DETAILED ACTION
Status of Claims
This is a non-final office action on the merits in response to the arguments and/or amendments filed on 13 November 2025 and the request for continued examination filed on 13 November 2025.
Claim(s) 1, 7, and 13 is/are amended.
Claim(s) 1-3, 7-9, 13-15, and 19-29 is/are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 13 November 2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 18 November 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 7-9, 13-15, and 19-29 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims not listed below are rejected for dependency.
Amended claim 1 recites the non-original limitation “wherein the one or more executable scripts are configured to automatically generate queries in a temporal query language optimized for analysis by an analytics engine without requiring manual user coding.” There does not appear to be support for the identified limitation in the originally filed disclosure.
Applicant’s remarks do not identify support for the identified limitation. What appear to be the most relevant portions of the original disclosure state:
[0020] In certain aspects, the cohort engine 104 is a high performance, distributed, in- memory database engine used to query structured temporal data. The cohort engine 104 can include a compressed memory model optimized to query data features on a patient's timeline, for example. The cohort engine 104 provides clustering capabilities such as, but not limited to clustering capabilities for parallel processing, dynamic schema configuration to allow users to model and populate custom data features, entity linking to preserve relational information from an underlying data source, an updated command syntax to allow for more complex query construction, and the ability to extend the query engine with external custom functions that can be invoked at run-time to support calculated clinical scores.
[0023] In certain aspects, the interpreter 106 is a specialized extraction, transformation, and loading (ETL) engine optimized for temporal databases. The interpreter 106 allows users to define the structure of an underlying temporal structure including data features, relationships, and timelines. In addition, users can configure operations to query and load data from external sources into a temporal structure for query. The ETL mechanism provides the ability to split loaded temporal data into multiple shards allowing for the clustering of multiple query engines for horizontal scaling.
[0024]Cohort engine 104 may be configured to interpret a consult request query (e.g., as represented in a completed study template, as described herein), sift through available patient data (e.g., patient objects), retrieve appropriate patient data for the consult request query, and push or provide retrieved patient data to analytics engine 112. For example, cohort engine 104 may obtain patient-timeline objects, processed with a selected knowledge graph by interpreter 106, from a memory or storage 120, and provide it to analytics engine 112 in a standard language format (e.g., temporal query language (TQL)). The standard language format may be configured to optimize the patient data for analysis by analytics engine 112. Such configuration may comprise a command or instruction regarding the retrieval of information by cohort engine 104 for analytics engine 112 (e.g., to include or exclude a type of lab result, to include simply a yes or no answer on whether a lab result is available rather than providing the actual lab result value, to include a most recent lab result, to include a patient's entire history of lab results, to include a patient's history of lab results for a given period of time, a given category of tests, and other commands and instructions).
None of the above disclosures describe or suggest automatically generating queries, much less automatically generate queries in a temporal query language. As such, these disclosures do not support the identified limitation. The remainder of the originally filed disclosure similarly fails to support the identified limitation.
Because the claimed invention includes a non-original limitation which is not supported by the originally filed disclosure, one of ordinary skill in the art would not recognize applicant as possessing the claimed invention at the time of filing. Therefore the claim is rejected under the written description requirement. Claims 7 and 13 are similarly rejected.
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 1-3, 7-9, 13-15, and 19-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1, which is representative of claims 7 and 13, recites a method for rapid informatics-based prognosis development, the method comprising:
accessing patient data with different coding schemes from a plurality of disparate data sources;
generating, based on the accessed patient data with different coding schemes, a plurality of patient objects corresponding to a plurality of patients, wherein each patient object of the plurality of patient objects represents at least a portion of a medical history of a patient of the plurality of patients over a period of time, wherein generating the plurality of patient objects comprises:
processing the accessed patient data with different coding schemes to conform to a coding hierarchy, wherein the coding hierarchy maps a plurality of codes to a phenotype, and
wherein the phenotype indicates a condition or a set of conditions;
accessing a consult request;
selecting a study template configured to design a study to develop a consult output in response to the consult request;
accessing a completed study template;
wherein the completed study template comprises a completed field in the study template based on information from the consult request, the completed field comprising a variable provided in at least one of a timeframe field, a phenotype field, a cohort field, or a demographic field, and
wherein the completed study template comprises
accessing,
generating the consult output based on the cohort data, the consult output comprising a result from an analysis of the cohort data according to a criterion and an instruction in the study template.
The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of gathering information, receiving an analysis request, analyzing the information according to a procedure, and generating results of the analysis. This concept describes a mental process that an analyst should follow to prepare a report similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping. As such, these limitation set forth a method of organizing human activity. Alternatively, the identified limitations encompass observations, evaluations, and judgements, and can be practically performed in the human mind. As such, these limitations set forth a mental process. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 7 recites the additional element of a system comprising a memory and a processor. Claim 13 recites the additional element of a non-transitory machine-readable storage medium. These additional elements are recited at an extremely high level of generality, and may be interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims recite the additional element of one or more executable scripts. This limitation amounts to instructions to implement the abstract idea with a computing device. As previously noted, implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application. As such, this limitation does not integrate the abstract idea into a practical application.
The claims recite the additional element of automatically generate queries without requiring manual user coding. This limitation amounts to instructions to implement the abstract idea with a computing device. As previously noted, implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application. As such, this limitation does not integrate the abstract idea into a practical application.
The claims recite the additional element of a temporal query language optimized for analysis by an analytics engine and structured temporal data. This additional element does not reflect any improvement to technology, does not require a particular machine, does not effect a transformation, and does not meaningfully limit the implementation of the abstract idea. Instead, this additional element only generally links the abstract idea to a computing environment involving temporal data. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional element of a distributed, in-memory database engine. This additional element does not reflect any improvement to technology, does not require a particular machine, does not effect a transformation, and does not meaningfully limit the implementation of the abstract idea. Instead, this additional element only generally links the abstract idea to a computing environment involving a distributed, in-memory database. As such, this additional element does not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the additional elements only generally link the abstract idea to computing environment with a distributed in-memory database. As such the combination of additional elements does not integrate the abstract idea into a practical application. As the additional elements, individually and as a combination, do not integrate the abstract idea into a practical application, the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not amount to significantly more.
As previously noted, the claims recite the additional element of a temporal query language optimized for analysis by an analytics engine and structured temporal data. However, Roth et al. (US 2015/0169667 A1) demonstrates (Temporal query languages have been well-studied. See at least [0143]) that temporal query languages and temporal data were well known long before the priority date of the claimed invention. As such, this additional element does not amount to significantly more.
As previously noted, the claims further recite the additional element of a distributed, in-memory database engine. Lee et al. (SAP HANA Distributed In-Memory Database System: Transaction, Session, and Metadata Management), SQLMaria (Oracle Launches TimeTen Scaleout!), and Apache (In-memory database with Apache Ignite) describe prior art, commercially available distributed in-memory databases. These references cumulatively demonstrate that distributed, in-memory databases were conventional before the priority date of the claimed invention. As such, this additional element does not amount to significantly more.
There are no further additional elements. When considered as a combination, the additional elements only generally link the abstract idea to computing environment with a distributed in-memory database. As such the combination of additional elements does not amount to significantly more than the abstract idea. Therefore, when considered individually and as a combination, the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Dependent claims 2, 3, 8, 9, 14, 15, and 19-29 further describe the abstract idea, but the claims continue to recite an abstract idea. Dependent claims 2, 3, 8, 9, 14, 15, do not recite any further additional elements. The previously identified additional elements, individually and as a combination, fail to integrate the narrowed abstract idea into a practical application for reasons similar to those indicated above. Therefore the claims are determined to be directed to an abstract idea. Further, the previously identified additional elements, individually and as a combination, do not amount to significantly more than the narrowed abstract idea for reasons similar to those indicated above. Dependent claim 19 recites the additional element of storing an object in a database. This additional element reflects no improvement to technology, no particular machine, and no transformation of an article. Instead, this additional element, individually and in combination with the prior additional elements, only generally links the abstract idea to a technological environment involving a computer. As such, this additional element does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea. Further, Per MPEP 2106.5(d), storing data in a memory is a well-known, routine, and conventional computer function. As such, this additional element, individually and in combination with the other computing device additional elements, does not amount to significantly more. Dependent claim 20 recites the additional element of using natural language processing. This additional element reflects no improvement to technology, no particular machine, and no transformation of an article. Instead, this additional element, individually and in combination with the prior additional elements, only generally links the abstract idea to a technological environment involving a computer implementing natural language processing. As such, this additional element does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea. Further, Berkan et al. (US 2003/0074353 A1) demonstrates (“Conventional natural language processing”, [0004]) that natural language processing was conventional long before the priority date of the claimed invention. As such, this additional element, individually and in combination with the other computing device additional elements, does not amount to significantly more. Dependent claims 21, 22, 24, 25, 27, and 28 further describe the additional element of a script. This additional element, individually and in combination with the prior additional elements, amounts to instructions to implement the abstract idea with a computing device. As such, this additional element does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea. Further, this additional element, individually and in combination with the prior additional elements, amounts to instructions to implement the abstract idea with a computing device. As such this additional element does not amount to significantly more. Dependent claims 23, 26, and 29 recite the additional element of a distributed, in-memory database. This additional element reflects no improvement to technology, no particular machine, and no transformation of an article. Instead, this additional element, individually and in combination with the prior additional elements, only generally links the abstract idea to a technological environment involving distributed storage. As such, this additional element, individually and in combination with the other computing device additional elements, does not amount to significantly more. Further, Lee et al. (SAP HANA Distributed In-Memory Database System: Transaction, Session, and Metadata Management), SQLMaria (Oracle Launches TimeTen Scaleout!), and Apache (In-memory database with Apache Ignite) describe prior art, commercially available distributed in-memory databases. These references cumulatively demonstrate that distributed, in-memory databases were conventional before the priority date of the claimed invention. As such, this additional element, individually and in combination with the other computing device additional elements, does not amount to significantly more.
Thus as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Claim Rejections - 35 USC § 103
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) 1-3, 7-9, 13-15, 19-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lefkofsky et al. (US 2020/0211716 A1) in view of Apache (In-memory database with Apache Ignite).
Regarding Claim 1, 7, and 13: Lefkofsky discloses a method for rapid informatics-based prognosis development, the method comprising:
accessing patient data with different coding schemes from a plurality of disparate data sources; generating, based on the accessed patient data with different coding schemes, a plurality of patient objects corresponding to a plurality of patients, wherein each patient object of the plurality of patient objects represents at least a portion of a medical history of a patient of the plurality of patients over a period of time, wherein generating the plurality of patient objects comprises: processing the accessed patient data with different coding schemes to conform to a coding hierarchy, wherein the coding hierarchy maps a plurality of codes to a phenotype, and wherein the phenotype indicates a condition or set of conditions (The patient data store 14 may be a pre-existing dataset that includes patient clinical history, such as demographics, comorbidities, diagnoses and recurrences, medications, surgeries, and other treatments along with their response and adverse effects details. … In one aspect, these datasets may be generated from one or more sources. … certain system users may be able to buy or license aspect to the datasets, such as when those users do not have immediate access to a sufficiently robust dataset, when those users are looking for even more records, and/or when those users are looking for specific data types, such as data reflecting patients having certain primary cancers, metastases by origin site and/or diagnosis site, recurrences by origin, metastases, or diagnosis sites, etc. See at least [0052]. Also: 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. See at least [0054]. Also: Features derived from structured, curated, or electronic medical or health records may include clinical features such as diagnosis, symptoms, therapies, outcomes, patient demographics such as patient name, date of birth, gender, ethnicity, date of death, address, smoking status, diagnosis dates for cancer, illness, disease, diabetes, depression, other physical or mental maladies, personal medical history… . See at least [0057]. Also: Feature collections may include a diverse set of fields available within patient health records. Clinical information may be based upon fields which have been entered into an electronic medical record (EMR) or an electronic health record (EHR) by a physician, nurse, or other medical professional or representative. Other clinical information may be curated from other sources, such as molecular fields from genetic sequencing reports. See at least [0055]).
accessing a consult request; selecting a study template configured to design a study to develop a consult output in response to the consult request; accessing a completed study template, wherein the completed study template comprises a completed field in the study template based on information from the consult request, the completed field comprising a variable provided in at least one of a timeframe field, a phenotype field, a cohort field, or a demographic field; wherein the completed study template comprises one or more executable scripts for performing an associated study type, wherein the one or more executable scripts are configured to automatically generate queries in a temporal query language optimized for analysis by an analytics engine without requiring manual user coding; accessing, using a cohort engine and at least one of the one or more executable scripts, cohort data based on the completed study template, the cohort data derived from the plurality of patient objects, wherein the cohort engine comprises a database engine configured to query structured temporal data stored as patient timeline objects; and generating the consult output based on the cohort data, the consult output comprising a result from an analysis of the cohort data according to a criterion and an instruction in the study template (notebooks provide a benefit to users by allowing the Interactive Analysis Portal 22 to provide custom templates to their selected data and leverage pre-built healthcare statistical models to provide results to users who are not sophisticated in programming. See at least [0263]. Also: A user may configure a reporting page for a notebook. A reporting page may include text, images, and graphs as selected and populated by the users. Preconfigured elements may be selected from a list, such as a dropdown list or a drag-and-drop menu. Preconfigured elements include statistical analysis modules and machine learning models. For example, a user may wish to perform linear regression on the data with respect to specific features. A user may select linear regression, and a menu with checkboxes may appear with features from their data set which should be supplied to the linear regression model. Once filled out, a template for reporting the linear regression results with respect to the selected features may be added to the reporting page at a location identified by the active cursor or the drop location for a drag- and drop-element. If a user wishes to solve a problem using a machine learning model, it may be added to the sheet. A header may be populated identifying the model, the hypertuning parameters, and the reported results. In some instances, a model that was previously trained may then be applied to the current cohort. See at least [0265] and Fig. 32. Also: One or more templates may be provided in template window 3050 for the user's convenience. Templates may include one or more cells preconfigured to operate on an input data such as the filtered patient cohort, run one or more cells of code to generate logical results, and run one or more cells of text or visualizations to report out the results of the performed logic on the input data in a convenient manner. Templates may exist for charts, graphs, regressions, dimension reductions, classifications, RNA or DNA normalization, and other commonly used features across templates available to the user. See at least [0275]. Also: A comprehensive collection of features in additional feature modules may combine a variety of features together across varying fields of medicine which may include diagnoses, responses to treatment regimens, genetic profiles, clinical and phenotypic characteristics, and/or other medical, geographic, demographic, clinical, molecular, or genetic features. See at least [0055]. Also: Processing device 3402 represents one or more general-purpose processing devices such as a microprocessor, a central processing unit, or the like. More particularly, the processing device may be … processor implementing other instruction sets, or processors implementing a combination of instruction sets. See at least [0291]. Also: Notebook editor 3200 may auto-populate with Title 3210 and one or more cells 3240A-D based upon the user selected workbook. … A cell UIE may provide selections pertaining to the currently selected cell 3240A-D having a block of code, such a commands for running the currently selected cell, terminating the currently selected cell, adding a cell, deleting a cell, running all cells, running all cells above, running all cells below, or terminating all cells. A kernel UIE may provide selections pertaining to one or more programming languages and/or available to the user such as … Structured Query Language (SQL) … . Selecting a kernel from the kernel UIE reloads the workbook so that the cells execute commands from the respective language. A widget UIE may provide selections pertaining to one or more supported code snippets for the active kernel. Code snippets may include code for creating visualizations such as a graph or a plot, code for simple arithmetic operations such as calculating a mean or a standard deviation, or code for more complex operations such as calculating a distribution and displaying a respective curve. See at least [0279]. Also: the back end layer 12 also may include a distributed computing and modeling layer 38 that receives data from the patient cohort timeline data storage 18 to provide inputs to a plurality of modules. See at least [0051]. Also: the system may include a patient timeline analysis module 28 that permits a user to review the sequence of events in the clinical life of each patient. See at least [0099]. Also: The Interactive Analysis Portal 22 allows the Notebook generation to perform one or more statistical models, analysis, and visualization or reporting of results to the narrowed down cohort without having the user code anything in the notebook as the selected models, analysis, visualizations, or reports of the notebook itself are configured to accept the cohort from the Interactive Analysis Portal 22 and provide the analysis on the cohort as is, without user intervention at the code level. See at least [0259]. Also: An associated user may then select a previously generated notebook which applies selected analysis to the narrowed down cohort without having the user code or recode anything in the notebook as the notebook itself is configured to accept the cohort from the Interactive Analysis Portal 22 and provide the notebook results without user intervention. See at least [0261]).
Lefkofsky does not describe a distributed, in-memory database engine. Apache teaches a distributed, in-memory database engine (“Apache Ignite® is a distributed in-memory database”, Page 1).
Lefkofsky provides a system which utilizes stored patient data, upon which the claimed invention’s use of a distributed, in-memory database can be seen as an improvement. However, Apache demonstrates that the prior art already knew of distributed, in-memory databases. One of ordinary skill in the art could have easily applied the techniques of Apache to the system of Lefkofsky. Further, one of ordinary skill in the art would have recognized that such an application of Apache would have resulted in an improved system which would more quickly access patient data. As such, the application of Apache, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Lefkofsky and the teachings of Apache.
Regarding Claim 2, 8, and 14: Lefkofsky in view of Apache makes obvious the above limitations. Lefkofsky further discloses wherein the study comprises one or both of a descriptive study and an analytical study (A user may configure a reporting page for a notebook. A reporting page may include text, images, and graphs as selected and populated by the users. Preconfigured elements may be selected from a list, such as a dropdown list or a drag-and-drop menu. Preconfigured elements include statistical analysis modules and machine learning models. For example, a user may wish to perform linear regression on the data with respect to specific features. A user may select linear regression, and a menu with checkboxes may appear with features from their data set which should be supplied to the linear regression model. Once filled out, a template for reporting the linear regression results with respect to the selected features may be added to the reporting page at a location identified by the active cursor or the drop location for a drag- and drop-element. If a user wishes to solve a problem using a machine learning model, it may be added to the sheet. A header may be populated identifying the model, the hypertuning parameters, and the reported results. In some instances, a model that was previously trained may then be applied to the current cohort. See at least [0265] and Fig. 32).
Regarding Claim 3, 9, and 15: Lefkofsky in view of Apache makes obvious the above limitations. Lefkofsky further discloses wherein the study template comprises one or more of a case series template, a sub-group template, a cohort template, and a case-control template (See at least Fig. 32, Element 3240A).
Regarding Claim 19: Lefkofsky in view of Apache makes obvious the above limitations. Lefkofsky further discloses wherein the patient objects are stored in a temporal database (The patient data store 14 may be a pre-existing dataset that includes patient clinical history, such as demographics, comorbidities, diagnoses and recurrences, medications, surgeries, and other treatments along with their response and adverse effects details. See at least [0052]. Also: the system may include a patient timeline analysis module 28 that permits a user to review the sequence of events in the clinical life of each patient. See at least [0099]. Also: a dataset that only utilizes backward-looking features, derived at each event point on the timeline. See at least [0142]).
Regarding Claim 20: Lefkofsky in view of Apache makes obvious the above limitations. Lefkofsky further discloses wherein obtaining cohort data is done at least in part using natural language processing to identify patient objects relevant to the study (In another embodiment, the user interface may include a natural language search style bar to facilitate filter criteria definition for the cohort, for example, in the “Ask Gene” tab 236 of the user interface or via a text input of the filtering interface. In one aspect, an ability to specify a query, either via keyboard-type input or via machine-interpreted dictation, may define one or more of the subsequent layers of a cohort funnel (described in greater detail in the next section). Thus, for example, when employing traditional natural language processing software or techniques, an input of “breast cancer patients” would cause the system to recognize a filter of “cancer_site==breast cancer” and add that as the next layer of filtering. Similarly, the system would recognize an input of “pancreatic patients with adverse reactions to gemcitabine” and translate it into multiple successive layers of filtering, for example, “cancer_site==pancreatic cancer” AND “medication==gemcitabine” AND “adverse reaction==not null.” See at least [0086]. Also: In a second aspect, the natural language processing may permit a user to use the system to query for general insights directly, thereby both narrowing down a cohort of patients via one or more funnel levels and also causing the system to display an appropriate summary panel in the user interface. Thus, in the situation that the system receives the query “What is the 5 years progression-free survival rate for stage III colorectal cancer patients, after radiotherapy?,” it would translate it into a series of filters such as “cancer_site==colorectal” AND “stage==III” AND “treatment==radiotherapy” and then display five-year progression-free survival rates using, for example, the patient survival analysis user interface 30. Similarly, the query “What percentage of female lung cancer patients are post-menopausal at a time of diagnosis?” would translate it into a series of patients such as “gender==female,” “cancer_site==lung,” and “temporal==at diagnosis,” determine how many of the resulting patients had data reflecting a post-menopause situation, and then determine the relevant percentage, for example, displaying the results through one or more statistical summary charts. See at least [0087]).
Regarding Claim 21, 24, and 27: Lefkofsky in view of Apache makes obvious the above limitations. Lefkofsky further discloses wherein the one or more executable scripts comprises instructions for querying a database or other storage for cohort data (the back end layer 12 also may include a distributed computing and modeling layer 38 that receives data from the patient cohort timeline data storage 18 to provide inputs to a plurality of modules. See at least [0051]).
Regarding Claim 22, 25, and 28: Lefkofsky in view of Apache makes obvious the above limitations. Lefkofsky further discloses wherein the one or more executable scripts is configured to reference at least part of a related study (Generating a notebook may be performed with a GUI for notebook editing. A user may configure a reporting page for a notebook. A reporting page may include text, images, and graphs as selected and populated by the users. Preconfigured elements may be selected from a list, such as a dropdown list or a drag-and-drop menu. Preconfigured elements include statistical analysis modules and machine learning models. For example, a user may wish to perform linear regression on the data with respect to specific features. A user may select linear regression, and a menu with checkboxes may appear with features from their data set which should be supplied to the linear regression model. Once filled out, a template for reporting the linear regression results with respect to the selected features may be added to the reporting page at a location identified by the active cursor or the drop location for a drag- and drop-element. If a user wishes to solve a problem using a machine learning model, it may be added to the sheet. A header may be populated identifying the model, the hypertuning parameters, and the reported results. In some instances, a model that was previously trained may then be applied to the current cohort. See at least [0265]).
Regarding Claim 23, 26, and 29: Lefkofsky in view of Apache makes obvious the above limitations. Lefkofsky further discloses wherein each patient object of the plurality of patient objects comprises structured temporal data (The patient data store 14 may be a pre-existing dataset that includes patient clinical history, such as demographics, comorbidities, diagnoses and recurrences, medications, surgeries, and other treatments along with their response and adverse effects details. See at least [0052]. Also: the back end layer 12 also may include a distributed computing and modeling layer 38 that receives data from the patient cohort timeline data storage 18 to provide inputs to a plurality of modules. See at least [0051]). As previously noted, Apache teaches a distributed, in-memory database engine (“Apache Ignite® is a distributed in-memory database”, Page 1). The motivation to combine Lefkofsky and Apache is the same as explained under claim 1 above, and is incorporated herein.
Response to Arguments
Applicant’s Argument Regarding 101 Rejections of claims 1-3, 7-9, 13-15, and 19-29:
Amended Claims 1, 7, and 13 recites “wherein the cohort engine comprises a distributed, in-memory database engine configured to query structured temporal data stored as patient timeline objects” and “wherein the one or more executable scripts are configured to automatically generate queries in a temporal query language optimized for analysis by an analytics engine without requiring manual user coding.” At least these technological limitations cannot be practically performed in the human mind and therefore amended Claims 1, 7, and 13 do not recite a judicial exception.
Amended Claims 1, 7, and 13 recite “wherein the cohort engine comprises a distributed, in-memory database engine configure to query structured temporal data stored as patient timeline objects.” Accordingly, in some implementations of the instant disclosure computer functionality may be improved by enabling rapid querying of complex temporal medical data structures.
Amended Claims 1, 7, and 13 additional recite “wherein the one or more executable scripts are configured to automatically generate queries in a temporal query language optimized for analysis by an analytics engine without requiring manual user coding.” Accordingly, some implementations of the present disclosure can provide a specific technological process that may automatically translate study parameters into optimized database queries without human intervention, representing an improvement in computer functionality.
The distributed, in-memory database engine configured to query structured temporal data stored as patient timeline objects” as recited in amended Claims 1, 7, and 13 can, as described in some implementations of the instant disclosure, represent a specific database architecture. This is not a generic database but a specialized system optimized for temporal medical data queries.
The aforementioned, recited features are not well-understood, routine, or conventional activities. The Examiner’s citation to prior art references regarding distributed in-memory databases does not establish that the specific combination of elements recited in the amended claims was conventional.
Examiner’s Response: Applicant's arguments filed 13 November 2025 have been fully considered but they are not persuasive.
The “practically performable” consideration of the eligibility analysis does not require that the entirety of the claim be performable in the human mind. Note MPEP 2106.04(a)(2)(III)(A) which states that “claims do recite a mental process when they contain limitations that can practically be performed in the human mind.” The identification of two limitations which cannot be performed in the human mind does not withdraw other limitations which can be practically performed in the human mind from an abstract idea.
Per MPEP 2106.05(a), “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” Here, the disclosure provides no information on how to implement the referenced distributed in-memory database engine, and appears to be entirely reliant on the existing knowledge of one of ordinary skill in the art to support the claimed functionality. Such a disclosure indicates that the identified limitation does not constitute a technical improvement.
Per MPEP 2106.05(a), “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” Here, the disclosure provides no information on how to implement the referenced automatic translation of study parameters, and appears to be entirely reliant on the existing knowledge of one of ordinary skill in the art to support the claimed functionality. Such a disclosure indicates that the identified limitation does not constitute a technical improvement.
While perhaps the claims “can” encompass particular architectures, at the level of generality that the current claims recite this feature, the additional element only generally links the abstract idea to an environment involving a type of database. Further, nothing in the claim appears that the technology involved is optimized for medical queries.
Examiner disagrees with Applicant’s implication that distributed, in-memory databases and structure data are unconventional. Further, current subject matter eligibility analysis does not require the establishment of the claim as a whole to be conventional to find the claim ineligible. Such a test would plainly contradict existing jurisprudence.
Applicant’s Argument Regarding 102 and 103 Rejections of claims 1-3, 7-9, 13-15, and 19-29:
Lefkofsky fails to disclose, for example, the integration of executable scripts within study templates that work in conjunction with a cohort engine to dynamically access cohort data.
While Lefkofsky states that “a user may select linear regression, and a menu with checkboxes may appear with features from their data set which should be supplied to the linear model,” this teaches user-driven selection of preconfigured elements, not executable scripts that “automatically generate queries in a temporal query language optimized for analysis by an analytics engine without requiring manual user coding.”
Examiner’s Response: Applicant's arguments filed 13 November 2025 have been fully considered but they are rendered moot by the amendment of claims. In the interest of prosecution, Applicant’s arguments above will be addressed here.
While Lefkofsky does not uses the phrase “executable scripts”, one of ordinary skill in the art would infer the templates of Lefkofsky include code which retrieves cohort data. See MPEP 2144.01: [I]n considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom.” In re Preda, 401 F.2d 825, 826, 159 USPQ 342, 344 (CCPA 1968).
First, Examiner notes that one of ordinary skill in the art would appreciate that SQL is a temporal query language post 2011. Lefkofsky discloses that the code of the notebooks may be SQL. Further, the selection of features from a menu reasonably reads on “without requiring manual user coding”, and one of ordinary skill in the art would infer that such notebooks would include code to retrieve identified data. As such, Lefkofsky reasonably discloses the referenced features.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 of the prior office action dated 28 January 2025.
Conclusion
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/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-02-20