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 .
Claim Status
Claims 1-20 are pending and examined herein.
Claims 1-20 are rejected.
Priority
Claims 1-20 are granted the claim to the benefit of priority to U.S. Provisional applications 62/947787 and 63/017705 filed 13 December 2019 and 30 April 2020. Thus, the effective filling date of claims 1-20 is 13 December 2019.
Information Disclosure Statement
The information disclosure statements (IDS) were received on 12 October 2022, 17 February 2024, and 01 April 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings received 11 June 2022 are objected to. The drawings are objected to because Figure 5 appears before Figure 4. The MPEP sets out the standards for drawings (37 C.F.R. 1.84) at 608.02(V) which states “The different views must be numbered in consecutive Arabic numerals, starting with 1, independent of the numbering of the sheets” (MPEP 608.02(V) in 37 C.F.R. 1.84(u)(1)). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
Claim 7 recites “wherein the learning element… provides an alert if the error values exceed a threshold value for a predetermined period of time”, claim 8 recites “wherein the learning element provides an alert if the patient parameter deviates from a desired range for a predetermined period of time”, and claim 20 recites “a learning element… provides an alert if the error values exceed a threshold value for a predetermined period of time” which are contingent limitations because providing an alert is contingent on the conditions of “the error values exceed a threshold value for a predetermined period of time” (in claim 7) and “the patient parameter deviates from a desired range for a predetermined period of time” (in claim 8) are met. The MPEP states at 2111.04(II) “The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur”. Therefore, the BRI of system claims 7, 8, and 20 requires structure for performing the alert should the conditions occur.
112/f Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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 is: “a therapeutic deliver system that delivers a therapeutic to a patient”, “a feature extractor that generates a set of features…”, “a predictive model that predicts a future value…”, and “a therapeutic delivery system controller that determines a desired dosage” in claims 1 and 18, “a learning element that stores… and compares the predicted future value…” in claim 6, “the learning element stores… and provides an alert…” in claim 7, “the learning element provides an alert…” in claim 8, and “a learning element that stores the features… compares the predicted future value… and provides an alert” in claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The structure of “a therapeutic deliver system that delivers a therapeutic to a patient” in claims 1 and 18 is shown by the instant disclosure as a device, controllable via an electrical signal, that provides a therapeutic to a patient (instant disclosure [0014]) which is interpreted as an infusion pump (instant disclosure [0016]) configured to deliver a therapeutic which is controllable via an electrical signal (by the pump controller shown in Fig. 1). The structure of “a predictive model that predicts a future value…” in claims 1 and 18 is shown by the instant disclosure as the predictive model can be implement as machine-readable instructions stored in a tangible memory and executed by an associated processor, either on a device local to the therapeutic delivery system, networked, or a remote system connected via an internet connection (instant disclosure [0017]) and algorithms which the predictive model utilizes as the predictive model can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical parameter to the user (instant disclosure [0019]-[0025]). The structure of “a therapeutic delivery system controller that determines a desired dosage” in claims 1 and 18 is shown by the pump controller of Fig. 1 and the algorithm is disclosed in [0046]). There is no disclosed algorithm for “a feature extractor that generates features…” (in claims 1 and 18) and there is no clear link to a structure for “a learning element that stores… and compares the predicted future value…” (in claim 6), “the learning element stores… and provides an alert…” (in claim 7), “the learning element provides an alert…” (in claim 8), and “a learning element that stores the features… compares the predicted future value… and provides an alert” (in claim 20). The limitations with no clear link to a structure are further addressed below under 112/b and 112/a.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
112/b
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.
Claims 1-12 and 18-20 are 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.
Claims 1 and 18 recite “the patient” in the feature extractor limitation of the claims which renders the metes and bounds of the claims indefinite. The indefiniteness arises because it is unclear if “the patient” is referring to the “patient” in the therapeutic delivery system limitation or the “patient” in the monitoring device limitation. Dependent claims 2-12 and 19-20 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, “the patient” in the feature extractor limitations of the claims is interpreted as referring to the “patient” in the monitoring device limitation.
Claims 1 and 18 recite “a feature extractor that generates a set of features for the patient, each of the set of features being associated with one of the patient and the therapeutic delivery system” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear if “each of the set of features being associated with one of the patient and the therapeutic delivery system” is meant to mean that each feature of the set of features is associated with one value of the patient (which includes values such as a patient parameter) and one value of the therapeutic delivery system (which includes values such as past and current dosage) (i.e., one feature is associated with one value of the patient and one value of the therapeutic delivery system) or if this is meant to mean that each feature of the set of features is associated with either the patient or the therapeutic delivery system, where the set of features includes features from both the patient and the therapeutic delivery system (i.e., one feature in the set of features is associated with either the patient or the therapeutic delivery system and the set of features includes features from both the patient and the therapeutic delivery system). Dependent claims 2-12 and 19-20 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, this limitation will be interpreted as one feature in the set of features is associated with either the patient or the therapeutic delivery system and the set of features includes features from both the patient and the therapeutic delivery system.
Claim 1 recites the limitation "the patient parameter" in line 11. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because the claim does not make clear what “the patient parameter” is. It is further unclear if the patient parameter is the same as “the biometric parameter” or if these parameters are distinct. Dependent claims 2-12 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, these parameters will be interpreted as being the same parameters.
Claim 9 recites “a user interface that displays” and claim 12 recites “a network interface that provides data” which renders the metes and bounds of the claim indefinite. The MPEP 2173.05(p)(II) states a single claim which claims both an apparatus and the method steps of using the apparatus is indefinite. The indefiniteness arises because claim 9 recites an apparatus “the system of claim 1, further comprising a user interface” and the method of using the apparatus “displays the patient parameter…” and claim 12 recites an apparatus “the system of claim 1, further comprising a network interface” and the method of using the apparatus “provides patient data…”. For the sake of furthering examination, claim 9 will be interpreted as “the system of claim 1, further comprising a user interface that is configured to display the patient parameter…” and claim 12 will being interpreted as “the system of claim 1, further comprising a network interface that is configured to provide patient data”.
112/b Indefiniteness based on 112/f claim interpretation
Claim limitations “a feature extractor that generates features…” (in claims 1 and 18), “a therapeutic delivery system controller that determines a desired dosage” (in claims 1 and 18), “a learning element that stores… and compares the predicted future value…” (in claim 6), “the learning element stores… and provides an alert…” (in claim 7), “the learning element provides an alert…” (in claim 8), and “a learning element that stores the features… compares the predicted future value… and provides an alert” (in claim 20) invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Claims 1 and 18 recite “a feature extractor that generates features…” and “a therapeutic delivery system controller that determines a desired dosage”. The written description fails to disclose corresponding structure (for a feature extractor and a therapeutic deliver system controller) for performing the entire claimed function of generating features or determining a dosage because there is no clear link between a structure (i.e., computer hardware such as a processor) and the functions of generating features and determining a desired dosage. Dependent claims 2-12, 19, and 20 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, “a feature extractor that generates features” and “a therapeutic delivery system controller that determines a desired dosage” is interpreted as a processor that is configured to perform these functions.
Claim 6 recites “a learning element that stores… and compares the predicted future value…”, claim 7 recites “the learning element stores… and provides an alert…”, claim 8 recites “the learning element provides an alert…”, and claim 20 recites “a learning element that stores the features… compares the predicted future value… and provides an alert”. The written description fails to disclose corresponding structure (for the learning element) for performing the entire claimed functions of storing data, providing alerts, and comparing values because there is no clear link between a structure (i.e., computer hardware such as a processor and memory) and the functions of storing data, comparing values and providing an alert. It is further unclear if the learning element in the disclosure is the same as the learning component or the learning model or if the learning element is distinct from the learning component or learning model. For the sake of furthering examination, the learning element is interpreted as being distinct from the learning component and the learning model. For the sake of furthering examination, the learning element is interpreted as a processor that is configured to perform these functions.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
112/a Written Description
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-13 and 17-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 18 recite “a feature extractor that generates features…”, Claim 6 recites “a learning element that stores… and compares the predicted future value…”, claim 7 recites “the learning element stores… and provides an alert…”, Claim 8 recites “the learning element provides an alert…”, and claim 20 recites “a learning element that stores the features… compares the predicted future value… and provides an alert”. There is an inadequate written description for the algorithm of “a feature extractor” which performs the function of generating features and there is an inadequate written description for the structure of “a learning element” which perform their respective function. Dependent claims 2-5, 9-13, and 19 are rejected by virtue of their dependency on a rejected claim without alleviating the rejection.
Claim 10 recites “the predictive model is implemented as a multi-parameter partial differential equation”, claim 11 recites “wherein the multi-parameter partial differential equation includes a set of hyper parameters…”, claim 17 recites “wherein the predictive model is implemented as a multi-parameter partial differential equation”, and claim 19 recites “the predictive model is expressed as a multi-parameter partial differential equation”. The MPEP states at 2163(II)(3)(a)(ii) “The written description requirement for a claimed genus may be satisfied through sufficient description of a representative number of species by actual reduction to practice… A "representative number of species" means that the species which are adequately described are representative of the entire genus. Thus, when there is substantial variation within the genus, one must describe a sufficient variety of species to reflect the variation within the genus” (MPEP 2163(II)(3)(a)(ii)). There is not an adequate written description of a multi-parameter partial differential equation model that predicts a future value for every monitored biometric parameter at a given time according to a set of features. The instant disclosure provides that “the model is implemented as a multi- parameter partial differential equation to predict a patient's systolic blood pressure at the next time step given temporally-spaced vital signs readings” (instant disclosure [0034]), “the predictive model is implemented as a multi-parameter partial differential equation” (instant disclosure [0042]), and “the current partial differential equation model yields an inherently explainable framework to interpret model decision making, which can be displayed to a physician” (instant disclosure [0047]). However, the instant disclosure does not provide an equation for the model which may be utilized for all possible monitored biometric parameters to predict a future value for the biometric parameter at all possible given times. Several methods have been disclosed for predicting future biometric parameters (such as blood pressure and blood glucose) such as described in Cinar et al. (US 20160354543 A1) which utilizes a model for predicting future glucose, Mulligan et al. (US 20150065826 A1) which utilizes a model for predicting future blood pressure, Saiti et al. (International Conference on Information Technology in Bio-and Medical Informatics. Cham: Springer International Publishing, 2017) which reviews models for predicting future glucose values, however there is no discussion of utilizing a multi-variable partial different equation for predicting future biometric parameters. Thus, the state of the art does not provide guidance on utilizing or deriving a multivariable partial differential equation for predicting future biometric values. Therefore, the instant disclosure does not provide an adequate written description for the multivariable partial differential equation for predicting future biometric parameters.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-12 and 18-20 fall under the statutory category of a machine and claims 13-17 fall under the statutory category of a process.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Claims 1 and 18 recite mathematical concepts of “a predictive model that predicts a future value for the biometric parameter at a given time according to the set of features”.
Independent claims 1 and 18 recite mental processes of “a feature extractor that generates a set of features for the patient, each of the set of features being associated with one of the patient and the therapeutic delivery system”, and “determine a dosage for the therapeutic according to the predicted future value for the patient parameter”.
Independent claim 13 recites a mathematical concept of “predicting a future value for the monitored patient parameter at a predictive model according to the extracted set of features”
Independent claim 13 recites mental processes of “extracting a set of features for a patient including at least the monitored patient parameter” and “selecting a dosage for the therapeutic delivery system according to the predicted future value of the monitored patient parameter”.
Dependent claim 6 recites a mathematical concept and mental process of “compare the predicted future value to the actual value to generate an error value for the predicted future value”. Dependent claim 14 recites a mathematical concept of “adjusting the predictive model according to the received set of hyperparameter values”. Dependent claim 15 recites a mathematical concept and mental process of “comparing the predicted future value to the actual value to generate an error value for the predicted future value”. Dependent claim 20 recites a mathematical concept and mental process of “compare the predicted future value to the actual value to generate an error value for the predicted future value”.
The claims recite as organizing data by generating features, making comparisons as comparing values to generate error values, and analyzing data as determining a dosage. The human mind is capable of organizing data, making comparisons, and analyzing data. The claims recite mathematical concepts of mathematical calculations as predicting future values (which encompasses partial differential equation models), adjusting parameters of the models based on hyperparameter values, and generating error values between a predicted value and a measured value (which encompasses mathematical calculations utilizing equations for calculating errors).
Dependent claims 2-5, 10, 11, 16, 17, and 19 (it is noted that claims 2-4, 16, and 19 also recite additional elements which are addressed under step 2A prong 2 and step 2B below) further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept.
(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). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claims 1, 13 and 18 of a therapeutic delivery system, the additional element in claim 2 of wherein the therapeutic delivery system is an infusion system, and the additional element in claim 3 of wherein the therapeutic delivery system is an insulin pump does into integrate the judicial exception into a practical application because this is generally linking the judicial exceptions to a particular technological environment of therapeutic delivery systems (see MPEP 2106.05(h)). This additional element only interacts with the judicial exceptions through a general link of a particular technological of a therapeutic delivery system and selecting a dosage through analyzing monitored data. This interaction constitutes as a general link because there is not limitation of how the selected dosage affects the therapeutic delivery system (i.e. there is no limitation of having the selected dosage administered to a patient utilizing the therapeutic delivery system).
The additional element in claims 1 and 18 of a monitoring device that includes a sensor for measuring a biometric parameter of a patient, the additional element in claim 4 of the sensors is a first sensor of a plurality of sensors, the additional element in claim 13 of monitoring a patient parameter, the additional element in claim 14 of receiving a set of hyperparameters values, and the additional element in claim 16 of wherein the monitoring a patient parameter comprises monitoring a plurality of patient parameters does not integrate the judicial exceptions into a practical application because these limitations are insignificant extra solution activity of data gathering. These additional elements only interact with the judicial exceptions by providing data to be processed by the judicial exceptions.
The additional elements in claims 6, 7, 9, 14, 15, and 20 of storing data and displaying data does not integrate the judicial exceptions into a practical application because these limitations are insignificant extra solution activity of outputting data. These additional elements only interact with the judicial exceptions by outputting data produced by the judicial exceptions. It is noted that the content of the data does not change the active step of storing data in a computer environment and displaying data on a user interface.
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-20 are directed to the abstract idea.
(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 additional element in claims 1 and 18 of a monitoring device that includes a sensor for measuring a biometric parameter of a patient, the additional element in claims 1, 13 and 18 of a therapeutic delivery system, the additional element in claim 2 of wherein the therapeutic delivery system is an infusion system, the additional element in claim 3 of wherein the therapeutic delivery system is an insulin pump the additional element in claim 4 of the sensors is a first sensor of a plurality of sensors, the additional element in claim 13 of monitoring a patient parameter, and the additional element in claim 16 of wherein the monitoring a patient parameter comprises monitoring a plurality of patient parameters as shown by are conventional as shown by Vettoretti et al. (BioMed Eng OnLine 18, 37 (2019); newly cited) which reviews the combination of a continuous glucose monitor and insulin pump, Cinar et al. (US 2016/0354543 A1; newly cited) which shows a continuous glucose monitor sensor along with physiological sensors and an insulin pump, Mulligan et al. (US 20150065826 A1; cited in IDS received 17 February 2024) which shows a plurality of sensors and a therapeutic delivery system as an infusion system, and Kee et al. (Anaesthesia, 2007, 62, pages 1251-1256; cited IDS 17 February 2024) which shows monitoring patient values with sensors and a therapeutic delivery systems as a syringe pump for infusing a drug.
The additional elements in claims 6, 7, 9, 14, 15, and 20 of storing data/displaying data and the additional element in claim 14 of receiving data in a computer environment are conventional as shown by MPEP 2106.05(d)(II) and 2106.05(b).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-7, 13, 15, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cinar et al. (US 2016/0354543 A1; newly cited).
Claim 1 is directed to a system comprising: a therapeutic delivery system that delivers a therapeutic to a patient; a monitoring device that includes a sensor for measuring a biometric parameter of a patient;
Cinar et al. shows a device for monitoring and treating patient glucose levels (a continuous glucose monitor and insulin pump) (Cinar et al. [0013]).
a feature extractor that generates a set of features for the patient, each of the set of features being associated with one of the patient and the therapeutic delivery system; a predictive model that predicts a future value for the biometric parameter at a given time according to the set of features;
Cinar et al. shows a prediction module adapted for automatically predicting a future glucose level using data measured by the glucose monitor system and the physiological monitoring system (Cinar et al. [0013]). Cinar et al. shows glucose predictions were obtained by using a multivariable time-series model where glucose concentration was expressed as a function of past glucose concentration, physical activity signal readings, and infused insulin amounts by using an ARMAX model and with every new measurement, this model was used for 30-minute prediction of glucose concentration which is interpreted as extracting and utilizing features from the patient (past glucose concentration and physical activity signal readings) and the therapeutic delivery system (infused insulin amounts) for predicting future values (Cinar et al. [0220]).
and a therapeutic delivery system controller that determines a dosage for the therapeutic according to the predicted future value for the patient parameter.
Cinar et al. shows a controller that controls an insulin infusion from the insulin pump as a function of predicted future glucose level (Cinar et al. [0013]).
Claim 18 is directed to a system comprising: a therapeutic delivery system that delivers a therapeutic to a patient; a monitoring device that includes a plurality of sensors for measuring a plurality of a patient parameters comprising a target patient parameter;
Cinar et al. shows a device for monitoring and treating patient glucose levels (a continuous glucose monitor, physiological status monitoring system, and insulin pump) (Cinar et al. [0013]).
a feature extractor that generates a set of features for the patient, each of the set of features being associated with one of the patient and the therapeutic delivery system and the set of features including a feature representing one or a current dosage and a past dosage of the therapeutic being provided by the therapeutic delivery system and at least two features derived from the plurality of patient parameters; a predictive model that predicts a future value for the target patient parameter at a given time according to the set of features;
Cinar et al. shows a prediction module adapted for automatically predicting a future glucose level using data measured by the glucose monitor system and the physiological monitoring system (Cinar et al. [0013]). Cinar et al. shows glucose predictions were obtained by using a multivariable time-series model where glucose concentration was expressed as a function of past glucose concentration, physical activity signal readings, and infused insulin amounts by using an ARMAX model and with every new measurement, this model was used for 30-minute prediction of glucose concentration which is interpreted as extracting and utilizing features from the patient (past glucose concentration and physical activity signal readings) and the therapeutic delivery system (infused insulin amounts which is interpreted total amount which includes past and current dosages) for predicting future values (Cinar et al. [0220]).
and a therapeutic delivery system controller that determines a dosage for the therapeutic according to the predicted future value for the patient parameter.
Cinar et al. shows a controller that controls an insulin infusion from the insulin pump as a function of predicted future glucose level (Cinar et al. [0013]).
Claim 3 is directed to wherein the therapeutic delivery system is an insulin pump, and the patient parameter is a blood glucose of the patient.
Cinar et al. shows an insulin pump and the patient parameter being blood glucose of the patient (Cinar et al. [0013]).
Claim 4 is directed to wherein the sensor is a first sensor of a plurality of sensors and the patient parameter is a first patient parameter of a plurality of patient parameters, the feature extractor generating at least two of the set of features from the plurality of patient parameters.
Cinar et al. shows a first sensor being a continuous glucose monitor which provides a patient parameter of blood glucose levels and provides more sensors as the physiological monitoring system which provide more patient parameters to be used as features for predicting future blood glucose levels (Cinar et al. [0013]).
Claim 5 is directed wherein the set of features includes one of a current and a past dosage with the therapeutic delivery system.
Cinar et al. shows glucose predictions were obtained by using a multivariable time-series model where glucose concentration was expressed as a function of past glucose concentration, physical activity signal readings, and infused insulin amounts by using an ARMAX model and with every new measurement, this model was used for 30-minute prediction of glucose concentration which is interpreted as extracting and utilizing features from the patient (past glucose concentration and physical activity signal readings) and the therapeutic delivery system (infused insulin amounts which is interpreted total amount which includes past and current dosages) for predicting future values (Cinar et al. [0220]).
Claim 6 is directed to a learning element that stores the set of features used for the prediction, the predicted future value, and an actual value for the monitored patient parameter at the given time and compares the predicted future value to the actual value to generate an error value for the predicted future value.
Cinar et al. shows comparing an actual measured value to the predicted value and updating the model based on this comparison (Cinar et al. [0043], [0047], and [0048]).
Claims 7 and 20 are directed to wherein the learning element stores a series of error values associated with a corresponding series of predicted future values and provides and alert if the error values exceed a threshold value for a predetermined period of time.
Cinar et al. shows a system which incorporates fault detection which utilizes information of the estimates of plasma glucose concentration (Cinar et al. [0111]). Cinar et al. further shows fault detection utilizing historical data which utilizes relations between variables, patterns that indicate various abnormalities, and statistical limits that indicate significant deviations are used in fault detection which is interpreted as analyzing error values in historical data to determine statistically significant abnormalities for a given time which are a result in faulty equipment (Cinar et al. [0115]).
Claim 13 is directed to a method for controlling a therapeutic delivery system according to a monitored patient parameter, the method comprising, monitoring a patient parameter,
Cinar et al. shows a device for monitoring and treating patient glucose levels (a continuous glucose monitor and insulin pump) (Cinar et al. [0013]).
extracting a set of features for a patient including at least the monitored patient parameter, predicting a future value for the monitored patient parameter at a predictive model according to the extracted set of features,
Cinar et al. shows a prediction module adapted for automatically predicting a future glucose level using data measured by the glucose monitor system and the physiological monitoring system (Cinar et al. [0013]).
and selecting a dosage for the therapeutic delivery system according to the predicted future value of the monitored patient parameter.
Cinar et al. shows a controller that controls an insulin infusion from the insulin pump as a function of predicted future glucose level (Cinar et al. [0013]).
Claim 15 is directed to storing the set of features used for the prediction, the predicted future value, and an actual value for the monitored patient parameter at the given time; and comparing the predicted future value to the actual value to generate an error value for the predicted future value.
Cinar et al. shows comparing an actual measured value to the predicted value and updating the model based on this comparison (Cinar et al. [0043], [0047], and [0048]).
Claims 13 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mulligan et al. (US 20150065826 A1; cited in IDS received 17 February 2024).
Claim 13 is directed to a method for controlling a therapeutic delivery system according to a monitored patient parameter, the method comprising, monitoring a patient parameter,
Mulligan et al. shows a system which comprise a therapeutic device which includes intravenous pump that can provide therapeutic compounds to a patient (Mulligan et al. [0055]). Mulligan et al. shows a system which includes sensors which obtain physiological data from the from a patient (Mulligan et al. [0053], [0056], and [0057]).
extracting a set of features for a patient including at least the monitored patient parameter, predicting a future value for the monitored patient parameter at a predictive model according to the extracted set of features,
Mulligan et al. shows the system includes a processor which performs data analysis to predict future blood pressure of the patient (Mulligan et al. [0058]).
and selecting a dosage for the therapeutic delivery system according to the predicted future value of the monitored patient parameter.
Mulligan et al. shows that the system includes instructing an intravenous pump to run at a certain rate to provide a certain dosage in response to a predicted future value (Mulligan et al. [0097]).
Claim 15 is directed to storing the set of features used for the prediction, the predicted future value, and an actual value for the monitored patient parameter at the given time; and comparing the predicted future value to the actual value to generate an error value for the predicted future value.
Mulligan et al. shows a self-learning process implemented on a system which utilizes a set of features for predicting future blood pressure values and receiving measured values for blood pressure (Mulligan et al. [0101]-[0104]). Mulligan et al. shows receiving data and if the data is understood by the predictive model and the output generated using the predictive model is not accurate which is interpreted as an error between a predicted value and an actual value (monitored data from patient), then the data and the outcome can be used to modify the predictive model (Mulligan et al. [0106]).
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.
Claims 1, 2, 5, 6, 8, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan et al. (US 20150065826 A1; cited in IDS received 17 February 2024) in view of Jeong et al. (Appl. Sci. 2019, 9, 5135; newly cited).
Independent claim 1 is directed to a system comprising: a therapeutic delivery system that delivers a therapeutic to a patient; a monitoring device that includes a sensor for measuring a biometric parameter of a patient;
Mulligan et al. shows a system which comprise a therapeutic device which includes intravenous pump that can provide therapeutic compounds to a patient (Mulligan et al. [0055]). Mulligan et al. shows a system which includes sensors which obtain physiological data from the from a patient (Mulligan et al. [0053], [0056], and [0057]).
a feature extractor that generates a set of features for the patient, each of the set of features being associated with one of the patient and the therapeutic delivery system; a predictive model that predicts a future value for the biometric parameter at a given time according to the set of features;
Mulligan et al. shows the system includes a processor which performs data analysis to predict future blood pressure of the patient (Mulligan et al. [0058]).
and a therapeutic delivery system controller that determines a dosage for the therapeutic according to the predicted future value for the patient parameter.
Mulligan et al. shows that the system includes instructing an intravenous pump to run at a certain rate to provide a certain dosage in response to a predicted future value (Mulligan et al. [0097]).
Mulligan et al. does not show a feature associated with the therapeutic delivery system or predicting a future value with a feature associated with the therapeutic delivery system.
Like Mulligan et al., Jeong et al shows a model for predicting future blood pressure values. Jeong et al. shows a model which intakes features associated with the patient and the therapeutic delivery system to predict future blood pressure (Jeong et al. page 3 figure 1, page 4, and page 6).
Independent claim 18 is directed to a therapeutic delivery system that delivers a therapeutic to a patient; a monitoring device that includes a plurality of sensors for measuring a plurality of patient parameters of a patient, the plurality of patient parameters comprising a target patient parameter;
Mulligan et al. shows a system which comprise a therapeutic device which includes intravenous pump that can provide therapeutic compounds to a patient (Mulligan et al. [0055]). Mulligan et al. shows a system which includes sensors which obtain physiological data from the from a patient (Mulligan et al. [0053], [0056], and [0057]).
a feature extractor that generates a set of features for the patient, each of the set of features being associated with one of the patient and the therapeutic delivery system and the set of features including a feature representing one of a current dosage and a past dosage of the therapeutic being provided by the therapeutic delivery system and at least two features derived from the plurality of patient parameters; a predictive model that predicts a future value for the target patient parameter at a given time according to the set of features;
Mulligan et al. shows the system includes a processor which performs data analysis to predict future blood pressure of the patient (Mulligan et al. [0058]).
and a therapeutic delivery system controller that determines a desired dosage for the therapeutic according to the predicted future value for the target patient parameter.
Mulligan et al. shows that the system includes instructing an intravenous pump to run at a certain rate to provide a certain dosage in response to a predicted future value (Mulligan et al. [0097]).
Mulligan et al. does not show a feature associated with the therapeutic delivery system or predicting a future value with a feature associated with the therapeutic delivery system.
Like Mulligan et al., Jeong et al shows a model for predicting future blood pressure values. Jeong et al. shows a model which intakes features associated with the patient and the therapeutic delivery system to predict future blood pressure (Jeong et al. page 3 figure 1, page 4, and page 6). Jeong et al. shows extracting and utilizing past dosage and current dosage associate with the therapeutic delivery system to predict future blood pressure (Jeong et al. page 3 figure 1, page 4, and page 6).
Claim 2 is directed to wherein the therapeutic delivery system is an infusion system, and the patient parameter is a blood pressure of the patient.
Mulligan et al. shows the therapeutic delivery system is an infusion system (intravenous pump) and the patient parameter is blood pressure of the patient (Mulligan et al. [0055] and [0058]).
Claim 5 is directed to wherein the set of features includes one of a current and a past dosage associated with the therapeutic delivery system.
Jeong et al. shows extracting and utilizing past dosage and current dosage associate with the therapeutic delivery system to predict future blood pressure (Jeong et al. page 3 figure 1, page 4, and page 6).
Claim 6 is directed to a learning element that stores the set of features used for the prediction, the predicted future value, and an actual value for the monitored patient parameter at the given time and compares the predicted future value to the actual value to generate an error value for the predicted future value.
Mulligan et al. shows a self-learning process implemented on a system which utilizes a set of features for predicting future blood pressure values and receiving measured values for blood pressure (Mulligan et al. [0101]-[0104]). Mulligan et al. shows receiving data and if the data is understood by the predictive model and the output generated using the predictive model is not accurate which is interpreted as an error between a predicted value and an actual value (monitored data from patient), then the data and the outcome can be used to modify the predictive model (Mulligan et al. [0106]).
Claim 8 is directed to wherein the learning element provides an alert if the patient parameter deviates from a range for a predetermined period of time.
Mulligan et al. shows if any blood pressure trends outside of the normal range various alarm conditions would be set off (Mulligan et al. [0098]).
Claim 12 is directed to a network interface that provides patient data from an electronic health records (EHR) database to the feature extractor, at least one features of the set of features being determined from the patient data.
Jeong et al. shows gathering patient data from an electronic health records database to extract features to be used for predicting future blood pressure of a patient (Jeong et al. page 3).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the prediction of future blood pressure of Mulligan et al. to incorporate the use of past and current dosages of a therapeutic device in the prediction of future blood pressure and gathering patient data from an electronic health records database to extract features as shown in Jeong et al. because this would allow for a model which utilizes parameters of therapeutic doses of a drug which, when administered, manipulates the blood pressure of an individual (Jeong et al. page 1 abstract and page 4). One would have a reasonable expectation of success because Mulligan et al. utilizes sensor data for predicting future blood pressure of a patient while Jeong et al. shows the use of data derived from a therapeutic delivery system for predicting future blood pressure.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Cinar et al. (US 20160354543 A1; newly cited) as applied to claim 1 in the 35 U.S.C. 102 rejection above, and further in view of Mazlish et al. (US 20190221307 A1; newly cited).
Claim 9 is directed to a user interface that displays the patient parameter and the dosage to a user and allows a user to change at least one parameter associated with the predictive model.
Cinar et al. does not show a user interface that displays the patient parameter and the dosage to a user and allows a user to change at least one parameter associated with the predictive model.
Like Cinar et al., Mazlish et al. shows a therapeutic delivery system with a continuous glucose monitor and insulin pump. Mazlish et al. shows a user interface for which displays current and projected glucose values, charts displaying glucose levels and insulin delivery data (Mazlish et al. abstract). Mazlish et al. shows that the user interface can allow a user to input a number of units of outside insulin (Mazlish et al. [0062]).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to combine reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have combined the therapeutic delivery system which predicts future glucose values based on infused insulin and past glucose measurements of Cinar et al. with the user interface to enter data of insulin delivery outside the therapeutic system of Mazlish et al. because this would allow for a system which can more accurately predict future glucose levels by being provided information of insulin administration that the system did not provide (Mazlish et al. [0060]-[0063]). One would have a reasonable expectation of success because Cinar et al. shows a therapeutic delivery system for infusing insulin which predicts future glucose levels based on infused insulin while Mazlish et al. provides a user interface for interacting with a therapeutic delivery system for infusing insulin.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Mulligan et al. (US 20150065826 A1; cited in IDS received 17 February 2024) as applied to claim 13 in the 35 U.S.C. 102 rejection above, and further in view of Jeong et al. (Appl. Sci. 2019, 9, 5135; newly cited).
Claim 16 is directed to wherein the monitoring a patient parameter comprises monitoring a plurality of patient parameters and the set of features includes a feature representing a current dosage and a past dosage of the therapeutic being provided by the therapeutic delivery system and at least two features derived from the plurality of patient parameters.
Mulligan et al. does not show features of a current dosage and a past dosage of the therapeutic for predicting future blood pressure.
Like Mulligan et al., Jeong et al shows a model for predicting future blood pressure values. Jeong et al. shows a model which intakes features associated with the patient and the therapeutic delivery system to predict future blood pressure (Jeong et al. page 3 figure 1, page 4, and page 6). Jeong et al. shows extracting and utilizing past dosage and current dosage associate with the therapeutic delivery system to predict future blood pressure (Jeong et al. page 3 figure 1, page 4, and page 6).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the prediction of future blood pressure of Mulligan et al. to incorporate the use of past and current dosages of a therapeutic device in the prediction of future blood pressure as shown in Jeong et al. because this would allow for a model which utilizes parameters of therapeutic doses of a drug which, when administered, manipulates the blood pressure of an individual (Jeong et al. page 1 abstract and page 4). One would have a reasonable expectation of success because Mulligan et al. utilizes sensor data for predicting future blood pressure of a patient while Jeong et al. shows the use of data derived from a therapeutic delivery system for predicting future blood pressure.
Conclusion
No claims are allowed.
Claims 10, 11, 14, 17, and 19 are free of the prior art of record. Cinar et al. is the closest prior art of record to claims 10, 11, 17, and 19 by showing a model for predicting a future biometric value. However, Cinar et al. does not show the use of a multi-variable partial differential equation for the prediction of a future biometric value. Cinar et al. is the closest prior art of record for claim 14 however there is no disclosure of receiving a set of hyper-parameter values for the predictive model from a user interface and adjusting the predictive model according to the hyperparameter values.
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/J.E.H./Examiner, Art Unit 1685
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685