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 .
Priority
Claims 1-12 and 40-47 are deemed to have an effective filing date of September 22, 2021.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “154” has been used to designate both a head (Figs. 1A-1B) and an undescribed electronic device of a cochlear implant system (Fig. 1D, above the RF transceiver reference numeral).
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “156” has been used to designate both output signals (Fig. 1D between module 158 and stimulator unit 142) and an undescribed electronic device of a cochlear implant system (Fig. 1D, within sensor 160 adjacent a sensor 160). It is unclear how sensor box 160 encloses a sensor 160? Thus, reference numeral “160” has designated two different elements.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “30” has been used to designate both an implantable device in the form of a cochlear implant (Fig. 9) and an implantable device in the form of a retinal prosthesis system (Fig. 11). The Examiner suggest using a prime (i.e., 30’) to distinguish the two reference numerals.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: Paragraph [0059], last line, does not end with punctuation (i.e., .). See last line on page 14 of the originally-filed specification.
Appropriate correction is required.
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 1-7, 12, 40-42, and 44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-7 and 12 claim a method (process) and claims 40-42 and 44 claim non-transitory computer readable storage media (product/manufacture) that stores a process. Therefore, the claims fall within the statutory categories.
Step 2A, Prong 1:
Claims 1-4 and 6-7 and 12 recite obtaining a plurality of clinical data sets and determining a relative priority based on the clinical data sets. Dependent claim 5 adds comparing first clinical data to second clinical data, and determining changes. The limitations, as drafted, describe a process that, under its broadest reasonable interpretation, includes performance of the limitations in the mind except for the recitation of “processor” and “storage media comprising instructions that, when executed by a processor, cause the processor to.” That is, other than reciting that a processor and storage media comprising instructions is performing these tasks, nothing in the claims precludes the steps from practically being performed in the human mind. MPEP 2106.04(a)(2)(III) states that the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper, to be an abstract idea. In this case, aside from the recitation of the “processor” and “storage media comprising instructions” , claims encompass a user observing the clinical data and making a judgement on which clinical data is more important. It is further noted that limitations, “a recipient of an implantable medical device” and “a different medical device recipient”, are associated with clinical data and not involved in performing any of the obtaining and determining steps.
Step 2A, Prong 2:
Claims recite “processor”, “storage media comprising instructions”, and “via a wide area network” to perform abstract idea steps. The specification discloses that wide area network is part of computer network (see [0044]). As such, these components read on a computer implemented system and are recited at a high level of generality, i.e., as a generic processor, performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional limitation does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial except into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The specification in [0044] and [0052] does not provide any indication that the computer is anything other than a generic, off-the-shelf computer component. Court decisions cited in MPEP 2106.05(d)(II) indicate that computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim, as a whole, amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). Accordingly, a conclusion that the generic computer functions merely being used to implement an abstract idea is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
Thus, the above-identified claims are directed to the judicial exception and ineligible since there is no inventive concept in the claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
Claims 40, 42, and 44 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Patent Application Publication No. 2020/0342968 to Avinash et al. (hereinafter referred to as “Avinash”).
Regarding claim 40, Avinash discloses one or more non-transitory computer readable storage media comprising instructions that, when executed by a processor, cause the processor (e.g., paragraph [0008]: a tangible computer-readable storage medium including instructions) to: obtain clinical data associated with a plurality of clinical profiles (e.g., paragraph [0008]: process data captured/obtained over time with respect to one or more patients [clinical profile of one or more patients]), wherein each of the plurality of clinical profiles is associated with a different medical device recipient (e.g., paragraph [0052]: medical devices are associated with a patient and are monitored to gather data regarding patient vitals, patient activity, medical device operation… and paragraph [0008]: data is captured/obtained over time for one or more patients – thus, each of the plurality of clinical profiles can be associated with different medical devices such as ventilator, anesthesia, intravenous infusion drip, etc.); analyze the clinical data to determine a relative priority between the plurality of clinical profiles (e.g., paragraphs [0079]-[0083]: patients and associated data can be post-processed so that data can be summarized, prioritized, and grouped for easy and quick inferencing; patients can be prioritized based on a clinical outcome or based on patient vitals); and display, at a display screen, a prioritized task list in which the plurality of clinical profiles are organized based on the determined relative priority (e.g., paragraphs [0083]-[0084]: using the prioritization, patients and events can be determined from the group of available patients and events for which a clinician is to be notified for immediate attention where the a “Christmas tree” display visualizes multiple criteria/events for multiple patients so that gross outliers can be visually identified/prioritized where the visualization can be generated from the prioritized data and use color/pattern/representation to indicated its relative value/urgency/categorization; and [0103]-[0105]: Fig. 10A is a generated visualization of a multi-patient prioritized clinician tasks where warnings 1032 and 1030 are shown on the display where the multi-patient view can show a prioritized patient 1020 or a prioritized event).
With respect to claim 42, Avinash discloses the one or more non-transitory computer readable storage media of claim 40, wherein analyzing the clinical data comprises analyzing physiological measurements associated with one or more recipients (e.g., paragraphs [0053]: physiological (e.g., vitals, etc.) data 220 are processed to determine an event, which is then prioritized; and [0086]: physiological data can be indicative of a physiological condition and as such, the time series physiological signal data can be processed by clinicians for decision making regarding a patient or medical equipment).
As to claim 44, Avinash discloses the one or more non-transitory computer readable storage media of claim 40, wherein analyzing the clinical data comprises analyzing contextual data associated with one or more recipients (e.g., paragraph [0080]: patients can be prioritized by analyzing data gathered from a cohort to which the patient belongs where the data is associated with one or more recipients/patients).
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.
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.
Claims 1-12, 41, 43, and 45-47 are rejected under 35 U.S.C. 103 as being unpatentable over Avinash in view of US Patent Application Publication No. 2018/0242090 to Sigwanz et al. (hereinafter referred to as “Sigwanz”).
Regarding claim 1, Avinash discloses a method, comprising: obtaining a plurality of clinical data sets, wherein each of the plurality of clinical data sets is associated with a respective one of a plurality of recipients (e.g., paragraphs [0008], [0027]: acquisition of time-series data is obtained from one or more medical machines and/or devices for more than one patient; [0052]: vitals (ECG, blood pressure, respiratory), patient activity, and medical device operation data of a patient is gathered (clinical data set); and [0030]: clinical data sets for a group of patients can be gathered and processed/organized with respect to each other and their associated signals); determining a relative priority of clinical support of the plurality of recipients based, at least in part, on the plurality of clinical data sets (e.g., paragraphs [0079]-[0080]: patients and associated patient data can be post-processed so that a clinician who attends to more than one patient can see a list of patients summarized, prioritized and grouped based on variance of their vitals); and displaying, based on the relative priority of clinical support, a prioritized clinician task list (e.g., paragraphs [0083]: using the prioritization, patients and events can be determined from the group of available patients and events for which a clinician is to be notified for immediate attention and visualization can be generated from the prioritized data; and [0103]-[0105]: Fig. 10A is a generated visualization of a multi-patient prioritized clinician tasks where warnings 1032 and 1030 are shown on the display where the multi-patient view can show a prioritized patient 1020 or a prioritized event). Avinash differs from the disclosed invention in that it does not expressly disclose that the recipients of medical devices have an implantable medical device. However, Sigwanz, in a related art: medical devices, teaches that adjusting a cochlear implant (implantable medical device for hearing) may be a medical device that is monitored (e.g., paragraphs [0007] and [0053]-[0054]). Accordingly, one of ordinary skill in the art would have recognized that a cochlear implant hearing device stores data about the device and adjusts the device in a fitting session based on data obtained from the device and the patient’s perception/input in view of the teachings of Sigwanz (abstract). Consequently, one of ordinary skill in the art would have modified the method of Avinash so that the recipients have an implantable medical device such as a cochlear implant as taught by Sigwanz as its medical device, and because the combination would have yielded a predictable result.
With respect to claim 2, Avinash in view of Sigwanz teaches the method of claim 1, wherein the implantable medical device comprises a hearing device and the plurality of clinical data sets comprise a plurality of fitting data sets (e.g., abstract of Sigwanz: fitting history includes fitting information at least one previous fitting session where each session is considered a clinical data set). Accordingly, one of ordinary skill in the art would have recognized that hearing devices obtain clinical data sets in each fitting session that adjusts/adapts the hearing device for generating optimized sound signals in view of the teachings of Sigwanz (abstract). Consequently, one of ordinary skill in the art would have modified the method of Avinash so that the implantable medical device is a hearing device and the method obtains a plurality of fitting data sets as the clinical data sets as taught by Sigwanz, and because the combination would have yielded a predictable result.
As to claim 3, Avinash in view of Sigwanz teaches the method of claim 1, wherein determining a relative priority of clinical support for the plurality of recipients comprises determining a relative priority of clinical support based on the plurality of clinical data sets and clinician data (e.g., paragraphs [0079]-[0081] of Avinash: patients and associated patient data can be post-processed to generate a summarized, prioritized visualization where the name of the patients is clinician data).
With respect to claim 4, Avinash in view of Sigwanz teaches the method of claim 1, wherein the obtaining comprises receiving, via a wide area network, fitting data from one or more remote computing devices (e.g., paragraph [0062] of Sigwanz: hearing care professional can host an online fitting session through a cloud service so that a user can be fitted remotely). Accordingly, one of ordinary skill in the art would have recognized the benefits of receiving, via a wide area network, fitting data from one or more remote computing devices in view of the teachings of Sigwanz. Consequently, one of ordinary skill in the art would have modified that method of Avinash in view of Sigwanz so that the obtaining comprises obtaining, via a wide area network, fitting data from one of more remote computing device in view of the teachings of Sigwanz that such was a well-known engineering protocol in the fitting of hearing devices art, and because the combination would have yielded a predictable result.
As to claim 5, Avinash in view of Sigwanz teaches the method of claim 1, further comprising: obtaining first clinical data applicable to a first time period and associated with a first recipient (e.g., paragraph [0036] of Sigwanz: obtaining the actual audiogram); obtaining second clinical data applicable to a second time period and associated with the first recipient (e.g., paragraphs of Sigwanz: [0036] obtaining the previous audiogram of a user; and [0067] compare different audiograms over time implies two different time periods); comparing the first clinical data to the second clinical data (e.g., paragraphs [0036] of Sigwanz: comparing the actual audiogram with a previous audiogram and [0067] of Sigwanz); and determining changes in hearing performance based on the comparing (e.g., paragraphs [0036] of Sigwanz: the comparison of audiograms may indicate a difference of hearing abilities of the user; [0083] of Sigwanz: a comparison of the actual audiogram with a previous audiogram may indicated a hearing loss), wherein the determining of the relative priority comprises determining a priority of the first recipient based on the changes in hearing performance (e.g., paragraph [0070] of Sigwanz: user experience value 54 may be stored in the fitting session information so that it is possible to judge the progression of a user experience with time; and the progression of a plurality of users may be compared and/or optimal profiles for sound processing parameters may be derived on these progressions; and (e.g., paragraphs [0079]-[0080] of Avinash: patients and associated patient data can be post-processed so that a clinician who attends to more than one patient can see a list of patients summarized, prioritized and grouped based on variance of their vitals). Accordingly, one of ordinary skill in the art would have recognized the benefits of comparing clinical data over time to determine hearing loss (or gain) in view of the teachings of Sigwanz. Consequently, one of ordinary skill in the art would have modified the method of Avinash in view of Sigwanz to determine changes in hearing performance and prioritizing those patients with the greatest hearing loss in view of the teachings of Sigwanz that a plurality of hearing performance of a plurality of users, and Avinash’s teachings that those with the a reading outside of normal should be prioritized over those with normal clinical data, and because the combination would have yielded a predictable result.
With respect to claim 6, Avinash in view of Sigwanz teaches the method of claim 1, wherein each of the plurality of clinical data sets indicates a physiological measurement of the respective recipient, and the determining of the relative priority is based on the physiological measurement (e.g., paragraph [0086] of Avinash: physiological data can be indicative of a physiological condition and as such, the time series physiological signal data can be processed by clinicians for decision making regarding a patient or medical equipment).
As to claim 7, Avinash in view of Sigwanz teaches the method of claim 1, wherein one or more of the plurality of clinical data sets indicates a request for assistance by a first recipient (e.g., Fig. 10A, 1030 – “TV is too High” for patient in Room 3), wherein the determining of the relative priority is based on the request for assistance (e.g., paragraph [0105] of Avinash: Fig. 10A, single-patient view includes the prioritized event 1042 “TV is too high” for Room 3, but Room 6 is the prioritized patient 1020 – thus, the relative priority of the TV compared to the low respiratory percentage is lower).
As to claim 8, Avinash in view of Sigwanz teaches the method of claim 1, further comprising: obtaining, in response to the displaying, operating parameters for the implantable medical device of a respective one of the plurality of recipients (e.g., paragraph [0016] of Sigwanz: sound processing parameters are determined/obtained based on user experience value); and providing the operating parameters to the implantable medical device of the respective one of the plurality of recipients to configure the operation of the implantable medical device of the respective one of the plurality of recipients (e.g., paragraph [0016] of Sigwanz: applying the determined sound processing parameters in the hearing device so that the hearing device is adapted for generating optimized sound signals based on the applied sound processing parameters). Accordingly, one of ordinary skill in the art would have recognized the benefits of determining operating parameters of a medical device based on clinical values obtained and applying the determined operating parameters of the medical device to the medical device so that the medical device’s performance is optimized in view of the teachings of Sigwanz. Consequently, one of ordinary skill in the art would have modified the method of Avinash in view of Sigwanz to obtain, in response to the prioritized clinical task list, new operating parameters of the medical device and applying the obtained/determined operating parameters to the medical device in view of the teachings of Sigwanz that such a well-known expedient in the hearing medical device art, and because the combination would have yielded a predictable result.
With respect to claim 9, Avinash in view of Sigwanz teaches the method of claim 1, further comprising: receiving input from a user interface, the input defining an adjustment to an implantable medical device of a first recipient (e.g., paragraph [0053] of Avinash: event predictor 280 forms an output 290 including an alert such as “adjust a machine/device’s settings/configuration); and communicating the adjustment to the implantable medical device of the first recipient (e.g., paragraphs [0103]-[0105] and Fig. 10A of Avinash: user interface (Fig. 10A) includes the alert to adjust the settings/configuration of the medical machine/device on the prioritized clinician task list so the clinician knows to make the adjustment).
As to claim 10, Avinash in view of Sigwanz teaches the method of claim 9, wherein communicating the adjustment to the implantable medical device of the first recipient comprises providing an indication of the adjustment to a machine learning model (e.g., paragraph [0042] of Avinash: a deep learning machine that utilizes transfer learning may properly connect data features affirmed by a human expert – settings and/or configuration information can be guided by learned use of settings and/or other configuration information), wherein the determination of the relative priority is based on the machine learning model (e.g., paragraphs [0044]-[0046] of Avinash: Neural network classifications can be confirmed or denied (e.g., by an expert user) and the visualization of data (Fig. 10A) can be driven by artificial intelligence).
With respect to claim 11, Avinash in view of Sigwanz teaches the method of claim 10, further comprising modifying the machine learning model based on the indication (e.g., paragraph [0044]: during operation of the learning machine, neural network classifications can be confirmed or denied to continue to improve neural network behavior – thus an indication of an adjustment to an implantable medical device of Avinash in view of Sigwanz modifies the machine learning model).
As to claim 12, Avinash in view of Sigwanz teaches the method of claim 1, further comprising: obtaining preference information from a user, wherein the determining of the relative priority is further based on the preference information (e.g., paragraph [0077] of Avinash: a recommender system or a recommendation system seeks to predict the “rating” or “preference” a user would give to an item In the healthcare context, such collaborative and/content-based filtering can be used to predict and/or categorize an event and/or classify a patient based on the events).
With respect to claim 41, Avinash discloses the one or more non-transitory computer readable storage media of claim 40, but does not expressly disclose that the plurality of clinical profiles comprises a plurality of fitting profiles, the clinical data comprises fitting data and wherein the instructions that cause the processor to analyze the fitting data comprise instructions that cause the processor to: analyze changes in hearing performance for one or more recipients. However, Sigwanz, in a related art: automatically determined user experience value for hearing device (medical device), teaches that a user profile comprising actual user information from a current session and a fitting history stored in database can be used to adjust the hearing device where the fitting history includes fitting information from at least one previous fitting session where each session is considered a clinical data set, and that a user experience value (analyzed hearing performance) is determined using the user information and the fitting history to determine operation parameters so that the hearing device can be adapted/changed to generate optimized sound signals (e.g., abstract of Sigwanz). Accordingly, one of ordinary skill in the art would have recognized the benefits of using fitting history/profiles and actual user information from a current session to analyze changes to hearing performance and to make changes to the hearing device to optimize sound signals in view of the teachings of Sigwanz. Consequently, one of ordinary skill in the art would have modified the computer readable storage media of Avinash for medical devices that are hearing devices so that the clinical profiles comprise a plurality of fitting profiles and the clinical data include fitting data so that the processor is configured to analyze changes in hearing performance for one or more recipients/patients in view of the teachings of Sigwanz that such were known medical protocol in the medical device art, and because the combination would have yielded a predictable result: determining a priority list for a hearing clinician.
As to claim 43, Avinash discloses the one or more non-transitory computer readable storage media of claim 40, further comprising instructions that cause the processor to: obtain, in response to the displaying, a determination of which patient/event needs immediate attention/action (e.g., paragraph [0083]), but does not expressly disclose causing the processor to obtain operating parameters for an implantable medical device of a medical device recipient; and provide the operating parameters to the implantable medical device of the medical device recipient to configure the operation of the implantable medical device of the medical device recipient. However, Sigwanz, in a related art, teaches that sound processing parameters are determined/obtained based on a user experience value or user’s hearing performance of a medical device that can be cochlear implant, and then are applied to the cochlear implant hearing device of the user to compensate for hearing deficiencies of the user (e.g., abstract and paragraphs [0007] of Sigwanz: hearing device may be a cochlear implant; and [0008]: hearing device can be adapted for processing sounds based on sound processing parameters stored in the hearing device such that hearing deficiencies of a user are compensated). Accordingly, one of ordinary skill in the art would have recognized the benefits of obtaining operating parameters for an implantable medical device based on clinical values obtained and applying the determined operating parameters to the implanted medical device so that the medical device’s performance is optimized in view of the teachings of Sigwanz. Consequently, one of ordinary skill in the art would have modified the computer readable storage media of Avinash to obtain, in response to the prioritized clinical task list, new operating parameters of the implanted medical device and applying the obtained/determined operating parameters to the implanted medical device in view of the teachings of Sigwanz that such a well-known expedient in the implantable medical device art, and because the combination would have yielded a predictable result.
With respect to claim 45, Avinash discloses the one or more non-transitory computer readable storage media of claim 40, further comprising instructions that cause the processor to: receive input from a user interface, the input defining an adjustment to a medical device of a first medical device recipient (e.g., paragraphs [0103]-[0105] and Fig. 10A of Avinash: user interface (Fig. 10A) includes the alert to adjust the settings/configuration of the medical machine/device on the prioritized clinician task list so the clinician knows to make the adjustment); and communicate the adjustment to the first medical device of the first medical device recipient (e.g., paragraphs [0103]-[0105] and Fig. 10A of Avinash: user interface (Fig. 10A) includes the alert to adjust the settings/configuration of the medical machine/device on the prioritized clinician task list so the clinician knows to make the adjustment). Avinash differs from the claimed invention in that its medical device of the medical device recipient is not expressly disclosed as an implantable medical device. However, Sigwanz, in a related art: medical devices, teaches that adjusting a cochlear implant (implantable medical device for hearing) may be a medical device that is monitored (e.g., paragraphs [0007] and [0053]-[0054]). Accordingly, one of ordinary skill in the art would have recognized that a cochlear implant hearing device stores data about the device and adjusts the device in a fitting session based on data obtained from the device and the patient’s perception/input in view of the teachings of Sigwanz (abstract). Consequently, one of ordinary skill in the art would have modified the method of Avinash so that the recipients have an implantable medical device such as a cochlear implant as taught by Sigwanz as its medical device, and because the combination would have yielded a predictable result.
As to claim 46, Avinash in view of Sigwanz teaches the one or more non-transitory computer readable storage media of claim 45, wherein instructions that cause the processor to communicate the adjustment to the implantable medical device of the first medical device recipient comprises instructions that cause the processor to: provide an indication of the adjustment to a machine learning model (e.g., paragraph [0042] of Avinash: a deep learning machine that utilizes transfer learning may properly connect data features affirmed by a human expert – settings and/or configuration information can be guided by learned use of settings and/or other configuration information), wherein the determined relative priority is based on the machine learning model (e.g., paragraphs [0044]-[0046] of Avinash: Neural network classifications can be confirmed or denied (e.g., by an expert user) and the visualization of data (Fig. 10A) can be driven by artificial intelligence).
With respect to claim 47, Avinash in view of Sigwanz teaches the one or more non-transitory computer readable storage media of claim 46, further comprising instructions that cause the processor to: modify the machine learning model based on the indication (e.g., paragraph [0044]: during operation of the learning machine, neural network classifications can be confirmed or denied to continue to improve neural network behavior – thus an indication of an adjustment to an implantable medical device of Avinash in view of Sigwanz modifies the machine learning model).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication No. 2022/0233109 to Ajemba et al. is directed to methods, systems and devices for improving continuous monitoring in the medical arts where machine learning modules are adjusted based on the signature of input features in the sensor data. That is, Ajemba teaches adjusting the plurality of machine learning models to feature or prioritize the one or more machine learning models that are associated with the identified signature of input features (e.g., paragraph [0061]).
US Patent Application Publication No. 2021/0265937 to Oliver et al. is directed to habilitation and/or rehabilitation methods for a cochlear implant (implantable medical device) and teaches that there is utilitarian values in fitting a cochlear implant to a recipient using machine learning.
US Patent Application Publication No. 2016/0239619 to Abou-Hawili et al. is directed to generating a role-based user interface that includes a patient information database which stores patient data relating to a plurality of patients being treated by one or more caregivers (e.g., abstract), and teaches prioritizing the elements of the user interface/display based on the received patient data (e.g., paragraph [0045]). In addition, Abou-Hawili teaches that if a patient event, such as a patient requesting assistance, occurs, the patient can be re-routed to another clinician if the primary caregiver is busy, unavailable, or not in the vicinity and another clinician is available (e.g., paragraph [0006]).
US Patent Application Publication No. 2004/0122709 to Avinash et al. is directed to medical procedure prioritization using patient-related information and other information in an integrated knowledge base to arrive at recommendations for patient care (e.g., abstract) where patient data can be collected by input devices that worn by, implanted in, or directly implemented by the patient (e.g., paragraph [0379]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CATHERINE M VOORHEES whose telephone number is (571)270-3846. The examiner can normally be reached Monday-Friday 8:30 AM to 4:30 PM.
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/CATHERINE M VOORHEES/Primary Examiner, Art Unit 3792