DETAILED ACTION
Notices to Applicant
This communication is a Final Office Action on the merits. Claims 16-30 as filed 07/11/2025, are currently pending and have been considered below.
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 present application is a 371 of PCT/EP2021/067475, filed 06/25/2021, which claims priority to EP20305712.0, filed 06/26/2020.
Claim Objections
Claims 16 and 22 are objected to because of the following informalities:
Claim 16, Line 5 recites “receiving data relating the patient health status, the received patient data comprising:” the claim limitation should read – “receiving patient data relating to the patient health status, the received patient data comprising:”.
Claim 22, Lines 4-5 recite “an input configured to receive data relating to the patient health status, the received patient data comprising:” the claim limitation should read – “an input configured to receive patient data relating to the patient health status, the received patient data comprising:”.
Appropriate correction is required.
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 16-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claims 16-21 are drawn to a computer-implemented method for assessing risks of a patient undergoing an anesthesia procedure, which is within the four statutory categories (i.e. method).
Independent Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 16 recites:
16. A computer-implemented method for assessing risks of a patient undergoing an anesthesia procedure using a global risk level allowing fine patient assessment and identification of subtle differences between two or more patients, the method comprising the following steps of: receiving data relating to the patient health status, the received patient data comprising: the patient's answers a questionnaire relating to at least a respiratory status and a cardiac status of the patient;
data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient; and
data from a repository comprising an anesthetic information and management record and/or an electronic medical record of the patient;
labelling each of the received patient data according to a set of tags comprising a "respiratory" tag and a "cardiac" tag;
from the received patient data, calculating a number N of index risks, the index risks comprising at least one respiratory risk calculated based on the patient data labeled as "respiratory" and at least one cardiac risk calculated based on the patient data labeled as "cardiac" by implementing a first algorithm, said first algorithm being a rule-based algorithm;
calculating said global risk level based on the index risks; and
outputting the global risk level so as to provide the risk of the patient undergoing an anesthesia procedure;
wherein the global risk level (32) is calculated via equation e1:
∑
i
=
1
N
k
i
*
R
i
(e1), N being the number of calculated index risks, k being a weighting factor, R being a numerical value associated with each calculated index risk.
The above limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded limitations of “at least one sensor” and “a repository,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient” and “a repository,” language, labelling each of the patient data as a “respiratory” tag and a “cardiac” tag, calculating a number of index risks and a global health risk level based on the index risks via an equation, and outputting the global risk level in the context of this claim encompasses the user manually collecting and analyzing patient health data for assessing risk. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind through observation, evaluation, judgment, and opinion but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, the limitation of “the global risk level (32) is calculated via equation e1:
∑
i
=
1
N
k
i
*
R
i
(e1)” falls under the abstract idea category of “Mathematical Concepts” as a recitation of mathematical formulas or equations. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements of “data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient” and “a repository,” to perform the above limitations. The elements in each of these steps are recited at a high-level of generality (i.e., a repository such as an electronic medical record stored in one or more local or remote databases and at least one sensor of a medical device e.g. a SpO2 sensor, ECG sensor, or a health-related smart device, or a camera of a mobile or smart phone to collect physiological signals as they relate to general purpose computer components (Application Specification at Pg. 15). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). 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 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 elements of “data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient” and “a repository,” to perform the above limitations amounts to no more than mere instructions to apply the exception using generic computer components. (i.e., a repository such as an electronic medical record stored in one or more local or remote databases and at least one sensor of a medical device e.g. a SpO2 sensor, ECG sensor, or a health-related smart device, or a camera of a mobile or smart phone as they relate to general purpose computer components (Application Specification at Pg. 15). (Application Specification at Pg. 15). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(f). The claim is not patent eligible.
Dependent claims 17-21 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond collecting and analyzing patient data. Dependent claim 18 recites “a medical database,” claim 19 recites “at least one medical guideline database,” claim 20 recites a “computer program product comprising instructions, when executed by a computer,” and claim 21 recites “a computer-readable storage medium comprising instructions, when the program is executed by a computer,” but these are recited at a high level of generality such that they amount to using generic computer components as a tool to perform the abstract idea. (See Application Specification at pgs. 15-16, 26, and 33-34). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claims 22-30 are drawn to a system for assessing risks of a patient undergoing an anesthesia procedure, which is within the four statutory categories (i.e. system).
Independent Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 22 recites:
22. A system for assessing risks of a patient undergoing an anesthesia procedure using a global risk level allowing fine patient assessment and identification of subtle differences between two or more patients, the system comprising:
an input configured to receive data relating to the patient health status, the received patient data comprising: the patient's answers a questionnaire relating to at least a respiratory status and a cardiac status of the patient;
data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient; and
data from a patient data repository;
a memory configured to store the patient data;
a processing unit configured to:
label each of the received patient data according to a set of tags comprising a "respiratory" tag and a "cardiac" tag;
based on the received data, calculate a number N of index risks, the index risks comprising at least one respiratory risk calculated based on the patient data labeled as "respiratory" and at least one cardiac risk calculated based on the patient data labeled as "cardiac" by implementing a first algorithm, said first algorithm being a rule-based algorithm;
calculate said global risk level based on the index risks; and
an output for outputting the global risk level so as to provide the risk of the patient undergoing an anesthesia procedure;
wherein the global risk level (32) is calculated via equation e1:
∑
i
=
1
N
k
i
*
R
i
(e1), N being the number of calculated index risks, k being a weighting factor, R being a numerical value associated with each calculated index risk.
The above limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded limitations of “data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient” and “a patient data repository; a memory configured to store the patient data; a processing unit,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient” and “a patient data repository; a memory configured to store the patient data; a processing unit,” language, labelling each of the patient data as a “respiratory” tag and a “cardiac” tag, calculating a number of index risks and a global health risk level based on the index risks via an equation, and outputting the global risk level in the context of this claim encompasses the user manually collecting and analyzing patient health data for assessing risk. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind through observation, evaluation, judgment, and opinion but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, the limitation of “the global risk level (32) is calculated via equation e1:
∑
i
=
1
N
k
i
*
R
i
(e1)” falls under the abstract idea category of “Mathematical Concepts” as a recitation of mathematical formulas or equations. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements of “data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient” and “a patient data repository; a memory configured to store the patient data; a processing unit,” to perform the above limitations. The elements in each of these steps are recited at a high-level of generality (i.e., a repository such as an electronic medical record stored in one or more local or remote databases, a memory and processing unit such as “in a general way to a processing device, which can for example include a computer,” and at least one sensor of a medical device or a health-related smart device as they relate to general purpose computer components (Application Specification at pgs.. 15-16, 26, and 33-34). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). 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 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 elements of “data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient” and “a patient data repository; a memory configured to store the patient data; a processing unit,” to perform the above limitations amounts to no more than mere instructions to apply the exception using generic computer components. (i.e., a repository such as an electronic medical record stored in one or more local or remote databases, a memory and processing unit such as “in a general way to a processing device, which can for example include a computer,” and at least one sensor of a medical device or a health-related smart device as they relate to general purpose computer components (Application Specification at Pg. 15). (Application Specification at pgs.. 15-16, 26, and 33-34). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(f). The claim is not patent eligible.
Dependent claims 23-30 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond collecting and analyzing patient data. Dependent claim 24 recites “a reference medical database,” and “a medical database,” claim 35 recites “at least one medical guideline database,” claim 26 recites “a machine learning algorithm,” and “anonymize and store the measured patient outcome and the patient data in the training dataset,” at a high level of generality and “a reference medical database,” and claim 28 recites “the at least one sensor is selected among: an optical sensor, a pressure senso; a force sense; a thermal sensor,” but these are recited at a high level of generality such that they amount to using generic computer components as a tool to perform the abstract idea. (See Application Specification at pgs. 14-16, 25, 26, 29 and 33-34). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
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.
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.
Claims 16-25 and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Pub. No. 2016/0232321 (hereinafter “Silverman”) in view of U.S. Patent Application Pub. No. 2008/0146893 A1 (hereinafter “Levendowski”).
RE: Claim 16 (Currently Amended) Silverman teaches the claimed:
16. A computer-implemented method for assessing risks of a patient undergoing an anesthesia procedure using a global risk level allowing fine patient assessment and identification of subtle differences between two or more patients, the method comprising the following steps: receiving data relating to the patient health status, the received data comprising: the patient's answers a questionnaire relating to at least a respiratory status and a cardiac status of the patient ((Silverman, [0051], [0192], [0195], [0200], [0201]) (an object of the present invention to provide a multi-dimensional system for assessing, coding, quantifying, displaying, integrating and communicating information relating to patient health and perioperative risk; The system includes a mechanism for inputting patient information and providing an output relating to the patient health and perioperative risk; The data may have been generated by a variety of ways, including prior entry by another caregiver, the same caregiver, a patient ( e.g., a questionnaire), or import from a testing facility; The healthcare provider is also prompted to rate the severity of the signs and symptoms attributable to smoking (which may be equivalent to (and co-populate) that for the features and subfeatures selected under the "RESPIRATORY" system); When medications are entered during the basic history and physical or via an alternative form of data entry (e.g., patient questionnaire), predetermined medications would co-populate one of the series of "potentially worrisome meds" screens; e.g. aspirin and Plavix));
data from a repository comprising an anesthetic information and management record and/or an electronic medical record of the patient ((Silverman, Fig. 3A, [0106]) (the present invention introduces the practice of assigning a 1 to 5(or 0 to 5) severity score for each major organ system based on information learned from the patient history and physical examination and associated testing; Other Applications & Interfaces, including remote data bases, reference sources, automated clinical records (which may be matched for linking of hard-coded entries)));
labelling each of the received patient data according to a set of tags comprising a "respiratory" tag and a "cardiac" tag ((Silverman, Fig. 4; [0638], [0733]) (Table 28 illustrates application of the inventive proposed common scale for monitoring parameters which otherwise have markedly disparate data ranges that would limit multiparameter graphical display and interparameter comparison. Each is centered at its consensus "normal" value; “CARD” (3) negative for murmur, dysthymia …; “RESP” (3) negative for COPD + asthma w/ rare symptoms …; Scoring according to the preferred embodiments essentially entails transparent conversion of clinical documentation to generate numeric representation of ACT and SEY at systemic as well as local (target and, when indicated, condition levels. The limited numeric vocabulary provides health care provider-and electronic health record-friendly tagging that is generalizable, substitutable and interoperable));
from the received patient data, calculating a number N of index risks, the index risks comprising at least one respiratory risk calculated based on the patient data labeled as "respiratory" and at least one cardiac risk calculated based on the patient data labeled as "cardiac" by implementing a first algorithm, said first algorithm being a rule-based algorithm ((Silverman, Fig. 4; [0138], [0144], [0638], [0733]) (The SISS™ scoring permits cumulative scoring based upon the number of disorders ( as may be ascertained from the number of positive feature categories) or diseased body systems and their degree of dysfunction in what is called herein the SHAPE™ Aggregate Disorders (SAD™) score (FIG. 3); Table 28 illustrates application of the inventive proposed common scale for monitoring parameters which otherwise have markedly disparate data ranges that would limit multiparameter graphical display and interparameter comparison. Each is centered at its consensus "normal" value; “CARD” (3) negative for murmur, dysthymia …; “RESP” (3) negative for COPD + asthma w/ rare symptoms …; ensuring identification and appropriate cumulation when more than one body system has a given score ( e.g., CNS, CARD and RESP all receive a score of 3)));
calculating said global risk level based on the index risks ((Silverman, [0138]) (SHAPE™ Aggregate Disorders (SAD™) score)); and
outputting the global risk level so as to provide the risk of the patient undergoing an anesthesia procedure ((Silverman, Fig. 3B) (OUTPUTS: scored display));
wherein the global risk level (32) is calculated via equation e1:
∑
i
=
1
N
k
i
*
R
i
(e1), N being the number of calculated index risks, k being a weighting factor, R being a numerical value associated with each calculated index risk ((Silverman, [0239]) (an aggregate score of the patient's disorders that is based upon the body system-specific 1-5 SISS™ scores (SAD™). described above. 2) What are referred to herein as SMASH™ (SHAPE™ Multifaceted Assessment of Surgical Harm) indices; the function may be for multiplying or weighting)).
Silverman fails to explicitly teach, but Levendowski et al. teaches the claimed:
data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient ((Levendowski et al., [0031], [0039]) (a single site wireless recorder that is affixed to the forehead by the patient, it is used to acquire oxygen saturation, pulse rate, airflow, respiratory effort)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the acquisition of oxygen saturation, pulse rate, airflow, and respiratory effort through a recorder monitor as taught by Levendowski et al. within the method and system for assessing, coding, quantifying, displaying, integrating and communicating information relating to patient health and perioperative risk as taught by Silverman with the motivation of assessing patient risk to make it easier for the clinician and patient to select the surgical option which maximized patient comfort and safety (Levendowski et al., [0011]).
RE: Claim 17 (Previously Presented) Silverman and Levendowksi et al. teach the claimed:
17. The method according to claim 16, further comprising the following steps: selecting at least one recommendation from a library of recommendations, the at least one recommendation being selected based at least on the global risk level ((Levendowski et al., [0047]) (other outputs from the system include data that can lead to a recommendation that can reduce the likelihood of perioperative complications; for example, the initiation of treatment with CPAP is recommended for bariatric patients approximately one month prior to surgery to help stabilize the patient’s respiratory system and improve their immune system));
receiving as input data relating to the health status of the patient measured after execution of the at least one recommendation; modifying each of the N index risks based on the data relating to the health status of the patient measured after execution of the at least one recommendation ((Levendowksi et al., [0047]) (so if the predictive model includes updated information about CPAP time-on-pressure, the likelihood of complications can be updated)); and
calculating an updated global risk level based on the modified index risks ((Levendowksi et al., [0047]) (patient’s who are non-compliant would be expected to have a greater risk of complications than the compliant than a compliant patient (all other factors held constant))).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine recommendations for a treatment based on a calculated patient risk and updating the calculated risk following the recommended treatment as taught by Levendowski et al. within the method and system for assessing, coding, quantifying, displaying, integrating and communicating information relating to patient health and perioperative risk as taught by Silverman with the motivation of assessing patient risk to make it easier for the clinician and patient to select the surgical option which maximized patient comfort and safety (Levendowski et al., [0011]).
RE: Claim 18 (Previously Presented) Silverman and Levendowksi et al. teach the claimed:
18. The method according to claim 16, further comprising the following steps: periodically, receiving data from a medical database comprising patient data and respective outcomes ((Silverman, [0059], [0078], [0212]) (Thick dashed line represents unique bidirectional relationship between SHAPE™ and "Subsequent Events and Outcomes"-SHAPE™ not only provides data to predict outcomes but also may be updated (with associated timestamp) based on events and outcomes; as information pertaining to SISS™, SOCU™ SICU™, Medications and Risk indicators becomes available, scores for existing indications may be updated automatically));
calculating at least one correlation between the received patient data and the respective outcomes ((Silverman, [0164]-[0166], [0330]) (define a "Weighting Code (vs. Multisystem Disorder) for each body system affected by the Multisystem Disorder; When a specific body system (e.g. "Cardiac") is affected by a multisystem condition ( e.g., "Amyloidosis"), the interaction is more complex than between two specific body systems because the user must determine if the multisystem condition's systemic impact is solely due to its effect on Cardiac (-could be designated"=" due to multiple effects on specific body systems, none of which merits a greater score than its cited effects on the Cardiac system (-could be designated as"+"); or due to factors wherein the multisystem disorder may have greater overall systemic impact than the effects cited for specific body systems such as Cardiac (--could be designated as "+>"); information from the cardiac evaluation may be used to determine beta-blocker use (while obviously not influencing the decision to perform an evaluation because it was generated by the evaluation). In such cases, the information ( e.g., from an echocardiogram or stress test) may be entered as a new potential scored "risk factor" or as justification for modifying the score of an existing factor));
modifying the inputs of equation e1 based on the at least one correlation; optionally, outputting the modified first algorithm ((Silverman, [0166], [0218]) (An example might be supermorbid obesity (typically assigned a score of 3) without significant organ dysfunction; the multisystem condition in and of itself may have significant impact during and after surgery. Likewise, fulminant amyloidosis may receive a higher score than its current manifestations on specific systems; Co-populate and thereby update integrated assessment scores and diagnostic and treatment algorithms)).
RE: Claim 19 (Previously Presented)Silverman and Levendowksi et al. teach the claimed:
19. The method according to claim 16, further comprising the following steps: periodically, receiving medical guidelines from at least one medical guideline database, each medical guideline being received at a respective time, the medical guidelines being stored in a database and being associated with a label ((Silverman, [0308]) (Table 18 Risk Index, ACC/AHA Guidelines e.g. Surgical Risk: High – SICU/SISS score 5, High Intermediate – score 4, Intermediate – score 3, Low Intermediate – score 2; Low – score 1));
comparing each received medical guideline with the respective medical guideline associated with the same label and being received at a preceding acquisition time ((Silverman, [0316]) (the SHAPE™ database may export the relevant text with either: its CNS System score; with the score of its co-populated inclusion in the "Nonspecific Risk Factors & Indicators for Ischemic Heart Disease" Subsystem of the Cardiac System; or with a converted score designed for the transition from SHAPE™ to the ACC/AHA Guidelines; the Cardiac Risk Index, the ACC/AHA guideline update on perioperative cardiovascular evaluation for noncardiac surgery, and a beta-blocker score all are available from the data tabulated in Table 18));
modifying the inputs of equation e1 based on the result of the comparison step; optionally, outputting the modified first algorithm ((Silverman, [0322]) (The default option would be not to include Exercise Tolerance in a cumulative score since this does not in and of itself constitute a disorder; Of special note-the ACC/ AHA Guidelines do not treat exercise tolerance as a minor, intermediate or major clinical risk factor. Instead, they rate it as a separate "good" or "poor" category which then augments or modulates the impact of the clinical risk factors. This is reflected in the inventive algorithm for converting the score assigned to exercise tolerance as a subsystem in FIG. 32 to its role as a modifier in the ACC/AHA guidelines in Tables 18a and 18b)).
RE: Claim 20 (Previously Presented) Silverman and Levendowksi et al. teach the claimed:
20. A computer program product for assessing the risks of a patient undergoing an anesthesia procedure, the computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method according to claim 16 ((Levendowski et al., [0035], [0055]) (an exemplary computer network system which implements embodiments of the invention; The server can be one or more computers or devices on a network that manages network resources; the system can be configured with a database or data storage area that can be any sort of internal or external memory device and may include both persistent and volatile memories; The function of the database is to maintain data in for long-term storage and also to provide efficient and fast access to instructions for applications that are executed; in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium including a network storage medium)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the computer system comprising a software module that can reside in memory as taught by Levendowski et al. within the method and system for assessing, coding, quantifying, displaying, integrating and communicating information relating to patient health and perioperative risk as taught by Silverman with the motivation of assessing patient risk to make it easier for the clinician and patient to select the surgical option which maximized patient comfort and safety (Levendowski et al., [0011]).
RE: Claim 21(Previously Presented) Silverman and Levendowksi et al. teach the claimed:
21. A computer-readable storage medium comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method according to claim 16 ((Levendowski et al., [0035], [0055]) (an exemplary computer network system which implements embodiments of the invention; The server can be one or more computers or devices on a network that manages network resources; the system can be configured with a database or data storage area that can be any sort of internal or external memory device and may include both persistent and volatile memories; The function of the database is to maintain data in for long-term storage and also to provide efficient and fast access to instructions for applications that are executed; in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium including a network storage medium)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the system comprising persistent and volatile memories and storage for instructions for applications that are executed as taught by Levendowski et al. within the method and system for assessing, coding, quantifying, displaying, integrating and communicating information relating to patient health and perioperative risk as taught by Silverman with the motivation of assessing patient risk to make it easier for the clinician and patient to select the surgical option which maximized patient comfort and safety (Levendowski et al., [0011]).
RE: Claim 22 (Currently Amended) Silverman teaches the claimed:
22. (Currently Amended) A system for assessing risks of a patient undergoing an anesthesia procedure using a global risk level allowing fine patient assessment and identification of subtle differences between two or more patients, the system comprising: an input configured to receive data relating to the patient health status, the received data comprising: the patient's answers to at least one questionnaire relating to at least a respiratory status and a cardiac status of the patient ((Silverman, [0051], [0192], [0195], [0200], [0201]) (an object of the present invention to provide a multi-dimensional system for assessing, coding, quantifying, displaying, integrating and communicating information relating to patient health and perioperative risk; The system includes a mechanism for inputting patient information and providing an output relating to the patient health and perioperative risk; The data may have been generated by a variety of ways, including prior entry by another caregiver, the same caregiver, a patient ( e.g., a questionnaire), or import from a testing facility; The healthcare provider is also prompted to rate the severity of the signs and symptoms attributable to smoking (which may be equivalent to (and co-populate) that for the features and subfeatures selected under the "RESPIRATORY" system); When medications are entered during the basic history and physical or via an alternative form of data entry (e.g., patient questionnaire), predetermined medications would co-populate one of the series of "potentially worrisome meds" screens; e.g. aspirin and Plavix));
data from a patient data repository; a memory configured to store the patient data ((Silverman, Fig. 3A, [0106], [0186]) (the present invention introduces the practice of assigning a 1 to 5(or 0 to 5) severity score for each major organ system based on information learned from the patient history and physical examination and associated testing; Other Applications & Interfaces, including remote data bases, reference sources, automated clinical records (which may be matched for linking of hard-coded entries; the actual data are stored in the database for subsequent applications as well as to provide greater information to the patient's caregivers)));
a processing unit configured to: label each of the received data according to a set of tags comprising a "respiratory" tag and a "cardiac" tag ((Silverman, Fig. 4; [0638], [0733]) (Table 28 illustrates application of the inventive proposed common scale for monitoring parameters which otherwise have markedly disparate data ranges that would limit multiparameter graphical display and interparameter comparison. Each is centered at its consensus "normal" value; “CARD” (3) negative for murmur, dysthymia …; “RESP” (3) negative for COPD + asthma w/ rare symptoms …; Scoring according to the preferred embodiments essentially entails transparent conversion of clinical documentation to generate numeric representation of ACT and SEY at systemic as well as local (target and, when indicated, condition levels. The limited numeric vocabulary provides health care provider-and electronic health record-friendly tagging that is generalizable, substitutable and interoperable));
based on the received data, calculate a number N of index risks, the index risks comprising at least one respiratory risk calculated based on the patient data labeled as "respiratory" and at least one cardiac risk calculated based on the patient data labeled as "cardiac" by implementing a first algorithm, said first algorithm being a rule-based algorithm; calculate said global risk level based on the index risks ((Silverman, Fig. 4; [0138], [0144], [0638], [0733]) (The SISS™ scoring permits cumulative scoring based upon the number of disorders ( as may be ascertained from the number of positive feature categories) or diseased body systems and their degree of dysfunction in what is called herein the SHAPE™ Aggregate Disorders (SAD™) score (FIG. 3); Table 28 illustrates application of the inventive proposed common scale for monitoring parameters which otherwise have markedly disparate data ranges that would limit multiparameter graphical display and interparameter comparison. Each is centered at its consensus "normal" value; “CARD” (3) negative for murmur, dysthymia …; “RESP” (3) negative for COPD + asthma w/ rare symptoms …; ensuring identification and appropriate cumulation when more than one body system has a given score ( e.g., CNS, CARD and RESP all receive a score of 3))); and
an output for outputting the global risk level so as to provide the risk of the patient undergoing an anesthesia procedure ((Silverman, Fig 3B, [0138]) (SHAPE™ Aggregate Disorders (SAD™) score; OUTPUTS: scored display));
wherein the global risk level is calculated via equation e1:
∑
i
=
1
N
k
i
*
R
i
(e1),N being the number of calculated index risks, k being a weighting factor, R being a numerical value associated with each calculated index risk ((Silverman, [0239]) (an aggregate score of the patient's disorders that is based upon the body system-specific 1-5 SISS™ scores (SAD™). described above. 2) What are referred to herein as SMASH™ (SHAPE™ Multifaceted Assessment of Surgical Harm) indices; the function may be for multiplying or weighting)).
Silverman fails to explicitly teach, but Levendowski et al. teaches the claimed:
data collected from at least one sensor configured to sense physiological signals representative of the respiratory status and the cardiac status of the patient ((Levendowski et al., [0031]) (a single site wireless recorder that is affixed to the forehead by the patient, it is used to acquire oxygen saturation, pulse rate, airflow, respiratory effort)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the acquisition of oxygen saturation, pulse rate, airflow, and respiratory effort through a recorder monitor as taught by Levendowski et al. within the method and system for assessing, coding, quantifying, displaying, integrating and communicating information relating to patient health and perioperative risk as taught by Silverman with the motivation of assessing patient risk to make it easier for the clinician and patient to select the surgical option which maximized patient comfort and safety (Levendowski et al., [0011]).
RE: Claim 23 (Previously Presented) Silverman and Levendowksi et al. teach the claimed:
23. The system according to claim 22, wherein the memory is further configured to store a library of recommendations, and the processing unit is further configured to: select at least one recommendation from the library of recommendations based at least on the global risk level ((Levendowski et al., [0047]) (other outputs from the system include data that can lead to a recommendation that can reduce the likelihood of perioperative complications; for example, the initiation of treatment with CPAP is recommended for bariatric patients approximately one month prior to surgery to help stabilize the patient’s respiratory system and improve their immune system));
receive as input data relating to the health status of the patient measured after execution of the at least one recommendation; modify each of the index risks based on the data relating to the health status of the patient measured after execution of the at least one recommendation ((Levendowksi et al., [0047]) (so if the predictive model includes updated information about CPAP time-on-pressure, the likelihood of complications can be updated)); and
calculating an updated global risk level based on the modified index risks ((Levendowksi et al., [0047]) (patient’s who are