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 .
Election/Restrictions
Applicant’s election without traverse of Group I, Claims 1-8 and 13-15, in the reply filed on 20 March 2026 is acknowledged. Claims 9-12 and 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention/group, there being no allowable generic or linking claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
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 of this title, 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 1-8 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde et al. (US 2017/0164876; hereinafter “Hyde”) in view of Lim et al. (US 2023/0061046; hereinafter “Lim”).
Regarding claim 1, Hyde discloses an apparatus for treatment of a patient for an inflammatory condition (e.g. ¶¶ 132 – “joint-based inflammation condition”), comprising: a sensor that detects any of a presence and a measure of each of one or more biomarkers of the patient (e.g. ¶¶ 210 – “chemical sensor 3340”), and that generates biomarker data indicative thereof, where the biomarkers include IL-6 and IL-8 (e.g. ¶¶ 210 – “a pro-inflammatory cytokine (IL-1α, IL-β, IL-6, TNFα, IL-8)”); an illumination source in proximity of the patient that applies a therapeutic dose of electromagnetic radiation thereto (e.g. ¶¶ 181, 222 – “optical stimulator 3506 is configured to generate infrared light”), and a controller in communications coupling with the sensor and with the illumination source (e.g. Fig. 33, #3406), the controller generating a dose control signal to effect application of the therapeutic dose by the illumination source (e.g. ¶¶ 219 – “The processor 1006 can coordinate operations of the system 1000 based on the one or more sense signals and identification of a physiological state (e.g., pain state) of the individual subject based on the one or more sense signals… the effector 1008 is operably coupled to the processor 1006 and is configured to effect a body portion of the individual subject responsive to control by the processor 1006” - where the examiner notes the chemical sensor provides one of the sense signals and the IR optical stimulator is one of the effectors), the controller determining an efficacy of applying such a dose by analyzing the biomarker data (¶¶ e.g. ¶¶ 229 – “feedback from the monitored movement/physiological parameter(s) (e.g., whereby detected movements or physiological parameters can result in the target value being adjusted to compensate for the detected values); or can depend on generalized health or fitness considerations (e.g., height, weight, body mass index, caloric needs or preferences, general health or fitness levels, etc.).”; e.g. ¶¶ 263 – “system 1000 can receive one or more feedback signals from an external device or object based on the one or more sense signals….processor 1006 can direct control of the effector 1008 responsive to the feedback signals to determine the stimulation settings for the effector 1008” – where the examiner if of the position that the controller determines the efficacy of the dose from the feedback in order to adjust the dose control) with training data indicative of modulation of the one or more biomarkers in each of plurality of members of a subpopulation in response to dosing of such electromagnetic radiation (e.g. ¶¶ 247 – “The system 1000 can monitor the individual and receive additional individual input regarding whether the pain intensifies, dissipates, differs in type, location, chronology, etc. The reference data can include user-specific personal history information, such as whether the individual has experienced a joint disorder, cardiovascular condition, other health condition, or the like, can depend on a feedback from monitored movement/physiological parameter(s) (e.g., whereby detected movements or physiological parameters can result in the target value being adjusted to compensate for the detected values), or can depend on generalized health or fitness considerations of the individual (e.g., height, weight, body mass index, caloric needs or preferences, general health or fitness levels, etc.).”
Hyde fails to expressly disclose analyzing the biomarker data with an artificial intelligence (AI) engine and a machine learning (ML) model. The examiner notes that the disclosure uses the AI Engine and Machine learning module to describe the performance of generic database access tasks without any regard to speed, sample/database sizing, or any details where an AI engine/ Machine learning model would be different or advantageous over a generic computer processor accessing a database. Regardless, Lim discloses the use of intelligence (AI) engine and a machine learning (ML) model in algorithms, in order to analyze and target stimulation based on pattern analysis and sampling biomarkers which include IL-6 analyte values as in Hyde (e.g. ¶¶ 38, 86, etc.). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to apply the known technique of analyzing the biomarker data with an artificial intelligence (AI) engine and a machine learning (ML) model, as taught by Lim, to the known device of Hyde, ready for improvement, to yield the predictable results of providing an increased efficiency to analyze more data when determining the feedback and adjustment of parameters for improvement in therapy.
Regarding claim 2, Hyde discloses the electromagnetic radiation is infrared light (e.g. ¶¶ 222 – “optical stimulator 3506 is configured to generate infrared light”), and the controller generates the dose control signal to effect application of the therapeutic dose timed as a function of changes in the measures of one or more biomarkers (e.g. ¶¶ 229 – “feedback from the monitored movement/physiological parameter(s) (e.g., whereby detected movements or physiological parameters can result in the target value being adjusted to compensate for the detected values); or can depend on generalized health or fitness considerations (e.g., height, weight, body mass index, caloric needs or preferences, general health or fitness levels, etc.).”; e.g. ¶¶ 263 – “system 1000 can receive one or more feedback signals from an external device or object based on the one or more sense signals….processor 1006 can direct control of the effector 1008 responsive to the feedback signals to determine the stimulation settings for the effector 1008”).
Regarding claim 3, Hyde discloses the illumination source applies the therapeutic dose to the patient in real-time substantially concurrently with detection of the presence and/or measure of the one or more biomarkers of the patient by the sensor (e.g. ¶¶ 181 – where the examiner notes the device operates in a continuous manner with live data).
Regarding claim 4, Hyde discloses the sensor is a blood sensor (e.g. ¶¶ 210 – “chemical sensor 3340 can include a transdermal sensor for sensing an analyte in tissue fluids (e.g., blood)”).
Regarding claim 5, Hyde discloses the dose control signal represents intensity of the therapeutic dose (e.g. ¶¶ 181 – where the range can be from low to high intensity for the effector or optical infrared stimulus).
Regarding claim 6, Hyde discloses the controller determines an efficacy of applying a said therapeutic dose by analyzing the biomarker data along with an indication of a phenotype, genotype, and/or demographic characterization of the patient with training data indicative of modulation of the one or more biomarkers in each of plurality of members of a subpopulation of at least comparable phenotype, genotype, and/or demographic characterization in response to dosing of such electromagnetic radiation (e.g. ¶¶ 247 – “The system 1000 can monitor the individual and receive additional individual input regarding whether the pain intensifies, dissipates, differs in type, location, chronology, etc. The reference data can include user-specific personal history information, such as whether the individual has experienced a joint disorder, cardiovascular condition, other health condition, or the like, can depend on a feedback from monitored movement/physiological parameter(s) (e.g., whereby detected movements or physiological parameters can result in the target value being adjusted to compensate for the detected values), or can depend on generalized health or fitness considerations of the individual (e.g., height, weight, body mass index, caloric needs or preferences, general health or fitness levels, etc.).
Hyde fails to expressly disclose analyzing with an artificial intelligence (AI) engine and a machine learning (ML) model. The examiner notes that the disclosure uses the AI Engine and Machine learning module to describe the performance of generic database access tasks without any regard to speed, sample/database sizing, or any details where an AI engine/ Machine learning model would be different or advantageous over a generic computer processor accessing a database. Regardless, Lim discloses the use of intelligence (AI) engine and a machine learning (ML) model in algorithms, in order to analyze and target stimulation based on pattern analysis and sampling biomarkers which include IL-6 analyte values as in Hyde (e.g. ¶¶ 38, 86, etc.). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to apply the known technique of analyzing the data with an artificial intelligence (AI) engine and a machine learning (ML) model, as taught by Lim, to the known device of Hyde, ready for improvement, to yield the predictable results of providing an increased efficiency to analyze more data when determining the feedback and adjustment of parameters for improvement in therapy.
Regarding claim 7, Hyde discloses the controller generates one or more dose control signals to effect application of a recommended therapeutic dose to the patient of a pharmaceutical/nutraceutical by analyzing the biomarker data with training data indicative of modulation of the one or more biomarkers in each of plurality of members of a subpopulation in response to dosing of each of electromagnetic radiation and such pharmaceutical/nutraceutical (e.g. ¶¶ 247 – “The system 1000 can monitor the individual and receive additional individual input regarding whether the pain intensifies, dissipates, differs in type, location, chronology, etc. The reference data can include user-specific personal history information, such as whether the individual has experienced a joint disorder, cardiovascular condition, other health condition, or the like, can depend on a feedback from monitored movement/physiological parameter(s) (e.g., whereby detected movements or physiological parameters can result in the target value being adjusted to compensate for the detected values), or can depend on generalized health or fitness considerations of the individual (e.g., height, weight, body mass index, caloric needs or preferences, general health or fitness levels, etc.).
Hyde fails to expressly disclose analyzing with an artificial intelligence (AI) engine and a machine learning (ML) model. The examiner notes that the disclosure uses the AI Engine and Machine learning module to describe the performance of generic database access tasks without any regard to speed, sample/database sizing, or any details where an AI engine/ Machine learning model would be different or advantageous over a generic computer processor accessing a database. Regardless, Lim discloses the use of intelligence (AI) engine and a machine learning (ML) model in algorithms, in order to analyze and target stimulation based on pattern analysis and sampling biomarkers which include IL-6 analyte values as in Hyde (e.g. ¶¶ 38, 86, etc.). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to apply the known technique of analyzing the data with an artificial intelligence (AI) engine and a machine learning (ML) model, as taught by Lim, to the known device of Hyde, ready for improvement, to yield the predictable results of providing an increased efficiency to analyze more data when determining the feedback and adjustment of parameters for improvement in therapy.
Regarding claim 8, Hyde discloses the pharmaceutical/nutraceutical as any of a steroid and a monoclonal antibody (e.g. ¶¶ 93, 122, etc.).
Regarding claim 13, Hyde discloses the electromagnetic radiation is infrared light (e.g. ¶¶ 181, 222 – “optical stimulator 3506 is configured to generate infrared light”) and the controller generates the dose control signal to effect application of the therapeutic dose timed as a function of changes in the measures of one or more biomarkers (e.g. ¶¶ 229 – “feedback from the monitored movement/physiological parameter(s) (e.g., whereby detected movements or physiological parameters can result in the target value being adjusted to compensate for the detected values); or can depend on generalized health or fitness considerations (e.g., height, weight, body mass index, caloric needs or preferences, general health or fitness levels, etc.).”; e.g. ¶¶ 263 – “system 1000 can receive one or more feedback signals from an external device or object based on the one or more sense signals….processor 1006 can direct control of the effector 1008 responsive to the feedback signals to determine the stimulation settings for the effector 1008”).
Regarding claim 14, Hyde discloses receiving the biomarker data in real time (e.g. ¶¶ 181 – where the examiner notes the device operates in a continuous manner).
Regarding claim 15, X Hyde discloses receiving the biomarker data from measurements based on the patient’s blood (e.g. ¶¶ 210 – “chemical sensor 3340 can include a transdermal sensor for sensing an analyte in tissue fluids (e.g., blood)”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael D’Abreu whose telephone number is (571) 270-3816. The examiner can normally be reached on 7AM-4PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at (571) 270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL J D'ABREU/Primary Examiner, Art Unit 3796
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