DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to an amendments filed on 09/26/2025. Claims 1-3 and 13-15 were amended. Claims 21-22 were added. Claims 9-12 were cancelled. Therefore, claims 1-8 and 13-22 are currently pending and have been examined.
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 1-8 and 13-22 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), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept — i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a method and system which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims recite the abstract idea (based on claim 1) of:
A system for treatment of a surgery-eligible patient using patient-specific post-surgery mortality prediction, comprising: determine that surgery is an ordinary standard of care for a disease diagnosed in a patient; receive a plurality of pre-operative factor indications for the patient, the plurality of pre-operative factor indications comprising at least one risk factor that was identified when the determining was performed, the at least one risk factor comprising one or more of: whether the patient was transferred from an emergency department; or whether the patient has septic shock; obtain a first model and an interpretable model; apply the plurality of pre-operative factor indications to the first model to obtain a plurality of confidence values corresponding to the plurality of pre-operative factor indications; apply the plurality of confidence values to the interpretable model to obtain a plurality of interpretation indications, the plurality of interpretation indications corresponding to a subset of the plurality of pre-operative factor indications, the plurality of interpretation indications most contributing to mortality of the patient, the plurality of interpretation indications being specific to the patient; and output a survival probability of the patient based on the plurality of interpretation indications; in response to the survival probability being above a threshold, determine to perform the surgery; and in response to the survival probability being below the threshold, determine to delay or cancel the surgery.
Independent claim 13 recites similar limitations and, therefore recites a similar abstract idea.
The claims, as illustrated by the limitations of Claim 1 above, recite an abstract idea within the “certain methods of organizing human activity” grouping — managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions and the “mathematical concepts” grouping — mathematical relationships, mathematical formulas or equations, and mathematical calculations.
The claims recite determining and outputting a survival probability for a patient post-surgery based on determining confidence values using pre-operative factors and interpretation indications most contributing to patient mortality from the confidence values and determining to perform or delay/cancel surgery based on survival probability. Determining and outputting a survival probability for a patient post-surgery based on determining confidence values using pre-operative factors and interpretation indications most contributing to patient mortality from the confidence values and determining to perform or delay/cancel surgery based on survival probability is a process that merely organizes human activity, as it involves following rules and instructions to receive factors, obtain models, obtain confidence values, apply confidence values to model, obtain indications, output survival probability, and determine to perform or delay/cancel surgery. It also involves an interaction between a person and a computer. Interaction between a person and computer qualifies as interaction under certain methods of organizing human activity. See MPEP 2106.04(a)(2)(II). Furthermore, the claims are directed toward mathematical concepts such as applying pre-operative factor indications to the first model to obtain a plurality of confidence values and applying the plurality of confidence values to the interpretable model to obtain a plurality of interpretation indications. As such, the claims recite an abstract idea within the categories of certain methods of organizing human activity and mathematical concepts.
The dependent claims 3, 6-8, 15, and 18-22 recite further abstract concept of mathematical concepts because they recite mathematical relationships and calculations, such 3/15 perform a combination of forward selection and a backward elimination to produce the plurality of pre-operative factor indications by reducing pre-operative factor dimensions; 6/18 the local interpretable model-agnostic explanation model produces the plurality of interpretation indications by: altering a first pre-operative factor indication of the plurality of pre-operative factor indications; monitoring a resultant impact of the first pre-operative factor indication to the plurality of confidence values; and producing the plurality of interpretation indications based on the resultant impact of the first pre-operative factor indication; 7/19 a first interpretation indication of the plurality of interpretation indications corresponding to the first pre-operative factor indication among the subset comprises the first pre-operative factor indication and a weight of the first pre-operative factor indication, the weight being determined based on the resultant impact of the first pre-operative factor indication; 8/20 the interpretable model produces each of the subset of the plurality of pre-operative factor indications and a respective weight of each of the subset of the plurality of pre-operative factor indications on the survival probability of the patient; 21/22 the output survival probability includes a respective weight for each of the received plurality of pre-operative factor indications.
STEP 2A PRONG TWO
The claims recite additional elements beyond those that encompass the abstract idea above including:
Independent claim 1:
an electronic surgical decision support tool comprising: a memory, and a processor communicatively coupled to the memory; wherein the memory stores a set of instructions which, when executed by the processor, cause the processor to:
trained machine learning
Dependent claim 3:
the set of instructions, when executed by the processor, further cause the processor:
Dependent claim 4:
trained machine learning
machine
Independent claim 13:
trained machine learning
Dependent claim 15:
the set of instructions, when executed by the processor, further cause the processor:
Dependent claim 16:
trained machine learning
machine
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with considerations laid out by the Supreme Court or the Federal Circuit. (see MPEP 2106.05 a-c and e) The additional elements integrate the abstract idea into a practical application when they: improve the functioning of a computer or improving any other technology, apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, apply the judicial exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The additional limitations do not integrate the abstract idea into a practical application when they merely serve to link the use of the abstract idea to a particular technological environment or field of use — i.e. merely uses the computer as a tool to perform the abstract idea; or recite insignificant extra-solution activity (see MPEP 2106.05 f - h).
The processor, memory, and trained machine learning are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Nothing in the claim recites specific limitations directed to an improved processor, memory, and trained machine learning. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception to computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a basic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claims do not integrate the abstract patient mortality determination process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract patient mortality determination process. The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting basic computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently straightforward such that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination, the limitations recited in the claims add nothing that is not already present when the steps are considered individually.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. For example, limitations 3/15 perform a combination of forward selection and a backward elimination to produce the plurality of pre-operative factor indications by reducing pre-operative factor dimensions; 6/18 the local interpretable model-agnostic explanation model produces the plurality of interpretation indications by: altering a first pre-operative factor indication of the plurality of pre-operative factor indications; monitoring a resultant impact of the first pre-operative factor indication to the plurality of confidence values; and producing the plurality of interpretation indications based on the resultant impact of the first pre-operative factor indication; 7/19 a first interpretation indication of the plurality of interpretation indications corresponding to the first pre-operative factor indication among the subset comprises the first pre-operative factor indication and a weight of the first pre-operative factor indication, the weight being determined based on the resultant impact of the first pre-operative factor indication; 8/20 the interpretable model produces each of the subset of the plurality of pre-operative factor indications and a respective weight of each of the subset of the plurality of pre-operative factor indications on the survival probability of the patient; 21/22 the output survival probability includes a respective weight for each of the received plurality of pre-operative factor indications are directed to the abstract ideas of mathematical concepts without integrating into a practical application or amounting to significantly more. Limitations 2/14 the plurality of pre-operative factor indications is at least one selected from the group of patient co-morbidity related factor indications, laboratory test result indications, patient demographics and disposition related factor indications; 4/16 the first trained machine learning model comprises a gradient boost machine model; 5/17 the interpretable model comprises a local interpretable model-agnostic explanation model merely serve to further narrow the abstract idea above. As such, the additional elements do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ouyang, et al. (US 2024/0212857 A1) in view of Kano, et al. (US 2023/0218345 A1) in further view of Cao, et al. (US 2014/0358587 A1).
With regards to claim 1, Ouyang teaches a system for patient-specific post-surgery mortality prediction, comprising: a memory (see at least figure 1); and a processor communicatively coupled to the memory (see at least figure 1); wherein the memory stores a set of instructions which, when executed by the processor, cause the processor (see at least claim 1) to: receive a plurality of pre-operative factor indications for the patient (see at least ¶ 0006, ECG of a patient may include one or more hidden risk markers that can be utilized to prognosticate post-operative outcomes including mortality and non-fatal major adverse cardiac events (MACE) …improve prediction of post-operative outcomes based on ECG …receiving, at a processor coupled to an ECG sensor system, an electrocardiogram (ECG) waveform data acquired from the ECG sensor system; and determining, by the processor, one or more post-operative risk metrics based on a trained neural network model, the trained neural-network model receiving the ECG waveform; ¶ 0136, Patient demographic, clinical, and outcomes data were assessed from the electronic health record at the time of each procedure. From these data, the pre-operative clinical characteristics needed for calculating the revised cardiac risk index (RCRI) were identified, including: coronary artery disease, congestive heart failure, stroke or transient ischemia attack, pre-operative insulin use, creatinine greater than 2 mg/dL, and elevated risk surgery as defined by American College of Cardiology and American Heart Association guidelines); obtain a first trained machine learning model (see at least ¶ 0006, a trained neural network model) and an interpretable model (see at least ¶ 0011, an interpretable model); apply …pre-operative factor indications to the first trained machine learning model to obtain a plurality of confidence values corresponding to the pre-operative factor indications (see at least ¶ 0006, determining post-operative risk metrics [confidence values] based on a trained neural network model, the trained neural network model receiving ECG waveform as input and outputting post-operative risk metrics of the patient); apply the plurality of confidence values to the interpretable model to obtain a plurality of interpretation indications, the plurality of interpretation indications corresponding to a subset of the plurality of pre-operative factor indications, the plurality of interpretation indications most contributing to mortality of the patient, the plurality of interpretation indications being specific to the patient (see at least ¶ 0011, for each prediction, one or more relevant ECG features extracted by the trained neural network model may be identified via an interpretable model. In one example, the one or more relevant ECG features may have a relevance score greater than a threshold relevance score. Thus, the one or more relevant ECG features that contributed to the prediction may be identified and indicated to the user. For example, the one or more important ECG features may be indicated via highlights on an ECG waveform graph that was used as input. The indication of the one or more important ECG features may be displayed on a user interface (e.g., as an overlay over an input ECG waveform graph, the waveform portion of the ECG including the one or more important ECG features may be enlarged and depicted separately along with the corresponding prediction and/or the input ECG waveform, etc.) in addition to indicating the post-operative risk output from trained the neural network model. In some examples, additionally or alternatively, one or more leads based on which the one or more important ECG features were identified may be determined and indicated); and output a survival probability of the patient based on the plurality of interpretation indications (see at least ¶ 0011, one or more relevant ECG features extracted by the trained neural network model may be identified via an interpretable model. In one example, the one or more relevant ECG features may have a relevance score greater than a threshold relevance score. Thus, the one or more relevant ECG features that contributed to the prediction may be identified and indicated to the user. For example, the one or more important ECG features may be indicated via highlights on an ECG waveform graph that was used as input. The indication of the one or more important ECG features may be displayed on a user interface (e.g., as an overlay over an input ECG waveform graph, the waveform portion of the ECG including the one or more important ECG features may be enlarged and depicted separately along with the corresponding prediction and/or the input ECG waveform, etc.) in addition to indicating the post-operative risk output from trained the neural network model; ¶ 0098, an output of the neural network may indicate that there is an 85% probability of post-operative mortality for a subject within 15 days from the date of surgery. As another non-limiting example, an output of the neural network may indicate that there is an 65% probability of post-operative MACE for a subject within 15 days and 30 days from the date of surgery).
Ouyang fails to teach applying determine that surgery is an ordinary standard of care for a disease diagnosed in a patient; …the plurality of…; the plurality of pre-operative factor indications comprising at least one risk factor that was identified when the determining was performed, the at least one risk factor comprising one or more of: whether the patient was transferred from an emergency department; or whether the patient has septic shock.
Kano teaches determine that surgery is an ordinary standard of care for a disease diagnosed in a patient (see at least ¶ 0062, For example, if aortic valvular stenosis is taken as an example, three options, i.e, surgical aortic valve replacement (SAVR), transcatheter aortic valve implantation (TAVI), and medication are assumed); …the plurality of… (see at least figure 3, ¶ 0042, examples of the feature include the age of a patient, the gender, the stage, the medical history, and the area of living). It would have been obvious to one of ordinary skill in the art to combine the medical treatment effect prediction method of Kano with the post-operative outcome prediction system of Ouyang with the motivation of more easily making judgements about medical treatment options (Kano, ¶ 0003).
Cao teaches the plurality of pre-operative factor indications comprising at least one risk factor that was identified when the determining was performed, the at least one risk factor comprising one or more of: whether the patient was transferred from an emergency department; or whether the patient has septic shock (see at least ¶ 0002, subject arrives at the emergency department; at least ¶ 0073, 0075-0077, 8:10 The patient historical medical data and the current condition data were sent to the hospital; Given the patient was 70 years old, had a history of hypertension and peripheral artery disease, our tool found the patients matched most with these risk factors needed a CABG. Therefore, there was a large chance that the patient would need a catheterization testing and a surgery of CABG. Preparation for angiogram and CABG started; 8:25 ECG data collected in the ambulance; Cath lab was made ready in the hospital; The patient arrived at the hospital. 8:40 Coronary angiogram was done; CABG surgery preparation was done. 8:50 Preliminary diagnosis was confirmed; started surgery). It would have been obvious to one of ordinary skill in the art to combine the emergency response protocol method of Cao with the post-operative outcome prediction system of Ouyang with the motivation of more rapid and efficient medical care (Cao, ¶ 0002).
Claim 13 recites similar limitations and is rejected for the same reasons.
With regards to claim 2, Ouyang teaches the system of claim 1, wherein the plurality of pre-operative factor indications comprises one or more of: patient co-morbidity related factor indications, laboratory test result indications, patient demographics, or disposition-related factor indications (see at least ¶ 0136, Patient demographic, clinical, and outcomes data were assessed from the electronic health record).
Claim 14 recites similar limitations and is rejected for the same reasons.
With regards to claim 3, Ouyang teaches the system of claim 1, wherein the set of instructions, when executed by the processor, further cause the processor to perform a combination of forward selection …to produce the plurality of pre-operative factor indications by reducing pre-operative factor dimensions (see at least ¶ 0060, the feature selection of the one or more important ECG features may be based on a forward selection (wherein features are added one by one based on their improvements to a ridge regression fit of the neural network model outcome)).
Furthermore, Kano teaches …and a backward elimination (see at least figure 3, ¶ 0047, A graph 36 illustrated in a right part of FIG. 3 illustrates a display example at a time when the icon 35 of the difference between medical treatments has been selected in the display state of the graph 30 of the left part of FIG. 3. For example, by selecting the icon 35 of the difference between medical treatments, only the bar graph of the second importance degree 33 is displayed). It would have been obvious to one of ordinary skill in the art to combine the medical treatment effect prediction method of Kano with the post-operative outcome prediction system of Ouyang with the motivation of more easily making judgements about medical treatment options (Kano, ¶ 0003).
Claim 15 recites similar limitations and is rejected for the same reasons.
With regards to claim 4, Ouyang teaches the system of claim 1, wherein the first trained machine learning model comprises a gradient boost machine model (see at least ¶ 0142, 0151).
Claim 16 recites similar limitations and is rejected for the same reasons.
With regards to claim 5, Ouyang teaches the system of claim 1, wherein the interpretable model comprises a local interpretable model-agnostic explanation model (see at least ¶ 0061).
Claim 17 recites similar limitations and is rejected for the same reasons.
With regards to claim 6, Kano teaches the system of claim 5, wherein the local interpretable model-agnostic explanation model (see at least ¶ 0131) produces the plurality of interpretation indications by: altering a first pre-operative factor indication of the plurality of pre-operative factor indications; monitoring a resultant impact of the first pre-operative factor indication to the plurality of confidence values; and producing the plurality of interpretation indications based on the resultant impact of the first pre-operative factor indication (see at least figure 3, ¶ 0046-0047, A graph 30 illustrated in a left part of FIG. 3 displays, as a cumulative bar graph, a bar graph of a first importance degree 32 and a second importance degree 33 with respect to each of features 31. Specifically, as the features 31, “Age”, “Frailty”, “Gender”, “Diabetes”, “Atrial fibrillation”, and “Valve pressure gradient” are exemplarily illustrated. With respect to each feature 31, the second importance degree 33 is displayed by being stacked after the bar graph of the first importance degree 32. Note that in the example of FIG. 3, the values of the first importance degree 32 and second importance degree 33 are displayed by normalized values, but the display mode is not limited to this. In addition, an icon 34 of a baseline effect for representing a state in which the first importance degree is displayed, and an icon 35 relating to a difference in effect between options for representing a state in which the second importance degree is displayed, and relating to a difference between medical treatments in the description below, are displayed under the graph 30. Note that the icons 34 and 35 and the graph 30 may be displayed in any positional relationship. In the graph 30, the features 31 may be displayed in the order of the magnitude of the added value of the first importance degree and the second importance degree. In the graph 30 in the left part of FIG. 3, the added value of the first importance degree and the second importance degree is highest in regard to the feature 31 “Age”, and is second highest in regard to the feature 31 “Frailty”. A graph 36 illustrated in a right part of FIG. 3 illustrates a display example at a time when the icon 35 of the difference between medical treatments has been selected in the display state of the graph 30 of the left part of FIG. 3. For example, by selecting the icon 35 of the difference between medical treatments, only the bar graph of the second importance degree 33 is displayed. It would have been obvious to one of ordinary skill in the art to combine the medical treatment effect prediction method of Kano with the post-operative outcome prediction system of Ouyang with the motivation of more easily making judgements about medical treatment options (Kano, ¶ 0003).
Claim 18 recites similar limitations and is rejected for the same reasons.
With regards to claim 7, Kano teaches the system of claim 6, wherein a first interpretation indication of the plurality of interpretation indications corresponding to the first pre-operative factor indication among the subset comprises the first pre-operative factor indication and a weight of the first pre-operative factor indication, the weight being determined based on the resultant impact of the first pre-operative factor indication (see at least figures 3-6, 8-12). It would have been obvious to one of ordinary skill in the art to combine the medical treatment effect prediction method of Kano with the post-operative outcome prediction system of Ouyang with the motivation of more easily making judgements about medical treatment options (Kano, ¶ 0003).
Claim 19 recites similar limitations and is rejected for the same reasons.
With regards to claim 8, Kano teaches the system of claim 1, wherein the interpretable model produces each of the subset of the plurality of pre-operative factor indications and a respective weight of each of the subset of the plurality of pre-operative factor indications on the survival probability of the patient (see at least figures 3-6, 8-12). It would have been obvious to one of ordinary skill in the art to combine the medical treatment effect prediction method of Kano with the post-operative outcome prediction system of Ouyang with the motivation of more easily making judgements about medical treatment options (Kano, ¶ 0003).
Claim 20 recites similar limitations and is rejected for the same reasons.
With regards to claim 21, Ouyang teaches the system of claim 1, wherein the output survival probability includes a respective weight for each of the received plurality of pre-operative factor indications (see at least ¶ 0058, one or more important ECG features having a weightage greater than a threshold weightage and contributing to post-operative risk prediction may be identified and indicated).
Claim 22 recites similar limitations and is rejected for the same reasons.
Response to Arguments
Applicant's arguments with respect to the 35 USC § 101 rejections set forth in the previous office action have been considered, but are not persuasive. In an effort to advance prosecution, the Examiner has provided a response to applicant's arguments. Applicant argues:
Applicant argues the limitations do not recite an abstract idea.
Applicant argues the limitations integrate any exception into a practical application of that exception and is significantly more because it provides an improvement to the technology.
In response to Applicant’s argument the limitations are not an abstract idea, the Examiner respectfully disagrees. The claims recite receiving pre-operative factor indications for a patient. Collecting information has been treated as within the realm of abstract ideas. See, e.g., Internet Patents, 790 F.3d at 1349; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011); Electric Power, LLC v. Alstom S.A., (Fed. Cir. 2016). The claims further recite analyzing the patient data using models and using the analysis to determine whether to perform surgery. Analyzing information has been treated as within the abstract-idea category. See, e.g., TLI Commc’ns, 823 F.3d at 613; Digitech, 758 F.3d at 1351; SmartGene, Inc. v. Advanced Biological Labs., SA, 555 F. App’x 950, 955 (Fed. Cir. 2014); Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011); SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 1333 (Fed. Cir. 2010); Electric Power, LLC v. Alstom S.A., (Fed. Cir. 2016); see also Mayo, 132 S. Ct. at 1301; Parker v. Flook, 437 U.S. 584, 589–90 (1978); Gottschalk v. Benson, 409 U.S. 63, 67 (1972). The invention further discloses outputting survival probability for the patient based on the analysis. Merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014); Electric Power, LLC v. Alstom S.A., (Fed. Cir. 2016). As such, the claims recite an abstract idea.
In response to Applicant’s argument the limitations integrate any exception into a practical application of that exception and is significantly more because it provides an improvement to the technology, the Examiner respectfully disagrees. The application discloses a processor, memory, and trained machine learning which are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Nothing in the claim recites specific limitations directed to an improved processor, memory, and trained machine learning. Similarly, the specification is silent with respect to these kinds of improvements. Furthermore, the specification discloses “[o]perative decision making and patient counseling in surgery (e.g., high-risk cardiac surgery) can be nuanced and challenging because uncertainty of outcome may complicate the decision process to intervene. Risk prediction modeling has been implemented to better inform surgeons and patients and provide clinical decision support.” See as-filed specification, ¶ 0003. Therefore, it appears the Applicant is applying generic computer components and mathematical concepts to the task of determining whether to perform surgery to make a decision maker (surgeon) more efficient. “As we have explained, ‘the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.’ Bancorp Servs., 687 F.3d at 1278.” FairWarning IP, LLC v. Iatric Systems, _ F.3d _, 120 U.S.P.Q.2d 1293 (Fed. Cir. 2016).
Applicant's arguments with respect to the 35 USC § 103 rejections set forth in the previous office action have been considered, but are moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ahn, et al. (US 2023/0030313 A1) which discloses a method, performed by at least one computing apparatus, of generating an interpretable prediction result for a patient. The method includes receiving medical image data of a subject patient, receiving additional medical data of the subject patient, and generating information about a prediction result for the subject patient, based on the medical image data of the subject patient and the additional medical data of the subject patient, by using a machine learning prediction model.
Siewerdsen, et al. (US 2022/0157459 A1) which discloses a device may receive a set of perioperative images including a set of pre-operative images depicting one or more anatomical structures of a surgical candidate. The set of pre-operative images may be processed using image analysis techniques to determine a first set of quantitative measures related to the anatomical structure(s) of the surgical candidate. The device may use a data model that has been trained based on perioperative data associated with a patient cohort sharing clinical characteristics with the surgical candidate to predict outcomes from one or more therapeutic options for the surgical candidate based on the first set of quantitative measures and a second set of quantitative measures related to a profile associated with the surgical candidate. Based on the predicted outcomes, the device may provide, to a client device, a recommendation relating to the therapeutic options for the surgical candidate and information to support the recommendation.
Gonçalves DM, Henriques R, Costa RS. Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis. Cancers (Basel). 2021 Jun 28;13(13):3217. doi: 10.3390/cancers13133217. PMID: 34203189; PMCID: PMC8269422 which discloses postoperative complications can impose a significant burden, increasing morbidity, mortality, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joey Burgess whose telephone number is (571)270-5547. The examiner can normally be reached Monday through Friday 9-6.
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/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3681