DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 06/04/2026.
Status of the Claims
Claims 1-25 are pending.
Claims 1 is amended.
Response to Arguments
Rejections Under 35 U.S.C.§103
Applicant’s Argument:
Applicant's argue see remarks pages 6-7, filed 06/04/2026 with respect to the rejection(s) of Claims that
“The Office has alleged that Rosenfeld teaches decision support algorithms for
cardiogenic shock and that Jentzer teaches SCAI shock stage classification. However, neither Rosenfeld nor Jentzer teaches the limitations recited in claim 1, as amended.
Claim 1, as amended, recites "continuously and automatically generating" shock stage
classification data. Rosenfeld' s system requires clinicians to manually navigate decision
flowcharts, with the user interface allowing "the physician to re-enter the physician resources
module at any particular location with results of the tests" to "continue with the diagnostic
algorithm processing." See Rosenfeld, paragraph [0218]. This manual, clinician-driven
process is fundamentally different from the continuous and automatic generation of shock stage classification data recited in claim 1,
Additionally, claim 1, as amended, recites generating an alert "in response to the
computer system detecting a transition between discrete shock stages." Rosenfeld's system uses threshold-based vital sign alerting where "[ o ]ne family of alarms looks for changes in vital signs over time, using pre-configured thresholds." See Rosenfeld, paragraph [0226].
These threshold-based alerts are triggered when individual vital sign parameters exceed thresholds, not when the computer system detects a transition between discrete shock stages as recited in claim 1, as amended.”
Examiner’s Response:
Applicant's arguments, see remarks pages 6-7, filed 06/04/2026 with respect to the rejection(s) of Claims 1 has been considered, and are not persuasive.
Examiner respectfully disagrees with the argument. Rosenfeld teaches the real time decision support for care of patients in Intensive Care Units (ICUs). The system monitors provide constant real time patient information based on algorithm for certain health condition. The algorithm of the support system includes a decision on “cardiogenic stage”. In the field of cardiology, the “cardiogenic shock” stages follow a standard criterion. The standard criteria to define a shock stage are known in the field of study. Rosenfeld teaches in [0232] “As noted earlier, a key objective of the present
invention is to standardize care and treatment across ICUs. This is effective in the present invention by providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition”. Therefore, examiner understood that Rosenfeld follows standard algorithm to classify cardiogenic shock and alert the command center based on patient’s health condition 24/7. See [0058] “The algorithms are selected from the group consisting of algorithms for treating; (…) Cardiogenic Shock”.
Applicant argument in regards to Rosenfeld paragraph [0218] and manual, clinician-driven process is not persuasive. This paragraph teaches treatment plan based on identification of a condition by the “decision support” algorithm. The examiner did not use this paragraph to address the shock stage classification. Examiner disagrees with this argument. Rosenfeld teaches continuous patient condition monitoring identifying cardiogenic shock following standard practice in the field and alerting the system. The treatment plan is administered accordingly. The applicant argument is not persuasive, and the rejection of claim 1 is maintained. See the rejections set forth below.
Applicant’s Argument:
Applicant's argue see remarks page 7, filed 06/04/2026 with respect to the rejection(s) of Claims that
“Jentzer is a retrospective clinical study that classified patients into SCAI stages for
research purposes, where "SCAI CS stages A through E were classified retrospectively using CICU admission data." See Jentzer, page 2119. Jentzer does not describe an automated staging algorithm that continuously and automatically generates shock stage classification data in real-time or detects transitions between discrete shock stages.
Accordingly, the combination of Rosenfeld and Jentzer does not teach or suggest
continuously and automatically generating shock stage classification data and generating an alert in response to detecting a transition between discrete shock stages. Applicant submits that claim 1, as amended, and its dependent claims 2-21, are patentable over the cited references.”
Examiner’s Response:
Applicant's arguments, see remarks page 7, filed 06/04/2026 with respect to the rejection(s) of Claims 1 has been considered, and are not persuasive.
Primary reference Rosenfeld teaches a standard support decision of “cardiogenic shock” condition 24/7 following standard practice in the field. Rosenfeld does not teach how the stage classifications are made. It is a built-in algorithm available and standard for the condition that decides. However, the secondary art Jentzer discloses how the criterion for stage classification is set. There are standard criterions followed in the field and the national college of cardiology. See the evidence reference as well.
Therefore, applicant argument is not persuasive. The rejection of claim 1 is maintained.
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.
Claims 1-15, and 18-21, 25 are rejected under 35 U.S.C. 103 as being unpatentable over Rosenfeld et al. (US 2014/0046674 A1, hereinafter Rosenfeld, IDS ref) and in view of Jentzer et al. (hereinafter Jentzer, IDS ref) “Cardiogenic Shock Classification to Predict Mortality in the Cardiac Intensive Care Unit” Journal of The American college of Cardiology, Vol. 74, No. 17, 2019-2028).
Evidence Reference: Kapur et al. “Criteria for Defining Stages of Cardiogenic shock Severity”, JOURNAL OF T H E AMERICAN COLLEGE OF CARDIOLOGY, VOL. 80, NO. 3, 2022
Regarding Claim 1, Rosenfeld teaches,
A method for generating an order set (Rosenfeld, Table 1, ALGORITHMS
& PRACTICE STANDARDS DECISION SUPPORT, Cardiovascular, Figure 34A steps 2202-2218, treatment plan) for a patient based on a shock stage, (Rosenfeld, Table 1, cardiogenic shock) the method comprising:
(a) receiving patient health data with a computer system, wherein the patient health data are associated with a patient and are continuously received in a real-time manner (Rosenfeld, [0036], “The command center/remote location is manned by intensivists 24 hours a day, seven days per week. Each ICU comprises a nurse's station, to which data flows from individual beds in the ICU. Each patient in the ICU is monitored by a video camera, as well as by clinical monitors typical for the intensive care unit. These monitors provide constant real time patient information to the nurse's station, which in turn provides that information over a dedicated T-1 (high bandwidth) line to the ICU command center/remote location” NOTE: data collected 24/7 from the patient);
(b) accessing a staging algorithm with the computer system, the staging algorithm being configured to generate shock stage classification data from patient health data (Rosenfeld, [0058], “the patient care management system further comprises a relational database for storing a plurality of decision support algorithms and for prompting intensivists to provide care to patients based upon any of the decision support algorithms. The algorithms are selected from the group consisting of algorithms for treating; (…) Cardiogenic Shock”). [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, Therefore, decision support for “cardiogenic shock” and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages). In cardiology field, “cardiogenic” shock is defined and classified using standard practice. See evidence reference above Kapur et al.)
(c) inputting the patient health data to the staging algorithm using the computer system as the patient health data are continuously received by the computer system in real-time (Rosenfeld, [0002] This invention relates generally to the care of
patients in Intensive Care Units (ICUs). More particularly this invention is a system and method for care of the critically ill that combines a real-time, multi-node telemedicine network and an integrated, patient care management system to enable specially-trained Intensivists to provide 24-hour/7-day-per-week patient monitoring and management to multiple, geographically dispersed ICUs from both on-site and remote locations);
continuously and automatically generating an output as shock stage classification data for the patient (Rosenfeld, [0060] “0060] Embodiments of the present invention provide methods for continuous expert critical care. Patients are monitored in a plurality of ICU's. Information from the patient monitoring is communicated to at least one command center over a first network”. (NOTE: the decision support algorithm generates the “cardiogenic shock stages” automatically based on standard practice guideline and the patient’s data. This data is generated continuously 24/7. See Rosenfeld, [0232], table 1, “This is effective in the present invention by providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition [0058]”),
(d) generating, with the computer system, an alert (Rosenfeld, Figure 19, Rule engine 642, Alert screen 644; [0225] Referring to FIG. 19. Thus, monitor 636 provides information in HL 7 form to the interface engine 638. The physiological data is then formatted by the interface engine for storage in the database 640 where all patient information is maintained. The rules engine 642 searches for patterns of data indicative of clinical deterioration. [0226] One family of alarms looks for changes in vital signs over time, using pre-configured thresholds. These thresholds are patient-specific and setting/disease-specific” in response to the computer system detecting a transition between discrete shock stages based on the shock stage classification data for the patient (Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, and [00232], As noted in Table I below, a wide variety of conditions is noted. Each of the conditions has an associated guideline of practice standard that can be presented to the intensivist who might be faced with that particular condition in a patient”. Therefore, standard decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages, Standard shock stage classification is used in all medical treatment facility).
(e) generating, with the computer system and in response to the alert, an order set based on the shock stage classification data for the patient (Rosenfeld, [0230] “In order to standardize treatment across ICUs at the highest possible level, decision support algorithms are used in the present invention. These include textural material describing the topic, scientific treatments and possible complications. This information is available in real time to assist in all types of clinical decisions from diagnosis to treatment to triage”. Figure 34A- steps 2200-2218, figure 34b, [0345] Initially, the intensivist is prompted to determine whether the patient is hemodynamically stable (no angina, heart failure, or hypotension (systolic less than 80 mm)) 2200. If this criterion is not met, the intensivist is prompted to go to the cardio-pulmonary guidelines algorithm which is generally known to those skilled in the art”. NOTE: for specific cardiogenic shock, standard decision support known in the medical field is used. For example. the stage classifications are standard criteria for treatment applied.)
(f) storing the order set in an electronic medical record (EMR) for the patient using the computer system. (Rosenfeld, Figure 9,208, Database Server/Warehouse, Figure 20, note stored in EMR line log updated, Figure 9, [0202], The database server/warehouse function 208 comprises the amassed information of a wide variety of patients, in their various conditions, treatments, outcomes, and other information of a statistical nature that will assist clinicians and intensivists in treating patients in the ICU. The headquarters' function also serves to allow centralized creation of decision support algorithms and a wide variety of other treatment information that can be centrally managed and thereby standardized across a variety of command center/remote locations”).
Rosenfeld teaches standard support decision algorithm for standard practice guideline for cardiogenic shocks stage decision ICU patients and consequently implement treatment plan, but Rosenfeld is silent on how the shock stage classification is done especially silent on generating an output as shock stage classification data for the patient. wherein the shock stage classification data indicate that the patient health data include features that are correlated with a particular shock stage classification.
However, Jentzer teaches generating an output as shock stage classification data for the patient. (Jentzer, Table 2, Figure 1, Page 2119, “method, SCAI CS stages A through E were classified retrospectively using CICU admission data based on the presence of hypotension or tachycardia, hypoperfusion, deterioration, and refractory shock. Hospital mortality in each SCAI shock stage was stratified by cardiac arrest (CA)”. Page 2120 right col. “We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration” NOTE: shock stage classification is correlated with patients’ health condition, and determined based on, hypertension, tachycardia, hypoperfusion etc. data ).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 2, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the shock stage classification data indicate an SCAI shock stage.
However, Jentzer teaches wherein the shock stage classification data indicate an SCAI shock stage. (Jentzer, Table 2, Figure 1, Page 2119,” method, SCAI CS stages A through E were classified retrospectively using CICU admission data based on the presence of hypotension or tachycardia, hypoperfusion, deterioration, and refractory shock. Hospital mortality in each SCAI shock stage was stratified by cardiac arrest (CA)”. Page 2120 right col. “We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 3, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the shock stage classification data indicate a numerical shock score value.
However, Jentzer teaches wherein the shock stage classification data indicate a numerical shock score value. (Jentzer, Table 4, TABLE 4 Severity of Illness Scores, Vital Signs, and Laboratory Data of Patients According to SCAI Shock Stage)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification score to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 4, combination of Rosenfeld and Jentzer teaches the method of claim 3,
Rosenfeld teaches shock score value comprises a cardiogenic shock score value. (Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1”).
Regarding Claim 5, combination of Rosenfeld and Jentzer teaches the method of claim 4,
Rosenfeld is silent on wherein generating the shock stage classification data further comprises correlating the cardiogenic shock score value with an SCAI shock stage using the computer system.
However, Jentzer teaches wherein generating the shock stage classification data further comprises correlating the cardiogenic shock score value with an SCAI shock stage using the computer system. (Jentzer, Page 2125, The SCAI statement authors clearly emphasize the added hazard posed by the presence of CA occurring in patients with or at risk of CS (12). In this cohort, the prevalence of CA increased substantially with increasing shock stage, highlighting the correlation between CA and severe shock in CICU patients. In our analysis, we clearly demonstrate the added mortality hazard posed by CA at all levels of shock severity, validating CA as a prognostically important modifier in the SCAI shock classification”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification score to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 6, combination of Rosenfeld and Jentzer teaches the method of claim 4,
Rosenfeld further teaches wherein the cardiogenic shock score value is computed based on data in the patient health data associated with measures of hypotension, lactate, vasopressor use, renal function, temporary mechanical support, and cardiac arrest. (Rosenfeld, Table 1, ALGORITHMS, & PRACTICE STANDARDS DECISION SUPPORT, Cardiovascular, MANAGEMENT OF HYPOTENSION, INOTROPES, MYOCARDIAL INFARCTION, renal dysfunction, see figure 33-37)
Regarding Claim 7, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the shock stage classification data indicate a risk stage for deterioration of the patient.
However, Jentzer teaches wherein the shock stage classification data indicate a risk stage for deterioration of the patient (Jentzer, 2120, right col. Deterioration was defined as increasing vasoactive drug requirements after the first hour or a rising lactate level after admission. We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration)”.
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification score to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 8, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches wherein the patient health data comprise EMR data for the patient. (Rosenfeld, Figure 9,208, Database Server/Warehouse, Figure 20, note stored in EMR line log updated, Figure 9, [0202], “The database server/warehouse function 208 comprises the amassed information of a wide variety of patients, in their various conditions, treatments, outcomes, and other information of a statistical nature that will assist clinicians and intensivists in treating patients in the ICU”).
Regarding Claim 9, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches wherein the alert comprises an electronic message generated by the computer system. Rosenfeld, Figure 19, Rule engine 642, Alert screen 644; [0225] Referring to FIG. 19. Thus, monitor 636 provides information in HL 7 form to the interface engine 638. The physiological data is then formatted by the interface engine for storage in the database 640 where all patient information is maintained. The rules engine 642 searches for patterns of data indicative of clinical deterioration. The data base for each individual patient is then reviewed and process rules are applied 460 to the vital sign data” [0214]. These process rules relate to certain alarming conditions which, if a certain threshold is reached, provides an alarm to the intensivist on duty. The vital sign alarm 462 is then displaced to the intensivist who can then take appropriate action [0226] One family of alarms looks for changes in vital signs over time, using pre-configured thresholds. These thresholds are patient-specific and setting/disease-specific”).
Regarding Claim 10, combination of Rosenfeld and Jentzer teaches the method of claim 9,
Rosenfeld further teaches wherein generating the alert comprises sending a page to a clinician by transmitting the page from the computer system to a pager. ”[0214]. These process rules relate to certain alarming conditions which, if a certain threshold is reached, provides an alarm to the intensivist on duty. The vital sign alarm 462 is then displaced to the intensivist who can then take appropriate action”).
Regarding Claim 11, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the alert further comprises a tier alert associated with an SCAI stage indicated by the shock stage classification data.
Jentzer teaches wherein the alert further comprises a tier alert associated with an SCAI stage indicated by the shock stage classification data (Jentzer, Page 2121, right col. The prevalence of CA increased across the SCAI shock stages, from 7.3% in stage A to 55.8% in stage E. As the SCAI shock stage increased, there were more extensive vital sign and laboratory abnormalities, higher severity of illness scores, and more frequent AKI (Table 4). The use and dosage of vasoactive medications and supportive therapies”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification score to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 12, combination of Rosenfeld and Jentzer teaches the method of claim 11,
Rosenfeld further teaches wherein the order set is updated based on a tier indicated by the tier alert. (Rosenfeld, Figure 33A-33C, [0345], “Therefore, the intensivist is lead through a decision support algorithm which prompts the
intensivist to determine the appropriate care to be given. [0345] Initially, the intensivist is prompted to determine whether the patient is hemodynamically stable (no angina, heart failure, or hypotension (systolic less than 80 mm)) 2200. If this criterion is not met, the intensivist is prompted to go to the cardio-pulmonary guidelines algorithm which is generally known to those skilled in the art. [0346] Alternatively, if this criterion is met, the intensivist is prompted to determine whether the patient is within 7 days of a myocardial infarction or at risk for myocardial ischemia 2202. If the patient is not within 7 days of a myocardial infarction or at risk for myocardial ischemia 2202, the intensivist is prompted to determine whether the wide complex QRS rhythm is sustained (greater than 30 seconds) 223”. Based on the prompted alert the internist follows the algorithm to treat the patient accordingly).
Regarding Claim 13, combination of Rosenfeld and Jentzer teaches the method of claim 11,
Rosenfeld further teaches wherein generating the alert comprises sending the alert to multiple users in a multidisciplinary health care team based on the tier indicated by the tier alert. (Rosenfeld, Figure 9, patient info front end at different locations. [0036], “The command center/remote location is manned by intensivists 24 hours a day, seven days per week. Each ICU comprises a nurse's station, to which data flows from individual beds in the ICU. Each patient in the ICU is monitored by a video camera, as well as by clinical monitors typical for the intensive care unit. These monitors provide constant real time patient information to the nurse's station, which in turn pro vides that information over a dedicated T-1 (high bandwidth) line to the ICU command center/remote location”. [0054] Intensivists detect impending problems by intermittently screening patient data, including both real time and continuously stored vital sign data. Patient severity of illness determines the frequency with which each patient's data is reviewed by the intensivists”).
Regarding Claim 14, combination of Rosenfeld and Jentzer teaches the method of claim 13,
Rosenfeld further teaches wherein generating the alert comprises providing, via the computer system, an access to a virtual videoconference room for the multiple users based on the tier indicated by the tier alert. (Rosenfeld, Figure 9, 11 [0052] Command center/remote location personnel communicate with ICU staff through videoconferencing and through "hot phones," which are dedicated telephones directly linked between the command center/remote location and the ICU. These communications links are used to discuss patient care issues and to communicate when a new order has been generated”).
Regarding Claim 15, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches further comprising generating a care path for the patient and displaying the care path for the patient to a clinician via the computer system, wherein the care path provides a visual depiction of escalation pathways and de-escalation pathways between different shock stages for the patient. (Rosenfeld, Figure 9, front end patient info, Figure 12, displays vital sign alarming 462, Display Real Time Vital Data, 472, Figure 11, remote surveillance display).
Regarding Claim 18, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches, wherein the shock stage comprises a cardiogenic shock stage. (Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1”).
Regarding Claim 19, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches wherein the shock stage comprises a non-cardiogenic shock stage. (Rosenfeld, Figure 31A, Step 2912, Non cardiogenic shock, such as “Septic shock”, “occult hemorrhage”).
Regarding Claim 20, combination of Rosenfeld and Jentzer teaches the method of claim 19,
Rosenfeld further teaches, wherein the non-cardiogenic shock stage comprises one of a disruptive shock stage, a hypovolemic shock stage, or an obstructive shock stage. (Rosenfeld, Figure 31A, Step 2912, hypovolemic shock, such as “occult hemorrhage” NOTE: hypovolemic shock (e.g., hemorrhagic shock)).
Regarding Claim 21, combination of Rosenfeld and Jentzer teaches the method of claim 20,
Rosenfeld further teaches wherein the disruptive shock stage comprises a septic shock stage (Rosenfeld, Figure 31A, Step 2912, Non cardiogenic shock, such as “Septic shock”,
Regarding Claim 25, Rosenfeld teaches,
A system for shock staging comprising:
by receiving patient health data in real-time; applying the patient health data to a staging model that classifies the patient health data (Rosenfeld, Figure 9, Abstract, “patient care module displays selected data elements of the hospitalized patients. A decision support module applies decision support algorithms to selected data elements of a hospitalized patient and to user input to provide patient care advice. [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1,”): as including features that are correlated with a particular shock stage classification(Rosenfeld, [0058], “the patient care management system further comprises a relational database for storing a plurality of decision support algorithms and for prompting intensivists to provide care to patients based upon any of the decision support algorithms;
a paging module to generate an alert (Rosenfeld, Figure 19, Rule engine 642, Alert screen 644; [0225] Referring to FIG. 19. Thus, monitor 636 provides information in HL 7 form to the interface engine 638. The physiological data is then formatted by the interface engine for storage in the database 640 where all patient information is maintained. The rules engine 642 searches for patterns of data indicative of clinical deterioration. [0226] One family of alarms looks for changes in vital signs over time, using pre-configured thresholds. These thresholds are patient-specific and setting/disease-specific”in response to the shock stage classification data indicating a change in a shock stage ((Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, and [00232], As noted in Table I below, a wide variety of conditions is noted. Each of the conditions has an associated guideline of practice standard that can be presented to the intensivist who might be faced with that particular condition in a patient”. Therefore, standard decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages); and
an engagement module (Figure 9-11, Front end Patient info, [0052] Command center/remote location personnel communicate with ICU staff through videoconferencing and through "hot phones," which are dedicated telephones directly linked between the command center/remote location and the ICU. These communications links are used to discuss patient care issues and to communicate when a new order has been generated. to generate an updated order set for the patient (Rosenfeld, [0230] “In order to standardize treatment across ICUs at the highest possible level, decision support algorithms are used in the present invention. These include textural material describing the topic, scientific treatments and possible complications. This information is available in real time to assist in all types of clinical decisions from diagnosis to treatment to triage”. Figure 34A- steps 2200-2218, figure 34b”) in response to the shock stage classification data indicating the change in the shock stage Rosenfeld, [0232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, and [00232], As noted in Table I below, a wide variety of conditions is noted. Each of the conditions has an associated guideline of practice standard that can be presented to the intensivist who might be faced with that particular condition in a patient”. Therefore, standard decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages a staging module to generate shock stage classification data).
Rosenfeld teaches standard support decision algorithm for cardiogenic shocks and consequently implement treatment plan, but Rosenfeld is silent on generate shock stage classification data. wherein the shock stage classification data indicate that the patient health data include features that are correlated with a particular shock stage classification
However, Jentzer teaches generate shock stage classification data. (Jentzer, Table 2, Figure 1, Page 2119, “method, SCAI CS stages A through E were classified retrospectively using CICU admission data based on the presence of hypotension or tachycardia, hypoperfusion, deterioration, and refractory shock. Hospital mortality in each SCAI shock stage was stratified by cardiac arrest (CA)”. Page 2120 right col. “We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration”)”. NOTE: see Jenstzer (see below) for the patient conditions and classification stage correlated to specific patient health condition. .
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
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Claims 16-17, and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Rosenfeld and in view of Jentzer as applied to claim 1, and in further view of Dong et al. (US 2023/0143235 A1, hereinafter, Dong).
Regarding Claim 16, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Combination Rosenfeld and Jentzer are silent on using machine learning algorithm wherein the staging algorithm comprises a machine learning model trained on training data to generate shock stage classification data from patient health data.
However, Dong teaches wherein the staging algorithm comprises a machine learning model trained on training data to generate shock stage classification data from patient health data. (Dong, [0012], “[0012] Optionally, the classification model comprises a machine-learning model trained to receive, as input, at least the numeric values and provide, as output, the predictive indicator. This embodiment provides a more accurate mechanism for generating the predictive indicator, e.g. compared to rule-based differential diagnosis. This is because the machine-learning model effectively acts as a super classifier for generating the predictive indicator, which has been identified as generate a more accurate prediction of the type of shock. [0014] The present invention is particularly advantageous when used with these three types of shock, as they all result in hemodynamic instability and form the dominant categories of shock. Thus, a hemodynamic analytics suite can be advantageously constructed that first predicts hemodynamic instability before identifying the type of shock.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate a machine learning model-based algorithm to modified Rosenfeld’s decision algorithm to classify cardiogenic shock as taught by Dong with the benefit of providing individualized health evaluation, risk stratification and early identification of impending catastrophic or worsening illness or other adverse events with more accurate early prediction. (Dong, [0002]).
Regarding Claim 17, combination of Rosenfeld, Jentzer, and Dong teaches the method of claim 16,
Rosenfeld and Jentzer are silent on wherein the machine learning model is a supervised learning model.
However, Dong teaches wherein the machine learning model is a supervised learning model. (Dong, [0062], “supervised learning technique.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate a machine learning model-based algorithm to modified Rosenfeld’s decision algorithm to classify cardiogenic shock as taught by Dong with the benefit of providing individualized health evaluation, risk stratification and early identification of impending catastrophic or worsening illness or other adverse events with more accurate early prediction. (Dong, [0002]).
Regarding Claim 22, Rosenfeld teaches,
A non-transitory computer-readable media having stored thereon instructions that when executed by a processor cause the processor to perform a method comprising: retrieving patient health data from a data storage in real-time, wherein the patient health data are associated with a patient (Rosenfeld, Figure 9, and 12, [0202]) Referring to FIG. 9, the distributed architecture of the present invention is shown. In concept, the distributed architecture comprises a headquarters component 200, The database server/warehouse function 208 comprises the amassed information of a wide variety of patients. in their various conditions, treatments, outcomes, and other information of a statistical nature that will assist clinicians and intensivists in treating patients in the ICU. The headquarters' function also serves to allow centralized creation of decision support algorithms and a wide variety of other treatment information that can be centrally managed and thereby standardized across a variety of command center/remote locations);
continuously retrieved from the data storage; and storing the shock stage classification data using the processor. (Rosenfeld, [0058], “the patient care management system further comprises a relational database for storing a plurality of decision support algorithms and for prompting intensivists to provide care to patients based upon any of the decision support algorithms. The algorithms are selected from the group consisting of algorithms for treating; (…) Cardiogenic Shock”). [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1,
Rosenfeld teaches standard support decision algorithm for cardiogenic shocks and consequently implement treatment plan, but Rosenfeld is silent on generating shock stage classification data for the patient in real-time.
However, Jentzer teaches generating shock stage classification data for the patient in real-time. (Jentzer, Table 2, Figure 1, Page 2119, “method, SCAI CS stages A through E were classified retrospectively using CICU admission data based on the presence of hypotension or tachycardia, hypoperfusion, deterioration, and refractory shock. Hospital mortality in each SCAI shock stage was stratified by cardiac arrest (CA)”. Page 2120 right col. “We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Combination Rosenfeld and Jentzer are silent on using machine learning algorithm to classify data,
However, Dong teaches accessing a machine learning model trained on training data to generate shock stage classification data from patient health data (Rosenfeld teaches); and by inputting the patient health data to the machine learning model as the patient health data (Dong, [0012], “[0012] Optionally, the classification model comprises a machine-learning model trained to receive, as input, at least the numeric values and provide, as output, the predictive indicator. This embodiment provides a more accurate mechanism for generating the predictive indicator, e.g. compared to rule-based differential diagnosis. This is because the machine-learning model effectively acts as a super classifier for generating the predictive indicator, which has been identified as generate a more accurate prediction of the type of shock. [0014] The present invention is particularly advantageous when used with these three types of shock, as they all result in hemodynamic instability and form the dominant categories of shock. Thus, a hemodynamic analytics suite can be advantageously constructed that first predicts hemodynamic instability before identifying the type of shock.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate a machine learning model-based algorithm to modified Rosenfeld’s decision algorithm to classify cardiogenic shock as taught by Dong with the benefit of providing individualized health evaluation, risk stratification and early identification of impending catastrophic or worsening illness or other adverse events with more accurate early prediction. (Dong, [0002]).
Regarding Claim 23, combination of Rosenfeld, Jentzer, and Dong teaches the non-transitory computer-readable media of claim 22
Rosenfeld further teach wherein the method performed by the processor further comprises generating an alert (Rosenfeld, Figure 19, Rule engine 642, Alert screen 644; [0225] Referring to FIG. 19. Thus, monitor 636 provides information in HL 7 form to the interface engine 638. The physiological data is then formatted by the interface engine for storage in the database 640 where all patient information is maintained. The rules engine 642 searches for patterns of data indicative of clinical deterioration. [0226] One family of alarms looks for changes in vital signs over time, using pre-configured thresholds. These thresholds are patient-specific and setting/disease-specific” when the shock stage classification data for the patient indicate a change in a shock stage. (Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, and [00232], As noted in Table I below, a wide variety of conditions is noted. Each of the conditions has an associated guideline of practice standard that can be presented to the intensivist who might be faced with that particular condition in a patient”. Therefore, standard decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages).
Regarding Claim 24, combination of Rosenfeld, Jentzer, and Dong teaches the non-transitory computer-readable media of claim 23,
Rosenfeld further teaches wherein the method performed by the processor further comprises in response to the alert, generating an order set based on the shock stage classification data for the patient (Rosenfeld, [0230] “In order to standardize treatment across ICUs at the highest possible level, decision support algorithms are used in the present invention. These include textural material describing the topic, scientific treatments and possible complications. This information is available in real time to assist in all types of clinical decisions from diagnosis to treatment to triage”. Figure 34A- steps 2200-2218, figure 34b, [0345] Initially, the intensivist is prompted to determine whether the patient is hemodynamically stable (no angina, heart failure, or hypotension (systolic less than 80 mm)) 2200. If this criterion is not met, the intensivist is prompted to go to the cardio-pulmonary guidelines algorithm which is generally known to those skilled in the art”. NOTE: for specific cardiogenic shock, standard decision support known in the medical field is used. For example. the stage classifications are standard criteria for treatment applied.); and
storing the order set in an electronic medical record (EMR) for the patient. (Rosenfeld, Figure 9,208, Database Server/Warehouse, Figure 20, note stored in EMR line log updated, Figure 9, [0202], The database server/warehouse function 208 comprises the amassed information of a wide variety of patients, in their various conditions, treatments, outcomes, and other information of a statistical nature that will assist clinicians and intensivists in treating patients in the ICU. The headquarters' function also serves to allow centralized creation of decision support algorithms and a wide variety of other treatment information that can be centrally managed and thereby standardized across a variety of command center/remote locations”).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Osypka et al. (US 2017/0053077 A1) recites “A shock probability determination system and method provides an output of probabilities for different types of shock based on input of selected patient demographic parameters and current clinical parameter values as well as normal ranges for each clinical parameter based on patient demographic data. The probability of different types of shock is determined based on comparison of current clinical parameter values of selected patient hemodynamic parameters to a normal range for each hemodynamic parameter. In one aspect, probabilities of cardiogenic shock, hypovolemic shock, septic shock, and anaphylactic shock are determined. In another aspect, a fluid status indicator is determined based on real-time probability of hypovolemic shock”(abstract).
Yale et al. “Early Prediction of Cardiogenic Shock Using Machine Learning”, Frontiers in Cardiovascular Medicine, 9, 862424, July 13, 2022.
Abstract: “Cardiogenic shock (CS) is a severe condition with in-hospital mortality of up to 50%. Patients who develop CS may have previous cardiac history, but that may not always be the case, adding to the challenges in optimally identifying and managing these patients. Patients may present to a medical facility with CS or develop CS while in the emergency department (ED), in a general inpatient ward (WARD) or in the critical care unit (CC). While different clinical pathways for management exist once CS is recognized, there are challenges in identifying the patients in a timely manner, in all settings, in a timeframe that will allow proper management. We therefore developed and evaluated retrospectively a machine learning model based on the XGBoost (XGB) algorithm which runs automatically on patient data from the electronic health record (EHR). The algorithm was trained on 8 years of de-identified data (from 2010 to 2017) collected from a large regional healthcare system. The input variables include demographics, vital signs, laboratory values, some orders, and specific pre-existing diagnoses. The model was designed to make predictions 2 h prior to the need of first CS intervention (inotrope, vasopressor, or mechanical circulatory support). The algorithm achieves an overall area under curve (AUC) of 0.87 (0.81 in CC, 0.84 in ED, 0.97 in WARD), which is considered useful for clinical use. The algorithm can be refined based on specific elements defining patient subpopulations, for example presence of acute myocardial infarction (AMI) or congestive heart failure (CHF), further increasing its precision when a patient has these conditions. The top-contributing risk factors learned by the model are consistent with existing clinical findings. Our conclusion is that a useful machine learning model can be used to predict the development of CS. This manuscript describes the main steps of the development process and our results”.
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/DILARA SULTANA/Examiner, Art Unit 2858
08/17/2026
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858
8/27/2026
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