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 .
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 1-12, 15-20, 43, and 46 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1-12, 15-20, 43, and 46 are within the four statutory categories. However, as will be shown below, claims 1-12, 15-20, 43, and 46 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 12, and 15 are representative of the inventive concept and recite:
Claim 1
A computer-implemented method for performing clinical trial endpoint adjudication, the method comprising: at a computing system,
receiving electronic data from a plurality of healthcare- related data sources;
determining that the data is unstructured or structured, wherein structured data is in a computer readable format and unstructured data is at least in part an image;
transforming unstructured data to the computer readable format;
attributing a confidence score to the transformed unstructured data;
generating embeddings relating to features in the unstructured data by applying a natural language processing model to the unstructured data, wherein the embeddings represent a predefined set of features for use in the clinical trial endpoint adjudication;
extracting features from the structured data;
mapping the extracted features to a predefined set of features;
and applying a pre-trained machine learning classification model to the embeddings from the unstructured data and the features extracted from the structured data to classify whether a healthcare event has occurred based on the embeddings and the features extracted from the structured data;
attributing a probability score as an attribute to the classification, wherein the probability score provides an indication of a likelihood of the healthcare event having occurred, the computing system automatically determining an adjudication outcome when the probability score is above a threshold and transmitting data for review when the probability score is below a threshold.
Claim 12
A method of training a machine learning classification model for performing clinical trial endpoint adjudication, the method comprising:
at a computing system, receiving electronic data from a plurality of healthcare-related data sources, the data comprising a set of adjudication dossiers from previous clinical trials and adjudication decisions relating to the set of adjudication dossiers;
analysing each data source to determine whether data held by the data source comprises structured and/or unstructured data, wherein structured data is in a computer readable format and unstructured data is at least in part an image;
transforming unstructured data to the computer readable format;
attributing a confidence score to the transformed unstructured data;
generating embeddings relating to features in the unstructured data by applying a natural language processing model to the unstructured data by extracting features from the structured data;
mapping the extracted features to a predefined set of features; and providing an adjudication decision based on the data from the set of adjudication dossiers;
updating the machine learning classification model based on the adjudication decision and the data from the set of adjudication dossiers;
storing the updated machine learning classification model in a relational database.
Claim 15
A computer-implemented method of harmonising and collating data from a plurality of healthcare-related sources for clinical trial endpoint adjudication, the method comprising:
at a computing system,
analysing each electronic data source to determine whether data held by the data source comprises structured and/or unstructured data, wherein structured data is in computer readable form and unstructured data is at least in part an image;
performing an optical character recognition process on a region or regions of the unstructured data not already in a machine-readable format;
attributing a confidence score as an attribute to the data based on at least one of (i) the data source, and (ii) a determined confidence based on the optical character recognition process;
generating embeddings relating to features in the unstructured data, the embeddings representing a predefined set of features for use in clinical trial endpoint adjudication;
performing a feature analysis on the data to extract features from the data;
mapping the extracted features to a predefined set of features;
publishing the mapped extracted features in a json format for use by a machine learning model in performing the clinical trial endpoint adjudication, wherein the confidence score is an attribute of the feature.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
analysing, transforming, attributing, mapping, updating, applying, extracting, and performing) or using pen and paper. Other than reciting generic computer components/functions such as “computer”, “computing system”, “machine learning classification model”, and “natural language processing model”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the generic computer language, the claim encompasses the user collecting data, processing it, and then making prediction based on the analysis of data. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions (mapping and publishing) also covers behavioral or interactions between people (i.e. a computer and user interface), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case a person is able to physically follow the steps to collect and process data), hence the claim falls under “Certain Methods of Organizing Human Activity”.
Dependent claims 2-11, 16-20, 43, and 46 recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 2, reciting a plurality of natural language processing models working with a second general model, but for recitation of generic computer components/functions).
Step 2A Prong Two:
This judicial exception is no integrated into a practical application. In particular, the claims recite the
following additional limitations:
Claim 1 recites “computer”, “computing system”, “natural language processing model”, “machine learning classification model”, “ receiving electronic data from a plurality of healthcare- related data sources”, and “generating embeddings relating to features in the unstructured data by applying a natural language processing model to the unstructured data, wherein the embeddings represent a predefined set of features for use in the clinical trial endpoint adjudication”
Claim 12 recites “machine learning classification model”, “computing system”, “receiving electronic data from a plurality of healthcare-related data sources, the data comprising a set of adjudication dossiers from previous clinical trials and adjudication decisions relating to the set of adjudication dossiers”, “generating embeddings relating to features in the unstructured data by applying a natural language processing model to the unstructured data by extracting features from the structured data”, and “storing the updated machine learning classification model in a relational database”.
Claim 15 recites “computing system”, “machine learning classification model”, “natural language processing model”, “performing an optical character recognition process on a region or regions of the unstructured data not already in a machine- readable format”, “generating embeddings relating to features in the unstructured data, the embeddings representing a predefined set of features for use in clinical trial endpoint adjudication”, and “json format”
In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of
are recited as being performed by a computer, computing system, natural language processing model, machine learning classification model, and optical character recognition. A computer is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The machine learning models are used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of “ receiving electronic data from a plurality of healthcare- related data sources”, and “generating embeddings relating to features in the unstructured data by applying a natural language processing model to the unstructured data, wherein the embeddings represent a predefined set of features for use in the clinical trial endpoint adjudication”, “receiving electronic data from a plurality of healthcare-related data sources, the data comprising a set of adjudication dossiers from previous clinical trials and adjudication decisions relating to the set of adjudication dossiers”, “generating embeddings relating to features in the unstructured data by applying a natural language processing model to the unstructured data by extracting features from the structured data”, and “storing the updated machine learning classification model in a relational database”, “performing an optical character recognition process on a region or regions of the unstructured data not already in a machine- readable format”, “generating embeddings relating to features in the unstructured data, the embeddings representing a predefined set of features for use in clinical trial endpoint adjudication”, and “json format”
Dependent claim 3 recites named-entity recognition model
Dependent claim 8 recites SHAP value
Dependent claim 9 recites local surrogate model
In particular, the additional elements do not integrate the abstract idea into a practical application,
other than the abstract idea per se, because the additional elements amount to no more limitations
which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of
are recited as being performed by a named-entity recognition model and local surrogate model. A computer is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The machine learning models are used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of SHAP value
Dependent claims 2, 4-7, 10-11, 16-20, 43, and 46 do not include any additional elements beyond those already recited in claims 3, 8, and 9, and hence do not integrate the aforementioned abstract idea into a
practical application. Looking at the limitations as an ordered combination adds nothing that is not
already present when looking at the elements taken individually. There is no indication that the
combination of elements improves the functioning of a computer, machine learning model,
or any other technology. Their collective function merely provides conventional computer
implementation and do not impose a meaningful limit to integrate the abstract idea into a practical
application.
Step 2B
Claims 1, 12, and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A system in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields as demonstrated by:
Recitation of receiving (claims 1 and 12) which is expressly used to acquire or accept (Col 7, Line 35, Vallee(US10621491B2) discloses: “The server 200 receives data from the remote subscriber computing device 100.”) in a manner that would be well-understood, routine, and conventional.
Recitation of updating (claim 12) which is expressly used to change by including the most recent information(Col 8, Line 34, Vallee(US10621491B2) discloses: “Every time a patient experiences an event or is being discharged from the program, the transformed dataset is updated and the model is retrained.”) in a manner that would be well-understood, routine, and conventional.
Recitation of storing (claim 12) which means to retain or enter for future electronic retrieval(Col 7, Line 2, Vallee(US10621491B2) discloses: “The remote subscriber computing device 100 further comprises a memory and a processor for storing data and instructions and for executing the instructions, respectively.”) in a manner that would be well-understood, routine, and conventional.
Recitation of extracting (claims 1, 6, 12, 15-16, and 20) which is expressly used to obtain from something using a specific method (Col 9, Line 12, Vallee(US10621491B2) discloses: “In order to reduce the input data dimensionality, a feature extraction algorithm can be run on the dataset.”) in a manner that would be well-understood, routine, and conventional.
Recitation of machine-readable (claim 15) which means a form that a computer can process (Col. 27, Line 27, Lucas(US10395772B1) discloses: “The intake pipeline 62 receives a clinical document that may include machine readable text or that may be received as an image file. “)in a manner that would be well-understood, routine, and conventional.
Recitation of json format (claims 15-16) which is a lightweight data-interchange format that is human-readable and machine-parsable (Col. 5, Line 37, Lucas(US10395772B1) discloses: “Exemplary electronic document captures may include a structured data form (such as JSON, XML, HTML, etc.)…”) in a manner that would be well-understood, routine, and conventional.
Recitation of SHAP value (claim 8) or Shapley Additive explanations, are way to understand how each feature in a machine learning model contributes to the model’s output for a specific prediction (Col. 3, Line 19, Gillies(US11587677B) discloses: “The present techniques also possess the ability to automatically generate explanations/interpretations for predictions generated by nonlinear models used in the present techniques using feature importance algorithms (e.g., Shapley Additive exPlanation (SHAP) value analysis).”) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2, 4-5, 7, 10-11, 17-20, 43, and 46 do not include any additional elements beyond those already recited in independent claims 1, 12, and 15 and dependent claims 3, 6, 8, 9, and 16. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 12, and 15, hence do not amount to “significantly more” than the abstract idea.
Subject Matter Free of Prior Art
Claim 1, 12, and 15 distinguish over the prior art for the following reasons.
Claim 1
A computer-implemented method for performing clinical trial endpoint adjudication, the method comprising: at a computing system,
“receiving electronic data from a plurality of healthcare- related data sources;
determining that the data is unstructured or structured, wherein structured data is in a computer readable format and unstructured data is at least in part an image…”
*Claims 12 and 15 recite similar limitations
The closest available prior art of record is as follows:
Dupont(US8887286B2) discloses an anomaly detection based on behavior modeling and heterogeneous information analysis but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Lucas(US10395772B1) discloses supplementation, extraction, and analysis of health records but does not fairly disclose or suggest the aforementioned configuration for the claimed invention.
Based on the evidence presented above, none of the closest available prior art record fairly discloses or suggest the underlined elements of the claimed invention. Claims 12 and 15 would also be found to be subject matter free or prior art for the same rationale as applied to claim 1. Claims 2-11, 16-20, 43, and 46 would also be considered to be subject matter free of prior art due to dependency.
Response to Arguments
Rejection under 35 U.S.C. 101
(Page 9) Regarding the assertion that during the interview, the office claimed “unstructured data”, “structured data” and “embeddings” to be indefinite.
Applicant's arguments filed have been fully considered but they are not persuasive. The office claimed “unstructured data”, “structured data” and “embeddings” to be abstract concepts even though they are defined in the specifications and well-known in the art. The concepts, as applied in the claims are broad and can be applied in various ways.
(Pages 10-11) Regarding the assertion that the claim limitations cannot be performed by the human mind.
Applicant's arguments filed have been fully considered but they are not persuasive. The claim as recited limitations can be performed by the human mind or using pen and paper.
(Pages 13) Regarding the assertion that the claim limitations cannot be performed by the human mind.
Applicant's arguments filed have been fully considered but they are not persuasive.
Rejection under 35 U.S.C. 103
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Slepian(US20190013093A1): Slepian discloses a system to analyze healthcare data. Some disclosures of this invention are similar to that of this pending instant application. (Specifications, page 16)
McKinney(US11984206B2): McKinney discloses a method for processing medical text and associated medical images. Some disclosures of this invention are similar to that of this pending instant application. (Specifications, page 30)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERYL GOPAL PATEL whose telephone number is (703)756-1990. The examiner can normally be reached Monday - Friday 5:30am to 2:30pm PST.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685