DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/22/2025 has been entered.
Status of Claims
Claims 1-20 are pending of which claims 1, and 12 are in independent form.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 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.
The arguments regarding the 35 USC 101 (Abstract Idea), has been considered, however examiner specifies that upon further examination, that the rejection have been maintained for the reasons mentioned below (please see the remapped rejection below).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) data qualification systems, for electronic health record data, and more specifically to systems capable of determining if the current values of input fields match stored values.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter.
Independent claim 1 are directed to a system, including memory and processor (statutory).
Independent claim 12, is directed to a method, which is a process (statutory).
Independent All other claims depend on claims 1, and 12. As such, claims 1-20 are directed to a statutory category.
Regarding claims 1, and 12
With respect to step 2A, prong one (Judicial Exception), it is noted that the independent claims recite an abstract idea falling within the Mental Process grouping of abstract ideas. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claim recites the following limitations directed to an abstract idea:
“receiving a qualification list … including … a property name, a property name value, a mnemonic, and a mnemonic value, an invert result property and an invert result property value” the limitation as drafted recites a mental process involving collecting and organizing information for evaluation;
“querying a database to return matches of a property name value using the mnemonic value” the limitation as drafted recites a mental process involving searching for information and identifying corresponding matches, which can be performed mentally;
“setting a result value to true to indicate that a match was found” the limitation as drafted recites a mental process involving evaluation and determination of a condition (match/no match);
“determining whether the invert result property value is true and inverting the result value in response to a determination that the invert result property value is true” the limitation as drafted recites a mental process involving application of a rule or logical condition (e.g. applying an exception) to modify an evaluation outcome;
“to enable one of continuing and halting subsequent data processing,” the limitation as drafted recites a mental process involving decision making and control flow based on evaluation results;
“overriding a qualification failure by inverting the result value from false to true” the limitation as drafted recites a mental process involving decision making to override a failure condition based on a rule or judgement.
These limitations correspond to concept that can be performed in the human mind (Mental Process) and therefore fall within the mental process category of abstract idea (see MPEP 2016.04(a)(2)).
Additionally, the claim recites organizing and structuring relationships between entities (nodes), which also falls within certain methods of organizing human activity, such as managing and structuring information.
The claims fall within:
Thus, the claims recite an abstract idea (mental process/mathematical concepts/information processing).
With respect to step 2A, Prong Two (Particular Application), the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims recite the use of:
“a memory storing one or more property collections; and a processor operably coupled to the memory and configured to execute machine-readable instructions” as drafted recites generic data storage component and generic processor performing generic computing functions, which performs generic computer functions and amount to mere instructions to apply the abstract idea on a computer (see MPEP 2106.05(f)(2)).
“spawn a first computer process … and to spawn a second computer process … spawned concurrently,” as drafted recites generic concurrent or parallel processing, which is a well-understood, routine, and conventional computer function and does not provide any technological improvement.
“receiving a qualification list … an invert result property value; querying a database …; setting a result value … determining … and inverting the result value …” as drafted recites: data gathering (receiving input), data retrieval (database query), data evaluation (determining match/no match), rule application (inverting results/overriding failure); which together constitutes insignificant extra-solution activity and implementation of abstract idea itself (see MPEP 2106.05(g)).
“enable one of continuing and halting subsequent data processing” as drafted recites result based control, which reflects the abstract idea itself (decision making), and does not impose a meaningful limit on how the abstract idea is performed.
The additional elements mentioned above fail to integrate the abstract idea into a practical application because the additional elements, individually and in combination, amount to no more that:
Generic computer components performing generic computer functions; and
Insignificant extra-solution activity, including data collection, analysis, scoring and presentation of results.
The claims do not:
Improve the functioning of a computer or processor;
Improve database query mechanisms or data structure;
Provide a specific technical solution for electronic health record data processing;
Implement a particular algorithm or technical mechanism for result inversion;
Improve concurrency or process execution in a technological way.
There are no improvements to computer functionality or any specific technical solution to a computer centric problem. Instead, the computer and semiconductor environment are used as tools to execute abstract mathematical encoding, data analysis, and decision making, with the result merely being applied in a generic manner.
There is no recitation of, a new data structure that changes computer operation, improved network functioning, an unconventional indexing technique, a specific hardware solution.
Instead, the computer components are used as tools to perform the abstract idea of collecting, organizing, and associating information about nodes and their relationships.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recited components are merely generic computer/database elements performing their routine, well-understood, and conventional functions. See Alive, MPEP 2016.05(d).
The steps mentioned in the independent claims are merely well-understood, routine and conventional activities. Courts have consistently helped such high-level information management operations are conventional.
The claims recite only functional, result oriented language, without specifying any technical mechanism for performing these operations in a non-conventional manner.
Considering claims as a whole, the ordered combination of elements also reflects nothing more than the typical workflow of distributed systems, and therefore DOES NOT add “significantly more” than the abstract idea.
Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Claims 2 and 12 add the limitation of "wherein automatically ranking the located
documents include placing the documents in an order of decreasing correspondence to the
event, with the documents having a highest correspondence first in the order. This limitation is
a process that, under the broadest reasonable interpretation, covers performance of the limitation
in the mind but for the recitation of generic computer components. That is, nothing in the
limitation precludes the step from practically being performed in the mind. This limitation, in the
context of this claim, encompasses the user thinking about ranking documents. Thus, this
limitation recites an abstract mental process under 2019 PEG because it can be performed in the
human mind either through observation, evaluation and judgment and opinion.
Claims 2 and 13 add the limitation, "the program steps further comprising converting
the qualification list from the first format into a property collection format. This limitation is a
process that, under the broadest reasonable interpretation, covers performance of the limitation in
the mind but for the recitation of generic computer components. That is, nothing in the limitation
precludes the step from practically being performed in the mind. This limitation, in the context of
this claim, encompasses the user thinking about converting data. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either
through observation, evaluation and judgment and opinion.
Claims 3 and 14 add the limitation, "the program steps further comprising retrieving a
property name value and a mnemonic value from the qualification list for all input field arrays.
This limitation is merely data extraction using computer as a tool which is considered to be
insignificant extra solution activity (MPEP 2106.05(g)).
Claims 4 and 15 add the limitation, "wherein the database can be the qualification items
array. This limitation is merely data execution using computer as a tool which is considered to
be insignificant extra solution activity (MPEP 2106.05(g)).
Claims 5 and 16 add the limitation, "wherein the database can be a Property
Collection. This limitation is merely data execution using computer as a tool which is
considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 6 adds the limitation, "the program steps further comprising determining a cache
result property is False and terminating the qualification. This limitation is merely data execution using computer as a tool which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claims 7 and 17 add the limitation, "the program steps further comprising determining a cache result property is True and caching the result value as the qualification result. This limitation is merely data extraction using computer as a tool which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claims 8 and 18 add the limitation, "the program steps further comprising returning the result value as the result of the qualification. This limitation is merely data extraction using computer as a tool which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claims 9 and 19 add the limitation, "wherein the processor addresses data using an
execution context property path (XPP)". This limitation is merely data execution using computer
as a tool which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claims 10 and 20 add the limitation, "wherein the at least one input field array also has an XPP property and an XPP value to retrieve the current value of the input field. This limitation is merely data extraction using computer as a tool which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 11 adds the limitation, "wherein the XPP value is an ASCII string. This limitation is merely data execution using computer as a tool which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Therefore, claims 1-20 are directed to an abstract idea and do not recite additional elements sufficient to amount to significantly more. The dependent claims provide specific implementations do not transform the abstract idea into a patent-eligible application. Therefore, the claims are not patent-eligible under 35 U.S.C. § 101.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over PATTERSON; NEAL L. (US 20120010905 A1) [Patterson] in view of Simon; Michael A. et al. (US 20210012904 A1) [Simon] in view of Yanes; Andres Matias (US 20110173590 A1) [Yanes].
Regarding claims 1, and 12, Patterson teaches, a system for electronic health record data qualification (Patterson [0053]: electronic health record), comprising:
a memory storing one or more property collections; and a processor (Patterson [0035]: processing unit, internal system memory.) operably coupled to the memory and configured to execute machine-readable instructions (Patterson [0036]: computer readable instructions), wherein the configuration of the processor to execute the machine-readable instructions includes configuration to spawn a first computer process configured to execute a first set of instructions of the machine-readable instructions to perform a first set of program steps for facilitating electronic health record data qualification (Patterson [0053]: The initial aggregator 110 also includes an electronic health record (EHR) exchange 159 and an EHR data store 160 containing centralized health records for persons interacting with the central computing system 102. At block 326, the EHR exchange 159 determines if an electronic health record exists in the EHR data store for the person identified in the integrated transaction. If an existing EHR is present, then the integrated transaction is stored to the EHR at block 328. If an EHR is not located in the EHR data store 160, then an EHR is created at block 328 based on the
clinical event details of the integrated transaction. Next, at block 330, the system stores the clinical event details to the EHR data store 160.); and
to spawn a second computer process configured to execute a second set of instructions of the machine-readable instructions to perform a second set of program steps for facilitating electronic health record data qualification (Patterson [0092-0093]: As set forth above, methods 1000 and 1100 may be used simultaneously. For example, if adjustment factors are determined in both methods, both factors may be applied to the base level of reimbursement to account for the quality of the care provided in the integrated transaction being adjudicated and the quality of care historically provided by the care provider. In addition, the system may make a quality adjustment simultaneously with the provision of care according to FIG. 10 for the specific length of stay for the patient.);
However, Patterson does not explicitly facilitate, wherein the first computer process and the second computer process are spawned concurrently.
Simon discloses, wherein the first computer process and the second computer process are spawned concurrently [0367 - 0369] teaches parallel processing: "Additionally, this coupling and/or connection may facilitate remote execution of program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more location without deviating from the scope of the disclosure. In addition, any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code and/or instructions. A central repository may provide program instructions to be executed on different devices."
wherein the first set of program steps, and the second set of program steps include one or more of program steps comprising: receiving a qualification list in a first format from a client, the qualification list including at least one input field array having a property name, a property name value, a mnemonic, and a mnemonic value, an invert result property and an invert result property value (Simon [0328 - 0329]: Tables 1 and 2 describe an example prioritization of the opportunities based on supporting documentation including exemplary methods for identifying the risk, mapping the risk to a diagnosis and examples of appropriate action to take based on the newly identified risk. For each source, the severity and associated coefficient are identified and tagged, with the source of information such that the data may be mapped to condition categories. Condition category mappings may vary based on member demographics, program enrollment, health status, and the like. The mapped data may then be stored at the 'person' level SO that the user can view risk at varying levels of detail and trace a risk assessment to the patient level and the specific documentation that contributed to the risk rating. The ability to easily identify the source of risks and view underlying activity may increase the ability of a reviewer to act upon the identified risk to resolve or reduce the risk. With reference to FIG. 13A, the resulting scores are highly multi-dimensional where categories may include one or more of demographics (e.g. gender, age, location), provider, facility, major risk category, minor risk category, broad categorization of open vs. closed risk sources, detailed categorization of risk sources, opportunities that are immediately actionable vs. opportunities that are strategic, evidence for the completed service year vs. evidence for the current service year, and the like and results may be filtered by category and viewed on an interactive browser or graphical user interface for improved navigation and insight.);
querying a database to return matches of a property name value using the mnemonic value (Simon [0334]: The query translation engine 144 allows a user to specify the desired information to be searched as a simple query phrase expressed in a predefined query generation syntax 1400 (FIG. 14). This query generation syntax 1400 allows the user to specify a request with simple phrases using common terminology familiar to those in the medical industry and, in certain embodiments, in a predefined order. This ability to specify a data request in simple, intuitive phrases facilitates users skilled in health care operations to craft request of the data warehouse 142 themselves rather than relying on an intermediate individual skilled in databases.);
setting a result value to true to indicate that a match was found (Simon [0357]: Referring to FIG. 23, the code generated by the query translation engine 144 may generate a table of results 2300 for all patients in the practice providing detailed traceability. In the illustrative example of FIG. 23, partial results from a data query (FIG. 17) are shown illustrating examples of possible outcomes. For patients who have a positive numerator (numerator=1), the corroborating data is shown. For the query of FIG. 17, the numerator will be positive if there was an LDL test 2302 in the last year with a result 2304 less than 100. Looking at the table of results 2300 the patients having had that test with those results may be identified including when the test was done and the actual results. For patients with a positive denominator (denominator=1), the corroborating data is also shown. For the query of FIG. 17, the denominator will be positive if there was a CAD/IVD procedure 2308 during the previous three years.); and
determining whether the invert result property value is true and inverting the result value in response to a determination that the invert result property value is true, to enable one of continuing and halting subsequent data processing (Simon [0296]: "The query generation module 114 integrates, as appropriate, newly received patient data into existing patient records on the basis of rules to match patients/members based attributes such as name, date of birth, gender, address, policy numbers, social security numbers, telephone number(s), and other such data to determine the patient data across the multiple different client databases 108. Phonetic algorithms such as a double metaphone algorithms and the like may be used to account for typos/misspellings when matching between different client databases 108. Patient data from one client database may include only data from the practice or network and not include data from specialists and out of network inpatient/emergency visits. Insurance data may not include information such as vitals, lab results, family history, and the like. By integrating the data a much richer patient history is created, facilitating more complex analysis.").
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Patterson et al. to the Simon's system by adding the feature of query matches. The references (Patterson and Simon) teach features that are analogous art and they are directed to the same field of endeavor, such as databases. Ordinary skilled artisan would have been motivated to do so to provide Patterson's system with enhanced data. (See Simon [Abstract], [0026], [0296], [0258], [0328-0329]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
However, neither Patterson nor Simon explicitly facilitates, wherein inverting the result value includes overriding a qualification failure by inverting the result value from false to true, wherein the qualification failure indicates that a match was not found and the result value is false.
Yanes discloses, wherein inverting the result value includes overriding a qualification failure by inverting the result value from false to true, wherein the qualification failure indicates that a match was not found and the result value is false (when the encapsulated logic of a declarative function is executed, there will exist, in many cases, one or more conditions under which execution is considered to have failed, such as a verify property declarative function not matching the property criteria it was assigned. When this occurs, or a sub-branch (i.e., other declarative functions that have been chained to the current declarative function) returns a failure, the declarative function returns to its immediate caller without executing any further logic or sub-branches. By default, it returns a failure to its caller. However, by means of a declarative function property this can be overridden to return success even on failure ¶ [0028]. Examiner specifies that overriding a failure condition to produce a success outcome which reads on “inverting the result value from false to true”).
It would have been obvious to one ordinary skilled in the art at the time of the filing of the present invention to combine the teachings of the cited references because Yanes' system would have allowed Patterson and Simon to facilitate wherein inverting the result value includes overriding a qualification failure by inverting the result value from false to true, wherein the qualification failure indicates that a match was not found and the result value is false. The motivation to combine is apparent in the Patterson and Simon’s reference, because there is a need to improve test automation, for the use of software to control the execution of tests, the comparison of actual outcomes to predicted outcomes, the setting up of test preconditions.
Regarding claims 2, and 13, the system of claim 1, the program steps further comprising converting the qualification list from the first format into a property collection format (Simon [0268]: "The data delivery module 132 may process the extracted data prior to transmission using methodologies such as: compression; encryption; restructuring the data to conform to a common schema, facilitating compliance with regulations covering protected health information such as HIPAA; and the like. The data delivery module 132 may deliver the data through a direct connection with the data receipt module 134, over a wireless or Internet connection, and the like. The data may be delivered directly to the data receipt module 134 or through one or more intermediate devices or servers designed to enhance data security by operating as a firewall, a single access point between the client extraction module 104 and the data organization module 110, and the like.")
Regarding claims 3, and 14, the system of claim 1, the program steps further comprising retrieving a property name value and a mnemonic value from the qualification list for all input field arrays (Patterson [0032]: "The data elements may include, for example, a variety of clinical, financial and administrative data elements as described in embodiments of the invention with reference to FIGS. 4-8. Clinical data elements are "clinical event details" submitted by the provider for the transaction being adjudicated and "EHR data elements" that pre-exist the submission of the particular transaction being adjudicated and are stored in the system as described below. For example, clinical data elements include, for example, patient identification data, diagnosis data, patient morbidity, mortality and recovery rates, drug prescription and other drug delivery and management information, and data elements traditional captured at the point of care and stored in an electronic medical record (EMR). Financial and administrative data elements may include hospital or other occupancy data, supply information, medical staff information, scheduling information, or other types of information related to clinical operations.")
Regarding claims 4, and 15, the system of claim 1, wherein the database can be the qualification items array (Simon [0270]: "Different data manipulation modules 140 may be directed to different use cases such as practice health assessment, smoking assessment, and others. A data manipulation module 140 for a particular use case may include processing the data received from different sites, various EHR implementations, different HER manufacturers, a plurality of providers and others into a set of precisely defined data points, statuses, metrics, and such. EHR data may vary in how the data is labeled, the order of data, the meaning of particular data terms, the use of unstructured and pseudo structured written data and notes as opposed to structured selections from a set, and others. A data manipulation module 140 may be operable to interpret the various incoming data and create output data and metrics in a common format.").
Regarding claims 5, and 16, the system of claim 1, wherein the database can be a Property Collection (Simon [0254]: "Referring to FIG. 1, an electronic health record data mining system 100 generally includes a data collection module 102 for specifying data to be collected and optionally initiating data collection, in communication with a client extraction module 104 for extracting specified data from one or more client databases 108. The client extraction module is in communication with a data organization module 110 for processing the extracted data. A user interaction module 112 is in communication with a query generation module 114 for transforming or converting data requests in the form of structured phrases from the user interaction module 112 into a formalized data base query. The data collection module 102, the data organization module 110, and the query generation module 114, may be located on a client instantiation server 115.").
Regarding claim 6, the system of claim 1, the program steps further comprising determining a cache result property is False and terminating the qualification (Patterson [0283]: "With reference to FIG. 7C, event categorization may begin with indexing each event entry by patient and ordering the entries by date and time (step 722). Once the event entries are indexed by patient, the events for a single patient may be processed beginning with determining if there are other events for the patient earlier the same day (step 724). For example, if this is the first event for the patient the system will create a new appointment (step 723) to associate with the patient, otherwise, the event will be added to the existing appointment (step 725). The schedule for the day will be reviewed to determine if scheduled appointment exists for this patient on this day (step 728). If a schedule appointment exists, it will be associated with the event entry (step 729) otherwise; the event entry will be categorized as extra activity (step 727).").
Regarding claims 7 and 17, the system of claim 1, the program steps further comprising determining a cache result property is True and caching the result value as the qualification result (Simon [0322]: "In an illustrative and non-limiting example, FIG. 12G shows a flow chart for inferring conditions from medication records and acting upon the inferences. Identify medications prescribed and filled (step 1230) using techniques such as pattern recognition of known medication identifiers, including National Drug Code (NDC) designations and medication names, natural language processing and the like. Determine if medication is highly correlated with specific conditions and risks (step 1232). If a specific condition is identified, determine presence of associated condition/diagnosis in the patient's HER (Step 1234). If the identified condition or diagnosis is not present, the condition may be flagged as a risk (step 1236) and one or more actions may occur such as contacting the care provider to see if they can document the inferred diagnostic condition (Step 1238), contacting the care provider to confirm the inferred diagnostic condition or arrange for appropriate testing and diagnosis of patient according to best medical practices (step 1240), and the like.").
Regarding claims 8, and 18, the system of claim 1, the program steps further comprising returning the result value as the result of the qualification (Simon [0275]: "The accuracy of the data manipulation modules 140 may be evaluated, in part, by comparing the results output from the data manipulation modules 140 with the results generated for comparable baseline sites which have, for example, similar numbers of patients, similar patient demographics, similar source system IT configuration and source organization business lines, and such. One or more of these factors may be considered in the selection of baseline sites for use in the automated reporting.").
Regarding claims 9, and 19, the system of claim 1, wherein the processor addresses data using an execution context property path (XPP) (Simon [0270]: "Different data manipulation modules 140 may be directed to different use cases such as practice health assessment, smoking assessment, and others. A data manipulation module 140 for a particular use case may include processing the data received from different sites, various EHR implementations, different EHR manufacturers, a plurality of providers and others into a set of precisely defined data points, statuses, metrics, and such. EHR data may vary in how the data is labeled, the order of data, the meaning of particular data terms, the use of unstructured and pseudo structured written data and notes as opposed to structured selections from a set, and others. A data manipulation module 140 may be operable to interpret the various incoming data and create output data and metrics in a common format.").
Regarding claims 10, and 20, the system of claim 1, wherein the at least one input field array also has an XPP property and an XPP value to retrieve the current value of the input field (Simon [0258]: "The query generation module 114 generally includes a query translation engine 144 to facilitate the creation of complex, database-executable data queries based on query phrases that correspond intuitively to the types of measures typically handled by a given client. The query generation module 114, facilitates data retrieval from the data warehouse 142. The query generation module 114 may facilitate system optimization by pre-retrieving common data requests such as most recent blood pressure test, date of last physical, and pre calculating common measures such as categorizing chronic conditions, and storing the results as additional parameters in the data warehouse 142. The query generation module 114 may be adapted to handle a variety of measures, statistics, figures of merit, variables, or the like (collectively referred to herein as "measures" except where context indicates otherwise) that are typically reported by the client extraction module 104 or that otherwise characterize the data collected from a client database 108. Without limitation, a measure may include quantified health care processes and their outcomes.").
Regarding claim 11, the system of claim 10, wherein the XPP value is an ASCII string (Simon [0320]: "The EHR data may be parsed based on data field or using one or more techniques such as direct string pattern matching (through tokenization of input), topic modeling, dictionary lookups through fuzzy matching, natural language processing, machine learning and the like to identify data of interest such as diagnosis codes, medications and medication codes, laboratory orders and results, vital statistics, annotations of interest, and the like. For example, the presence of diagnosis codes 1214 in a claim or billing document, notes for an assessment or encounter, a "Problem List" entry, or other diagnosis category may be used. Text notations may be processed using one or more techniques such as direct string pattern matching (through tokenization of input), topic modeling, dictionary lookups through fuzzy matching, natural language processing, machine learning and the like to identify words and phrases that may be mapped to a specific diagnosis code 1214, diagnostic groups 1216 or condition category 1218, as specified above.").
Conclusion
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4/28/2026
/MOHAMMAD S ROSTAMI/ Primary Examiner, Art Unit 2154
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