DETAILED ACTION
This office action is based on the claim set filed on 06/04/2026.
Claims 1 and 14 have been amended.
Claims 7, 12, 17, and 19 have been canceled.
Claims 1-6, 8-11, 13-16, 18, and 20 are currently pending and have been examined.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/06/2026 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.
Claim 1-6, 8-11, 13-16, 18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-6, 8-11, and 13 are drawn to a method and Claim 14-16, 18, and 20 are drawn to a system/device, and each of which is within the four statutory categories (i.e., a machine and a process). Claim 1-6, 8-11, 13-16, 18, and 20 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for predicting a future health based on behavior of a user. Collecting a user image, characteristics, and behavior data to estimate future health and predict the user future image are steps that could have been performed by a human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Metal Process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 1 recites the steps of:
“training a reinforcement learning model to predict types of visualizations that influence users to modify user behavior to improve user future health using training data comprising different types of visualizations shown to individuals visualizing individual predicted future health conditions overtime, wherein when an individual's behavior improves over time after being shown a certain type of visualization, the type of visualization shown to the individual receives a positive reward during training, and when an individual's behavior does not improve over time after being shown a certain type of visualization, the type of visualization shown to the individual is given a negative reward during training:
receiving an image of a user;
receiving demographic information, health information, and behavior information associated with the user;
predicting, with a mathematical model, future health information for the user based on the demographic information, the health information, and the behavior information associated with the user;
determining, with a reinforcement learning model, visualizations that are most effective for the user based on the demographic information, the health information, and the behavior information;
generating a prompt to be input into a generative artificial intelligence model, where the prompt is generated in response to the predicted future health information
generating, by inputting the prompt into the generative artificial intelligence model, one or more predicted future images of the user based on the image of the user, the predicted future health information, and the determined visualizations”.
Independent Claim 14 recites similar steps as in Claim 1.
These limitations, as drafted, given the broadest reasonable interpretation cover performance of the limitations by a human mind with aid of pen and paper reciting an abstract idea for Mental Process along with Mathematical Calculations and relationships that constitute Mathematical Concepts but for the recitation of generic computer components. For example, predicting with a mathematical model is Mathematical Concepts. These limitations encompass a user the ability to collect a user image, characterizes or attributes, and behavior data that includes to predict future health likelihood and image accordingly, which are steps that that could have been performed by a human to implement the abstract idea and are steps reciting mental process that could have been performed using a human mind with aid of pen and paper and mathematical concepts, but other than the mere nominal recitation of "processor, generative artificial intelligence model ", to implement the abstract idea for performing the steps of observing, evaluating, judgment and opinion which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “processor, generative artificial intelligence model, reinforcement learning model” that iteratively takes input data and analyzes said data to determine an output to performing generic computer functions for predicting a future health such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f), generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). For example, the generative artificial intelligence model, reinforcement learning model is/are recited in the claims in a high level of generality and while the claims and specification describes a receiving positive and negative rewards, the training is described in the specification in an arbitrary form without disclosing using the available data for allowing the model to learn patterns and relationships within the data and implement it to perform the claimed function. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2-6, 8-11, 13, 15-16, 18, and 20 include all of the limitations of claim(s) 1 and 14, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 2-6, 8-10, and 15-16 the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 11, 13, 18, and 20, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process but for the recitation of generic computer components. The claims recite additional elements “processor, reinforcement learning model, collaborative filtering, generative artificial intelligence model” that implement the identified abstract idea. These hardware components are recited at a high level of generality to perform the steps that amounts to no more than the words "apply it" with a computer because it appears to intend to do so, i.e., display[ing], which would still amount to mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Additionally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements amount to more than mere instruction to apply the exception using generic computer component and have been re-evaluated under the “significantly more” analysis. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-10, 13-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. (US 2013/0325493 A1- “Wong”) in view of El-Sallam et al. (US 2022/0087533 A1- “El-Sallam”) in view of Islam (US 2024/0225454 A9)
Regarding Claim 1 (Currently Amended), Wong teaches a method comprising:
training a reinforcement learning model to predict types of visualizations that influence users to modify user behavior to improve user future health using training data comprising different types of visualizations shown to individuals visualizing individual predicted future health conditions overtime, wherein when an individual's behavior improves over time after being shown a certain type of visualization, the type of visualization shown to the individual receives a positive reward during training, and when an individual's behavior does not improve over time after being shown a certain type of visualization, the type of visualization shown to the individual is given a negative reward during training: Wong discloses one or more visualizations to motivate the user change his/her behavior and improve health, for example, a medical avatar eats a meal, patient can see the resulting restriction in food consumption and potential changes in weight over a specified time period, along with possible complications and other factors that may affect outcomes, are also shown on the medical avatar as such the medical avatar may be used to visualize the positive effects of treatment compliance and the potential negative effects of non-compliance. In addition, the visualizations serve patient education purposes and help patients make informed decisions together with their care providers such as importing a visualization of a smoker lungs to motivate smoking cessation and illustrate the potential positive effects of treatment and help promote patient compliance and other visualizations show wrinkles caused by smoking (Wong: [0047-0048], [0053], [0096])
receiving an image of a user Wong discloses a medical avatar application using a photograph of a user where the user takes photos of himself/herself and uploaded [receiving] for use by the medical avatar application (Wong: [0050-0051], [0070]);
receiving demographic information, health information, and behavior information associated with the user Wong discloses the user selecting information to input into the medical avatar application that includes gender [demographic information], conditions, medications, procedures [health information], diet and activates [behavior information] (Wong: [0044], [0071], [0083], [0085], [0096])
predicting, with a mathematical model, future health information for the user based on the demographic information, the health information, and the behavior information associated with the user Wong discloses the medical avatar application being customized to predict, using standard regression analysis [mathematical model], outcome(s) for potential future health issues, (e.g., change in weight, a disease likely to occur, physical effects, etc.) based on the user past and current health condition [health information], diet, exercise, and food consumption, [behavior information], and demographic (Wong: [0044-0048], [0096])
determining, with the reinforcement learning model, visualizations that are most effective for the user based on the demographic information, the health information, and the behavior information Wong discloses the medical avatar application using the provided health, demographic, and behavior information may determine and provide an immediate visualization of the user that may impact and effect outcomes over a time period as such the visualization can educate the user to help making decisions (Wong: [Fig. 9], [0047], [0096])
generating a prompt to be input into a generative artificial intelligence model, where the prompt is generated in response to the predicted future health information
generating, by inputting the prompt into the generative artificial intelligence model, one or more predicted future images of the user based on the image of the user, the predicted future health information, and the determined visualizations Wong discloses generating photographs documents on-going changes (e.g., weight loss) simulation over time based on the visualization image of the user and the predicated future health (Wong: [Fig. 14], [0128-0131]).
Wong discloses generating a predicated future image(s) that includes 2D/3D images using the provided health, demographic, and behavior information. However, Wong does not expressly disclose using generative artificial intelligence model trained to generate future images based on the input data via that may include information such as health, demographics, etc., and receive rewards for the image generation and provide a prompt as input based on predicated information as underlined.
El-Sallam discloses on the basis of the analysis generating, using an artificial intelligence model, an input by a user that causes the artificial intelligence model to an output via display predicted future images of the user comprises an estimation of an individual’s three-dimensional (3D) body shape and associated body measurements and future predictive body compositions based on their previous and current estimates (El-Sallam: [Fig. 15 a-b], [0036], [0040], [0203], [0333-0334], [0336]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Wong generating predicted future images to incorporate using an artificial intelligence model to generate predicated future images, as taught by El-Sallam, which provides the ability to identify user’s risk and allows early and tailored interventions to be recommended to the user over their lifespan to help improve a user's short and long term their health and wellness risk status (El-Sallam,: [0354]).
The combination of Wong and El-Sallam does not expressly discloses using a generative artificial intelligence model and generation of a prompt to be input into the generative artificial intelligence model.
Islam teaches
generating a prompt to be input into a generative artificial intelligence model, where the prompt is generated in response to the predicted future health information Islam discloses a machine learning (ML) and generative artificial intelligence (GAI) models that could possibly be trained to predict future physiological parameter based on past trends where the ML/AI is tuned using reinforcement learning agent may learn/train or improve through continuous feedback provided by a human such as if an action [behavior] benefits the agent where the learning agent receives positive rewards or reinforcement and if the action harms the agent, the agent receives negative reward and where an input prompt which is fed to the model(s) that causes the artificial intelligence model to output via display predicted future physiology of the user based on their previous and current estimates that may repeat the prompt to provide output(s) such that to optimize the output (Islam: [0450], [0542], [0546-0547], [0568-0570], [0579], [0581], [0585], [0587]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Wong generating of predicted future images to incorporate using a reward reinforcement learning agent and generative artificial intelligence model to generate predicated future parameters, as taught by Islam, which helps ML/AI tuning from reinforming learning agent that learns from the consequences of its actions through continuous feedback, making decisions based on trial and error (Islam: [0568]).
Regarding Claim 2 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the demographic information includes an age and gender of the user (Wong: [0040-0041], [0044]).
Regarding Claim 3 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the health information includes a height and weight of the user (Wong: [0040], [0044], [0091-0092]).
Regarding Claim 4 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the health information includes medical history of the user (Wong: [Fig. 9], [0045], [0096]).
Regarding Claim 5 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the behavior information includes diet and exercise habits associated with the user (Wong: [Fig. 9], [0043], [0096]).
Regarding Claim 6 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the mathematical model is trained, using training data comprising demographic information, health information, and behavior information associated with a plurality of users, to receive the demographic information, the health information, and the behavior information associated with the user as input, and output the predicted future health information Wong discloses data inputted to models comprising gender [demographic information], conditions, medications, procedures [health information], diet and activates [behavior information] and output display content regarding future health (Wong: [Fig. 9], [0083], [0093-0096]).
Regarding Claim 8 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the determined visualizations indicate one or more visual styles of the user Wong discloses visualization of the user in 2D, 3D, (Wong: [Fig. 1-4], [0049]).
Regarding Claim 9 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the determined visualizations indicate perspectives of the user Wong discloses a visualization indicating views [perspective] of the user (Wong: [Fig. 2-4], [0049]).
Regarding Claim 10 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the determined visualizations indicate one or more features of the user Wong discloses visualization indicate body fat, body shape, etc. (Wong: [Fig. 1-4], [0043], [0049]).
Regarding Claims 13 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, further comprising:
displaying the one or more predicted future images to the user Wong discloses using the user characteristics, attributes and behavior to display predicted future visualization such as progression of a disease (Wong: [0051], [0053], [0096], [0098]);
receiving revised behavior information associated with the user Wong discloses receiving a modification to the user behavior such as weight loss (Wong: [0096], [0131]);
predicting, with the mathematical model, revised future health information for the user based on the demographic information, the health information, and the revised behavior information; Wong discloses based on the user information and modified behavior (e.g., weight loss), predict, using standard regression analysis [mathematical model], outcome(s) for potential future health issues, (e.g., change in weight, a disease likely to occur, physical effects, etc.) based on the user past and current health condition [health information], modifying diet, exercise, and food consumption, [behavior information], and demographic predicate future health (Wong: [0096], [0132])
generating, with the generative artificial intelligence model, one or more revised predicted future images of the user based on the image of the user, the revised future health information, and the determined visualizations Wong discloses generating photographs documents on-going changes (e.g., weight loss) simulation over time based on the visualization image of the user and the predicated future health and review their progress (Wong: [Fig. 14], [0128-0131], [0134]). El-Sallam discloses on the basis of the analysis generating, using an artificial intelligence model, an output via display predicted future images of the user comprises an estimation of an individual’s three-dimensional (3D) body shape and associated body measurements and future predictive body compositions based on their previous and current estimates (El-Sallam: [Fig. 15 a-b], [0036], [0040], [0203], [0333-0334], [0336]). Islam discloses a generative model (Islam: [0450]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
Regarding Claims 14 (Currently Amended), Wong teaches a computing device comprising a processor configured to:
the claims recite substantially similar limitations to claim 6, as such, are rejected for similar reasons as given above.
Regarding Claims 15-16 and 20 (Original), the claims recite substantially similar limitations to claim 5-6 and 13, as such, are rejected for similar reasons as given above.
Claims 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. (US 2013/0325493 A1- “Wong”) in view of El-Sallam et al. (US 2022/0087533 A1- “El-Sallam”) in view of Islam (US 2024/0225454 A9) in view of Avinash et al. (US 2020/0342968 A1 – “Avinash”)
Regarding Claims 11 (Original), the combination of Wong, El-Sallam, and Islam teaches the method of claim 1, wherein the reinforcement learning model is trained using collaborative filtering
the combination of Wong, El-Sallam, and Islam teaches the reinforcement learning model.
Avinash discloses visualization process to transform a user medical data into graphical representations using an artificial intelligence that includes a reinforcement learning model that may provide data for visualization and action trained to summarize past events related to the predicted future events and to display the predicted future events and the pertinent past events of the patient and collaborative filtering to determine another input of the user to generate a predictive outcome (Avinash: [0046], [0054], [0060], [0071-0072], [0077]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have the combination of Wong, El-Sallam, and Islam generating of predicted future images to incorporate using a reinforcement learning model part of the artificial intelligence and neural network and collaborative filtering used for training, as taught by Avinash, which helps predicting an event classify a patient based on the event (Avinash: [0077]).
Regarding Claims 18 (Original), the claims recite substantially similar limitations to claim 11, as such, are rejected for similar reasons as given above.
Response to Amendment
Applicant's arguments filed 06/04/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to Double Patenting (DP) rejection on page 6-7.
In response to the Applicant argument that “the Examiner acknowledges that the independent claims of the '800 application are broader and more generic than the instant application. Office Action, Pg. 5. As such, the independent claims of the instant application cannot be anticipated by or obvious in view of the '800 application since a broader claim cannot anticipate a narrow claim”, Examiner finds the Applicant argument is persuasive. Therefore, Examiner withdraws the DP rejection.
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 7-8.
On page 7-8 of the remarks, the Applicant argues “However, Applicant respectfully submits that independent claims 1 and 14 are allowable based on the reasoning set forth in Ex parte Desjardins, Appeal 2024-000567 ... Similarly, independent claim 1 of the instant application recites "training a reinforcement learning model to predict types of visualizations that influence users to modify user behavior to improve user future health ... Applicant respectfully submits that, similar to Desjardins, the claimed feature of receiving a positive reward during training”, Examiner respectfully disagree. First, the claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. The claim limitations, given their broadest reasonable interpretation, recite steps, i.e., receiving images and information of a user to determine a visualization of the user based on the received information and predict future images of the user, which categorized as part of an abstract idea (i.e., data collection, manipulation, display) that is similar to obtaining/collecting, determining, comparing (analyzing) and predicting or provide an opinion, which are steps of observing, evaluating, judgment, and opinion that are citing a process for which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III), but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea.
Second, The PTAB had found the Desjardins invention ineligible under Section 101 as being directed to an abstract idea however the Appeals Review Panel (ARP) found the claims to be directed to methods for training artificial intelligence/machine learning (AI) models and the claims improved the functioning of the computer itself by reducing storage requirements and preserving task performance across sequential training as such improves the operation of a machine learning system by enhancing its training efficiency or preserving prior learning, as such it is not “directed to” an abstract idea under Alice Step 1. In contrast and as mentioned above, the “training a reinforcement learning model” and “generative artificial intelligence model”, is/are recited in the claims in a high level of generality and is in described in the specification, (see Applicant [Fig. 4, 5], [0032], [0037], [0057]), in an arbitrary form without disclosing a specific algorithm and implementing the claimed invention for allowing the model to learn patterns and relationships within the data and implement these additional elements to perform the claimed function rather the trained model is recited at a high level of generality and describing a general concept of using a machine learning/ reinforcement learning model to perform task(s) which is a mere in instruction(s) that may be performed by human as such the claim when viewed as a whole, recite a Mental process and the recitation of reinforcement learning model and generative artificial intelligence model have been analyzed under Step 2A, Prong Two as an additional element cited as a tool for implementing claim steps that amounts to no more than mere instructions to implement “apply” the exception using a generic computer component and no more than adding the words "apply it" (or an equivalent) with the judicial exception. that it amounts no more than adding the words "apply it" (or an equivalent).
Furthermore, receiving a reward during the training of a reinforcement learning (RL) model is generally considered an abstract concept that represents feedback, rather than a physical or tangible object and symbolizes mechanism for guiding behavior. Therefore, the instant claim(s) are not similar to in Desjardins
Therefore, the Examiner has addressed the Applicant argument(s) and found this argument is not found to be persuasive. Hence, Examiner remains the 101 rejections of claims which have been updated to address Applicant's amendments.
Applicant's arguments with respect to the 35 U.S.C. § 103 rejection on page 8-10.
On page of the remarks, the Applicant argues “Applicant respectfully submits that the cited references fail to teach or suggest the features of amended independent claims 1 and 14”, Examiner respectfully disagree. The Applicant argument is directed to a new feature that was not examined in the prior analysis however, the argued new feature is expressly disclosed by the reference “Islam” as described in the above rejection.
Furthermore, Applicant argues on page 10 that “However, none of the cited references teach generating a prompt to be input into a generative artificial intelligence model to generate future images of the user based on the image of the user, the predicted future health information, and the determined visualizations, as recited in amended independent claim 1...”, Examiner respectfully disagree. It is respectfully submitted that the Examiner finds the applicant is addressing each reference individual to teach all the claim elements ignoring the fact that the Examiner rejection is based on nonobviousness 35 USC 103. Therefore, in response to applicant's arguments against the references individually, one cannot show nonobviousness by arguing references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413,208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Therefore, Examiner finds that the Applicant argument against the references is unpersuasive.
Prior Art Cited but not Applied
The following document(s) were found relevant to the disclosure but not applied:
Vodrahalli et al. US 2023/0092766 A1 – discloses obtaining an image from a subject indicates a disease associated with the subject and processing the image data to generate a temporal sequence of images with corresponding predicted disease states.
The references are relevant since it discloses prediction an image of future health/medical status.
Conclusion
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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