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 .
Acknowledgements
This communication is in response to Remarks filed on 12/16/2025.
Claims 1-11, 13-14, 18, 20 are amended
Claims 1-20 are currently pending and have been examined.
Claims 1-20 have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/25/2021 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a device and method for calculating a recovery trajectory.
The limitations of receive, […], clinical information regarding the patient, receive, […], a raw biometric data comprising continuous electroencephalogram (EEG) waveform data; analyze the raw biometric data and the clinical information to produce a set of analytic measures; categorize the set of analytic measures by feature to generate a reference data set that is separate from the raw biometric data stream; continuously transform the raw EEG waveform data into the analytic measures and recompute the reference data set based on characteristic shifts in the analytic measures over a minutes-to-hours temporal window; continuously calculate and update, based on the reference data set and comparative data from prior patients having a similar brain injury, a recovery trajectory indicative of changes in the severity of the injury over time without requiring direct assessment of the raw EEG waveform data; and […], the recovery trajectory. as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a system implemented by a processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the processor, communication interface and display screen, this claim encompasses a person receiving data, analyzing data and calculating a recovery trajectory in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of (claim 1) a processor that implements the identified abstract idea. The processor with a database is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing a generic computer functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a biometric sensor that implements the identified abstract idea. The biometric sensor is not described by the applicant and is recited at a high-level of generality (i.e., a generic sensor performing a generic functions of sensing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of an communication interface and a display. The communication interface and display merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Utilization of the interactive user equates to saying “apply it.” MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor with a database to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a biometric sensor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of an communication interface and display was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
Claims 2-10, 12-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 2, 12 merely describes set of analytic measures. Claim 4, 5, 14, 15 merely describes the biometric data. Claim 6-18, 16-18 merely describes the recovery trajectory. Claim merely describes 9, 19 merely describes the injury.
Claims 3, 10, 13, 20 also includes the additional element of “machine learning” which is not described by the applicant and is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Claim 3, 13 merely describes analyzing the set measures. Claim 10, 20 merely describes the recovery trajectory.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Brunner (US 20170249434) in view of Gandhi (US 20140128764)
CLAIM 1, 11
Brunner teaches A continuous recovery trajectory monitoring device for brain injury configured to calculate a recovery trajectory of a patient based on a severity of an injury of the patient, the continuous recovery trajectory monitoring device comprising: a communication interface; a display; an electronic processor configured to: (Brunner Fig 1. And para 32 teach assessing recovery of a patient. Brunner para 19 teaches a GUI user interface for person, patient, caregiver, physician. Examiner notes a GUI requires a display. Para 20 teaches a system including processor and a memory.)
receive, via the communication interface, clinical information regarding the patient (Brunner para 47 teaches acquisition of data including data from the GUI, clinical lab, context including name, sex, date of birth)
receive, via the biometric sensor, a raw biometric data comprising continuous electroencephalogram (EEG) waveform data; (Brunner para 35 teaches data from a sensor including EEG. Para 118, 162 teaches passive continuous acquisition of EEG. )
analyze the raw biometric data and the clinical information to produce a set of analytic measures, (Brunner para 62 teaches analyte data referring to data pertaining to sensors registering substances such as glucose, calcium)
categorize the set of analytic measures by feature to generate a reference data set that is separate from the raw biometric data stream; (Brunner para 130 teaches a reformatted dataset that extracts time series characteristics through recalling, normalization, rearrangement of time series including moving window, log, or other transformations, compression, dimension reduction, etc)
[…] recompute the reference data set based on characteristic shifts in the analytic measures over a minutes-to-hours temporal window; (Brunner para 31 teaches period of time may be minutes to hours to weeks. Para 47 teaches new data being included. Para 63 teaches obtain new knowledge and deriving predictions. Para 64 teaches historical, new, and/or streaming data. Para 130 teaches a reformatted dataset that extracts time series characteristics through recalling, normalization, rearrangement of time series including moving window, log, or other transformations, compression, dimension reduction. Para 198 teaches analysis on a frequent basis to ensure the data is always analyzed in the best possible way.)
continuously calculate and update, based on the reference data set and comparative data from prior patients having a similar brain injury, a recovery trajectory indicative of changes in the severity of the injury over time without requiring direct assessment of the raw EEG waveform data; and (Brunner para 47 teaches new data being included. Para 63 teaches obtain new knowledge and deriving predictions. Para 64 teaches historical, new, and/or streaming data. Para 154 teaches assessing health state and trajectory. Fig. 6 shows a recovery trajectory to be displayed.)
display, on the display, the recovery trajectory. (Brunner Para 154 teaches assessing health state and trajectory. Fig. 6 shows a recovery trajectory to be displayed.)
Brunner does not teach:
continuously transform the raw EEG waveform data into the analytic measures and recompute the reference data set based on characteristic shifts in the analytic measures over a minutes-to-hours temporal window;
Gandhi does teach
continuously transform the raw EEG waveform data into the analytic measures and recompute the reference data set based on characteristic shifts in the analytic measures over a minutes-to-hours temporal window; (Gandhi para 104, 105, 110 teach predicting levels of substances using EEG data. Para 41 teaches a window of 40 minutes after baseline EEG acquisition 72 teaches 20 minute window. )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the data as taught by Brunner with the transforming the EEG data into analytic measures as taught by Gandhi. It would be beneficial to predict levels of substances using EEG in order to avoid problems of acquisition, storage and transport of bio fluids as taught by Gandhi para 6.
CLAIM 2, 12
Brunner teaches wherein the electronic processor is configured to produce the set of analytic measures based on a predetermined content within the raw biometric data related to the injury and a characteristic shift of one or more features within the raw biometric data. (Brunner para 62 teaches analyte data referring to data pertaining to sensors registering substances such as glucose, calcium. Para 166 teaches processing of data such as ECG data to remove motor artifacts. Para 168 further teaches data may including estimates of physiological, behavioral, and biometric data including continuous and discrete and subjective data. )
CLAIM 3, 13
Brunner teaches wherein the electronic processor is configured to analyze, via machine learning, the set of analytic measures and clinical information to calculate the recovery trajectory. (Brunner para 68 teaches machine learning to analyze data to make predictions, capture patterns and estimate and/or quantify differences in data, quantify time series stability or instability patterns, identify change points in times series, and/or their predictors. Para 199 teaches model fitting to subject trajectory. Fig. 6 shows a recovery trajectory)
CLAIM 4, 14
Brunner teaches wherein the raw biometric data includes at least one selected from the group consisting of EEG data, […], and electrocardiogram data. (Brunner para 35 teaches EKG, EEG data. Examiner notes additional limitations interpreted as optional due to claim limitations “at least one …”)
CLAIM 5, 15
Brunner teaches wherein the raw biometric data is a real-time continuous feed. (Brunner para 118 teaches continuous acquisition of data. Para 157 teaches data collected in real time.)
CLAIM 6, 16
Brunner teaches wherein the electronic processor is further configured to recalculate the recovery trajectory in response to receiving either or both new clinical information and new raw biometric data. (Brunner para 47 teaches providing new derived data. Para 63-64 teach algorithm and the system acquire new and or streaming data and provides reports, visualization and answers. Fig. 6 shows a recover trajectory. Brunner para 62 teaches analyte data referring to data pertaining to sensors registering substances such as glucose, calcium. Para 166 teaches processing of data such as ECG data to remove motor artifacts. Para 168 further teaches data may including estimates of physiological, behavioral, and biometric data including continuous and discrete and subjective data.)
CLAIM 7, 17
Brunner teaches wherein the electronic processor is configured to calculate the recovery trajectory by comparing the analytic measures and clinical information for the patient to historical data for other patients; (Brunner para 173, 239 teaches comparing data against a group or baseline. )
identifying a trend in changes to a determined severity of the injury of the patient over a period of time; and (Brunner para 241 teaches analyzing a dataset and extracting change points which specify a change larger than expected to find general health deterioration )
determining a predicted recovery outcome based on the comparison and the identified trend. (Brunner para 241 teaches analyzing a dataset and extracting change points which specify a change larger than expected to find general health deterioration. Para 239-240 teaches comparing data to a database belonging to a healthy population and then classifying the subject as a healthy person or probability that person has a disease. )
CLAIM 9, 19
Brunner teaches wherein the injury of the patient includes a brain injury, and (Brunner para 146 teaches an example of a brain tumor)
wherein the electronic processor is configured to receive raw biometric data by receiving a continuous stream of EEG data for the patient. (Brunner para 35 teaches EKG, EEG data. Para 118 teaches continuous acquisition of data. Para 157 teaches data collected in real time.)
CLAIM 10, 20
Brunner teaches wherein the electronic controller is configured to calculate the recovery trajectory by receiving a plurality of data streams for the patient including a text data stream providing text-format clinical information for the patient and a biosignal data stream providing raw biometric data for the patient from the biometric sensor, (Brunner para 47 teaches acquisition of data including data from the GUI, clinical lab, context including name, sex, date of birth. para 62 teaches analyte data referring to data pertaining to sensors registering substances such as glucose, calcium. Para 168 further teaches data may including estimates of physiological, behavioral, and biometric data including continuous and discrete and subjective data.)
applying a separate machine-learning classifier of a plurality of machine-learning classifiers to each data stream of the plurality of data streams, wherein each machine- learning classifier is configured to produce an output indicating a relative probability for each of a plurality of severity classes for the injury, and wherein each machine- learning classifier is configured to produce an output indicating a relative probability for each of a plurality of severity classes for the injury, and (Brunner para 47 teaches an ensemble module which activates a algorithm selection module and then query answers are aggregated in a ensemble metalearner module that provides an integrated answer. Para 90 further teaches an ensemble algorithm is a machine learning paradigm that uses multiple machine learning algorithms to solve a problem )
determining a recovery trajectory for the patient based at least in part on an averaging of the probability for each of the severity classification from each of the plurality of machine-learning classifiers. (Brunner para 194 teaches ensemble averaging, and simply results are weighted and averaged. Fig. 6 shows a recovery trajectory. Para 154 teaches assessing health state and trajectory. Para 160 teaches the model used for a subjects trajectory)
Claim 8, 18 is rejected under 35 U.S.C. 103 as being unpatentable over Brunner (US 20170249434) view of Gandhi (US 20140128764) in view of Kanada (US 20180040088)
CLAIM 8, 18
Brunner teaches wherein the electronic processor is configured to: display the recovery trajectory by displaying a graph on the display […] wherein the […] derived from the analytic measures and the reference data set, and (Brunner 62 teaches analyte data referring to data pertaining to sensors registering substances such as glucose, calcium. Para 130 teaches a reformatted dataset that extracts time series characteristics through recalling, normalization, rearrangement of time series including moving window, log, or other transformations, compression, dimension reduction. Para 154 teaches assessing continuous data to assess baselines and trajectories. Fig. 6 shows a recovery trajectory to be displayed.)
wherein the recovery trajectory is continuously plotted […] as the analytic measures are updated over time. (Brunner para 62 teaches analyte data referring to data pertaining to sensors registering substances such as glucose, calcium. Para 154 teaches assessing continuous data to assess baselines and trajectories. Fig. 6 shows a recovery trajectory to be displayed. Para 79, 118 teaches continuous time series data. Para 221 teaches trends over time such as daily measures. Para 239 teaches updating a trend over time as new data is received )
Brunner does not teach
wherein the electronic processor is configured to: display the recovery trajectory by displaying a graph on the display divided into three regions, including a first region indicative of poor recovery conditions, a second region indicative of average recovery conditions, and a third region indicative of excellent recovery;
wherein the three regions are automatically defined based on severity classifications derived from the analytic measures and the reference data set, and
wherein the recovery trajectory is continuously plotted relative to the three regions as the analytic measures are updated over time.
Kanada does teach
wherein the electronic processor is configured to: display the recovery trajectory by displaying a graph on the display divided into three regions, including a first region indicative of poor recovery conditions, a second region indicative of average recovery conditions, and a third region indicative of excellent recovery; (Kanada para 172 and Fig. 23 teach a graph divided into regions from poor to good)
wherein the three regions are automatically defined based on severity classifications derived from the analytic measures and the reference data set, and (Kanada para 172 and Fig. 23 teach a graph divided into regions from poor to good)
wherein the recovery trajectory is continuously plotted relative to the three regions as the analytic measures are updated over time. (Kanada para 172 and Fig. 23 teach a graph divided into regions from poor to good)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the graph as taught by Brunner in view of Gandhi with the dividing into regions including a poor, average, and excellent recovery as taught by Kanada. It would be beneficial for the graph to show regions because it helps to predict the future medical condition of the medical examination patient since it is possible to see the tendency of good or poor treatment outcome as taught by Kanada para 172.
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20160199662 Wundrich
Para 15, 22 teach Biomarker calculation based on EEG signal
US 20190174039 Jung
Claim 12 EEG sensor is configured to generate an EEG signal based on electrical activity of a brain of the user; the at least one processor is operable to execute instructions to further cause the computer to perform the method comprising: filtering the EEG signal to remove noise; performing fast Fourier transform (FFT) on the filtered EEG signal to obtain an FFT EEG signal; calculating biomarkers from the FFT EEG signal;
Response to Arguments Regarding U.S.C. 101 Rejections
Applicant argues pg. 11
The amended claims are directed to a continuous recovery trajectory monitoring device and corresponding method that operate on raw electroencephalogram (EEG) waveform data to automatically compute, update, and display a recovery trajectory indicative of changes in injury severity over time. Importantly, the claims are not directed merely to collecting information, analyzing data, or displaying results. Rather, they recite a specific technical architecture that includes, inter alia:
[…]
This ordered combination of steps defines a trajectory-centric monitoring system, not a generic analytical abstraction. The recovery trajectory itself is a machine generated construct that emerges from continuous signal processing and comparative analysis, not from human reasoning.
Examiner responds:
The Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow receiving data, analyzing data and calculating a recovery trajectory. Applicant points to receipt of raw data, transformation, categorization of measures, recalculation and updating over a time window which Examiner has identified as encompassed by the abstract idea. Applicant points out automation of activities which Examiner has addressed in Step 2A2 analysis and has not the use of a computer to automate as merely applying a computer to implement the abstract idea and not integrating the identified abstract idea into a practical application. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to receiving data, analyzing data and calculating a recovery trajectory, the claimed invention is directed to an abstract idea.
Applicant argues pg. 11:
As described in the specification (see, e.g., paragraphs [0017], [0033], [0035], [0044], and [0045]), the invention replaces direct waveform review with an automatically computed recovery trajectory, thereby fundamentally altering how physiologic data is processed and presented in a brain injury monitoring context. Accordingly, the focus of the claims is a concrete technological solution to a technical problem in medical signal processing, not an abstract idea.
Examiner responds:
Applicant has not identified a technical problem in medical signal processing nor a concrete technological solution to a technical problem.
Examiner looks to citations of the specification, specifically para 17 in search of a technical problem/solution and finds para 17 recites “While EEG can also be represented by values of blood pressure, heart rate, and background frequencies, when this data is stored for use in complex analytical assessments, it is not possible to include the waveforms in the analysis directly. To do so would require massive computing capacity, automated process for recognizing file structures and data types, and the application of algorithms with varied accuracy at recognizing a pre-determined pattern within the waveform data. Embodiments described herein solve this complex problem by converting the waveforms into a dataset that can be queried for characteristic content without requiring direct assessment of the raw biometric data stream.”
Applicant characterizes problems in analyzing EEG data as a computing problem, however the problems are problems arising from analyzing a large volume of stored data and not from a problem in medical signal processing or in computer functioning, technology or technical field.
Further, Examiner finds that the claim requires analysis of the raw waveforms directly because Claim 1 recites “receive, via the biometric sensor, a raw biometric data comprising continuous electroencephalogram (EEG) waveform data; analyze the raw biometric data and the clinical information to produce a set of analytic measures;”. Thus Applicant claims include identified problems of “massive computing capacity, automated process for recognizing file structures and data types, and the application of algorithms with varied accuracy at recognizing a pre-determined pattern within the waveform data” relating to storing waveform data. Because Applicant does not explicitly solve an identified technical problem, a technical solution is not found and the claims are not integrated into a practical application.
Examiner has considered and cannot find support of a technical problem/solution in cited paragraphs [0033], [0035], [0044], and [0045].
Applicant argues pg. 11-12:
The USPTO's August 4, 2025, memorandum clarifies that a claim should not be rejected as a "mental process" where it recites operations that cannot practically be performed in the human mind.
Examiner responds:
Examiner has not characterized the claims as directed to a mental process.
Applicant argues pg. 12-13:
Even if the claims were considered, arguendo, to involve an abstract idea at some level of generality, the amended claims include significantly more than any such idea. The inventive concept resides in the specific, non-conventional arrangement of components and processing steps, including: […] This architecture is not routine or conventional and is expressly described in the specification. For example, paragraph [0017] explains that embodiments "convert the waveforms into a dataset that can be queried for characteristic content without requiring direct assessment of the raw biometric data stream," thereby eliminating the need for direct waveform interpretation. This constitutes a technical improvement to medical monitoring systems, enabling continuous, autonomous injury-state tracking that was not previously achievable using conventional EEG monitoring or clinical decision support systems. Courts have consistently recognized that claims directed to specific improvements in medical signal processing and monitoring technology are patenteligible (see, e.g., CardioNet, LLC v. InfoBionic, Inc.). Like the claims upheld in CardioNet, the present claims are directed to how physiological signals are processed and used by a machine, not merely to the collection or analysis of data.
Examiner responds:
The Examiner respectfully disagrees. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry (emphasis added).” Further, MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).” As such, it is only the additional elements identified by the Examiner to not be part of the abstract idea that are analyzed to determine whether they represent well-understood, routine, conventional activities in the field of the invention.
In that regard, MPEP 2106.05(d)(I) indicates that in determining whether the additional elements represent are well-understood, routine, conventional activities, the Examiner should consider whether the additional elements (1) provide an improvement to the technological environment to which the claim is confined, (2) whether the additional elements are mere instructions to apply the judicial exception, or (3) whether the additional elements represent insignificant extra-solution activity. The additional elements of the claims do not provide significantly more based on this inquiry.
Taking these in turn, whether the additional elements of the claim provide an improvement was analyzed/addressed in the 2A2 analysis as no improvement was present. The technological environment to which the claims are confined (a general-purpose computer performing generic computer functions) is recited at a high level of generality and has been found by the courts to be insufficient to provide a practical application (see MPEP 2106.05(d)(II); Alice Corp.). None of the additional elements of the claim were found to represent extra-solution activity and thus no well-understood, routine, conventional analysis is required. As such, when viewed either individually or as an ordered combination, the additional elements do not provide significantly more to the abstract idea and the claims are not subject matter eligible.
Response to Arguments Regarding U.S.C. 102/103 Rejections
Applicant argues pg. 14
Even assuming arguendo that Brunner processes physiological data, Brunner does not disclose the specific processing pipeline and trajectory-centric output structure now required by the amended claims. […] Brunner Does Not Continuously Generate and Update a Recovery Trajectory […] Brunner Does Not Disclose Conversion of Raw EEG Waveforms into a Separate Analytic Reference Data Set […] Brunner Does Not Operate Over an Acute Minutes-to-Hours Temporal Window […]
Examiner responds:
Examiner has applied new art in light of Applicant’s amendments. See 103 rejection above.
Applicant argues pg. 16-17:
Kanada Is Even Further Removed from the Claimed Invention. Kanada is directed to medical examination result visualization and comparison, not continuous recovery trajectory monitoring.
Examiner responds:
Examiner does not use Kanada to teach continuous recovery trajectory monitoring. It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the graph as taught by Brunner with the dividing into regions including a poor, average, and excellent recovery as taught by Kanada. It would be beneficial for the graph to show regions because it helps to predict the future medical condition of the medical examination patient since it is possible to see the tendency of good or poor treatment outcome as taught by Kanada para 172.
Applicant argues pg. 19-20:
In response, as amended, claims 8 and 18 now recite computationally defined recovery regions that are derived from analytic measures and a reference data set, and require that a recovery trajectory be continuously plotted relative to those regions as the analytic measures are updated over time.
Applicant infers in the Office Action that the Examiner's original rationale relied on treating the claimed regions as static graphical overlays. That rationale no longer applies. The amended claims require that the regions themselves are machine-generated outputs of the recovery trajectory engine, not merely visual divisions applied for presentation purposes.
Examiner responds:
Examiner respectfully disagrees. See 103 rejection above. It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the graph as taught by Brunner with the dividing into regions including a poor, average, and excellent recovery as taught by Kanada. It would be beneficial for the graph to show regions because it helps to predict the future medical condition of the medical examination patient since it is possible to see the tendency of good or poor treatment outcome as taught by Kanada para 172.
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 ANDREW KYLE TAPIA whose telephone number is (703)756-1662. The examiner can normally be reached 830 - 530.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687