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 .
Priority
The application claims the benefit of U.S. Provisional Patent Application No. 63/339,759 and has an effective priority date of 9 May 2022.
Claim Amendment
In the amended claims received 08 October 2024 the following occurred: claims 2,5,12,16,17,23,26 were amended. Claims 3,7,10,15,18,21,24-25,27-29,32-33 were cancelled.
Distinguishing Prior Art
The prior art of record fails to teach or suggest several of the specific visualized combination of features (lists of features that are being visualized). For example:
Regarding Claim 11
The method of claim 1, further comprising: illustrating, by the visualization tool, data across multiple organ systems, the data including cardiac (left ventricular ejection fraction, right ventricular systolic pressure, and right heart catheterization data), pulmonary (percent predicted forced vital capacity and diffusing capacity), cutaneous (modified Rodnan skin score), gastrointestinal (Medsger GI severity scores and body mass index) peripheral vasculature (Medsger Raynaud’s scores capturing damage including digital pits, ulcerations and gangrene), muscle (proximal muscle strength on a 0-5 scale), laboratory measurements, and patient reported outcomes (HAQ-DI).
Commentary: the prior art failed to teach the specific combination of visualized features.
Regarding Claim 22
The system of claim 12, further comprising: plotting, by the visualization tool, critical events of the patient, the critical events including clinically significant interstitial lung disease, severe interstitial lung disease, cardiomyopathy, pulmonary hypertension, mean pulmonary arterial pressure, severe gastrointestinal dysmotility, myopathy, renal crises, and cancer diagnosis.
Commentary: the prior art failed to teach the specific combinations of visualized features collectively on one plot.
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: visualization tool (at least in claims 1,11,12,22,23), analytics platform (at least in claims 2,6,8,9,12,13,17,19,20,31).
The structure for the visualization tool is processor connected to a memory.
The structure for the analytics platform interpreted to be the cloud because of Fig. 3.
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 § 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 1-33 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.
Claims 1,12 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method, system, non-transitory computer readable medium for providing an interactive patient level data visualization and analysis tool that illustrates a patient’s health trajectory across multiple organ systems, which are within a statutory category (or are interpreted to be within a statutory category for subject matter eligibility analysis purposes).
Step 2A1
The limitations of illustrating a patient’s health trajectory across multiple organ systems, the method comprising: integrating data from an electronic medical record […] and one or more research databases into an analytics platform; plotting […] the patient’s health trajectory and overlaying […] data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to courses of other patients, with a same disease, selected by a user as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a system comprising a processor, a memory connected with the processor, a non-transitory computer readable medium the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the system comprising a processor, memory connected with the processor and non-transitory computer readable medium, this claim encompasses illustrating a patient’s health trajectory across multiple organ systems by integrating data from an medical record and one or more research databases into an analytics platform, plotting the patient’s health trajectory and overlaying data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to courses of other patients, with a same disease, selected by a user in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people 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.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a system comprising a processor, a memory connected with the processor and a non-transitory computer readable medium that implements the identified abstract idea. These additional elements are not described by the applicant and are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of an visualization tool an analytics platform. The visualization tool and analytics platform merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a system comprising a processor, a memory connected with the processor and a non-transitory computer readable medium to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
The claim further recites the additional element of an visualization tool and analytics platform. The visualization tool and analytics platform merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application.
Claims 2,4-6,8-9,11,13-14,16-17,19-20,22,26 and 30-31 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 merely describe(s) the data integrated from the electronic medical record system and the one or more research databases into the analytics platform is real-time-data, and the real-time data is provided using a certain type of technology. Claim 4 merely describes performing calculations and processing of the data from the electronic medication system and the one or more research databases for each patient individually and for all reference patients collectively. Claim 5 merely describes performing of the calculations and the processing of the data from the medical records and the one or more research databases for each patient individually and for all reference patients collectively further comprises: receiving certain data and a specific speed and starting location and updating the calculations and the processing based on the received data. Claim 6 merely describes receiving filters to compare the patient’s health trajectory to a user-specified subgroup based on demographic, clinical and biological characteristics. Claim 8 merely describes modeling, respective latent health states of disease patients based on using cardiopulmonary and cutaneous parameters. Claim 9 merely describes projecting the disease patient’s health trajectory into a future; calculating, by the analytics platform, respective probabilities that parameters of the disease patient will fall below or rise above clinically set boundaries; and presenting the respective probabilities with a corresponding visualization of the parameters. Claim 11 merely describes illustrating data across multiple organ systems. Claim 13 merely describes the data integrated from the electronic medical record system and the one or more research databases into the analytics platform is a certain type of data. Claim 14 merely describes wherein the real time data is provided using a certain data type. Claim 16 merely describes the performing of the calculations and the processing of the data from certain sources for each patient individually and for all reference patients collectively further comprises receiving new real time data from the electronic medical record system and the one or more research databases; and updating the calculations and the processing based on the received new real time data. Claim 17 merely describe receiving filters to compare the patient’s health trajectory to a user-specified subgroup based on demographic, clinical and biological characteristics, wherein the demographic, the clinical and the biological characteristics include age at disease onset, race, sex, cutaneous subtype, and autoantibody status. Claim 19 merely describes modeling respective latent health states of disease patients based on using cardiopulmonary and cutaneous parameters. Claim 20 merely describes projecting the patient’s health trajectory into a future, calculating respective probabilities and parameters of the patient will fall below or rise above clinically set boundaries; and presenting the respective probabilities with a corresponding visualization of the parameters. Claim 22 merely describes plotting critical events of the patient, the critical events including clinically significant interstitial lung disease, severe interstitial lung disease, cardiomyopathy, pulmonary hypertension, mean pulmonary arterial pressure, severe gastrointestinal dysmotility, myopathy, renal crises, and cancer diagnosis. Claim 26 merely describes performing calculations and processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patients collectively, wherein the performing of the calculations and the processing of the data from the medical record and the one or more research databases for each patient individually and for all reference patient collectively, comprises: receiving new real-time data from the electronic medical record system and the one or more research databases; and updating the calculations and the processing based on the received new real-time data. Claim 30 modeling respective latent health states and disease patients based on using cardiopulmonary and cutaneous parameters. Claim 31 merely describes projecting the patient’s health trajectory into a future, calculating respective probabilities that parameters of the patient will fall below or rise above clinically set boundaries; and presenting the respective probabilities with a corresponding visualization of the parameters.
The dependent additional elements include an analytics platform, an electronic medical records system, research databases, a non-transitory computer readable medium, a visualization tool. The visualization too and analytics platform generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. The remaining additional elements were analyzed as were the computer part(s) of the independent claims.
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.
Claim(s) 1,4,6,8,9,12,17,19,20,23,30 and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen (interactive Visualization for Patient to Patient Comparison) to US 11,875,903 B2 (hereafter Schaeffer).
Regarding Claim 1
Nguyen teaches:
A method of providing an interactive patient-level data visualization and analysis tool that illustrates a patient’s health trajectory across multiple organ systems, the method comprising: integrating data from an electronic medical record system and one or more research databases into an analytics platform; [Nguyen teaches at Fig. 2 2D visualization of the entire patient sample. Nguyen teaches at Fig. 2D the entire 100 patients in the 2D similarity space with various mapping attributes. The mapping attributes are interpreted to corresponding to the data saved to one or more research databases (partitioning of data on a disk generating an arbitrary number of databases). The visualization tool is an analytics platform.]
plotting via a visualization tool, the patient’s health trajectory; [Nguyen teaches at Figure 4 visualization at an exploration stage. Nguyen teaches at Figure 4 patients with medium risk who were treated with the Study 8 and BFM 95 protocols. This is interpreted as plotting via a visualization tool, the patient’s health trajectory.]
Nguyen may not explicitly teach:
and overlaying, by the visualization tool, data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to courses of other patients, with a same disease, selected by a user.
Schaeffer teaches:
and overlaying, by the visualization tool, data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to courses of other patients, with a same disease, selected by a user. [Schaeffer teaches at col. 4 line 20-21 Fig. 14 is another example of a data summary window in a patient timeline analysis user interface. Schaeffer teaches at Fig. 14 Item 332 compare cohort option. Schaeffer teaches at Fig. 14 Item 304 age at diagnosis data comparison visualization.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer with the motivation of addressing that despite this wealth of data, there is a dearth of meaningful ways to compile and analyze the data quickly, efficiently, and comprehensively (Schaeffer at col. 1, line 33-line 35).
Regarding Claim 4
Nguyen/Schaeffer teach the method of claim 1. Nguyen/Schaeffer further teach:
further comprising: performing calculations and processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patients collectively. [Schaeffer teaches at col. 4 line 20-21 Fig. 14 is another example of a data summary window in a patient timeline analysis user interface. Schaeffer teaches at Fig. 14 Item 332 compare cohort option. Schaeffer teaches at Fig. 14 Item 304 age at diagnosis data comparison visualization. Collectively, Fig. 14 teaches performing calculations and processing of the data from (the electronic medical record system and the one or more research databases taught elsewhere by Sutton), for each patient individually and for all reference patients collectively.]
Regarding Claim 6
Nguyen/Schaeffer teach the method of claim 1. Nguyen/Schaeffer further teach:
further comprising: receiving, by the analytics platform, filters to compare the patient’s health trajectory to a user-specified subgroup based on the demographic, clinical, and biological characteristics. [Nguyen teaches at Figure 2 a drop down menu for filtering a plot of patient data with multiple groups selected that includes age at diagnosis, the phenotype, and immunophenotype. The age at diagnosis is the demographic filter, the phenotype is the clinical filter and the immunophenotype the biological characteristics filter.]
Regarding Claim 8
Nguyen/Schaeffer teach the method of claim 1. Nguyen/Schaeffer further teach:
further comprising: modeling, by the analytics platform, respective latent health states of disease patients based on using cardiopulmonary and cutaneous parameters. [Nguyen teaches at Figure 2 a drop down menu for filtering a plot of patient data with multiple groups selected that includes age at diagnosis, the phenotype, and immunophenotype. The age is interpreted as both a cardiopulmonary and cutaneous parameter.]
Regarding Claim 9
Nguyen/Schaeffer teach the method of claim 8. Nguyen/Schaeffer further teach:
further comprising: projecting, by the analytics platform, the disease patient’s health trajectory into a future; [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. Collectively, Schaeffer teaches projecting, by the analytics platform, the patient’s health trajectory into a future.]
calculating, by the analytics platform, respective probabilities that parameters of the disease patient will fall below or rise above clinically set boundaries; [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. This is a prediction concerning survival months from progression based on respective probabilities with a corresponding visualization of the parameters (the parameters are interpreted to be listed at Item 334 on Fig. 20, one of them is Folfirinox. The clinically set boundary is interpreted as survival or death.]
and presenting the respective probabilities with a corresponding visualization of the parameters. [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. This is a prediction concerning survival months from progression based on respective probabilities with a corresponding visualization of the parameters (the parameters are interpreted to be listed at Item 334 on Fig. 20, one of them is Folfirinox. Collectively, Schaeffer teaches presenting the respective probabilities with a corresponding visualization of the parameters.]
Regarding Claim 12
Nguyen teaches:
A system for providing an interactive patient-level data visualization and analysis tool that illustrates a patient health trajectory across multiple organ systems, the system comprising: a processor; and a memory connected with the processor, the memory including computer-readable instructions for the processor to perform a plurality of operation comprising: integrating data tables from an electronic medical record system and one or more research databases into an analytics platform; [Nguyen teaches at Fig. 2 2D visualization of the entire patient sample. Nguyen teaches at Fig. 2D the entire 100 patients in the 2D similarity space with various mapping attributes. The mapping attributes are interpreted to corresponding to the data saved to one or more research databases (partitioning of data on a disk generating an arbitrary number of databases). The visualization tool is an analytics platform.]
[…]
plotting, via a visualization tool, the patient’s health trajectory; [Nguyen teaches at Figure 4 visualization at an exploration stage. Nguyen teaches at Figure 4 patients with medium risk who were treated with the Study 8 and BFM 95 protocols. This is interpreted as plotting via a visualization tool, the patient’s health trajectory.]
[…].
Nguyen may not explicitly teach:
performing calculations and processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patients collectively;
and overlaying, by the visualization tool, data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to courses of other patients, with a same disease, selected by a user.
Schaeffer teaches:
performing calculations and processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patients collectively; [Schaeffer teaches at col. 4 line 20-21 Fig. 14 is another example of a data summary window in a patient timeline analysis user interface. Schaeffer teaches at Fig. 14 Item 332 compare cohort option. Schaeffer teaches at Fig. 14 Item 304 age at diagnosis data comparison visualization. Collectively, Fig. 14 teaches performing calculations and processing of the data from (the electronic medical record system and the one or more research databases taught elsewhere by Sutton), for each patient individually and for all reference patients collectively.]
and overlaying, by the visualization tool, data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to courses of other patients, with a same disease, selected by a user. [Schaeffer teaches at col. 4 line 20-21 Fig. 14 is another example of a data summary window in a patient timeline analysis user interface. Schaeffer teaches at Fig. 14 Item 332 compare cohort option. Schaeffer teaches at Fig. 14 Item 304 age at diagnosis data comparison visualization.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer with the motivation of addressing that despite this wealth of data, there is a dearth of meaningful ways to compile and analyze the data quickly, efficiently, and comprehensively (Schaeffer at col. 1, line 33-line 35).
Regarding Claim 17
Nguyen/Schaeffer teach the system of claim 12. Nguyen/Schaeffer further teach:
wherein the plurality of operations further comprise: receiving, by the analytics platform, filters to compare the patient’s health trajectory to a user-specified subgroup based on demographic, clinical, and biological characteristics, wherein the demographic, the clinical, and the biological characteristics include age at disease onset, race, sex, cutaneous subtype, and autoantibody status. [Nguyen teaches at Figure 2 in the Legend an ethnicity drop down option interpreted receiving, by the analytics platform, filters to compare the patient’s health trajectory to a user-specified subgroup based on a clinical characteristics. Nguyen teaches at Figure 2 in the Legend an phenotype drop down box interpreted to be receiving, by the analytics platform, filters to compare the patient’s health trajectory to a user-specified subgroup based on biological characteristics. Nguyen teaches at Figure 2 in the Legend a date of birth drop down box interpreted to be receiving, by the analytics platform, filters to compare the patient’s health trajectory to a user-specified subgroup based on demographics.]
Regarding Claim 19
Nguyen/Schaeffer teach the system of claim 12. Nguyen/Schaeffer further teach:
wherein the plurality of operations further comprise: modeling, by the analytics platform, respective latent health states of disease patients based on using cardiopulmonary and cutaneous parameters. [Nguyen teaches at Figure 2 a drop down menu for filtering a plot of patient data with multiple groups selected that includes age at diagnosis, the phenotype, and immunophenotype. The age is interpreted as both a cardiopulmonary and cutaneous parameter.]
Regarding Claim 20
Nguyen/Schaeffer teach the system of claim 19. Nguyen/Schaeffer further teach:
wherein the plurality of operations further comprise: projecting, by the analytics platform, the patient’s health trajectory into a future; [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. Collectively, Schaeffer teaches projecting, by the analytics platform, the patient’s health trajectory into a future.]
calculating, by the analytics platform, the patient’s health trajectory into a future; [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. Collectively, Schaeffer teaches calculating, by the analytics platform, the patient’s health trajectory into a future.]
calculating, by the analytics platform, respective probabilities and parameters of the patient will fall below or rise above clinically set boundaries; [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. This is a prediction concerning survival months from progression based on respective probabilities with a corresponding visualization of the parameters (the parameters are interpreted to be listed at Item 334 on Fig. 20, one of them is Folfirinox. The clinically set boundary is interpreted as survival or death.]
and presenting the respective probabilities with a corresponding visualization of the parameters. [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. This is a prediction concerning survival months from progression based on respective probabilities with a corresponding visualization of the parameters (the parameters are interpreted to be listed at Item 334 on Fig. 20, one of them is Folfirinox. Collectively, Schaeffer teaches presenting the respective probabilities with a corresponding visualization of the parameters.]
Regarding Claim 30
Nguyen/Schaeffer teach the non-transitory computer-readable medium of claim 23. Nguyen/Schaeffer further teach:
wherein the plurality of operations further comprise: modeling, by the analytics platform, respective latent health states of disease patients based on using cardiopulmonary and cutaneous parameters. [Nguyen teaches at Figure 2 a drop down menu for filtering a plot of patient data with multiple groups selected that includes age at diagnosis, the phenotype, and immunophenotype. The age is interpreted as both a cardiopulmonary and cutaneous parameter.]
Regarding Claim 31
Nguyen/Schaeffer teach the non-transitory computer-readable medium of claim 30. Nguyen/Schaeffer further teach:
wherein the plurality of operations further comprise: projecting, by the analytics platform, the patient’s health trajectory into a future; [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. Collectively, Schaeffer teaches projecting, by the analytics platform, the patient’s health trajectory into a future.]
calculating, by the analytics platform, the patient’s health trajectory into a future; [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. Collectively, Schaeffer teaches calculating, by the analytics platform, the patient’s health trajectory into a future.]
calculating, by the analytics platform, respective probabilities that parameters of the patient will fall below or rise above clinically set boundaries; [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. The clinically set boundary is interpreted as survival or death.]
and presenting the respective probabilities with a corresponding visualization of the parameters. [Schaeffer teaches at col. 4, line 33-34 that Fig. 20 is another example of a patient survival analysis user interface. Schaeffer teaches at Fig. 20 Item 302 a chart of progression free survival vs. time. This is a prediction concerning survival months from progression based on respective probabilities with a corresponding visualization of the parameters (the parameters are interpreted to be listed at Item 334 on Fig. 20, one of them is Folfirinox. Collectively, Schaeffer teaches presenting the respective probabilities with a corresponding visualization of the parameters.]
Claim(s) 5,13,16,23,26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen (interactive Visualization for Patient to Patient Comparison) to US 11,875,903 B2 (hereafter Schaeffer) in view of US 11,177,041 B1 (hereafter Sutton).
Regarding Claim 5
Nguyen/Schaeffer teach the method of claim 4. Nguyen/Schaeffer may not explicitly teach:
wherein the performing of the calculations and the processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patients collectively further comprises: receiving new real-time data from the electronic medical record system and the one or more research databases;
and updating the calculations and the processing based on the received new real-time data.
Sutton teaches:
wherein the performing of the calculations and the processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patients collectively further comprises: receiving new real-time data from the electronic medical record system and the one or more research databases; [Sutton teaches at col. 6 health system server 110 includes one or more EMR databases 210. Sutton teaches at col. 6 line 23-26 teaches that batch database will be operably connected to health system server and will receive batch data a pre-specified time intervals from the EMR database via network connection. Sutton teaches at col. 6 real time database will also be operably connected to health system and will receive data in real-time or near real-time upon user request via network connection 213. The real time database is interpreted as the research database.]
and updating the calculations and the processing based on the received new real-time data. [Sutton teaches at col. 29 line 25-26 the user interface dashboard is updated in real time or near real time. Sutton teaches at col. 29 line 26-29 patients presenting in the ed with unspecified chest pain will have their cardiac predictive risk estimate displayed in column 610, and their predicted outcome in column 620. Collectively, this is interpreted as updating the calculations and processing based on the received new real-time data.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer to the method and system for cardiac risk assessment of a patient using historical and real-time data of Sutton with the motivation of addressing the of cardiac risk assessment of a patient using historical and real-time data.
Regarding Claim 13
Nguyen/Schaeffer teach the system of claim 12. Nguyen/Schaeffer may not explicitly teach:
wherein the data integrated from the electronic medical record system and the one or more research databases into the analytics platform is real-time-data.
Sutton teaches:
wherein the data integrated from the electronic medical record system and the one or more research databases into the analytics platform is real-time-data. [Sutton teaches at col. 6 health system server 110 includes one or more EMR databases 210. Sutton teaches at col. 6 line 23-26 teaches that batch database will be operably connected to health system server and will receive batch data a pre-specified time intervals from the EMR database via network connection. Sutton teaches at col. 6 real time database will also be operably connected to health system and will receive data in real-time or near real-time upon user request via network connection 213. The real time database is interpreted as the research database.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer to the method and system for cardiac risk assessment of a patient using historical and real-time data of Sutton with the motivation of addressing the of cardiac risk assessment of a patient using historical and real-time data.
Regarding Claim 16
Nguyen/Schaeffer teach the system of claim 12. Nguyen/Schaeffer may not explicitly teach:
wherein the performing the calculations and the processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patient collectively further comprises: receiving new real-time data from the electronic medical record system and the one or more research databases;
and updating the calculations and the processing based on the received real-time data.
Sutton teaches:
wherein the performing the calculations and the processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patient collectively further comprises: receiving new real-time data from the electronic medical record system and the one or more research databases; [Sutton teaches at col. 6 health system server 110 includes one or more EMR databases 210. Sutton teaches at col. 6 line 23-26 teaches that batch database will be operably connected to health system server and will receive batch data a pre-specified time intervals from the EMR database via network connection. Sutton teaches at col. 6 real time database will also be operably connected to health system and will receive data in real-time or near real-time upon user request via network connection 213. The real time database is interpreted as the research database.]
and updating the calculations and the processing based on the received real-time data. [Sutton teaches at col. 29 line 25-26 the user interface dashboard is updated in real time or near real time. Sutton teaches at col. 29 line 26-29 patients presenting in the ed with unspecified chest pain will have their cardiac predictive risk estimate displayed in column 610, and their predicted outcome in column 620. Collectively, this is interpreted as updating the calculations and processing based on the received real-time data.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer to the method and system for cardiac risk assessment of a patient using historical and real-time data of Sutton with the motivation of addressing the of cardiac risk assessment of a patient using historical and real-time data.
Regarding Claim 23
Nguyen teaches:
A non-transitory computer readable medium having stored thereon instructions for a processor to perform a plurality of operations comprising: integrating data tables from an electronic medical record system and one or more research databases into an analytics platform; [Nguyen teaches at Fig. 2 2D visualization of the entire patient sample. Nguyen teaches at Fig. 2D the entire 100 patients in the 2D similarity space with various mapping attributes. The mapping attributes are interpreted to corresponding to the data saved to one or more research databases (partitioning of data on a disk generating an arbitrary number of databases). The visualization tool is an analytics platform.]
plotting, via a visualization tool, a patient’s health trajectory; [Nguyen teaches at Figure 4 visualization at an exploration stage. Nguyen teaches at Figure 4 patients with medium risk who were treated with the Study 8 and BFM 95 protocols. This is interpreted as plotting via a visualization tool, the patient’s health trajectory.]
Nguyen may not explicitly teach:
and overlaying, by the visualization tool, data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to course of other patients, with a same disease, selected by a user.
Schaeffer teaches:
and overlaying, by the visualization tool, data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to course of other patients, with a same disease, selected by a user. [Schaeffer teaches at col. 4 line 20-21 Fig. 14 is another example of a data summary window in a patient timeline analysis user interface. Schaeffer teaches at Fig. 14 Item 332 compare cohort option. Schaeffer teaches at Fig. 14 Item 304 age at diagnosis data comparison visualization.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer with the motivation of addressing that despite this wealth of data, there is a dearth of meaningful ways to compile and analyze the data quickly, efficiently, and comprehensively (Schaeffer at col. 1, line 33-line 35).
Nguyen/Schaffer may not explicitly teach:
wherein the data integrated from the electronic medical record system and the one or more research databases into the analytics platform is real-time-data.
Sutton teaches:
wherein the data integrated from the electronic medical record system and the one or more research databases into the analytics platform is real-time-data. [Sutton teaches at col. 6 health system server 110 includes one or more EMR databases 210. Sutton teaches at col. 6 line 23-26 teaches that batch database will be operably connected to health system server and will receive batch data a pre-specified time intervals from the EMR database via network connection. Sutton teaches at col. 6 real time database will also be operably connected to health system and will receive data in real-time or near real-time upon user request via network connection 213. The real time database is interpreted as the research database.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer to the method and system for cardiac risk assessment of a patient using historical and real-time data of Sutton with the motivation of addressing the of cardiac risk assessment of a patient using historical and real-time data.
Regarding Claim 26
Nguyen/Schaeffer teach the non-transitory computer-readable medium of claim 23. Nguyen/Schaeffer further teach:
wherein the plurality of operations further comprise: performing calculations and processing of the data from the electronic medical record system and the one or more research databases for each patient individually and for all reference patients collectively further comprises: [Schaeffer teaches at col. 4 line 20-21 Fig. 14 is another example of a data summary window in a patient timeline analysis user interface. Schaeffer teaches at Fig. 14 Item 332 compare cohort option. Schaeffer teaches at Fig. 14 Item 304 age at diagnosis data comparison visualization. Collectively, Fig. 14 teaches performing calculations and processing of the data from (the electronic medical record system and the one or more research databases taught elsewhere by Sutton), for each patient individually and for all reference patients collectively.]
Nguyen/Schaeffer may not explicitly teach:
receiving new real-time data from the electronic medical record system and the one or more research databases;
and updating the calculation and the processing based on the received new real-time data.
Sutton teaches:
receiving new real-time data from the electronic medical record system and the one or more research databases; [Sutton teaches at col. 6 health system server 110 includes one or more EMR databases 210. Sutton teaches at col. 6 line 23-26 teaches that batch database will be operably connected to health system server and will receive batch data a pre-specified time intervals from the EMR database via network connection. Sutton teaches at col. 6 real time database will also be operably connected to health system and will receive data in real-time or near real-time upon user request via network connection 213. The real time database is interpreted as the research database.]
and updating the calculation and the processing based on the received new real-time data. [Sutton teaches at col. 29 line 25-26 the user interface dashboard is updated in real time or near real time. Sutton teaches at col. 29 line 26-29 patients presenting in the ed with unspecified chest pain will have their cardiac predictive risk estimate displayed in column 610, and their predicted outcome in column 620. Collectively, this is interpreted as updating the calculations and processing based on the received real-time data.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer to the method and system for cardiac risk assessment of a patient using historical and real-time data of Sutton with the motivation of addressing the of cardiac risk assessment of a patient using historical and real-time data.
Claim(s) 2 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen (interactive Visualization for Patient to Patient Comparison) to US 11,875,903 B2 (hereafter Schaeffer) in view of US 11,177,041 B1 (hereafter Sutton) in view of Walinjkar (Personalized Wearable Systems for Real-Time ECG Classification and Healthcare Interoperability).
Regarding Claim 2
Nguyen/Schaeffer teach the method of claim 1. Nguyen/Schaeffer may not explicitly teach:
wherein the data integrated from the electronic medical record system and the one or more research databases into the analytics platform is real-time-data,
and the real-time data is provided using FHIR technology.
Sutton teaches:
wherein the data integrated from the electronic medical record system and the one or more research databases into the analytics platform is real-time-data, [Sutton teaches at col. 6 health system server 110 includes one or more EMR databases 210. Sutton teaches at col. 6 line 23-26 teaches that batch database will be operably connected to health system server and will receive batch data a pre-specified time intervals from the EMR database via network connection. Sutton teaches at col. 6 real time database will also be operably connected to health system and will receive data in real-time or near real-time upon user request via network connection 213. The real time database is interpreted as the research database.]\
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer to the method and system for cardiac risk assessment of a patient using historical and real-time data of Sutton with the motivation of addressing the of cardiac risk assessment of a patient using historical and real-time data.
Nguyen/Schaeffer/Sutton may not explicitly teach:
and the real-time data is provided using FHIR technology.
Walinjkar teaches:
and the real-time data is provided using FHIR technology. [Walinjkar teaches at the Abstract ECG abnormalities based on annotation in MITDB could be classified and these ECG observation could be logged to a server implementation based on FHIR standards. Walinjkar teaches at the title personalized wearable systems for real-time classification and healthcare interoperability: real-time ECG classification and FHIR interoperability.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer to the method and system for cardiac risk assessment of a patient using historical and real-time data of Sutton to the personalized wearable systems for real time ECG classification and healthcare interoperability of Walinjkar with the motivation of addressing modern wearable health monitoring devices, which have become easily available in the consumer market, however, real-time analyses and prediction along with alerts and alarms about a health hazard are not adequately addressed in such devices (Walinjkar at the Abstract).
Regarding Claim 14
Nguyen/Schaeffer/Sutton teach the system of claim 13. Nguyen/Schaeffer/Sutton may not explicitly teach:
wherein the real-time data is provided using FHIR technology.
Walinjkar teaches:
wherein the real-time data is provided using FHIR technology. [Walinjkar teaches at the Abstract ECG abnormalities based on annotation in MITDB could be classified and these ECG observation could be logged to a server implementation based on FHIR standards. Walinjkar teaches at the title personalized wearable systems for real-time classification and healthcare interoperability: real-time ECG classification and FHIR interoperability.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the interactive visualization for patient to patient comparison of Nguyen to the method and process for predicting and analyzing patient cohort response, progression, and survival of Schaeffer to the method and system for cardiac risk assessment of a patient using historical and real-time data of Sutton to the personalized wearable systems for real time ECG classification and healthcare interoperability of Walinjkar with the motivation of addressing modern wearable health monitoring devices, which have become easily available in the consumer market, however, real-time analyses and prediction along with alerts and alarms about a health hazard are not adequately addressed in such devices (Walinjkar at the Abstract).
Conclusion
Tangentially related to the specification:
Crabb et al. Innovation in resuscitation: A novel clinical decision display system for advanced cardiac life support. The American Journal of Emergency Medicine, Volume 43, 2021, Pages 217-223. Crab teaches clinical decision display for advanced cardia life support.
US 20140236630 A1 teaches systems and method for collecting, sharing, and analyzing data of electronic medical records for improved health analysis.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683