DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed March 16, 2026.
Claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment(s)
Claims 1-20 are pending.
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 to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 13, and 20:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 1 falls within the statutory category of method
Independent claim 13 falls within the stutory category of article of manufacture.
Independent claim 20 falls within the statutory category of system
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 13, and 20 claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in the independent claims 1, 13, and 20 (claim 1 being representative) are:
A method comprising: treat a condition by delivering a therapy, to a patient;
identifying, , a first patient state distribution,
wherein the first patient state distribution includes a percentage of time the patient spent in each of a plurality of patient states over a first time period;
identifying a second patient state distribution, wherein the second patient state distribution includes a percentage of time the patient spent in each of the plurality of patient states over a second time period;
capturing a change in patient state distributions, the change being a difference between the first patient state distribution and the second patient state distribution;
and monitor the change in the patient state distributions;
triggering, in response to the change in the patient state distributions, an alert;
and identifying, based on the change in the patient state distributions, one or more settings in which to suggest alterations for delivering the therapy to the patient.
The abstract idea is “certain methods of organizing human activity” by following rules and instructions to determine a change in a patient state distribution when monitoring a patient to identify alterations to therapy delivered. (see MPEP § 2106.04(a)(2))
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1, 13, and 20 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional elements below:
a medical device
at least one hardware processor
a monitoring platform
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
No additional element is recited as executing the abstract idea. The additional elements, (a), is recited as “apply-it” (e.g. “configured to”)
The additional element, (b), is recited as a general purpose computer component ([see [00192]) as “apply-it” or an equivalent to gather data
The additional element, (c), is recited as “apply-it” or an equivalent to communicate data
Independent claim 13 recites the additional elements below not already recited in the independent claim 1:
A machine-storage medium embodying instructions that, when executed by a machine
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), is recited as executing the abstract idea as “apply-it” or an equivalent
Independent claim 20 recites the additional elements below not already recited in the independent claim 1:
one or more processors; and one or more memory storing instructions
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), is recited as executing the abstract idea as “apply-it” or an equivalent
Accordingly, independent claims 1, 13, and 20 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, are merely applying the abstract idea and therefore, do not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2-12 and 14-19
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Dependent claims 2-12 fall within the statutory category of method.
Dependent claims 14-19 fall within the statutory category of article of manufacture.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-12 and 14-19 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims 1 and 13 abstract idea by (1) further limiting the analysis of patient state distributions, (2) further limiting the underlying cause and alerts, and (3) further limiting suggestions of parameters and therapy. Therefore, the dependent claims inherit the same abstract idea of “certain methods of organizing human activity” by following rules and instructions to determine a change in a patient state distribution when monitoring a patient to identify alterations to therapy delivered. (see MPEP § 2106.04(a)(2))
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2-12 and 14-19 this judicial exception is not integrated into a practical application.
The dependent claims recite the below additional elements not already recited in the independent claims:
neurostimulation programming.
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), are recited as generally linking the abstract idea to neurostimulation
Accordingly, dependent claims 2-12 and 14-19 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that amount to significantly more for the same reasons given in Prong 2. The claims are patent ineligible.
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 1-5, 8, 10-12, 13-16, 19, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Javed et. al (hereinafter Javed) (US12245870В2) in view of Bharmi et, al (hereinafter Bharmi) (US20250037867Al)
As per claim 1, Javed teaches:
A method comprising: using a medical device configured to treat a condition by delivering a therapy, to a patient; (Col. 11 lines 35-39 discloses, “In other forms of the present technology, an RPT device 4000 that is configured to supply respiratory pressure therapy to the patient 1000 via an air circuit 4170 to a patient interface 3000, as illustrated in FIG. 1, may also be configured as a monitoring apparatus.”)
identifying, by at least one hardware processor, a first patient state distribution; (Col. 6 lines 4-19 discloses, “According to a fifth aspect, there is provided a method of monitoring chronic disease state of a patient. The method, carried out in one or more processors, comprises: extracting, for each of a plurality of monitoring sessions, a respiratory feature from a respiratory signal indicative of the patient's respiration during the monitoring session, the respiratory signal derived from at least one sensor; computing a stability measure of the patient for a monitoring session. The stability measure represents a probability of a change point having occurred at the monitoring session in a statistical distribution of the respiratory feature. The computing comprises: computing a posterior distribution of run length for the monitoring session given values of the respiratory feature up to and including the monitoring session; and computing a sum of values of the posterior distribution of run length.”)
identifying a second patient state distribution, ( Col. 25 lines 56-67 and Col. 26 lines 1-5 discloses, “FIG. 8 contains a graph 8000 showing example results obtained from the monitoring apparatus 7000 using the method 7100. The upper trace 8010 shows one of the above-mentioned respiratory features, namely 75t" percentile of respiratory rate over the session, over 400 sessions indexed by t. The grey band 8015 shows a 28-session interval symmetrically surrounding an ADHF event (indicated by the upward arrow 8020) experienced by the patient at approximately session number 182. The lower trace 8050 shows peaks at the sessions t where the stability measure S, computed by the retrospective approach exceeded a threshold, and hence step 7150 generated an alert. In particular the double peak 8060 coincides with the ADHF event. Other peaks, e.g. 8070, do not coincide with ADHF events and therefore represent "false positives".” / examiner notes that under BRI patient state distribution is a distribution of a patients state such as respiratory state as disclosed with the first and second patient states being time points in the series which differ)
capturing a change in patient state distributions, the change being a difference between the first patient state distribution and the second patient state distribution; (Col. 17 lines 28-42 discloses, “Clinical event prediction from respiratory features is an example of a highly imbalanced dataset with very small number of events within a large number of stable sessions, so a robust approach is needed to minimize the number of false positive predictions. The assumption underlying the present technology is that when the patient is stable, the respiratory feature follows one statistical distribution and, at some point before a clinical event, passes through a "change point" to follow a different distribution. The stability measure is therefore computed such that a change of distribution at monitoring session indexed by t results in a rise in the stability measure at or near the monitoring session indexed by t. In other words, the stability measure for a session is an indication of a change point having occurred in the distribution of the respiratory feature at that session.” And see Col. 25 lines 56-67 and Col. 26 lines 1-5 / examiner notes the stability measure of a changepoint under BRI is a difference between a first and second example measures such as points surrounding an ADHF event as taught in the prior art cited)
and providing a monitoring platform to monitor the change in the patient state distributions. (Col. 11 lines 35-39 discloses, “In other forms of the present technology, an RPT device 4000 that is configured to supply respiratory pressure therapy to the patient 1000 via an air circuit 4170 to a patient interface 3000, as illustrated in FIG. 1, may also be configured as a monitoring apparatus.” And see Col. 12 lines 7-17 discloses, “The RPT device 4000 preferably has an electrical power supply 4210, one or more input devices 4220, a central controller 4230, a therapy device controller 4240, a pressure generator 4140, one or more protection circuits 4250, memory 4260, transducers 4270, data communication interface 4280 and one or more output devices 4290. Electrical components 4200 may be mounted on a single Printed Circuit Board Assembly (PCBA) 4202. In an alternative form, the RPT device 4000 may include more than one PCBA 4202” and see Col. 12 lines 43-67 and Col. 13 lines 1-67 discloses, “In some forms of the present technology, the central controller 4230 is configured to implement the one or more processes described herein expressed as computer programs stored in a non-transitory computer readable storage medium, such as memory 4260. Data communication interface 4280 may be connectable to a remote external communication network 4282 and/or a local external communication network 4284. The remote external communication network 4282 may be connectable to a remote external device 4286. The local external communication network 4284 may be connectable to a local external device 4288. The data communication interface 4280 may use wired communication (e.g. via Ethernet, or optical fibre) or a wireless protocol (e.g. CDMA, GSM, LTE) to connect to the Internet. In one form, local external communication network 4284 utilises one or more communication standards, such as Bluetooth, or a consumer infrared protocol. The local external device 4288 may be a personal computer, mobile phone, tablet or remote control. In one form, remote external communication network 4282 is the Internet. In one form, remote external device 4286 is one or more computers, for example a cluster of networked computers. In one form, remote external device 4286 may be virtual computers, rather than physical computers. In either case, such a remote external device 4286 may be accessible to an appropriately authorised person such as a clinician. An output device 4290 may take the form of one or more of a visual, audio and haptic unit. A visual display may be a Liquid Crystal Display (LCD) or Light Emitting Diode (LED) display.A display driver 4292 receives as an input the characters, symbols, or images intended for display on the display 4294, and converts them to commands that cause the display 4294 to display those characters, symbols, or images. A display 4294 is configured to visually display characters, symbols, or images in response to commands received from the display driver 4292. 8.1.3 Monitoring Process In one aspect of the present technology, a monitoring apparatus carries out a monitoring process to monitor the patient's cardio-pulmonary health from a respiratory signal that is indicative of the respiration of the patient 1000. 5 10 15 20 25 30 In the form of the present technology in which the monitoring apparatus is the unobtrusive apparatus 7000 illustrated in FIG. 7B and the respiratory signal is the respiratory movement signal derived from the movement signal 7003, the monitoring process may be carried out by the processor 7006 of the contactless sensor unit 1200, configured by instructions stored on computer-readable storage medium such as the memory 7002. Alternatively, a processor of the external computing device 7005 may implement all or part of the described monitoring process, having obtained the required data, either raw or partly processed, from the sensor unit 1200 and any other sensors in the apparatus 7000 via the connection 7008 as described above. In such implementations, the above descriptions of the visual display 7015 and the audio output 7017 of the monitoring apparatus 7000 apply equally to comparable elements of the external computing device 7005. In one 35 example, the external computing device 7005 is a clinician accessible device such as a multi-patient monitoring device that allows a clinician to review data from multiple remote patient data recording devices such as the monitoring apparatus 7000. In these systems, a database may be provided to 40 record patient monitoring data. Through such an external computing device 7005, clinicians may receive a report or alert that a particular patient may require closer observation or should be brought to hospital. In the form of the present technology in which the monitoring apparatus is the RPT device 4000 and the respiratory signal is a signal representing the respiratory flow rate Qr of the patient 1000 that is derived from one or more of the transducers 4270, the monitoring process may be carried out by the central controller 4230 of the RPT device 4000 configured by instructions stored on computer readable storage medium such as the memory 4260. Alternatively, the local or remote external device 4288 or 4286 may implement all or part of the described processing, having obtained the required data, either raw or partly processed, from RPT device 4000 via the data communication interface 4280 as described above. In such implementations, the output functions of the output device 4290 of the RPT device 4000 are carried out by comparable elements of the local or remote external device 4288 or 4286.” And see Col. 14 lines 8-17 discloses, “The method 7100 then at step 7130 uses the extracted respiratory feature(s) from the just-completed monitoring session, and possibly respiratory features from one or more previous monitoring sessions, to compute a stability measure. The so-created stability measure, or a history of consecutively computed stability measures on a session-by session basis, may be stored in one or more memories, for example the memory 7002 of the sensor unit 1200 or that of the external computing device 7005 or other memory associated with a processor that computes the stability measure.” And see Col. 17 lines 32-34 discloses, “The assumption underlying the present technology is that when the patient is stable, the respiratory feature follows one statistical distribution and, at some point before a clinical event, passes through a "change point" to follow a different distribution. The stability measure is therefore computed such that a change of distribution at monitoring session indexed by t results in a rise in the stability measure at or near the monitoring session indexed by t. In other words, the stability measure for a session is an indication of a change point having occurred in the distribution of the respiratory feature at that session. Step 7130 according to the present technology is therefore distribution based in nature.” / examiner notes that the monitoring apparatus includes hardware and software algorithm as well as memory, network interfaces, interface to communicate information, and processor which under BRI in this context someone of ordinary skill in the art would understand this to be a monitoring platform, further the stability measure is determining changes in timepoints of data and is in distribution form)
However Javed does not teach:
wherein the first patient state distribution includes a percentage of time the patient spent in each of a plurality of patient states over a first time period;
wherein the second patient state distribution includes a percentage of time the patient spent in each of the plurality of patient states over a second time period;
triggering, in response to the change in the patient state distributions, an alert on the monitoring platform;
and identifying, based on the change in the patient state distributions, one or more settings in which to suggest alterations for delivering the therapy to the patient.
However, Bharmi does teach:
wherein the first patient state distribution includes a percentage of time the patient spent in each of a plurality of patient states over a first time period; ( [0208] discloses, “Chronic pain state data 174 may include data associated with five states ( e.g., A, B, C, D, E) described herein that define the chronic pain state of a patient. In an example, state A through state E may indicate relatively increasing amounts of severity of chronic pain. For example, state A may indicate the least severity and state E may represent the highest severity of chronic pain. “ and see [0209] discloses, “The chronic pain state data 174 may include temporal information associated with the states (e.g., change in states over time). Example aspects of the chronic pain state data 174 are described with reference to FIGS. 5-7, 9A, 9B, llA, and 11B and at the Exhibits provided with the present disclosure. The chronic pain state data 174 may be stored in one or more electronic data records (e.g., as one or more entries in the memory 106, the database 130, or the like).”)
wherein the second patient state distribution includes a percentage of time the patient spent in each of the plurality of patient states over a second time period; ( e.g. in [0302] discloses, “Metrics 1445 includes % Time a patient is in a given Body Position (e.g., upright) or state (e.g., active, at rest, etc.). In some aspects, a case in which the amount of time a patient has spent upright and active over time is greater than the amount of time the patient has spent at rest may indicate improvement in the patient's functional status. In some aspects, the duration and time (e.g., in the evening) of less activity may be indicative that the patient may be having better rest and higher sleep quality, and may indicate an improvement in the patient's functional status.”)
triggering, in response to the change in the patient state distributions, an alert on the monitoring platform; ([0319] discloses, “In some aspects, the digital health platform application may output a notification prompting medical personnel to implement device reprogramming for cases in which the holistic measure deviates from a target value for an extended period of time (e.g., longer than a threshold duration). That is, for example, large deviations for an unacceptable time may trigger rep/physician alert for reprogramming.”)
and identifying, based on the change in the patient state distributions, one or more settings in which to suggest alterations for delivering the therapy to the patient. ([0320] discloses, “In some aspects, the digital health platform application may suggest changes to therapy settings based on the holistic measure, the wearable device data, and/or the trial device data. Non-limiting examples of therapy settings include cycling or endurance settings ( e.g., for battery longevity).”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Javed’s teachings of analyzing certain types of medical data for distributions of changing parameters as previously cited with Bharmi’s teachings, as previously cited, the motivation being Javed’s is already monitoring patient data as previously cited and concerned with the obtrusive nature and patient compliance (see Col. 4) and the data is a choice therefore combining with Bharmi’s patient states of percentage time periods and alerts would not change the type of analysis or operability of Javed but would increase the depth of data for the statistical analysis to increase precision and accuracy of the changepoint analysis for the distributions while remaining unobtrusive and increasing patient compliance in any medical monitoring scenario.
As per claim 2, Javed further teaches:
The method of claim 1, further comprising: dividing a sequence of observed patient states into non-overlapping partitions, wherein each partition includes a distribution; and defining a changepoint based on delineations between each partition. (See Fig. 8 and see Col. 17 lines 32-41 discloses, “The assumption underlying the present technology is that when the patient is stable, the respiratory feature follows one statistical distribution and, at some point before a clinical event, passes through a "change point" to follow a different distribution. The stability measure is therefore computed such that a change of distribution at monitoring session indexed by t results in a rise in the stability measure at or near the monitoring session indexed by t. In other words, the stability measure for a session is an indication of a change point having occurred in the distribution of the respiratory feature at that session.”)
As per claim 3, Javed further teaches:
The method of claim 1, further comprising: detecting delineations between each partition among a plurality of partitions. (See Fig. 8 and Col. 5 lines 51-64 discloses, “The method comprises: extracting, in a processor, for each of a plurality of monitoring sessions, a respiratory feature from a respiratory signal indicative of the patient's respiration during the monitoring session, the respiratory signal derived from at least one sensor; and computing, in a processor, a stability measure of the patient for a monitoring session, the stability measure representing an indication of a change point having occurred at the monitoring session in a statistical distribution of the respiratory feature.” Examiner notes the delineations under BRI one of ordinary skill in the art would understand to be a distinct area of interest in the patient data and partitioned to be understood as distinct areas of data not overlapping)
As per claim 4, Javed further teaches:
The method of claim 3, further comprising: prespecifying a threshold for the change in the patient state distributions; (Col. 14 lines 25-49 discloses, “The stability measure is then evaluated at step 7140 to determine whether it meets a criterion, such as by comparison with one or more thresholds. For example, the stability measure may be compared with a threshold at step 7140, such as in a processor. If the stability measure exceeds the threshold, for example ("Y"), a change point is detected, and step 7150 may generate an alert. If not ("N"), the method 7100 concludes at step 7160. The choice of the threshold affects the sensitivity and specificity of the monitoring process in detecting potential clinical events, and is chosen based on desired levels of sensitivity and specificity when the monitoring process is executed on training data. In some implementations, the threshold may be adjusted between monitoring sessions based on observed false positive and false negative detections. Other evaluations at step 7140 may determine whether the stability measure resides in a particular range, such by a comparison with one or more thresholds attributable to one or more ranges. Accordingly, the automated monitoring process effectively converts, through processing, respiratory signal data, which might appear to be innocuous, into a tool for patient monitoring, i.e., the stability measure, improving not only the monitoring apparatus but also the ability of clinicians in the field to more effectively monitor their patients, such as for making timely and necessary changes in treatment.”)
estimating, in a joint manner, a partition run length and the distribution using a changepoint detection algorithm; (Col. 20 lines 37-60 discloses, “The method 7300 starts at step 7310, where the joint likelihood p(r, y1.,) (i.e. y) is initialised (i.e. assigned a value for t=0), to 1 in one implementation. The step 7310 is 40 only carried out at the first iteration of the method 7300, and hence is shown dashed in FIG. 7E. At step 7320, the current time t is incremented and the current sample y, is received. Step 7330 follows, at which the method 7300 computes the current posterior predictive probability p(y,ly) using the 45 UPM and the current sample y, using Eqs. 15 to 17. Step 7340 then computes the current joint likelihood p(r У1:)=Y from the previous joint likelihood Y1 and the current posterior predictive probability p(y, y,") using Eq. 11. For run length r, equal to zero, there are t terms in the sum of Eq. 50 11. However, for values of run length r, that are greater than zero, there is only one term in the sum of Eq. 11, as there is only one value of r,1 at which the change point prior (Eq. 12) is non-zero, namely r_1=r,-1. This fact gives the on-line approach its computational efficiency. At the next step 7350, the method 7300 computes the current posterior run length distribution p(r,ly1:,) by normalising the current joint likelihood p(r, У1:) as in Eq. 10. Step 7360 then computes the current stability measure S, from the current posterior run length distribution p(rly1:) using Eq. 19. The method 7300 then concludes.”)
and testing a divergence of the distribution over time. (Col. 20 lines 62-67 discloses, “The retrospective approach to step 7130 works by comparing the probability distributions of sub-sequences of the time series {y(t)} before and after a certain time. Step 7130 65 under the retrospective approach computes the stability measure at that time as the dissimilarity between the two distributions.” And see Col. 22 lines 3-5 discloses, “In one implementation, the convex function f used in the definition of the f-divergence (Eq. 22) is the Kullback Leibler divergence defined as f(t)=t log(t).”)
As per claim 5, Javed further teaches:
The method of claim 1, further comprising: identifying one or more settings in which to suggest alterations for delivering the therapy to the patient, the therapy being at least partially defined based on the change in the patient state distributions. (Col. 21 lines 41-45 discloses, “Optionally, in some embodiments, the alert message may even express that the patient should be considered for additional treatment, hospitalization, or an evaluation due to the detection of a 45 potential clinical event.” And see Col. lines 2-10 discloses, “In another form of the present technology, the processor 7006 may condition an alert on responses to a patient query that may serve to avoid unnecessary alerts. In a variant of the method 7100, upon the stability measure meeting a criterion (step 7140), rather than immediately generating an alert, as at step 7150, the processor 7006 may prompt the patient 1000 to take an action, such as take their prescribed medication, or trigger a presentation of a query to the patient 1000 to provide a response.”)
As per claim 8, Javed further teaches:
The method of claim 1, wherein the change in the patient state distributions includes at least one of a change in patient state variability, a change in patient state dwell time percentage, or a change in patient state event detection. (see Col. 7 lines 35-42 discloses, “FIG. 7C is a flow chart illustrating a method of monitoring chronic disease state of a patient, as carried out by the monitoring apparatus of FIG. 7B according to one form of the present technology. FIG. 7D is a block diagram illustrating a method that may be used to implement the feature extraction step in the method of FIG. 7C according to one form of the present technology.” And see Col. 16 lines 38-67 and see Col. 17 lines 1-5 discloses, “FIG. 7D is a block diagram illustrating a method 7200 that may be used to implement the feature extraction step 7120 in the method of FIG. 7C in one form of the present technology. In the method 7200, an activity estimation and movement detection module 7210 generates an activity count signal and a movement flag series from the (pre-processed) movement signal. (Under the combined or single-channel approach, there is only one (pre-processed) movement signal.) A presence/absence detection module 7220 generates a presence/absence flag series from the (pre-processed) movement signal and the movement flag series. A sleep/wake 50 analysis module 7230 calculates a hypnogram from the presence/absence flag series, the movement flag series, and 55 60 65 the activity count signal. A breathing rate estimation module 7240 generates a series of estimates of the breathing rate of the patient from the (pre-processed) movement signal and the hypnogram. A signal selection module 7250 selects sections of the (pre-processed) movement signal, using the movement flag series and the hypnogram. A modulation cycle metrics calculation module 7255 generates an estimate of the modulation cycle length of the patient's respiration from the selected sections of the (preprocessed) movement signal. An envelope generation module 7260 generates envelopes of the selected sections of the (pre-processed) movement signal using the estimated breathing rate. An SDB event detection module 7265 generates candidate SDB events from the selected sections of the (pre-processed) movement signal using the estimated modulation cycle length. An SDB event confirmation mod- ule 7270 confirms the candidate SDB events generated by the SDB event detection module 7265 using the estimated modulation cycle length. Finally, a feature calculation module 7280 calculates respiratory feature values from the confirmed SDB events.” And see Col. 17 lines 28-36 discloses, “Clinical event prediction from respiratory features is an example of a highly imbalanced dataset with very small number of events within a large number of stable sessions, so a robust approach is needed to minimize the number of false positive predictions. The assumption underlying the present technology is that when the patient is stable, the respiratory feature follows one statistical distribution and, at some point before a clinical event, passes through a "change point" to follow a different distribution.”)
As per claim 10, Javed does not teach:
The method of claim 1, wherein the triggering includes triggering , in response to the change in the patient state distributions, the alert to a customer service assistance entity on the monitoring platform.
However Bharmi teaches:
The method of claim 1, wherein the triggering includes triggering , in response to the change in the patient state distributions, the alert to a customer service assistance entity on the monitoring platform. ([0319] discloses, “In some aspects, the digital health platform application may output a notification prompting medical personnel to implement device reprogramming for cases in which the holistic measure deviates from a target value for an extended period of time (e.g., longer than a threshold duration). That is, for example, large deviations for an unacceptable time may trigger rep/physician alert for reprogramming.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Javed’s teachings of analyzing certain types of medical data for distributions of changing parameters as previously cited with Bharmi’s teachings, as previously cited, the motivation being the same as given for claim 1.
As per claim 11, Javed further teaches:
The method of claim 10, further comprising: causing an action based at least in part on the alert. (Col. 24 lines 17-45 discloses, “The clinical alert generated at step 7150 may include a warning or alert message taking a number of forms. For example, the processor 7006, to generate a clinical alert to the patient 1000, may activate a status light (e.g., an LED or an icon on the display device 7015) of the monitoring apparatus 7000. A more detailed message concerning the assessment of the indicator may also be displayed to the patient 1000 on the display device 7015. Optionally, the processor 7006 may also, or alternatively, send an alert message via the connection 7008 to the external computing device 7005 associated with a clinician. Such a message may take the form of a wired or wireless communication. For example, the processor 7006 may generate an alert message 30 via a paging system such as by automatically dialing a paging system. The processor 7006 may also be configured to generate an automated voice phone call message. The processor 7006 may also send the alert message by a fax transmission. In some embodiments, the processor 7006 35 may also send an alert message via any internet messaging protocol, such as an email message, or by any other internet data file transport protocol. The alert messages may even be encrypted to keep patient information confidential. A typical alert message may identify the patient. Such a message may 40 also include data recorded by the monitoring apparatus 7000 or any other recorded patient information. Optionally, in some embodiments, the alert message may even express that the patient should be considered for additional treatment, hospitalization, or an evaluation due to the detection of a 45 potential clinical event.”)
As per claim 12, Javed further teaches:
The method of claim 11, further comprising: identifying at least one underlying cause of the change in the patient state distributions. (Col. 25 lines 65-67 and 26 lines 1-2 discloses, “The lower trace 8050 shows peaks at the sessions t where the stability measure S, computed by the retrospective approach exceeded a threshold, and hence step 7150 generated an alert. In particular the double peak 8060 coincides with the ADHF event.”)
As per claims 14-16 they are an article of manufacture claims which repeats the same limitations of claim 1-8, the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Javed and Bharmi as well as motivations to combine disclose the underlying process steps that constitute the method of claims 1-8 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claims 14-16 are rejected for the same reasons given above for claims 1-8.
As per claim 20 it is a system claim which repeat the same limitations of claim 1 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Javed and Bharmi as well as motivations to combine disclose the underlying process steps that constitute the methods of claim 1, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 20 is rejected for the same reasons given above for claim 1.
Claims 6, 7, 9, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Javed et. al (hereinafter Javed) (US12245870В2) In view of Bharmi et, al (hereinafter Bharmi) (US20250037867Al) and in view of Rezai et. al (hereinafter Rezai ) (US12343160В2)
As per claim 6, Javed and Bharmi does not teach:
The method of claim 1, further comprising: receiving information on a plurality of patient states, the plurality of patient states representing an overall patient health based on a combination of parameter sets comprising: a pain parameter; a medication parameter; an activities of daily living parameter; a mood parameter; a sleep parameter; an alertness parameter; and a mobility parameter.
However, Rezai does teach:
The method of claim 1, further comprising: receiving information on a plurality of patient states, the plurality of patient states representing an overall patient health based on a combination of parameter sets comprising: a pain parameter; a medication parameter; an activities of daily living parameter; a mood parameter; a sleep parameter; an alertness parameter; and a mobility parameter. (see TABLE III TABLE IV and TABLE V and TABLE VII and see Col. 4 lines 42-51 discloses, “TABLE III provides non-limiting examples of parameters associated with movement and activity of the user, referred to herein alternatively for ease of reference as "motor parameters," that can be measured and exemplary tests, devices, and methods. The use of portable monitoring, in-vivo sensing, and portable computing devices allows the motor parameters to be measured. Using embedded accelerometer, GPS, and cameras, the user's movements can be captured and quantified to see how pain affects them and related to the pain-relevant parameters. “ and see Col. 4 lines 63-67 discloses, “TABLE IV provides non-limiting examples of parameters associated with sensory acuity of the user, referred to herein alternatively for ease of reference as "sensory parameters," that can be measured and exemplary tests, devices, and methods.” And see Col. 5 lines 1-67 discloses, “TABLE V provides non-limiting examples of parameters associated with a sleep quantity and quality of the user, referred to herein alternatively for ease of reference as "sleep parameters," that can be measured and exemplary tests, devices, and methods.” And see “Table VII provides non-limiting examples of psychosocial and behavioral parameters, referred to herein alternatively for ease of reference as "psychosocial parameters," that can be measured and exemplary tests, devices, and methods.” And see Col. 7 lines 50-61 discloses, “The predictive model 124 can also utilize user data 126 stored at the remote server 120, including, for example, employment information (e.g., title, department, shift), age, sex, home zip code, genomic data, nutritional information, medication intake, household information (e.g., type of home, number and age of residents), social and psychosocial data, consumer spending and profiles, financial data, food safety information, the presence or absence of physical abuse, and relevant medical history. In addition, the model can combine multiple users to interact together to refine the prediction, such as a social model of spouse, children, family, co-workers, friends and others.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Javed’s teachings of analyzing certain types of medical data for distributions of changing parameters as previously cited and Bharmi as previously cited with Rezai’s teachings of specific types of parameter data, as previously cited, the motivation being Javed’s is already monitoring patient data as previously cited and concerned with the obtrusive nature and patient compliance (see Col. 4) and the data is a choice therefore combining with Rezai neurostimulation data and parameters of groupings would not change the type of analysis or operability of Javed but would increase the depth of data for the statistical analysis to increase precision and accuracy of the changepoint analysis for the distributions while remaining unobtrusive and increasing patient compliance in any medical monitoring scenario.
As per claim 7, Javed and Bharmi do not teach:
The method of claim 1, further comprising: generating a suggestion for one or more new combinations of parameter sets; and providing the suggestion to the monitoring platform.
However, Rezai does teach:
The method of claim 1, further comprising: generating a suggestion for one or more new combinations of parameter sets; and providing the suggestion to the monitoring platform. (Col. 13 lines 7-38 discloses, “Additionally or alternatively, the user can be assigned a set of wellness values representing an overall wellness of the user from the values for the first and second pain-relevant parameters, and the value assigned to the user can be 10 assigned based on the set of wellness values. The set of wellness values can include, for example, a first value representing fatigue, a second value representing emotional stress, a third value representing physical stress, and a fourth value representing sleep quality. In one implementation, 15 feedback, in the form of a self-reported pain level from the user, can be used to refine the predictive model. For example, the self-reported pain level can be compared to the value assigned to the user via a predictive model, and a parameter associated with the predictive model can be 20 changed according to the comparison. In one example, this can be accomplished by generating a reward for a reinforcement learning process based on a similarity of the measured outcome to the value assigned to the user and changing the parameter via the reinforcement learning process. It will be appreciated that each of the wellness values and the value assigned to the user can be provided, for example, via a user interface or network interface, to one or more of the user, the user's health care provider, the user's care team, a research team, a user's workplace, or other interested entities. This allows the value to be used to make decisions about the user's care and activities including the improvement or optimization of a diagnosis of chronic pain and pain severity, pain management, decisions on return to work, performance, and function, and guiding therapies for pain including medications, lesioning, and other procedures such as neuromodulation including neurostimulation, spinal cord stimulation, infusion and other approaches.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Javed’s teachings and Bharmi’s teachings with Rezai’s teachings for the same reasons given for claim 6.
As per claim 9, Javed and Bharmi do not teach:
The method of claim 1, further comprising: analyzing data for neurostimulation programming.
However, Rezai does teach:
The method of claim 1, further comprising: analyzing data for neurostimulation programming. (Col. 13 lines 31-46 discloses, “This allows the value to be used to make decisions about the user's care and activities including the improvement or optimization of a diagnosis of chronic pain and pain severity, pain management, decisions on return to work, performance, and function, and guiding therapies for pain including medications, lesioning, and other procedures such as neuromodulation including neurostimulation, spinal cord stimulation, infusion and other approaches. Feedback provided to the user can be used to improve the user's awareness, perception and interpretation of being in an overall positive (e.g., decreased pain) and negative (e.g., increased pain) states, allowing the user to learn strategies for avoiding negative states and inducing positive states. The provided wellness data can also be used for improvement or optimization of cognitive, motor, sensory, and behavioral function as well as generally attempting to improve the user's quality of life.” And see Col. 12 lines 30-37 discloses, “It will be appreciated that the index can be used for clinical studies to determine a response to pain treatment or responses to stimuli. Further, the index can be used to dispense medication or another therapeutic intervention to a patient for pain, either by providing the index for use by a medical professional or automatically, by actuating an infusion pump, spinal cord stimulator, or other device in response to an index meeting a threshold value.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Javed’s teachings and Bharmi’s teachings with Rezai’s teachings for the same reasons given for claim 6.
As per claims 17-18 they are an article of manufacture claims which repeats the same limitations of claim 6-7, the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Javed, Bharmi, and Rezai as well as motivations to combine disclose the underlying process steps that constitute the method of claims 6-7 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claims 17-18 are rejected for the same reasons given above for claims 6-7.
Response to Arguments Regarding 35 U.S.C § 101 Rejection
The applicant argues on pages 1-4 of the submitted remarks that the rejection under 35 U.S.C § 101 should be withdrawn in light of the below arguments. Claims 1-20 were rejected under 35 U.S.C. § 101 as allegedly being directed to non- statutory subject matter. Applicant respectfully traverses. The Examiner rejected claims 13-19 as directed to signals per se, citing paragraph [00255] of the specification for the proposition that the broadest reasonable interpretation of "machine- storage medium" encompasses transitory signals. Applicant respectfully submits that the Examiner cited the wrong definitional paragraph. Paragraph [00256] - not [00255] - defines the actual claim term. Paragraph [00256] states that "machine-storage media," "computer-storage media," and "device-storage media" "specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term 'signal medium."' The specification thus draws a clear distinction between "machine-storage media" (non-transitory) and "transmission media" or "signal media" (transitory), and the claim term falls in the former category. Notwithstanding, to advance prosecution beyond this issue, Applicant has amended claim 13 to recite "non-transitory machine-storage medium."
Applicant respectfully traverses the rejection of claims 1-20 as directed to an abstract idea for at least the following reasons. The Examiner's abstract idea categorization is incorrect. The Examiner characterized the claims as "certain methods of organizing human activity" by "following rules and instructions to determine a change in a patient state distribution when monitoring a patient." This category encompasses fundamental economic practices, commercial or legal interactions, and managing personal behavior or relationships between people. MPEP § 2106.04(a)(2)(II). In contrast, the amended claims recite computing patient state distributions - each comprising a percentage of time a patient spent in each of a plurality of patient states over a time period - detecting a change between those distributions, triggering an alert, and identifying therapy delivery settings to suggest alterations based on the change. These are computational operations performed by a hardware processor on multi-dimensional health data that feed results back into therapy delivery. None involves managing personal behavior, commercial interactions, or legal interactions. Additionally, the subject matter of the claims cannot be practically performed by the human mind; therefore it does not qualify as a mental process.
Applicant continues to argue, the claims are integrated into a practical application. The Examiner dismissed the medical device as merely "linking the abstract idea to the medical environment" and the monitoring platform as "apply-it" to communicate data. These characterizations do not account for the amended claims as a whole.
The amended claims recite a closed-loop method: (a) using a medical device delivering therapy to a patient; (b) computing percentage-based distributions of time across multiple patient states over respective time periods; (c) detecting a change between those distributions; (d) triggering an alert on a monitoring platform; and (e) identifying, based on the change, therapy delivery settings to suggest alterations. The final step ties the computational analysis directly to the physical medical device recited in the first step - the therapy delivered by that device is the subject of the suggested alteration. This is not merely linking to a field of use; it is using the computational result to inform how a physical device delivers therapy to a patient. See FIGS. 5, 6, 8 (element 870: "Patient Alert: Offer New Program Setting?"), and 23 (elements 2342, 2350-2356: neurostimulation programming information flowing to program implementation logic, parameter adjustment algorithm, and program selection/modification). The claims also address a specific problem in remote patient monitoring: existing approaches require clinicians to individually review each patient's data, resulting in delays of weeks or months from onset to treatment implementation ([0079]-[0081]). The claimed solution - detecting distribution shifts and triggering targeted alerts of changes warranting treatment modifications rather than presenting all data ([00142]) - constitutes an improvement to the functioning of the monitoring and therapy delivery system itself. The Examiner provided no evidence that the ordered combination of elements is well- understood, routine, or conventional. Under Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018), such factual assertions require evidentiary support. None was provided.
Additional argument by applicant. Finally, Applicant respectfully asserts that the USPTO Examples of Eligible Subject Matter support a finding that the present claims are patentable subject matter. USPTO Example 46, Claim 21 (Livestock Activity Monitoring - Eligible). Claim 2 was found eligible because it added specific sensor hardware, a specific data processing step (computing a moving average), a threshold-based detection step, and a concrete responsive action (generating an alert). The Office found these limitations reflected "an improvement in the monitoring of animals" and were "not merely 'applying' the exception." The amended claims follow the same structure: medical device delivering therapy, a specific processing step (computing percentage-based distributions across patient states over time periods), a change-detection step (difference between distributions), a concrete responsive action (triggering an alert), and a further step beyond Example 46 - identifying therapy delivery settings to suggest alterations based on the change.
USPTO Example 42, Claim 12 (Notifications - Eligible). Example 42 found eligibility where specific data conversion, storage, and transmission steps provided a technical improvement to notification systems. The amended claims similarly recite a specific data structure (multi-state percentage distributions), a specific comparison (difference between distributions over different time periods), and specific responsive actions that together improve remote patient monitoring by reducing data overload and enabling timely therapy adjustments. See [0142].
The dependent claims independently warrant eligibility. The Examiner grouped all dependent claims together and identified only "neurostimulation programming" (claim 9) as an additional element. Claims 5, 11, and 12 were not individually analyzed. Claim 5 further specifies that the therapy is at least partially defined based on the distribution change. Claim 11 requires causing an action based on the alert. Claim 12 requires identifying an underlying cause of the change. Each independently supports eligibility and warrants individual analysis.
For at least the foregoing reasons, Applicant respectfully requests withdrawal of the § 101 rejection.
Examiner appreciates applicant’s distinction and applicant has overcome the signals per se rejection. Examiner notes it wasn’t a question the differences of transitory and non-transitory rather it needed to be clear the scope of the claim only includes the non-transitory medium.
Examiner appreciates applicants arguments but does not find them persuasive. The MPEP 2106 states The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I); (Mathematical Calculations - A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.)
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above.
The MPEP 2106.04(a)(2) (II) states, “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping.”
Examiner approaches the claim under broadest reasonable interpretation and must take the substance of the positive recitation of the claim without reading the specification into the claims but in light of the specification. The examiner is required to identify the elements within the claims which are abstract and identify the additional elements of the claim, therefore it was not a conclusory statement but rather a clear record of what elements are abstract idea and what elements are additional. The elements identified as abstract are reasonably interpreted to be a certain method of organizing human activity or, more particularly, following rules or instructions since the substance of the claim recites abstract steps of determining a change in a patient state distribution when monitoring a patient to identify alterations to therapy delivered. When additional element computer tools are used to only make the monitoring, analysis, and capturing of changes in patient distribution data more efficient does not make it dispositive of being certain methods of human activity and an abstract idea. The question is not would a human do it but could a human do it even if it involves a computer or multiple people and examiner notes a human could follow abstract statistical steps to identify, capture, and monitor patient states and their distributions over time to suggest alterations in therapies as is done today. The claim is directed to the abstract idea of certain methods of human activity.
Examiner appreciates applicant’s arguments but does not find them persuasive. Examiner does not read the specification into the claims but rather examines the scope of the recited claim. Therefore, applicants argument that the recited claims provide an additional element which is tangible changed in state to deliver a therapy or particular machine is incorrect. The therapy is broadly claimed as is the medical device. The medical device is “apply-it” level as it uses the language for e.g. “configured to” with no affirmative deliverance of any controller based therapy delivered. Further the settings are suggested alterations thus abstract in nature and again further not affirmative recitation of an additional element delivering adjusted therapy thus not a practical application or significantly more. Further examiner did not need to provide evidence as examiner did not on the record not any well understood routine or conventional element rather cited from applicants on specification any additional element information as “apply-it” which does not require this additional analysis, so yes none was provided as it was not required by examiner. Further limitations are executed by a processor therefore, the instant application claims do not provide as argued an improvement to the computer in which the claims are confined or device for therapy delivery or the system itself, rather as argued the real-world problem resulting in delays of weeks or months from onset to treatment implementation is abstract and the abstract idea recited in the claims cannot bring forth the practical application. Examiner notes that the determination of whether claims positively recited an abstract idea is not determined based on what a human would do but rather could a human do it, whether that be alone, with others, with a computer, or with a pen and paper. The claimed invention is using a computer as a tool and any improvement present would be an improvement to the abstract idea. Finally, were applicants line of reasoning correct, the invention in Alice Corp. would have been subject matter eligible because it was an improvement to the technology of settlement risk mitigation.
Examiner appreciates applicants arguments but does not find them persuasive. There is no nexus with example 42 or 46 for the same reasons aforementioned in this argument. There is no particular machine or additional element where a tangible state change or unconventional steps occur. Further there is no clear technical problem identified for the instant application confined to the computer environment and no reflection or recitation in the claims of this technological improvement, rather the abstract idea is recited and improved and thus cannot bring for the practical application. Each case turns on its on facts and while these examples are guidance are not precedential case law and do not have the same fact pattern as the instant application.
Finally, examiner did in fact analyze the dependent claims individually and as a whole. They do not warrant eligibility or they would have been labelled as such. Rather the examiner analyzed the claim by noting the additional element present and the way the abstract idea is further limited. No specific template is required to be used. No argument of specific limitations by applicant warranted any additional element analysis rather were just further abstract idea thus do not support practical application or significantly more as the abstract idea cannot bring forth the practical application or significantly more. The analysis was completed by examiner.
Examiner must maintain the 35 U.S.C 101 rejection.
Response to Arguments Regarding 35 U.S.C § 103 Rejections
Applicant argues on pages 5-6 of the remarks for claims rejected under 35 U.S.C § 103.
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The examiner must maintain the 35 U.S.C 103 rejection.
Prior Art not cited but made of record
US20230139196A1- LOPEZ, et. al
A system and method are provided for patient monitoring. The system and method may receive measurement data comprising measured values of physiological parameters of a patient at consecutive time instances. On a display, a trend view 510 may be provided which provides a longitudinal visualization of the measurement data by setting out the measured values of a first set of the physiological parameters at the consecutive time instances against a common timeline. Simultaneously with the trend view, a patient status view 500 may be provided which provides a visualization of a physiological state of the patient and which visualization is adapted to the measured values of a second set of the physiological parameters at a select time instance. The trend view and the patient status view may be dynamically linked by, when user input is received which is indicative of a selection of a past time instance, adapting the visualization of the physiological state to the measurements of the second set physiological parameters at the past time instance.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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