovskiy and Madabhushi, the teaching of Alizadeh based on this incentive without yielding unexpected results.
As per claim 7,
The combined teaching of Teverovskiy, Madabhushi and Alizadeh teaches the limitations of claim 6.
Alizadeh further teaches wherein a data ratio of the training set to the validation set is 9:1:
(Paragraph [0185] of Alizadeh. The teaching describes that LSTM networks with 32, 64 and 128 neurons were assessed and from one to four layers deep with a 9:1 training: validation scheme (FIG. 10B).)
Conclusion
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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