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 Rejections - 35 USC § 112
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 1-20 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.
In regard to claim 1, the specification does not reasonably convey to one of ordinary skill in the art that the applicant had possession of the claimed invention at the time of filing. Claim 1 recites, inter alia, “a processor programmed with a machine learning algorithm, the algorithm adapted to receive a systolic blood pressure measurement from the blood pressure measuring device, perform a probabilistic determination to predict a blood pressure response to a particular dose of an intravenous medication and develop a dosage recommendation to achieve a predetermined target systolic blood pressure goal”. The novelty of the claim appears drawn to this limitation. The specification discloses generally that the processor is programmed with a machine learning algorithm that is adapted to receive a systolic blood pressure measurement and develop a dosage recommendation to achieve a predetermined target systolic blood pressure. However, the specification fails to describe how the systolic blood pressure measurement is processed by the machine learning algorithm, what the output of the algorithm is, and how the output of the algorithm corresponds to the recited dosage recommendation. All of the disclosure of the machine learning algorithm in the applicant’s originally filed application is in terms of what the algorithm does and fails to describe how the algorithm does it. Please see par. [0007], [0009], [0012], [0018], [0038]-[0056] of the pg-publication of the instant action. At best these passages disclose either generic data processing techniques such as Bayesian, random forest, and/or decision tree methodology and smart BP PID algorithms (see par. [0038] and [0039] or generic use of variables such as a plurality of data elements listed in par. [0014]. But there is in no in-depth discussion of how the techniques work or how the data is handled. The drawings only show the algorithm as a black box with no disclosure of specific steps on how the data is handled. The applicant only discloses the desired result of developing a dosage recommendation using a machine learning algorithm rather than describing the invention itself. The specification does not reasonably convey to one of ordinary skill in the art that the applicant was in possession of the full scope of the claimed machine learning algorithm at the time of filing. Therefore, the written description requirement of 35 USC 112(a) is not satisfied.
The independent claims 9 and 18 include similar limitations and are rejected for the same reason.
The dependent claims are rejected by virtue of their dependency on the rejected independent claim.
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 1-20 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.
In regard to claim 1, the claim is rendered indefinite because of the limitation “a processor programmed with a machine learning algorithm, the algorithm adapted to receive a systolic blood pressure measurement from the blood pressure measuring device, perform a probabilistic determination to predict a blood pressure response to a particular dose of an intravenous medication and develop a dosage recommendation to achieve a predetermined target systolic blood pressure goal”. As established above, the applicant has failed to provide support for the machine-learning algorithm in the specification. Because of that failure the ordinary skilled artisan would not be reasonably apprised of the scope of the limitation. The applicant has only described the intended result of the algorithm rather than disclosed the substance of the algorithm.
The independent claims 9 and 18 include similar limitations and are rejected for the same reason.
The dependent claims are rejected by virtue of their dependency on the rejected independent claim.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 9-15, and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lamontagne et al (US 2024/0238518; hereafter Lamontagne).
Regarding claim 1, Lamontagne discloses an automated system for managing blood pressure of a patient (system provided for the patient monitoring device is of the type that can continuously monitor at least the arterial pressure of the patient, such as systolic and diastolic pressure; Figs. 1A-C, 2, 2B, 3, 4A, 4B, 5A-5C & and at least Abstract) comprising: a blood pressure measuring device (11; see at least par. [0044]-[0049]); a controller (13, 20, 30) communicatively coupled with the blood pressure measuring device (see at least par. [0044]-[0049]), the controller comprising: a processor programmed with a machine learning algorithm (see at least par. [0067]-[0074]), the algorithm adapted to receive a systolic blood pressure measurement from the blood pressure measuring device (see at least par. [0067]-[0074])), perform a probabilistic determination to predict a blood pressure response to a particular dose of an intravenous medication and develop a dosage recommendation to achieve a predetermined target systolic blood pressure goal (see at least par. [0049]-[0075]); a memory adapted to store a plurality of data elements including data relating to the intravenous medication, data relating to the patient, data relating to a plurality of hemodynamic parameters, and data relating to other patients with a same or a similar condition (see at least par. [0049]-[0050], [0061]-[0069]); and a user interface including a display (see par. [0044]-[0045]); and an infusion pump apparatus communicatively coupled with the controller and comprising an infusion pump (12; see par. [0044],[0046]), a medication bag fluidly coupled with the infusion pump (see par. [0046]), and an intravenous port fluidly coupled with the infusion pump (the infusion pump may be a volumetric (fluidly) intravenous infusion pump, that is automatically operated in order to infuse a medication agent to a patient; the infusion pump may have the appropriate intravenous system, or other administration mechanism, by which the medicinal agent is administered to the patient; the intravenous system for perfusion of the vasopressor into the patient includes a line (e.g., tubing), a needle or syringe, medication bag; para [0046]); wherein the machine learning algorithm uses at least one of the plurality of data elements to develop the dosage recommendation (based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient, with a view to efficiently take into consideration multiple variables to suggest a dose parameter with a view to improving the patient's condition; Abstract & para [0072]), the controller presents the dosage recommendation to an operator using the user interface (a system management device may also be provided so that parameters of the system may be monitored and/or modified by an operator; the system management device may also be known as a user interface, and may take various forms such as an integrated keyboard and screen, a portable device (e.g., smart phone, tablet); para [0044]), and the controller instructs the infusion pump to implement the dosage recommendation (upon receiving this data, the controller module may control the operation of the infusion pump to vary the amount of medicinal agent administered to the patient; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient; para [0049], [0072]).
Regarding claim 2, Lamontagne discloses the system of claim 1. In addition, Lamontagne discloses wherein the data relating to the medication includes at least one of known pharmacology of the medication, historic dose response data for the current patient, and last dose change duration for the current patient (such record entries of time dependent data may accrue into historical data associated with a given patient and/or treatment episode; based on the available data, the central controller determines the dose parameters, and communicates with the controller module to control its associated pump; patient-specific administration history, and eventually, outcomes of the vasopressor administration episodes; para [0064], [0068]).
Regarding claim 3, Lamontagne discloses wherein the data relating to a plurality of hemodynamic parameters includes at least one of patient heart rate, patient stroke volume, patient cardiac output, and patient total peripheral resistance (a dose parameter as a function of a plurality of variables; the variables may include time-dependent patient- specific variables, such as any of the vital signs measured by the patient monitoring device, including one or more of arterial pressure (i.e., blood pressure), heart rate, blood oxygen saturation, respiratory rate, and current or past medical conditions; para [0049]).
Regarding claim 4, Lamontagne discloses wherein the machine learning algorithm automatically, alters itself based on patient blood pressure response to the medication after a predetermined number of sequential dose adjustments (based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient, with a view to efficiently take into consideration multiple variables to suggest a dose parameter with a view to improving the patient's condition; Abstract & para [0072]), and develops the dosage recommendation according to the altered algorithm (based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient, with a view to efficiently take into consideration multiple variables to suggest a dose parameter with a view to improving the patient's condition; Abstract & para [0072]).
Regarding claim 5, Lamontagne discloses wherein the system further comprises a second infusion pump apparatus communicatively coupled with the controller (the dose parameters, or pump control instructions associated thereto, are sent from the central controller to the controller modules for the controller modules to control their respective pumps; para [0062]), the second infusion pump apparatus (pumps; para [0062]) comprising a second infusion pump and a second medication bag fluidly coupled with the second infusion pump (the intravenous system for perfusion of the vasopressor into the patient includes a line (e.g., tubing), a needle or syringe, medication bag; pumps; para [0046], [0062]), the first infusion pump apparatus dispensing a first medication and the second infusion pump apparatus dispensing a second medication different from the first medication (pumps; by taking observations of a sufficient number of administration episodes, the administration assistance system may identify standards or deviations from standards based on the afore-mentioned patient profile values of concurrent medications; para [0062], [0070]), the algorithm being further adapted to perform a probabilistic determination to predict a blood pressure response to a particular dose of each of the first and second medications (determining, based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; as such patient profile may have an impact on a patient's reaction to a dose rate of a given vasopressor agent; Abstract & para [0071]) and develop a dosage recommendation for each of the first and second medications to achieve the predetermined target systolic blood pressure goal (based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient, with a view to efficiently take into consideration multiple variables to suggest a dose parameter with a view to improving the patient's condition; Abstract & para [0072]).
Regarding claim 6, Lamontagne discloses further comprising a heart rate sensor communicatively coupled to the controller (other vital signs of the patient may be monitored by the patient monitoring device sensor), such as the heart rate; para [0045]).
Regarding claim 9, Lamontagne discloses an apparatus for determining an optimal intravenous medication dosage to manage blood pressure in a patient (monitoring device for determining, with the at least one processing unit, based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; based on the available data, the central controller determines the dose parameters, and communicates with the controller module to control its associated pump; Figs. 1A-C, 2, 2B, 3, 4A, 4B, 5A-5C, Abstract & para [0044], [0064]), the apparatus comprising: a blood pressure measuring device (11; monitor at least the arterial pressure of the patient, such as systolic and diastolic pressure; the patient monitoring device may thus be said to include at least an arterial pressure monitoring device; para [0045]); a controller (13, 20, 30) communicatively coupled with the blood pressure measuring device (monitor at least the arterial pressure of the patient, such as systolic and diastolic pressure; the patient monitoring device may thus be said to include at least an arterial pressure monitoring device; the controller module has the capacity of communicating with the patient monitoring device; para [0045], [0049]), the controller (controller; para [0049]) comprising: a processor programmed with a machine learning algorithm (the machine-learning module acquires the data from one or more of the central controllers; para [0066]), the algorithm adapted to receive a systolic blood pressure measurement from the blood pressure measuring device (monitor at least the arterial pressure of the patient, such as systolic and diastolic pressure; the patient monitoring device may thus be said to include at least an arterial pressure monitoring device; para [0045]), perform a probabilistic determination to predict a blood pressure response to a particular dose of an intravenous medication and develop a dosage recommendation to achieve a predetermined target systolic blood pressure goal (the system provided for administration of a vasopressor agent, the patient monitoring device is of the type that can continuously monitor at least the arterial pressure of the patient, such as systolic and diastolic pressure; the intravenous system for perfusion of the vasopressor into the patient includes a line (e.g., tubing), a needle or syringe, medication bag or other source of the vasopressor; the controller module may also take into consideration a target arterial pressure, or a target range of arterial pressure, in comparison to the measured (current) arterial pressure; para [0045], [0046], [0049]); a memory adapted to store a plurality of data elements including data relating to the intravenous medication, data relating to the patient (the memory of the controller module communicatively coupled to the processing unit and comprising computer-readable program instructions executable by the processing unit for: receiving, by the processing unit, a current arterial pressure of a patient; determining, with the processing unit, based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure and controlling an operation of a pump administering the vasopressor agent as a function of the dose parameter; para [0061]), data relating to a plurality of hemodynamic parameters (a dose parameter as a function of a plurality of variables; the variables may include time-dependent patient-specific variables, such as any of the vital signs measured by the patient monitoring device, including one or more of arterial pressure (i.e., blood pressure), heart rate, blood oxygen saturation, respiratory rate, and current or past medical conditions; para [0049]), and data relating to other patients with a same or a similar condition (controller communicate with the patient monitoring devices and controller modules of other networked systems associated with other patients; the data may be in the form of digital files, patient profile data acquired may include family conditions; para [0064], [0068]); and a user interface including a display (a user interface, and may take various forms such as an integrated screen; para [0044]); wherein the machine learning algorithm uses at least one of the plurality of data elements to develop the dosage recommendation (based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient, with a view to efficiently take into consideration multiple variables to suggest a dose parameter with a view to improving the patient's condition; Abstract & para [0072]), the controller presents the dosage recommendation to an operator using the user interface (a system management device may also be provided SO that parameters of the system may be monitored and/or modified by an operator; the system management device may also be known as a user interface, and may take various forms such as an integrated keyboard and screen, a portable device (e.g., smart phone, tablet); para [0044]), and the controller instructs the infusion pump to implement the dosage recommendation (upon receiving this data, the controller module may control the operation of the infusion pump to vary the amount of medicinal agent administered to the patient; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient; para [0049], [0072]).
Regarding claim 10, Lamontagne discloses further comprising an infusion pump apparatus communicatively coupled with the controller and comprising an infusion pump (an infusion pump and a controller module used in a closed loop with the patient monitoring device and the infusion pump; para [0044]), a medication bag fluidly coupled with the infusion pump (the infusion pump may have the appropriate intravenous system; the intravenous system for perfusion of the vasopressor into the patient includes a medication bag or other source of the vasopressor; para [0046]), a medication bag fluidly coupled with the infusion pump, and an intravenous port fluidly coupled with the infusion pump (the infusion pump may have the appropriate intravenous system; the intravenous system for perfusion of the vasopressor into the patient includes a medication bag or other source of the vasopressor; para [0046])), wherein the controller instructs the infusion pump to implement the dosage recommendation (upon receiving this data, the controller module may control the operation of the infusion pump to vary the amount of medicinal agent administered to the patient; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient; para [0049], [0072]).
Regarding claim 11, Lamontagne discloses wherein the data relating to the medication includes at least one of known pharmacology of the medication, historic dose response data for the current patient, and last dose change duration for the current patient (such record entries of time dependent data may accrue into historical data associated with a given patient and/or treatment episode; based on the available data, the central controller determines the dose parameters, and communicates with the controller module to control its associated pump; patient-specific administration history, and eventually, outcomes of the vasopressor administration episodes; para [0064], [0068]).
Regarding claim 12, Lamontagne discloses wherein the data relating to a plurality of hemodynamic parameters includes at least one of patient heart rate, patient stroke volume, patient cardiac output, and patient total peripheral resistance (a dose parameter as a function of a plurality of variables; the variables may include time-dependent patient- specific variables, such as any of the vital signs measured by the patient monitoring device, including one or more of arterial pressure (i.e., blood pressure), heart rate, blood oxygen saturation, respiratory rate, and current or past medical conditions; para [0049]).
Regarding claim 13, Lamontagne discloses wherein the machine learning algorithm automatically alters itself based on patient blood pressure response to the medication after a predetermined number of sequential dose adjustments (based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient, with a view to efficiently take into consideration multiple variables to suggest a dose parameter with a view to improving the patient's condition; Abstract & para [0072]), and develops the dosage recommendation according to the altered algorithm (based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient, with a view to efficiently take into consideration multiple variables to suggest a dose parameter with a view to improving the patient's condition; Abstract & para [0072]).
Regarding claim 14, Lamontagne discloses wherein the apparatus further comprises a second infusion pump apparatus communicatively coupled with the controller (the dose parameters, or pump control instructions associated thereto, are sent from the central controller to the controller modules for the controller modules to control their respective pumps; para [0062]), the second infusion pump apparatus (pumps; para [0062]) comprising a second infusion pump and a second medication bag fluidly coupled with the second infusion pump (the intravenous system for perfusion of the vasopressor into the patient includes a line (e.g., tubing), a needle or syringe, medication bag; pumps; para [0046], [0062]), the first infusion pump apparatus dispensing a first medication and the second infusion pump apparatus dispensing a second medication different from the first medication (pumps; by taking observations of a sufficient number of administration episodes, the administration assistance system may identify standards or deviations from standards based on the afore- mentioned patient profile values of concurrent medications; para [0062], [0070]), the algorithm being further adapted to perform a probabilistic determination to predict a blood pressure response to a particular dose of each of the first and second medications (determining, based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; as such patient profile may have an impact on a patient's reaction to a dose rate of a given vasopressor agent; Abstract & para [0071]) and develop a dosage recommendation for each of the first and second medications to achieve the predetermined target systolic blood pressure goal (based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure; using the trained machine learning algorithm, and with the data specific to a patient, the administration assistance module may propose (recommend) dose parameter specific to the patient, with a view to efficiently take into consideration multiple variables to suggest a dose parameter with a view to improving the patient's condition; Abstract & para [0072]).
Regarding claim 15, Lamontagne discloses further comprising a heart rate sensor communicatively coupled to the controller (other vital signs of the patient may be monitored by the patient monitoring device sensor), such as the heart rate; para [0045]).
In regard to claims 18-20, please see the rejections above as the examiner believes all of the limitations are adequately addressed. The disclosure of the recited structure and accompanying disclosure of how the structure works anticipates the recited method steps.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lamontagne in view of US 2021/0369941 A1 to Korshøj et al. (hereafter Korshøj).
Regarding claims 7 and 16, Lamontagne fails to disclose further comprising an intracranial pressure sensor communicatively coupled to the controller.
Korshøj discloses further comprising an intracranial pressure sensor communicatively coupled to the controller (an intracranial pressure sensor can be implanted in the brain and connected to the control interface along with the flow pump controlling the fluid exchange; para [0192]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include further comprising an intracranial pressure sensor communicatively coupled to the controller as taught by Korshøj into the system of Lamontagne for the purpose of providing and upgrading current technologies from labor-demanding and passive information providers to automated and controlled monitoring, i.e. active therapy that can accelerate neuro-intensive care and patient healing in order to improve clinical outcome with reduction of secondary disabilities after treatment.
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lamontagne in view of US 2018/0085532 A1 to Desborough et al. (hereafter Desborough .
Regarding claims 8 and 17, Lamontagne fails to disclose wherein the display of the user interface is touch sensitive and presents icons enabling the operator to accept, reject, or adjust the dosage recommendation.
Desborough discloses wherein the display of the user interface is touch sensitive and presents icons enabling the operator to accept, reject, or adjust the dosage recommendation (the user interface may be adapted to display a bolus recommendation based on the blood glucose data and a selection of one of the user-selectable icons; a pen cap that includes a touch screen user interface that can include buttons and display a recommended dosage; para [0017], [0065]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the display of the user interface is touch sensitive and presents icons enabling the operator to accept, reject, or adjust the dosage recommendation as taught by Desborough into the system of Lamontagne for the purpose of providing systems, and devices that further reduce the cognitive burden on the user while improving the accuracy of a recommended bolus dosage.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE J STIGELL whose telephone number is (571)272-8759. The examiner can normally be reached M-F 9-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Tsai can be reached at 571-270-5246. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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THEODORE J. STIGELL
Primary Examiner
Art Unit 3783
/THEODORE J STIGELL/ Primary Examiner, Art Unit 3783
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