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 .
DETAILED ACTION
Information Disclosure Statement
Applicants’ Information Disclosure Statements, filed 10/22/2025 and 12/10/2024, have been received, entered into the record, and considered. See attached form PTO-1449.
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 are directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention are directed to an abstract idea without significantly more.
Claims 1, 14 recite “a method/apparatus, comprising: constructing a medical library that includes data items extractable from a data warehouse …; obtaining a request to generate a database…; determining… a specific data items…; generating a table…; and generating a database….”.
These limitations are processes that, under their broadest reasonable
interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting "a memory, a processor", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, but for the memory, the processor, the steps of “constructing a medical library that includes data items extractable from a data warehouse …; obtaining a request to generate a database…; determining… a specific data items…; generating a table…; and generating a database…” in the context of this claim encompasses a user gathering, collecting and organizing data mentally, with the aid of pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
This judicial exception is not integrated into a practical application. In particular, the claims recite additional element – using “the memory, the processor” to “constructing a medical library that includes data items extractable from a data warehouse …; obtaining a request to generate a database…; determining… a specific data items…; generating a table…; and generating a database….”, these limitations amount to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
“generating a table…; generating a database…”; these limitations are mere generic transmissions and presentations of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
The memory, the processor is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “constructing a medical library that includes data items extractable from a data warehouse …; obtaining a request to generate a database…; determining… a specific data items…; generating a table…; and generating a database….”. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). 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 because, when considered separately and in combination, they do not add significantly more to the exception. Considered separately and as an ordered combination, the claim elements do not provide an improvement to another technology or technical field; do not provide an improvement to the functioning of the computer itself.
The limitations of “constructing a medical library that includes data items extractable from a data warehouse …; obtaining a request to generate a database…; determining… a specific data items…; generating a table…; and generating a database….” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claim 14 is an apparatus to perform the method of claim 1; is similar in scope
to claim 1; and therefore, are rejected under similar rationale.
Dependent claims 2-13, 15-20 merely add further details of the abstract steps recited in claims 1, 14, respectively, without including an improvement to another technology or technical field, an improvement to the functioning of the abstract idea to a particular technological environment. Therefore, dependent claims 2-13, 15-20 are also directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Colley et al. (US Pub No. 2021/0090694).
As to claims 1, 14, Colley teaches a method of an apparatus operating by at least one processor, the method comprising:
constructing a medical data library that includes data items extractable from
a clinical data warehouse for disease-specific study, and generates a table of each data item by applying a disease-specific fabric condition that includes medical conditions for extracting a disease-specific patient group (i.e. The term “clinical trial” will be used to refer to a research study, [0207]; capturing all relevant clinical trial and patient data, including disease/condition data, trial eligibility criteria, trial site features and constraints, and/or clinical trial status … structuring that data to optimally drive different system activities, [0221]; lake database 170 also includes raw unmodified data 168 from sources 102, [0966]; data vault database 180 includes data that has been normalized and optimally structured for storage and database manipulation. For instance, raw original clinical medical records stored at 168 in lake database 170 may be processed to normalize data formats and placed in specific structured data fields optimized for data searching and other data manipulation processes, [0968]; operational and analytical applications 188 and 192 … operational applications also include application programs used by an abstractor specialist to convert unstructured raw clinical medical records or semi-structured records to system optimized structured records, [0986]);
obtaining request to generate a database for study of a specific disease (i.e. Analytical applications 192, in contrast, include application programs that are provided primarily for research purposes and use by either provider client researchers or provider specialist researchers. For instance, analytical applications 192 include programs that enable a researcher to generate and analyze data sets or derived data sets corresponding to a researcher specified subset of de-identified (e.g., not associated with a specific patient) cancer state characteristics. Here, analysis may include various data views and manipulation tools which are optimized for the types of data presented, [0987]; A feature collection associated with all the different field of omics, including: cognitive genomics, a collection of features comprising the study of the changes in cognitive processes associated with genetic profiles, [1947]; Referring again to FIG. 21, the cohort tool shown allows a physician to select different cancer state filters 2120 to be applied to the system database thereby changing the set of patients for which the system presents treatment efficacy data to help the physician explore effects of different factors on efficacy which is intended to lead to new treatment insights like factor-treatment-efficacy relationships, [1099]);
determining, from the medical data library, specific data items relevant to the study of the specific disease (i.e. Selecting a cancer origin site, a cancer metastasis site, an anchor event, and/or a survival curve group may further filter the cohort to only patients which have the respective prerequisite event or outcome in their patient records, or those patients who receive the selected prediction, [01976]; should be able to explore different data sets associated with different cancer state factors, different treatments and different treatment efficacies, [0999]);
generating a table of each of the specific data items using data for a patient group of the specific disease defined by applying a fabric condition of the specific disease (i.e. the cohort option 1518 can be selected to access an analytical tool that enables the physician to explore prior treatment responses of patients that have the same type of cancer as the patient that the physician is planning treatment for in light of similarities in molecular data between the patients, [1094]; analytical applications 192 are shown to include, among other applications, “self-service” applications. Here, the phrase “self-service” is used to refer to applications that enable a system user to, in effect, use query tools and data visualization tools, to access and manipulate data sets that are not optimally supported by other user applications … The self-service tools are designed to allow an authorized system user to develop different data visualizations, unique SQL or other database queries and/or to prepare data in whatever format desired, [0999]); and
generating a database for the study of the specific disease using the tables of the specific data items (i.e. This may be performed by using a table with every clinical trial (study) having its own row, [2602]; The metastasis sites may be displayed in a number of different formats. A first format may include an image of a human body which regions having metastasis predictions highlighted therein. Highlighting for regions with predictions may be color coded based upon the value of the prediction, [01977]; Area 2004 shows summary information presented when the summary option is selected from the option list 2002. When other list options are selected, related information is used to populate area 2004 with additional related information, [1093]).
As per claim 9, Colley teaches a method of an apparatus operating by at least one processor, the method comprising:
obtaining a table specification of a target item to be added in a medical data library (i.e. The collected original data is stored in the lake database 170 as raw original data (e.g., documents, images, records, files, etc.), [1002]; at least a subset of the collected data is “shaped” or otherwise processed to generate structured data that is optimal for database access, searching, processing and manipulation … At step 408 the database optimized shaped data is added to similarly structured data already maintained in data vault database 180, [1003]; Continuing, at block 410, at least a subset of the data vault data or the lake data is “shaped” or otherwise processed to generate structured data that is optimal to support specific user application programs 188 and 192 … At step 412 the optimized application structured data is added to similarly structured data already maintained in data marts database 190, [1004]); File gateway 314 receives source files and controls the process of adding those files to lake database 170, [0996]);
classifying the target item into any one or a core part, an extension part, or an analysis part of the medical data library based on a classification criterion (i.e. Classification Codes for Mapping Features Between Data Stores, [2566]; the input micro-services 1167 may also run a variant classification engine 1360 on the variant files utilizing a knowledge database of variant information 1175 to calculate many different types of variant criteria, further classification and addition database insertion, [1055]; tumors may be classified into different categories, [1052]; ETL platform 360 extracts the data to restructure, transforms the data to the system or application specific data structure required and then loads that data into the respective database 180 or 190, [0998]; a genomic sequencing order may be received at file gateway 314 and, once ingested, may be stored in lake database 170 for subsequent consumption … At 1118 alterations from raw molecular data are called and at block 1120 pathogenicity of the variants is classified. At 1122 genomic phenotypes may be calculated. At 1123 an MSI assay may be performed. At 1124 at least a subset of the genomic data and/or an analysis of at least the subset of the genomic data
is stored in system database 160, [1026]); and
generating table information and structure of the target item into a structured query language code, and registering the target item into a structured (i.e. This may be performed by using a table with every clinical trial (study) having its own row , [2602]; Other micro-services 1179 can query 1181 samples, findings, variants, classifications, etc. via an interface 1177 and SQL queries 1187. Authorized users may also be permitted to register samples and post classifications via the other micro-services, [1056]; Referring still to FIG. 3, analytical applications 192 are shown to include, among other applications, “self-service” applications. Here, the phrase “self-service” is used to refer to applications that enable a system user to, in effect, use query tools and data visualization tools, to access and manipulate data sets that are not optimally supported by other user applications … The self-service tools are designed to allow an authorized system user to develop different data visualizations, unique SQL or other database queries and/or to prepare data in whatever format desired. Hereinafter, unless indicated otherwise, the term “explore” will be used to refer to any self-service activities performed within the disclosed system, [0999]);
wherein the medical data library is constructed to include data items extractable from a clinical data warehouse for disease-specific study, and to generate a table of each data item by applying a disease-specific fabric condition that includes medical conditions for extracting a disease-specific patient group (i.e. The term “clinical trial” will be used to refer to a research study, [0207]; capturing all relevant clinical trial and patient data, including disease/condition data, trial eligibility criteria, trial site features and constraints, and/or clinical trial status … structuring that data to optimally drive different system activities, [0221]; lake database 170 also includes raw unmodified data 168 from sources 102, [0966]; data vault database 180 includes data that has been normalized and optimally structured for storage and database manipulation. For instance, raw original clinical medical records stored at 168 in lake database 170 may be processed to normalize data formats and placed in specific structured data fields optimized for data searching and other data manipulation processes, [0968]; operational and analytical applications 188 and 192 … operational applications also include application programs used by an abstractor specialist to convert unstructured raw clinical medical records or semi-structured records to system optimized structured records, [0986]);
As to claims 2, 16, Colley teaches the determining the specific data items comprises:
providing a user interface with selectable data items for the study of the specific disease (i.e. Referring again to FIG. 14, upon selecting cell 1404 associated with a patient named Dwayne Holder, the system presents the screenshot 1500 shown in FIG. 15 that includes a second level navigation bar 1502 near the top of the screen 1500 and a workspace 1504 below bar 1502. Navigation bar 1502 persistently identifies the patient 1506 associated with the data currently being viewed by the physician throughout the screenshots illustrated and also includes a separate hyperlink text term for each of several system data views or application programs that can be selected by the physician. In FIG. 15 the view and applications options include an “Overview” option 1508, a “Reports” option 1510, an “Alterations” option 1512, a “Trials” option 1514, an “Immunotherapy” option 1516, a “Cohort” option 1518, a “Board” option 1520 and a “Modelling” option 1522. Many other options will be added to bar 1502 over time as they are developed. A view or application currently accessed by the physician is underlined or otherwise visually distinguished in bar 1502. For instance, in FIG. 15 the overview icon 1508 is shown highlighted to indicate that the information presented in workspace 1504 is associated with the overview data view, [1073]; and
determining data items confirmed in the user interface to the specific data items (i.e. Referring again to FIG. 15, general cancer state and patient information at 1550 includes diagnosis, stage, patient date of birth and gender information 1530 as well as an anatomical image that shows a representation of a tumor within a body that is generally consistent with the patient's cancer state. In some cases, the tumor representation is just representative of the patient's condition as opposed to directly tied to actual tumor images while in other cases the tumor representation is derived from actual medical images of the patient's tumor, [1077]).
As to claims 3, 17, Colley teaches the providing the user interface comprises:
managing default items selected from the medical data library by disease or study purpose, and providing the user interface with default items related to the request to generate the database (i.e. Referring again to FIG. 15, to access specific issued reports associated with the patient the physician selects reports icon 1510 to access a reports screen 1600 shown in FIG. 16. Reports screen 1600 shows the reports icon 1510 highlighted to help orient the physician and includes a report list indicating all reports stored in the system that are associated with the patient, [1081]; The physician can select one of the report images to access the full report. For instance, if the physician selects image icon 1602, the screenshot 1700 shown in FIG. 17 is presented that splits the display screen into a report list section 1702 along the left edge of the screen and an enlarged report section 1704 that covers about the right two thirds of the screen where the selected report is presented in a larger format for viewing, [1082]; With respect to FIG. 115, the “genetic sequencing genes—default” value set is selected, [1336]; For instance, as shown in FIG. 117, a portion of values in the “diagnosis site—default” value set are displayed in a pop-up window. The values include a description of the diagnosis site and an associated URL with a SNOMED code associated to the diagnosis site. It should be apparent that the values may contain more than as many type or quantity of data fields in order to sufficiently characterize the value, including but not limited to text descriptors, number descriptors, URLs, FHIR elements, and so forth, [1337]).
As to claims 4, 12, 18, Colley teaches the generating the table of each of the specific data items comprises (i.e. a system may poll new abstraction entries for each patient, identify new data elements populated in the newest document, and re-evaluate patient's eligibility across all of the available clinical trials. This may be performed by using a table with every clinical trial (study) having its own row, where the each inclusion or exclusion expression is given a row, the cell where each row and column meet contains information on whether the study requires satisfaction of the expression (T), fails satisfaction of the expression (F), or does not require the expression (Null). If a patient satisfies the expression for all (T) and does not satisfy the expression for all (F), then they are indicated as eligible for the associated clinical trial, [2602]):
when a table of a specific data item needs to be generated from unstructured data, extracting a table record value from the unstructured data using an artificial intelligence model to generate the table of the specific data item (i.e. An artificial intelligence program auto-populates the shell based on rulesets and information curated via machine learning. Sub-process 2414 enables a pathology specialist to confirm or modify AI populated report information and add additional information derived during review, [1210]; Stored classifications module 5520—A feature collection associated with the variant characterization machine learning models, classification models, or other artificial intelligence derived features, [1807]; Artificial Intelligence for Predicting Patient Eligibility for Clinical Trials or Criteria, [2587]; An abstraction software suite may be programmed or utilize a trained artificial intelligence to recognize a document type from a source and extract all relevant information from the document and storing a digital representation in a structured format according to the above disclosure, [2594]).
As to claims 5, 11, 19, Colley teaches the generating the table of each of the specific data items comprises:
determining an order of table generation of the specific data items (i.e. As another example, a normalized RNA data set may be utilized in connection with one or more methods to cluster samples in order, for instance, to identify disease subtype, [2795]; in at least some cases it is contemplated that the above system could be used to manage other complex medical order processes, patient treatments or clinical activities, orders related to other disease states, [3492]); and
generating the table according to the order (i.e. For each category, the system then may determine which subset of the cohort has a largest spread of progress free survival vs. non-survival and treat the feature split which generated the largest spread as an edge between nodes and the features themselves as nodes, [3103]).
As per claim 6, Colley teaches the method of claim 1, wherein the data items in the medical data library are classified into any one of a core part, an extension part, or an analysis part,
the core part includes data items related to essential clinical information being used as a default for studies (i.e. In an example, a computer-implemented method comprises: performing clustering on RNA expression data corresponding to a plurality of samples, where each sample is assigned to at least one of a plurality of clusters; generating a deconvoluted RNA expression data model comprising at least one cluster identified as corresponding to biological indication of one or more pathologies; receiving additional RNA expression data of a sample of tumor tissue; deconvoluting the additional RNA expression data based in part on the deconvoluted RNA expression data model; and classifying the sample of tumor tissue as the biological indication of one or more pathologies, [0363]),
the extension part includes data items related to clinical information being
optionally used based on a disease or study purpose (i.e. FIGS. 264A through 264F illustrate the accuracy of an exemplary digital tissue segmented for all test data, for test data classified as stage I or stage II cancer cases, or for test data classified as stage Ill or stage IV cancer cases, [0737]), and
the analysis part includes data items related to derivative information necessary for studying and analyzing clinical information (i.e. An exemplary micro-service set for genomic variant characterization includes but is not limited to the following set: (1) Variant characterization (a data package containing characterized variant calls for a case, which may include overall classification, reference criteria and other singles used to determine classification, exclusion rules, other flags, etc.); (2) Therapy match (including therapies matched to a variant characterization's list of SNV, indel, CNV, etc. variants via therapy templates); (3) Report (a machine-readable version of the data delivered to a physician for a case); (4) Variants reference sets (a set of unique variants analyzed across all cases); (5) Unique indel regions reference sets (gene-specific regions where pathogenic inframe indels and/or frameshift variants are known to occur); (6) DNA reports; (7) RNA reports; (8) Tumor Mutation Burden (TMB) calculations, etc. Once genomic variant characterization and classification has been completed, other applications and micro-services provide tools for variant scientists or other clinicians or even other micro-services to act upon the data results, [0979]; Fifty-one cases were randomly selected from the study group with a range of tumor mutational burden profiles. Their variants were re-evaluated using a tumor-only analytical pipeline. After filtering the dataset using a population database and focusing on coding variants from the 51 samples, 2,544 variants were identified that had a false positive rate of 12.5%. By further filtering with an internally developed list of technical artifacts (e.g., artifacts from DNA sequencing process), an internal pool of matched normal samples, and classification criteria, 74% of the false somatic variants (false positive rate of 2.3%) were removed while still retaining all true somatic alterations, [1148]).
As per claim 7, Colley teaches the method of claim 1, wherein the obtaining the request to generate the database comprises,
receiving an input of a disease name of the specific disease (i.e. For each clinical trial, details 4198 associated with the name of the clinical trial, [1709]; Trial metadata 9101 can be used to view, update, and sort data corresponding to clinical trials. As shown, for example, the trial data 9102 can be summarized via a displayed table on GUI 9100. The trial data 9102 can include separate table entries for each clinical trial. As an example, each clinical trial may be listed with the corresponding national clinical trial (NCT ID), the trial name, the disease type relating to the clinical trial, annotation status, an approved status, a review status, and/or the date of last update, [2471]).
As per claim 8, Colley teaches the method of claim 1, further comprising:
after obtaining the request to generate the database, extracting a number of patients with the specific disease from the clinical data warehouse and providing the number of patients of the specific disease (i.e. FIG. 306A is an example of an image incorporated into a user interface for visually examining similarity within a patient cohort, [0790]; FIG. 349 is a graph showing Predicted TCGA cancer types for samples within each xT 500 cohort cancer type. Cancer type predictions are based on a random forest model trained on an internal reference dataset. Bubble size corresponds to the percentage of samples from the cohort predicted to have a given TCGA cancer type, [0840]; FIG. 374 is a graph illustrating the distribution of gene expression calls with reported therapeutic evidence. Frequency of expression calls by reportable gene matched to therapeutic evidence, stratified by cancer cohort (n=133 patients). Therapeutic evidence for the genes represented on this plot are provided in the table shown in FIG. 367, [0865]; Fifty-one cases were randomly selected from the study group with a range of tumor mutational burden profiles, [1148].
As to claims 10, 20, Colley teaches the classifying comprises:
when the target item is essential clinical information being used as a default for studies, classifying the target item into the core part (i.e. In an example, a computer-implemented method comprises: performing clustering on RNA expression data corresponding to a plurality of samples, where each sample is assigned to at least one of a plurality of clusters; generating a deconvoluted RNA expression data model comprising at least one cluster identified as corresponding to biological indication of one or more pathologies; receiving additional RNA expression data of a sample of tumor tissue; deconvoluting the additional RNA expression data based in part on the deconvoluted RNA expression data model; and classifying the sample of tumor
tissue as the biological indication of one or more pathologies, [0363]);
when the target item is clinical information being optionally used for a disease or study purposes, classifying the target item into the extension part (i.e. FIGS. 264A through 264F illustrate the accuracy of an exemplary digital tissue segmenter for all test data, for test data classified as stage I or stage II cancer cases, or for test data classified as stage Ill or stage IV cancer cases, [0737]); and
when the target item is derivative information necessary for studying and analyzing clinical information, classifying the target item into the analysis part (i.e. An exemplary micro-service set for genomic variant characterization includes but is not limited to the following set: (1) Variant characterization (a data package containing characterized variant calls for a case, which may include overall classification, reference criteria and other singles used to determine classification, exclusion rules, other flags, etc.); (2) Therapy match (including therapies matched to a variant characterization's list of SNV, indel, CNV, etc. variants via therapy templates); (3) Report (a machine-readable version of the data delivered to a physician for a case); (4) Variants reference sets (a set of unique variants analyzed across all cases); (5) Unique indel regions reference sets (gene-specific regions where pathogenic inframe indels and/or frameshift variants are known to occur); (6) DNA reports; (7) RNA reports; (8) Tumor Mutation Burden (TMB) calculations, etc. Once genomic variant characterization and classification has been completed, other applications and micro-services provide tools for variant scientists or other clinicians or even other micro-services to act upon the data results, [0979]; Fifty-one cases were randomly selected from the study group with a range of tumor mutational burden profiles. Their variants were re-evaluated using a tumor-only analytical pipeline. After filtering the dataset using a population database and focusing on coding variants from the 51 samples, 2,544 variants were identified that had a false positive rate of 12.5%. By further filtering with an internally developed list of technical artifacts (e.g., artifacts from DNA sequencing process), an internal pool of matched normal samples, and classification criteria, 74% of the false somatic variants (false positive rate of 2.3%) were removed while still retaining all true somatic alterations, [1148]).
As to claims 13, 15, Colley teaches:
obtaining a request to generate a database for study of a specific disease (i.e. A structured Patient Inclusion Report may be generated at either or both a single point-in-time as well as regenerated as new information about a patient or trial becomes available. Through the use of validation contracts that represent clinical trial /protocol inclusion & exclusion criteria, programmatic and automated evaluation of a patient's eligibility for any given clinical trial can be evaluated, [2606]);
determining, from the medical data library, specific data items relevant to the study of the specific disease (i.e. a system may poll new abstraction entries for each patient, identify new data elements populated in the newest document, and re-evaluate patient's eligibility across all of the available clinical trials. This may be performed by using a table with every clinical trial (study) having its own row, where the each inclusion or exclusion expression is given a row, the cell where each row and column meet contains information on whether the study requires satisfaction of the expression (T), fails satisfaction of the expression (F), or does not require the expression (Null). If a patient satisfies the expression for all (T) and does not satisfy the expression for all (F), then they are indicated as eligible for the associated clinical trial, [2602]);
generating a table of each of the specific data items using data for a patient group of the specific disease defined by applying a fabric condition of the specific disease (i.e. the data elements may be separated into a requirements table and a calculations table such that a study is only considered once all data elements that appear in the study's inclusion/exclusion criteria have been satisfied. Even further, data elements may be split into static and temporal classifications where a static classification is a data element that is not expected to change over time (gender, cancer site, previous treatments received, etc.) and temporal classification is a data element that is subject to change (age, treatments not yet received, metastasis, smoking, blood pressure, white/red blood cell counts, etc.). A patient may be recommended as potentially eligible for a clinical trial once the static classifications are all met, and the patient may be informed of the temporal classifications which need to be met. In this manner, a patient who would otherwise be eligible for a clinical trial, except that they have not had a blood test performed in the last six months may be informed that pending the results of a blood test, they may be eligible for the clinical trial. Thusly, encouraging the patient to consider getting a blood test to make their patient record more robust and potentially entering into an applicable clinical trial, [2063]); and
generating a database for the study of the specific disease using the table of the specific data items (i.e. The features of the data store are aggregated from millions of documents across thousands of sources which may be impossible for an abstractor to keep in mind all the types of features that may be extracted from any particular document from any particular source. An abstraction software suite may be programmed or utilize a trained artificial intelligence to recognize a document type from a source and extract all relevant information from the document and storing a digital representation in a structured format according to the above disclosure, [2594]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ozeran et al. (US Pub. 2020/0381087) – discloses facilitating the extraction and analysis of data embedded within clinical trial information and patient records.
Frasier et al. (US Pat. 12,009,068) discloses building intuitive clinical trial applications.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MIRANDA LE whose telephone number is (571)272-4112. The examiner can normally be reached M-F 7AM-5PM.
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phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MIRANDA LE/ Primary Examiner, Art Unit 2153
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