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 .
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed 02/28/2025 has been considered by the Examiner.
Response to Arguments
Rejections under 35 USC 102/103
Applicant's arguments filed 02/18/2026 have been fully considered but they are not persuasive.
Applicant argues that the previously presented prior art of Esteller does not teach all of the limitations of the claimed invention, namely that of feature data that both determines control algorithm relationships and serves as a reference point for anomaly detection.
Examiner would like to identify the following elements as requested by Applicant in the Remarks:
Esteller’s feature data: Feature data may be regarded as an Evoked Compound Action Potential (ECAP), which is a neural response to a stimulation waveform. At least one feature of the ECAP may be indicative of the shape/size of the ECAP, a peak height or width, an area of the ECAP or of any ECAP peak, a length of any portion of the ECAP, a time defining a duration of any portion of the ECAP, or a time delay from stimulation to issuance of the ECAP (Esteller [0014-0015]).
Control algorithm relationships determined using such feature data: ECAP feature data can be used to determine threshold values including an extracted ECAP threshold, a visual ECAP threshold, a perception threshold, discomfort threshold, and/or motor threshold which compare a feature of the ECAP, for example amplitude, with a stimulation intensity and determine a relationship between the two to determine a threshold value (Esteller [0084-0089]). The control algorithm also includes a process of personalizing settings during a calibration mode, which determines which ECAP metrics (features) are most sensitive to changes (Esteller [0089]). A set point may be determined based on the calibration mode based on the optimization goal and the measured metrics (Esteller [0090]).
Use of feature data for determining anomalies in detected features: ECAP features are used to determine a set point for the stimulation, and the set point is used to maintain therapy as defined by the optimization goal, therefore as the metric extracted from the ECAP changes, the stimulation parameter selected as the controlled variable will be adjusted to maintain the same volume of activation (Esteller [0090]).
Applicant argues that the control algorithm of Esteller does not meet the limitations of the claimed invention because it does not disclose determining whether detected features are anomalous with respect to the feature data used to determine the control algorithm’s relationships, nor does it describe remedial actions triggered by such anomalies, where an anomalous feature indicated an abnormal condition.
Examiner respectfully disagrees and argues that Applicant is reading in additional limitations to the claim which are not present. Esteller uses a calibration mode to measure ECAP features in response to a stimulation, and use those features as an input to the control algorithm to determine a set point and optimization goal. The determined set point and optimization goal are then compared to features of subsequent ECAPs, to determine if one of the features is anomalous with respect to the feature data used to determine one or more of the relationships, which would be feature data used to calculate the set point and optimization goal, and then perform remedial action when it is determined that at least one feature is anomalous, in the form of adjusting stimulation parameters.
Applicant notes on page 9 of the Remarks, that the claimed anomalous features indicate an abnormal condition, such as data integrity issues or lead migration, which may require remedial action beyond parameter adjustment. However, these limitations are not present in the claim. The claim only includes the limitations of identifying an anomalous feature with respect to feature data used to determine one or more relationship defined by the control algorithm, and performing an action in the case of an anomalous feature being indicated. Examining the claimed invention, the prior art device of Esteller discloses the claimed method, and the rejection is maintained.
Rejections under 35 USC 101
Applicant's arguments filed 02/18/2026 have been fully considered but they are not persuasive.
Applicant argues that Examiner has not provided evidence that using feature data to determine control algorithm relationships and detecting anomalies with respect to that same feature data is well-understood, routine, or conventional. This argument is predicated on Applicant’s assertion that the control algorithm relationships determined using feature data, and determination of anomalous features as taught by Esteller does not fulfill the claim limitations. Examiner points to the discussion above regarding the addition of limitations which are not explicitly recited by the claims. With respect to the limitations recited in the claims, it would be possible for a human to use feature data, and in the mind or with the aid of pen and paper, determine control algorithm relationships, which may include using feature data to determine thresholds or optimization goals of the stimulation therapy. It would also be possible for a human in the mind or with the aid of pen and paper to determine anomalous feature data with respect to the feature data used to determine the control algorithm relationships, which may include comparing subsequently measured feature data to the thresholds previously determined using feature data. A human would be able to identify an anomalous feature data point in this regard and then perform a remedial action, where the remedial action may be an adjustment of stimulation parameters. Examiner notes that the claim does not require the delivery of stimulation using any new stimulation parameters determined from the one or more relationships determined using feature data. Examiner also notes that ‘remedial action’ is claimed broadly and could be interpreted to be almost anything including noting a medical condition, suggesting a doctor’s visit, outputting an alert, etc.
As the claims are currently presented, they amount to only the computer implementation of a mental process/abstract idea. Examiner is making this determination based solely on the limitations present in the claims as currently presented. With this in consideration, the rejection is maintained.
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 USC 101 because the claimed invention is directed to an abstract idea/mental process without significantly more.
Step 1
Claim 1 recites a method and claim 16 recites a manufacture and claim 20 recites a machine.
Step 2A, Prong 1
Claims 1, 16, and 20 recite the limitations of determining a relationship between stimulation waveform parameters and features of resulting evoked compound action potentials and performing remedial action if the features are found to be anomalous. These steps, given their Broadest Reasonable Interpretation are directed to an abstract idea/mental process. A human could practically determine a relationship between stimulation waveform parameters and ECAP features, for example a visual threshold to determine the lowest stimulation intensity from which a resulting ECAP is visible. Using these relationships, a human could compare feature data to the features used to determine the relationship and evaluate if the features were anomalous. These actions amount to a mental process.
Step 2A, Prong 2
Claims 1, 16, and 20 do not include any additional elements that integrate the abstract idea/mental process into a practical application.
Claims 1 and 20 include the additional elements of a stimulator having at least one electrode to deliver an electrical waveform, sensing circuitry to sense an electrical potential, a controller which performs the process of determining a relationship between the waveform parameters and the ECAP features, providing closed-loop control of the simulator using a control algorithm, and performing remedial action if a feature is determined to be anomalous.
The limitation of a stimulator delivering an electrical waveform via at least one electrode and sensing electrical potentials using sensing circuitry is insignificant extra-solution of data gathering. This initial delivery of an electrical stimulation is performed in order to gather data for the mental analysis step of determining a relationship between the waveform parameters and the feature data, and is a necessary precursor for all uses of the recited exception. See MPEP 2106.04(d)(2). This data gathering is the performance of clinical tests on an individual to obtain an input for an equation wherein the stimulation pulses are performed to obtain ECAP features which are used as inputs for the control algorithm to determine a relationship between waveform parameters and ECAP features. See In re Grams, 888 F.2d 83, MPEP 2106.05(g). The extra-solution activity of data gathering as described does not integrate the judicial exception into practical application.
The controller as claimed does not integrate the abstract idea into a practical application because it generally links the abstract idea to a particular technological environment or field of use. See MPEP 2106.05(h). The controller uses a control algorithm which provides closed-loop control of the stimulator; however the closed-loop control algorithm fails to integrate the abstract idea/mental process into practical application because it has a nominal or insignificant relationship to the exception. The claims do not connect the result of the mental process of determining a relationship between the waveform parameters and the feature data to the actual control of the stimulation therapy. See MPEP 2106.04(d)(2).
Thus, taking remedial action based on anomalous data is broadly claimed such that it has no particular steps and is instead merely instructions to ‘apply’ the exception in a generic way. Therefore, the remedial action does not integrate the mental process into a practical application. See MPEP 2106(d)(2).
Claim 16 does not include any additional elements.
Step 2B
Claims 1, 16, and 20 do not include any additional elements that amount to significantly more than the abstract idea itself.
Claims 1 and 20 include the additional elements of a stimulator having at least one electrode to deliver an electrical waveform, sensing circuitry to sense an electrical potential, a controller which performs the process of determining a relationship between the waveform parameters and the ECAP features, and performing remedial action if a feature is determined to be anomalous. The limitation of a stimulator delivering an electrical waveform via at least one electrode and sensing electrical potentials using sensing circuitry is insignificant extra-solution of data gathering. This data gathering is the performance of clinical tests on an individual to obtain an input for an equation wherein the stimulation pulses are performed to obtain ECAP features which are used as inputs for the control algorithm to determine a relationship between waveform parameters and ECAP features. See In re Grams, 888 F.2d 83, MPEP 2106.05(g). The controller as claimed does not amount to significantly more than the abstract idea because it generally links the abstract idea to a particular technological environment or field of use. See MPEP 2106.05(h). The determination of the relationships between waveform parameters and the ECAP features is the mental process itself and remedial action taken if the features are determined to be anomalous is insignificant extra-solution activity. Taking remedial action based on anomalous data is broadly claimed such that anything could be a remedial action, for example, adjusting stimulation parameters, generating an alarm indication, etc., and does not amount to significantly more than the abstract idea as the remedial action does not amount to an inventive concept. See MPEP 2106.05(g).
The additional elements of an electrical stimulator comprising at least one electrode, sensing circuitry, and a controller are all well-understood, routine, and conventional in the art of electrical stimulation therapy. See Esteller (US 20190209844 A1) which teaches an electrical stimulation system having a plurality of electrodes which are attached to a patient and control circuitry which delivers a stimulation waveform to the patient and senses a neural response using one or more sensing electrodes of the plurality of electrodes in paragraphs [0012-0014].
Claim 16 does not include any additional elements.
Claims 2-4 further define the extra-solution activity of data gathering to obtain input values for an equation such as sensing electrical potentials including neural activity or muscle activity and specifying what may be considered as a feature of the sensed potentials.
Claims 5 and 11-15 further define the abstract idea/mental process as performed by specifying the order of sensing/analyzing events before determining them to be anomalous and providing remedial action based on the determination in the form of adjusting stimulation parameters or communicating with the patient.
Claims 6-10 and 17-19 further define the abstract idea/mental process by detailing statistical methods that could be used to determine if a feature is anomalous.
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) 1-5, 7, 11-16, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Esteller et al (US 20190209844 A1).
Regarding claim 1, Esteller teaches a method, comprising:
delivering an electrical therapy using a stimulator operably connected to at least one electrode (see [0014]; a plurality of electrodes coupled to a patient’s tissue) by delivering an electrical waveform according to waveform parameters (see [0014]; the medical device issues a stimulation waveform using a stimulation program, [0042]; stimulation programs define parameters including which electrodes are active, polarity, amplitude, pulse width, frequency, cycling, and waveform shape);
sensing electrical potentials using sensing circuitry (see [0040]; ECAPS evoked using stimulation are sensed using a sense electrode on a lead 14); and
using a controller (102) to automatically perform a process, wherein the automatically performed process includes:
detecting at least one feature in the sensed electrical potentials (see [0014-0015]; detecting a feature of the ECAP resulting from an electrical stimulation);
providing closed-loop control of the stimulator using a control algorithm and the detected at least one feature as an input into the control algorithm (see [0090-0091]; in running mode 1104, the ECAP algorithm 124 detects ECAPs and adjusts stimulation parameters based on the features of the detected signals in comparison to a feature threshold),
wherein the control algorithm defines one or more relationships between at least one feature and the one or more of the waveform parameters (see [0084-0089]; the ECAP feature data can be used to determine threshold values including an extracted ECAP threshold, a visual ECAP threshold, a perception threshold, discomfort threshold, and/or motor threshold which compare a feature of the ECAP, for example amplitude, with a stimulation intensity and determine a relationship between the two to determine a threshold value),
the one or more relationships being determined using feature data (see [0089]; deriving the relation between the stimulation parameter and the ECAP metric, which includes metrics labeled in Fig. 9);
determining whether the detected at least one feature is anomalous with respect to the feature data used to determine the one or more relationships (see [00090-0091]; ECAP features are compared to set point parameters/thresholds determined in the calibration mode to detect any ECAPs exceeding a threshold value); and
performing remedial action when it is determined that the at least one feature is anomalous with respect to the feature data (see [0091]; adjusts the stimulation parameters to maintain the metrics according to the determined threshold/set point).
Regarding claim 2, Esteller teaches the method of claim 1, wherein the sensing electrical potentials includes sensing local field potentials evoked compound action potentials (see [0013]; the neural response may comprise an Evoked Compound Action Potential).
Regarding claim 3, Esteller teaches the method of claim 1, wherein the sensing electrical potentials includes sensing neural activity or sensing muscle activity (see [0013]; the neural response may comprise an Evoked Compound Action Potential).
Regarding claim 4, Esteller teaches the method of claim 1, wherein the detecting at least one feature (see Fig. 9, [0065-0075] where ECAP features are labeled and described) includes detecting at least one peak, the at least one peak including
a minimum peak (H_N1);
a maximum peak (H_P2);
detecting an area under a curve (A N1 and/or A tot);
detecting a curve length (L_P1toN2); or
detecting a rate of decay for a peak amplitude (see [0073]; any time defining a duration of at least a portion of an ECAP).
Regarding claim 5, Esteller teaches the method of claim 1, wherein the providing closed-loop control includes providing closed-loop control based on a feature change for the detected at least one feature with respect to a baseline or a feature difference (see [0090-0091]; running mode 1104 operates continuously to sense and analyze ECAP features and adjusts the stimulation parameters to maintain the metrics according to the set points).
Regarding claim 7, Esteller teaches the method of claim 1, wherein the determining whether the detected at least one feature is anomalous includes performing statistical analysis to determine that the detected at least one feature is anomalous with respect to the feature data (see [0088]; the ECAP algorithm may have an optimization goals which, for example, keeps the ECAP features at 50% between an extracted ECAP threshold and a perception threshold, the comparison of subsequent ECAP features to the thresholds and optimization goal can be considered to be statistical analysis).
Regarding claim 11, Esteller teaches the method of claim 1, wherein the determining whether the detected at least one feature is anomalous is performed before detecting a subsequence instance of the at least one feature in a sensed evoked signal (see Fig. 7B, [0050]; the ECAP algorithm enables ECAP sensing between pulses 133a and 133b, and if the total duration of the ECAP is sensed to be longer than the quiet period between two subsequent pulses, the ECAP algorithm may not enable subsequent pulses until the ECAP measurement has finished).
Regarding claim 12, Esteller teaches the method of claim 1, wherein the detected at least one feature is determined to be not anomalous before adjusting at least one waveform parameter based on the detected at least one feature (see [0090-0091]; the set point is used during running mode to maintain therapy as defined by an optimization goal).
Regarding claim 13, Esteller teaches the method of claim 1, further comprising storing a plurality of instances of the detected at least one feature, and auditing the plurality of instances to determine if any one or more of the instances is detected as being anomalous with respect to the feature data (see [0089] and [0093]; sensed spinal cord signals are recorded and their extracted ECAP metrics are stored for each posture so that the resulting ECAP metrics are related to varied stimulation parameters/positions/activities, it can be appreciated that these values are then used to create various threshold values that subsequent ECAP values are compared).
Regarding claim 14, Esteller teaches the method of claim 1, wherein the performing remedial action includes automatic and/or manual processes for:
disabling or adjusting the closed-loop control (see [0089-0091]; calibration mode allows for determination of threshold values that determine the adjust of stimulation parameters); or
reconfiguring the feature data used to determine the one or more relationships between the at least one feature and the one or more waveform parameters (see [0093]; ECAP metric measurements can be recorded and stored and can then be used to evaluate and/or modify therapy).
Regarding claim 15, Esteller teaches the method of claim 1, wherein the performing remedial action includes communicating with a patient to troubleshoot (see [0093-0094]; patient may be able to adjust stimulation parameters including the set points of the ECAP metrics, which may represent patient discomfort).
Regarding claim 16, Esteller teaches a non-transitory machine-readable medium including instructions, which when executed by a machine, cause the machine to perform a method (see [0014]; a non-transitory computer-readable medium storing instructions to cause the circuitry to perfom the method) comprising:
delivering an electrical therapy by delivering an electrical waveform according to waveform parameters (see [0014]; the medical device issues a stimulation waveform using a stimulation program, [0042]; stimulation programs define parameters including which electrodes are active, polarity, amplitude, pulse width, frequency, cycling, and waveform shape);
sensing electrical potentials (see [0040]; ECAPS evoked using stimulation are sensed using a sense electrode on a lead 14); and
automatically perform a process, wherein the automatically performed process includes:
detecting at least one feature in the sensed electrical potentials (see [0014-0015]; detecting a feature of the ECAP resulting from an electrical stimulation);
providing closed-loop control using a control algorithm and the detected at least one feature as an input into the control algorithm (see [0090-0091]; in running mode 1104, the ECAP algorithm 124 detects ECAPs and adjusts stimulation parameters based on the features of the detected signals in comparison to a feature threshold),
wherein the control algorithm defines one or more relationships between at least one feature and the one or more of the waveform parameters (see [0084-0089]; the ECAP feature data can be used to determine threshold values including an extracted ECAP threshold, a visual ECAP threshold, a perception threshold, discomfort threshold, and/or motor threshold which compare a feature of the ECAP, for example amplitude, with a stimulation intensity and determine a relationship between the two to determine a threshold value),
the one or more relationships being determined using feature data (see [0089]; deriving the relation between the stimulation parameter and the ECAP metric, which includes metrics labeled in Fig. 9);
determining whether the detected at least one feature is anomalous with respect to the feature data used to determine the one or more relationships (see [00090-0091]; ECAP features are compared to set point parameters/thresholds determined in the calibration mode to detect any ECAPs exceeding a threshold value); and
performing remedial action when it is determined that the at least one feature is anomalous with respect to the feature data (see [0091]; adjusts the stimulation parameters to maintain the metrics according to the determined threshold/set point).
Regarding claim 20, Esteller teaches a system, comprising:
a stimulator operably connected to at least one stimulation electrode (see [0014]; medical device issues stimulation via a plurality of electrodes),
and configured to deliver an electrical therapy using the at least one stimulation electrode by delivering an electrical waveform according to waveform parameters (see [0014]; the medical device issues a stimulation waveform using a stimulation program, [0042]; stimulation programs define parameters including which electrodes are active, polarity, amplitude, pulse width, frequency, cycling, and waveform shape);
sensing circuitry operably connected to at least one sensing electrode, and configured to sense electrical potentials (see [0040]; ECAPS evoked using stimulation are sensed using a sense electrode on a lead 14); and
a controller (102) operably connected to the stimulator and the sensing circuitry, wherein the controller is configured to:
detect at least one feature in the sensed electrical potentials (see [0014-0015]; detecting a feature of the ECAP resulting from an electrical stimulation);
provide closed-loop control of the stimulator using a control algorithm and the detected at least one feature as an input into the control algorithm (see [0090-0091]; in running mode 1104, the ECAP algorithm 124 detects ECAPs and adjusts stimulation parameters based on the features of the detected signals in comparison to a feature threshold),
wherein the control algorithm defines one or more relationships between at least one feature and the one or more of the waveform parameters (see [0084-0089]; the ECAP feature data can be used to determine threshold values including an extracted ECAP threshold, a visual ECAP threshold, a perception threshold, discomfort threshold, and/or motor threshold which compare a feature of the ECAP, for example amplitude, with a stimulation intensity and determine a relationship between the two to determine a threshold value),
the one or more relationships being determined using feature data (see (see [0089]; deriving the relation between the stimulation parameter and the ECAP metric, which includes metrics labeled in Fig. 9);
determine whether the detected at least one feature is anomalous with respect to the feature data used to determine the one or more relationships (see [00090-0091]; ECAP features are compared to set point parameters/thresholds determined in the calibration mode to detect any ECAPs exceeding a threshold value); and
perform remedial action when it is determined that the at least one feature is anomalous with respect to the feature data (see [0091]; adjusts the stimulation parameters to maintain the metrics according to the determined threshold/set point).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Esteller et al (US 20190209844 A1) in view of Etkin (US 20180236255 A1).
Regarding claim 6, Esteller teaches the method of claim 1. Esteller is silent regarding implementing unsupervised machine learning technique to determine whether the detected at least one feature is anomalous with respect to the feature data, wherein the unsupervised machine learning techniques include a density-based supervised clustering of apps with noise (DB SCAN) or an isolation forest.
Etkin teaches a system for applying an electrical stimulation to a patient and measuring the evoked physiological signals. The signals are then analyzed in order to determine if there is an abnormality (Etkin [0004-0007]).
The method of determining an abnormality comprising implementing unsupervised machine learning techniques (see Etkin [0096]; machine learning model is unsupervised) to determine whether the detected at least one feature is anomalous with respect to the feature data (see Etkin [0096]; a machine learning model is used to cluster novel data points to determine if the measured response is normal or abnormal) wherein the unsupervised machine learning techniques include a density-based supervised clustering of apps with noise (DBSCAN) (see Etkin [0090]; the machine learning model can use clustering techniquest include density-based clustering with applications of noise).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method disclosed by Esteller by the adding unsupervised machine learning model and density-based supervised clustering of Etkin. One of ordinary skill in the art would have been motivated to make this modification in order to utilize a machine learning model for efficient classification and differentiation of normal and abnormal conditions with regard to the specific trends/fluctuations of the physiological signals of an individual (Etkin [0012]).
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Esteller et al (US 20190209844 A1) in view of Tirungari et al (S. Tirunagari, D. Abasolo, A. Iorliam, A. T. S. Ho and N. Poh, "Using Benford's law to detect anomalies in electroencephalogram: An application to detecting alzheimer's disease," 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Manchester, 2017, pp. 1-6, doi: 10.1109/CIBCB.2017.8058547.)
Esteller teaches the method of claim 7 and the non-transitory machine-readable medium of claim 16. Esteller is silent regarding wherein the determining whether the detected at least one feature is anomalous includes performing statistical analysis to determine that the detected at least one feature is anomalous with respect to the feature data.
Tirungari teaches a method for detecting abnormalities in physiological signals, namely EEG signals, using Benford’s Law to diagnose brain disorders associated with anomalous signals. The method comprising wherein the detected at least one feature is quantified using digits (see Tirungari [Page 2, Col 1]; signals are quantified as time derivatives), and the statistical analysis includes analyzing a most significant digit for a quantified value using Benford's law (see Tirungari [Page 2, Col 1]; time derivatives of physiological signals follow Benford’s law and can be used to discriminate between Alzheimer’s patients and control patients, it can be appreciated that in this instance the Alzheimer’s patient’s signals are interpreted to be anomalous with respect to the control signals).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method for determining an anomalous feature as taught by Esteller with Benford’s Law as taught by Tirungari. One of ordinary skill in the art would have been motivated to make this modification in order to capture descriptive probabilities from the first digit features of physiological signals which can be input into a machine model for classification (Tirungari [Page 2, Col 1]).
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Esteller et al (US 20190209844 A1) in view of Krauss et al (US 8679029 B2).
Esteller teaches the method of claim 7 and the non-transitory computer-readable medium of claim 16. Esteller is silent regarding wherein the statistical analysis includes a Z- score calculated as a difference between a data point for the at least one feature and a mean of training data, wherein the mean is divided by a standard deviation of the training data, wherein the at least one feature is determined to be anomalous when the Z-score exceeds a threshold.
Krauss teaches a system for monitoring, diagnosing, and treating a patient by means of running statistical analysis on physiological signals of a patient to determine a medical condition that may need intervention (Krauss Abstract). The system teaches statistical analysis wherein the statistical analysis includes a Z- score calculated as a difference between a data point for the at least one feature and a mean of training data (see Krauss [Col 89, Line 60-Col 90, Line 16]; statistical analysis on a parameter including a Z-score to indicate how many standard deviations a data point is away from a mean value), wherein the mean is divided by a standard deviation of the training data, wherein the at least one feature is determined to be anomalous when the Z-score exceeds a threshold (see Krauss [Col 89, Line 60-Col 90, Line 16]; the Z-score analysis is used to determine statistical significance of the indicated diagnosis based on the feature being considered).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method for determining an anomalous feature as taught by Esteller with the use to Z-scores as taught by Krauss. One of ordinary skill in the art would have been motivated to make this modification in order to advantageously enhance the diagnosis of a medical condition based on a statistically significant difference between an anomalous data point and an average (Krauss [Col 89, Line 60-Col 90, Line 16]).
Claim(s) 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Esteller et al (US 20190209844 A1) in view of Rossi et al (US 20170340292 A1).
Esteller teaches the method of claim 7 and the non-transitory computer-readable medium of claim 16. Esteller is silent regarding wherein the statistical analysis includes a boxplot derived from training data, wherein the at least one feature is determined to be anomalous when a data point for the at least one feature is greater than or less than a factor of an upper limit for an interquartile range or a factor of a lower limit for the interquartile range.
Rossi teaches a method for detecting anomalies within physiological signals, namely electrocardiogram signals, using statistical analysis. The method comprising wherein the statistical analysis includes a boxplot derived from training data (see Rossi Fig. 24 picturing a box-plot derived from training data), wherein the at least one feature is determined to be anomalous when a data point for the at least one feature is greater than or less than a factor of an upper limit for an interquartile range or a factor of a lower limit for the interquartile range (see Rossi [0070-0072]; a signal s can be classified as anomalous when it falls outside of a confidence region defined using the interquartile range as a consideration).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method for determining an anomalous feature as taught by Esteller with the use of statistical analysis including box-plot ranges with consideration for the interquartile ranges of the data as taught by Rossi. One of ordinary skill in the art would have been motivated to make this modification in order to utilize a well-understood, routine, and conventional method of statistical analysis in order to determine anomalies by building a confidence region with consideration to the interquartile ranges of the dataset (Rossi [0070]).
Conclusion
THIS ACTION IS MADE FINAL. 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 ALISHA J SIRCAR whose telephone number is (571)272-0450. The examiner can normally be reached Monday - Thursday 9-6:30, Friday 9-5:30 CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.J.S./Examiner, Art Unit 3792
/ALLEN PORTER/ Primary Examiner, Art Unit 3796
Prosecution Insights