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 1-20 are currently pending in this case and have been examined and
addressed below. This communication is a Final Rejection in response to the
Amendment to the Claims and Remarks filed on 01/26/2026.
Claims 1, 11, and 18 are amended claims.
Claims 2-10, 12-17, and 19-20 are original claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/03/2025, 01/26/2026, 02/26/2026, and 04/23/2026, 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 1-20 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 1-20 are drawn to a system, method, and article of manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system comprising to receive one or more dosage
compliance plans that, when applied to one or more users, encourage the one or more users to comply with medical prescriptions; receive data associated with the user, wherein the data comprises the at least one modified attribute of the user and the at least one attribute of the medical prescription; receive one or more constraints, wherein the one or more constraints comprise rules pertaining to dosage amounts associated with the one or more dosage compliance plans, generate an optimal dosage compliance plan for the user, wherein the optimal dosage compliance plan comprises the at least one modified attribute of the user associated with the at least one attribute of the medical prescription; generate, a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan and includes one or more operating parameters for one or more components of the treatment apparatus to enable user adherence to the optimal dosage compliance plan; and generate an estimate of the duration during which the user will be taking the medical prescription as a result of the user complying with the treatment plan that is associated with the optimal dosage compliance plan.
Independent claim 11 recites a method comprising receiving one or more dosage compliance plans that, when applied to one or more users, encourage the one or more users to comply with medical prescriptions; receiving data associated with the user, wherein the data comprises at least one modified attribute of the user and at least one attribute of the medical prescription; receiving one or more constraints, wherein the one or more constraints comprise rules pertaining to dosage amounts associated with the one or more dosage compliance plans; generating an optimal dosage compliance plan for the user comprising the at least one modified attribute of the user associated with the at least one attribute of the medical prescription; generating, via the artificial intelligence engine, a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan and includes one or more operating parameters for one or more components of the treatment apparatus to enable user adherence to the optimal dosage compliance plan; and generate an estimate of the duration during which the user will be taking the medical prescription as a result of the user complying with the treatment plan that is associated with the optimal dosage compliance plan.
Independent claim 18 recites an article of manufacture comprising to receive one or more dosage compliance plans that, when applied to one or more users, encourage the one or more users to comply with medical prescriptions; receive data associated with the user, wherein the data comprises the at least one modified attribute of the user and the at least one attribute of the medical prescription; receive one or more constraints, wherein the one or more constraints comprise rules pertaining to dosage amounts associated with the one or more dosage compliance plans; and generate an optimal dosage compliance plan for the user comprising the at least one modified attribute of the user associated with the at least one attribute of the medical prescription; generate a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan and includes one or more operating parameters for one or more components of the treatment apparatus to enable user adherence to the optimal dosage compliance plan; and generate an estimate of the duration during which the user will be taking the medical prescription as a result of the user complying with the treatment plan that is associated with the optimal dosage compliance plan.
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 an artificial intelligence engine, treatment apparatus, patient interface, server computing device, a memory device, and processing device.
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.
Claims 1, 11, and 18 recite transmit the estimate such that the estimate is enabled to be presented on the patient interface. This limitation is recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Claims 1, 11 and, 18 recite control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion. This step amounts to insignificant extra solution activity. When determining if a particular treatment and prophylaxis as a practical application under Step 2A Prong Two, Examiner considered the factors presented in the MPEP 2106.04(d)(2).
Factor A: The Particularity Or Generality Of The Treatment Or Prophylaxis. The treatment plan from the abstract idea is not "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). The treatment plan is never specifically stated and the particular disease or condition that the treatment plan is treating is never specifically stated. Therefore, the claims recite a high-level recitation of a treatment plan without explicitly providing a particular treatment for a particular disease or medical condition.
Factor B. Whether the Limitation(s) Have More Than a Nominal or Insignificant Relationship to the Exception. The treatment limitation does not have a significant relationship to the judicial exception – that is it does not integrate the law of nature into a practical application. As stated above, because the treatment plan and the particular disease or condition is never explicitly stated, any possible treatment could not reasonably be considered known in the art as a treatment for any disease.
Factor C. Whether the Limitation(s) Are Merely Extra-Solution Activity or A Field of Use. The treatment or prophylaxis limitation does not impose meaningful limits on the judicial exception and is only extra-solution activity or a field-of-use (see MPEP § 2106.05(g))). Controlling a treatment apparatus based on a treatment plan is well known, and amounts to necessary data output similar to that of In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (See Analysis for Step 2B). The step does not add a meaningful limitation to the process of determining a treatment plan for a patient.
Therefore, the claims only recite the prophylactic step as a tool which only serves as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
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 an artificial intelligence engine, treatment apparatus, patient interface, server computing device, a memory device, processing device, and transmit the estimate such that the estimate is enabled to be presented on the patient interface.
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.
Claims 1, 11, and 18 recite control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion. The use of controlling the operation of a treatment apparatus based on a treatment plan is well-understood, routine, and conventional. This position is supported by (1) Shen et al, Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer (2019), teaching on determining values of the set of variables defining a treatment plan that are converted into control parameters of a treatment machine, namely a medical linear accelerator in external-beam radiation therapy (EBRT) and a remote afterloader in high-dose-rate brachytherapy (HDRBT), based on which the optimized treatment plan is delivered (Pgs. 2-3 1. Introduction); (2); Fraass et al, The impact of treatment complexity and computer-control delivery technology on treatment delivery errors (1998), teaching on computer-controlled multileaf collimators (MLCs) treated patients under the control of the computer- controlled conformal radiotherapy system (CCRS), which downloads the treatment delivery plan from the planning system and performs some (or all) of the machine set up and treatment delivery for each field (Pg. 1 Abstract); and (3) Marchal-Crespo et al., Review of control strategies for robotic movement training after neurologic injury (2009), teaching on adaptive controllers that modify control parameters based on ongoing participant performance (Pg. 1 Abstract). Therefore, controlling the operation of a treatment apparatus based on a treatment plan is not sufficient to amount to significantly more than the recited judicial exception.
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 claims 2, 12, and 19 recite generates the optimal dosage compliance plan based on the one or more dosage compliance plans and the one or more constraints, wherein each of the optimal dosage compliance plans conforms to the one or more constraints.
Dependent claim 3, 13, and 20 recite transmit the optimal dosage compliance plan to be presented on the patient interface associated with the user, wherein the transmission of the optimal dosage compliance plan is operatively configured to generate the alert for the user on the patient interface, and the alert is configured to recognize a selection by the user, wherein the selection from the user is representative of the user indicating that the user has complied with the optimal dosage compliance plan.
Dependent claims 4 and 14 recite the alert comprises one or more compliance responses configured for the selection by the user, wherein at least one of the one or more compliance responses comprises a positive indication.
Dependent claim 5 and 15 recites the positive indication comprises information representative of the user indicating that the user has complied with the optimal dosage compliance plan.
Dependent claim 6 recites an act of selecting one or more compliance responses causes a second alert comprising dosage information determined by the optimal dosage compliance plan.
Dependent claim 7 recites the alert is configured to display information pertaining to a medication, wherein the medication is associated with the optimal dosage compliance plan, and the medication is capable of being identified by the user based upon a general description of the medication.
Dependent claim 8 and 16 recite the at least one modified attribute of the user comprises dosage time information, dosage amount information, allergy information, prescribed drug information, patient-or provider-identified over-the- counter drug, vitamin, mineral, amino acid, nootropic or other nutraceutical information, side effect information, personal information, or some combination thereof.
Dependent claim 9 and 17 recite the at least one attribute of the medical prescription of the user comprises number of medications, types of medications, brands of medications, generic names of medications, the side-effects of the medication, the contraindications for using the medication, the context for taking the medication, the injury type, comorbidities, risks of taking the medication, indications to stop taking the medication, indications to inform the healthcare provider of a problem or possible side-effect of the medication, injury severities, or some combination thereof.
Dependent claim 10 recites receive refill information associated with the user; based upon the refill information, determine one or more refill outcomes for the user; and based on (i) the one or more refill outcomes for the user, (ii) the data associated with the user, (iii) the one or more dosage compliance plans, and (iv) the one or more constraints, generate the optimal dosage compliance plan comprising the at least one modified attribute of the user associated with the at least one attribute of the medical prescription.
Each of these steps of the preceding dependent claims 2-10, 12-17, and 19-20 only serve to further limit or specify the features of independent claims 1, 11, or 18 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(s) 2, 10, 12, and 19 also recite the additional element of “an artificial intelligence engine” which is analyzed the same as the “a computing device” and does not provide a practical application or significantly more for the same reasons.
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) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kraft (US 20220258935 A1) in view of Gnanasambanda (US 20230047253 A1) in view of Arric (US 20190392936 A1) in view of Bissonnette (US 20220016482 A1) in view of Alaklabi (US 20130079925 A1).
As per Claim 1, Kraft teaches a computer-implemented system for generating, by an artificial intelligence engine, a dosage compliance plan associated with a treatment apparatus, the computer- implemented system comprising:
receive data associated with the user, wherein the data comprises the at least one modified attribute of the user and the at least one attribute of the medical prescription; ([Para. 0104] receive patient information and/or reference information, including the patient information may include information relating to one or more of: weight; age; sex: body mass index; metabolism; renal function; liver enzymes; pharmacokinetics; risk factors for disease; current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; partial or full genome SNP screening data; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history; biomarker information; demographic information; patient's medical history; diagnostic information; and tissue expression profiling.)
receive one or more constraints, wherein the one or more constraints comprise rules pertaining to dosage amounts associated with the one or more dosage compliance plans;([Para. 0104] receive patient information and/or reference information, including current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history. [Para. 0065] the look-up table may include reference information 114 regarding coagulation measurements (e.g., prothrombin time (PT) and/or partial thromboplastin time (PTT)) (i.e. dosage amounts)and SNP genetic profile or full genomic sequence information . )
and generate, via the artificial intelligence engine, an optimal dosage compliance plan for the user, wherein the optimal dosage compliance plan comprises the at least one modified attribute of the user associated with the at least one attribute of the medical prescription.([Para. 0002] recommending or medicating an optimal treatment protocol and/or an optimal drug selection, combination and dosage for a particular patient, in particular, by utilizing patient information (i.e. one modified attribute of the user associated) in combination with available medical and other relevant information and datasets (i.e. at least one attribute of the medical prescription) to determine, predict or suggest an optimal drug or therapy.)
Kraft does not explicitly disclose, however Gnanasambanda discloses
receive one or more dosage compliance plans that, when applied to one or more users, encourage the one or more users to comply with medical prescriptions; ([Para. 0004] receiving a selection of a type of the care plan for the patient. [Para. 0276] the user selects the menu item Health Plans (element 812b). Accordingly, in response to the receiving the selection of the menu item Health Plans, types of health plans are shown, as illustrated in screen shot 805. The types of health plans shown with respect to Nathan's profile include: Diabetes (element 824), Cardiovascular, Asthma, and Back Pain. [Para. 0125] The patient graph for each condition may also include an engagement profile that may be used to determine a compliance of the user with the care plan. )
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 determining an optimal combination drug product as taught by Kraft and incorporate dynamically managing a goal in a care plan of a patient as taught by Gnanasambanda, with the motivation of improving the health outcomes of a group by improving clinical outcomes while lowering costs (Gnanasambanda Para. 0002).
Kraft/ Gnanasambanda do not explicitly disclose, however Arric discloses
a patient interface associated with the user, wherein the patient interface is operatively enabled to display an alert for the user; ([Para. 0086] FIGS. 10A-10B show examples of user interfaces that may be shown to a user related to receiving alerts or notifications about a prescription.)
a server computing device configured to generate the dosage compliance plan, wherein the dosage compliance plan comprises at least one modified attribute of the user, and, further, wherein the at least one modified attribute of the user is associated with at least one attribute of a medical prescription; ([Para. 0079] the medication management platform may be used with a tabletop dispenser 770, which may display information to the user on the screen 714, and the platform may also be used with a mobile application, which may utilize a smartphone or similar device (i.e. server computing device) to display information to the user. )
and a memory device including instructions that, when executed by the computing device, configure the server computing device to: ([Para. 0046] A mobile device may have a processor, a memory (i.e. memory device), a transceiver, an input, and an output.)
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 determining an optimal combination drug product as taught by Kraft, dynamically managing a goal in a care plan of a patient as taught by Gnanasambanda, and incorporate managing the dispensation of medication as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
Kraft/ Gnanasambanda/ Arric do not explicitly teach, however Bissonnette teaches
the treatment apparatus configured to be manipulated by a user; ([Para. 0010] a user performs an exercise using the exercise device (i.e. treatment apparatus).[Para. 0130] The exercise machine (i.e. treatment apparatus) 100 may be an osteogenic, muscular strengthening, isometric exercise and/or rehabilitation assembly. Solid state, static, or isometric exercise and rehabilitation equipment (e.g., exercise machine 100) can be used to facilitate osteogenic exercises that are isometric in nature and/or to facilitate muscular strengthening exercises. [Para. 0004] The terms “exercise apparatus,” “exercise device,” “electromechanical device,” “exercise machine,” “rehabilitation device,” “cycling machine” “balance board,” and “isometric exercise and rehabilitation assembly” may be used interchangeably. )
generate, via the artificial intelligence engine, a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan and includes one or more operating parameters for one or more components of the treatment apparatus to enable user adherence to the optimal dosage compliance plan; ([Para. 0010] Generating, by the artificial intelligence engine, a machine learning model trained to receive as input both onboarding data associated with a user and an onboarding protocol and, based on the onboarding data and the onboarding protocol, output an exercise plan. [Para. 0123] The improved exercise plan may be dynamically updated based on characteristics of the user, selected physical activity levels, performance measurements, user-reported difficulties of the exercises, user-reported pain levels, and the like. To comply with the exercise plan, the exercise machine may be controlled using a signal that indicates changing an attribute of an operating parameter of the exercise machine. The control system may change the attribute of the operating parameter in response to receiving the signal. [Para. 0319] The desired target zone for each user may be tailored based on the one or more characteristics of the user. The one or more characteristics may pertain to personal information, performance information, and/or measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed (i.e. optimal dosage compliance plan), a comorbidity, or some combination thereof.)
and control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion. ([Para. 0123] To comply with the exercise plan, the exercise machine may be controlled using a signal that indicates changing an attribute of an operating parameter of the exercise machine. The control system may change the attribute of the operating parameter in response to receiving the signal. [Para. 0204] The pedals 1922, 1924 may be moved along the pedal arms based on operating parameters provided in a control instruction generated by a machine learning model 60. For example, a motor and/or actuator communicatively coupled to the pedals 1922, 1924 may cause the pedals to move along the pedal arms to desired positions associated with desired range of motions. [Para. 0205] A machine learning model 60 may be trained to receive input (e.g., measurements) and to output a control instruction that causes an operating parameter of the exercise device to change. The operating parameter may represent or correspond to one or more resistances provided by one or more pedals 1922, 1924; a range of motion of the one or more pedals 1922. [Para. 0295] While the user performs the improved exercise plan, the processing device may receive data pertaining to the user. The data pertaining to the user may include one or more characteristics of the user, performance measurements, sensor measurements, user-reported difficulty of an exercise, user-reported pain level, or some combination thereof. [Para. 0312] Responsive to determining that the one or more measurements and/or the one or more characteristics of the user satisfy the trigger condition, the processing device may transmit the control instruction to the exercise device 100. In some embodiments, transmitting the control instruction to the exercise device 100 may cause the exercise device 100 to modify the resistance (i.e. range of motion) of the one or more pedals in real-time or near real-time.)
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 determining an optimal combination drug product as taught by Kraft, dynamically managing a goal in a care plan of a patient as taught by Gnanasambanda, managing the dispensation of medication as taught by Arric, and incorporate the determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Bissonnette, with the motivation of facilitating exercise, strength training, osteogenesis, and/or rehabilitation of a user (Para. 0003).
Kraft/ Gnanasambanda/ Arric/ Bissonnette does not explicitly teach, however Alaklabi teaches
generate, via the artificial intelligence engine, an estimate of the duration during which the user will be taking the medical prescription as a result of the user complying with the treatment plan that is associated with the optimal dosage compliance plan; ([Para. 0053] Sends a report after a certain period of use, which gives the patient an estimate about his/her medication compliance. It congratulates the patient when not missing any doses, or it warns him/her that medication noncompliance could cause dangerous complications when a specific number of doses are missed. This report could be exchanged between the medication management device and various mobile devices 12. [Para. 0055] The device provides a detailed medication history of the patient through a detailed report that contains all the names of the drugs being taken by the patient, doses and dose period (i.e. estimate of the duration) as shown in FIG. 4.)
transmit the estimate such that the estimate is enabled to be presented on the patient interface; ([Para. 0053] Sends a report after a certain period of use, which gives the patient an estimate about his/her medication compliance. It congratulates the patient when not missing any doses, or it warns him/her that medication noncompliance could cause dangerous complications when a specific number of doses are missed. This report could be exchanged between the medication management device and various mobile devices 12. [Para. 0055] The device provides a detailed medication history of the patient through a detailed report that contains all the names of the drugs being taken by the patient, doses and dose period (i.e. estimate of the duration) as shown in FIG. 4. [Para. 0036] The medication management device may also communicate and display information stored thereon via mobile devices 12, computers or electronic files stored at a doctor's office 14.)
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 determining an optimal combination drug product as taught by Kraft, dynamically managing a goal in a care plan of a patient as taught by Gnanasambanda, managing the dispensation of medication as taught by Arric, the determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Bissonnette, and incorporate the medication management device as taught by Alaklabi, with the motivation of managing medication and more specifically it relates to a medication management device for assisting a patient with management of health information and medication as well as communicating directly with a health provider regarding compliance (Alaklabi Para. 0004).
As per Claim 2, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 1, Kraft further teaches wherein the artificial intelligence engine generates the optimal dosage compliance plan based on the one or more dosage compliance plans and the one or more constraints, wherein each of the optimal dosage compliance plans conforms to the one or more constraints. ([Para. 0002] recommending or medicating an optimal treatment protocol and/or an optimal drug selection, combination and dosage for a particular patient, in particular, by utilizing patient information (i.e. one modified attribute of the user associated) in combination with available medical and other relevant information and datasets (i.e. at least one attribute of the medical prescription) to determine, predict or suggest an optimal drug or therapy.)
As per Claim 3, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 1, Arric further teaches wherein the server computing device is further configured to: transmit the optimal dosage compliance plan to be presented on the patient interface associated with the user, ([Para. 0084] To scan and import prescription information from a medication label, first, a user of the medication management system may launch the mobile application to access the system. Next, the mobile application may automatically launch the camera of the mobile device being used to access the mobile application. The camera may then be used to capture the medication label. The user may be presented with a user interface screen as seen in FIG. 8A, for example. )
wherein the transmission of the optimal dosage compliance plan is operatively configured to generate the alert for the user on the patient interface, and the alert is configured to recognize a selection by the user, wherein the selection from the user is representative of the user indicating that the user has complied with the optimal dosage compliance plan. ([Para. 0084] Next, when an image of the label is properly captured, a character recognition algorithm may be used to detect a patient name 876a, medication name 876b, instructions for medication intake and frequency of intake 876c, medication dosage 876d, provider name (not shown), and pharmacy name 876e. The algorithm may then convert the found information into discrete text, and prompt the user to verify whether the information was correctly captured. Next, after confirmation of the information, the information may be imported into the user's profile within the medication management system. The mobile application may next prompt the user to confirm whether or not consumption of the medication has already taken place.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, and Bissonnette, incorporate transmitting medication information and recognizing the selection of the user as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
As per Claim 4, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 3, Arric further teaches wherein the alert comprises one or more compliance responses configured for the selection by the user, wherein at least one of the one or more compliance responses comprises a positive indication. ([Para. 0084] The mobile application may next prompt the user to confirm whether or not consumption of the medication has already taken place. If “Yes,” the mobile application may next prompt the user to verify the known time of the last consumption. Examiner interprets the selection of “yes” to be indicative of a positive indication.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, and Bissonnette, and incorporate determining medication compliance as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
As per Claim 5, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 4, Arric further teaches wherein the positive indication comprises information representative of the user indicating that the user has complied with the optimal dosage compliance plan. ([Para. 0084] The mobile application may next prompt the user to confirm whether or not consumption of the medication has already taken place. If “Yes,” the mobile application may next prompt the user to verify the known time of the last consumption. Next, the medication management system may implement an algorithm for monitoring consumption times and notifying or alerting the user of the need to consume medication according to the prescription in their profile.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, and Bissonnette, and incorporate determining medication compliance as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
As per Claim 6, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 4, Arric further teaches wherein an act of selecting one or more compliance responses causes a second alert comprising dosage information determined by the optimal dosage compliance plan.([Para. 0084] Next, the medication management system may implement an algorithm for monitoring consumption times and notifying or alerting the user of the need to consume medication according to the prescription in their profile.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, and Bissonnette, and incorporate determining medication compliance as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
As per Claim 7, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 3, Arric further teaches wherein the alert is configured to display information pertaining to a medication, wherein the medication is associated with the optimal dosage compliance plan, and the medication is capable of being identified by the user based upon a general description of the medication. ([Para. 0007] the medication dispenser displays an alert to the user when medication consumption is needed according to a medication regimen associated with the user. [Para. 0116] when the medication management system determines that it is time for a user to consume medication, the compartmental dispenser 1960 may send an alert through either the lights 1937, the speaker 1964, the screen 1914, or any combination thereof. The screen 1914 may display the dosage and any other medication instructions.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, and Bissonnette, and incorporate determining medication compliance as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
As per Claim 8, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 1, Kraft further teaches wherein the at least one modified attribute of the user comprises dosage time information, dosage amount information, allergy information, prescribed drug information, patient-or provider-identified over-the- counter drug, vitamin, mineral, amino acid, nootropic or other nutraceutical information, side effect information, personal information, or some combination thereof. ([Para. 0104] receive patient information and/or reference information, including the patient information may include information relating to one or more of: weight; age; sex: body mass index; metabolism; renal function; liver enzymes; pharmacokinetics; risk factors for disease; current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; partial or full genome SNP screening data; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history; biomarker information; demographic information; patient's medical history; diagnostic information; and tissue expression profiling.)
As per Claim 9, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 1, Kraft further teaches wherein the at least one attribute of the medical prescription of the user comprises number of medications, types of medications, brands of medications, generic names of medications, the side-effects of the medication, the contraindications for using the medication, the context for taking the medication, the injury type, comorbidities, risks of taking the medication, indications to stop taking the medication, indications to inform the healthcare provider of a problem or possible side-effect of the medication, injury severities, or some combination thereof. ([Para. 0104] receive patient information and/or reference information, including the patient information may include information relating to one or more of: weight; age; sex: body mass index; metabolism; renal function; liver enzymes; pharmacokinetics; risk factors for disease; current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; partial or full genome SNP screening data; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history; biomarker information; demographic information; patient's medical history; diagnostic information; and tissue expression profiling.)
As per Claim 10, Kraft/ Gnanasambanda/ Arric/ Bissonnette/ Alaklabi teach the computer-implemented system of claim 1, Arric further teaches wherein the server computing device is further configured to: receive refill information associated with the user; based upon the refill information, determine, via the artificial intelligence engine, one or more refill outcomes for the user; and based on (i) the one or more refill outcomes for the user, (ii) the data associated with the user, (iii) the one or more dosage compliance plans, and (iv) the one or more constraints, generate, via the artificial intelligence engine, the optimal dosage compliance plan comprising the at least one modified attribute of the user associated with the at least one attribute of the medical prescription. ([Para. 0117] the compartmental dispenser working together with the medication management platform may also send reminders to users, such as reminders to refill and pickup medications related to chronic illnesses such as diabetes, hypertension, cholesterol management, mental health, asthma, and diseases such as diabetes requiring treatment with statin. When a refill of medication is received by a user, the following exemplary process may be carried out. First, the user may verify a connection (such as via Bluetooth) between the compartmental dispenser and their electronic or mobile device. Next, the user may scan or manually input their medication information from a prescription or medication bottle into the mobile application. Next, if the medication management platform finds that the medication already exists within the user's profile, the platform may prompt the user to confirm whether the new scan is a refill of the exact medication that previously existed in the system. Next, if the user confirms “Yes” via the mobile application, the mobile application may automatically send a signal to the compartmental dispenser 1960 and highlight a compartment 1961 that housed the same medication using a blinking light or group of lights 1937.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, Bissonnette, and Alaklabi, and incorporate determining medication compliance as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
Claim(s) 11-12 and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kraft (US 20220258935 A1) in view of Gnanasambanda (US 20230047253 A1) in view of Bissonnette (US 20220016482 A1) in view of Alaklabi (US 20130079925 A1).
As per Claim 11, Kraft teaches a computer-implemented method for generating, by an artificial intelligence engine, a dosage compliance plan associated with a treatment apparatus, the computer- implemented method comprising:
receiving data associated with the user, wherein the data comprises at least one modified attribute of the user and at least one attribute of the medical prescription; ([Para. 0104] receive patient information and/or reference information, including the patient information may include information relating to one or more of: weight; age; sex: body mass index; metabolism; renal function; liver enzymes; pharmacokinetics; risk factors for disease; current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; partial or full genome SNP screening data; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history; biomarker information; demographic information; patient's medical history; diagnostic information; and tissue expression profiling.)
receiving one or more constraints, wherein the one or more constraints comprises rules pertaining to dosage amounts associated with the one or more dosage compliance plans; ([Para. 0104] receive patient information and/or reference information, including current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history. [Para. 0065] the look-up table may include reference information 114 regarding coagulation measurements (e.g., prothrombin time (PT) and/or partial thromboplastin time (PTT)) (i.e. dosage amounts)and SNP genetic profile or full genomic sequence information . )
and generating an optimal dosage compliance plan for the user comprising the at least one modified attribute of the user associated with the at least one attribute of the medical prescription. ([Para. 0002] recommending or medicating an optimal treatment protocol and/or an optimal drug selection, combination and dosage for a particular patient, in particular, by utilizing patient information (i.e. one modified attribute of the user associated) in combination with available medical and other relevant information and datasets (i.e. at least one attribute of the medical prescription) to determine, predict or suggest an optimal drug or therapy.)
Kraft does not explicitly disclose, however Gnanasambanda
receiving one or more dosage compliance plans that, when applied to one or more users, encourage users to comply with medical prescriptions; ([Para. 0004] receiving a selection of a type of the care plan for the patient. [Para. 0276] the user selects the menu item Health Plans (element 812b). Accordingly, in response to the receiving the selection of the menu item Health Plans, types of health plans are shown, as illustrated in screen shot 805. The types of health plans shown with respect to Nathan's profile include: Diabetes (element 824), Cardiovascular, Asthma, and Back Pain. [Para. 0125] The patient graph for each condition may also include an engagement profile that may be used to determine a compliance of the user with the care plan. )
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 generating a dosage compliance plan as taught by Kraft and incorporate receive one or more dosage compliance plans that, when applied to one or more users, encourage users to comply with medical prescriptions as taught by Gnanasambanda, with the motivation of improving the health outcomes of a group by improving clinical outcomes while lowering costs (Gnanasambanda Para. 0002).
Kraft/ Gnanasambanda do not explicitly disclose, however Bissonnette discloses
generating, via the artificial intelligence engine, a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan and includes one or more operating parameters for one or more components of the treatment apparatus to enable user adherence to the optimal dosage compliance plan; ([Para. 0010] Generating, by the artificial intelligence engine, a machine learning model trained to receive as input both onboarding data associated with a user and an onboarding protocol and, based on the onboarding data and the onboarding protocol, output an exercise plan. [Para. 0123] The improved exercise plan may be dynamically updated based on characteristics of the user, selected physical activity levels, performance measurements, user-reported difficulties of the exercises, user-reported pain levels, and the like. To comply with the exercise plan, the exercise machine may be controlled using a signal that indicates changing an attribute of an operating parameter of the exercise machine. The control system may change the attribute of the operating parameter in response to receiving the signal. [Para. 0319] The desired target zone for each user may be tailored based on the one or more characteristics of the user. The one or more characteristics may pertain to personal information, performance information, and/or measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed (i.e. optimal dosage compliance plan), a comorbidity, or some combination thereof.)
and controlling, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion. ([Para. 0123] To comply with the exercise plan, the exercise machine may be controlled using a signal that indicates changing an attribute of an operating parameter of the exercise machine. The control system may change the attribute of the operating parameter in response to receiving the signal. [Para. 0204] The pedals 1922, 1924 may be moved along the pedal arms based on operating parameters provided in a control instruction generated by a machine learning model 60. For example, a motor and/or actuator communicatively coupled to the pedals 1922, 1924 may cause the pedals to move along the pedal arms to desired positions associated with desired range of motions. [Para. 0205] A machine learning model 60 may be trained to receive input (e.g., measurements) and to output a control instruction that causes an operating parameter of the exercise device to change. The operating parameter may represent or correspond to one or more resistances provided by one or more pedals 1922, 1924; a range of motion of the one or more pedals 1922. [Para. 0295] While the user performs the improved exercise plan, the processing device may receive data pertaining to the user. The data pertaining to the user may include one or more characteristics of the user, performance measurements, sensor measurements, user-reported difficulty of an exercise, user-reported pain level, or some combination thereof. [Para. 0312] Responsive to determining that the one or more measurements and/or the one or more characteristics of the user satisfy the trigger condition, the processing device may transmit the control instruction to the exercise device 100. In some embodiments, transmitting the control instruction to the exercise device 100 may cause the exercise device 100 to modify the resistance of the one or more pedals in real-time or near real-time.)
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 determining an optimal combination drug product as taught by Kraft, dynamically managing a goal in a care plan of a patient as taught by Gnanasambanda, and incorporate the determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Bissonnette, with the motivation of facilitating exercise, strength training, osteogenesis, and/or rehabilitation of a user (Para. 0003).
Kraft/ Gnanasambanda/ Bissonnette does not explicitly teach, however Alaklabi teaches
generate, via the artificial intelligence engine, an estimate of the duration during which the user will be taking the medical prescription as a result of the user complying with the treatment plan that is associated with the optimal dosage compliance plan; ([Para. 0053] Sends a report after a certain period of use, which gives the patient an estimate about his/her medication compliance. It congratulates the patient when not missing any doses, or it warns him/her that medication noncompliance could cause dangerous complications when a specific number of doses are missed. This report could be exchanged between the medication management device and various mobile devices 12. [Para. 0055] The device provides a detailed medication history of the patient through a detailed report that contains all the names of the drugs being taken by the patient, doses and dose period (i.e. estimate of the duration) as shown in FIG. 4.)
transmit the estimate such that the estimate is enabled to be presented on the patient interface; ([Para. 0053] Sends a report after a certain period of use, which gives the patient an estimate about his/her medication compliance. It congratulates the patient when not missing any doses, or it warns him/her that medication noncompliance could cause dangerous complications when a specific number of doses are missed. This report could be exchanged between the medication management device and various mobile devices 12. [Para. 0055] The device provides a detailed medication history of the patient through a detailed report that contains all the names of the drugs being taken by the patient, doses and dose period (i.e. estimate of the duration) as shown in FIG. 4. [Para. 0036] The medication management device may also communicate and display information stored thereon via mobile devices 12, computers or electronic files stored at a doctor's office 14.)
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 determining an optimal combination drug product as taught by Kraft, dynamically managing a goal in a care plan of a patient as taught by Gnanasambanda, managing the dispensation of medication as taught by Arric, the determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Bissonnette, and incorporate the medication management device as taught by Alaklabi, with the motivation of managing medication and more specifically it relates to a medication management device for assisting a patient with management of health information and medication as well as communicating directly with a health provider regarding compliance (Alaklabi Para. 0004).
As per Claims 12, Kraft/ Gnanasambanda/ Bissonnette/ Alaklabi teach computer-implemented method of claim 11, Kraft further teaches wherein the artificial intelligence engine generates the optimal dosage compliance plan based on the one or more dosage compliance plans and the one or more constraints, wherein each of the optimal dosage compliance plans conforms to the one or more constraints. ([Para. 0002] recommending or medicating an optimal treatment protocol and/or an optimal drug selection, combination and dosage for a particular patient, in particular, by utilizing patient information (i.e. one modified attribute of the user associated) in combination with available medical and other relevant information and datasets (i.e. at least one attribute of the medical prescription) to determine, predict or suggest an optimal drug or therapy.)
As per Claim 16, Kraft/ Gnanasambanda/ Bissonnette/ Alaklabi teach the computer-implemented method of claim 11, Kraft further teaches wherein the at least one modified attribute of the user comprises dosage time information, dosage amount information, allergy information, prescribed drug information, patient-or provider-identified over-the-counter drug, vitamin, mineral, amino acid, nootropic or other nutraceutical information, side effect information, personal information, or some combination thereof. ([Para. 0104] receive patient information and/or reference information, including the patient information may include information relating to one or more of: weight; age; sex: body mass index; metabolism; renal function; liver enzymes; pharmacokinetics; risk factors for disease; current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; partial or full genome SNP screening data; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history; biomarker information; demographic information; patient's medical history; diagnostic information; and tissue expression profiling.)
As per Claim 17, Kraft/ Gnanasambanda/ Bissonnette/ Alaklabi teach the computer-implemented method of claim 11, Kraft further teaches wherein the at least one attribute of the medical prescription of the user comprises number of medications, types of medications, brands of medications, generic names of medications, the side-effects of the medication, the contraindications for using the medication, the context for taking the medication, the injury type, comorbidities, risks of taking the medication, indications to stop taking the medication, indications to inform the healthcare provider of a problem or possible side-effect of the medication, injury severities, or some combination thereof. ([Para. 0104] receive patient information and/or reference information, including the patient information may include information relating to one or more of: weight; age; sex: body mass index; metabolism; renal function; liver enzymes; pharmacokinetics; risk factors for disease; current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; partial or full genome SNP screening data; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history; biomarker information; demographic information; patient's medical history; diagnostic information; and tissue expression profiling.)
As per Claim 18, Kraft teaches for storing instructions for generating, by an artificial intelligence engine, a dosage compliance plan associated with a treatment apparatus that modifies at least one attribute of a user, wherein the at least one attribute of the user is associated with the at least one attribute of a medical prescription, wherein a processing device executes the instructions to:
receive data associated with the user, wherein the data comprises the at least one modified attribute of the user and the at least one attribute of the medical prescription; ([Para. 0104] receive patient information and/or reference information, including the patient information may include information relating to one or more of: weight; age; sex: body mass index; metabolism; renal function; liver enzymes; pharmacokinetics; risk factors for disease; current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; partial or full genome SNP screening data; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history; biomarker information; demographic information; patient's medical history; diagnostic information; and tissue expression profiling.)
receive one or more constraints, wherein the one or more constraints comprises rules pertaining to dosage amounts associated with the one or more dosage compliance plans; ([Para. 0104] receive patient information and/or reference information, including current medications; other medications; other minerals/vitamins/supplements, history of prior side effects to one or more medications; analysis of pharmacogenomic and/or pharmacogenetic profile; drug-drug interaction information; drug-diet interaction information; whole or partial genome analysis; vitamin deficiencies; diet; drug allergies and/or sensitivities; environmental, toxin or other allergy history. [Para. 0065] the look-up table may include reference information 114 regarding coagulation measurements (e.g., prothrombin time (PT) and/or partial thromboplastin time (PTT)) (i.e. dosage amounts)and SNP genetic profile or full genomic sequence information . )
and generate, via an artificial intelligence engine, an optimal dosage compliance plan for the user comprising the at least one modified attribute of the user associated with the at least one attribute of the medical prescription. ([Para. 0002] recommending or medicating an optimal treatment protocol and/or an optimal drug selection, combination and dosage for a particular patient, in particular, by utilizing patient information (i.e. one modified attribute of the user associated) in combination with available medical and other relevant information and datasets (i.e. at least one attribute of the medical prescription) to determine, predict or suggest an optimal drug or therapy.)
Kraft does not explicitly disclose, however Gnanasambanda discloses
A tangible, non-transitory computer-readable medium ([Para. 0006] a tangible, non-transitory computer-readable medium stores instructions)
receive one or more dosage compliance plans that, when applied to one or more users, encourage users to comply with medical prescriptions; ([Para. 0004] receiving a selection of a type of the care plan for the patient. [Para. 0276] the user selects the menu item Health Plans (element 812b). Accordingly, in response to the receiving the selection of the menu item Health Plans, types of health plans are shown, as illustrated in screen shot 805. The types of health plans shown with respect to Nathan's profile include: Diabetes (element 824), Cardiovascular, Asthma, and Back Pain. [Para. 0125] The patient graph for each condition may also include an engagement profile that may be used to determine a compliance of the user with the care plan. )
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 generating a dosage compliance plan as taught by Kraft and incorporate a tangible, non-transitory computer-readable medium, by an artificial intelligence engine, processing device, receive one or more dosage compliance plans that, when applied to one or more users, encourage users to comply with medical prescriptions as taught by Gnanasambanda, with the motivation of improving the health outcomes of a group by improving clinical outcomes while lowering costs (Gnanasambanda Para. 0002).
Kraft/ Gnanasambanda do not explicitly teach, however Bissonnette teaches
generate, via the artificial intelligence engine, a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan and includes one or more operating parameters for one or more components of the treatment apparatus to enable user adherence to the optimal dosage compliance plan; ([Para. 0010] Generating, by the artificial intelligence engine, a machine learning model trained to receive as input both onboarding data associated with a user and an onboarding protocol and, based on the onboarding data and the onboarding protocol, output an exercise plan. [Para. 0123] The improved exercise plan may be dynamically updated based on characteristics of the user, selected physical activity levels, performance measurements, user-reported difficulties of the exercises, user-reported pain levels, and the like. To comply with the exercise plan, the exercise machine may be controlled using a signal that indicates changing an attribute of an operating parameter of the exercise machine. The control system may change the attribute of the operating parameter in response to receiving the signal. [Para. 0319] The desired target zone for each user may be tailored based on the one or more characteristics of the user. The one or more characteristics may pertain to personal information, performance information, and/or measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed (i.e. optimal dosage compliance plan), a comorbidity, or some combination thereof.)
and control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion. ([Para. 0123] To comply with the exercise plan, the exercise machine may be controlled using a signal that indicates changing an attribute of an operating parameter of the exercise machine. The control system may change the attribute of the operating parameter in response to receiving the signal. [Para. 0204] The pedals 1922, 1924 may be moved along the pedal arms based on operating parameters provided in a control instruction generated by a machine learning model 60. For example, a motor and/or actuator communicatively coupled to the pedals 1922, 1924 may cause the pedals to move along the pedal arms to desired positions associated with desired range of motions. [Para. 0205] A machine learning model 60 may be trained to receive input (e.g., measurements) and to output a control instruction that causes an operating parameter of the exercise device to change. The operating parameter may represent or correspond to one or more resistances provided by one or more pedals 1922, 1924; a range of motion of the one or more pedals 1922. [Para. 0295] While the user performs the improved exercise plan, the processing device may receive data pertaining to the user. The data pertaining to the user may include one or more characteristics of the user, performance measurements, sensor measurements, user-reported difficulty of an exercise, user-reported pain level, or some combination thereof. [Para. 0312] Responsive to determining that the one or more measurements and/or the one or more characteristics of the user satisfy the trigger condition, the processing device may transmit the control instruction to the exercise device 100. In some embodiments, transmitting the control instruction to the exercise device 100 may cause the exercise device 100 to modify the resistance of the one or more pedals in real-time or near real-time.)
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 determining an optimal combination drug product as taught by Kraft, dynamically managing a goal in a care plan of a patient as taught by Gnanasambanda, and incorporate managing the dispensation of medication as taught by Arric, and incorporate the determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Bissonnette, with the motivation of facilitating exercise, strength training, osteogenesis, and/or rehabilitation of a user (Para. 0003).
Kraft/ Gnanasambanda/ Bissonnette does not explicitly teach, however Alaklabi teaches
generate, via the artificial intelligence engine, an estimate of the duration during which the user will be taking the medical prescription as a result of the user complying with the treatment plan that is associated with the optimal dosage compliance plan; ([Para. 0053] Sends a report after a certain period of use, which gives the patient an estimate about his/her medication compliance. It congratulates the patient when not missing any doses, or it warns him/her that medication noncompliance could cause dangerous complications when a specific number of doses are missed. This report could be exchanged between the medication management device and various mobile devices 12. [Para. 0055] The device provides a detailed medication history of the patient through a detailed report that contains all the names of the drugs being taken by the patient, doses and dose period (i.e. estimate of the duration) as shown in FIG. 4.)
transmit the estimate such that the estimate is enabled to be presented on the patient interface; ([Para. 0053] Sends a report after a certain period of use, which gives the patient an estimate about his/her medication compliance. It congratulates the patient when not missing any doses, or it warns him/her that medication noncompliance could cause dangerous complications when a specific number of doses are missed. This report could be exchanged between the medication management device and various mobile devices 12. [Para. 0055] The device provides a detailed medication history of the patient through a detailed report that contains all the names of the drugs being taken by the patient, doses and dose period (i.e. estimate of the duration) as shown in FIG. 4. [Para. 0036] The medication management device may also communicate and display information stored thereon via mobile devices 12, computers or electronic files stored at a doctor's office 14.)
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 determining an optimal combination drug product as taught by Kraft, dynamically managing a goal in a care plan of a patient as taught by Gnanasambanda, managing the dispensation of medication as taught by Arric, the determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Bissonnette, and incorporate the medication management device as taught by Alaklabi, with the motivation of managing medication and more specifically it relates to a medication management device for assisting a patient with management of health information and medication as well as communicating directly with a health provider regarding compliance (Alaklabi Para. 0004).
As per Claims 19, Kraft/ Gnanasambanda/ Bissonnette/ Alaklabi teach computer-readable medium of claim 18, Kraft further teaches wherein the artificial intelligence engine generates the optimal dosage compliance plan based on the one or more dosage compliance plans and the one or more constraints, wherein each of the optimal dosage compliance plans conforms to the one or more constraints. ([Para. 0002] recommending or medicating an optimal treatment protocol and/or an optimal drug selection, combination and dosage for a particular patient, in particular, by utilizing patient information (i.e. one modified attribute of the user associated) in combination with available medical and other relevant information and datasets (i.e. at least one attribute of the medical prescription) to determine, predict or suggest an optimal drug or therapy.)
Claim(s) 13-15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kraft (US 20220258935 A1) in view of Gnanasambanda (US 20230047253 A1) in view of Bissonnette (US 20220016482 A1) in view of Alaklabi (US 20130079925 A1) in view of Arric (US 20190392936 A1).
As per Claim 13, Kraft/ Gnanasambanda/ Bissonnette/ Alaklabi teach the computer-implemented method of claim 11, however Arric teaches further comprising transmitting the optimal dosage compliance plan to be presented on the patient interface associated with the user, ([Para. 0084] To scan and import prescription information from a medication label, first, a user of the medication management system may launch the mobile application to access the system. Next, the mobile application may automatically launch the camera of the mobile device being used to access the mobile application. The camera may then be used to capture the medication label. The user may be presented with a user interface screen as seen in FIG. 8A, for example.)
wherein the transmission of the optimal dosage compliance plan generates the alert for the user on the patient interface, and a selection is received from the user, wherein the selection from the user is representative of the user indicating that the user has complied with the optimal dosage compliance plan. ([Para. 0084] Next, when an image of the label is properly captured, a character recognition algorithm may be used to detect a patient name 876a, medication name 876b, instructions for medication intake and frequency of intake 876c, medication dosage 876d, provider name (not shown), and pharmacy name 876e. The algorithm may then convert the found information into discrete text, and prompt the user to verify whether the information was correctly captured. Next, after confirmation of the information, the information may be imported into the user's profile within the medication management system. The mobile application may next prompt the user to confirm whether or not consumption of the medication has already taken place.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, Bissonnette, and Alaklabi and incorporate transmitting medication information and recognizing the selection of the user as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
As per Claim 14, Kraft/ Gnanasambanda/ Bissonnette/ Alaklabi teach the computer-implemented method of claim 11, however Arric teaches wherein the alert comprises one or more compliance responses configured for the selection by the user, wherein at least one of the one or more compliance responses comprises a positive indication. ([Para. 0084] The mobile application may next prompt the user to confirm whether or not consumption of the medication has already taken place. If “Yes,” the mobile application may next prompt the user to verify the known time of the last consumption. Examiner interprets the selection of “yes” to be indicative of a positive indication.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, Bissonnette, and Alaklabi and incorporate determining medication compliance as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
As per Claim 15, Kraft/ Gnanasambanda/ Bissonnette/ Alaklabi teach the computer-implemented method of claim 14, however Arric teaches wherein the positive indication comprises information representative of the user indicating that the user has complied with the optimal dosage compliance plan. ([Para. 0084] The mobile application may next prompt the user to confirm whether or not consumption of the medication has already taken place. If “Yes,” the mobile application may next prompt the user to verify the known time of the last consumption. Next, the medication management system may implement an algorithm for monitoring consumption times and notifying or alerting the user of the need to consume medication according to the prescription in their profile.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, Bissonnette, and Alaklabi and incorporate determining medication compliance as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
As per Claim 20, Kraft/ Gnanasambanda/ Bissonnette/ Alaklabi teach the computer-readable medium of claim 18, however Arric teaches further comprising transmitting the optimal dosage compliance plan to be presented on the patient interface associated with the user, ([Para. 0084] To scan and import prescription information from a medication label, first, a user of the medication management system may launch the mobile application to access the system. Next, the mobile application may automatically launch the camera of the mobile device being used to access the mobile application. The camera may then be used to capture the medication label. The user may be presented with a user interface screen as seen in FIG. 8A, for example.)
wherein the transmission of the optimal dosage compliance plan generates the alert for the user on the patient interface, and a selection is received from the user, wherein the selection from the user is representative of the user indicating that the user has complied with the optimal dosage compliance plan. ([Para. 0084] Next, when an image of the label is properly captured, a character recognition algorithm may be used to detect a patient name 876a, medication name 876b, instructions for medication intake and frequency of intake 876c, medication dosage 876d, provider name (not shown), and pharmacy name 876e. The algorithm may then convert the found information into discrete text, and prompt the user to verify whether the information was correctly captured. Next, after confirmation of the information, the information may be imported into the user's profile within the medication management system. The mobile application may next prompt the user to confirm whether or not consumption of the medication has already taken place.)
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 determining an optimal combination drug product, dynamically managing a goal in a care plan of a patient, and determination of an exercise plan for a patient and the control instruction for the exercise machine as taught by Kraft, Gnanasambanda, Bissonnette, and Alaklabi and incorporate transmitting medication information and recognizing the selection of the user as taught by Arric, with the motivation of helping the patient of physician decide whether a particular medication needs to be stopped, adjusted, or added to, leading to better outcomes, adherence, and cost savings (Arric Para. 0016).
Response to Arguments
Applicant's arguments, see pgs. 9-13 “Rejections under 35 U.S.C. 101” filed 01/26/2026 have been fully considered but they are not persuasive.
Applicant argues that “control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion” does not amount to insignificant extra solution activity because this recitation is an affirmative step that enables user adherence to an optimal dosage compliance plan, which is a paramount step of the independent claims. Examiner respectfully disagrees. The limitation of control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion is analyzed under Step 2A, Prong Two and Step 2B, as an additional element that only recites the prophylactic step as a tool which only serves as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception. Examiner considered the factors presented in the MPEP 2106.04(d)(2), which include (A) the particularity or generality of the treatment or prophylaxis, (B) whether the limitation(s) have more than a nominal or insignificant relationship to the exception, and (C) whether the limitation(s) are merely extra-solution activity or a field of use. In regards to Factor A, the claims recite a high-level recitation of a treatment plan without explicitly providing a particular treatment for a particular disease or medical condition. Furthermore, the claims do not explicitly state the particular disease or medical condition that the treatment plan is aimed to treat. In regards, to Factor B, due to the lack of clarity of which particular disease or medical condition that the treatment plan aims to treat, any possible treatment combination could not reasonably be considered known in the art as a treatment for any disease. In regards to Factor C, the use of controlling the operation of a treatment apparatus based on a treatment plan is well-understood, routine, and conventional. This position is supported by (1) Shen et al, Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer (2019); (2) Fraass et al, The impact of treatment complexity and computer-control delivery technology on treatment delivery errors (1998); and (3) Marchal-Crespo et al., Review of control strategies for robotic movement training after neurologic injury (2009). Therefore, controlling the operation of a treatment apparatus based on a treatment plan is not sufficient to amount to significantly more than the recited judicial exception. Therefore, the claims only recite the prophylactic step as a tool which only serves as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
Applicant argues that reciting control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion, cannot be performed in the human mind and is not an abstract idea. Examiner notes that control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion has never been identified as being part of the abstract idea. The limitation of control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion is analyzed under Step 2A, Prong Two and Step 2B, as an additional element that only recites the prophylactic step as a tool which only serves as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
Applicant argues that the claim limitations are indicative of a partial application of an abstract idea include “applying the judicial exception with, or by use of, a particular machine”. MPEP 2106.05(b) describes that the particular machine needs to have a specific structure (e.g., Eibel's Fourdrinier machine), which the recited pedal of the treatment apparatus does have, and that the particular machine not be a generic computer. Further, regarding MPEP 2106.05(f), the pedal of the treatment apparatus claimed and described in the specification is significantly more because it "plays a significant part in permitting the claimed method to be performed”. Examiner respectfully disagrees. The treatment apparatus is not recited to any degree of particularity in the claim limitation(s). The Specification states: “The system 10 also includes a treatment apparatus 70 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment apparatus 70 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment apparatus 70 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, or the like.” (Para. 0095). The use of the treatment apparatus and the pedal of the treatment apparatus is not considered a particular machine because the treatment apparatus is not recited to any degree of particularity and the treatment apparatus is being utilized for its intended use. No technical modification is made to the treatment apparatus or the pedal of the treatment apparatus that would provide any technical improvement. The claim limitations are directed towards generating a dosage compliance plan associated with a treatment apparatus. The limitation of control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion is not integral to claim generating a treatment plan, therefore this claim limitation does not amount to significantly more because it plays a significant part in permitting the claimed method to be performed.
Applicant argues that the claims do not fall into the abstract category of organizing human activity and features of the claim do not recite a mental process because they are not recited at “a high level of generality such that they could practically be performed in the human mind”. Examiner respectfully disagrees. The steps of (Claim 11 being representative) receiving one or more dosage compliance plans that, when applied to one or more users, encourage users to comply with medical prescriptions; receiving data associated with the user, wherein the data comprises at least one modified attribute of the user and at least one attribute of the medical prescription; receiving one or more constraints, wherein the one or more constraints comprises rules pertaining to dosage amounts associated with the one or more dosage compliance plans; generating an optimal dosage compliance plan for the user comprising the at least one modified attribute of the user associated with the at least one attribute of the medical prescription; generating, via the artificial intelligence engine, a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan, and generate an estimate of the duration during which the user will be taking the medical prescription as a result of the user complying with the treatment plan that is associated with the optimal dosage compliance plan- these actions are at best characterized as human tasks. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or person would follow to obtain and extract medical data, the claimed invention is directed to an abstract idea. The step of transmit the estimate such that the estimate presented on the patient interface is recited are recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception. The step of controlling, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion amounts to a claim limitation that only recites the prophylactic step as a tool which only serves as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception. Examiner notes that mental process has never been identified as the basis of the 101 rejection.
Applicant argues that the claim limitations improve an existing technological process and are not directed to a mere abstract idea. Specifically, implementing at least one limitation of claim 1 requires complex processing and therefore imposes meaningful limits on the claim. Examiner respectfully disagrees. There is no recitation of how controlling the operation of treatment apparatus by using a treatment plan including a range of motion associated with an optimal dosage compliance plan is an improvement to an existing technological process. The Specification states: “The system 10 also includes a treatment apparatus 70 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment apparatus 70 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment apparatus 70 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, or the like.” (Para. 0095). The treatment apparatus is not specified to any degree of particularity. The operation of the treatment apparatus is not being utilized beyond its intended use. The claims recite the additional elements of an artificial intelligence engine, treatment apparatus, patient interface, server computing device, a memory device, and processing device that conduct the processing of dosage compliance plans, attributes of the user and the medical prescription, and the constraints of dosage amounts to generate a treatment plan for the user. 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 utilization of these additional elements does not amount to complex processing when the additional elements are merely being utilized as mere instruction to apply the exception.
Applicant argues that the limitations of these claims amount to significantly more than any abstract idea because the recited structural elements are required to execute the limitations of the claims and are therefore inseparable from these limitations. Further, these limitations, taken as a whole, constitute an improvement to the technological field of predictive performance of controlling operation of treatment apparatuses by using a treatment plan including a range of motion associated with an optimal dosage compliance plan. Examiner respectfully disagrees. The claims recite an artificial intelligence engine, treatment apparatus, patient interface, server computing device, a memory device, and processing device, which are additional elements that amount to mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons. The limitation of control, using the treatment plan, wherein the treatment plan includes at least one operating parameter of at least one pedal of the treatment apparatus that enables a range of motion, operation of the at least one operating parameter of the at least one pedal of the treatment apparatus that enables the range of motion is analyzed under Step 2A, Prong Two and Step 2B, as an additional element that only recites the prophylactic step as a tool which only serves as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception. The Specification states: “The system 10 also includes a treatment apparatus 70 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment apparatus 70 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment apparatus 70 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, or the like.” (Para. 0095). The use of the treatment apparatus and the pedal of the treatment apparatus is not considered a particular machine because the treatment apparatus is not recited to any degree of particularity and the treatment apparatus is being utilized for its intended use.
Applicant's arguments, see pgs. 13-15 “Rejections under 35 U.S.C. 103” filed 01/26/2026 have been fully considered but they are not persuasive.
Applicant argues that the cited prior art does not teach or suggest the teaching of generate, via the artificial intelligence engine, a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan and includes one or more operating parameters for one or more components of the treatment apparatus to enable user adherence to the optimal dosage compliance plan. Examiner respectfully disagrees.
Bissonnette teaches at Para. 0010 that generating, by the artificial intelligence engine, a machine learning model trained to receive as input both onboarding data associated with a user and an onboarding protocol and, based on the onboarding data and the onboarding protocol, output an exercise plan. Para. 0123 further teaches the improved exercise plan may be dynamically updated based on characteristics of the user, selected physical activity levels, performance measurements, user-reported difficulties of the exercises, user-reported pain levels, and the like. To comply with the exercise plan, the exercise machine may be controlled using a signal that indicates changing an attribute of an operating parameter of the exercise machine. The control system may change the attribute of the operating parameter in response to receiving the signal. Para. 0319 teaches the desired target zone for each user may be tailored based on the one or more characteristics of the user. The one or more characteristics may pertain to personal information, performance information, and/or measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed (i.e. optimal dosage compliance plan), a comorbidity, or some combination thereof. This is indicative of generate, via the artificial intelligence engine, a treatment plan for the user, wherein the treatment plan is associated with the optimal dosage compliance plan and includes one or more operating parameters for one or more components of the treatment apparatus to enable user adherence to the optimal dosage compliance plan.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/P.K.E./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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