DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to Applicant’s communication filed on October 2, 2025.
Claims 1, 24, 26 and 28 have been amended and are hereby entered.
Claims 1-19 and 21-28 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on October 2, 2025 has been entered.
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 1-19 and 21-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
Claims 1 and 26 are directed to a system and a method respectively and therefore all fall into one of the four statutory categories. (Step 1: Yes, the claims fall into one of the four statutory categories).
Step 2A analysis - Prong one:
The substantially similar independent system and method claims, taking claim 26 as exemplary, recite the following limitations: receiving, by one or more processors, one or more physiological measurements of a user; applying, by the one or more processors, the one or more measurements to a machine learning model, wherein the machine learning model is trained using a plurality of examples, each example comprising one or more sample physiological measurements of a sample user and a corresponding sample weight loss medication administered to the sample user; generating, by the one or more processors, based on applying the one or more physiological measurements to the machine learning model, a metric indicating an expected outcome associated with a weight loss medication for the user; generating, based on the metric, a customized recommendation comprising one or more administration parameters for the weight loss medication or comprising preventative actions to be taken by the user; and providing, by the one or more processors, to an application executing on a device associated with the user or a clinician, the customized recommendation.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations above, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a method implemented by a processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the additional elements identified/bolded above, this claim encompasses a person receiving user data, analyzing the data, determining an expected outcome associated with a weight loss drug for the user, and then creating a personalized recommendation to the user to administer the drug or take preventative actions in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A – Prong 1: Yes, the claims are abstract).
Step 2A analysis - Prong two:
Claims 1 and 26 recite additional elements beyond the abstract idea. Claims 1 and 26 recite one or more processors, using a trained machine learning model, an application, and a device associated with the user or a clinician. The application appears to be software.
This judicial exception is not integrated into a practical application. In particular, the claims recite one or more processors, an application, and a device associated with the user or a clinician which are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the processor receives inputs, applies the input to an algorithm, analyzes data, outputs a result, etc. (see Applicant’s specification paras 17, 98).
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1 and 26 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application).
Step 2B analysis:
For the next step of the analysis, it must be determined whether the limitations present in the claims represent a patent-eligible application of the abstract idea. A claim directed to a judicial exception must be analyzed to determine whether the elements of the claim, considered both individually and as an ordered combination are sufficient to ensure that the claim as a whole amounts to significantly more than the exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of well-understood, routine, and conventional activities previously known to the industry. Further, the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention. See MPEP 2106.05(d).
Applicant’s specification discloses the following:
Applicant describes embodiments of the disclosure at a very high level to include the use of a wide variety of instrumentation devices/wearables, processors, servers, networks, storage mediums, databases, computers, input/output devices, etc. (see Applicant’s specification paras 45, 99-113). The invention, may use any computer via any transmission medium (a communication network or broadcast waves) capable of transmitting the program.
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions appear to be implemented using conventional computer systemization.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of one or more processors, using a trained machine learning model, an application, and a device to perform all of the steps discussed above amount to no more than mere instructions to apply the exceptions using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims do not provide an inventive concept significantly more than the abstract idea. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2B: No, the claims do not provide significantly more).
The instant claims, as currently recited, merely indicate intended use of the claimed invention and do not provide a particular treatment or prophylaxis. The claims may be interpreted to recite a practical application of the abstract idea and overcome the 101 rejection if and only if the independent claims are amended to recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. See MPEP 2106.04(d)(2).
Dependent Claims 2-19, 21-25 and 27-28 further define the abstract idea that is presented in independent Claims 1 and 26, and are further grouped as certain methods of organizing human activity and are abstract for the same reasons and basis as presented above. Further, Claims 15 and 21 recite additional elements beyond the abstract idea. Claim 15 recites a video and an avatar. Claim 21 recites an instrumentation device. These additional elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. For example, as noted above, the Applicant’s specification indicates the use of known instrumentation devices. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not recite additional elements that integrate the judicial exception into a practical application when considered both individually and as an ordered combination. Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-19 and 21-28 are rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8, 10, 13-15 and 18-19, 21-28 are rejected under 35 U.S.C. 103 as being unpatentable over Bleich et al. (WO 2025006572 A1) in view of Lebovitz et al. (US 9061153), further in view of Ferraro et al. (WO 2006071892).
Regarding Claim 1, Bleich discloses the following limitations:
A system, comprising: one or more processors configured to: receive one or more physiological measurements of a user; (Bleich discloses methods and systems for predicting changes in a target physiological parameter (e.g., weight loss) of a target patient expected to result from a medication (e.g., an anti-obesity medication) to be administered to the target patient that the one or more computing devices receives user input indicative of a value for each of one or more starting physiological parameters for a target patient (receive one or more physiological measurements of a user). For example, if the set of starting physiological parameters includes a target patient’s starting weight, A1C level, resting pulse rate, and height, the one or more processors may receive user input indicative of values for each of these starting physiological parameters (e.g., starting weight = 279 lbs., starting A1C level = 8.2, starting pulse rate = 88 beats per minute, starting height = 5 ft., 7 in.). – abstract; paras 3-4, 51, 57)
apply, by the one or more processors, the one or more physiological measurements to a…model, (Bleich discloses that the methods and systems may comprise deriving one or more parameter-estimation functions (a model) based on historical data, wherein each parameter-estimation function models how a separate parameter of a prediction function varies in accordance with one or more starting physiological parameters (applying the one or more physiological measurements). – abstract)
wherein the…model is trained using a plurality of examples, each example comprising one or more sample physiological measurements of a sample user and a corresponding sample weight loss medication administered to the sample user; (Bleich discloses using historical data (a plurality of examples) indicative of changes observed over an observation period in the target physiological parameter of a plurality of patients (one or more sample physiological measurements of a sample user) resulting from administration of the medication (a corresponding sample weight loss medication administered to the sample user) in order to derive one or more parameter-estimation functions. – para 3)
generate, based on applying the one or more physiological measurements to the…model, a metric indicating an expected outcome associated with a weight loss medication for the user; (Bleich discloses estimating the amount of weight that a specific patient may expect to lose (generate a metric) as a result of taking this medication (indicating an expected outcome associated with a weight loss medication for the user). – paras 29, 31, 35)
generate, based on the metric, a customized recommendation comprising one or more administration parameters for the weight loss medication or comprising preventative actions to be taken by the user; and provide, to an application executing on a device associated with the user or a clinician, the customized recommendation. (Bleich discloses that the predicted changes in the target physiological parameter of the target patient (generating, based on the metric, a customized recommendation) displayed on the user interface (providing the customized recommendation) may be calculated based at least in part on the target dose level to assist at least one of the target patient and the medical professional in determining whether the target dose level of the medication should be administered to the target patient (administration parameters for the weight loss medication). The user may be the patient (the user), or someone entering data on behalf of the patient, such as a caregiver, family member, or health care provider (HCP) (or a clinician). – paras 3, 10, 29, 35, 68; FIG. 3B item 312)
Bleich does not disclose the following limitations met by Lebovitz:
apply the one or more physiological measurements to a machine learning model, (Lebovitz teaches a learning module (optionally implementing machine learning methods known in the art) (a machine learning model) is used for the estimation which module is trained to predict triglyceride level based on one or more patient parameters (apply the one or more physiological measurements). – col 13, lines 40-49)
wherein the machine learning model is trained using a plurality of examples, each example comprising one or more sample physiological measurements of a sample user and a corresponding sample weight loss medication administered to the sample user; (Lebovitz teaches that the learning module is trained to predict triglyceride level based on one or more patient parameters (e.g., blood glucose levels) (one or more sample physiological measurements of a sample user). Further, the method further comprises selecting the patient for treatment according to the patient taking at least one oral diabetes medication (a corresponding sample weight loss medication administered to the sample user). – col 6, lines 45-49; col 13, lines 40-49)
generate, based on applying the one or more physiological measurements to the machine learning model, a metric… (Lebovitz teaches using the learning module (based on applying the one or more physiological measurements to the machine learning model) to predict a duration and/or a magnitude of a treatment (generate a metric), comprising determining a level of triglycerides and predicting one or both of a duration and an expected magnitude of effect of said treatment wherein said effect may comprise weight loss. – col 5, lines 36-46; col 12, lines 52-63)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified analyzing patient physiological parameters in order to predict weight loss as a result from a medication as disclosed by Bleich to incorporate inputting patient parameters into a machine learning module as taught by Lebovitz in order to assist in predicting outcomes and/or selecting treatments (see Lebovitz col 50, lines 31-44).
Regarding Claim 2, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the one or more processors are further configured to determine the weight loss medication from a plurality of weight loss medications based on a plurality of expected outcomes associated with the plurality of weight loss medications. (Bleich discloses that the medication (the weight loss medication) may be any pharmaceutical medication (a plurality of weight loss medications) that is expected to effect a change in the one or more target physiological parameter (based on a plurality of expected outcomes associated with the plurality of weight loss medications). For example, the medication may be any of a class of medications that, if administered to the patient, either once or repeatedly over a period of time, are expected to change the patient’s A1C level or the patient’s weight. – para 39)
Regarding Claim 3, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the one or more physiological measurements comprise at least one of: body-mass index, weight, blood pressure, heart rate, smoking status, glucose excretion, comprehensive metabolic panel, complete blood count, lipase levels, thyroid panel, magnesium level, HgA1c, fasting glucose, energy expenditure, physical activity, hormone levels, body weight, body fat percentage, a genetic marker, an evaluation of gut microbiome, or energy intake. (Bleich discloses that the set of starting physiological parameters may include, for example, a target patient’s starting weight (weight)(body weight), A1C level (HgA1c), resting heart rate (heart rate), etc. – paras 6, 31, 51, 68)
Regarding Claim 4, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the machine learning model comprises one or more corresponding weights to generate one or more values, the one or more corresponding weights comprising at least one of a binary weight or a continuous weight; (Lebovitz teaches that the triglyceride levels considered in selecting and/or applying treatment are one or more (e.g., a combination, optionally weighted) (the one or more corresponding weights comprising a continuous weight) of plasma triglyceride levels, fasting plasma triglyceride levels, hepatic triglyceride levels, food lipid levels and/or a triglyceride level estimate. – col 13, lines 30-39)
wherein to generate the metric, the one or more processors are further configured to generate the metric based on the one or more values. (Lebovitz teaches using the learning module (based on the one or more values) to predict a duration and/or a magnitude of a treatment (generate the metric), comprising determining a level of triglycerides and predicting one or both of a duration and an expected magnitude of effect of said treatment wherein said effect may comprise weight loss. – col 5, lines 36-46; col 12, lines 52-63)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified analyzing patient physiological parameters in order to predict weight loss as a result from a medication as disclosed by Bleich to incorporate inputting patient parameters into a machine learning module as taught by Lebovitz in order to assist in predicting outcomes and/or selecting treatments (see Lebovitz col 50, lines 31-44).
Regarding Claim 5, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the weight loss medication is selected from a GLP-1 receptor agonist or a GIP receptor agonist. (Bleich discloses that the medication may be any of a class of medications that, if administered to the patient, either once or repeatedly over a period of time, are expected to change the patient’s A1C level or the patient’s weight (the weight loss medication is selected). Such medications may include an SGLT2, a GLP-1 agonist (a GLP-1 receptor agonist), a GIP/GLP1 agonist (a GLP-1 receptor agonist and a GIP receptor agonist), a GIP/GLP1/Glucagon agonist (a GLP-1 receptor agonist and a GIP receptor agonist), a sulfonyluria, an insulin, an insulin-analog, and/or other medication approved by a regulatory agency for use in treating diabetes and/or chronic weight management. – paras 39, 68)
Regarding Claim 6, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 5, wherein the GLP-1 receptor agonist is selected from one or more of semaglutide, liraglutide, exenatide, and dulaglutide, and wherein the GIP receptor agonist comprises tirzepatide. (Bleich discloses that the medication may be any of a class of medications that, if administered to the patient, either once or repeatedly over a period of time, are expected to change the patient’s A1C level or the patient’s weight. Such medications may include an SGLT2, a GLP-1 agonist, a GIP/GLP1 agonist, a GIP/GLP1/Glucagon agonist, a sulfonyluria, an insulin, an insulin-analog, and/or other medication approved by a regulatory agency for use in treating diabetes and/or chronic weight management. Such medications may include, for example, but not limited to, tirzepatide (wherein the GIP receptor agonist comprises tirzepatide), semaglutide (wherein the GLP-1 receptor agonist is selected from semaglutide), retatrutide, metformin, insulin glargine, insulin lyspro, and the like. – para 39)
Regarding Claim 7, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the expected outcome further comprises at least one administration parameter for the weight loss medication, the administration parameter comprising at least one of a dosage of the weight loss medication, a timing of administration of the weight loss medication, a frequency of administration, a route of administration, a dose escalation protocol, circumstances of administration, or any combination thereof. (Bleich discloses that FIG.5A and 5B are exemplary screens of the web interface for presenting predicted changes (the expected outcome) in the body weight of a patient expected to result from a course of medication (the weight loss medication). The screen includes a dropdown menu for the user to select a dose level (a dosage of the weight loss medication) and then the screen shows predictions for the level of the target physiological parameter (e.g., weight) at a plurality of time points over a forecast period. – paras 22, 69-70; FIG.5A-5B)
Regarding Claim 8, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the expected outcome further comprises identifying at least one of a therapy discontinuation, side effects, or therapeutic efficacy. (Bleich discloses that the screen that shows the predictions (the expected outcome), selection of the side effect profile link 524 takes the user to a screen that informs the user about potential side effects (side effects) of the medication that the user is taking. Selection of the details link 522 takes the user to a screen that provides additional details regarding how the web interface calculates expected changes to the user’s body weight and/or A1C level (therapeutic efficacy). – para 73; FIG.5A-5B)
Regarding Claim 10, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein to generate the metric, the one or more processors are further configured to generate, based on applying the one or more physiological measurements to the…model, a plurality of metrics for a plurality of expected outcome parameters. (Bleich discloses allowing the user to view predictions for a selected target physiological parameter (e.g., whether to view predicted changes for the patient’s A1C level or the patient’s weight) (the metric) and a selected dose level (e.g., between 5, 15, or 15 mg). The predictions are calculated based on the selected target physiological parameter and dose level and then displayed via a results panel; showing predictions for the level of the target physiological parameter at a plurality of time points over the forecast period including a curve 516a that shows the average expected level, a curve 516b that shows a maximum expected level, and a curve 516c that shows the minimum expected level over time (a plurality of metrics for a plurality of expected outcome parameters).– paras 69-71; FIG.5A-5B)
Bleich does not disclose the following limitations met by Lebovitz:
applying the one or more physiological measurements to the machine learning model (Lebovitz teaches a learning module (optionally implementing machine learning methods known in the art) (the machine learning model) is used for the estimation which module is trained to predict triglyceride level based on one or more patient parameters (applying the one or more physiological measurements). – col 13, lines 40-49)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified analyzing patient physiological parameters in order to predict weight loss as a result from a medication as disclosed by Bleich to incorporate inputting patient parameters into a machine learning module as taught by Lebovitz in order to assist in predicting outcomes and/or selecting treatments (see Lebovitz col 50, lines 31-44).
Regarding Claim 13, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 8, wherein the expected outcome further comprises an expected outcome parameter, the expected outcome parameter comprising at least one of weight loss, fat loss, fasting blood glucose, cholesterol levels, hormone levels, duration of fat loss, risk of weight regain, a change in body mass index, or any combination thereof. (Bleich discloses presenting predicted changes (the expected outcome further comprises an expected outcome parameter) in the body weight (weight loss) of a patient expected to result from a course of medication. FIG. 5A shows the patient’s body weight being predicted to decrease overtime (weight loss). – para 22; FOG. 5A-5B)
Regarding Claim 14, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the one or more processors are further configured to generate, by the one or more processors, a simulation identifying a plurality of expected outcomes over a corresponding plurality of timepoints. (Bleich discloses allowing the user to view predictions for a selected target physiological parameter (e.g., whether to view predicted changes for the patient’s A1C level or the patient’s weight) and a selected dose level (e.g., between 5, 15, or 15 mg). The predictions are calculated based on the selected target physiological parameter and dose level and then displayed via a results panel (a simulation); showing predictions for the level of the target physiological parameter at a plurality of time points over the forecast period (over a corresponding plurality of timepoints) including a curve 516a that shows the average expected level, a curve 516b that shows a maximum expected level, and a curve 516c that shows the minimum expected level over time (a plurality of expected outcomes over a corresponding plurality of timepoints).– paras 69-71; FIG.5A-5B)
Regarding Claim 15, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 14, wherein the simulation comprises a representation of the plurality of expected outcomes over the corresponding plurality of timepoints, wherein the representation comprises at least one of a timeline, a graph, a video, an audio, or an avatar. (Bleich discloses predicting changes in the target physiological parameter of the target patient comprises predicting, for each time point of the plurality of future time points (a timeline), an expected change in the target physiological parameter and a prediction interval for the expected change. In graph 514, the horizontal axis represents time while the vertical axis represents a magnitude or level of the target physiological parameter. – paras 7, 70-71; FIG.5A-5B)
Regarding Claim 18, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 14, wherein the plurality of expected outcomes identified by the simulation comprise an expected outcome parameter, the expected outcome parameter comprising at least one of weight loss, fat loss, fasting blood glucose, cholesterol levels, hormone levels, duration of fat loss, risk of weight regain, a change in body mass index, or any combination thereof. (Bleich discloses presenting predicted changes (the expected outcome further comprises an expected outcome parameter) in the body weight (weight loss) of a patient expected to result from a course of medication. FIG. 5A shows the patient’s body weight being predicted to decrease overtime (weight loss). – para 22; FOG. 5A-5B)
Regarding Claim 19, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the user has a BMI greater than 25, a body fat percentage greater than 20%, Type I Diabetes, Type II Diabetes, or nonalcoholic steatohepatitis (NASH). (Lebovitz teaches selecting patients, especially patients with type II diabetes (the user has Type II Diabetes). – col 10, lines 11-16)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified analyzing patient physiological parameters in order to predict weight loss as a result from a medication as disclosed by Bleich to incorporate selecting patients that have type II diabetes as taught by Lebovitz in order to assist in predicting outcomes and/or selecting treatments (see Lebovitz col 50, lines 31-44).
Regarding Claim 21, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the one or more physiological measurements of the user are obtained by at least one of the device or an instrumentation device on the user. (Bleich discloses that the one or more computing devices receive user input indicative of a value for one or more starting physiological parameters for the target patient (the one or more physiological measurements of the user are obtained) via a user interface. FIG.4 presents an exemplary screen 400 of a web browser or web-enabled application interface (hereinafter referred to as a “web interface”) on a mobile device for receiving user input (obtained by the user device) from a user. – paras 3, 68; FIG.4)
Regarding Claim 22, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein to generate the metric indicating the expected outcome, the one or more processors is further configured to determine the metric based on at least one of: (i) an average of the plurality of examples, (ii) a weighted combination of the plurality of examples, or (iii) a comparison with a dataset comprised of the plurality of examples. (Bleich discloses that the way in which the patient’s expected weight loss (the metric indicating the expected outcome) varies with the aforementioned starting physiological parameters may be estimated by analyzing historical data (the plurality of examples) indicative of outcomes in a population of users that exhibited different starting height, weight, resting heart rates, and A1C levels. By taking these current physiological parameters into account, and by analyzing the historical data ((iii) a comparison with a dataset), the amount of weight that a specific patient may expect to lose as a result of taking this medication may be estimated with greater accuracy. – para 31)
Regarding Claim 23, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein to receive the one or more physiological measurements, the one or more processors are further configured to receive, over a time period, the one or more physiological measurements from at least one of the device or an instrumentation device, (Bleich discloses that the one or more computing devices receive user input indicative of a value for one or more starting physiological parameters for the target patient (receive the one or more physiological measurements) via a user interface. FIG. 4 presents an exemplary screen 400 of a web browser or web-enabled application interface (hereinafter referred to as a “web interface”) on a mobile device for receiving user input from a user (from the device). The method may further comprise receiving, at the one or more computing devices, further user input indicative of actual changes to the target physiological parameter observed in the target patient (the one or more physiological measurements) in response to a course of the medication previously administered (over a time period) to the target patient. – paras 3, 11, 68; FIG.4)
wherein to apply the one or more physiological measurements to the…model, the one or more processors are further configured to apply the one or more physiological measurements to the…model, responsive to elapsing of the time period, (Bleich discloses further user input indicative of actual changes to the target physiological parameter observed in the target patient in response to a course of the medication previously administered to the target patient, wherein the calculated parameter values of the prediction function are calculated based at least in part on the further user input (apply the one or more physiological measurements to the…model, responsive to elapsing of the time period). – para 11)
wherein to generate the metric, the one or more processors are further configured to generate the metric indicating the expected outcome associated with the weight loss medication for a subsequent time period. (Bleich discloses showing predictions for the level of the target physiological parameter (generate the metric indicating the expected outcome) as a result from administration of the medication (associated with the weight loss medication) at a plurality of time points over a forecast period (for a subsequent time period) (e.g., 1 to 12 months into the future). – paras 69-70; FIG.5A-5B)
Bleich does not disclose the following limitations met by Lebovitz:
wherein to apply the one or more physiological measurements to the machine learning model (Lebovitz teaches a learning module (optionally implementing machine learning methods known in the art) (the machine learning model) is used for the estimation which module is trained to predict triglyceride level based on one or more patient parameters (applying the one or more physiological measurements). – col 13, lines 40-49)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified analyzing patient physiological parameters in order to predict weight loss as a result from a medication as disclosed by Bleich to incorporate inputting patient parameters into a machine learning module as taught by Lebovitz in order to assist in predicting outcomes and/or selecting treatments (see Lebovitz col 50, lines 31-44).
Regarding Claim 24, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the one or more processors are further configured to: receive one or more subsequent physiological measurements of the user; apply, responsive to receipt of the one or more subsequent physiological measurements, the one or more subsequent physiological measurements to the machine learning model; generate, based on applying the one or more subsequent physiological measurements to the machine learning model, a subsequent metric indicating a subsequent expected outcome associated with the weight loss medication for the user; generate, based on the subsequent metric, a subsequent customized recommendation comprising one or more subsequent administration parameters for the weight loss medication or comprising subsequent preventative actions to be taken by the user; and provide, to the application executing on the device within a defined time period relative to the receipt of the one or more subsequent physiological measurements, the subsequent customized recommendation. (Lebovitz teaches that after applying the treatment, the triglyceride levels may be measured again in step 204 or may be estimated based on one or more patient parameters (receive one or more subsequent physiological measurements of the user) and the process is repeated (subsequent) (col 25, lines 54-62; col 28, lines 40-46; FIG. 2). A machine learning module trained to predict triglyceride level based on one or more patient parameters (applying the one or more subsequent physiological measurements to the machine learning model) is utilized (col 13, lines 40-49; col 25, lines 54-62; FIG. 2). A duration and an expected magnitude of effect of said treatment is predicted and then a treatment according to said prediction is selected (col 5, lines 36-42). (Claim 24 recites substantially similar limitations to those recited in claim 1. Examiner interprets claim 24, under broadest reasonable interpretation, to mean that the process described above in claim 1 may be repeated with new/different/changed/updated input data. Bleich is being relied upon to disclose the described process, as recited in claim 1 above, and Lebovitz is being relied upon to teach the limitations regarding the machine learning model and repeating a process with new/different data)
While Bleich discloses that a user may want to repeat the process of calculating an expected change in the target physiological parameter of a target patient (see para 65), Bleich does not explicitly disclose repeating the process using subsequent data and generating subsequent expected outcomes and recommendations. However, Lebovitz teaches a feedback loop and repeating the process, starting by measuring triglyceride levels.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified analyzing patient physiological parameters in order to predict weight loss as a result from a medication as disclosed by Bleich to incorporate inputting patient parameters into a machine learning module and repeating the process of selecting a treatment plan as taught by Lebovitz in order to assist in predicting outcomes and/or selecting treatments (see Lebovitz col 50, lines 31-44).
Regarding Claim 25, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 24, wherein the defined time period ranges between 1 second to 1 hour. (Lebovitz teaches providing sensor measurements of the one or more body parameters in real-time (the defined time period ranges between 1 second to 1 hour). – col 22, lines 31-38)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified analyzing patient physiological parameters in order to predict weight loss as a result from a medication as disclosed by Bleich to incorporate inputting real-time patient parameters into a machine learning module and repeating the process of selecting a treatment plan as taught by Lebovitz in order to assist in predicting outcomes and/or selecting treatments (see Lebovitz col 50, lines 31-44).
Regarding Claim 26, Bleich discloses the following limitations:
A method, comprising: receiving, by one or more processors, one or more physiological measurements of a user; (Bleich discloses methods and systems for predicting changes in a target physiological parameter (e.g., weight loss) of a target patient expected to result from a medication (e.g., an anti-obesity medication) to be administered to the target patient that the one or more computing devices receives user input indicative of a value for each of one or more starting physiological parameters for a target patient (receive one or more physiological measurements of a user). For example, if the set of starting physiological parameters includes a target patient’s starting weight, A1C level, resting pulse rate, and height, the one or more processors may receive user input indicative of values for each of these starting physiological parameters (e.g., starting weight = 279 lbs., starting A1C level = 8.2, starting pulse rate = 88 beats per minute, starting height = 5 ft., 7 in.). – abstract; paras 3-4, 51, 57)
applying, by the one or more processors, the one or more measurements to a…model, (Bleich discloses that the methods and systems may comprise deriving one or more parameter-estimation functions (a model) based on historical data, wherein each parameter-estimation function models how a separate parameter of a prediction function varies in accordance with one or more starting physiological parameters (applying the one or more measurements). – abstract)
wherein the…model is trained using a plurality of examples, each example comprising one or more sample physiological measurements of a sample user and a corresponding sample weight loss medication administered to the sample user; (Bleich discloses using historical data (a plurality of examples) indicative of changes observed over an observation period in the target physiological parameter of a plurality of patients (one or more sample physiological measurements of a sample user) resulting from administration of the medication (a corresponding sample weight loss medication administered to the sample user) in order to derive one or more parameter-estimation functions. – para 3)
generating, by the one or more processors, based on applying the one or more physiological measurements to the…model, a metric indicating an expected outcome associated with a weight loss medication for the user; (Bleich discloses estimating the amount of weight that a specific patient may expect to lose (generate…a metric) as a result of taking this medication (indicating an expected outcome associated with a weight loss medication for the user). – para 31)
generating, based on the metric, a customized recommendation comprising one or more administration parameters for the weight loss medication or comprising preventative actions to be taken by the user; and providing, by the one or more processors, to an application executing on a device associated with the user or a clinician, the customized recommendation. (Bleich discloses that the predicted changes in the target physiological parameter of the target patient (generating, based on the metric, a customized recommendation) displayed on the user interface (providing the customized recommendation) may be calculated based at least in part on the target dose level to assist at least one of the target patient and the medical professional in determining whether the target dose level of the medication should be administered to the target patient (administration parameters for the weight loss medication). The user may be the patient (the user), or someone entering data on behalf of the patient, such as a caregiver, family member, or health care provider (HCP) (or a clinician). – paras 3, 10, 29, 35, 68; FIG. 3B item 312)
(Bleich discloses displaying, on the user interface, the predicted changes in the target physiological parameter of the target patient (the customized recommendation) to assist in determining whether the medication should be administered.– paras 3, 10, 68; fig. 3B item 312)
(The broadest reasonable interpretation includes alternative form for this limitation; therefore, a citation is not required by the Examiner. However, in the interest of compact prosecution, a citation is provided) (Bleich discloses that when outcomes are predicted (based on the metric indicating the expected outcome) for multiple types of medications (identifying at least one recommended weight loss medication), these predicted outcomes may help a patient and his/her caregivers (providing, to an application executing on a device associated with the user or a clinician) to compare and contrast the likely effects of different medications, and ultimately help the patient and his/her caregivers decide which medication to take. – paras 29, 35)
Bleich does not disclose the following limitations met by Lebovitz:
applying, by the one or more processors, the one or more measurements to a machine learning model, (Lebovitz teaches a learning module (optionally implementing machine learning methods known in the art) (a machine learning model) is used for the estimation which module is trained to predict triglyceride level based on one or more patient parameters (apply the one or more physiological measurements). – col 13, lines 40-49)
wherein the machine learning model is trained using a plurality of examples (Lebovitz teaches that the learning module is trained to predict triglyceride level based on one or more patient parameters (e.g., blood glucose levels) (one or more sample physiological measurements of a sample user). Further, the method further comprises selecting the patient for treatment according to the patient taking at least one oral diabetes medication (a corresponding sample weight loss medication administered to the sample user). – col 6, lines 45-49; col 13, lines 40-49)
generate, based on applying the one or more physiological measurements to the machine learning model, a metric… (Lebovitz teaches using the learning module (based on applying the one or more physiological measurements to the machine learning model) to predict a duration and/or a magnitude of a treatment (generate a metric), comprising determining a level of triglycerides and predicting one or both of a duration and an expected magnitude of effect of said treatment wherein said effect may comprise weight loss. – col 5, lines 36-46; col 12, lines 52-63)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified analyzing patient physiological parameters in order to predict weight loss as a result from a medication as disclosed by Bleich to incorporate inputting patient parameters into a machine learning module as taught by Lebovitz in order to assist in predicting outcomes and/or selecting treatments (see Lebovitz col 50, lines 31-44).
Regarding Claim 27, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The system of claim 1, wherein the metric comprises a value corresponding with a likelihood of a physiological response to a GLP-1 receptor agonist or a GIP receptor agonist based on the one or more physiological measurements. (Bleich discloses that the web interface is configured to predict changes in the patient’s body weight (a value corresponding with a likelihood of a physiological response) expected to result from a course of a GIP/GLP-1 agonist administered to the patient; and that the medications may include a GLP-1 agonist, a GIP/GLP1 agonist and a GIP/GLP1/Glucagon agonist (a GLP-1 receptor agonist or a GIP receptor agonist). The prediction may be based on the patent’s starting physiological parameters. (based on the one or more physiological measurements). – paras 39, 41, 68; FIGs. 4-5A)
Regarding Claim 28, Bleich, Lebovitz and Ferraro disclose all the limitations above and further disclose the following limitations:
The method of claim 26, wherein the metric comprises a value corresponding with a likelihood of a physiological response to a GLP-1 receptor agonist or a GIP receptor agonist based on the one or more physiological measurements. (Bleich discloses that the web interface is configured to predict changes in the patient’s body weight (a value corresponding with a likelihood of a physiological response) expected to result from a course of a GIP/GLP-1 agonist administered to the patient; and that the medications may include a GLP-1 agonist, a GIP/GLP1 agonist and a GIP/GLP1/Glucagon agonist (a GLP-1 receptor agonist or a GIP receptor agonist). The prediction may be based on the patent’s starting physiological parameters. (based on the one or more physiological measurements). – paras 39, 41, 68; FIGs. 4-5A)
Claims 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bleich et al. (WO 2025006572 A1) in view of Lebovitz et al. (US 9061153) in view of Ferraro et al. (WO 2006071892), further in view of Okuda (US 20230169473).
Regarding Claim 11, Bleich, Lebovitz and Ferraro disclose all the limitations above, however do not disclose the following limitations met by Okuda:
The system of claim 8, wherein the expected outcome further comprises at least one expected outcome parameter, the expected outcome parameter comprising at least one of a timing of discontinuation, a cause of discontinuation, a probability of discontinuation, a discontinuation mitigation, or any combination thereof. (Okuda teaches predicting the probability of medication discontinuation (a probability of discontinuation) for a particular patient and or medication. – para 54, 70)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified displaying predicting changes in a target physiological parameter as disclosed by Bleich to incorporate predicting the probability of medication discontinuation as taught by Okuda in order to prevent wasting medications (see Okuda para 8).
Regarding Claim 16, Bleich, Lebovitz, Ferraro and Okuda disclose all the limitations above and further disclose the following limitations:
The system of claim 14, wherein the plurality of expected outcomes identified by the simulation comprise at least one expected outcome parameter, the expected outcome parameter comprising at least one of a timing of discontinuation, a cause of discontinuation, a probability of discontinuation, a discontinuation mitigation, or any combination thereof. (Okuda teaches predicting the probability of medication discontinuation (a probability of discontinuation) for a particular patient and or medication. – para 54, 70)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified displaying predicting changes in a target physiological parameter as disclosed by Bleich to incorporate predicting the probability of medication discontinuation as taught by Okuda in order to prevent wasting medications (see Okuda para 8).
Claims 9, 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bleich et al. (WO 2025006572 A1) in view of Lebovitz et al. (US 9061153) in view of Ferraro et al. (WO 2006071892), further in view of Geatz et al. (US 20030144829).
Regarding Claim 9, Bleich, Lebovitz and Ferraro disclose all the limitations above, however do not disclose the following limitations met by Geatz:
The system of claim 8, wherein the side effects are selected from nausea, vomiting, diarrhea, early satiety, loss of appetite, anorexia, dizziness, increased heart rate, indigestion, headache, hypoglycemia, calculus of a kidney or ureter, pancreatitis, diabetic retinopathy, depression, suicidal ideation or attempts, pain in abdomen, acute kidney injury, muscle wasting and atrophy, constipation, or any combination thereof. (Geatz teaches predicting the onset of one or more symptoms such as dizziness (dizziness), nausea or vomiting (nausea, vomiting), depression (depression), migraines (headache), – abstract; paras 3, 25, 45, 63)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the systems and methods for predicting changes in a target physiological parameter expected to result from a medication as disclosed by Bleich to incorporate predicting the onset of certain symptoms as taught by Geatz in order so that the patient can engage in or seek the proper preventative measures (see Geatz para 35).
Regarding Claim 12, Bleich, Lebovitz, Ferraro and Geatz disclose all the limitations above and further disclose the following limitations:
The system of claim 8, wherein the expected outcome further comprises at least one expected outcome parameter, the expected outcome parameter comprising at least one of timing of onset of the side effects, duration of the side effects, probability of the side effects, side effect mitigations, or any combination thereof. (Geatz teaches predicting the onset of symptoms (timing of onset of side effects) so that the patient can be alerted ahead of time that symptoms will ensue. – abstract; para 35)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the systems and methods for predicting changes in a target physiological parameter expected to result from a medication as disclosed by Bleich to incorporate predicting the onset of symptoms as taught by Geatz in order so that the patient can engage in or seek the proper preventative measures (see Geatz para 35).
Regarding Claim 17, Bleich, Lebovitz, Ferraro and Geatz disclose all the limitations above and further disclose the following limitations:
The system of claim 14, wherein the plurality of expected outcomes identified by the simulation comprise at least one expected outcome parameter, the expected outcome parameter comprising at least one of timing of onset of side effects, duration of side effects, probability of side effects, side effect mitigations, or any combination thereof. (Geatz teaches predicting the onset of symptoms (timing of onset of side effects) so that the patient can be alerted ahead of time that symptoms will ensue. – abstract; para 35)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the systems and methods for predicting changes in a target physiological parameter expected to result from a medication as disclosed by Bleich to incorporate predicting the onset of symptoms as taught by Geatz in order so that the patient can engage in or seek the proper preventative measures (see Geatz para 35).
Response to Arguments
Regarding rejections under 35 USC § 101 to Claims 1-19 and 21-28, Applicant’s arguments have been fully considered, and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(a) Amended claim 1 recites a combination of steps directed towards generating and providing a recommendation customized based on a metric indicating an expected outcome associated with a weight loss medication by using a trained machine learning model to generate the metric, among other things, that cannot be performed outside of a computer, let alone in the human mind. (p. 10).
Regarding (a), Examiner respectfully disagrees. Examiner does not assert mental process as an abstract idea grouping for the instant claims, thus this argument is moot. See updated rejection above.
(b) Further, amended claim 1 is directed to specific technical features, including a trained machine learning model, generation of a metric using the trained machine learning model, and generation of a customized recommendation based on the generated metric, which are not any of the aforementioned methods of organizing human activity. (p. 10).
Regarding (b), Examiner respectfully disagrees. MPEP 2106.04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the claimed invention represents a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to, to paraphrase, select a weight loss medication for a user. The Examiner notes that Applicants Background describes prescribing weight loss medication and gathering additional user data as a human task (see Applicant’s Spec. para 4). 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 instruction that a person or persons would follow to prescribe or select an appropriate weight loss drug for a user, the claimed invention is directed to an abstract idea.
(c) Like Example 39, amended claim 1 here "does not recite any mathematical relationships, formulas, or calculations." (See PEG 2019: Abstract Ideas.) While the claim limitations…may be "based on mathematical concepts, the mathematical concepts are not recited in the claims." (See PEG 2019: Abstract Ideas.) Thus, amended claim 1 is eligible. (p. 11).
Regarding (c), Examiner does not assert mathematical concepts as an abstract idea grouping for the instant claims, thus this argument is moot. See updated rejection above.
(d) This is an improvement in conventional methods of weight loss medications. Further, amended claim 1 recites a combination of steps directed to providing a customized recommendation by receiving at least one physiological measurement, applying it to a trained machine learning model, generating a metric indicating an expected outcome associated with a weight loss medication, generating a customized recommendation, and providing the customized recommendation. Thus, even assuming arguendo if amended claim 1 is directed to a judicial exception (which Applicant does not concede), amended claim 1 imposes meaningful limits on the alleged judicial exception and integrates the alleged judicial exception into a practical application. (p. 12).
Regarding (d), Examiner respectfully disagrees. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer or any other technology/technical field. See also MPEP2106.05(a)(I). Here there is no improvement to the computer nor is there an improvement to another technology. Because neither type of improvement is present in the claims, an improvement to technology is not present and there is no practical application. Further, Examiner notes that the stated problems of improving conventional methods of weight loss medications are interpreted as not being rooted in technology. The problems are not caused by nor related to computer technology and the claims do not provide any limitations that may be interpreted as technical improvements to computer technology. The claimed invention is using a computer as a tool and any improvement present is an improvement to the abstract idea of, to paraphrase, select weight loss medication for a user and update user data.
(e) Applicant respectfully requests that the rejection of claim 1 and its dependents under 35 U.S.C. § 101 be withdrawn. Applicant further requests that the rejection of independent claim 26, and its respective dependents, be withdrawn at least because claim 26 contains limitations similar to claim 1. (p. 12).
Regarding (e), Examiner respectfully disagrees. Based on response to arguments above, claim 1 is unpatentable and therefore similar independent claim 26, as well as all claims depending therefrom, are unpatentable according to the same rationale.
Regarding rejections under 35 USC § 103 to Claims 1-19 and 21-28, Applicant’s arguments have been fully considered and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(f) the references fail to disclose at least "apply the one or more physiological measurements to a machine learning model, wherein the machine learning model is trained using a plurality of examples, each example comprising one or more physiological measurements of a sample user and a corresponding sample weight loss medication administered to the sample user," "generate, based on applying the one or more physiological measurements to the machine learning model, a metric indicating an expected outcome associated with a weight loss medication for the user," "generate, based on the metric, a customized recommendation comprising one or more administration parameters for the weight loss medication or comprising preventative actions to be taken by the user," and "providing, to an application executing on a device associated with the user or clinician, the customized recommendation." Thus, Applicant respectfully requests that the rejection of claim 1, and its dependents, under 35 U.S.C. § 103 be withdrawn. (p. 13).
Regarding (f), Examiner respectfully disagrees. Examiner will evaluate each argued claim limitation below:
(f.1) The claim recites “apply the one or more physiological measurements to a machine learning model, wherein the machine learning model is trained using a plurality of examples, each example comprising one or more sample physiological measurements of a sample user and a corresponding sample weight loss medication administered to the sample user;”. Bleich discloses deriving one or more parameter-estimation functions (a model) based on historical data, wherein each function models how a separate parameter of a prediction function varies in accordance with one or more starting physiological parameters of the user (apply the one or more physiological measurements to a model). While Bleich does not disclose a trained machine learning model, the Examiner relies upon Lebovitz to teach a machine learning model that is trained (wherein the machine learning model is trained using a plurality of examples) to predict triglyceride levels based on one or more patient parameters such as blood glucose levels (each example comprising one or more sample physiological measurements of a sample user) and diabetes medication (a corresponding sample weight loss medication administered to the sample user).
(f.2) The claim recites “generate, based on applying the one or more physiological measurements to the machine learning model, a metric indicating an expected outcome associated with a weight loss medication for the user;”. Bleich discloses estimating the amount of weight that a specific patient may expect to lose (generate a metric based on applying the one or more physiological measurements to the model) as a result of taking this medication (indicating an expected outcome associated with a weight loss medication for the user). Examiner notes that while Bleich does not disclose the machine learning model, Bleich does disclose applying physiological measurements to a model and estimating an expected outcome. Further, Lebovitz is relied upon to teach a trained machine learning model that generates a metric, for example, a duration and/or a magnitude of effect of a treatment regarding weight loss.
(f.3) The claim recites “generate, based on the metric, a customized recommendation comprising one or more administration parameters for the weight loss medication or comprising preventative actions to be taken by the user;”. Bleich discloses that the predicted changes in the target physiological parameter of the target patient (generating, based on the metric, a customized recommendation) displayed on the user interface may be calculated based at least in part on the target dose level to assist at least one of the target patient and the medical professional in determining whether the target dose level of the medication should be administered to the target patient (comprising one or more administration parameters for the weight loss medication).
(f.4) The claim recites “provide, to an application executing on a device associated with the user or a clinician, the customized recommendation.”. Bleich discloses that the user may be the patient (the user) or someone entering data on behalf of the patient such as a caregiver or healthcare provider (a clinician), and that the predicted changes (the customized recommendation) are displayed on the user interface (provide to an application executing on a device).
Accordingly, the prior art discloses each and every limitation recited in claim 1, and therefore similar independent claim 26, as well as all claims depending therefrom, are unpatentable according to the same rationale. See updated prior art rejection above.
Conclusion
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/K.E.V./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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