Detailed Notice
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
Claims 1-21 are currently pending.
Claims 1, 11, and 21 are amended.
Claims 1-21 are rejected.
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 1-21 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.
Step 1:
In the instant case, claims 1-10 are directed towards a computer-implemented method (i.e., process), claims 11-20 are directed toward an apparatus (i.e., machine), and claims 21 is directed toward a non-transitory computer-readable media (i.e., manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 11, and 21 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A computer-implemented method for predicting a risk of persistent post-operative pain for an individual, the computer-implemented method comprising: receiving, by one or more processors, a prediction input data object comprising multivariate intra-operative vital sign data of the individual, wherein the multivariate intra- operative vital sign data comprises hemodynamic data; processing, by the one or more processors, the multivariate intra-operative vital sign data of the individual to determine phase information related to propagating dynamics of one or more hemodynamic responses; providing, by the one or more processors, at least the processed multivariate intra- operative vital sign data to a cohort predictive model associated with a cohort of the individual, wherein (a)the cohort predictive model is initialized with a plurality of historical data objects associated with a post-operative timepoint, and (b) each of the plurality of historical data objects comprises respective phase information in a common coordinate system; generating, by the one or more processors, a risk prediction data object comprising a classification of the phase information determined based at least in part on the cohort predictive model, wherein the risk prediction data object is associated with the post-operative timepoint, and wherein the risk prediction data object comprises an indication of one or more of a likelihood of or a classification of whether the individual will experience a specified level of persistent post-operative pain; and providing, by the one or more processors, the risk prediction data object for display by a remote computing device”.
The limitations of receiving, by one or more processors, a prediction input data object comprising multivariate intra-operative vital sign data of the individual, wherein the multivariate intra- operative vital sign data comprises hemodynamic data; processing, by the one or more processors, the multivariate intra-operative vital sign data of the individual to determine phase information related to propagating dynamics of one or more hemodynamic responses; providing, by the one or more processors, at least the processed multivariate intra-operative vital sign data to a cohort predictive model associated with a cohort of the individual, wherein (a) the cohort predictive model is initialized with a plurality of historical data objects associated with a post-operative timepoint, and (b) each of the plurality of historical data objects comprises respective phase information in a common coordinate system; generating, by the one or more processors, a risk prediction data object comprising a classification of the phase information determined based at least in part on the cohort predictive model, wherein the risk prediction data object is associated with the post-operative timepoint, and wherein the risk prediction data object comprises an indication of one or more of a likelihood of or a classification of whether the individual will experience a specified level of persistent post-operative pain; and providing, by the one or more processors, the risk prediction data object for display by a remote computing device, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, processing, providing, and generating, which is properly interpreted as a “personal behavior”), and/or a mental process that a doctor should determine when discharging a patient that may have a high or low risk of being readmitted (i.e. in this cased the aforementioned limitations recite steps a doctor would normally mentally perform to analyze the chance of readmitting a patient, but instead automates the process via a computer model), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claim 11 and 21 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Dependent claims 2-10 and 12-20 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 11, and 21. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-21 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “processors”, “predictive model”, “apparatus”, “non-transitory memory”, “program code”, “remote device”, and “non-transitory computer-readable media”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1-2 and [0011]-[0025], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “by one or more processors”, “to a cohort predictive model”, “by the remote computing device”, “the apparatus comprising at least one or more processors and at least one non-transitory memory including program code, the at least one non-transitory memory and the program code configured to, with the one or more at least one processors, cause the apparatus to at least”, and “One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving… a prediction input data object comprising multivariate intra-operative vital sign data of the individual, wherein the multivariate intra- operative vital sign data comprises hemodynamic data”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-10 and 12-20 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 11, and 21, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-10 and 12-20include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 11, and 21, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving… a prediction input data object comprising multivariate intra-operative vital sign data of the individual, wherein the multivariate intra- operative vital sign data comprises hemodynamic data”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-6, 10-13, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (US 20200268457 A1), hereinafter Wolf, in view of Taylor (US 20200188029 A1).
Regarding claim 1, Wolf teaches a computer-implemented method for predicting a risk of persistent post-operative pain for an individual (Wolf, Abstract, [0023], [0025], [00558], and [0706]), the computer-implemented method comprising: receiving, by one or more processors, a prediction input data object comprising multivariate intra-operative vital sign data of the individual (Wolf, [0019]: “The operations for populating a post-operative report of a surgical procedure may include receiving an input of a patient identifier, receiving an input of an identifier of a health care provider, and receiving an input of surgical footage of a surgical procedure performed on the patient by the health care provider”, [0024]: “Embodiments may involve receiving, in real time, intracavitary video of a surgical procedure”, [0281], and [0351]-[0352]); processing, by the one or more processors, the multivariate intra-operative vital sign data of the individual (Wolf, [0415]: “information for populating at least part of a post-operative report may be obtained from surgical footage of a surgical procedure. Such information may be referred to as image-based information. Additionally, information about a surgical procedure may be obtained from notes of a healthcare provider or a user, previously filed forms for a patient (e.g., a medical history for the patient), medical devices used during a surgical procedure, and the like. Such information may be referred to as auxiliary information. In an example embodiment, auxiliary information may include vital signs, such as pulse, blood pressure, temperature, respiratory rate, oxygen levels, and the like reported by various medical devices used during a surgical procedure. Image-based information and auxiliary information may be processed by a suitable computer-based software application and the processed information may be used to populate a post-operative report. For example, FIG. 24A shows an example of a process 2401 for processing information and populating a post-operative report 2301”, [0424], [0507], and [0567]: “a method for providing decision support for surgical procedures may include receiving a vital sign of a patient, and a recommendation may be based on an accessed correlation and a vital sign. A vital sign may be received from a medical instrument, a device, an external device, a data storage, a sensor, and/or any other computing component, and may include any indicator a condition of a patient health status (e.g., a heart rate, a breathing rate, a brain activity, and/or other vital sign). In some embodiments, vital signs may be received via a network from a connected device, and may be detected either via a traditional sensor or through analysis of video footage”); providing, by the one or more processors, at least the processed multivariate intra-operative vital sign data to a cohort predictive model associated with a cohort of the individual (Wolf [0025], [0121]: “a decision making junction may refer to a part of a procedure in which a surgeon is faced with two or more viable alternatives, and where choosing the better alternative of the two or more viable alternatives (for example, the alternative that is predicted to reduce a particular risk, the alternative that is predicted to improve outcome, the alternative that is predicted to reduce cost, etc.) is based on at least a particular number of factors (for example, is based on at least two factors, on at least five factors, on at least ten factors, on at least one hundred factors, and so forth)” and [0718]: “Aspects of embodiments for predicting post discharge risk may also include identifying a characteristic of a patient and determining a predicted outcome associated with the surgical procedure based on the identified patient characteristic. The predicted outcome associated with the surgical procedure based on the identified patient characteristic may be determined using a suitable machine-learning model”, [0722], [0726], and [0743]), wherein (a) the cohort predictive model is initialized with historical data objects associated with a post-operative timepoint, (Wolf, [0675], [0718], [0722], [0726], and [0743]); and (b) each of the plurality of historical data objects comprises respective phase information in a common coordinate system (Wolf, [0088], [0154]-[0155], [0306], and [0319]); generating, by the one or more processors, a risk prediction data object comprising a classification of phase information determined based at least in part on the cohort predictive model, wherein the risk prediction data object is associated with the post-operative timepoint (Wolf, [0080], [0209]: “the type of the intraoperative surgical event may be any category in which the intraoperative surgical event may be classified. For example, the type may include the type of procedure being performed, the phase of the procedure, whether or not the intraoperative surgical event is adverse, whether the intraoperative surgical event is part of the planned procedure, the identity of a surgeon performing the intraoperative surgical event, a purpose of the intraoperative surgical event, a medical condition associated with the intraoperative surgical event, or any other category or classification”, [0262], and [0496]), and wherein the risk prediction data object comprises an indication of one or more of a likelihood of or a classification of whether the individual will experience a specified level of persistent post-operative pain (Wolf, [0706]: “outcome C2 may correspond to a specific post-discharge adverse event (e.g., bleeding, pain, nausea, confusion, or any other adverse event), outcome C3 may correspond to a post-discharge complication (e.g., paralysis, pain, bleeding, or any other complication), and outcome C4 may correspond to an elevated risk of readmission. It should be noted that any other suitable outcomes may be used to evaluate the surgical procedure (e.g., an outcome that evaluates an objective measure of a patient's “well-being” several days after the surgical procedure). In an example embodiment, the height of probability bars 3211A-3217A and 3211B-3217B may relate to a probability of occurrence of corresponding outcomes C1-C4”); and providing, by the one or more processors, the risk prediction data object for display by a remote computing device (Wolf, [0496]: “Additionally or alternatively, the measure may list recommended events that were not performed during the surgical procedure (e.g., if suturing was required but not performed, such event may be listed as not being performed). Furthermore, the measure may list events during the surgical procedure that were performed but are not recommended events. For example, an event of administering a pain-relieving medicine to a patient during the surgical procedure may be performed and may not be recommended. Additionally, the machine-learning model may output deviations between characteristics of events performed during the surgery and the corresponding recommended events, as described above”, [0534]: “Conventional approaches for providing decision support for surgical procedures may be unable to be performed in real time or may be unable to determine decision making junctions in surgical videos and develop recommendations to perform specific actions that improve surgical outcomes. In such situations, surgeons may miss critical decision-making points and/or fail to perform particular actions that can improve outcomes, and surgeries may result in suboptimal outcomes for patients”, [0536]: “(i.e., a method for providing decision support for surgical procedures may be performed in real time during a surgical procedure). Real-time recommendations may include providing recommendations via an interface in an operating room (e.g., an operating room depicted in FIG. 1). Real-time recommendations may be updated during a surgical procedure”, and [0565]).
Wolf does not teach wherein the multivariate intra- operative vital sign data comprises hemodynamic data and determine phase information related to propagating dynamics of one or more hemodynamic responses.
However, Taylor teaches wherein the multivariate intra- operative vital sign data comprises hemodynamic data (Taylor, [0308]-[0309], [0317], and [0341]) and determine phase information related to propagating dynamics of one or more hemodynamic responses (Taylor, [0308]-[0309], [0317], and [0341]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Wolf to incorporate the teachings of Taylor and account for a method for assessing coronary anatomy, myocardial perfusion, and coronary artery flow noninvasively. Such a method and system may benefit cardiologists who diagnose and plan treatments for patients with suspected coronary artery disease. In addition, a need exists for a method to predict coronary artery flow and myocardial perfusion under conditions that cannot be directly measured, e.g., exercise, and to predict outcomes of medical, interventional, and surgical treatments on coronary artery blood flow and myocardial perfusion (Taylor, Abstract and [0004]-[0008]).
Regarding claims 2 and 12, Wolf further teaches processing the multivariate intra-operative vital sign data comprises complexifying the multivariate intra-operative vital sign data of the individual (Wolf, [0253 ]-[0254], [0259]-[0266], and [0274]-[292]), and wherein providing at least the processed multivariate intra-operative vital sign data to a cohort predictive model comprises projecting the processed multivariate intra-operative vital sign data onto a three-dimensional manifold of the cohort predictive model and determining phase information of the projection of the processed multivariate intra-operative vital sign data (Wolf, [0078], [0091], [0166], [0167], [0172], and [0324]).
Regarding claims 3 and 13, Wolf further teaches the cohort predictive model is generated and initialized based at least in part by (Wolf, [0079]): receiving a historical data object for each of a cohort comprising a plurality of individuals, each historical data object associated with a binary classification and comprising multivariate intra-operative vital sign data for a corresponding individual (Wolf, [0079], [0209], [0262], [0264], and [0496]); processing the plurality of historical data objects to generate a plurality of first dimension mode data objects, a plurality of second dimension mode data objects, and a plurality of third dimension mode data objects (Wolf, [0078], [0091], [0166], [0167], [0172], and [0324]); generating a cohort predictive model based at least in part on the plurality of first dimension mode data objects and the plurality of second dimension mode data objects, wherein the plurality of first dimension mode data objects and the plurality of second dimension mode data objects are processed to generate a three-dimensional manifold (Wolf, [0078], [0091], [0166], [0167], [0172], and [0324]); and initializing the cohort predictive model with the plurality of historical data objects based at least in part on the plurality of third dimension mode data objects and each binary classification (Wolf, [0025], [0121], [0722], [0726], and [0743]).
Regarding claims 5 and 15, Wolf further teaches each of the plurality of first dimension mode data objects comprises a weight for each of one or more vital sign variate types (Wolf, [0078], [0091 ], [0166], [0167], [0172], and [0324]); each of the plurality of second dimension mode data objects comprises a weight for each of a plurality of intra-operative timepoints (Wolf, [0078], [0091], [0166], [0167], [0172], and [0324]); and each of the plurality of third dimension mode data objects comprises a weight for each of the plurality of individuals (Wolf, [0078], [0091], [0166], [0167], [0172], and [0324]).
Regarding claims 6 and 16, Wolf further teaches initializing the cohort predictive model comprises determining a relationship between phase information of the projection of the plurality of historical data objects onto the three-dimensional manifold and a binary classification (Wolf, [0078], [0091], [0166], [0167], [0172], and [0324]).
Regarding claims 10 and 20, Wolf further teaches the one or more risk prediction-based actions for the individual comprises displaying the risk prediction data object with a three-dimensional manifold, wherein the three-dimensional manifold is generated based at least in part on the historical data objects (Wolf, [0078], [0091], [0166], [0167], [0172], and [0324]).
Regarding claim 11, Wolf teaches an apparatus for predicting a risk of persistent post-operative pain for an individual, the apparatus comprising at least one or more processors and at least one non-transitory memory including program code, the at least one non-transitory memory and the program code configured to, with the one or more at least one processors, cause the apparatus to at least (Wolf, Abstract, [0023], [0025], [00558], and [0706]): receive a prediction input data object comprising multivariate intra-operative vital sign data of the individual (Wolf, [0019]: “The operations for populating a post-operative report of a surgical procedure may include receiving an input of a patient identifier, receiving an input of an identifier of a health care provider, and receiving an input of surgical footage of a surgical procedure performed on the patient by the health care provider”, [0024]: “Embodiments may involve receiving, in real time, intracavitary video of a surgical procedure”, [0281], and [0351]-[0352]); process the multivariate intra-operative vital sign data of the individual (Wolf [0415]: “information for populating at least part of a post-operative report may be obtained from surgical footage of a surgical procedure. Such information may be referred to as image-based information. Additionally, information about a surgical procedure may be obtained from notes of a healthcare provider or a user, previously filed forms for a patient (e.g., a medical history for the patient), medical devices used during a surgical procedure, and the like. Such information may be referred to as auxiliary information. In an example embodiment, auxiliary information may include vital signs, such as pulse, blood pressure, temperature, respiratory rate, oxygen levels, and the like reported by various medical devices used during a surgical procedure. Image-based information and auxiliary information may be processed by a suitable computer-based software application and the processed information may be used to populate a post-operative report. For example, FIG. 24A shows an example of a process 2401 for processing information and populating a post-operative report 2301”, [0424], [0507], and [0567]: “a method for providing decision support for surgical procedures may include receiving a vital sign of a patient, and a recommendation may be based on an accessed correlation and a vital sign. A vital sign may be received from a medical instrument, a device, an external device, a data storage, a sensor, and/or any other computing component, and may include any indicator a condition of a patient health status (e.g., a heart rate, a breathing rate, a brain activity, and/or other vital sign). In some embodiments, vital signs may be received via a network from a connected device, and may be detected either via a traditional sensor or through analysis of video footage”); provide at least the processed multivariate intra-operative vital sign data to a cohort predictive model associated with a cohort of the individual (Wolf [0025], [0121]: “a decision making junction may refer to a part of a procedure in which a surgeon is faced with two or more viable alternatives, and where choosing the better alternative of the two or more viable alternatives (for example, the alternative that is predicted to reduce a particular risk, the alternative that is predicted to improve outcome, the alternative that is predicted to reduce cost, etc.) is based on at least a particular number of factors (for example, is based on at least two factors, on at least five factors, on at least ten factors, on at least one hundred factors, and so forth)” and [0718]: “Aspects of embodiments for predicting post discharge risk may also include identifying a characteristic of a patient and determining a predicted outcome associated with the surgical procedure based on the identified patient characteristic. The predicted outcome associated with the surgical procedure based on the identified patient characteristic may be determined using a suitable machine-learning model”, [0722], [0726], and [0743]), wherein (a) the cohort predictive model is initialized with historical data objects associated with a post-operative timepoint (Wolf, [0675], [0718], [0722], [0726], and [0743]) and (b) each of the plurality of historical data objects comprises respective phase information in a common coordinate system (Wolf, [0088], [0154]-[0155], [0306], and [0319]); generate a risk prediction data object comprising a classification of phase information determined based at least in part on the cohort predictive model, wherein the risk prediction data object is associated with the post-operative timepoint (Wolf, [0080], [0209]: “the type of the intraoperative surgical event may be any category in which the intraoperative surgical event may be classified. For example, the type may include the type of procedure being performed, the phase of the procedure, whether or not the intraoperative surgical event is adverse, whether the intraoperative surgical event is part of the planned procedure, the identity of a surgeon performing the intraoperative surgical event, a purpose of the intraoperative surgical event, a medical condition associated with the intraoperative surgical event, or any other category or classification”, [0262], and [0496]); and initiate the performance perform one or more risk prediction-based actions for the individual (Wolf, [0496]: “Additionally or alternatively, the measure may list recommended events that were not performed during the surgical procedure (e.g., if suturing was required but not performed, such event may be listed as not being performed). Furthermore, the measure may list events during the surgical procedure that were performed but are not recommended events. For example, an event of administering a pain-relieving medicine to a patient during the surgical procedure may be performed and may not be recommended. Additionally, the machine-learning model may output deviations between characteristics of events performed during the surgery and the corresponding recommended events, as described above”, [0534]: “Conventional approaches for providing decision support for surgical procedures may be unable to be performed in real time or may be unable to determine decision making junctions in surgical videos and develop recommendations to perform specific actions that improve surgical outcomes. In such situations, surgeons may miss critical decision-making points and/or fail to perform particular actions that can improve outcomes, and surgeries may result in suboptimal outcomes for patients”, [0536]: “(i.e., a method for providing decision support for surgical procedures may be performed in real time during a surgical procedure). Real-time recommendations may include providing recommendations via an interface in an operating room (e.g., an operating room depicted in FIG. 1). Real-time recommendations may be updated during a surgical procedure”, and [0565]).
Wolf does not teach wherein the multivariate intra- operative vital sign data comprises hemodynamic data and determine phase information related to propagating dynamics of one or more hemodynamic responses.
However, Taylor teaches wherein the multivariate intra- operative vital sign data comprises hemodynamic data (Taylor, [0308]-[0309], [0317], and [0341]) and determine phase information related to propagating dynamics of one or more hemodynamic responses (Taylor, [0308]-[0309], [0317], and [0341]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Wolf to incorporate the teachings of Taylor and account for a method for assessing coronary anatomy, myocardial perfusion, and coronary artery flow noninvasively. Such a method and system may benefit cardiologists who diagnose and plan treatments for patients with suspected coronary artery disease. In addition, a need exists for a method to predict coronary artery flow and myocardial perfusion under conditions that cannot be directly measured, e.g., exercise, and to predict outcomes of medical, interventional, and surgical treatments on coronary artery blood flow and myocardial perfusion (Taylor, Abstract and [0004]-[0008]).
Regarding claim 21, Wolf teaches one or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: receive a prediction input data object comprising multivariate intra-operative vital sign data of an individual (Wolf, [0019]: “The operations for populating a post-operative report of a surgical procedure may include receiving an input of a patient identifier, receiving an input of an identifier of a health care provider, and receiving an input of surgical footage of a surgical procedure performed on the patient by the health care provider”, [0024]: “Embodiments may involve receiving, in real time, intracavitary video of a surgical procedure”, [0281], and [0351 ]-[0352]); process the multivariate intra-operative vital sign data of the individual (Wolf, [0415]: “information for populating at least part of a post-operative report may be obtained from surgical footage of a surgical procedure. Such information may be referred to as image-based information. Additionally, information about a surgical procedure may be obtained from notes of a healthcare provider or a user, previously filed forms for a patient (e.g., a medical history for the patient), medical devices used during a surgical procedure, and the like. Such information may be referred to as auxiliary information. In an example embodiment, auxiliary information may include vital signs, such as pulse, blood pressure, temperature, respiratory rate, oxygen levels, and the like reported by various medical devices used during a surgical procedure. Image-based information and auxiliary information may be processed by a suitable computer-based software application and the processed information may be used to populate a post-operative report. For example, FIG. 24A shows an example of a process 2401 for processing information and populating a post-operative report 2301”, [0424], [0507], and [0567]: “a method for providing decision support for surgical procedures may include receiving a vital sign of a patient, and a recommendation may be based on an accessed correlation and a vital sign. A vital sign may be received from a medical instrument, a device, an external device, a data storage, a sensor, and/or any other computing component, and may include any indicator a condition of a patient health status (e.g., a heart rate, a breathing rate, a brain activity, and/or other vital sign). In some embodiments, vital signs may be received via a network from a connected device, and may be detected either via a traditional sensor or through analysis of video footage”); provide at least the processed multivariate intra-operative vital sign data to a cohort predictive model associated with a cohort of the individual (Wolf, [0025], [0121]: “a decision making junction may refer to a part of a procedure in which a surgeon is faced with two or more viable alternatives, and where choosing the better alternative of the two or more viable alternatives (for example, the alternative that is predicted to reduce a particular risk, the alternative that is predicted to improve outcome, the alternative that is predicted to reduce cost, etc.) is based on at least a particular number of factors (for example, is based on at least two factors, on at least five factors, on at least ten factors, on at least one hundred factors, and so forth)” and [0718]: “Aspects of embodiments for predicting post discharge risk may also include identifying a characteristic of a patient and determining a predicted outcome associated with the surgical procedure based on the identified patient characteristic. The predicted outcome associated with the surgical procedure based on the identified patient characteristic may be determined using a suitable machine-learning model”, [0722], [0726], and [0743]), wherein (a) the cohort predictive model is initialized with historical data objects associated with a post-operative timepoint (Wolf, [0675], [0718], [0722], [0726], and [0743]) and (b) each of the plurality of historical data objects comprises respective phase information in a common coordinate system (Wolf, [0088], [0154]-[0155], [0306], and [0319]); generate a risk prediction data object comprising a classification of phase information determined based at least in part on the cohort predictive model, wherein the risk prediction data object is associated with the post-operative timepoint (Wolf [0080], [0209]: “the type of the intraoperative surgical event may be any category in which the intraoperative surgical event may be classified. For example, the type may include the type of procedure being performed, the phase of the procedure, whether or not the intraoperative surgical event is adverse, whether the intraoperative surgical event is part of the planned procedure, the identity of a surgeon performing the intraoperative surgical event, a purpose of the intraoperative surgical event, a medical condition associated with the intraoperative surgical event, or any other category or classification”, [0262], and [0496]); and initiate the performance perform one or more risk prediction-based actions for the individual (Wolf, [0496]: “Additionally or alternatively, the measure may list recommended events that were not performed during the surgical procedure (e.g., if suturing was required but not performed, such event may be listed as not being performed). Furthermore, the measure may list events during the surgical procedure that were performed but are not recommended events. For example, an event of administering a pain-relieving medicine to a patient during the surgical procedure may be performed and may not be recommended. Additionally, the machine-learning model may output deviations between characteristics of events performed during the surgery and the corresponding recommended events, as described above”, [0534]: “Conventional approaches for providing decision support for surgical procedures may be unable to be performed in real time or may be unable to determine decision making junctions in surgical videos and develop recommendations to perform specific actions that improve surgical outcomes. In such situations, surgeons may miss critical decision-making points and/or fail to perform particular actions that can improve outcomes, and surgeries may result in suboptimal outcomes for patients”, [0536]: “(i.e., a method for providing decision support for surgical procedures may be performed in real time during a surgical procedure). Real-time recommendations may include providing recommendations via an interface in an operating room (e.g., an operating room depicted in FIG. 1). Real-time recommendations may be updated during a surgical procedure”, and [0565]).
Wolf does not teach wherein the multivariate intra- operative vital sign data comprises hemodynamic data and determine phase information related to propagating dynamics of one or more hemodynamic responses.
However, Taylor teaches wherein the multivariate intra- operative vital sign data comprises hemodynamic data (Taylor, [0308]-[0309], [0317], and [0341]) and determine phase information related to propagating dynamics of one or more hemodynamic responses (Taylor, [0308]-[0309], [0317], and [0341]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Wolf to incorporate the teachings of Taylor and account for a method for assessing coronary anatomy, myocardial perfusion, and coronary artery flow noninvasively. Such a method and system may benefit cardiologists who diagnose and plan treatments for patients with suspected coronary artery disease. In addition, a need exists for a method to predict coronary artery flow and myocardial perfusion under conditions that cannot be directly measured, e.g., exercise, and to predict outcomes of medical, interventional, and surgical treatments on coronary artery blood flow and myocardial perfusion (Taylor, Abstract and [0004]-[0008]).
Claim(s) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf and Taylor, in view of Alter (US 20140249762 A1).
Regarding claims 4 and 14 An and Taylor does not teach the plurality of historical data objects is aggregated and processed together using complex higher-order singular value decomposition (HOSVD), and wherein the three-dimensional manifold is generated based at least in part on ranks of components generated by the HOSVD.
However, Alter teaches the plurality of historical data objects is aggregated and processed together using complex higher-order singular value decomposition (HOSVD), and wherein the three-dimensional manifold is generated based at least in part on ranks of components generated by the HOSVD (Alter, [0082], [0092], [0101], [0101], [0110], and [0113]-[0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify An and Taylor to incorporate the teachings of Alter and account for a more effective method for disease related characterization of biological data. The subject technology provides such characterization (Alter, Abstract and [0004]-[0006]).
Claim(s) 7-9 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf and Taylor, in view of Alter Principe et al. (US 20100197258 A1), hereinafter Principe.
Regarding claims 7 and 17 An and Taylor does not teach the plurality of first dimension mode data objects comprises eigenvectors of a first correntropy matrix, wherein the first correntropy matrix is generated based at least in part on the plurality of historical data objects; the plurality of second dimension mode data objects comprises eigenvectors of a second correntropy matrix, wherein the second correntropy matrix is generated based at least in part on the plurality of historical data objects; and the plurality of third dimension mode data objects comprises eigenvectors of a third correntropy matrix, wherein the third correntropy matrix is generated based at least in part on the plurality of historical data objects.
However, Principe teaches the plurality of first dimension mode data objects comprises eigenvectors of a first correntropy matrix, wherein the first correntropy matrix is generated based at least in part on the plurality of historical data objects (Principe, Abstract, [0005]-[0007]); the plurality of second dimension mode data objects comprises eigenvectors of a second correntropy matrix, wherein the second correntropy matrix is generated based at least in part on the plurality of historical data objects (Principe, [0006]-[0007], [0029], and [0043]); and the plurality of third dimension mode data objects comprises eigenvectors of a third correntropy matrix, wherein the third correntropy matrix is generated based at least in part on the plurality of historical data objects (Principe, [0005], [0032], [0035], [0040], and [0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify An and Taylor to incorporate the teachings of Principe and account for a method of robust signal detection that is data independent (Principe, Abstract and [0003]-[0004]).
Regarding claims 8 and 18 An and Taylor does not teach the first correntropy matrix is generated by applying a first cross-correntropy function to a first moment matrix, wherein the first moment matrix is generated based at least in part on a first mode matrix unfolding of a third-order tensor; the second correntropy matrix is generated by applying a second cross-correntropy function to a second moment matrix, wherein the second moment matrix is generated based at least in part on a second mode matrix unfolding of the third-order tensor; and the third correntropy matrix is generated by applying a third cross-correntropy function to a third moment matrix, wherein the third moment matrix is generated based at least in part on a third mode matrix unfolding of the third-order tensor, wherein the third-order tensor represents the plurality of historical data objects.
However, Principe teaches the first correntropy matrix is generated by applying a first cross-correntropy function to a first moment matrix, wherein the first moment matrix is generated based at least in part on a first mode matrix unfolding of a third-order tensor (Principe, Abstract, [0005]-[0007]); the second correntropy matrix is generated by applying a second cross-correntropy function to a second moment matrix, wherein the second moment matrix is generated based at least in part on a second mode matrix unfolding of the third-order tensor (Principe, [0006]-[0007], [0029], and [0043]); and the third correntropy matrix is generated by applying a third cross-correntropy function to a third moment matrix, wherein the third moment matrix is generated based at least in part on a third mode matrix unfolding of the third-order tensor, wherein the third-order tensor represents the plurality of historical data objects (Principe, [0005], [0032], [0035], [0040], and [0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify An and Taylor to incorporate the teachings of Principe and account for a method of robust signal detection that is data independent (Principe, Abstract and [0003]-[0004]).
Regarding claims 9 and 19 An and Taylor does not teach each of the first, second, and third cross-correntropy functions is based on a Gaussian function.
However, Principe teaches each of the first, second, and third cross-correntropy functions is based on a Gaussian function (Principe, [0007], [0022], [0025], and [0039]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify An and Taylor to incorporate the teachings of Principe and account for a method of robust signal detection that is data independent (Principe, Abstract and [0003]-[0004]).
Response to Arguments
Applicant's arguments filed 08/28/2025 have been fully considered but they are not persuasive. Regarding the 35 U.S.C. 101 Rejection, Applicant argues that generating a risk prediction data object using a trained cohort predictive model and providing this risk prediction data object for display by a remote computing device does not fall within the abstract idea of a certain method of organizing human activity. Examiner respectfully disagrees. The cohort predictive model and remote computing device are not a part of the abstract idea, but additional elements. The generating a risk prediction and displaying are a part of the abstract idea of certain method of organizing human activity because a person, persons, or person with computer tools (i.e., the model and device). Using the additional elements to perform the abstract idea is apply or linking, however the claim still recites an abstract idea.
Applicant also argues the claims recite additional elements or combination of additional elements that integrates the abstract idea into a practical application. More specifically, the claims and specification recite improved methods for analyzing multivariate-operative vital sign data, which allows for more efficient and accurate predictions of the risk of persistent post-operative pain for individuals of interest. Applicant also argues the claims recite a graph-based machine learning model which provides an improvement in the field of graph modeling, are a thus sufficient for integrating an alleged abstract idea. Examiner respectfully disagrees. The technological improvement must be to the technology or technical field itself and its functioning. Improving the analysis of data is not improvement to the technology, but the to the abstract idea. An abstract idea cannot integrate itself into a practical application. Additionally, the machine learning or cohort model is recited at a high level of generality and amounts to generic computer tools that merely apply or link to the abstract idea, which is not an improvement. Therefore, the claims do not recite additional elements (or combination) that integrate the abstract idea into a practical application.
Applicant also argues the claims amount to significantly more. Examiner respectfully disagrees. As recite above the additional elements (alone or in combination) amount to generic computer tools that merely apply or link the abstract idea into a practical application. Or the claims recite steps that amount to extra solution activity in the form of data gathering. Therefore, the 35 U.S.C. 101 Rejection is maintained.
Applicant’s arguments with respect to 35 U.S.C. 102/103 Rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.S.S./Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681
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