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 Amendments
Claims 1-16 are currently pending in this case and have been examined and
addressed below. This communication is a Non-Final Rejection in response to the
Amendment to the Claims and Remarks filed on 07/14/2025.
Claims 15-16 are amended claims.
Claims 1-14 are original claims.
Priority
This application claims benefit to the U.S. Provisional Serial No. 63337465, filed 05/02/2022, which are hereby incorporated by reference herein in its entirety.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/28/2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-16 are drawn to a method, system, and article of manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1, 15, and 16 recites a method (Claim 1 being representative) for retrieving a stored healthcare treatment model that has been trained to identify, for each of a plurality of health conditions, one or more respective treatment programs, wherein each of the treatment programs includes a respective treatment to modify respective behavior associated with one or more neurohumoral factors that are associated with the respective health condition; and in response to receiving input that specifies a first health condition of the one or more health conditions: using the healthcare treatment model to select one or more treatment programs corresponding to the first health condition; and providing for the one or more treatment programs.
These steps amount to certain methods of organizing human activity which includes functions relating to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
The claims recite the additional elements of a computing device, one or more processor, memory, respective treatment user interface, a non-transitory computer readable storage medium, and display.
These elements are recited at a high-level of generality such that it amounts to mere instructions to apply the exception because this is an example of applying the abstract idea by use of general-purpose computer which does not integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
As discussed above with respect to integration of the abstract idea into a
practical application, the claims recite the additional elements of a computing device, one or more processor, memory, respective treatment user interface, a non-transitory computer readable storage medium, and display.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
For the reasons stated, these claims fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claim 2 recites in response to receiving input that specifies a second health condition of the one or more health conditions, wherein the second health condition is different from the first health condition: using the healthcare treatment model to select one or more treatment programs corresponding to the second health condition, wherein the one or more treatment programs corresponding to the second health condition differ from the one or more treatment programs corresponding to the first health condition; and providing the treatment user interfaces for the one or more treatment programs corresponding to the second health condition.
Dependent claim 3 recites generating a treatment regimen for the first health condition, wherein the treatment regimen includes the one or more treatment programs corresponding to the first health condition.
Dependent claim 4 recites in response to an indication that the healthcare treatment model has been updated, retrieving the updated healthcare treatment model and updating the treatment regimen for the first health condition according to the updated healthcare treatment model, wherein the updated treatment regimen (i) includes one or more treatment programs not previously in the treatment regimen and/or (ii) omits one or more treatment programs previously in the treatment regimen.
Dependent claim 5 recites receiving information measuring adherence to the one or more treatment programs.
Dependent claim 7 recites in response to an indication that the healthcare treatment model has been updated, retrieving the updated healthcare treatment model and updating at least one treatment program in accordance with the updated healthcare treatment model.
Dependent claim 8 recites wherein the plurality of health conditions consists of health conditions other than: myopia, cancer cachexia, social communication disorder, mild cognitive impairment, and ophthalmologic rehabilitation.
Dependent claim 9 recites wherein the one or more treatment programs are provided for treatment regimens other than: improving antiviral immunology and strengthening a pelvic floor muscle.
Dependent claim 10 recites the first health condition is chronic pain, the stored healthcare treatment model includes a model for treating chronic pain, and the one or more treatment programs are selected from the group consisting of avoiding silver, performing external activity, applying cold temperature, moisturizing, applying warm temperatures, and providing games or other fun activities.
Dependent claim 11 recites the first health condition is hypertension, the stored healthcare treatment model includes a model for treating hypertension, and the one or more treatment programs are selected from the group consisting of performing anti- inflammatory exercises, avoiding nicotine, avoiding methane, avoiding aldehyde, and having an appropriate concentration of natrium in diet.
Dependent claim 12 recites the first health condition is peripheral arterial disease (PAD) in low extremities, the stored healthcare treatment model includes a model for treating PAD, and the one or more treatment programs are selected from the group consisting of performing anti- inflammatory exercises, avoiding nicotine, avoiding methane, avoiding aldehyde, and having an appropriate concentration of natrium in diet.
Dependent claim 13 recites the first health condition is chronic renal failure (CRF), the stored healthcare treatment model includes a model for treating CRF, and the one or more treatment programs are selected from the group consisting of performing pelvic exercises, following a specialized dietary plan, and removing chemical stimuli.
Dependent claim 14 recites the first health condition is chronic obstructive pulmonary disease (COPD), the stored healthcare treatment model includes a model for treating COPD, and the one or more treatment programs are selected from the group consisting of avoiding nicotine, avoiding alcohol, performing acute, aerobic and moderate exercise, implementing biofeedback- training of breathing, and performing Vagus Nerve Stimulation (VNS).
Each of these steps of the preceding dependent claims 2-5 and 7-14 only serve to further limit or specify the features of independent claim 1 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Dependent claim 6 recites one or more of the treatment interfaces are configured to monitor one or more specific patient activities using sensors of an electronic device on which the treatment interfaces are presented, the method further comprising selecting a first specific patient activity to monitor according to a first treatment interface of the provided treatment interfaces. The steps of monitor one or more specific patient activities on which the treatment interfaces are presented and selecting a first specific patient activity to monitor according to a first treatment interface of the provided treatment interfaces are part of the abstract idea, which further specifies and limits the independent claims. The sensors of an electronic device is an additional element and does not provide application or significantly more than the judicial exception.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1-16 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6-15, and 20 of U.S. Patent No. 12,380,977. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed invention is merely a recitation of the broader “generating treatment regimens for one or more conditions” embodiment to the “treating myopia” of the US Patent. The claims are directed towards retrieving a stored healthcare treatment model that had been trained to identify a plurality of health conditions, receiving input to specify first condition, using the health treatment model to select one or more treatment programs, and providing the treatment user interfaces for the one or more treatment programs. As the narrower treating myopia embodiment anticipates the instant application, the instant application is rejected under nonstatutory double patenting.
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.
Claim(s) 1-3, 5-6, 8-9, and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kutzko (US 20200273578 A1) in view of Kang (KR 20170013649 A).
REGARDING CLAIM 1
Kutzko teaches a method of generating treatment regimens for one or more health conditions, performed at a computing device having one or more processors and memory storing one or more programs configured for execution by the one or more processors:
retrieving a stored healthcare treatment model that has been trained to identify, for each of a plurality of health conditions, one or more respective treatment programs; ([Para. 0119] The artificial intelligence module 152 may function as or comprise a machine/deep learning/artificial intelligence platform that interrogates the healthcare information (i.e. neurohumoral factors) or data of the system 100 and learns about healthcare behaviors and trends of one or more patients 101B. [Para. 0125] One or more unsuccessful therapies 126 and/or successful therapies 125 for each condition 121 may be determined by the artificial intelligence module 152 based on the therapeutic behavior pattern. [Para. 0126] One or more unsuccessful therapies 126 and/or successful therapies 125 for each condition 121 may be determined by the artificial intelligence module 152 based on the one or more limiting factors 122 of step 502. Optionally, after assessment of a current disease state status, the artificial intelligence module 152 may aggregate a review of the one or more limiting factors 122 of the patient 101B. )
and in response to receiving input that specifies a first health condition of the one or more health conditions: ([Para. 0129] The method 600 may start 601 and healthcare data for a patient 101B may be received in step 602. In preferred embodiments, the healthcare data for a patient 101B received in step 602 may include one or more new conditions 121 for the patient 101B. The artificial intelligence module 152 may assess the current disease state status of a condition 121 of the patient 101B)
using the healthcare treatment model to select one or more treatment programs corresponding to the first health condition; ([Para. 0129] In step 603 one or more successful therapies 125 and/or unsuccessful therapies 126 may be determined for the one or more new conditions 121 for the patient 101B. Preferably, the artificial intelligence module 152 may determine one or more therapeutic categories along with the unsuccessful therapies 126 and successful therapies 125. In some embodiments, the artificial intelligence module 152 may assess the current disease state status of a condition 121 of the patient 101B, and the artificial intelligence module 152 may aggregate a review of therapies, such as medications, that have been tried and failed (or those of similar mechanism of action and/or potency). Therapies 125, 126, may include: procedures, such as laboratory tests; medical devices and digital health technologies; prescriptive drugs, such as prescription drugs, compounded drugs, veterinary prescription drugs, specialty pharmacy medications, medical cannabis; phytocannabinoids; terpenoid molecules; other compounds; vaccines and monoclonal antibodies; or any other therapy which may be used to treat a condition 121 for that patient 101B. In further embodiments, all possible therapeutic and pharmacologic combinations of therapies may be examined and determined to be pharmacologically and therapeutically appropriate.)
and providing the treatment user interfaces for the one or more treatment programs. ([Para. 0136] FIG. 7A illustrates a patient dashboard 700 providing access to all health information for the patient. Because all of a patient's data is aggregated in one location on the distributed ledger, the patient dashboard 700 allows the patient to view all of their data. The patient dashboard 700 further allows patients to access alternative healthcare solutions unique to their needs, which are generated by the artificial intelligence system. The patient's unique treatment plans, generated by the artificial intelligence system, are available to be viewed, inspected, and selected on the patient dashboard 700.)
Kutzko does not explicitly teach, however Kang teaches
wherein each of the treatment programs includes a respective treatment user interface to modify respective behavior associated with one or more neurohumoral factors that are associated with the respective health condition ([Pg. 2] The database 200 stores images and documents for the solution for brain health promotion (a personalized wellness recommendation solution for brain health promotion, Prescription). [Pg. 3] The brain health improving solution (i.e. treatment programs) may be provided for each neurotransmitter (i.e. one or more neurohumoral factors), and may include a prescription capable of promoting the synthesis of each neurotransmitter. In addition, the brain health improving solution may further include a prescription capable of inhibiting secretion of a neurotransmitter. The user interface 120 can display the generated results (brain health promoting solution recommendation result, evaluation result sheet, recommendation sheet, etc.) through a display unit, a print, or the like. [Pg. 7] The comprehensive evaluator 190 may receive a non-pharmacological prescription (a brain health promotion solution) that can enhance the deficient neurotransmitter of the subject from the database 200. The comprehensive evaluator 190 may provide at least one of the evaluation result sheet, the advice sheet, and the brain health promotion solution recommendation result sheet to the subject through the user interface 120 such as a print and display means.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of predicting the health and therapeutic behavior of patients and making treatment plan recommendations as taught by Kutzko and incorporate a wellness recommendation system as taught by Kang, with the motivation of providing customized wellness promoting solutions that can solve the imbalance of neurotransmitters (Kang Para. 0011).
REGARDING CLAIM 2
Kutzko/ Kang teaches the method of claim 1, Kutzko teaches further comprising: in response to receiving input that specifies a second health condition of the one or more health conditions, wherein the second health condition is different from the first health condition: ([Para. 0130] The probability of disease progression 128 of a new condition 121 of the patient 101B may be calculated. In some embodiments, the artificial intelligence module 152 may perform the calculation for each condition 121 of the patient 101B. For example, the patient 101B may have a primary condition 121, secondary condition 121, tertiary condition 121 of any other number of conditions 121.)
using the healthcare treatment model to select one or more treatment programs corresponding to the second health condition, wherein the one or more treatment programs corresponding to the second health condition differ from the one or more treatment programs corresponding to the first health condition; ([Para. 0130] The artificial intelligence module 152 may perform the calculation for each condition 121 of the patient 101B. For example, the patient 101B may have a primary condition 121, secondary condition 121, tertiary condition 121 of any other number of conditions 121. [Para. 0131] One or more possible therapies 127 for a new condition 121 may be determined by the artificial intelligence module 152.)
and providing the treatment user interfaces for the one or more treatment programs corresponding to the second health condition.([Para. 0136] FIG. 7A illustrates a patient dashboard 700 providing access to all health information for the patient. Because all of a patient's data is aggregated in one location on the distributed ledger, the patient dashboard 700 allows the patient to view all of their data. The patient dashboard 700 further allows patients to access alternative healthcare solutions unique to their needs, which are generated by the artificial intelligence system. The patient's unique treatment plans, generated by the artificial intelligence system, are available to be viewed, inspected, and selected on the patient dashboard 700.)
REGARDING CLAIM 3
Kutzko/ Kang teaches the method of claim 1, Kutzko teaches further comprising: generating a treatment regimen for the first health condition, wherein the treatment regimen includes the one or more treatment programs corresponding to the first health condition. ([Para. 0125] One or more unsuccessful therapies 126 and/or successful therapies 125 for each condition 121 may be determined by the artificial intelligence module 152 based on the therapeutic behavior pattern 124 of step 503. Therapies 125, 126, may include: procedures, such as laboratory tests; medical devices and digital health technologies; prescriptive drugs, such as prescription drugs, compounded drugs, veterinary prescription drugs, specialty pharmacy medications, medical cannabis; phytocannabinoids; terpenoid molecules; other compounds; or any other therapy which may be used to treat a condition 121 for that patient 101B. In some embodiments, after assessment of a current disease state status, the artificial intelligence module 152 may aggregate a review of medications and therapies that have been tried and failed (or those of similar mechanism of action and/or potency) as well as the current medications being taken. From this evaluation the artificial intelligence module 152 may compute and determine precluded or unsuccessful therapies 126 (those that wouldn't be prescribed) and possible successful therapies 125, including pharmacotherapeutic considerations, for that patient 101B. )
REGARDING CLAIM 5
Kutzko/ Kang teaches the method of claim 1, Kutzko teaches further comprising: receiving information measuring adherence to the one or more treatment programs. ([Para. 0123] A compliance record 123 of a patient 101B may include data which describes the dosing schedule for one or more medications prescribed to the patient 101B along with the amount and timing of the refills for the one or more medications that the patient 101B has received. As an example, in step 502, the artificial intelligence module 152 may receive healthcare data of a patient 101B having an existing condition 121 of eczema, a new condition 121 of athletes' foot, a limiting factor 122 of bipolar disorder, and a compliance record 123 for the existing condition 121 that includes refill information on an oral prescription and a topical prescription for the existing condition 121 of eczema.)
REGARDING CLAIM 6
Kutzko/ Kang teaches the method of claim 1, Kutzko further teaches wherein one or more of the treatment interfaces are configured to monitor one or more specific patient activities using sensors of an electronic device on which the treatment interfaces are presented, the method further comprising selecting a first specific patient activity to monitor according to a first treatment interface of the provided treatment interfaces. ([Para. 0152] the artificial intelligence system is connected to a smart-home device which is operable to interact with the patient audibly, visibly, or both. The smart-home device is operable to detect side effects of disease-related issues, such as, but not limited to, slurred speech and nystagmus. For example, the artificial intelligence system uses the smart-home device to triage, screen, and monitor for symptoms of specific diseases, such as COVID-19. In another embodiment, the artificial intelligence system uses the smart-home device to monitor progression or remission of diseases as well as drug-related issues. In another embodiment, the artificial intelligence system uses other consumer devices, such as, but not limited to, smart phones and/or smart watches, to interact with the patient audibly, visibly, or both.)
REGARDING CLAIM 8
Kutzko/ Kang teaches the method of claim 1, Kutzko further teaches wherein the plurality of health conditions consists of health conditions other than: myopia, cancer cachexia, social communication disorder, mild cognitive impairment, and ophthalmologic rehabilitation. ([Para. 0095] The health condition 121 is operable to be one or more of diabetes, hypertension or high blood pressure, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), etc. Examiner interprets that the list cited in the claimed invention does not recite the claimed conditions.)
REGARDING CLAIM 9
Kutzko/ Kang teaches the method of claim 1, Kutzko further teaches wherein the one or more treatment programs are provided for treatment regimens other than: improving antiviral immunology and strengthening a pelvic floor muscle. ([Para. 0129] From this evaluation the artificial intelligence module 152 may compute and determine precluded or unsuccessful therapies 126 (those that wouldn't be prescribed) and possible successful therapies 125, including pharmacotherapeutic considerations, for that patient 101B. Therapies 125, 126, may include: procedures, such as laboratory tests; medical devices and digital health technologies; prescriptive drugs, such as prescription drugs, compounded drugs, veterinary prescription drugs, specialty pharmacy medications, medical cannabis; phytocannabinoids; terpenoid molecules; other compounds; vaccines and monoclonal antibodies)
REGARDING CLAIM 15
Kutzko/ Kang teach the method of claim 1, Kutzko further teaches a computer system for building models for selecting healthcare treatment programs, comprising: one or more processors; memory; and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs comprising instructions. ([Para. 0107] The processor 402 is configured to execute software stored within the memory 410,)
REGARDING CLAIM 16
Kutzko/ Kang teach a method of claim 1, Kutzko further teaches a non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having one or more processors, memory, and a display, the one or more programs comprising instructions for a method of claim 1. ([Para. 0153] A computer-readable storage medium having computer readable code stored thereon for programming a computer, server, appliance, device, etc. each of which may include a processor. [Para. 0108] System output is able to be provided via a display screen 404A such as a liquid crystal display (LCD), touch screen, and the like.)
Claim(s) 4 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kutzko (US 20200273578 A1) ) in view of Kang (KR 20170013649 A) in view of Vaughn (US 20190019581 A1).
REGARDING CLAIM 4
Kutzko/ Kang teach the method of claim 3, however Vaughn teaches further comprising: in response to an indication that the healthcare treatment model has been updated, retrieving the updated healthcare treatment model and updating the treatment regimen for the first health condition according to the updated healthcare treatment model, wherein the updated treatment regimen (i) includes one or more treatment programs not previously in the treatment regimen and/or (ii) omits one or more treatment programs previously in the treatment regimen. ([Para. 0019] The diagnostic module may be configured to received updated subject data from the subject in response to the therapy of the subject and generate updated diagnostic data from the subject. The therapeutic module may be configured to receive the updated diagnostic data and output an updated personal treatment plan for the subject in response to the diagnostic data and the updated diagnostic data. [Para. 0198] The diagnosis module receives new data from the subject. [Para. 0200] The new data is fitted to the assessment model to generate an updated assessment model. This assessment model may comprise an initial diagnosis for a previously untreated subject, or an updated diagnosis for a previously treated subject. [Para. 0201] In step 218, the updated assessment model is provided to the therapy module, which determines what progress has been made as a result of any previously recommended therapy. The therapy module scores the therapy based on the amount of progress in the assessment model, with larger progress corresponding to a higher score, making a successful therapy and similar therapies more likely to be recommended to subjects with similar assessments in the future. The set of therapies available is thus updated to reflect a new assessment of effectiveness, as correlated with the subject's diagnosis. [Para. 0202] A new therapy is recommended based on the assessment model, the degree of success of the previous therapy, if any, and the scores assigned to a collection of candidate therapies based on previous uses of those therapies with the subject.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of predicting the health and therapeutic behavior of patients and making treatment plan recommendations as taught by Kutzko, a wellness recommendation system as taught by Kang, and incorporate providing digital diagnostics and digital therapeutics to patients as taught by Vaughn, with the motivation of improving both the accuracy and efficiency for diagnosis and treatment (Vaughn Para. 0005).
REGARDING CLAIM 7
Kutzko/ Kang teach the method of claim 1, however Vaughn teaches further comprising: in response to an indication that the healthcare treatment model has been updated, retrieving the updated healthcare treatment model and updating at least one treatment program in accordance with the updated healthcare treatment model. ([Para. 0019] The diagnostic module may be configured to received updated subject data from the subject in response to the therapy of the subject and generate updated diagnostic data from the subject. The therapeutic module may be configured to receive the updated diagnostic data and output an updated personal treatment plan for the subject in response to the diagnostic data and the updated diagnostic data. [Para. 0198] The diagnosis module receives new data from the subject. [Para. 0200] The new data is fitted to the assessment model to generate an updated assessment model. This assessment model may comprise an initial diagnosis for a previously untreated subject, or an updated diagnosis for a previously treated subject. [Para. 0201] In step 218, the updated assessment model is provided to the therapy module, which determines what progress has been made as a result of any previously recommended therapy. The therapy module scores the therapy based on the amount of progress in the assessment model, with larger progress corresponding to a higher score, making a successful therapy and similar therapies more likely to be recommended to subjects with similar assessments in the future. The set of therapies available is thus updated to reflect a new assessment of effectiveness, as correlated with the subject's diagnosis. [Para. 0202] A new therapy is recommended based on the assessment model, the degree of success of the previous therapy, if any, and the scores assigned to a collection of candidate therapies based on previous uses of those therapies with the subject.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of predicting the health and therapeutic behavior of patients and making treatment plan recommendations as taught by Kutzko, a wellness recommendation system as taught by Kang, and incorporate providing digital diagnostics and digital therapeutics to patients as taught by Vaughn, with the motivation of improving both the accuracy and efficiency for diagnosis and treatment (Vaughn Para. 0005).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kutzko (US 20200273578 A1) ) in view of Kang (KR 20170013649 A) in view of Rosenbluth (US 20230178247 A1).
REGARDING CLAIM 10
Kutzko/ Kang teach the method of claim 1, Kutzko further teaches wherein: the first health condition is chronic pain, the stored healthcare treatment model includes a model for treating chronic pain, ([Para. 0119] The artificial intelligence module 152 may function as or comprise a machine/deep learning/artificial intelligence platform that interrogates the healthcare information or data of the system 100 and learns about healthcare behaviors and trends of one or more patients 101B. [Para. 0125] One or more unsuccessful therapies 126 and/or successful therapies 125 for each condition 121 may be determined by the artificial intelligence module 152 based on the therapeutic behavior pattern. [Para. 0151] The condition is chronic pain.)
Kutzko does not explicitly teach, however Rosenbluth teaches
and the one or more treatment programs are selected from the group consisting of avoiding silver, performing external activity, applying cold temperature, moisturizing, applying warm temperatures, and providing games or other fun activities. ([Para. 0002] Fibromyalgia (FM) is a medical condition where an individual suffers from chronic musculoskeletal pain (i.e. chronic pain). [Para. 0038] The treatment planning module 170 generates a treatment plan to a user of the digital therapy system 140 to alleviate a user's FM. A treatment plan is a set of one or more treatment assignments for the user to perform as part of treating or mitigating their FM in accordance with cognitive behavioral therapy (CBT) techniques. A treatment assignment is an exercise or activity that assists the user in developing effective strategies for alleviating symptoms of FM. For example, treatment assignments may include exercises and activities such as talk therapy, physical movements (i.e. performing external activity), recommendations in favor or against certain activities, recommendations in favor or against certain pharmacological solutions to the user's symptoms, reading materials, presentations of a user's progress.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of predicting the health and therapeutic behavior of patients and making treatment plan recommendations as taught by Kutzko, a wellness recommendation system as taught by Kang, and incorporate chronic pain management through digital therapy system as taught by Rosenbluth, with the motivation of providing a customized treatment plan to a user suffering from fibromyalgia (Para. 0005).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kutzko (US 20200273578 A1) ) in view of Kang (KR 20170013649 A) in view of Ozemek (Impact of therapeutic lifestyle changes in resistant hypertension (2020)).
REGARDING CLAIM 11
Kutzko/ Kang teach the method of claim 1, Kutzko further teaches wherein: the first health condition is hypertension, the stored healthcare treatment model includes a model for treating hypertension, [Para. 0119] The artificial intelligence module 152 may function as or comprise a machine/deep learning/artificial intelligence platform that interrogates the healthcare information or data of the system 100 and learns about healthcare behaviors and trends of one or more patients 101B. [Para. 0125] One or more unsuccessful therapies 126 and/or successful therapies 125 for each condition 121 may be determined by the artificial intelligence module 152 based on the therapeutic behavior pattern. [Para. 0149] a patient has a condition such as hypertension.)
Kutzko does not explicitly teach, however Ozemek teaches
and the one or more treatment programs are selected from the group consisting of performing anti- inflammatory exercises, avoiding nicotine, avoiding methane, avoiding aldehyde, and having an appropriate concentration of natrium in diet. ([Pg. 1 Abstract] Adopting healthy lifestyles, such as being active on ≥4 days per week, weight-loss in the presence of obesity, consuming a diet rich in fruits and vegetables, and sodium below the recommended threshold, avoiding high alcohol consumption and refraining from smoking have been effective lifestyle therapies to prevent or control stage 1 hypertension (HTN).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of predicting the health and therapeutic behavior of patients and making treatment plan recommendations as taught by Kutzko, a wellness recommendation system as taught by Kang, and incorporate adopting lifestyle and wellness changes to prevent or control hypertension as taught by Ozemek, with the motivation of quantifying the blood pressure lowering effects of certain therapeutic lifestyles in patients with resistant hypertension (Ozemek Pg. 1 Abstract).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kutzko (US 20200273578 A1) ) in view of Kang (KR 20170013649 A) in view of Khan (Life-style Modification in Peripheral Arterial Disease (2005)).
REGARDING CLAIM 12
Kutzko/ Kang teach the method of claim 1, Kutzko further teaches wherein: the first health condition is peripheral arterial disease (PAD) in low extremities, the stored healthcare treatment model includes a model for treating PAD, ([Para. 0119] The artificial intelligence module 152 may function as or comprise a machine/deep learning/artificial intelligence platform that interrogates the healthcare information or data of the system 100 and learns about healthcare behaviors and trends of one or more patients 101B. [Para. 0125] One or more unsuccessful therapies 126 and/or successful therapies 125 for each condition 121 may be determined by the artificial intelligence module 152 based on the therapeutic behavior pattern. ([Para. 0095] The health condition 121 is operable to be one or more of peripheral vascular disorders. [Para. 0146] the patient also has a comorbidity, such as peripheral artery disease.)
Kutzko does not explicitly teach, however Khan teaches
and the one or more treatment programs are selected from the group consisting of performing anti- inflammatory exercises, avoiding nicotine, avoiding methane, avoiding aldehyde, and having an appropriate concentration of natrium in diet. ([Pg. 1 Introduction] giving up smoking, increased exercise, and improved diet and weight loss. [Pg. 2 Smoking cessation and PAD] Combination of exercise therapy and smoking cessation (i.e. avoiding nicotine) may also be beneficial. [Pg. 3 Exercise] Exercise in PAD usually involves treadmill walking although other modalities including upper limb exercise and pole-striding. [Pgs. 3-4 Sodium Restriction] Patients with PAD develop a higher degree of cardiac hypertrophy than other hypertensive subjects with the same level of mean arterial pressure. Sodium intake rather than the mechanical factors seems to be the major modulation which influences the degree of cardiac hypertrophy. A daily intake of not more than 100 mmol (6 g of sodium chloride) is recommended (i.e. appropriate concentration of natrium in diet).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of predicting the health and therapeutic behavior of patients and making treatment plan recommendations as taught by Kutzko, a wellness recommendation system as taught by Kang, and incorporate review the published evidence supporting the use of life-style modification in peripheral arterial disease (PAD) as taught by Khan, with the motivation of reduce cardiovascular risk and suggests clinically based strategies (Khan Pg.1 Introduction).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kutzko (US 20200273578 A1) ) in view of Kang (KR 20170013649 A) in view of Maschio (Effects of dietary protein and phosphorus restriction on the progression of early renal failure (1982)).
REGARDING CLAIM 13
Kutzko/ Kang teach the method of claim 1, Kutzko further teaches wherein: the first health condition is chronic renal failure (CRF), the stored healthcare treatment model includes a model for treating CRF, ([Para. 0095] The health condition 121 is operable to be one or more of renal disease or failure. [Para. 0119] The artificial intelligence module 152 may function as or comprise a machine/deep learning/artificial intelligence platform that interrogates the healthcare information or data of the system 100 and learns about healthcare behaviors and trends of one or more patients 101B. [Para. 0125] One or more unsuccessful therapies 126 and/or successful therapies 125 for each condition 121 may be determined by the artificial intelligence module 152 based on the therapeutic behavior pattern.)
Kutzko does not explicitly teach, however Maschio teaches
and the one or more treatment programs are selected from the group consisting of performing pelvic exercises, following a specialized dietary plan, and removing chemical stimuli. ([Pg. 6 Discussion] Dietary protein and phosphorus restriction (i.e. specialized dietary plan) in patients with chronic renal failure)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of predicting the health and therapeutic behavior of patients and making treatment plan recommendations as taught by Kutzko, a wellness recommendation system as taught by Kang, and incorporate dietary restrictions for chronic renal failure patients, with the motivation of providing beneficial effects on the structure and function of the kidney (Maschio Pg. 1).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kutzko (US 20200273578 A1) ) in view of Kang (KR 20170013649 A) in view of Staats (The Use of Non-invasive Vagus Nerve Stimulation to Treat Respiratory Symptoms Associated With COVID-19: A Theoretical Hypothesis and Early Clinical Experience (2020)).
REGARDING CLAIM 14
Kutzko/ Kang teach the method of claim 1, Kutzko further teaches wherein: the first health condition is chronic obstructive pulmonary disease (COPD), the stored healthcare treatment model includes a model for treating COPD, ([Para. 0095] The health condition 121 is operable to be one or more of chronic obstructive pulmonary disease (COPD). [Para. 0119] The artificial intelligence module 152 may function as or comprise a machine/deep learning/artificial intelligence platform that interrogates the healthcare information or data of the system 100 and learns about healthcare behaviors and trends of one or more patients 101B. [Para. 0125] One or more unsuccessful therapies 126 and/or successful therapies 125 for each condition 121 may be determined by the artificial intelligence module 152 based on the therapeutic behavior pattern.)
Kutzko does not explicitly teach, however Staats teaches
and the one or more treatment programs are selected from the group consisting of avoiding nicotine, avoiding alcohol, performing acute, aerobic and moderate exercise, implementing biofeedback- training of breathing, and performing Vagus Nerve Stimulation (VNS). ([Pg. 2 Introduction] Non-invasive Vagus nerve stimulation (nVNS; gammaCore™, electroCore, Inc.; Fig. 1) is a neuromodulatory. It is CE marked in the EU for the treatment and prevention of symptoms of reactive airway disease, which includes chronic obstructive pulmonary disease (COPD), as well as for a number of other conditions. Clinical data suggest that nVNS might provide benefits in patients who have respiratory symptoms that are sometimes associated with COVID-19, such as acute bronchoconstriction due to asthma and respiratory distress associated with COPD.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of predicting the health and therapeutic behavior of patients and making treatment plan recommendations as taught by Kutzko, a wellness recommendation system as taught by Kang, and incorporate treatment and prevention of symptoms of reactive airway disease as taught by Staats, with the motivation of t provide benefits in patients who have respiratory symptoms (Staats Pg. 2 Introduction).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mainwaring et al (US 20150379232 A1), which discloses diagnostic computer systems and diagnostic user interfaces.
Gary et al, Combined exercise and cognitive behavioral therapy improves outcomes in patients with heart failure, which discloses interventions designed to improve both physical and psychological symptoms may provide the best method for optimizing functioning and enhancing health-related quality of life in patients with heart failure.
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/P.K.E./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682