DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim status
2. Claims 1-14 are currently pending and under exam herein.
Claims 1-14 are rejected.
Priority
3. This application is 371 of International Application No. PCT/EP2022/062019,
which designated the United States and was filed on 4 May 2022. Claimed benefit of domestic priority U.S. Provisional Application No. 63/184893, filed on 6 May 2021 is acknowledged. In this action, all claims are examined as though they had an effective filing date of 6 May 2021. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 1 November 2023 is being considered by the examiner.
Drawings
5. The drawing submitted on 1 November 2023 are accepted by the examiner.
Claim Interpretation
6. Interpretation of terms in claims 1, 6, 8 and 13 are described below.
The ‘clinical care system’ claimed in claims 6 and 13 is described in the specification para. 0013 as a system that monitors multiple life signs for a patient; therefore it is interpreted to be a computer/data processing system that receives and monitors already generated data rather than an apparatus that acquires the measurements.
Regarding claims 1 and 8, the terms ‘deviation value’ and ‘deviation time’ will be interpreted to refer to metrics of evaluating a clinical state that relate to a rate-of-change of a value as the metric that defines the clinical state. For this metric, a deviation associated with an adverse clinical state typically has a large change in a short period of time (see para. 0083 and 0049 of the specification).
Regarding claims 1 and 8, the terms ‘value threshold’ and ‘value time’ will be interpreted to refer to metrics of evaluating a clinical state that relate to a value being outside of a normal range for a sustained period of time. For this metric, an abnormality associated with an adverse clinical state typically has a value outside of the normal range for a sustained period of time (see para. 0083 and 0049 of the specification).
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “the batch deviation detection module” in claims 1 and 8.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Para. 0015 of the specification describes that the batch deviation detection module is used for comparing the received plurality of measurements for the patient to the defined deviation value and the defined deviation time.
Para. 0049 of the specification describes that the batch deviation detection module functions by computing whether there is a significant deviation (increase or decrease) greater than [v_dev] in a short amount of time under [dt_dev], and the batch abnormal value detection module computes whether the value stays abnormal above or below a physiologically-critical threshold [v_value] in for a significant amount of time [dt_value].
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
7. Claims 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 does not depend on a parent claim, therefore, the terms “the method” and “the feature” lack antecedent basis. A possible correction is to make claim 4 dependent on claim 1. For the purposes of review, claim 4 will be considered to be dependent on claim 1.
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.
8. Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1 and 8 recite: identifying (140), within the plurality of measurements for at least one feature for a patient, a deviation and/or an abnormality predicting the clinical state,
Claims 1 and 8 recite: comparing (142) the received plurality of measurements for the patient to the defined deviation value and the defined deviation time
Claims 1 and 8 recite: identifying (144), based on the comparing step, a deviation if the received plurality of measurements taken over the span of time for the patient is above the defined deviation value and below the defined deviation time
Claims 1 and 8 recite: and/or comparing (146), the received plurality of measurements for the patient to the defined value threshold and the defined value time
Claims 1 and 8 recite: identifying (148), based on the comparing step, an abnormality if the received plurality of measurements taken over the span of time for the patient is above the defined value threshold and below the defined value time
Claims 1 and 8 recite: predicting (150), based upon identification of a deviation and/or abnormality, that the patient is susceptible to or experiencing the clinical state
Claims 2 and 9 recite: the method further comprises the step of organizing (132) the plurality of measurements with individual patients within the plurality of patients
Claims 3 and 10 recite: The method of claim1, further comprising the step of extracting (134) one or more features from the received plurality of measurements.
Claims 5 and 12 recite: (ii) extracting (520) a plurality of features from the received training data; and (iii) training (530) the batch deviation detection module
Claims 6 and 13 recite: configured to monitor multiple life signs for a patient,
Claims 7 and 14 recite: the method of claim1 [or claim 8], wherein the event monitoring system is utilized to determine a set of parameter definitions for a clinical state during training of the event monitoring system
The limitations regarding ‘determine a set of parameter definitions’, ‘monitor multiple life signs’, ‘extracting (520) a plurality of feature’, ‘extracting (134) one or more features’, ‘predicting that the patient is susceptible’, ‘identifying an abnormality’, ‘comparing the received plurality of measurements for the patient to the defined value threshold and the defined value time’, ‘identifying a deviation’, ‘identifying a deviation and/or an abnormality’ and ‘organizing (132) the plurality of measurements’ are generically recite data analysis steps that can be practically performed in the human mind because the human mind is capable of identifying relevant information, comparing values, and determining information from other values. Therefore, these limitations fall under the "Mental processes" groupings of abstract idea.
The limitation regarding ‘training (530) the batch deviation detection module’ is a verbal equivalents that describe a mathematical calculation that is performed as the limitation. Therefore, these limitations fall under the "Mathematical concepts" groupings of abstract ideas.
While claims 1 and 8 recite performing some aspects of the analysis with ‘using a batch deviation module of the event monitoring system’, there are no additional limitations that indicate that this system requires anything other than carrying out the recited mental process in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the "Mental processes" grouping of abstract ideas.
As such, claims 1-14 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or insignificant extra-solution activity. Specifically, the claims recite the following additional elements:
Claims 1 and 8 recite: receiving (120) a set of parameter definitions for a clinical state, the set of parameter definitions comprising a definition for a deviation value, a definition for a deviation time, a definition for a value threshold, and a definition for a value time
Claims 1 and 8 recite: receiving (130) a plurality of measurements for at least one feature for a patient, the plurality of measurements taken over a span of time
Claims 1 and 8 recite: using a batch deviation detection module of the event monitoring system
Claims 1 and 8 recite: providing (160), via a user interface of the event monitoring system, an alert that the patient is susceptible to or experiencing the clinical state.
Claims 2 and 9 recite: The method of claim 1, wherein the plurality of measurements comprise measurements for a plurality of patients
Claims 4 and 11 recite: The method of , wherein the feature is a vital sign, a lab result, and/or a clinical score
Claims 5 and 12 recite: The method of claim1, further comprising the steps of: (i) receiving (510) training data comprising medical information and clinical state information for a plurality of patients
Claims 6 and 13 recite: The method of claim1, wherein the event monitoring system comprises a clinical care system
Claim 6 and 13 recite: wherein the user interface of the event monitoring system comprises a display of the monitored multiple life signs for the patient, and wherein the alert that the patient is susceptible to or experiencing the clinical state comprises a textual alert on the display.
Claim 8 recites: an event monitoring system
The limitations for ‘receiving (120) a set of parameter definitions’, ‘receiving training data’ and ‘receiving (130) a plurality of measurements’ merely serve to gather data that is used an input for the judicial exception. Therefore, these limitations are mere data gathering activities. As set forth in MPEP 2106.05(g), mere data gathering activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application. The limitations ‘wherein the plurality of measurements comprise measurements for a plurality of patients’, those that limits ‘parameter definitions’ and limit features to ‘vital sign, a lab result, and/or a clinical score’ further limit the type of data received, but do not change the position of receiving the measurements as a data gathering activities. Furthermore, these limitations to particular data types amount to the ‘field-of-use’ in which to apply the judicial exception and do not amount to significantly more than the exception itself, and thus cannot integrate a judicial exception into a practical application (MPEP 2106.05(h)).
The limitations for ‘a textual alert on a display’, ‘providing an alert’ and ‘providing a display’ are post-solution activity steps that merely serve to output data from the judicial exception. As set forth in MPEP 2106.05(g), output activity that is incidental to the primary process are insignificant extra-solution activity that do not have a practical application. The limitations that further limit the type of data output fail to integrate the judicial exception into a practical application because they merely further limit the tangential output activities but do not change their position as output activities.
There are no limitations that indicate that the ‘batch deviation detection module’, the ‘user interface’, or the ‘clinical care system’ of the ‘event monitoring system’ requires anything other than a generic computing system to receive, analyze and display data. As such, these limitations equate to mere instructions to implement the abstract idea in a generic computing environment that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
As such, claims 1-14 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, and conventional activity.
The limitations directed to receiving data, and the limitations directed to providing an alert or displaying data do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As set forth in MPEP section 2106.05(g), the courts have decided that limitations that merely add an insignificant extra-solution activity, do not amount to an inventive concept, particularly when the activities are well-understood and conventional. As set forth in MPEP section 2106.05(d), the courts have recognized that limitations directed to data gathering that are claimed as insignificant extra-solution activity are routine, well understood and conventional. Specifically, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) are deemed well-understood, routine and conventional. Additionally, outputting data (i.e. storing and retrieving information from memory) has also been deemed well-understood, routine and conventional activity (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The limitations pertaining to the ‘batch deviation detection module’, the ‘user interface’, or the ‘clinical care system’ of the ‘event monitoring system’ used to execute the method, are directed to performing judicial exceptions with a generic computing system on a generic computer. These limitations are not sufficient to amount to significantly more than the judicial exception because, as set forth in the MPEP section 2106.05(d)(II)), using a generic computing environment or generic computer to perform the judicial exception, has been deemed well-understood, routine and conventional activity including receiving or transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362), performing repetitive calculations (Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)), and storing and retrieving information in memory (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more.
Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-14 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
9. Claims 1, 3-4, 6, 8, 10-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Stapelfeldt et al. (US20140107504A1, filed 11 October 2013), in view of Bhat et al. (US20200054291A1, filed 25 October 2019). The italicized text corresponds to the instant claim limitations.
Regarding claim 8, Stapelfeldt et al. disclose a non-transitory computer readable medium stores machine executable instructions executable at an associated processor to predict patient outcomes related to a procedure, Stapelfeldt et al. further discloses a user interface is configured to provide the calculated risk metric to a user in a human comprehensible form (para. 0005; an event monitoring system comprised of a batch deviation detection module; a processor and a user interface).
Regarding claims 1 and 8, Stapelfeldt et al. discloses systems and methods for monitoring severity and duration of aberrant physiological parameters of a patient during anesthesia or a procedural sedation. Stapelfeldt et al. further discloses that the method uses a model to predict a calculated risk or patient outcome based on the analysis. Stapelfeldt et al. further discloses automation: it is programmed to automatically select the information according to the real-time conditions of the patient, preferences of the user and other information related to an event that is occurring and provide an encounter GUI that is populated with such selected information to provide timely patient encounter information, guidance and alerts to users so that informed decisions can be made in a time-critical environment (para. 0002; 0005-0006; 00454-0055; a method for automated prediction of a clinical state using an event monitoring system).
Regarding claims 1 and 8, Stapelfeldt et al. disclose that the various exposure limits can be determined as a maximum amount of time that a patient can remain below the associated threshold without experiencing a certain increase in the risk of post-procedure morbidity or mortality. The exposure limits can vary for the plurality of thresholds, and can be standardized, determined individually for each patient according to an associated patient history, for example, by querying an EHR database for certain diagnoses or by entering such information via an appropriate input device. Stapelfeldt et al. further discloses that postoperative outcome may be associated not only with the severity of hypotension but also the duration of hypotensive periods of time spent below a range of mean arterial blood pressure (MAP) thresholds that are commonly encountered during anesthesia. This disclosure teaches a definition of a clinical state based on value being outside of a normal range for a sustained period of time and is described further in the claim interpretation section of this office action under ‘value threshold’ and ‘value time’ (para. 0028, 0040; receiving a set of parameter definitions for a clinical state, the set of parameter definitions comprising a definition for a value threshold, and a definition for a value time).
Pertaining to claims 1 and 8, Stapelfeldt et al. disclose that respective cumulative times are measured for which the monitored physiological parameter of the patient meets each of a plurality of threshold values during the procedure. Stapelfeldt et al. discloses that this can include for example, measuring respective cumulative times for which the blood pressure of the patient was below each of a plurality of threshold values during a procedure (para. 0004; 0007; receiving a plurality of measurements for at least one feature for a patient, the plurality of measurements taken over a span of time).
Regarding claims 1 and 8, Stapelfeldt et al. discloses that a predictive model is configured to calculate a risk metric, representing an effect of a monitored physiological parameter on a patient outcome related to the procedure from the measured cumulative times for which the monitored physiological parameter of the patient met each of the plurality of threshold values. This disclosure teaches the second of the two options claimed (which relates to a value being outside of a normal range for a sustained period of time as discussed in the claim interpretation section of this office action under ‘value threshold’ and ‘value time’) (para. 0005; identifying, within the plurality of measurements for at least one feature for a patient, a deviation and/or an abnormality predicting the clinical state, comprising: comparing, using a batch deviation detection module of the event monitoring system, the received plurality of measurements for the patient to the defined deviation value and the defined deviation time; and identifying, based on the comparing step, a deviation if the received plurality of measurements taken over the span of time for the patient is above the defined deviation value and below the defined deviation time; and/or comparing, using the batch deviation detection module of the event monitoring system, the received plurality of measurements for the patient to the defined value threshold and the defined value time; and identifying, based on the comparing step, an abnormality if the received plurality of measurements taken over the span of time for the patient is above the defined value threshold and below the defined value time).
Regarding claims 1 and 8, Stapelfeldt et al. disclose that a risk metric is calculated for the patient, representing an effect of monitored physiological parameter on a patient outcome related to the procedure from the measured cumulative times for which the monitored physiological parameter of the patient met each of the plurality of threshold values. Stapelfeldt et al. further disclose that the adverse outcome can be one of in-hospital morbidity or mortality, morbidity or mortality within a thirty-day period after a procedure, an increase in length of hospital stay, or readmission to a hospital within a thirty-day period after a procedure (para. 0004; 0016; predicting, based upon identification of a deviation and/or abnormality, that the patient is susceptible to or experiencing the clinical state).
Pertaining to claims 1 and 8, Stapelfeldt et al. discloses that a user is alerted when any one of the cumulative times exceeds a predetermined exposure limit for its associated threshold value, portending a certain percent increase in risk of adverse outcome. Furthermore, a final risk score can be provided at the end of a procedure based on the total number of exposure limits that were exceeded over the course of the procedure (para. 0007; providing, via a user interface of the event monitoring system, an alert that the patient is susceptible to or experiencing the clinical state).
Regarding claims 1 and 8, Stapelfeldt et al. is silent to receiving a set of parameter definitions for a clinical state, the set of parameter definitions comprising a definition for a deviation value, a definition for a deviation time. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Bhat et al.
Pertaining to claims 1 and 8, Bhat et al. discloses an event detection device with a variable threshold. Bhat et al. further discloses that in some embodiments, the model comprises rules of assigning a higher diagnostic score to the candidate condition of “pulmonary disease” if (1) respiration rate (RR) substantially increases from the reference (RRRef ) by at least a threshold value and (2) change in RR in a time period (ΔRR/Δt) exceeds a threshold range, which indicates a sudden onset in rise of respiration rate. This is an example wherein a rate-of-change of a value is a metric/feature that defines the clinical state, which relates to the interpretation of ‘deviation value’ and ‘deviation time’ in the Claim Interpretation section of this office action (para. 0081; receiving a set of parameter definitions for a clinical state, the set of parameter definitions comprising a definition for a deviation value, a definition for a deviation time).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Bhat et al. discloses that a composite index of factors can indicate worsening heart failure, including sudden onset of a rise in respiration rate. (para. 0081). Therefore, one of ordinary skill in the art would have been motivated to utilize the method of measuring and monitoring the rate of change of a value as a feature in a prediction model taught by Bhat et al. in the method and system to predict adverse outcomes from monitored parameters taught by Stapelfeldt et al. in order to predict an adverse outcome in patients experiencing a heart failure episode. Furthermore, one of ordinary skill in the art would predict that the monitoring of a rate of change of a value taught by Bhat et al. could be readily added to the method of Stapelfeldt et al. with a reasonable expectation of success because they both pertain to devices and methods for detecting adverse events from longitudinally collected physiological information using models based on multiple thresholds and features. The invention is therefore prima facie obvious.
Regarding claims 3 and 10, Stapelfeldt et al. discloses that a feature extractor is configured to provide a plurality of features from a physiological parameter measured for a patient during the procedure. The plurality of features includes at least cumulative time periods for which a value of a parameter meets various thresholds of the monitored parameter (para. 0005; the method/system of claim1/claim 8, further comprising the step of extracting (134) one or more features from the received plurality of measurements).
Pertaining to claims 4 and 11, Stapelfeldt et al. discloses that the modern electronic anesthesia information systems is a vital sign monitor and measure for example mean arterial blood pressure to predict an outcome. Stapelfeldt et al. further discloses that in one example, the risk metric can comprise many features such as a number of mean arterial blood pressure thresholds that were met for cumulative times exceeding certain predetermined values, such as one minute or a certain number of minutes. that features used to predict outcome can include mean arterial blood pressure (para. 0040; 0018-0019; the method of claims 1, wherein the feature is a vital sign, a lab result, and/or a clinical score).
Regarding claim 6 and 13, Stapelfeldt et al. discloses a patient monitoring system that includes a plurality of sensors to measure physiological parameters of a patient during a procedure under anesthesia, which can include electrocardiography (ECG) data, heart rate, blood pressure, inspired and expired gases, oxygen saturation of the blood (pulse oximetry), temperature, urine output, central venous pressure, pulmonary artery pressure and pulmonary artery occlusion pressure, cardiac output, cerebral activity, and neuromuscular function. Stapelfeldt et al. further discloses that data from the plurality of sensors is provided to a processing assembly that monitors and records the received data
(Fig. 2; para. 0020; the method of claim 1, wherein the event monitoring system comprises a clinical care system configured to monitor multiple life signs for a patient).
Pertaining to claims 6 and 13, Stapelfeldt et al. discloses that a user interface is configured to provide the calculated risk metric to a user in a human comprehensible form. Stapelfeldt et al. further discloses that the alert can be displayed on the user device, as well as other notifications and relevant information, corresponding to patient conditions that are monitored. Stapelfeldt et al. further discloses that user device can show various patient conditions such as vital sign trends in the form of text, graphics or a combination of texts and graphics (para. 0005, 0057-0058; 0065; wherein the user interface of the event monitoring system comprises a display of the monitored multiple life signs for the patient, and wherein the alert that the patient is susceptible to or experiencing the clinical state comprises a textual alert on the display).
10. Claims 2, 5, 7, 9, 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Stapelfeldt et al. (US20140107504A1, filed 11 October 2013), in view of Bhat et al.(US20200054291A1, filed 25 October 2019) as applied to claims 1, 3-4, 6, 8, 10-11 and 13, further in view of Cheng et al. (BMC Medical Informatics and Decision Making, 2020, Vol. 20, p. 1-23). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1, 3-4, 6, 8, 10-11 and 13 are taught by Stapelfeldt et al. and Bhat et al. above.
Regarding claims 2, 5, 7, 9, 12 and 14, Stapelfeldt et al. and Bhat et al. are silent to: the method of claim 1, wherein the plurality of measurements comprise measurements for a plurality of patients, and the method further comprises the step of organizing the plurality of measurements with individual patients within the plurality of patients (claims 2 and 9); the method of claim1, further comprising the steps of: (i) receiving training data comprising medical information and clinical state information for a plurality of patients; (ii) extracting a plurality of features from the received training data; and (iii) training the batch deviation detection module (claims 5 and 12); and the method of claim1, wherein the event monitoring system is utilized to determine a set of parameter definitions for a clinical state during training of the event monitoring system (claims 7 and 14). However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Cheng et al.
Pertaining to claims 2 and 9, Cheng et al. teaches a method called the MedGP framework for estimating temporal dependencies from time series data to improve patient outcome. Cheng et al. further disclose analyzing medical time-series data consisting of electronic health records (EHRs) from1365 septic patients, 952 patients with heart failure, and 4723 patients with neoplasms, and jointly modeled many clinical covariates, including patient demography, vital signs, and lab results. Cheng et al. further discloses selected three groups of discharged patients from these data: 1365 septic patients, 952 patients with heart failure, and 4723 patients with neoplasms. For the patients with neoplasms or heart failure, they used the data from full patient length-of-stay in training and testing. For septic patients, the disease progression varies substantially across patients, and the distribution of the covariates changes dramatically depending on the disease phase. To address this issue, they segmented the time series data into four disjoint partitions based on clinical status: no sepsis, pre-sepsis, sepsis, and recovery and modeled them separately (p. 9, col. 2, para. 3 - p. 10, col. 1, para. 2; Table 1; abstract; the method of claim 1, wherein the plurality of measurements comprise measurements for a plurality of patients, and the method further comprises the step of organizing the plurality of measurements with individual patients within the plurality of patients).
Pertaining to claims 5 and 12, Cheng et al. discloses that MedGP is a statistical framework that incorporates 24 clinical covariates and supports a rich reference data set from which relationships between observed covariates are inferred and exploited for high-quality inference of patient state over time. Cheng et al. further discloses the reference data are HUP medical time-series data consisting of electronic health records from >260,999 patients including ICD-9 codes; patient demography, length-of-stay, vital signs and lab results. Cheng et al. further discloses jointly model multiple clinical covariates—vital signs and lab tests—over time for each patient using GP regression and representing medical covariates in the model by kernels followed by parameter learning. Cheng et al. further disclose training a GP kernel on each reference patient separately, and using these models to estimate the empirical population-level model using nonparametric density estimation. In this training step, they define the features of each kernel (i.e. extract features) and cluster the kernels and for each cluster parameters and weight coefficients are estimated. This step describes feature extraction (p. 5, col. 1, para. 3 – p. 8, col. 2, para. 3; Fig. 1; abstract; the method of claim 1, further comprising the steps of: (i) receiving training data comprising medical information and clinical state information for a plurality of patients; (ii) extracting a plurality of features from the received training data; and (iii) training the batch deviation detection module).
With respect to claims 7 and 14, Cheng et al. discloses For each identified kernel cluster (group of data points), they estimated one set of parameters (μq and vq ) for the basis kernel, and the weight coefficients. Cheng et al. discloses estimating each new parameter using density-weighted means with the density from the univariate KDE as the weights. Cheng et al. further teaches online updating with new parameters by estimating the elements of the new empirical Aq matrix and λq vector corresponding to each new Bq matrix using singular value decomposition (SVD). (p. 8, col. 2, para. 3 – p. 9, col. 1, para. 1; the method of claim 1, wherein the event monitoring system is utilized to determine a set of parameter definitions for a clinical state during training of the event monitoring system).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Cheng et al. disclosed that Gaussian processes (GPs) are useful approaches for time series analysis because they can naturally capture irregular time series observations and estimate prediction uncertainties in a probabilistic framework; therefore, GPs are applicable to the modeling of medical time series data (p. 2, col. 2, para. 4). Therefore, one of ordinary skill in the art would have been motivated to utilize the modeling of medical time series data taught by Cheng et al. in the method and system to used medical time series data to predict outcome events taught by Stapelfeldt et al. and Bhat et al. in order to capture irregular time series observations and estimates of prediction uncertainties. Furthermore, one of ordinary skill in the art would predict that the biomarker development approach taught by Cheng et al. could be readily added to the method of Stapelfeldt et al. and Bhat et al. with a reasonable expectation of success because both inventions pertain to real-time monitoring of patients to get inferences about health status using information from clinical covariates and lab test results to improve medical interventions and improve patient outcome. Furthermore, Stapelfeldt et al. discloses that their predictive model can utilize any appropriate classification or regression model (i.e. a Gaussian process), such as an artificial neural network, a support vector machine, a statistical classifier, or a similar model, to calculate the risk metric (para. 0019). The invention is therefore prima facie obvious.
Conclusion
11. No claims are allowed.
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/J.J.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
