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 .
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 1-11 and 18 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 a method of treatment of a patient for an inflammatory condition (e.g. ¶¶ 132 – “joint-based inflammation condition”), comprising applying to a patient a therapeutically effective combination of (i) one or more doses of an electromagnetic radiation (e.g. ¶¶ 222 – “an optical stimulator configured to generate infrared light”), and(ii) one or more doses of any of a pharmaceutical and a nutraceutical (collectively, "pharmaceutical/nutraceutical") (e.g. ¶¶ 84 – “MEMS drug delivery system”; 96, 122, etc.) where the electromagnetic radiation comprises infrared radiation to which the patient is exposed via one or more external infrared light sources (e.g. ¶¶ 222 – “an optical stimulator configured to generate infrared light”), where the therapeutically effective combination is to reduce an inflammatory condition of the patient when applied in connection with zero, one or more doses of any of the electromagnetic radiation and/or a said 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 disclose prediction by an artificial intelligence (Al) engine and a trained 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 9, Hyde discloses a method of treatment of a patient for an inflammatory condition, comprising: determining any of a presence and a measure of each of one or more biomarkers of the patient (e.g. ¶¶ 141), applying to a patient a therapeutically effective combination of (i) one or more doses of an electromagnetic radiation, and(ii) one or more doses of any of a pharmaceutical and a nutraceutical (collectively, "pharmaceutical/nutraceutical") where the electromagnetic radiation comprises infrared radiation to which the patient is exposed via one or more external infrared light sources (e.g. ¶¶ 222 – “an optical stimulator configured to generate infrared light”; e.g. ¶¶ 84 – “MEMS drug delivery system”; 96, 122, etc.), where the applying step includes determining, based on any of the presence and the measure of the one or more biomarkers, any of (i) a timing, intensity, duration and/or wavelength of said one or more doses of the electromagnetic radiation, and(ii) a timing, quantity and selection of the pharmaceuticals/nutraceuticals (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.).”).
Regarding claim 18, Hyde discloses method of generating electromagnetic radiation, comprising: determining a modulation of one or more of biomarkers in each of plurality of members of a subpopulation of any of humans, mammals or other animals in response to infrared light by, for each of the plurality members (e.g. ¶¶ ), receiving biomarker data indicative of any of a presence and a measure of each of one or more biomarkers of the member prior to application to that member of infrared light (e.g. ¶¶ ), applying infrared light to the member, where a timing, duration, intensity and wavelength of the applied infrared light is determined by analyzing the received biomarker (e.g. ¶¶ 222 – “an optical stimulator configured to generate infrared light”; 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), identifying a change in the one or more of the biomarkers subsequent to application of the infrared light (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 disclose analyzing and updating the trained 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 2, Hyde discloses detecting any of a presence and a measure of each of one or more biomarkers of the patient to determine an efficacy in inflammation reduction of application of any of the (i) one or more doses of the electromagnetic radiation, and (ii) one or more doses of the pharmaceutical/nutraceutical (e.g. ¶¶ 141).
Regarding claim 3, Hyde fails to disclose the applying step includes analyzing data regarding the one or more biomarkers with an artificial intelligence (Al) engine to determine (i) any of a timing, intensity, duration and/or wavelength of said one or more doses of the electromagnetic radiation, and (ii) any of a timing, quantity and selection of the pharmaceuticals/nutraceuticals. 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. In the same field of endeavor, 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 claims 4 and 10, Hyde discloses the analyzing step includes analyzing the data regarding the one or more biomarkers with the artificial intelligence (Al) engine and the machine learning (ML) model trained with training data indicative of modulation of the one or more biomarkers in any of each of (i) plurality of members of a subpopulation in response to dosing of such electromagnetic radiation and/or pharmaceuticals/nutraceuticals, (ii) the patient or another individual in response to such dosing (e.g. ¶¶ 181).
Regarding claims 5-6 and 11, Hyde discloses the one or more biomarkers include an oxidative stress biomarker, including one or more of catalase, superoxide dismutase (SOD),isoprostane (IsoPs), malondialdehyde (MDA), S-glutathionylation (Hemoglobin),myeloperoxidase, Nrf-2, PGC-la and Glutathione Peroxidase (GPX-1) (e.g. ¶¶ 141-147).
Regarding claims 7-8, Hyde discloses the pharmaceutical/nutraceutical is any of a corticosteroid and a monoclonal antibody (e.g. ¶¶ 93, 122, etc.).
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
