DETAILED ACTION
This communication is in response to the Application filed on 12/07/2023. Claims 1-20 are pending and have been examined. Claims 1, 8 and 15 are independent. This Application was published as U.S. Pub. 2025/0053790A1.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/07/2023 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Applicant’s claims for benefit of a provisional application 63/518,555 submitted on 08/09/2023 is acknowledged.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: a large language model (LLM), an attribute extraction module, and a trial simulation module in claim 1; an LLM training module in claim 2; a latent variable module in claim 3; a patient selection module in claim 6; an analytics module in claim 7.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-2, 7-9 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Schaeffer et al. (US Pub 2022/0059240) in view of Benedum et al. ("Replication of real-world evidence in oncology using electronic health record data extracted by machine learning." Cancers 15.6 (2023): 1853.).
Regarding Claim 1,
Schaeffer discloses a trial simulation system (Fig.1, par [084], "…a system 10 for predicting and analyzing patient cohort response, progression, and survival...") comprising: a processor (Fig.67, par [506], "…Processing device 6702 represents one or more general-purpose processing devices such as a microprocessor, a central processing unit..."); a large language model (LLM) (paras [110-115], "…Artificial Intelligence Models, neural networks (NN), or machine learning algorithms (MLA) may be trained from a training data set.", "…MLAs include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated)") configured to:
receive, as at least one input, a medical document that includes medical text associated with a patient (Fig.1, par [087], The patient data store 14; par [089], "…A patient data store may include one or more feature modules which may comprise a collection of features available for every patient in the system 10..."; paras [090-109], Feature collections may include a diverse set of fields available within patient health records (e.g., EMR, HER, imaging features, features from research based Omic fields, etc.); and
generate, as at least one output in response to the at least one input (par [112], "…A set of transformation steps may be performed to convert the data from the Patient Data Store into a format suitable for analysis..."), one or more predicted values for one or more medical attributes of the patient based on the medical text included in the medical document (par [111], "…Training may include providing optimized datasets, labeling these traits as they occur in patient records, and training the MLA to predict or classify based on new inputs...Some MLA may identify features of importance and identify a coefficient, or weight, to them...");
a trial simulation module (paras [140-149], Patient Survival Analysis Module) configured to perform a survival model simulation that computes estimations of hazard ratio (HR) between cases and controls (Figs.15-20, par [141], "…The system further may provide survival analysis for the subset of patients through use of the patient survival analysis user interface 30..."; Fig.33, par [318], example of propensity adjusted survival curve between control cohort and a treatment cohort of patients; Fig.29 See Hazard Ratio Exploration workbook. It is construed that the hazard ratio could be estimated from survival curves for control and treatment cohorts as shown in Fig.33.) using real-world data of the plurality of patients extracted in the first attribute extraction and second attribute extraction.
Schaeffer discloses artificial intelligence models such as neural network and machine learning algorithms for prediction and classification and natural language processing (par [122]), but does not explicitly teach the attribute extraction with a large language model as disclosed in the limitation, "using real-world data of the plurality of patients extracted in the first attribute extraction and second attribute extraction."
However, Benedum, in the analogous field of endeavor, teaches an attribute extraction module (Benedum, Fig.1, 1. Introduction, "…natural language processing (NLP) with machine learning (ML) techniques (i.e., ML extraction) is increasingly being applied to EHR data for more efficient and scalable generation of RWD (Box 1). ML extraction techniques can learn and recognize language patterns to provide automated solutions for extracting clinically relevant information, thereby enabling research and RWE generation at scale [8] (Figure 1)...") configured to:
perform first attribute extraction from a plurality of structured medical documents of a plurality of patients, including extracting values for a first plurality of attributes associated with the plurality of patients (2.1. Data Source, Table 1, "...We obtained the key analysis variables from structured data sources in the patient’s EHR (Table 1)..."); and
perform second attribute extraction from a plurality of unstructured medical documents of the plurality of patients using the LLM, including extracting predicted values for a second plurality of attributes associated with the plurality of patients (Figure 1, Panel (B): Data curation by ML extraction; See Table 1 Study variables and Unstructured data source (e.g., clinic notes, PDF lab reports, radiology images, etc.); 2.1.2. Machine Learning Extraction, "…Each of the 18 variables has been extracted through NLP of clinical notes, followed by an advanced ML or deep learning model, including LSTM and XGBoost...");
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified a system and method for analyzing a patient data store as taught by Schaeffer with the natural language processing (NLP) with machine learning (ML) techniques (i.e., ML extraction) of Benedum with a reasonable expectation of success to efficiently recognize language patterns to provide automated extraction of clinically relevant real-world data (RWD) at a speed and scale that far exceeds manual data curation (Benedum, Abstract, 1. Introduction).
Regarding Claim 2,
Schaeffer in view of Benedum discloses the trial simulation system of claim 1, further comprising an LLM training module configured to train the LLM using a plurality of labeled training documents, each labeled training document being labeled with a value for at least one labeled attribute (Benedum, 2.1.2. Machine Learning Extraction, "…models are trained on the data labeled by expert abstraction to recognize, interpret, and curate free text into structured variable values in order to mimic the abstraction process. Models used between 35,710 and 211,581 expert-abstracted labels for training, validation, and testing, depending on the variable...").
Regarding Claim 7,
Schaeffer in view of Benedum discloses the trial simulation system of claim 1, further comprising an analytics module configured to perform a test diagnostic on output of the survival model simulation to evaluate a quality of the survival model simulation (Schaeffer, paras [175-183], a hybrid two-model approach, "…2. Training a model on such a dataset, to derive predictions for expected future survival at each time point…6. Comparing the expected survival predictions to the actual survival based on the forward looking model...").
Claim 8 is a method claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 1.
Claim 9 is a method claim with limitations similar to the limitations of Claim 2 and is rejected under similar rationale.
Claim 14 is a method claim with limitations similar to the limitations of Claim 7 and is rejected under similar rationale.
Claim 15 is a device claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale. Additionally,
Schaeffer discloses a computer storage device having computer-executable instructions stored thereon, which, on execution by a computer, cause the computer to perform operations (Fig.67, par [503], "…an example machine of a computer system 6700 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed...")
…
Rationale for combination is similar to that provided for Claim 1.
Claim 16 is a device claim with limitations similar to the limitations of Claim 2 and is rejected under similar rationale.
Claims 3-4, 10-11 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Schaeffer in view of Benedum further in view of Mulyadi et al. ("Uncertainty-aware variational-recurrent imputation network for clinical time series." IEEE Transactions on Cybernetics 52.9, pp 9684-9694, (2021)).
Regarding Claim 3,
Schaeffer in view of Benedum discloses the trial simulation system of claim 1, but does not explicitly teach a latent variable module.
Mulyadi discloses a latent variable module configured to: generate second predicted values for one or more attributes of the second plurality of attributes using a latent variable model (Mulyadi, Fig.1, I. Introduction, "…We propose a novel variational-recurrent imputation network (V-RIN) which unifies the imputation and prediction networks for multivariate time series EHR data..."; B. VAE-based Imputation Network, "…The imputation network is devised based on VAEs to capture the latent distribution of the sparse data by means of its inference network (i.e., encoder E). Then, the subsequent generative network of VAEs (i.e., decoder D) estimates the reconstructed data distribution..."); and
update values of the one or more attributes with the second predicted values (III. Proposed Method, "…We regard its reconstructed values as the imputation estimates...").
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified a system and method for analyzing a real-world patient data and clinical trial emulation as taught by Schaeffer in view of Benedum with a novel variational-recurrent imputation network of Mulyadi with a reasonable expectation of success to exploit those patterns from missing values to improve the prediction of the clinical outcomes as the downstream task (Mulyadi, Abstract, 1. Introduction).
Regarding Claim 4,
Schaeffer in view of Benedum further in view of Mulyadi discloses the trial simulation system of claim 3, wherein the LLM is further configured to
generate a confidence score for each value of the one or more predicted values (Mulyadi, 1. Introduction, "…we introduce the uncertainty as the imputation fidelity of estimates, which compensates for the potential impairment of imputation estimates..."; B. VAE-based Imputation Network, "…we regard the variance of reconstructed data as the uncertainty to be further utilized in the recurrent imputation process…we quantify this uncertainty as the fidelity score of the missing value estimates..."; i.e., inverse of uncertainty is construed as a confidence or fidelity.),
wherein generating the second predicted values further comprises generating second predicted values for one or more attributes of the second plurality of attributes (B. VAE-based Imputation Network, "…as an output of this VAE-based imputation network, we acquire the set {X, U} denoting the imputed values (i.e., second predicted values) and their corresponding uncertainty, respectively.") when an associated confidence score is below a threshold (Abstract, "…it is appropriate for the method to handle the less certain information differently than the reliable data. In this regard, we can use the uncertainties in estimating the missing values as the fidelity score to be further utilized to alleviate the risk of biased missing value estimates."; i.e., a mechanism for differentiating imputed data based on fidelity score maps the concept of confidence score threshold ).
Claim 10 is a method claim with limitations similar to the limitations of Claim 3 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 3.
Claim 11 is a method claim with limitations similar to the limitations of Claim 4 and is rejected under similar rationale.
Claim 17 is a device claim with limitations similar to the limitations of Claim 3 and is rejected under similar rationale.
Claim 18 is a device claim with limitations similar to the limitations of Claim 4 and is rejected under similar rationale.
Claims 5-6, 12-13 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schaeffer in view of Benedum further in view of Krishnan et al. (US Pub 2020/0090796).
Regarding Claim 5,
Schaeffer in view of Benedum discloses the trial simulation system of claim 1. Schaeffer discloses patient feature store (par [109]) where all patients' feature sets are stored ,and Benedum discloses the extraction of plurality of attributes from structured and unstructured medical documents, but does not explicitly discloses the storage of attributes in matrix.
Krishnan discloses wherein performing the first attribute extraction further comprises storing the values for the first plurality of attributes in a matrix that identifies unique patients in a first dimension of the matrix and unique attributes in a second dimension of the matrix, wherein each cell in the matrix stores one value of an associated attribute for a particular patient (Fig.2, par [027], "…In act 204, the facility extracts features from this accessed clinical data...", i.e., first attributes from the structured medical data of patients),
wherein performing the second attribute extraction further comprises storing the predicted values for the second plurality of attributes associated with the plurality of patients in the matrix (Fig.2, par [025], "…In act 202, the facility processes the image data to extract image-based features...", i.e., second attributes from the unstructured medical data of patients; par [028], "…In act 205, the facility combines the features extracted from the scans and the clinical text to obtain a single feature vector for a particular patient. For combining the features, the facility concatenates or merges the feature vectors of scans and the clinical data...", i.e., concatenation of features of each patient in vector form. Constructing feature matrix from concatenated feature vectors of all patient in the group is a routine process for a skilled person of art. ).
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified a system and method for analyzing a real-world patient data and clinical trial emulation as taught by Schaeffer in view of Benedum with concatenation and merging of extracted features of patients as feature vector of Krishnan with a reasonable expectation of success to exploit those patterns from missing values to study the effectiveness of new treatment regimen and improve the prediction of the clinical outcomes as the downstream task (Krishnan, par [002]).
Regarding Claim 6,
Schaeffer in view of Benedum further in view of Krishnan discloses the trial simulation system of claim 5, Schaeffer further discloses comprising a patient selection module configured to: identify a plurality of eligible patients from the matrix based on eligibility criteria (paras [116-119], Patient Cohort Filtering User Interface, "…The system also may permit a user to filter patient data according to any of the criteria listed herein including those listed under the heading "Features and Feature Modules..."); and create a trial matrix that includes data associated with the plurality of eligible patients from the matrix (Figs.3-9, paras [124-131], "…the cohort funnel and population analysis user interface 26 may be configured to permit a user to conduct analysis of a cohort, for the purpose of identifying key inflection points in the distribution of patients exhibiting each attribute of interest, relative to the distributions in the general patient population or a patient..."; par [131], "…The final filtered cohort of interest may form the basis for further detailed analysis in the modules or other user interfaces..."),
wherein performing the survival model simulation includes using the trial matrix as input data for the survival model simulation (Figs.15-20, paras [140-149], Patient "Survival" Analysis Module, "…The system further may provide survival analysis for the subset of patients through use of the patient survival analysis user interface 30...").
Claim 12 is a method claim with limitations similar to the limitations of Claim 5 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 5.
Claim 13 is a method claim with limitations similar to the limitations of Claim 6 and is rejected under similar rationale.
Claim 19 is a device claim with limitations similar to the limitations of Claim 5 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 5.
Claim 20 is a device claim with limitations similar to the limitations of Claim 6 and is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hafez et al. (US Pub 2021/0142910) discloses systems and methods are provided for implementing a tool for evaluating an effect on an event, such as a medication or treatment, on a subject's condition, using a propensity model that identifies matched treatment and control cohorts within a base population of subjects (Hafez, Abstract).
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/JANGWOEN LEE/Examiner, Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
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