yNotice 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 .
DETAILED ACTION
In the response filed on 24 April 2026, claims 1, 4, 6, 13 and 19-20 have been amended.
Now claims 1-20 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 24 April 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite methods and system for performing the limitations of:
Claim 1, which is representative of claims 19 and 20
[… building …] a medical outcome prognostication function based on […] processing a training set that includes: a first plurality of device-captured medical image data of at least one device-captured medical image data type; and a corresponding plurality of medical outcome data for a first medical outcome type; obtaining a second plurality of device-captured medical image data of the at least one device-captured medical image data type, wherein each of the second plurality of device-captured medical data corresponds to pre-trial medical image data for a corresponding one of a plurality of prospective clinical trial participants of a clinical trial having a primary endpoint corresponding to the first medical outcome type; and generating a plurality of medical outcome prognosis scores corresponding to the first medical outcome type […] to perform the […] medical outcome prognostication function upon each of the second plurality of device-captured medical image data based on applying the corresponding […] model to generate a corresponding medical outcome prognosis score of the plurality of medical outcome prognosis scores; selecting a first proper subset of the plurality of prospective clinical trial participants and a second proper subset of the plurality of prospective clinical trial participants by processing the plurality of medical outcome prognoses scores using a […] algorithm based on applying a medical outcome prognosis score-based stratification factor of the […] algorithm, wherein the first proper subset and the second proper subset are mutually exclusive; and wherein trial arm assignment data indicating assignment of the first proper subset of the plurality of prospective clinical trial participants to a control trial arm of the clinical trial and further indicating assignment of the second proper subset of the plurality of prospective clinical trial participants to an experimental trial arm of the clinical trial is [… output …] to an entity associated with conducting the clinical trial.
, as drafted, is a method, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with at least one processor and at least one memory (claim 20), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for the at least one processor and at least one memory (claim 20), the claim encompasses collection of various data, organization of the various collected data into a model, use of the organized data to provide a result for a human user to use in their human activity organization of a clinical trial. 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 “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of at least one processor and at least one memory (claim 20), which implements the abstract idea. The at least one processor and at least one memory (claim 20) is recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification paragraph [0685]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “training a computer vision-based medical outcome prognostication function based on utilizing artificial intelligence to train a corresponding computer vision model via processing training set… applying the corresponding computer vision model”, “a randomization algorithm”, and “communicated to an entity”. The “training a computer vision-based medical outcome prognostication function based on utilizing artificial intelligence to train a corresponding computer vision model via processing training set… applying the corresponding computer vision model” is recited at a high-level of generality (i.e., training and using a generic off-the-shelf computer-vision based machine learning model in a generic way) and amounts to generally linking the abstract idea to a particular technological environment. The “a randomization algorithm” is recited at a high-level of generality (i.e., using a generic off-the shelf algorithm) and amounts to generally linking the abstract idea to a particular technological environment. The “communicated to an entity” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does 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 at least one processor and at least one memory (claim 20) to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “training a computer vision-based medical outcome prognostication function based on utilizing artificial intelligence to train a corresponding computer vision model via processing training set… applying the corresponding computer vision model”, “a randomization algorithm”, and “communicated to an entity” were considered generally linking the abstract idea to particular technological environment and/or extra-solution activity. The “training a computer vision-based medical outcome prognostication function based on utilizing artificial intelligence to train a corresponding computer vision model via processing training set… applying the corresponding computer vision model” have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Buckler (20220409160): see below but at least paragraph [0104], [0115]; Barnes (20200321102): paragraphs [0013], [0100]-[0101]; Beck (10650929): Column 2, lines 20-40; Bernard (10140421): Column 5, lines 25-65; Lyman (20200352518): paragraph [0031]; McKinney (20210065859): paragraph [0017]; Roth (20220366220): paragraph [0502]; training a computer-vision based machine learning model using artificial intelligence is well-understood, routine, and conventional. The “a randomization algorithm” have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Fisher (20220157413): paragraph [0082]; Dwyer (20130211805: paragraph [0016]; Tourtelloutte (20170154168: paragraphs [0055], [0066]; using a randomization algorithm is well-understood, routine, and conventional. The “communicated to an entity” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-18 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claim 2 further describes determination of a probability, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 3 further describes the randomization algorithm that was already considered above and is incorporated herein.
Claim 4 describes organization of data and subsequent use of the already considered randomization algorithm that was already considered above and is incorporated herein.
Claim 5 describes determination of a range of scores, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 6 describes ordering of subjects, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 7 describes the randomization algorithm minimizing a difference, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 8-12 and 16 describe the type of data gathered, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 13 and 14 describe labels on data, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 15 describes the subsequent performance of the additional elements described in claim 1, however this high-level training, use of a randomization algorithm and communication was already considered above and is incorporated herein.
Claims 17 and 18 describe a human performing a clinical trial, which is human activity as part of the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 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.
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.
Claim(s) 1-2, 4-5, 7-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. No. 20220415454 (hereafter “Schuler da Costa Ferro”), in view of U.S. Patent App. No. 20220157413 (hereafter “Fisher”), in view of U.S. Patent App. No. 20220409160 (hereafter “Buckler”).
Regarding (Currently Amended) claim 1, Schuler da Costa Ferro teaches a method (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraph [0006], “Systems and methods for estimating treatment effects in randomized controlled trials”) comprising:
training a […] medical outcome prognostication function based on utilizing artificial intelligence to train a corresponding […] model via processing a training set (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0006]-[0007], “receiving external data of previous randomized clinical trials… training a prognostic model using the received external data”, paragraph [0013], “the prognostic model is a model-based generative machine learning model”, paragraph [0034], “A conceptual illustration of the stratification and estimation process is illustrated in FIG. 2. Process 200 trains (210) a prognostic model using acquired external data from previous trials”) that includes:
a first plurality of device-captured medical […] data of at least one […] medical […] data type; and a corresponding plurality of medical outcome data for a first medical outcome type (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0005]-[0007], “baseline characteristics are collected and measured… receiving external data of previous randomized clinical trials”, paragraph [0028], “process 100 acquires (110) external data of trial subjects from previous randomized clinical trials. In some embodiments, external data may be from high quality observational studies. External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects, and/or their eventual trial outcomes from the previous randomized clinical trials. In many embodiments, prognostic models are trained with acquired external data, and the models can be used to estimate outcomes for patients under control conditions”, paragraph [0048], “External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects and their eventual trial outcomes from the previous randomized clinical trials”);
obtaining a second plurality of […] medical […] data of the at least one […] medical […] data type, wherein each of the second plurality of […] medical […] data corresponds to pre-trial medical […] data for a corresponding one of a plurality of prospective clinical trial participants of a clinical trial having a primary endpoint corresponding to the first medical outcome type (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0005]-[0009], “baseline characteristics are collected and measured before random assignments, statistician retain the ability to test for treatment effects across the randomized trial groups by adjusting known covariates of the randomized trial groups… generating sets of one or more subject characteristics of a plurality of trial subjects… the sets of one or more characteristics of a plurality of trial subjects include baseline covariates of trial subjects”, paragraph [0029], “Process 100 generates (120) sets of one or more subject characteristics of trial subjects of a target trial. In certain embodiments, subject characteristics include baseline covariates of each trial subject”, paragraph [0048], “TTE endpoints refer to the time point where certain events occur in a trial. Treatment effects detected from TTE endpoints can be another indicator of efficacy of new treatments… estimating TTE treatment effects using pseudovalue regression with a covariate acquired from a generative model is illustrated in FIG. 3… the event of interest for purposes of estimating TTE treatment effects is whether trial subjects have a favorable or unfavorable outcome on study… External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects and their eventual trial outcomes from the previous randomized clinical trials”. The Examiner notes second data for prospective trial participants is collected and the endpoint corresponds to the outcome of the first data, which teaches what is required of the claim under the broadest reasonable interpretation); and
generating a plurality of medical outcome prognosis scores corresponding to the first medical outcome type based on utilizing artificial intelligence to perform the […] medical outcome prognostication function upon each of the second plurality of device-captured medical […] data based on applying the corresponding […] model to generate a corresponding medical outcome prognosis score of the plurality of medical outcome prognosis scores (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0006]-[0008], “estimating TTE treatment effects of trial subjects using pseudovalue regression, where the method includes training a prognostic model using the received external data, generating prognostic scores of trial subjects using the prognostic model and the generated trial subjects' subject characteristics, and estimating TTE treatment effects for trial subjects using a pseudovalue regression model and the prognostic scores”, paragraph [0013], “the prognostic model is a model-based generative machine learning model”, paragraphs [0048]-[0050], “Process 300 generates (320) prognostic scores for trial subjects using the trained prognostic model and subjects' subject characteristics. In certain embodiments, prognostic scores may be expected values of treatment outcome predictions predicted by the prognostic model”);
selecting a first proper subset of the plurality of prospective clinical trial participants and a second proper subset of the plurality of prospective clinical trial participants by processing the plurality of medical outcome prognoses scores […] based on applying a medical outcome prognosis score-based stratification factor […] (Schuler de Costa Ferro: Figs. 2-3, 5-6, paragraph [0001], “Prognostic Score Stratification”, paragraph [0007], “training a prognostic model using the received external data, generating outcome predictions for trial subjects using the prognostic model, defining a variable to stratify the trial subjects based on the outcome predictions, stratifying all trial subjects by the variable in to a plurality of strata”, paragraphs [0026]-[0028], “trial subjects may be partitioned into nonoverlapping groups by a certain characteristic of the trial subjects… stratification of trial subjects may be performed multiple times based on multiple subject characteristics. Machine learning models in accordance with a number of embodiments of the invention can be used to estimate outcomes under control conditions, which can be used to identify optimal groupings that may be used to stratify the trial subjects… process 100 acquires (110) external data of trial subjects from previous randomized clinical trials… External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects, and/or their eventual trial outcomes from the previous randomized clinical trials. In many embodiments, prognostic models are trained with acquired external data, and the models can be used to estimate outcomes for patients under control conditions”, paragraph [0035], “prognostic models generate outcome predictions using the entire set of one or more subject characteristics… predictions of the continuous variable itself may be used as stratifying variables”, paragraphs [0038]-[0040], “Process 200 defines (230) a variable X based on the predicted outcomes to use to stratify the trial subjects. In several embodiments, X may be defined as the probability p.sub.j of observing outcome Y… separate the trial subjects into strata based on their probability of a binary outcome occurring during the study. In several embodiments, this can allow for a more flexible application of the prognostic information in a range of baseline variables to create strata, where said strata are based on outcome predictions under control conditions… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J”, paragraph [0049], “prognostic scores may be expected values of treatment outcome predictions predicted by the prognostic model”. Also see, paragraph [0030]. The Examiner notes patient prognostic scores are determined, a variable (i.e., the prognosis score) is defined to stratify the patients into groups, and the patients are stratified using the variable as a stratification factor into groups (i.e., a first and second group), the Examiner interprets that the determined prognostic score being used as the variable defined stratification factor would be prima facie obvious under the broadest reasonable interpretation),
wherein the first proper subset and the second proper subset are mutually exclusive; and wherein trial arm assignment data indicating assignment of the first proper subset of the plurality of prospective clinical trial participants to a control trial arm of the clinical trial and further indicating assignment of the second proper subset of the plurality of prospective clinical trial participants to an experimental trial arm of the clinical trial […] (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0004], “Randomized controlled trials (RCT) are one method used to conduct a clinical trial. An RCT generally has two arms, namely the treatment arm and the control arm. Enrolled subjects are assigned to each arm randomly, and the efficacy of a proposed new treatment is determined by comparing trial outcomes of subjects enrolled in the treatment arm that received the new treatment against trial outcomes of subjects enrolled in the control arm that received an existing treatment”, paragraphs [0037]-[0040], “subjects assigned to the treatment arm… Process 200 defines (230) a variable X based on the predicted outcomes to use to stratify the trial subjects. In several embodiments, X may be defined as the probability pj of observing outcome Y and can be ordinal… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J. In many embodiments, p0j and p1j denote the expected outcome probabilities under control and treatment arms respectively for a stratum”. The Examiner notes the groups are arms of a clinical trial and are mutually exclusive).
Schuler da Costa Ferro may not explicitly teach (underlined below for clarity):
a first plurality of device-captured medical […] data of at least one device-captured medical […] data type; and a corresponding plurality of medical outcome data for a first medical outcome type; obtaining a second plurality of device-captured medical […] data of the at least one device-captured medical […] data type, wherein each of the second plurality of device-captured medical […] data corresponds to pre-trial medical […] data for a corresponding one of a plurality of prospective clinical trial participants of a clinical trial having a primary endpoint corresponding to the first medical outcome type; and
selecting a first proper subset of the plurality of prospective clinical trial participants and a second proper subset of the plurality of prospective clinical trial participants by processing the plurality of medical outcome prognoses scores using a randomization algorithm based on applying a medical outcome prognosis score-based stratification factor of the randomization algorithm,
wherein the first proper subset and the second proper subset are mutually exclusive; and wherein trial arm assignment data indicating assignment of the first proper subset of the plurality of prospective clinical trial participants to a control trial arm of the clinical trial and further indicating assignment of the second proper subset of the plurality of prospective clinical trial participants to an experimental trial arm of the clinical trial is communicated to an entity associated with conducting the clinical trial.
Fisher teaches a first plurality of device-captured medical […] data of at least one device-captured medical […] data type; and a corresponding plurality of medical outcome data for a first medical outcome type; obtaining a second plurality of device-captured medical […] data of the at least one device-captured medical […] data type, wherein each of the second plurality of device-captured medical […] data corresponds to pre-trial medical […] data for a corresponding one of a plurality of prospective clinical trial participants of a clinical trial having a primary endpoint corresponding to the first medical outcome type (Fisher: Figures 3, 5-7, paragraph [0069], “receive historical data that can be used to pre-train generative models and/or to determine a prior distribution for Bayesian analyses. Historical data in accordance with numerous embodiments of the invention can include (but is not limited to) control arms from historical control arms, patient registries, electronic health records, and/or real world data”, paragraph [0082], “an untrained generative model of the control condition is trained using historical data, such as (but not limited to), data from previously completed clinical trials, electronic health records, and/or other studies”, paragraph [0123], “Peripherals 1210 can include any of a variety of components for capturing data, such as (but not limited to) cameras, displays, and/or sensors. In a variety of embodiments, peripherals can be used to gather inputs”. The Examiner notes sensors gathering input reads on device captured under the broadest reasonable interpretation); and
selecting a first proper subset of the plurality of prospective clinical trial participants and a second proper subset of the plurality of prospective clinical trial participants by processing the plurality of medical outcome prognoses scores using a randomization algorithm based on applying a medical outcome prognosis score-based stratification factor of the randomization algorithm (Fisher: Figures 3, 5-7, paragraphs [0033]-[0035], “a group of subjects with particular characteristics are randomly assigned to one or more experimental groups receiving new treatments or to a control group receiving a comparative treatment (e.g., a placebo… a subject can only be assigned to one of the treatment arms”, paragraph [0082], “a patient population is randomly divided into a control group and a treatment group as part of a randomized controlled trial. Patients from the population can be randomized into the control and treatment groups with unequal randomization in accordance with a variety of embodiments of the invention”),
wherein the first proper subset and the second proper subset are mutually exclusive; and wherein trial arm assignment data indicating assignment of the first proper subset of the plurality of prospective clinical trial participants to a control trial arm of the clinical trial and further indicating assignment of the second proper subset of the plurality of prospective clinical trial participants to an experimental trial arm of the clinical trial is communicated to an entity associated with conducting the clinical trial (Fisher: Figures 3, 5-7, paragraph [0046], “determines (220) target trial parameters based on the estimated correlation and variance. Target trial parameters in accordance with a number of embodiments of the invention can include (but are not limited to) sample size, control arm size, and/or treatment arm size”, paragraph [0082], “a patient population is randomly divided into a control group and a treatment group as part of a randomized controlled trial. Patients from the population can be randomized into the control and treatment groups with unequal randomization in accordance with a variety of embodiments of the invention”, paragraph [0132], “Output engines in accordance with several embodiments of the invention can provide a variety of outputs to a user, including (but not limited to) decision rules, treatment effects, generative model biases, recommended RCT designs, etc.”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a randomization algorithm and sensor data as taught by Fisher within the training and use of machine learning for stratification as taught by Schuler da Costa Ferro with the motivation of “improve RCT design by reducing the number of subjects required for different arms of the RCT” (Fisher: paragraph [0031]).
Schuler da Costa Ferro and Fisher may not explicitly teach (underlined below for clarity):
training a computer vision-based medical outcome prognostication function based on utilizing artificial intelligence to train a corresponding computer vision model via processing a training set that includes: a first plurality of device-captured medical image data of at least one device-captured medical image data type […]; obtaining a second plurality of device-captured medical image data of the at least one device-captured medical image data type, wherein each of the second plurality of device-captured medical image data corresponds to pre-trial medical image data for a corresponding one of a plurality of prospective clinical trial participants of a clinical trial having a primary endpoint corresponding to the first medical outcome type; and generating a plurality of medical outcome prognosis scores corresponding to the first medical outcome type based on utilizing artificial intelligence to perform the computer vision-based medical outcome prognostication function upon each of the second plurality of device-captured medical image data based on applying the corresponding computer vision model to generate a corresponding medical outcome prognosis score of the plurality of medical outcome prognosis scores;
Buckler teaches training a computer vision-based medical outcome prognostication function based on utilizing artificial intelligence to train a corresponding computer vision model via processing a training set that includes: a first plurality of device-captured medical image data of at least one device-captured medical image data type […]; obtaining a second plurality of device-captured medical image data of the at least one device-captured medical image data type, wherein each of the second plurality of device-captured medical image data corresponds to pre-trial medical image data for a corresponding one of a plurality of prospective clinical trial participants of a clinical trial having a primary endpoint corresponding to the first medical outcome type; and generating a plurality of medical outcome prognosis scores corresponding to the first medical outcome type based on utilizing artificial intelligence to perform the computer vision-based medical outcome prognostication function upon each of the second plurality of device-captured medical image data based on applying the corresponding computer vision model to generate a corresponding medical outcome prognosis score of the plurality of medical outcome prognosis scores (Buckler: Figures 1-8, paragraph [0010], “methods and systems for selecting and recommending a suitable therapeutic treatment plan for a patient… analyze and process non-invasively obtained data, such as imaging data, e.g., computed tomography angiography (CTA) data”, paragraph [0104], “an image processing software”, paragraph [0115], “As described in further detail below, the virtual 'omics models are built from a variety of machine learning models… The machine (e.g., a computer or processor) will “learn,” for example, by identifying patterns, categories, statistical relationships, etc., exhibited by training data. The result of the learning is then used to predict whether new data exhibits the same patterns, categories, and statistical relationships”, paragraphs [0119]-[0120], “one or more neural network(s) can be generated and/or updated with virtual 'omics from vascular CT images processed as described in FIGS. 2A and 2B”, paragraph [0153], “During training, the virtual 'omics engine 310 identifies features in CTA imaging data (e.g., a particular plaque morphology) that are predictive of the molecular measurements. After training, the virtual 'omics engine 310 is validated”);
One of ordinary skill in the art before the effective filing date would have found it obvious to include using imaging modality data to train and use a computer vision model as taught by Buckler within the sensor data and machine learning as taught by Schuler da Costa Ferro and Fisher with the motivation of “improvements in the ability to provide patient-specific recommendations of therapies” (Buckler: paragraph [0050]).
Regarding (Previously Presented) claim 2, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach wherein each of the plurality of medical outcome prognosis scores indicate a corresponding probability value denoting a predicted probability that a corresponding prospective clinical trial participant will attain the first medical outcome type based on the computer vision-based medical outcome prognostication function being trained to predict probability of the first medical outcome type as a function of device-captured medical data having the at least one device-captured medical image data type (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0034]-[0035], “outcome predictions generated in many embodiments of the invention may also be binary in nature as the scores predict the outcome probability between the two possible outcomes”, paragraphs [0037]-[0040], “subjects assigned to the treatment arm… Process 200 defines (230) a variable X based on the predicted outcomes to use to stratify the trial subjects. In several embodiments, X may be defined as the probability pj of observing outcome Y and can be ordinal… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J. In many embodiments, p0j and p1j denote the expected outcome probabilities under control and treatment arms respectively for a stratum”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 4, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach wherein performing the randomization algorithm to facilitate selecting the first proper subset and the second proper subset is based on: determining a deterministic mapping of a plurality of possible medical outcome prognosis scores to a plurality of score-based groups, wherein the deterministic mapping indicates a mapping of each of the plurality of possible medical outcome prognosis scores to exactly one of the plurality of score-based groups, and wherein the plurality of possible medical outcome prognosis scores render a full score range that includes all possible medical outcome prognosis scores generated via performance of the computer vision-based medical outcome prognostication function (Schuler da Costa Ferro: paragraph [0034], “derive the probability of a binary outcome for each trial participant”, paragraphs [0039]-[0040], “Let Y={0,1} be the outcome vector that denotes outcomes for subjects i, and ZX, be the vector of covariates for subjects i… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J. In many embodiments, p0j and p1j denote the expected outcome probabilities under control and treatment arms respectively for a stratum”);
segregating the plurality of prospective clinical trial participants across the plurality of score-based groups by including each of the plurality of prospective clinical trial participants in a corresponding one of the plurality of score-based groups based on determining which one of the plurality of score-based groups is mapped to by the corresponding medical outcome prognosis score generated from corresponding device-captured medical image data of the each of the plurality of prospective clinical trial participants (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0026], “trial subjects may be partitioned into nonoverlapping groups by a certain characteristic of the trial subjects. In several embodiments, stratification of trial subjects may be performed multiple times based on multiple subject characteristics. Machine learning models in accordance with a number of embodiments of the invention can be used to estimate outcomes under control conditions, which can be used to identify optimal groupings that may be used to stratify the trial subjects”, paragraph [0030], “Binary treatment outcomes may be estimated using a stratified analysis whereby the entirety of trial subjects is partitioned into nonoverlapping groups known as strata by a certain subject characteristic that all trial subjects possess, thus allowing researchers to observe the correlation between certain subject characteristics and the binary trial outcome”, paragraphs [0038]-[0040], “Process 200 defines (230) a variable X based on the predicted outcomes to use to stratify the trial subjects. In several embodiments, X may be defined as the probability pj of observing outcome Y and can be ordinal… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J. In many embodiments, p0j and p1j denote the expected outcome probabilities under control and treatment arms respectively for a stratum”); and
applying a randomization sub-algorithm of the randomization algorithm to each of the plurality of score-based groups independently to distribute prospective clinical trial participants in a given score-based group of the plurality of score-based groups between the first proper subset and the second proper subset separately from distributing other prospective clinical trial participants in other score-based groups of the plurality of score-based groups between the first proper subset and the second proper subset (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0026], “trial subjects may be partitioned into nonoverlapping groups by a certain characteristic of the trial subjects. In several embodiments, stratification of trial subjects may be performed multiple times based on multiple subject characteristics. Machine learning models in accordance with a number of embodiments of the invention can be used to estimate outcomes under control conditions, which can be used to identify optimal groupings that may be used to stratify the trial subjects”; Fisher: Figures 3, 5-7, paragraphs [0033]-[0035], “a group of subjects with particular characteristics are randomly assigned to one or more experimental groups receiving new treatments or to a control group receiving a comparative treatment (e.g., a placebo… a subject can only be assigned to one of the treatment arms”, paragraph [0082], “a patient population is randomly divided into a control group and a treatment group as part of a randomized controlled trial. Patients from the population can be randomized into the control and treatment groups with unequal randomization in accordance with a variety of embodiments of the invention”. The Examiner notes multiple stratification is performed which in combination one of ordinary skill in the art would understand teaches what is required under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 5, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 4, and further teach wherein the plurality of possible medical outcome prognosis scores correspond to numeric values in the full score range, and wherein determining the deterministic mapping is based on: determining a plurality of medical outcome prognosis score ranges to define the plurality of score-based groups, wherein the plurality of medical outcome prognosis score ranges contiguously render the full score range that includes all possible medical outcome prognosis scores generated via performance of the computer vision-based medical outcome prognostication function, and wherein each of the plurality of medical outcome prognosis score ranges include multiple corresponding ones of the plurality of possible medical outcome prognosis scores (Schuler da Costa Ferro: paragraph [0034], “derive the probability of a binary outcome for each trial participant”, paragraphs [0039]-[0040], “Let Y={0,1} be the outcome vector that denotes outcomes for subjects i, and ZX, be the vector of covariates for subjects i… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J. In many embodiments, p0j and p1j denote the expected outcome probabilities under control and treatment arms respectively for a stratum”; Fisher: paragraph [0103], “the treatment can be declared effective if Prob(b0≥0) exceeds a pre-specified threshold”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 7, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach wherein performing the randomization algorithm to automatically select the first proper subset and the second proper subset includes distributing prospective clinical trial participants between the first proper subset and the second proper subset based on at least one of: minimizing a first difference between: a first number of prospective clinical participants included the first proper subset; and a second number of prospective clinical trial participants included in the second proper subset; and minimizing a second difference between: a first mean medical outcome prognosis score of first medical outcome type prognosis scores generated from medical image data of prospective clinical trial participants included in the first proper subset; and a second mean medical outcome prognosis score of second medical outcome prognosis scores generated from second medical image data of second prospective clinical trial participants included in the second proper subset (Fisher: paragraphs [0011]-[0013], “minimizing a number of samples for the control arm of the target random control trial… minimizing a number of samples for the treatment arm of the target random control trial”, paragraph [0057], “Equation 4 can be computationally optimized over n0 and n1 in the desired randomization ratio n0/n1 until the minimum values of n0 and n1 are found such that the output power meets or exceeds the desired value (e.g., with a numerical optimization scheme)”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 8, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach wherein each device-captured medical image data the first plurality of device-captured medical image data and of the second plurality of device-captured medical image data indicates a plurality of sensor data values captured by at least one medical imaging device of at least one corresponding medical imaging device type, wherein the medical outcome prognostication function is trained based on processing a first plurality of feature vectors that each include a first corresponding plurality of sensor data values for a corresponding one of the first plurality of device-captured medical image data, and wherein the computer vision-based medical outcome prognostication function is performed upon the second plurality of device-captured medical image data based on processing a second plurality of feature vectors that each include a second corresponding plurality of sensor data values for a corresponding one of the second plurality of device-captured medical image data (Fisher: Figures 3, 5-7, paragraph [0034], “each subject i in an RCT can be described by a vector”, paragraph [0069], “receive historical data that can be used to pre-train generative models and/or to determine a prior distribution for Bayesian analyses. Historical data in accordance with numerous embodiments of the invention can include (but is not limited to) control arms from historical control arms, patient registries, electronic health records, and/or real world data”, paragraph [0082], “an untrained generative model of the control condition is trained using historical data, such as (but not limited to), data from previously completed clinical trials, electronic health records, and/or other studies”, paragraph [0123], “Peripherals 1210 can include any of a variety of components for capturing data, such as (but not limited to) cameras, displays, and/or sensors. In a variety of embodiments, peripherals can be used to gather inputs”. The Examiner notes sensors gathering input reads on device captured under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 9, Schuler da Costa Ferro and Fisher teach the limitations of claim 8, and further teaches wherein the at least one device-captured medical image data type includes at least one medical imaging modality, and wherein the first plurality of device-captured medical image data includes a first plurality of medical imaging data of the at least one medical imaging modality, and wherein the second plurality of device-captured medical image data is a second plurality of medical imaging data of the at least one medical imaging modality (Buckler: paragraph [0010], “methods and systems for selecting and recommending a suitable therapeutic treatment plan for a patient with cardiovascular disease, such as atherosclerosis. For example, physicians and other healthcare providers can use the new methods and systems to analyze and process non-invasively obtained data, such as imaging data, e.g., computed tomography angiography (CTA) data”, paragraphs [0119]-[0120], “one or more neural network(s) can be generated and/or updated with virtual 'omics from vascular CT images processed as described in FIGS. 2A and 2B”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 10, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 9, and further teach wherein the at least one medical imaging modality includes a computed tomography (CT) scan modality, wherein the first plurality of medical imaging data includes a first plurality of CT scans, and wherein the second plurality of medical imaging data includes a second plurality of CT scans (Buckler: paragraph [0010], “methods and systems for selecting and recommending a suitable therapeutic treatment plan for a patient with cardiovascular disease, such as atherosclerosis. For example, physicians and other healthcare providers can use the new methods and systems to analyze and process non-invasively obtained data, such as imaging data, e.g., computed tomography angiography (CTA) data”, paragraph [0019], “the non-invasively obtained data is imaging data, such as, for example, radiological imaging data, which can be obtained by computed tomography (CT)”, paragraphs [0119]-[0120], “one or more neural network(s) can be generated and/or updated with virtual 'omics from vascular CT images processed as described in FIGS. 2A and 2B”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 11, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 9, and further teach wherein the at least one device-captured medical image data type further corresponds to at least one of: at least one anatomical region captured via the at least one medical imaging modality; or at least one image plane by which the at least one medical imaging modality is captured (Buckler: paragraphs [0010], “imaging data, e.g., computed tomography angiography (CTA) data, of arteries from patients”, paragraph [0020], “obtained imaging data to obtain quantitative plaque morphology data including structural anatomy data, tissue composition data, or both”. The image is of an anatomical region (i.e., the patients’ arteries), which teaches what is required under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 13, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach wherein the training set includes a plurality of training data instances corresponding to a plurality of individuals, wherein each training data instance includes, for a corresponding one of the plurality of individuals: one of the first plurality of device-captured medical image data captured for the corresponding one of the plurality of individuals as input feature data; and one of the corresponding plurality of medical outcome data determined for the corresponding one of the plurality of individuals as output label data, wherein the plurality of individuals are distinct from the plurality of prospective clinical trial participants (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0005]-[0007], “baseline characteristics are collected and measured… receiving external data of previous randomized clinical trials”, paragraph [0028], “process 100 acquires (110) external data of trial subjects from previous randomized clinical trials. In some embodiments, external data may be from high quality observational studies. External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects, and/or their eventual trial outcomes from the previous randomized clinical trials. In many embodiments, prognostic models are trained with acquired external data, and the models can be used to estimate outcomes for patients under control conditions”, paragraph [0034], “A conceptual illustration of the stratification and estimation process is illustrated in FIG. 2. Process 200 trains (210) a prognostic model using acquired external data from previous trials.”, paragraph [0048], “External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects and their eventual trial outcomes from the previous randomized clinical trials”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 14, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 13, and further teach wherein the input feature data of the each training data instance further includes, for the corresponding one of the plurality of individuals, at least one of: demographic data for the corresponding one of the plurality of individuals; patient history data for the corresponding one of the plurality of individuals; medical report text data for the corresponding one of the plurality of individuals; or risk factor data for the corresponding one of the plurality of individuals (Schuler da Costa Ferro: paragraph [0043], “a historical dataset”; Fisher: paragraphs [0039]-[0041], “probabilistic generative models that can be trained on various data, such as (but not limited to) one or more of historical, registry, and/or real world data… borrowing from a historical dataset”, paragraph [0069], “Historical data in accordance with numerous embodiments of the invention can include (but is not limited to) control arms from historical control arms, patient registries, electronic health records, and/or real world data”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 15, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach training a plurality of medical outcome prognostication functions corresponding to a plurality of different medical outcome types, wherein the medical outcome prognostication functions is one of the plurality of medical outcome prognostication functions and the first medical outcome type is one of the plurality of different medical outcome types, wherein the medical outcome prognostication function is selected for performance upon the second plurality of device-captured medical image data for the plurality of prospective clinical trial participants of the clinical trial based on the clinical trial having the primary endpoint corresponding to the first medical outcome type (Schuler da Costa Ferro: paragraph [0028], “prognostic models are trained with acquired external data, and the models can be used to estimate outcomes for patients under control conditions. Embodiments of the invention can leverage these estimated outcomes to improve precision of estimated treatment effects”; Fisher: paragraph [0053], “Prognostic models in accordance with many embodiments of the invention can be trained (e.g., based on a prior trial) or pre-trained.”, paragraph [0058], “Processes in accordance with several embodiments of the invention can use multiple prognostic models (e.g., one to predict each outcome of interest) and/or a multivariate prognostic model”, paragraph [0070], “generative models can be trained directly on a specific outcome p(y|x0). For example, if a goal of using the generative model is to increase the statistical power for the primary analysis of a randomized controlled trial then it may be sufficient (but not necessary) to only use a model of p(y|x0)”);
obtaining a third plurality of device-captured medical image data, wherein each of the third plurality of device captured medical image data corresponds pre-treatment medical image data for a corresponding one of a second plurality of prospective clinical trial participants of a second clinical trial having another primary endpoint corresponding to a second medical outcome of the plurality of different medical outcome types (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0005]-[0009], “baseline characteristics are collected and measured before random assignments… generating sets of one or more subject characteristics of a plurality of trial subjects”, paragraphs [0026]-[0029], “trial subjects may be partitioned into nonoverlapping groups by a certain characteristic of the trial subjects. In several embodiments, stratification of trial subjects may be performed multiple times based on multiple subject characteristics… Process 100 generates (120) sets of one or more subject characteristics of trial subjects of a target trial. In certain embodiments, subject characteristics include baseline covariates of each trial subject”; Fisher: paragraph [0004], “correlate to the expected outcome or participants with specific pre-treatment covariates”, paragraph [0083], “a vector of pre-treatment covariates”, paragraph [0087], “using a bootstrap by repeatedly resampling the data (with replacement) and re-fitting the model”. The Examiner notes additional data sets can be sampled (i.e., a third data set), which teaches what is required under the broadest reasonable interpretation);
selecting a second medical outcome prognostication function of the plurality of medical outcome prognostication functions for performance upon the third plurality of device-captured medical image data for the second plurality of prospective clinical trial participants of the second clinical trial based on the second clinical trial having a second primary endpoint corresponding to the second medical outcome; generating a second plurality of medical outcome prognosis scores corresponding to the second medical outcome based on utilizing artificial intelligence to perform the second medical outcome prognostication function upon each of the third plurality of device-captured medical image data to generate a second corresponding medical outcome prognosis score of the second plurality of medical outcome prognosis scores (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0006]-[0008], “estimating TTE treatment effects of trial subjects using pseudovalue regression, where the method includes training a prognostic model using the received external data, generating prognostic scores of trial subjects using the prognostic model and the generated trial subjects' subject characteristics, and estimating TTE treatment effects for trial subjects using a pseudovalue regression model and the prognostic scores”, paragraph [0013], “the prognostic model is a model-based generative machine learning model”, paragraphs [0048]-[0050], “Process 300 generates (320) prognostic scores for trial subjects using the trained prognostic model and subjects' subject characteristics. In certain embodiments, prognostic scores may be expected values of treatment outcome predictions predicted by the prognostic model”; Fisher: paragraph [0058], “Processes in accordance with several embodiments of the invention can use multiple prognostic models (e.g., one to predict each outcome of interest) and/or a multivariate prognostic model”, paragraph [0070], “generative models can be trained directly on a specific outcome p(y|x0). For example, if a goal of using the generative model is to increase the statistical power for the primary analysis of a randomized controlled trial then it may be sufficient (but not necessary) to only use a model of p(y|x0)”. The Examiner notes in combination a model is selected and a set of prognosis scores is generated, which teaches what is required under the broadest reasonable interpretation);
processing the second plurality of medical outcome prognosis scores via the randomization algorithm to automatically select another first proper subset of the second plurality of prospective clinical trial participants and another second proper subset of the second plurality of prospective clinical trial participants based on utilizing the second medical outcome as the medical outcome prognosis score-based stratification factor of the randomization algorithm (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0026], “trial subjects may be partitioned into nonoverlapping groups by a certain characteristic of the trial subjects. In several embodiments, stratification of trial subjects may be performed multiple times based on multiple subject characteristics. Machine learning models in accordance with a number of embodiments of the invention can be used to estimate outcomes under control conditions, which can be used to identify optimal groupings that may be used to stratify the trial subjects”, paragraph [0030], “Binary treatment outcomes may be estimated using a stratified analysis whereby the entirety of trial subjects is partitioned into nonoverlapping groups known as strata by a certain subject characteristic that all trial subjects possess, thus allowing researchers to observe the correlation between certain subject characteristics and the binary trial outcome”, paragraphs [0038]-[0040], “Process 200 defines (230) a variable X based on the predicted outcomes to use to stratify the trial subjects. In several embodiments, X may be defined as the probability pj of observing outcome Y and can be ordinal… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J. In many embodiments, p0j and p1j denote the expected outcome probabilities under control and treatment arms respectively for a stratum”; Fisher: Figures 3, 5-7, paragraphs [0033]-[0035], “a group of subjects with particular characteristics are randomly assigned to one or more experimental groups receiving new treatments or to a control group receiving a comparative treatment (e.g., a placebo… a subject can only be assigned to one of the treatment arms”, paragraph [0082], “a patient population is randomly divided into a control group and a treatment group as part of a randomized controlled trial. Patients from the population can be randomized into the control and treatment groups with unequal randomization in accordance with a variety of embodiments of the invention”),
wherein the another first proper subset and the another second proper subset are mutually exclusive; and communicating second clinical trial assignment data indicating assignment of the another first proper subset of the plurality of prospective clinical trial participants to a second control trial arm of the clinical trial and further indicating assignment of the another second proper subset of the second plurality of prospective clinical trial participants to a second experimental trial arm of the second clinical trial (Fisher: Figures 3, 5-7, paragraph [0046], “determines (220) target trial parameters based on the estimated correlation and variance. Target trial parameters in accordance with a number of embodiments of the invention can include (but are not limited to) sample size, control arm size, and/or treatment arm size”, paragraph [0082], “a patient population is randomly divided into a control group and a treatment group as part of a randomized controlled trial. Patients from the population can be randomized into the control and treatment groups with unequal randomization in accordance with a variety of embodiments of the invention”, paragraph [0132], “Output engines in accordance with several embodiments of the invention can provide a variety of outputs to a user, including (but not limited to) decision rules, treatment effects, generative model biases, recommended RCT designs, etc.”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 16, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach wherein the first medical outcome type corresponds to at least one of: a mortality metric; a hospitalization metric; a count metric; a medically-defined scale-based metric; objective tumor response; a metric for measured change; a metric for pain; or at least one biomarker (Schuler da Costa Ferro: paragraph [0030], “treatment outcomes may be binary in that they account for whether trial subjects have achieved the desired treatment outcome or not”; Fisher: paragraph [0049], “estimate the effect of a new intervention on a given outcome”. The Examiner notes a binary outcome for effective treatment reads on at least a metric for measured change under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 17, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach wherein the entity conducts the clinical trial based on: conducting the clinical trial for the first proper subset of the plurality of prospective clinical trial participants in conjunction with the control trial arm of the clinical trial to generate control trial arm result data; conducting the clinical trial for the second proper subset of the plurality of prospective clinical trial participants in conjunction with the experimental trial arm of the clinical trial to generate experimental trial arm result data; and generating corresponding clinical trial results based on the control trial arm result data and the experimental trial arm result data (Fisher: paragraph [0082], “a patient population is randomly divided into a control group and a treatment group as part of a randomized controlled trial. Patients from the population can be randomized into the control and treatment groups with unequal randomization in accordance with a variety of embodiments of the invention”, paragraph [0093], “a randomized controlled trial is conducted (potentially with unequal randomization). The generative model is used to create digital subjects, and all of the data are incorporated into a statistical analysis (including the prior from step 610 if the analysis is Bayesian) to estimate the treatment effects”, paragraph [0126], “RCT data in accordance with a variety of embodiments of the invention can be divided into control and treatment arms”, paragraph [0132], “Output engines in accordance with several embodiments of the invention can provide a variety of outputs to a user”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 18, Schuler da Costa Ferro and Fisher teach the limitations of claim 1, and further teach wherein the clinical trial is conducted to test a corresponding medical product, and wherein the corresponding medical product is commercially manufactured by a corresponding medical product manufacturing entity based on clinical trial results of the clinical trial comparing favorably to regulation-mandated acceptance criteria (Schuler da Costa Ferro: paragraphs [0003]-[0005], “study the safety and efficacy of biomedical or behavioral interventions on humans. When new drugs and medical devices are invented, they must undergo rigorous trials to generate data on its efficacy and safety in order to be approved by the relevant authorities for clinical use. Test articles that do not produce satisfactory safety or efficacy levels will not be approved for mass commercial use”; Fisher: paragraph [0049], “estimate the effect of a new intervention on a given outcome”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 19, Schuler de Costa Ferro teaches a method (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraph [0006], “Systems and methods for estimating treatment effects in randomized controlled trials”) comprising:
training a […] outcome prediction function based on utilizing artificial intelligence to process a training set (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0006]-[0007], “receiving external data of previous randomized clinical trials… training a prognostic model using the received external data”, paragraph [0013], “the prognostic model is a model-based generative machine learning model”, paragraph [0034], “A conceptual illustration of the stratification and estimation process is illustrated in FIG. 2. Process 200 trains (210) a prognostic model using acquired external data from previous trials”) that includes:
a first plurality of scientific […] data of at least one […] data type; and a corresponding plurality of outcome data for a first outcome (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0005]-[0007], “baseline characteristics are collected and measured… receiving external data of previous randomized clinical trials”, paragraph [0028], “process 100 acquires (110) external data of trial subjects from previous randomized clinical trials. In some embodiments, external data may be from high quality observational studies. External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects, and/or their eventual trial outcomes from the previous randomized clinical trials. In many embodiments, prognostic models are trained with acquired external data, and the models can be used to estimate outcomes for patients under control conditions”, paragraph [0048], “External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects and their eventual trial outcomes from the previous randomized clinical trials”);
obtaining a second plurality of scientific […] data of the at least one […] data type, wherein each of the second plurality of scientific image data corresponds pre-study scientific […] data for a corresponding one of a plurality of prospective participants of a scientific study having a primary endpoint corresponding to the first outcome (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0005]-[0009], “baseline characteristics are collected and measured before random assignments, statistician retain the ability to test for treatment effects across the randomized trial groups by adjusting known covariates of the randomized trial groups… generating sets of one or more subject characteristics of a plurality of trial subjects… the sets of one or more characteristics of a plurality of trial subjects include baseline covariates of trial subjects”, paragraph [0029], “Process 100 generates (120) sets of one or more subject characteristics of trial subjects of a target trial. In certain embodiments, subject characteristics include baseline covariates of each trial subject”, paragraph [0048], “TTE endpoints refer to the time point where certain events occur in a trial. Treatment effects detected from TTE endpoints can be another indicator of efficacy of new treatments… estimating TTE treatment effects using pseudovalue regression with a covariate acquired from a generative model is illustrated in FIG. 3… the event of interest for purposes of estimating TTE treatment effects is whether trial subjects have a favorable or unfavorable outcome on study… External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects and their eventual trial outcomes from the previous randomized clinical trials”. The Examiner notes second data for prospective trial participants is collected and the endpoint corresponds to the outcome of the first data, which teaches what is required of the claim under the broadest reasonable interpretation);
generating a plurality of outcome prediction scores corresponding to the first outcome based on utilizing artificial intelligence to perform the […] outcome prediction function upon each of the second plurality of scientific […] data to generate a corresponding outcome score of the plurality of outcome prediction scores (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0006]-[0008], “estimating TTE treatment effects of trial subjects using pseudovalue regression, where the method includes training a prognostic model using the received external data, generating prognostic scores of trial subjects using the prognostic model and the generated trial subjects' subject characteristics, and estimating TTE treatment effects for trial subjects using a pseudovalue regression model and the prognostic scores”, paragraph [0013], “the prognostic model is a model-based generative machine learning model”, paragraphs [0048]-[0050], “Process 300 generates (320) prognostic scores for trial subjects using the trained prognostic model and subjects' subject characteristics. In certain embodiments, prognostic scores may be expected values of treatment outcome predictions predicted by the prognostic model”);
selecting a first proper subset of the plurality of prospective participants and a second proper subset of the plurality of prospective participants by processing the plurality of medical outcome prognoses scores […] based on utilizing the first outcome as a stratification factor […] (Schuler de Costa Ferro: Figs. 2-3, 5-6, paragraph [0001], “Prognostic Score Stratification”, paragraph [0007], “training a prognostic model using the received external data, generating outcome predictions for trial subjects using the prognostic model, defining a variable to stratify the trial subjects based on the outcome predictions, stratifying all trial subjects by the variable in to a plurality of strata”, paragraphs [0026]-[0030], “trial subjects may be partitioned into nonoverlapping groups by a certain characteristic of the trial subjects… stratification of trial subjects may be performed multiple times based on multiple subject characteristics. Machine learning models in accordance with a number of embodiments of the invention can be used to estimate outcomes under control conditions, which can be used to identify optimal groupings that may be used to stratify the trial subjects… process 100 acquires (110) external data of trial subjects from previous randomized clinical trials… External data in accordance with several embodiments of the invention may include subject characteristics of trial subjects, and/or their eventual trial outcomes from the previous randomized clinical trials. In many embodiments, prognostic models are trained with acquired external data, and the models can be used to estimate outcomes for patients under control conditions”, paragraph [0035], “prognostic models generate outcome predictions using the entire set of one or more subject characteristics… predictions of the continuous variable itself may be used as stratifying variables”, paragraphs [0038]-[0040], “Process 200 defines (230) a variable X based on the predicted outcomes to use to stratify the trial subjects. In several embodiments, X may be defined as the probability p.sub.j of observing outcome Y… separate the trial subjects into strata based on their probability of a binary outcome occurring during the study. In several embodiments, this can allow for a more flexible application of the prognostic information in a range of baseline variables to create strata, where said strata are based on outcome predictions under control conditions… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J”, paragraph [0049], “prognostic scores may be expected values of treatment outcome predictions predicted by the prognostic model”. Also see, paragraph [0030]. The Examiner notes first outcome data (i.e., previous outcomes from past trials) is collected and used to train a prognostic model, patient prognostic scores are determined using the prognostic model which was learned using the first outcome data, a variable (i.e., a outcome learned from the first outcome data that trains the prognostic model) is defined to stratify the patients into groups, and the patients are stratified using the variable as a stratification factor into groups (i.e., a first and second group), the Examiner interprets that the first outcome data used to train the prognostic model being used as the variable defined stratification factor would be prima facie obvious under the broadest reasonable interpretation),
wherein the first proper subset and the second proper subset are mutually exclusive; and wherein trial arm assignment data indicating assignment of the first proper subset of the plurality of prospective participants to a control trial arm of the scientific study and further indicating assignment of the second proper subset of the plurality of prospective participants to an experimental trial arm of the scientific study […] (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0004], “Randomized controlled trials (RCT) are one method used to conduct a clinical trial. An RCT generally has two arms, namely the treatment arm and the control arm. Enrolled subjects are assigned to each arm randomly, and the efficacy of a proposed new treatment is determined by comparing trial outcomes of subjects enrolled in the treatment arm that received the new treatment against trial outcomes of subjects enrolled in the control arm that received an existing treatment”, paragraphs [0037]-[0040], “subjects assigned to the treatment arm… Process 200 defines (230) a variable X based on the predicted outcomes to use to stratify the trial subjects. In several embodiments, X may be defined as the probability pj of observing outcome Y and can be ordinal… Process 200 stratifies (240) the trial subjects by the variable X. into j strata, where j=1,2, . . . , J. In many embodiments, p0j and p1j denote the expected outcome probabilities under control and treatment arms respectively for a stratum”. The Examiner notes the groups are arms of a clinical trial and are mutually exclusive).
Schuler da Costa Ferro may not explicitly teach (underlined below for clarity):
selecting a first proper subset of the plurality of prospective participants and a second proper subset of the plurality of prospective participants by processing the plurality of medical outcome prognoses scores using a randomization algorithm based on utilizing the first outcome as a stratification factor of the randomization algorithm,
wherein the first proper subset and the second proper subset are mutually exclusive; and wherein trial arm assignment data indicating assignment of the first proper subset of the plurality of prospective participants to a control trial arm of the scientific study and further indicating assignment of the second proper subset of the plurality of prospective participants to an experimental trial arm of the scientific study is communicated to an entity associated with conducting the scientific study.
Fisher teaches selecting a first proper subset of the plurality of prospective participants and a second proper subset of the plurality of prospective participants by processing the plurality of medical outcome prognoses scores using a randomization algorithm based on utilizing the first outcome as a stratification factor of the randomization algorithm (Fisher: Figures 3, 5-7, paragraphs [0033]-[0035], “a group of subjects with particular characteristics are randomly assigned to one or more experimental groups receiving new treatments or to a control group receiving a comparative treatment (e.g., a placebo… a subject can only be assigned to one of the treatment arms”, paragraph [0082], “a patient population is randomly divided into a control group and a treatment group as part of a randomized controlled trial. Patients from the population can be randomized into the control and treatment groups with unequal randomization in accordance with a variety of embodiments of the invention”),
wherein the first proper subset and the second proper subset are mutually exclusive; and wherein trial arm assignment data indicating assignment of the first proper subset of the plurality of prospective participants to a control trial arm of the scientific study and further indicating assignment of the second proper subset of the plurality of prospective participants to an experimental trial arm of the scientific study is communicated to an entity associated with conducting the scientific study (Fisher: Figures 3, 5-7, paragraph [0046], “determines (220) target trial parameters based on the estimated correlation and variance. Target trial parameters in accordance with a number of embodiments of the invention can include (but are not limited to) sample size, control arm size, and/or treatment arm size”, paragraph [0082], “a patient population is randomly divided into a control group and a treatment group as part of a randomized controlled trial. Patients from the population can be randomized into the control and treatment groups with unequal randomization in accordance with a variety of embodiments of the invention”, paragraph [0132], “Output engines in accordance with several embodiments of the invention can provide a variety of outputs to a user, including (but not limited to) decision rules, treatment effects, generative model biases, recommended RCT designs, etc.”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a randomization algorithm as taught by Fisher within the training and use of machine learning for stratification as taught by Schuler da Costa Ferro with the motivation of “improve RCT design by reducing the number of subjects required for different arms of the RCT” (Fisher: paragraph [0031]).
Schuler da Costa Ferro and Fisher may not explicitly teach (underlined below for clarity):
training a computer vision-based outcome prediction function based on utilizing artificial intelligence to process a training set that includes: a first plurality of scientific image data of at least one image data type; and a corresponding plurality of outcome data for a first outcome; obtaining a second plurality of scientific image data of the at least one image data type, wherein each of the second plurality of scientific image data corresponds pre-study scientific image data for a corresponding one of a plurality of prospective participants of a scientific study having a primary endpoint corresponding to the first outcome; generating a plurality of outcome prediction scores corresponding to the first outcome based on utilizing artificial intelligence to perform the computer vision-based outcome prediction function upon each of the second plurality of scientific image data to generate a corresponding outcome score of the plurality of outcome prediction scores;
Buckler teaches training a computer vision-based outcome prediction function based on utilizing artificial intelligence to process a training set that includes: a first plurality of scientific image data of at least one image data type; and a corresponding plurality of outcome data for a first outcome; obtaining a second plurality of scientific image data of the at least one image data type, wherein each of the second plurality of scientific image data corresponds pre-study scientific image data for a corresponding one of a plurality of prospective participants of a scientific study having a primary endpoint corresponding to the first outcome; generating a plurality of outcome prediction scores corresponding to the first outcome based on utilizing artificial intelligence to perform the computer vision-based outcome prediction function upon each of the second plurality of scientific image data to generate a corresponding outcome score of the plurality of outcome prediction scores (Buckler: Figures 1-8, paragraph [0010], “methods and systems for selecting and recommending a suitable therapeutic treatment plan for a patient… analyze and process non-invasively obtained data, such as imaging data, e.g., computed tomography angiography (CTA) data”, paragraph [0104], “an image processing software”, paragraph [0115], “As described in further detail below, the virtual 'omics models are built from a variety of machine learning models… The machine (e.g., a computer or processor) will “learn,” for example, by identifying patterns, categories, statistical relationships, etc., exhibited by training data. The result of the learning is then used to predict whether new data exhibits the same patterns, categories, and statistical relationships”, paragraphs [0119]-[0120], “one or more neural network(s) can be generated and/or updated with virtual 'omics from vascular CT images processed as described in FIGS. 2A and 2B”, paragraph [0153], “During training, the virtual 'omics engine 310 identifies features in CTA imaging data (e.g., a particular plaque morphology) that are predictive of the molecular measurements. After training, the virtual 'omics engine 310 is validated”);
One of ordinary skill in the art before the effective filing date would have found it obvious to include using imaging modality data to train and use a computer vision model as taught by Buckler within the sensor data and machine learning as taught by Schuler da Costa Ferro and Fisher with the motivation of “improvements in the ability to provide patient-specific recommendations of therapies” (Buckler: paragraph [0050]).
REGARDING CLAIM(S) 20
Claim(s) 20 is/are analogous to Claim(s) 1, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
Claim(s) 3, 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. No. 20220415454 (hereafter “Schuler da Costa Ferro”), U.S. Patent App. No. 20220157413 (hereafter “Fisher”) and U.S. Patent App. No. 20220409160 (hereafter “Buckler”) as applied to claim 1 above, and further in view of U.S. Patent App. No. 20130211805 (hereafter “Dwyer”).
Regarding (Original) claim 3, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 1, and further teach wherein the randomization algorithm is performed to select the first proper subset and the second proper subset based on applying a stratification function based on utilizing the first medical outcome type as a stratification factor (Schuler da Costa Ferro: Figures 2-3, 5-6, paragraphs [0026], “trial subjects may be partitioned into nonoverlapping groups by a certain characteristic of the trial subjects. In several embodiments, stratification of trial subjects may be performed multiple times based on multiple subject characteristics. Machine learning models in accordance with a number of embodiments of the invention can be used to estimate outcomes under control conditions, which can be used to identify optimal groupings that may be used to stratify the trial subjects”, paragraph [0030], “Binary treatment outcomes may be estimated using a stratified analysis whereby the entirety of trial subjects is partitioned into nonoverlapping groups known as strata by a certain subject characteristic that all trial subjects possess, thus allowing researchers to observe the correlation between certain subject characteristics and the binary trial outcome”, paragraphs [0038]-[0040], “Process 200 defines (230) a variable X based on the predicted outcomes to use to stratify the trial subjects. In several embodiments, X may be defined as the probability pj of observing outcome Y and can be ordinal”), and […].
Schuler da Costa Ferro, Fisher and Buckler may not explicitly teach (underlined below for clarity):
further includes applying a permuted block randomization technique.
Dwyer teaches further includes applying a permuted block randomization technique (Dwyer: paragraph [0139], “Subjects were randomized in the cohorts using a permuted block randomization scheme”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include a block randomization as taught by Dwyer with the randomization algorithm as taught by Schuler da Costa Ferro, Fisher and Buckler with the motivation of “improve the quality and predictive value of a randomized study” (Dwyer: Abstract).
Regarding (Currently Amended) claim 6, Schuler da Costa Ferro, Fisher and Buckler teach the limitations of claim 4, but may not explicitly teach wherein applying the randomization sub-algorithm of the randomization algorithm to the each of the plurality of score-based groups is based on: generating a random trial assignment ordering for the each of the plurality of score-based groups, wherein the random trial assignment ordering includes an ordered plurality of assignments based on participant distribution proportion data; and determining an ordering of prospective clinical trial participants in the each of the plurality of score-based groups, wherein distributing prospective clinical trial participants in the each of the plurality of score-based groups is based on applying the random trial assignment ordering to the ordering of prospective clinical trial participants.
Dwyer teaches wherein applying the randomization sub-algorithm of the randomization algorithm to the each of the plurality of score-based groups is based on: generating a random trial assignment ordering for the each of the plurality of score-based groups, wherein the random trial assignment ordering includes an ordered plurality of assignments based on participant distribution proportion data; and determining an ordering of prospective clinical trial participants in the each of the plurality of score-based groups; wherein distributing prospective clinical trial participants in the each of the plurality of score-based groups is based on applying the random trial assignment ordering to the ordering of prospective clinical trial participants (Dwyer: paragraph [0069], “the p-values for the cohorts corresponding to a particular randomization scheme/cohort size may be grouped together, and the associated p-values may be sorted. The proportion of p-values falling below a certain threshold for significance for the randomization scheme/cohort size group may be used as the risk score for that particular randomization scheme/cohort size combination”, paragraphs [0119]-[0125], “In randomizing the subjects into groups, the ranks may be taken into account in order to establish a preference for allocating subjects based on higher-ranked covariates. By testing different ranking schemes, different techniques of allocating randomization capital can be evaluated”).
The motivation to combine is the same as in claim 3, incorporated herein.
Regarding (Previously Presented) claim 12, Schuler da Costa Ferro, Fisher, Buckler and Dwyer teach the limitations of claim 6, and further teach wherein the training set further includes an additional plurality of device-captured medical data of at least one additional device-captured medical data type, wherein the at least one additional device-captured medical data type includes at least one of: a histological slide type; a DNA sequence data type; a test strip type; or at least one health sensor data type corresponding to health sensor data captured by a wearable device that includes at least one of: heart rate data; blood oxygen measurement data; blood pressure data; internal temperature data; respiratory rate data; pedometer data; or sleep cycle data (Buckler: paragraphs [0059]-[0061], “a series of histology images”, paragraph [0148], “gene expression data is obtained from microarray, RNA sequencing, single cell RNA sequencing, or reverse transcriptase PCR”).
The motivation to combine is the same as in claim 3, incorporated herein.
Response to Arguments
Applicant's arguments filed 24 April 2026 have been fully considered but they are not persuasive. Applicants' arguments will be addressed herein below in the order in which they appear in the response filed on 24 April 2026.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-20, the Examiner has considered the Applicant's arguments but does not find them persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
The Applicant submits that the claims, when considered as a whole, amount to significantly more than any abstract concept identified by the Office… When considered as a whole, the Applicant's claims provide a technological solution that provides enhanced functionality to conventional modeling platforms used in clinical trials. Paragraph [0297] discusses some of the improvements provided by embodiments of the claimed Invention… Paragraph [0298] discusses other technical improvements… In view of the above reasons, the Applicant submits that the claims are, in fact, eligible for patent protection because they provide an improvement to existing technologies.
The Examiner respectfully disagrees.
It is respectfully submitted, that performance of a clinical trial is not a technical problem rooted in computer hardware technology, performance of a clinical trial is human activity performed by humans on other humans, any alleged improvement, is a non-technical human activity solution to a non-technical human activity problem, which may improve upon the abstract idea of organizing clinical trials between for human users, nevertheless an abstract idea is still abstract idea. As the claimed additional elements do not provide a technical solution to a technical problem recited in applications specification and/or improvement in the functionality of the computer the argument is not persuasive.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1-20, the Examiner has considered the applicant’s arguments; however, the arguments are not persuasive as addressed herein. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Claim 1 has been amended to recite selecting prospective clinical trial participants… That claim element now affirmatively recites selecting, and thus can no longer be considered a statement of intended use… More specifically, the cited portions of Schuler da Costa Ferro disclose grouping trial subjects based on subject characteristics, and using the treatment outcomes for later correlation between subject characteristics and trial outcomes… selecting subsets of trial participants based on "a medical outcome prognosis score-based stratification factor," is not equivalent to grouping trial participants based on participant characteristics… There is no evidence of record that a stratification factor based on a medical outcome prognosis is a characteristic of a trial subject. Should the Office take the position that a prognosis is a characteristic of a trial participant, the Applicant requests the Office to either provide support for that assumption, or take Official Notice. See… No prima facie case of obviousness has been established, because none of the cited references, taken alone or in combination, disclose selecting subsets of prospective participants in a way that uses " the first outcome as a stratification factor of the randomization algorithm," as recited by claim 19.
The Examiner respectfully disagrees.
It is respectfully submitted, the combination of Fisher within Schuler de Costa Ferro teach the argued limitation, firstly the examiner notes paragraph [0001], explicitly states “Prognostic Score Stratification”, the examiner notes that additionally, Figures 2-3, and at least paragraphs [0007],[0026]-[0028], [0035], [0038]-[0040], further explicitly teach collection of historical outcome data for previous trials (i.e., first outcome data), using the first outcome data to train a prognostic model, using the trained prognostic model learned from the first outcome data for determination of prognosis scores, defining a variable to stratify patients based on the predicted outcome (i.e., the prognosis scores or previously acquired outcomes that were used to train the model), and stratifying the patients into groups (i.e., the first and second group) using the variable. The variable as taught by Schuler de Costa Ferro is not limited to subject characteristics as argued, firstly, the Examiner notes that the rest of paragraph [0026], that has been omitted the Applicant reads “Machine learning models in accordance with a number of embodiments of the invention can be used to estimate outcomes under control conditions, which can be used to identify optimal groupings that may be used to stratify the trial subjects” the machine learning model in this paragraph is the trained prognosis model that is described in, see above but at least paragraphs [0007], [0035], [0038]-[0040], which explicitly recite that the predicted outcomes and variables associated with the training the prognosis model (i.e., the prognosis scores and first outcome data) can be used as the variable to stratify the patients, the Examiner interprets that a person of ordinary skill in the art would find it prima facie obvious to define this variable to stratify the patients using the prognosis scores and the first outcome data that is used to train the model to generate the prognosis scores under the broadest reasonable interpretation. Although, Schuler de Costa Ferro teaches stratification and of outcomes of previously randomized trials it may not explicitly teach use of a randomization algorithm, nevertheless, Fisher (see above but at least paragraph [0082]), explicitly teaches this and would be prima facie obvious to combine with the motivation of “improve RCT design by reducing the number of subjects required for different arms of the RCT” (Fisher: paragraph [0031]).
Finally, with respect to claim 19, the claim was drafted as an intended use, the claim amendments removing the intended use language has required an updated rejection, and is not persuasive for reasons similar to claim 1 above.
In addition, the Examiner respectfully notes that the cited reference was never applied as a reference under 35 U.S.C. 102 against the pending claims. As such, the Examiner respectfully submits that the issue at hand is not whether the applied prior art specifically teaches the claimed features, per se, but rather, whether or not the prior art, when taken in combination with the knowledge of average skill in the art, would put the artisan in possession of these features. Regarding this issue, it is well established that references are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969). The issue of obviousness is not determined by what the references expressly state but by what they would reasonably suggest to one of ordinary skill in the art, as supported by decisions in In re DeLisle 406 Fed 1326, 160 USPQ 806; In re Kell, Terry and Davies 208 USPQ 871; and In re Fine, 837 F.2d 1071, 1074, 5 USPQ 2d 1596, 1598 (Fed. Cir. 1988) (citing In re Lalu, 747 F.2d 703, 705, 223 USPQ 1257, 1258 (Fed. Cir. 1988)). Further, it was determined in In re Lamberti et al, 192 USPQ 278 (CCPA) that:
(i) obviousness does not require absolute predictability;
(ii) non-preferred embodiments of prior art must also be considered; and
(iii) the question is not express teaching of references, but what they would suggest.
According to In re Jacoby, 135 USPQ 317 (CCPA 1962), the skilled artisan is presumed to know something more about the art than only what is disclosed in the applied references. In In re Bode, 193 USPQ 12 (CCPA 1977), every reference relies to some extent on knowledge of persons skilled in the art to complement that which is disclosed therein.
Conclusion
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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