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 .
Status of Claims
This action is in reply to Applicant’s communication filed on May 4, 2026.
Claims 1, 13 and 18 have been amended and are hereby entered.
Claims 1, 3-5, 8-9 and 11-21 are currently pending and have been examined.
Priority
Acknowledgment is made of Applicant’s claim for priority under 35 U.S.C. § 371 of International Application No. PCT/US2020/053059, filed September 28, 2020, which claims the benefit of U.S. Provisional Application No. 62/906,939, filed September 27, 2019.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 and 18-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the refined therapeutic intervention" on page 3, lines 14-15. There is insufficient antecedent basis for this limitation in the claim. The claim does not recite that the therapeutic intervention becomes "a refined therapeutic intervention" after calibration. The claim recites that the therapeutic intervention is calibrated "to refine subsequent administration of the therapeutic intervention". Therefore, the Examiner interprets “the refined therapeutic intervention” to be the calibrated therapeutic intervention as previously recited in claim 1. However, appropriate correction is required.
Claim 18 recites substantially similar limitations to those in claim 1 and is rejected on the same basis as stated above. Claims 2-12 and 19-21 are further rejected as being dependent on a rejected base claim.
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, 3-5, 8-9 and 11-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
Claims 1 and 13 are each directed to a method and Claim 18 is directed to a system. Therefore, all claims fall into one of the four statutory categories. (Step 1: Yes, the claims fall into one of the four statutory categories).
Step 2A analysis - Prong one:
The substantially similar independent method and system claims, taking claim 1 as exemplary, recite the following limitations: receiving and storing in a memory an initial speech sample of a human patient according to a selected model speech sample; administering, as part of a therapeutic intervention, an initial dose of pain-relieving drugs to the human patient; after administering the initial dose, receiving and storing in the memory a first response speech sample of the human patient; administering, as part of the therapeutic intervention, a second dose of pain-relieving drugs to the human patient; after administering the second dose, receiving and storing in the memory a second response speech sample of the human patient according to the selected model speech sample; analyzing, with a processing device, the initial speech sample, the first response speech sample, and the second response speech sample to produce an intervention-response relationship for the human patient, the intervention-response relationship produced by extracting objective speech-response features from the initial speech sample, first response speech sample, and second response speech sample and mapping a speech-pain gradient according to the extracted objective speech-response features without reference to previous data and based on the selected model speech sample; receiving additional objective data for the human patient, the additional objective data comprising at least one of a video sample of the human patient, a facial scan of the human patient, eye movement of the human patient, a thermal sample of the human patient, a writing sample of the human patient, a heart rate of the human patient, or respiration data of the human patient, wherein the intervention-response relationship is further produced by analyzing the additional objective data for objective indicia of a response of the human patient to the therapeutic intervention, and wherein the first response speech sample and the second response speech sample are received after an amount of time appropriate for the therapeutic intervention to take effect; calibrating the therapeutic intervention according to the intervention-response relationship for the human patient to refine subsequent administration of the therapeutic intervention; monitoring a response of the human patient to the refined therapeutic intervention by receiving and analyzing one or more additional response speech samples at intervals for objective indicia of the response of the human patient to the therapeutic intervention; recalibrating the refined therapeutic intervention responsive to the analyzing the one or more additional response speech samples; and administering the recalibrated refined therapeutic intervention according to the intervention-response relationship.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations are interpreted as a series of steps describing managing personal behavior or relationships or interactions between people including following rules or instructions and are thus are grouped as certain methods of organizing human activity which are abstract ideas. The un-bolded limitations, as drafted, recite 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 implementing the method by a processing device (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the identified additional elements above, this claim encompasses a person listening to a patient speak before and after administering a dose of drugs, deciding if the patient is in more, less or the same amount of pain than before the drug administration, and determining a next dose 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 2A – Prong 1: Yes, the claims are abstract).
Step 2A analysis - Prong two:
Claims 1, 13 and 18 recite additional elements beyond the abstract idea. Claims 1, 13 and 18 recite a processing device. Claims 1 and 13 further recite storing data in a memory. Claim 13 further recites calibrating an automated therapeutic device. Claim 18 further recites an intervention administering device.
This judicial exception is not integrated into a practical application. In particular, the claims recite a processing device, storing data in a memory, calibrating an automated therapeutic device, and an intervention administering device which are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the processing device represents one or more general-purpose processing devices that executes processing logic in instructions for performing the operations and steps discussed (see Applicant’s spec. para 48). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore, Claims 1, 13 and 18 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application).
Step 2B analysis:
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 elements of using a processing device, a memory, an automated therapeutic device, and an intervention administering device to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions appear to be implemented using conventional computer systemization. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). See MPEP2106.05(I)(A).
Applicant describes embodiments of the disclosure at a very high level to include the use of an intervention administering device/computer system which includes the use of a wide variety of machine learning models/techniques, processing devices, memories, data storage devices, computer-readable medium, device displays, input/output devices, etc. (see Applicant’s Spec. paras 33, 46-52).
The claims do 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 elements of a processing device, a memory, an automated therapeutic device and an intervention administering device to perform all of the steps discussed above amount to no more than mere instructions to apply the exceptions using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2B: No, the claims do not provide significantly more).
Dependent Claims 3-5, 8-9, 11-12, 14-17 and 19-21 further define the abstract idea that is presented in independent Claims 1, 13 and 18 and are further grouped as certain methods of organizing human activity and are abstract for the same reasons and basis as presented above. Further, Claims 9, 15 and 19-21 recite additional elements beyond the abstract idea. Claims 9 and 15 recite a patient-controlled analgesia (PCA) device. Claim 19 recites an audio input device. Claims 20-21 recite an output device. These additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. For example, as noted above, the Applicant’s specification indicates the use of known drug administration devices and input/output devices. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not recite additional elements that integrate the judicial exception into a practical application when considered both individually and as an ordered combination. Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1, 3-5, 8-9 and 11-21 are rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
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.
Claims 1, 3-5, 9 and 11-21 are rejected under 35 U.S.C. 103 as being unpatentable over Rosenbek et al. (US 20170119302) in view of Srivastava et al. (US 20180193652).
Regarding Claim 1, Rosenbek discloses the following limitations:
A method for assessing a response of a human patient to a therapeutic intervention, the method comprising:
receiving and storing in a memory an initial speech sample of a human patient according to a selected model speech sample; (Rosenbek discloses collecting (receiving) speech sample from subjects (an initial speech sample of a human patient) for determining baseline acoustic measures and storing the baseline acoustic measures in a memory (storing in a memory - para 48). The screening app on the phone may prompt the user (a selected model speech sample) to record a sample of their speech and/or request a sample already stored in the phone's memory. Articulation measures from read speech (a selected model speech sample) or spontaneous speech may be used for identification of a disease stage. – paras 37, 48, 51, 110-111; FIG. 1 & 2A)
administering, as part of a therapeutic intervention, an initial dose of…drugs to the human patient; (Rosenbek discloses methods that can be used to monitor a change in a neurological or other disease in a subject (the human patient), and/or to monitor effects of specific drugs, surgical treatments or rehabilitative efforts (administering, as part of a therapeutic intervention, an initial dose of drugs). – paras 32, 94-95)
after administering the initial dose, receiving and storing in the memory a first response speech sample of the human patient; (Rosenbek discloses receiving speech samples (a first response speech sample). The identification device is an analytical tool that includes an interface, a processor and a memory (storing in the memory) and receives input via the interface, one or more speech samples from a subject (S210 of FIG. 2B). The subject systems can be used to monitor therapy. In one embodiment a subject's adherence and performance on a particular treatment/rehabilitation program can be monitored via continued use of the subject systems. By monitoring adherence to a treatment, this implies the speech samples may occur after the treatment/therapy has occurs (after administering the initial dose), in order to determine the patients state. – paras 32, 48, 94; FIG. 2B)
administering, as part of the therapeutic intervention, a second dose of…drugs to the human patient; (Rosenbek discloses methods that can be used to monitor a change in a neurological or other disease in a subject (the human patient), and/or to monitor effects of specific drugs, surgical treatments or rehabilitative efforts. In addition, monitoring speech/language changes over periods of time can help determine whether or not a particular treatment (drugs/rehabilitation exercise) (administering, as part of the therapeutic intervention, a second dose of drugs) is slowing down the progression of the disease. – paras 32, 94-95) (Examiner interprets the monitoring of drug treatment over periods of time as disclosed by Rosenbek to mean that the drug is administered in measured doses over periods of time)
after administering the second dose, receiving and storing in the memory a second response speech sample of the human patient according to the selected model speech sample; (Rosenbek discloses receiving one or more speech samples from a subject and that the user may produce speech samples that correspond to a scheduled time, day, week, or month that repeats at a predetermined frequency (receiving and storing in the memory a second response speech sample of the human patient). The subject systems can be used to monitor therapy. In one embodiment a subject's adherence and performance on a particular treatment/rehabilitation program can be monitored via continued use of the subject systems. By monitoring adherence to a treatment, this implies the speech samples may occur after the treatment/therapy has occurs (after administering the second dose), in order to determine the patients state. The screening app on the phone may prompt the user to record a sample of their speech (according to the selected model speech sample). – paras 48, 51, 54, 94, 110-111; FIG. 2B)
analyzing, with a processing device, the initial speech sample, the first response speech sample, and the second response speech sample to produce an intervention-response relationship for the human patient, (Rosenbek discloses that the identification device (a processing device) compares (analyzing) the baseline acoustic measures (the initial speech sample) with the speech samples (the first response speech sample, and the second response speech sample) (S230 of FIG. 2B). The processor can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (produce an intervention-response relationship for the human patient) (S240 of FIG. 2B). – paras 32, 48, 94; FIG. 2A-2B)
the intervention-response relationship produced by extracting objective speech-response features from the initial speech sample, first response speech sample, and second response speech sample…according to the extracted objective speech-response features without reference to previous data and based on the selected model speech sample; (Rosenbek discloses determining a health state of the subject based upon the results of comparing the identified acoustic measures (extracting objective speech-response features) from the baseline acoustics (the initial speech sample) with the speech samples (first response speech sample, and second response speech sample) or by tracking the rate of change (without reference to previous data) in specific baseline acoustic measures. The screening app on the phone may prompt the user to record a sample of their speech and may be read speech (based on the selected model speech sample). – paras 48, 51, 110-111; FIG. 2B) [Examiner interprets the limitation “without reference to previous data”, using broadest reasonable interpretation, to mean that current/most recent data is analyzed in order to produce the intervention-response relationship at each moment in time; for example, the slope, rate of change or gradient of the data at each moment is produced to create the intervention-response relationship (see also Applicant’s specification paras 9, 30 33).]
receiving additional objective data for the human patient, the additional objective data comprising at least one of a video sample of the human patient, a facial scan of the human patient, eye movement of the human patient, a thermal sample of the human patient, a writing sample of the human patient, a heart rate of the human patient, or respiration data of the human patient, (Rosenbek discloses that biomarkers for respiratory diseases may include cough. The analysis for such disease or medical conditions includes evaluating the frequency, intensity, or other characteristics of cough (receiving additional objective data comprising respiration data of the human patient) during a conversation. – paras 19, 43, 45, 76-77, 80, 82, 84)
wherein the intervention-response relationship is further produced by analyzing the additional objective data for objective indicia of a response of the human patient to the therapeutic intervention, (Rosenbek discloses that the processor can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (wherein the intervention-response relationship is further produced). The exact nature and duration of the cough can vary from one disease to another, but the intensity (strength), frequency (number of occurrences) and the duration for which a cough lasts (time since onset) are variables he analysis can include evaluation of the frequency (i.e. the number of occurrences), the intensity (i.e. the strength) or other characteristics of cough during a conversation (analyzing the additional objective data for objective indicia). The method provided can be used to monitor effects of specific drugs, surgical treatments or rehabilitative efforts (a response of the human patient to the therapeutic intervention). – paras 19-20, 32, 43, 48, 76-77)
and wherein the first response speech sample and the second response speech sample are received after an amount of time appropriate for the therapeutic intervention to take effect; (Rosenbek discloses the speech and language of a speaker may be monitored (the first response speech sample and the second response speech sample are received) over different periods/intervals, ranging from a few minutes to several days, weeks, months, or even years (received after an amount of time appropriate for the therapeutic intervention to take effect). During this monitoring, candidate biomarkers can be tracked to determine their presence/absence or the degree to which these change over time. – paras 30, 32, 41, 59)
calibrating the therapeutic intervention according to the intervention-response relationship for the human patient to refine subsequent administration of the therapeutic intervention; (Rosenbek discloses methods that can be used to monitor effects of specific drugs, surgical treatments or rehabilitative efforts. The processor 202 can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (according to the intervention-response relationship) (S240 of FIG. 2B). The model that outputs the diagnosis and treatment plan, which can include the use of drugs or rehabilitation exercises, after the health state S240 is determined, is iteratively trained (calibrating the therapeutic intervention – para 133). Articulation characteristics are measured using the standard deviation sum of cepstral coefficients and delta coefficients extracted from speech. These parameters may be used to monitor disease progression, efficacy of treatment, and/or need for changes in treatment (refine subsequent administration of the therapeutic intervention). – paras 32, 48, 92, 94-95, 108, 133; FIG. 2B) (Examiner notes that this iterative training, as disclosed by Rosenbek, is in effect calibrating the treatment plan to be provided to the patient based upon the patients’ health state from the speech analysis)
monitoring a response of the human patient to the refined therapeutic intervention by receiving and analyzing one or more additional response speech samples at intervals for objective indicia of the response of the human patient to the therapeutic intervention; (Rosenbek discloses monitoring disease progression, efficacy of treatment, and/or need for changes in treatment (monitoring a response of the human patient to the refined therapeutic intervention). Further analysis of the speech samples (receiving and analyzing one or more additional response speech samples) can be provided based on potential changes in the speech samples taken at the specified intervals (at intervals). Using acoustic measures (objective indicia) as a biomarker involves evaluating changes in various aspects (or subsystems of speech) over time (of the response of the human patient to the therapeutic intervention). – paras 34, 54, 108)
recalibrating the refined therapeutic intervention responsive to the analyzing the one or more additional response speech samples; (Rosenbek discloses analyzing speech samples collected over different periods/intervals (days, months, years, etc.) (analyzing the one or more additional response speech samples) in order to monitor disease progression, efficacy of treatment, and/or need for changes in treatment (recalibrating the refined therapeutic intervention). – paras 30, 34, 42, 54, 108)
and administering the recalibrated refined therapeutic intervention according to the intervention-response relationship. (Rosenbek discloses that the methods provided herein can be used to monitor effects of specific drugs (administering the recalibrated refined therapeutic intervention). Medical practitioners having access to the prescribed treatment program (the recalibrated refined therapeutic intervention) may follow the prescribed treatment programs or augment them based on the individual needs of the subject. – paras 32, 42, 94-95, 108) (Examiner notes that in monitoring effects of drugs over time, the drugs must be administered over time)
Rosenbek does not disclose the following limitations met by Srivastava:
pain-relieving drugs (Srivastava teaches an implantable neuromodulator device (IND) which may be configured as a therapeutic device for treating or alleviating the pain. In some examples, the IND 112 may include a drug delivery system such as a drug infusion pump that can deliver pain medication (the pain reliever) to the patient, such as morphine sulfate or ziconotide, among others. – para 47)
and mapping a speech-pain gradient according to the extracted objective speech-response features…; (Srivastava teaches that the closed-loop control of the electrostimulation may be further based on the type of the pain, such as chronic or acute pain. In an example, the pain analyzer circuit 220 may trend the signal metric over time (mapping a speech-pain gradient) to compute an indication of abruptness of change of the signal metrics, such as a rate of change of vocal expression metrics (according to the extracted objective speech-response features). The pain episode may be characterized as acute pain if the signal metric changes abruptly (e.g., the rate of change of the signal metric exceeding a threshold), or as chronic pain if the signal metric changes gradually (e.g., the rate of change of the signal metric falling below a threshold). The controller circuit 312 may control the therapy unit 250 to deliver, withhold, or otherwise modify the pain therapy in accordance with the pain type. Speech motor slowness such as slower syllable pronunciation, or an increased variability of accuracy in syllable pronunciation, may indicate intensity or duration of pain (mapping a speech-pain gradient). – paras 82-83, 100) (Examiner interprets the “gradient”, using broadest reasonable interpretation, to be the rate of change (slope) at a given time.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate pain medication and generating a trend of signal metrics over time to indicate intensity or duration of pain to further adjust the pain therapy as taught by Srivastava in order to provide an efficient way of managing appropriate pain therapy (see Srivastava para 7).
Regarding Claim 3, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations: -
monitoring a response of the human patient to the calibrated initial dose of…drugs by receiving and analyzing one or more additional response speech samples for objective indicia…; (Rosenbek discloses that the monitoring of effects of specific drugs, surgical treatments or rehabilitative efforts (monitoring a response of the human patient to the calibrated initial dose of…drugs) includes receiving and analyzing one or more speech samples (receiving and analyzing one or more additional response speech samples) from a subject (S210 of FIG. 2B). The speech samples can be collected at a predetermined frequency or scheduled time; they may be taken at specified intervals. The trimmed WAV files were analyzed using the ITU-T P.56 standard, Method B (“Objective measurement of active speech level,” ITU-T Recommendation P.56, 2011), to measure the active speech level (ASL) which measured the signal level in active (non-silence) regions of speech (analyzing one or more additional response speech samples for objective indicia). – paras 32, 48, 54, 116; FIG. 2B)
and recalibrating the personalized dose of…drugs responsive to the analyzing the one or more additional response speech samples. (Rosenbek discloses determining the health state of the subject by comparing (analyzing) the acoustic measures from the one or more speech samples (the one or more additional response speech samples) with the baseline acoustic measures. Results derived using overtrained models may be accurate for the particular dataset employed in the experiment but grossly misleading for the greater population of talkers in general. Generalization may be tested by splitting a dataset into test data and training data (which may be further split to include a validation set for iterative training or feature selection). The model that outputs the diagnosis and treatment plan, which can include the use of specific drugs, after the health state S240 is determined, is iteratively trained. This iterative training is in effect calibrating the therapeutic intervention to be provided to the patient based upon the health state from the speech analysis. The recalibration occurs when new speech samples are received (recalibrating the personalized dose of drugs). – paras 32, 42, 48, 54, 133; FIG. 2B)
Rosenbek does not disclose the following limitations met by Srivastava:
analyzing one or more additional response speech samples for objective indicia of pain; (Srivastava teaches analyzing the recorded speech signal to generate a plurality of speech features to generate a pain score (objective indicia of pain) to provide objective pain assessment. FIG. 5 shows that the process of analyzing vocal expression may be repeated (analyzing one or more additional response speech samples). – paras 7, 15, 42, 95; FIG. 5)
pain-relieving drugs (Srivastava teaches an implantable neuromodulator device (IND) which may be configured as a therapeutic device for treating or alleviating the pain. In some examples, the IND 112 may include a drug delivery system such as a drug infusion pump that can deliver pain medication (the pain reliever) to the patient, such as morphine sulfate or ziconotide, among others. – para 47)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate the systems and methods of objective pain assessment and the use of pain medication as taught by Srivastava in order to improve pain therapy efficacy (see Srivastava para 42).
Regarding Claim 4, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
further comprising: administering the recalibrated personalized dose of pain-relieving drugs to the human patient. (Rosenbek discloses methods that may be used to monitor effects of specific drugs (administering the recalibrated personalized dose of pain-relieving drugs), surgical treatments or rehabilitative efforts. – paras 32, 94-95)
Regarding Claim 5, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
wherein administering the personalized dose of pain-relieving drugs comprises setting at least one of an initial loading dose, a demand dose, a lockout interval, an infusion rate, or an administration time limit for the pain-relieving drugs. (Srivastava teaches a drug delivery system such as a drug infusion pump that can deliver pain medication to the patient. The process includes computer-implemented generation of recommendations or an alert to the system user regarding pain medication (e.g., medication dosage and time for taking a dose), electrostimulation therapy, or other pain management regimens. In some examples, the IND 112 may include a drug delivery system (a demand dose) such as a drug infusion pump (an infusion rate) that can deliver pain medication (the pain-relieving drugs.) to the patient, such as morphine sulfate or ziconotide, among others. – paras 47, 56, 74, 85-87, 106; FIG. 2) (Examiner interprets providing a dosage as being a demand dose, and using an infusion pump would include an infusion rate and is therefore being interpreted as an infusion rate)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified iteratively training a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate adaptively adjusting drug dosage as taught by Srivastava in order to provide an efficient way of managing appropriate pain therapy (see Srivastava para 7).
Regarding Claim 9, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
wherein the pain reliever is administered by a patient-controlled analgesia (PCA) device. (Srivastava teaches a drug delivery system, such as an intrathecal drug delivery pump that may be surgically placed under the skin, which may be programmed to inject medication or biologics through a catheter to the area around the spinal cord. Other examples of drug delivery system may include a computerized patient-controlled analgesia pump (a patient-controlled analgesia (PCA)) that may deliver the prescribed pain medication to the patient such as via an intravenous line. – para 74)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified monitoring therapy efficacy as disclosed by Rosenbek to incorporate delivery of pain therapy via a patient-controlled analgesia pump as taught by Srivastava in order to provide an efficient way of managing appropriate pain therapy, thereby providing the appropriate dosage to the patient based upon the pain levels determined by the speech (see Srivastava para 7).
Regarding Claim 11, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
further comprising receiving additional objective data for the human patient; wherein the intervention-response relationship is further produced by analyzing the additional objective data for objective indicia…in the human patient. (Rosenbek discloses a screening system that can monitor for a respiratory disease. In one embodiment, a similar system as described with respect to FIG. 5 can be used, where the screening for respiratory diseases can be accomplished by using cough as a biomarker. For the respiratory diseases, cough can be found (receiving additional objective data) and analyzed (analyzing the additional objective data). This may be accomplished via an automatic speech recognition-based analysis. In further embodiments, the acoustic analysis can be performed to quantify metrics (by analyzing the additional objective data for objective indicia in the human patient) including, but not limited to fundamental frequency characteristics, intensity, articulatory characteristics, speech/voice quality, prosodic characteristics, and speaking rate. Once the information from the speech/cough analysis is obtained, comparators 512 can be used to reach a diagnostic decision. The decision provides information indicative of a likelihood and type of disease. A base line of cough data for respiratory-type infections can be created by obtaining cough samples from a variety of sources. Additional respiration data related to a cough can be analyzed along with the speech data can be used determine the health state with an output that is the treatment response (the intervention-response relationship is further produced by analyzing the additional objective data). – paras 80, 82, 84, 94; FIG. 5)
Rosenbek does not disclose the following limitations met by Srivastava:
analyzing…for objective indicia of pain in the human patient. (Srivastava teaches analyzing the recorded speech signal to generate a plurality of speech features to generate a pain score (objective indicia of pain in the human patient) to provide objective pain assessment. FIG. 5 shows that the process of analyzing (analyzing) vocal expression may be repeated to manage pain of a patient. – paras 7, 15, 42, 95; FIG. 5)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate the systems and methods of objective pain assessment as taught by Srivastava in order to improve pain therapy efficacy (see Srivastava para 42).
Regarding Claim 12, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
wherein the additional objective data comprises at least one of: a video sample of the human patient; a facial scan of the human patient; eye movement of the human patient; a thermal sample of the human patient; a writing sample of the human patient; a heart rate of the human patient; or respiration data of the human patient. (Rosenbek discloses a screening system that can monitor for a respiratory disease. In one embodiment, a similar system as described with respect to FIG. 5 can be used, where the screening for respiratory diseases can be accomplished by using cough as a biomarker (respiration data). For the respiratory diseases, cough can be found (receiving additional objective data) and analyzed (analyzing the additional objective data). This may be accomplished via an automatic speech recognition-based analysis. In further embodiments, the acoustic analysis can be performed to quantify metrics including, but not limited to fundamental frequency characteristics, intensity, articulatory characteristics, speech/voice quality, prosodic characteristics, and speaking rate. Once the information from the speech/cough analysis is obtained, comparators 512 can be used to reach a diagnostic decision. The decision provides information indicative of a likelihood and type of disease. A base line of cough data for respiratory-type infections can be created by obtaining cough samples from a variety of sources. – paras 80, 82, 84, 94; FIG. 5)
Regarding Claim 13, Rosenbek discloses the following limitations:
A method for calibrating an automated therapeutic device, the method comprising:
receiving and storing in a memory an initial sample of objective data from a human patient; (Rosenbek discloses collecting (receiving) speech sample from subjects (an initial speech sample of objective data from a human patient) for determining baseline acoustic measures and storing the baseline acoustic measures in a memory (storing in a memory - para 48) – paras 37, 48; FIG. 1 & 2A)
receiving and storing in the memory a first response sample of objective data after administering, as part of a therapeutic intervention, a dose of…drugs; (Rosenbek discloses receiving speech samples (a first response sample of objective data). The identification device is an analytical tool that includes an interface, a processor and a memory (storing in the memory) and receives input via the interface, one or more speech samples from a subject (S210 of FIG. 2B). The subject systems can be used to monitor therapy. In one embodiment a subject's adherence and performance on a particular treatment/rehabilitation program can be monitored via continued use of the subject systems. By monitoring adherence to a treatment, this implies the speech samples may occur after the treatment/therapy has occurs (after administering, as part of a therapeutic intervention, a dose of drugs), in order to determine the patients state. – paras 32, 48, 94; FIG. 2B)
analyzing, with a processing device, the initial sample and the first response sample to produce an intervention-response relationship according to objective indicia of a response to the human patient to the therapeutic intervention in the initial sample and first response sample, (Rosenbek discloses that the identification device (a processing device) compares (analyze) the baseline acoustic measures (the initial speech sample) with the speech samples (first response speech sample) (S230 of FIG. 2B). The processor can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (produce an intervention-response relationship for the human patient according to objective indicia of a response to the human patient to the therapeutic intervention in the initial speech sample and first response speech sample) (S240 of FIG. 2B). – paras 32, 48, 94; FIG. 2A-2B)
wherein the intervention-response relationship is produced by extracting objective response features from the initial sample and first response sample and mapping a response gradient according to the extracted objective response features without reference to previous data; (Rosenbek discloses determining a health state of the subject based upon the results of comparing the identified acoustic measures (extracting objective response features) from the baseline acoustics (the initial sample) with the speech samples (first response sample) or by tracking the rate of change in specific baseline acoustic measures (and mapping a response gradient without reference to previous data). – para 48; FIG. 2B) [Examiner interprets the limitation “without reference to previous data”, using broadest reasonable interpretation, to mean that current/most recent data is analyzed in order to produce the intervention-response relationship at each moment in time; for example, the slope, rate of change or gradient of the data at each moment is produced to produce the intervention-response relationship (see also Applicant’s specification paras 9, 30 33).]
calibrating the automated therapeutic device according to the intervention-response relationship to refine subsequent administration of the therapeutic intervention; (Rosenbek discloses methods that can be used to monitor effects of specific drugs, surgical treatments or rehabilitative efforts. The processor 202 can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (the intervention-response relationship.) (S240 of FIG. 2B). The processor 202 can then output a diagnosis. Results derived using overtrained models may be accurate for the particular dataset employed in the experiment but grossly misleading for the greater population of talkers in general. Generalization may be tested by splitting a dataset into test data and training data (which may be further split to include a validation set for iterative training or feature selection). The model that outputs the diagnosis and treatment plan, which can include the use of specific drugs, after the health state S240 is determined, is iteratively trained. This iterative training is in effect calibrating the therapeutic intervention to be provided to the patient based upon the health state from the speech analysis (calibrating the automated therapeutic device) Articulation characteristics are measured using the standard deviation sum of cepstral coefficients and delta coefficients extracted from speech. These parameters may be used to monitor disease progression, efficacy of treatment, and/or need for changes in treatment (refine subsequent administration of the therapeutic intervention) – para 133). – paras 32, 48, 92, 94-95, 108, 133; FIG. 2B)
and administering the therapeutic intervention according to the intervention-response relationship. (Rosenbek discloses that the methods provided herein can be used to monitor effects of specific drugs (administering the therapeutic intervention). – paras 32, 94-95, 108) (Examiner notes that in monitoring effects of drugs over time, the drugs must be administered over time)
Rosenbek does not disclose the following limitations met by Srivastava:
pain-relieving drugs (Srivastava teaches an implantable neuromodulator device (IND) which may be configured as a therapeutic device for treating or alleviating the pain. In some examples, the IND 112 may include a drug delivery system such as a drug infusion pump that can deliver pain medication (pain-relieving drugs) to the patient, such as morphine sulfate or ziconotide, among others. – para 47)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate the use of pain medication as taught by Srivastava in order to improve pain therapy efficacy (see Srivastava para 42).
Regarding Claim 14, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
calibrating the automated therapeutic device comprises setting at least one of an initial loading dose, a demand dose, a lockout interval, an infusion rate, or an administration time limit for the pain-relieving drug. (Srivastava teaches a drug delivery system such as a drug infusion pump that can deliver pain medication to the patient. The process includes computer-implemented generation of recommendations or an alert to the system user regarding pain medication (e.g., medication dosage and time for taking a dose), electrostimulation therapy, or other pain management regimens. In some examples, the IND 112 may include a drug delivery system (a demand dose) such as a drug infusion pump (an infusion rate) that can deliver pain medication (the pain-relieving drugs.) to the patient, such as morphine sulfate or ziconotide, among others. – paras 47, 56, 74, 85-87, 106; FIG. 2) (Examiner interprets providing a dosage as being a demand dose, and using an infusion pump would include an infusion rate and is therefore being interpreted as an infusion rate)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified iteratively training a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate adaptively adjusting drug dosage as taught by Srivastava in order to provide an efficient way of managing appropriate pain therapy (see Srivastava para 7).
Regarding Claim 15, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
wherein the automated therapeutic device comprises a patient-controlled analgesia (PCA) device; (Srivastava teaches a drug delivery system, such as an intrathecal drug delivery pump that may be surgically placed under the skin, which may be programmed to inject medication or biologics through a catheter to the area around the spinal cord. Other examples of drug delivery system may include a computerized patient-controlled analgesia pump (a patient-controlled analgesia (PCA)) that may deliver the prescribed pain medication to the patient such as via an intravenous line. – para 74)
and analyzing the initial sample and the first response sample comprises analyzing an objective pain level of the human patient in response to the PCA device administering a pain reliever. (Srivastava teaches a pain analyzer circuit may generate a pain score using signal metrics of facial or vocal expression extracted from the sensed information. The pain score may be output to a user or a process. The system may additionally include a neurostimulator that can adaptively control the delivery of pain therapy (the PCA device administering a pain reliever) by automatically adjusting stimulation parameters based on the pain score. The pain analyzer circuit includes a speech processor which may be configured to analyze the recorded voice or speech, and generate a vocal expression metric from the recorded voice or speech. Patient with chronic pain may present with impaired cognitive function, emotional and psychological distress, depression, and motor control impairment. Chronic pain can directly or indirectly result in abnormality in speech motor control. The speech processor 223 may process the recorded voice or speech by performing one or more of speech segmentation, transformation, feature extraction, and pattern recognition. Speech motor slowness such as slower syllable pronunciation, or an increased variability of accuracy in syllable pronunciation, may indicate intensity or duration of pain (analyzing an objective pain level of the human patient in response). – abstract; paras 7, 62, 74, 100; FIGs. 2, 3)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified monitoring therapy efficacy as disclosed by Rosenbek to incorporate delivery of pain therapy via a patient-controlled analgesia pump as taught by Srivastava in order to provide an efficient way of managing appropriate pain therapy, thereby providing the appropriate dosage to the patient based upon the pain levels determined by the speech (see Srivastava para 7).
Regarding Claim 16, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
further comprising: after calibrating the automated therapeutic device, receiving a second response sample of objective data; (Rosenbek discloses monitoring effects of specific drugs, surgical treatments or rehabilitative efforts that includes receiving and analyzing one or more speech samples from a subject (receiving a second response sample of objective data) (S210 of FIG. 2B). The processor 202 can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (the intervention-response relationship) (S240 of FIG. 2B). The processor 202 can then output a diagnosis. The model that outputs the diagnosis and treatment plan, which can include the use of specific drugs, after the health state S240 is determined, is iteratively trained. This iterative training is in effect calibrating the therapeutic intervention to be provided to the patient based upon the health state from the speech analysis (after calibrating the automated therapeutic device). – paras 32, 48, 94, 133; FIG. 2B)
analyzing the second response speech sample to produce an updated intervention-response relationship according to objective indicia…in the second response sample; (Rosenbek discloses that the monitoring of effects of specific drugs, surgical treatments or rehabilitative efforts includes receiving and analyzing one or more speech samples (analyzing the second response speech sample) from a subject (S210 of FIG. 2B). The speech samples can be collected at a predetermined frequency or scheduled time; they may be taken at specified intervals. The trimmed WAV files were analyzed using the ITU-T P.56 standard, Method B (“Objective measurement of active speech level,” ITU-T Recommendation P.56, 2011), to measure the active speech level (ASL) which measured the signal level in active (non-silence) regions of speech (according to objective indicia). The processor 202 can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (to produce an updated intervention-response relationship) (S240 of FIG. 2B). – paras 32, 48, 54, 116; FIG. 2B)
and recalibrating the automated therapeutic device according to the updated intervention response relationship to produce a recalibrated personalized dose of pain-relieving drugs. (Rosenbek discloses monitoring effects of specific drugs, surgical treatments or rehabilitative efforts. The processor 202 can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (the intervention-response relationship.) (S240 of FIG. 2B). The processor 202 can then output a diagnosis. Results derived using overtrained models may be accurate for the particular dataset employed in the experiment but grossly misleading for the greater population of talkers in general. Generalization may be tested by splitting a dataset into test data and training data (which may be further split to include a validation set for iterative training or feature selection). The model that outputs the diagnosis and treatment plan, which can include the use of specific drugs, after the health state S240 is determined, is iteratively trained. This iterative training is in effect calibrating the therapeutic intervention to be provided to the patient based upon the health state from the speech analysis (recalibrating the automated therapeutic device to produce a recalibrated personalized dose of pain-relieving drugs). – paras 32, 48, 94, 133; FIG. 2B)
Rosenbek does not disclose the following limitations met by Srivastava:
objective indicia of pain (Srivastava teaches analyzing the recorded speech signal to generate a plurality of speech features to generate a pain score (objective indicia of pain) to provide objective pain assessment. FIG. 5 shows that the process of analyzing vocal expression may be repeated to manage pain of a patient. – paras 7, 15, 42, 94-95; FIG. 5)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate the systems and methods of objective pain assessment as taught by Srivastava in order to improve pain therapy efficacy (see Srivastava para 42).
Regarding Claim 17, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
wherein the objective data comprises at least one of a speech sample, a video sample, a facial scan, eye movement data, a thermal sample, a writing sample, heart rate data, or respiration data. (Rosenbek discloses monitoring effects of specific drugs, surgical treatments or rehabilitative efforts that includes receiving and analyzing one or more speech samples from a subject (the objective data comprises a speech sample) (S210 of FIG. 2B). A person's speech can include other vocal behaviors such as cough or laugh. In one embodiment, a similar system as described with respect to FIG. 5 can be used, where the screening for respiratory diseases can be accomplished by using cough as a biomarker (respiration data). For the respiratory diseases, cough can be found (the objective data comprises respiration data) and analyzed. In further embodiments, the acoustic analysis can be performed to quantify metrics including, but not limited to fundamental frequency characteristics, intensity, articulatory characteristics, speech/voice quality, prosodic characteristics, and speaking rate. Once the information from the speech/cough analysis is obtained, comparators 512 can be used to reach a diagnostic decision. – paras 19, 48, 82,84, 94; FIG. 5)
Regarding Claim 18, Rosenbek discloses the following limitations:
A system for administering a therapeutic intervention to a human patient, the system comprising:
…and a processing device configured to: receive an initial speech sample of the human patient; (Rosenbek discloses using a processor (a processing device) for collecting (receive) speech sample from subjects (an initial speech sample of the human patient) for determining baseline acoustic measures and storing the baseline acoustic measures in a memory. – paras 37, 48; FIG. 1 & 2A)
…administer, as part of a therapeutic intervention, a dose of…drugs to the human patient; (Rosenbek discloses methods that can be used to monitor a change in a neurological or other disease in a subject (the human patient), and/or to monitor effects of specific drugs, surgical treatments or rehabilitative efforts (administering, as part of a therapeutic intervention, an initial dose of drugs). – paras 32, 94-95)
receive a first response speech sample of the human patient after administering the dose; (Rosenbek discloses receiving speech samples (a first response speech sample). The identification device is an analytical tool that includes an interface, a processor and a memory and receives input via the interface, one or more speech samples from a subject (receive a first response speech sample of the human patient) (S210 of FIG. 2B). The subject systems can be used to monitor therapy. In one embodiment a subject's adherence and performance on a particular treatment/rehabilitation program can be monitored via continued use of the subject systems. By monitoring adherence to a treatment, this implies the speech samples may occur after the treatment/therapy occurs (after administering the initial dose), in order to determine the patients state. – paras 32, 48, 94; FIG. 2B)
analyze the initial speech sample and the first response speech sample to produce an intervention-response relationship for the human patient according to objective indicia of a response of the human patient to the therapeutic intervention in the initial speech sample and first response speech sample, (Rosenbek discloses that the identification device (a processing device) compares (analyze) the baseline acoustic measures (the initial speech sample) with the speech samples (first response speech sample) (S230 of FIG. 2B). The processor can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (produce an intervention-response relationship for the human patient) (S240 of FIG. 2B). Using acoustic measures (objective indicia) as a biomarker involves evaluating changes in various aspects (or subsystems of speech) over time (of a response of the human patient to the therapeutic intervention in the initial speech sample and first response speech sample). – paras 32, 34, 48, 94, 108; FIG. 2A-2B)
wherein the intervention-response relationship is produced by extracting objective speech-response features from the initial speech sample and first response speech sample and mapping a speech-response gradient according to the extracted objective speech-response features without reference to previous data; (Rosenbek discloses determining a health state of the subject based upon the results of comparing the identified acoustic measures (extracting objective response features) from the baseline acoustics (the initial sample) with the speech samples (first response sample) or by tracking the rate of change (and mapping a speech-response gradient without reference to previous data) in specific baseline acoustic measures (according to the extracted objective speech-response features). – para 48; FIG. 2B) [Examiner interprets the limitation “without reference to previous data”, using broadest reasonable interpretation, to mean that current/most recent data is analyzed in order to produce the intervention-response relationship at each moment in time; for example, the slope, rate of change or gradient of the data at each moment is produced to produce the intervention-response relationship (see also Applicant’s specification paras 9, 30 33).]
calibrate the therapeutic intervention according to the intervention-response relationship to refine subsequent administration of the therapeutic intervention; (Rosenbek discloses methods that can be used to monitor effects of specific drugs, surgical treatments or rehabilitative efforts. The processor 202 can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (according to the intervention-response relationship) (S240 of FIG. 2B). The model that outputs the diagnosis and treatment plan, which can include the use of drugs or rehabilitation exercises, after the health state S240 is determined, is iteratively trained (calibrate the therapeutic intervention – para 133). Articulation characteristics are measured using the standard deviation sum of cepstral coefficients and delta coefficients extracted from speech. These parameters may be used to monitor disease progression, efficacy of treatment, and/or need for changes in treatment (to refine subsequent administration of the therapeutic intervention). – paras 32, 48, 92, 94-95, 108, 133; FIG. 2B) (Examiner notes that this iterative training, as disclosed by Rosenbek, is in effect calibrating the treatment plan to be provided to the patient based upon the patients’ health state from the speech analysis)
and administer the refined therapeutic intervention according to the intervention-response relationship. (Rosenbek discloses that the methods provided herein can be used to monitor effects of specific drugs (administering the refined therapeutic intervention). – paras 32, 94-95, 108) (Examiner notes that in monitoring effects of drugs over time, the drugs must be administered over time)
Rosenbek does not disclose the following limitations met by Srivastava:
an intervention administering device (Srivastava teaches a therapy circuit/unit (an intervention administering device) that may include a drug delivery system such as a computerized patient-controlled analgesia pump that may deliver the prescribed pain medication to the patient such as via an intravenous line. – paras 47, 62, 74; FIG. 2, item 250; FIG. 3)
cause the intervention administering device to administer…a dose of pain-relieving drugs to the human patient; (Srivastava teaches a therapy circuit/unit (the intervention administering device) that may include a drug delivery system such as a computerized patient-controlled analgesia pump that may deliver the prescribed pain medication to the patient such as via an intravenous line (cause the intervention administering device to administer a dose of pain-relieving drugs to the human patient). – paras 47, 62, 74; FIG. 2, item 250; FIG. 3)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate pain medication as taught by Srivastava in order to provide an efficient way of managing appropriate pain therapy (see Srivastava para 7).
Regarding Claim 19, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
further comprising an audio input device configured to receive the initial speech sample and the first response speech sample. (Rosenbek discloses an input to the identification device 200 (an audio input device) which can include a microphone, which is connected to the device in such a manner that a speech sample can be recorded into the device 200 (configured to receive). Alternately, a speech sample can be recorded on another medium and copied (or otherwise transmitted) to the device 200. Once the speech sample is input to the device 200, the processor of the computer or mobile device can provide the processor 202 of the device 200 and perform the identification procedures to determine the health state of the subject. The one or more speech samples may be received in this way. – paras 48, 50, 64; FIG. 2A-2B, 5)
Regarding Claim 20, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
further comprising an output device configured to request the human patient to elicit the initial speech sample and the first response speech sample. (Rosenbek discloses the identification device 200 can be located at the testing site of a patient. In one such embodiment, the identification device 200 can be part of a computer or mobile device such as a smartphone. The interface 201 (an output device) can include a user interface such as a graphical user interface (GUI) provided on a screen or display. An input to the identification device 200 can include a microphone, which is connected to the device in such a manner that a speech sample can be recorded into the device 200. The screening app on the phone may prompt the user (request the human patient to elicit) to record a sample of their speech (the initial speech sample and the first response speech sample). – paras 48, 50-51; FIG. 2A-2B)
Regarding Claim 21, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
the output device provides a selected model speech sample to the human patient; (Rosenbek discloses that a speech sample can be recorded by the phone through the phone's microphone. The screening app on the phone may prompt the user to record a sample of their speech and/or request a sample already stored in the phone's memory (provides a selected model speech sample), which may provide the memory of the identification device when the screening app and baseline acoustic measures are stored entirely on the phone. The screening app can perform the steps to determine the health state of the subject. The user interface can prompt the user with exactly what to say as part of the selected model speech sample to be analyzed by the processor. – paras 48, 50-51, 54; FIG. 2A-2B)
and the processing device is configured to analyze the initial speech sample and the first response speech sample based on the selected model speech sample. (Rosenbek discloses that once the speech sample is input to the device 200, the processor of the computer or mobile device can provide the processor 202 of the device 200 and perform the identification procedures (the processing device is configured to analyze) to determine the health state of the subject. In an embodiment, a speech sample (the initial speech sample and the first response speech sample) can be recorded by the phone through the phone's microphone. The screening app on the phone may prompt the user to record a sample of their speech and/or request a sample already stored in the phone's memory (the selected model speech sample), which may provide the memory 203 of the identification device 200 when the screening app and baseline acoustic measures are stored entirely on the phone. The screening app can perform the steps to determine the health state of the subject. The subject systems can be used to monitor therapy. In one embodiment a subject's adherence and performance on a particular treatment/rehabilitation program can be monitored via continued use of the subject systems. – paras 32, 48, 50-51, 94; FIG. 1, 2A-2B)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Rosenbek et al. (US 20170119302) in view of Srivastava et al. (US 20180193652), further in view of Berisha et al. (US 20220338804) (Applicant admitted prior art).
Regarding Claim 8, Rosenbek and Srivastava teach all the limitations above and further teach the following limitations:
and the method further comprises providing a personally calibrated dose of…drugs according to the intervention-response relationship. (Rosenbek discloses methods that can be used to monitor effects of specific drugs, surgical treatments or rehabilitative efforts. The processor 202 can determine a health state of the subject based upon the results of the comparison or by tracking the rate of change in specific baseline acoustic measures (according to the intervention-response relationship) (S240 of FIG. 2B). The processor 202 can then output a diagnosis. Results derived using overtrained models may be accurate for the particular dataset employed in the experiment but grossly misleading for the greater population of talkers in general. Generalization may be tested by splitting a dataset into test data and training data (which may be further split to include a validation set for iterative training or feature selection). The model that outputs the diagnosis and treatment plan, which can include the use of specific drugs, after the health state S240 is determined, is iteratively trained. This iterative training is in effect calibrating the therapeutic intervention to be provided to the patient based upon the health state from the speech analysis (providing a personally calibrated dose of drugs – para 133). – paras 32, 48, 94, 133; FIG. 2B)
pain-relieving drugs (Srivastava teaches an implantable neuromodulator device (IND) which may be configured as a therapeutic device for treating or alleviating the pain. In some examples, the IND 112 may include a drug delivery system such as a drug infusion pump that can deliver pain medication (the pain reliever) to the patient, such as morphine sulfate or ziconotide, among others. – para 47)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate pain medication as taught by Srivastava in order to provide an efficient way of managing appropriate pain therapy (see Srivastava para 7).
Rosenbek and Srivastava do not teach the following limitations met by Berisha:
administering the initial dose of pain-relieving drugs to the human patient comprises administering a dose of the pain-relieving drugs according to normative data from other human patients; (Berisha teaches Table 1 which illustrates common concentrations which have been previously determined for opioid-naive patients across large populations (see para 6) (pain-relieving drugs). The traditional approach for programming and setting the PCA device (administering a dose of the pain-relieving drugs) relies on normative data from tables like Table 1 (according to normative data from other human patients) (see para 7). Traditionally, the pump is programmed via normative data for different patient populations or through subjective assessment of the patient's pain scores (see para 37). – Applicant’s Spec. paras 6-7, 37, 39; Table 1; FIG. 1)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified a model which outputs a health state of a subject by including the use of specific drugs as disclosed by Rosenbek to incorporate normative drug concentration data for different patient populations as taught by Berisha because one of ordinary skill in the art would have recognized that applying a well-known “traditional” technique to a known drug administration device (i.e., a PCA device) would have yielded predictable results and resulted in an improved system.
Relevant Prior Art of Record Not Currently Being Applied
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
Pulliam et al. (US 9782122) discloses Pain treatment or therapy delivery time refers to the amount of time it takes for the device or system to determine, based on the quantified pain level, whether the subject needs pain treatment or therapy, or needs a current level of pain treatment or therapy to be adjusted, to transmit a signal to the pain treatment or therapy device, and for the pain treatment or therapy device to administer, deliver, instruct, or otherwise provide the appropriate treatment or therapy to the subject. The system records the subject's speech and performs a signal processing function to isolate the speech patterns or syllables of interest, and then processes the various measures or metrics of those patterns or syllables to help quantify the level of the subject's pain.
Response to Arguments
Regarding the Claim Objection to Claim 1, Applicant’s arguments have been fully considered and are persuasive. The objection has been withdrawn in light of latest amendments.
Regarding rejections under 35 USC § 112(a) to Claims 1, 3-5, 8-9 and 11-21, Applicant’s arguments have been fully considered and are persuasive. The rejection has been withdrawn in light of latest amendments.
Regarding rejections under 35 USC § 101 to Claims 1, 3-5, 8-9 and 11-21, Applicant’s arguments have been fully considered and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(a) These amended limitations are not directed to a mere mental process or generalized clinical judgment. A human mind cannot practically perform the claimed combination of structured speech-sample acquisition according to a selected model speech sample, objective feature extraction from multiple speech samples, generation of an intervention-response relationship and speech-pain gradient without reference to previous data, analysis of additional objective patient data of the expressly recited types, and iterative recalibration of treatment based on interval monitoring. Rather, the claims are directed to a particular computer-implemented technique for objectively assessing response to treatment and controlling subsequent treatment administration using multimodal patient data. (p. 11).
Regarding (a), Examiner respectfully disagrees. Examiner notes that the claims have not been characterized as a mental process and this this argument is moot. Even so, 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 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 to asses a patients’ response to treatment. Applicant has not pointed to anything in the claims that fall outside of this characterization, therefore, the claimed invention is directed to an abstract idea.
(b) The claims also integrate any alleged abstract idea into a practical application. The amended claims do not merely observe a patient and report a result. Instead, the claims use the objectively derived intervention-response relationship to calibrate and then recalibrate subsequent administration of a therapeutic intervention for that patient. The claims therefore recite a concrete treatment-related application in which structured patient speech analysis and additional objective patient data are used to refine how a therapeutic intervention is subsequently administered. This is a specific application of the claimed data processing, not a generalized instruction to apply an abstract idea on a computer. (p. 11).
Regarding (b), Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicate that a practical application may be present where the claimed invention provides a technical solution to a technical problem or improves the functioning of a computer or any other technology/technical field. Here, the Examiner cannot find, nor has the Applicant identified, any technological problem that was caused by the technological environment to which the claims are confined.
Further, MPEP 2106.04(d)(2) indicates that a practical application may be present where the abstract idea effects a particular treatment or provides particular prophylaxis for a disease or medical condition. A particular treatment/prophylaxis is present where (a) there is a particular (i.e., named/described) treatment/prophylaxis that occurs when the claim is implemented; (b) the treatment/prophylaxis has more than a nominal connection/correlation to the abstract idea; and (c) the administration is more than extra-solution activity or a field of use. Here, while the claim recites that a treatment/prophylaxis is provided/administered to the patient, there is no particularity to the treatment; the claim does not state what the actual treatment/prophylaxis is, how often it is applied, the amount/concentration of treatment, the length of treatment, etc. Because a particular treatment/prophylaxis is not present in the claims, a practical application is not present.
(c) Claims 13 and 18 are similarly amended to recite a more concrete intervention- response calibration framework and to remove the unsupported self-supervised-model language. In view of the amendments, Applicant respectfully submits that the claims are directed to patent-eligible subject matter, and withdrawal of the 101 rejections is respectfully requested. (p. 11-12).
Regarding (c), Examiner respectfully disagrees. Based on response to arguments above, claim 1 is unpatentable and therefore similar independent claims 13 and 18, as well as all claims depending therefrom, are unpatentable according to the same rationale.
Regarding rejections under 35 USC § 103 to Claims 1, 3-5, 8-9 and 11-21, Applicant’s arguments have been fully considered and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(d) As an initial matter, amended claims 1, 13, and 18 no longer recite mapping a speech-pain gradient "using a self-supervised model." The Office Action expressly relied on Gfeller for that limitation. Once that limitation is removed, the Office Action's articulated basis for relying on Gfeller no longer applies. Rosenbek does not teach or suggest the presently amended combination of limitations requiring, among other things, receipt of an initial speech sample according to a selected model speech sample, administration of initial and second doses as part of a therapeutic intervention, receipt of corresponding response speech samples, mapping a speech-pain gradient without reference to previous data and based on the selected model speech sample, receiving additional objective data of expressly recited types, further producing the intervention-response relationship by analyzing that additional objective data for objective indicia of the patient's response to the therapeutic intervention, receiving the response samples after an amount of time appropriate for the therapeutic intervention to take effect, and iteratively monitoring and recalibrating subsequent administration of the therapeutic intervention using additional response speech samples at intervals. Srivastava likewise does not cure these deficiencies. Amended claims 13 and 18 are likewise patentably distinct. (p. 12-13).
Regarding (d), Examiner agrees that, based on Applicant’s amendments, the reliance upon Gfeller is moot. Therefore the Examiner has withdrawn the rejection. However, upon further consideration, a new grounds of rejection necessitated by Applicant’s amendments is made and the Examiner continues to rely upon Rosenbek and Srivastava to teach all of the limitations recited in amended claim 1. See updated rejection above.
Based on response to arguments above, claim 1 is unpatentable and therefore similar independent claims 13 and 18, as well as all claims depending therefrom, are unpatentable according to the same rationale.
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 KIMBERLY VANDER WOUDE whose telephone number is (703)756-4684. The examiner can normally be reached M-F 9 AM-5 PM.
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/K.E.V./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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