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 .
This action is responsive to communication filed on 07/19/2023.
Claims 1-20 are pending.
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.
Claim 1-11, 13-20 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.
Regarding claims 1, 10, 13 and 18, the term “high occlusion probability” recited by claims 1, 10, 13 and 18 is a relative term which renders the claim indefinite. The term “high occlusion probability” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The metes and bounds of the claimed “high occlusion probability” are not defined for ascertaining what would constitute "high occlusion probability". The original disclosure of the invention disclose “[a] high occlusion probability can be present if the probability of an occlusion occurring, which is output by the control unit, is higher than a limit value, for example 50%, preferably higher than 70%. In particular, the predefined limit value may be variably adjustable before the start of a treatment. However, other limit values for a change of the operating mode from the low to the high occlusion probability are also conceivable, as long as they have a positive value, i.e. also about 20%. If now an occlusion probability is estimated and this exceeds a limit value or an alarm condition, then in a sense an occurrence of an occlusion can be determined directly”, however although some examples of limit values for probability of occlusion occurring are described (i.e. 50%, preferably higher than 70%, also about 20%), no particular definition has been given for the claimed “high occlusion probability” as to ascertain what would constitute a “high occlusion probability”. Clarification and correction is required.
Dependent claims 2-11 and 18-20 are rejected under 35 U.S.C. 112(b) for the reasons discussed above with respect to independent claim 1 from which they depend.
Dependent claims 14-17 are rejected under 35 U.S.C. 112(b) for the reasons discussed above with respect to independent claim 13 from which they depend.
Regarding claim 14, the recitation ”selecting a zero with a minimum value from the zeros” recited in lines 4-5 of the claim, renders the claim indefinite. It is unclear from the claim how a zero with a minimum value form the zeros is being selected from “the zeros”. It is unclear as to what the minimum value from the zeros is referring to and the metes and bounds of the scope of the invention the claimed limitation intends to cover. Clarification and correction is required.
For examination on the merits the claims will be interpreted as best understood in light of the 35 USC 112(b) rejections above.
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 an abstract idea without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Under Step 1 of the analysis, claim 1, belongs to a statutory category namely a method. Likely claim 12 belongs to a statutory category, namely it is a method and claim 13 belongs to a statutory category, namely it is a device.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim(s) 1, 12 and 13 recite(s) concepts related to mathematical algorithms/concepts, mental processes and concepts performed in the human mind e.g. observation, evaluation, judgment, opinion and/or human activities for “determining an occlusion probability by a control unit based on the pressure course; detecting a volume delivered by a pump for an operating range with high occlusion probability by the control unit; detecting an actual occurrence of an occlusion at a detection time by a detection unit when an alarm condition is exceeded” (claim 1); “detecting occurrences of occlusion events at the pressure courses; combining the pressure courses and the occurrences of occlusion events into a training data set; creating a system for machine learning having the pressure courses as input values and the occurrences of occlusion events as output values; inputting the subsequent pressure course into the system; and ... estimate of the occlusion probability in real time based on the subsequent pressure course by the system” (claim 12); and “…detect an occurrence of an occlusion; and … detect a volume delivered by the medical pump for an operating range with high occlusion probability” (claim 13).
The concepts discussed above can be considered to describe mental processes, namely concepts performed in the human mind or with pen and paper, and/or mathematical concepts, namely a series of calculations leading to one or more numerical results or answers. Although, the claim does not spell out any particular equation or formula being used, the lack of specific equations for individual steps merely points out that the claim would monopolize all possible calculations in performing the steps. These steps recited by the claims, therefore amount to a series of mental or mathematical steps, making these limitations amount to an abstract idea.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
This judicial exception is not integrated into a practical application because the abstract idea is not performed by using any particular device and because the “control unit” (claims 1 and 13), “machine learning” (claim 12), “detection unit” (claim 13) amounts to the recitation of a general purpose computer used to apply the abstract idea; the recitation of “sensor unit” and “detecting a pressure course in the fluid guide unit by a sensor unit” recited by claim 1, “detecting pressure courses by a sensor unit” recited by claim 12, “a sensor unit adapted to detect a pressure course in the fluid guide unit” recited by claim 13, is mere gathering recited at high level of generality, post-solution activities recited at a high level of generality generally linking the abstract idea to a field of use (i.e. pumping back the volume delivered by the pump…) and the results of the algorithm are merely output/stored as part of insignificant post-solution activity (i.e. outputting an estimate of the occlusion probability… (claim 12)) and are not used In any particular matter as to integrate the abstract idea in a practical application.
Under Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer “control unit” (claims 1 and 13), “machine learning” (claim 12), “detection unit” (claim 13), used to apply the abstract idea and mere data gathering/output recited at a high level of generality and insignificant extra-solution activity that when further analyzed under Step 2B is found to be well-understood, routine and conventional activities as evidenced by MPEP 2106.05(d)(II); and because the data of performing the algorithm must necessarily be “obtained” and the use of a general purpose computer to implement the abstract idea for performing the algorithm does not amount to significantly more than the recitation of the abstract idea itself.
Therefore, claims 1, 12 and 13 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-11 and 14-20 merely expand on the abstract idea by appending additional steps to the mathematical algorithm on their respective independent claims 1 and 13 and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore these claims are found ineligible for the reasons described for their respective parent claims 1 and 13.
Dependent claim 2 recite “storing the pressure course in a memory unit after the pressure course is detected” however this merely amounts to insignificant extra solution activity and well understood routine and conventional activities and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claim 3 recite “detecting a number of motor steps of a pump motor or of a rotation angle of the pump motor” however this merely expand on the abstract idea by appending additional steps to the mathematical algorithm and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claim 4 recite “retracting the number of motor steps of the pump motor or the rotation angle of the pump motor” however this merely amounts to insignificant extra solution activity and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claim 5 recite “wherein the alarm condition is a pressure limit value”, however this merely expand on the abstract idea by appending additional steps to the mathematical algorithm and is mere data characterization of the data and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claim 6 recite “creating a trained system for machine learning with the pressure course as an input value and the occlusion probability as an output value” however this merely expand on the abstract idea by appending additional steps to the mathematical algorithm and the steps of “inputting the pressure course in real time into the trained system for machine learning; and outputting the occlusion probability in real time by the trained system for machine learning based on the pressure course”, merely amounts to insignificant extra solution activity, and because the results of the algorithm are merely output/stored as part of insignificant post-solution activity and are not used In any particular matter as to integrate the abstract idea in a practical application or amount to significantly more that the judicial exception.
Dependent claim 7 recite “combining the pressure course and the occurrences of occlusion events into a training data set”; “detecting occurrences of occlusion events” and “training the trained system for machine learning with the pressure course as the input value and the occurrences of occlusion events as the output value”, however this merely expand on the abstract idea by appending additional steps to the mathematical algorithm; and the steps of “detecting the pressure course by the sensor unit” is mere data gathering recited at a high level of generality generally linking the abstract idea to a field of use and the input/output of values, merely amounts to insignificant extra solution activity, and because the results of the algorithm are merely output/stored as part of insignificant post-solution activity and are not used In any particular matter as to integrate the abstract idea in a practical application or amount to significantly more that the judicial exception.
Dependent claim 8, recite “the trained system for machine learning is a neural network” however this merely expand on the abstract idea by appending additional steps to the mathematical algorithm and it is merely defining the system for machine learning which amounts to the recitation of a general purpose computer and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claim 11 recite “reducing pressure by pumping back fluid through the pump and simultaneously detecting the pressure course through the sensor unit; and stopping pumping when the pressure reaches a pressure level at which the pressure is constant to safely stop pumping back and prevent overpumping” however, this merely expand on the abstract idea by appending additional steps to the mathematical algorithm (i.e. when pressure reaches a pressure level) and mere data gathering recited at a high level of generality and insignificant post solution activities generally linking the abstract idea to a field of use and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claim 16 recite “the safety device is adapted for the predefined limit value to be variably adjustable before a start of a treatment” however this amounts to insignificant post solution activities generally linking the abstract idea to a field of use and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claim 17 recite “detecting a number of motor steps of a pump motor or a rotation angle of the pump motor” however this amounts to mere data gathering recited at high level of generality and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claim 18 recite “a safety device comprising: a sensor unit adapted to detect a pressure course in the fluid guide unit; a detection unit adapted to detect an occurrence of an occlusion; and a control unit adapted to detect a volume delivered by the medical pump for an operating range with high occlusion probability” however this merely expand on the abstract idea by appending additional steps to the mathematical algorithm (i.e. detect occurrence of an occlusion, detect a volume delivery…for an operating range with high occlusion probability), mere data gathering recited at a high level of generality (i.e. “a sensor unit…to detect a pressure...”) and the “safety device” and “control unit” amounts to the recitation of a general purpose computer to implement the abstract idea and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Dependent claims 19 and 20 respectively recite “fluid guide unit is an outlet line of the medical pump” (claim 19) and “the medical pump is an infusion pump” (claim 20), however this is generally linking the abstract idea to a field of use and do not integrate the abstract idea into a practical application or amount to significantly more that the judicial exception.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
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.
(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) 13-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by PAI SAMEER WO 2022125272 A1 (hereinafter Pai).
Regarding claim 13, Pai disclose a safety device (see abstract) for reducing a bolus in a fluid guide unit of a medical pump (see Figs. 1-2; abstract; page 1, ll. 4-5 wherein an infusion pump is disclosed), the safety device comprising:
a sensor unit (“pressure sensor”) adapted to detect a pressure course in the fluid guide unit (see Figs. 1-2; page 3, ll. 10-16, page 7, ll. 3-6; page 10, ll. 12-14, wherein pressure is detected within the cartridge 118 and/or infusion set 104);
a detection unit adapted to detect an occurrence of an occlusion (see page 5, ll. 4-9; page 16, ll. 14-22; page 18, ll. 20-23; page 19, ll. 1-7,wherein an occlusion probability is determined; see page 7, ll. 7-11; see page 8, ll. 1-8, wherein a control unit is disclosed); and
a control unit adapted to detect a volume delivered by the medical pump for an operating range with high occlusion probability (page 14, ll. 17-21, page 15, ll. 17-23; page 16, ll. 1-11, discuss a volume delivered and that system compliance value is determined based on a volume, and is compared against expected values through Bayesian interference; and page 17, ll. 1-3, and 6-9, wherein a determination of a probability of an occlusion is made based on the estimated system compliance; see page 8, ll. 1-8, wherein a control unit is disclosed).
Regarding claim 14, Pai disclose the materials discussed above with respect to claim 13 and further disclose the control unit is configured to determine an occurrence time of the occlusion (see abstract; page 4, ll. 20-24) by one of the following:
calculating a first derivation of the pressure course and a second derivation of the pressure course (see page 14, ll. 3-9), determining zeros of the second derivation of the pressure course, and selecting a zero with a minimum value from the zeros;
deriving the pressure course and determining a change in a slope of the pressure course to a minimum value at which the slope increases (see Fig. 5; page 4, ll.19-24; page 5, ll. 1-3; page 15, ll. 17-23); and
determining inflection points of the pressure course, wherein a smallest inflection point represents the occurrence time of the occlusion (see Fig. 5; page 4, ll.19-24; page 5, ll. 1-3; page 15, ll. 17-23, wherein trend line 302 with a positive slope above a given threshold may be indicative of an occlusion and wherein the inflection points resides in the sensed force readings 301A-C and which represents occurrence time of an occlusion).
Examiner Note: It is noted that the limitations recited by claim 14 are required in the alternative. For examination on the merits the claim have been interpreted as requiring only one of the alternatives.
Regarding claim 15, Pai disclose the materials discussed above with respect to claim 13. Pai further disclose wherein the control unit is adapted to classify an occlusion probability as high if the occlusion probability exceeds a predefined limit value (see abstract; page 3, ll. 19-24, wherein presence of an occlusion is identified; page 18, ll. 14-19, “a determination of a probability above a threshold can trigger an occlusion alert”).
Claim(s) 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Paiam et al. US20210330881A1 (hereinafter Paiam).
Regarding claim 12, Paiam disclose a forecast method for determining an occlusion probability (see abstract; para. 0087) comprising the steps of:
detecting pressure courses by a sensor unit (page 3, ll. 10-16; page 4, ll. 17-24; page 5, ll. 1-3; page 10, ll. 12-14; wherein pressure is measured by a force sensor);
detecting occurrences of occlusion events at the pressure courses (see abstract, para. 0086-0089);
combining the pressure courses and the occurrences of occlusion events into a training data set (see para. 0043, 0095-0096, 0178, wherein training data sets of values of threshold (i.e. pressure) associated with known occlusion conditions is disclosed);
creating a system for machine learning having the pressure courses as input values and the occurrences of occlusion events as output values (see para. 0043, 0095-0096, 0178, wherein machine learning techniques are used to adaptively adjust the threshold by using training data sets of values of threshold (i.e. pressure) associated with known occlusion conditions to train models used to determine if an occlusion exists);
training the system for machine learning with the training data set (see para. 0043, 0095-0096, 0178, wherein models are trained with training data sets of values of threshold (i.e. pressure) associated with known occlusion);
detecting a subsequent pressure course in real time by the sensor unit (see abstract, para. 0012, 0086-0087, 0132, wherein first and second pressures are disclosed);
inputting the subsequent pressure course into the system (abstract, para. 0012, 0086-0087, 0132, wherein the first and second pressures are used (inputs) in order to determine a magnitude of a difference between the first and second pressures in order to determine the presence on an occlusion); and
outputting an estimate of the occlusion probability in real time based on the subsequent pressure course by the system (see abstract; para. 0012, 0043, 0063, 0065-0066, 0087, 0095-0096, 0178, wherein occlusion is detected at a time in which the fluidic pressure decreases until the fluidic pressure reaches the set upstream occlusion threshold and an alarm is issued, wherein the occlusion is based on subsequent pressures [first and second pressures], which are used in order to determine an occlusion is likely [occlusion probability]).
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.
Claim(s) 1-7, 9-11, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over PAI SAMEER WO 2022125272 A1 (hereinafter Pai) in view of Paiam et al. US20210330881A1 (hereinafter Paiam).
Regarding claim 1, Pai disclose a method for reducing a bolus in a fluid guide unit comprising the steps of:
detecting a pressure course in the fluid guide unit by a sensor unit (page 3, ll. 10-16, page 10, ll. 12-14);
determining an occlusion probability by a control unit based on the pressure course (see page 5, ll. 4-9; page 16, ll. 14-22; page 18, ll. 20-23; page 19, ll. 1-7,wherein an occlusion probability is determined; see page 7, ll. 7-11; see page 8, ll. 1-8, wherein a control unit is disclosed);
detecting a volume delivered by a pump for an operating range with high occlusion probability by the control unit (see page 9, ll. 7-10; page 14, ll. 17-21, page 15, ll. 17-23; page 16, ll. 1-11, discuss a volume delivered and that system compliance value is determined based on a volume, and is compared against expected values through Bayesian interference; and page 17, ll. 1-3, and 6-9, wherein a determination of a probability of an occlusion is made based on the estimated system compliance);
detecting an actual occurrence of an occlusion at a detection time by a detection unit when an alarm condition is exceeded (page. 4, ll. 17-24; page 5, ll. 4-13; page 10, ll. 12-19; page 16, ll. 14-16).
However, Pai do not expressly or explicitly disclose, pumping back the volume delivered by the pump for the operating range with high occlusion probability.
Paiam disclose as system for fast detection of occlusion (see abstract, para. 0007 -0009) and further disclose a syringe pump including a back-off function that provides pressure relief to reduce a volume of a bolus after release of the occlusion (see para. 0060).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings disclosed by Paiam to configure the system of Pai for pumping back the volume delivered by the pump for the operating range with high occlusion probability for the benefit of allowing for faster detection of occlusions in infusion devices to conserve device resources and minimize possible harm to the patient by reducing the size of an inadvertent an potentially harmful bolus by allowing a smaller amount of the bolus volume to be infused to the patient after an occlusion release (see para. 0004-0005).
Regarding claim 2, the combination of Pai and Paiam disclose the materials as discussed with respect to claim 1.
Pai further disclose a memory unit for storing data and algorithm (see second paragraph page 7, memory 115, Fig. 2; see second paragraph of page 8) and wherein the data is received into the algorithm stored in the memory (see last paragraph of page 16). Pai further disclose that the system compliance is being updated and that the system compliance is determined based on a change in pressure (see last paragraph on page 18 through first paragraph on page 19, claim 14).
Although Pai do not explicitly disclose a step of specifically storing pressure course in a memory after pressure course is detected. However from the sections discussed above, it is implied that the values of pressure is being updated and stored in the memory storing the algorithm.
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention for storing the pressure course in a memory unit after the pressure course is detected for the benefit of providing easy access of the data in order to perform data analysis and necessary calculations in order to accurately determine the state of occlusion of the system.
Regarding claim 3, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 1. However Pai is silent as to disclose that the step of detecting the volume delivered by the pump comprises detecting a number of motor steps of a pump motor or of a rotation angle of the pump motor.
Paiam disclose the step of detecting a volume delivered by the pump (see para. 0060, 0071, 0074, 0081) comprising detecting a number of motor steps of a pump motor or of a rotation angle of the pump motor (see para. 0085, 0117, stepper motor in which each burst moves the stepper motor 25 steps).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Pai as modified by Paiam for detecting a number of motor steps of a pump motor or of a rotation angle of the pump motor for the benefit of maintaining continuity of the volume of fluid delivered (para. 0085) and to reduce the size of this inadvertent and potentially harmful bolus (see para. 0005).
Examiner Note: It is noted that the recited detection of the volume by the pump requires the detection of a number of motor steps of a pump motor or of a rotation angle of the pump motor in the alternative. The claimed limitation have been interpreted as requiring only one of the alternatives.
Regarding claim 4, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 3. Pai do not expressly or explicitly disclose wherein the step of pumping back the volume delivered by the pump comprises retracting the number of motor steps of the pump motor or the rotation angle of the pump motor.
Paiam disclose wherein the step of pumping back the volume delivered by the pump comprises retracting the number of motor steps of the pump motor or the rotation angle of the pump motor (see para. 0080, 0085, 0117, wherein the infusion pump moves the fluid in a reverse direction and wherein a flow rates below 40ml/hours, the LVP pump works in burst mode in which in which each burst moves the stepper motor 25 steps).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Pai as modified by Paiam such that the step of pumping back the volume delivered by the pump comprises retracting the number of motor steps of the pump motor or the rotation angle of the pump motor for the benefit of maintaining continuity of the volume of fluid delivered (para. 0085) and to reduce the size of this inadvertent and potentially harmful bolus (see para. 0005).
Examiner Note: It is noted that the recited retracting the number of motor steps of a pump motor or of a rotation angle of the pump motor in the alternative. The claimed limitation have been interpreted as requiring only one of the alternatives.
Regarding claim 5, the combination of Pai and Paiam disclose the materials as applied with respect to claim 1. Pai further disclose wherein the alarm condition is a pressure limit value (see second paragraph on page 3, “wherein force experienced by pressure sensor exceeds a predetermined threshold force, a processor connected to the pressure sensor generates a signal indicating that an occlusion has possibly occurred or is possibly occurring”).
Regarding claim 6, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 1. However Pai do not expressly or explicitly disclose wherein the step of determining the occlusion probability comprises the following steps: creating a trained system for machine learning with the pressure course as an input value and the occlusion probability as an output value; inputting the pressure course in real time into the trained system for machine learning; and outputting the occlusion probability in real time by the trained system for machine learning based on the pressure course.
Paiam disclose wherein the step of determining the occlusion probability comprises the following steps: creating a trained system for machine learning with the pressure course as an input value and the occlusion probability as an output value (see para. 0043, 0095-0096, 0178, wherein machine learning techniques are used to adaptively adjust the threshold by using training data sets of values of threshold (i.e. pressure) associated with known occlusion conditions to train models used to determine if an occlusion exists); inputting the pressure course in real time into the trained system for machine learning (see para. 0043, 0095-0096, 0178, wherein training data sets of values of threshold (i.e. pressure) associated with known occlusion conditions to train models used to determine if an occlusion exists is disclosed); and outputting the occlusion probability in real time by the trained system for machine learning based on the pressure course (see abstract, para. 0012, 0043, 0063, 0065-0066, 0095-0096, 0178, wherein occlusion is detected at a time in which the fluidic pressure decreases until the fluidic pressure reaches the set upstream occlusion threshold and an alarm is issued).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Pai as modified by Paiam for creating a trained system for machine learning with the pressure course as an input value and the occlusion probability as an output value; inputting the pressure course in real time into the trained system for machine learning; and outputting the occlusion probability in real time by the trained system for machine learning based on the pressure course, for the benefit of providing an enhanced system capable of accurately detect the presence of occlusion in an infusion line and o detect the occlusion condition as early as possible to reduce the size of this inadvertent and potentially harmful bolus (see para. 0005).
Regarding claim 7, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 6. Pai further disclose detecting the pressure course by the sensor unit (page 3, ll. 10-16; page 4, ll. 17-24; page 5, ll. 1-3; page 10, ll. 12-14; wherein pressure is measured by a force sensor); detecting occurrences of occlusion events (see abstract; page 3, ll. 19-24; page 5, ll. 4-9; page 16, ll. 14-22; page 18, ll. 20-23; page 19, ll. 1-7,wherein an occlusion is determined).
However Pai do not expressly or explicitly disclose combining the pressure course and the occurrences of occlusion events into a training data set and training the trained system for machine learning with the pressure course as the input value and the occurrences of occlusion events as the output value.
Paiam further disclose wherein the step of creating the trained system for machine learning comprises the following steps: detecting the pressure course by the sensor unit (see para. 0039); detecting occurrences of occlusion events (see abstract, para. 0086-0089); combining the pressure course and the occurrences of occlusion events into a training data set (see para. 0043, 0095-0096, 0178, wherein training data sets of values of threshold (i.e. pressure) associated with known occlusion conditions to train models); and training the trained system for machine learning with the pressure course as the input value and the occurrences of occlusion events as the output value (see para. 0043, 0095-0096, 0178, wherein training data sets of values of threshold (i.e. pressure) associated with known occlusion conditions to train models used to determine if an occlusion exists is disclosed).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Pai as modified by Paiam for combining the pressure course and the occurrences of occlusion events into a training data set and training the trained system for machine learning with the pressure course as the input value and the occurrences of occlusion events as the output value, for the benefit of providing an enhanced system capable of accurately detect the presence of occlusion in an infusion line and o detect the occlusion condition as early as possible to reduce the size of this inadvertent and potentially harmful bolus (see para. 0005).
Regarding claim 9, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 1. Pai further disclose wherein the step of detecting the volume delivered by the pump (see page 5, ll. 9-13; page 14, ll. 10-23) comprises the following steps: determining an occurrence time of the occlusion (see fig. 5; page 10, ll. 17-19; page14, ll. 3-9, wherein pressure is determined over a period of time in order to determine an occluded system based in pressure characteristics determined over time); determining a time span and/or a number of revolutions of the pump between the occurrence time of the occlusion and the detection time of the occlusion by the control unit (see fig. 5; page 10, ll. 17-19; page14, ll. 3-9; see page. 14, ll. 10-23; page 15, ll. 12-23, wherein analysis is performed over time (time-span) in order to determine an occlusion and wherein a reduction in period of time required to determine the presence of an occlusion is disclosed); and determining a volume delivered by the pump between the occurrence time of the occlusion and the detection time of the occlusion from the time span and/or the number of revolutions of the pump by the control unit (see page 16, ll. 1-11, wherein the change is pressure is divided by the change in volume, therefore the volume is being determined).
Regarding claim 10, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 9. Pai further disclose wherein the occurrence time of the occlusion is a time of a beginning of an operating range with high occlusion probability (see Figs. 5, page 17, ll. 6-9, 15-24; page 18, ll.3-19; claims 7 and 11, wherein control unit is configured to plot a trend line by connecting a force sensed at some instant in time within a burst cycle, wherein a trend line slope above a threshold triggers an occlusion alarm, and wherein at least one occlusion alert alarm or notification is triggered upon determining that the computed probability of a system occlusion exceeds a threshold).
Regarding claim 11, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 1. Pai further disclose reducing pressure and simultaneously detecting the pressure course through the sensor unit (see page 5, ll. 7-13; page 13, ll. 9-23); and further disclose at least one of a measurement of pressure reduction after motor halt (see page. 17, ll. 1-5, claim 12).
Although, Pai disclose a system compliance being computed by measurement of a pressure reduction after cessation of a drive mechanism activation, where the system compliance is computed as a function of an expected decay in frictional force and by dividing a change in pressure as measured by a force sensor by a change in infusate volume (see page 5, ll. 7-13) and to solve issues related to the delivery of pressurized fluid to the patient in a large bolus of infusate upon a sudden release of the occlusion even after the drive mechanism has been stopped (overpumping prevention) (see page 10, ll. 12-22).
However, Pai do not expressly or explicitly disclose the step of pumping back the volume delivered by the pump further comprising: reducing pressure by pumping back fluid through the pump and simultaneously detecting the pressure course through the sensor unit; and stopping pumping when the pressure reaches a pressure level at which the pressure is constant to safely stop pumping back and prevent overpumping (emphasis added).
Paiam disclose wherein the step of pumping back the volume delivered by the pump (see para. 0060, wherein syringe pump includes a back-off function that provides pressure relief, allowing the syringe to reduce a volume of a bolus after release of the occlusion) further comprises: reducing pressure by pumping back fluid through the pump (see para. 0060, “back-off function that provides pressure relief) and simultaneously detecting the pressure course through the sensor unit (see para. 0059-0060, pressure sensor measurements are used to detect occlusion condition in a syringe pump and wherein syringe pump includes a back-off function that provides pressure relief to reduce a volume of a bolus); and stopping pumping when the pressure reaches a pressure level at which the pressure is constant to safely stop pumping back and prevent overpumping (see Fig. 4, 5A; para. 0060, 0080-0081, 0085, 0134, when flow rates have negative value the infusion pump moves the fluid in a reverse detection, the fluidic pressure will depend on the flow rate, when an occlusion condition is encountered pressure flow profile may be flat because F1=0ml/hour (flow is paused/stopped) during T1 and T3, in the flow profile 400 shown in Fig. 4, therefore it is implied that the pressure is constant during T1 and T3 and that pumping have stopped since F1=0ml/hour and as a result overpumping is prevented by allowing the syringe to reduce a volume of a bolus).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Pai as modified by Paiam with a step of pumping back the volume delivered by the pump further comprises: reducing pressure by pumping back fluid through the pump and simultaneously detecting the pressure course through the sensor unit; and stopping pumping when the pressure reaches a pressure level at which the pressure is constant to safely stop pumping back and prevent overpumping for the benefit of providing an enhanced system that allows for faster detection of occlusions in infusion devices in order to conserve device resources and to minimize possible harm to the patient (see para. 0004, 0005) and to allow adjustment or prevention of administration of a fluid to a patient (para. 0134).
Regarding claim 16, Pai discloses the materials discussed for claim 15. Pai further disclose infusion pumps for managing the delivery and dispensation of a prescribed amount or dose or a drug, fluid, fluid like substance, or infusate to patients for accurately delivering infusates. And further disclose the pumps are used in various settings and can be used to administer medication through a variety of delivery methods (see page 1, ll. 8-21); and further disclose that the medication syringe is mechanically driven under a microprocessor control to deliver a prescribed dose of medication at a controlled rate to a patient (see page 1, ll. 22-24).
However Pai do not expressly or explicitly disclose the safety device is adapted for the predefined limit value to be variably adjustable before a start of a treatment.
Paiam disclose a safety device is adapted for the predefined limit value to be variably adjustable before a start of a treatment (see para. 0053, 0095, 0134, 0178, wherein the threshold is adaptively adjusted based at least in part on a parameter of the infusion device, and wherein a particular configuration is selected based at least in patient specific information such as treatment prescription, therefore it is implied that the adjustments take place before a start of a treatment).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of Paiam discussed above to configure the system of Pai such that the safety device is adapted for the predefined limit value to be variably adjustable before a start of a treatment, for the benefit of ensuring the system to be properly configured based on patient specific information such that proper treatment prescription is administered, ensuring proper patient care and treatment by detection of an occlusion interrupting an infusion of a medical fluid to the patient and minimizing possible harm to the patient (see para. 0003-0004).
Regarding claim 17, Pai disclose the materials discussed above with respect to claim 13. However Pai is silent as to disclose to detect the volume delivered by the pump comprises detecting a number of motor steps of a pump motor or of a rotation angle of the pump motor.
Paiam disclose the detection of the volume delivered by the pump (see para. 0060, 0071, 0074, 0081) by detecting a number of motor steps of a pump motor or of a rotation angle of the pump motor (see para. 0085, 0117, wherein stepper motor in which each burst moves the stepper motor 25 steps, therefore the number of motor steps is being determined).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of Paiam discussed above to configure the system of Pai to detect of the volume delivered by the pump by detecting a number of motor steps of a pump motor or of a rotation angle of the pump motor for the benefit of maintaining continuity of the volume of fluid delivered (para. 0085) and to reduce the size of this inadvertent and potentially harmful bolus (see para. 0005).
Examiner Note: It is noted that the recited detection of the volume by the pump requires the detection of a number of motor steps of a pump motor or of a rotation angle of the pump motor in the alternative. The claimed limitation have been interpreted as requiring only one of the alternatives.
Regarding claim 18, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 1. Pai further disclose the control unit is adapted to perform the method of claim 1 (see abstract; see page 8, ll. 1-8, wherein a control unit is disclosed); and a safety device (see abstract; see Figs. 1-2; abstract; page 1, ll. 4-5 wherein an infusion pump is disclosed) comprising:
a sensor unit (“pressure sensor”) adapted to detect a pressure course in the fluid guide unit (see Figs. 1-2; page 3, ll. 10-16, page 7, ll. 3-6; page 10, ll. 12-14, wherein pressure is detected within the cartridge 118 and/or infusion set 104);
a detection unit adapted to detect an occurrence of an occlusion (see page 5, ll. 4-9; page 16, ll. 14-22; page 18, ll. 20-23; page 19, ll. 1-7,wherein an occlusion probability is determined; see page 7, ll. 7-11; see page 8, ll. 1-8, wherein a control unit is disclosed); and
a control unit adapted to detect a volume delivered by the medical pump for an operating range with high occlusion probability (page 14, ll. 17-21, page 15, ll. 17-23; page 16, ll. 1-11, discuss a volume delivered and that system compliance value is determined based on a volume, and is compared against expected values through Bayesian interference; and page 17, ll. 1-3, and 6-9, wherein a determination of a probability of an occlusion is made based on the estimated system compliance; see page 8, ll. 1-8, wherein a control unit is disclosed).
Regarding claim 19, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 18. Pai further disclose wherein the fluid guide unit is an outlet line of the medical pump (see Figs. 1-2; page 7, ll. 3-6; page 10, ll. 12-14, page 17, ll. 18-21, wherein infusion set 104 is an outlet line of the infusion pump).
Regarding claim 20, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 18. Pai further disclose that the medical pump is an infusion pump (see Figs. 1-2, depicting an infusion pump; page 7, ll. 3-8).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over PAI SAMEER WO 2022125272 A1 (hereinafter Pai) in view of Paiam et al. US20210330881A1 (hereinafter Paiam) in further view of Massaron et al. 2021 publication “Machine Learning for Dummies” (hereinafter Massaron).
Regarding claim 8, the combination of Pai and Paiam disclose the materials discussed above with respect to claim 7.
However the combination of Pai and Paiam do not expressly or explicitly disclose that the machine learning is a neural network.
However, before the effective filing date of the claimed invention, one of ordinary skill in the art would have recognized that neural networks are a common ML algorithm, as disclosed by Massaron (P. 4-5 "common algorithms" "neural networks"), and would have found it obvious to modify Paiam analysis using a machine learning/AI model to include specifically a neural network with the motivation to utilize a known algorithm that’s easily accessible (e.g. Massaron P. 14 "When working with Python, you gain the benefit of not having to reinvent the wheel when it comes to algorithms. There is a package available to meet your specific needs—you just need to know which one to use. The following table provides you with a listing of common Python packages.") and for the inherent benefits of using ML/AI to perform the analysis.
Conclusion
The prior art made of record cited in form PTOL-892 and not relied upon is considered pertinent to applicant's disclosure.
Wolff et al. US Patent 6834242 disclose a method to analyze the pressure variation in a perfusion device including multiple perfusion modules each equipped with a pump to deliver a liquid to be perfused in a line placed downstream from the pump as well as a means to measure the pressure in the line. Wolff further disclose determining whether there is an acceptable explanation for a pressure variation (increase or reduction in the flow rate in a module) and whether this variation concerns modules other than that which detected it first, and which ones, to then act on the perfusion device according to the result of this analysis, either by modifying the analytical parameters in the various modules affected if the pressure variation has an acceptable explanation or by simultaneously stopping all the modules affected by this variation in flow rate and by dealing with the source of the malfunction (rupture or obstruction) (see abstract).
Lariner, JR. et al. US20110190694 disclose a wearable infusion pump (see abstract) and further disclose detection of occlusions/leaks, as well as occlusion type (see para. 0861-0870).
Oruklu et al. US20140350513A1 disclose an infusion system detecting whether there is a partial occlusion or a total occlusion in the fluid delivery line and a probability of air being in the fluid delivery line (see abstract; para. 0006, 0033).
Labarthe US Patent 10814063 B2 disclose a metho for detecting an occlusion in an infusion line during an infusion process (see abstract; claims 1-3).
Zheng et al. US 20210030953A1 disclose a system for monitoring operation of a medical infusion pump for occlusion (abstract) and further disclose pumping back to the pump reservoir that occurs due to an occlusion (see Fig. 11, para. 0037, 0086).
Kessel et al. US20210393870A1 disclose a wearable infusion pump assembly (abstract) and detection of occlusions along the fluid delivery path of infusion pump assembly (see para. 0879-0888).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YARITZA H PEREZ BERMUDEZ whose telephone number is (571)270-1520. The examiner can normally be reached Monday-Friday.
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/YARITZA H. PEREZ BERMUDEZ/
Examiner
Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857
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