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 .
This communication is in response to the amendment received on 03/10/2026. Claims 1, 3, 6, 8-22 and 31 remain pending in this application.
The objection to the specification, 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph and 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph have been withdrawn in light of the amendments.
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, 6, 8-22 and 31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1, 3, 6, 8-22 and 31 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claim 1 has been amended to recite:
“A computer-implemented data integration method for data exchange across medical data formats, the method comprising:
receiving, at a predictive analytics and data integration platform, an insurance discovery query comprising information in a non-standardized data format from a medical records and billing interface associated with a first medical entity, the insurance discovery query comprising demographic information for a first patient, wherein the insurance discovery query indicates that at least a portion of insurance information for the first patient is unknown, wherein the predictive analytics and data integration platform is communicatively coupled to a data repository comprising a networked collection of non-transitory storage devices associated with a plurality of medical entities, and wherein the non-standardized data format is one of multiple different non-standardized data formats recognized by the predictive analytics and data integration platform and used across the plurality of medical entities,
converting, by the predictive analytics and data integration platform, the information in the insurance discovery query from the non- standardized data format to a standardized data format that is a common data format recognized by the plurality of medical entities, the standardized data format conforming to a communication standard comprising one of a Health Level 7 (HL7®) format or a Fast Healthcare Interoperability Resources (FHIR®) format,
generating a single sign-on (SSO) interface, using a set of SSO credentials, for accessing the information in the insurance discovery query having been converted to the standardized data format,
providing, by the predictive analytics and data integration platform and via the SSO interface, remote access to the plurality of medical entities to the information in the insurance discovery query having been converted to the standardized data format,
receiving a plurality of patient records based on the information in the insurance discovery query from the plurality of medical entities in the standardized data format,
generating, by the predictive analytics and data integration platform, insurance discovery data for the first patient in the standardized data format, the insurance discovery data comprising insurance information predicted based on the demographic information for the first patient and the plurality of patient records, and
transmitting, by the predictive analytics and data integration platform, the insurance discovery data to the medical records and billing interface associated with the first medical entity to resolve the insurance information for the first patient being unknown,
wherein the receiving of the insurance discovery query, the converting of the information in the insurance discovery query, the providing of the remote access, the generating of the insurance discovery data, and the transmitting of the insurance discovery data are performed in real-time.”
The limitations of “receiving an insurance discovery query…, converting the information in the insurance discovery query from the non-standardized data format to a standardized format…, receiving a plurality of patient records based on information in the insurance discovery query…, generating insurance discovery query data for the patient in the standardized data format, the insurance discovery data comprising insurance information predicted based on demographic information for the first patient and the plurality of patient records, …” correspond to an abstract idea of “certain methods of organizing human activity”. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic predictive analysis and fata integration platform does not take the claim out of the methods of organizing human interactions grouping. Thus, the claim recites an abstract idea.
The current specification describes the predictive analysis and data integration platform as a combination of generic software and hardware components, such as “…a predictive analytics computing platform including hardware logic and/or software logic configured for execution on processing circuitry, where the predictive analytics computing platform is configured to receive, from a claims processing system, claims data corresponding to a medical claim for a first patient, access, from the predictive analytics platform, a set of requirements corresponding to at least one first payer of the number of payers to which the medical claim is directed, where the set of requirements was generated at least in part through training at least one machine learning classifier with claims data corresponding to a subset of the number of patients having coverage provided by one or more payers of the at least one first payer, verify the claims data in view of the set of requirements, and provide predictive analytic output to the claims processing system responsive to verifying the claims data…” in [0005] and also “Various aspects of the embodiments disclosed herein are performed on one or more computing devices, such as a laptop computer, tablet computer, mobile phone or other handheld computing device, or one or more servers….” in [0266]. Hence, the predictive analysis and data integration platform recited in the claims are directed to generic computer components.
Claim 11 recites “executing a set of machine learning classifiers by the predictive analytics and data integration platform, each of the machine learning classifiers having been trained by: applying predictive analytics to the data repository comprising information for a plurality of second patients and a plurality of insurance payers, and determining trends in payer coverage based on one or more of geographic information, employer information, guarantor information, and age demographics associated with the data repository, applying the determined trends to the information for the first patient, and identifying one or more likely payer candidates as the insurance discovery data for the first patient based on the determined trends”. The limitations of “executing a set of machine learning classifiers by the predictive analytics and data integration platform, each of the machine learning classifiers having been trained by: applying predictive analytics to the data repository comprising information for a plurality of second patients and a plurality of insurance payers, and determining trends in payer coverage based on one or more of geographic information, employer information, guarantor information, and age demographics associated with the data repository” correspond to mathematical relationships, which falls within the “mathematical concepts” of the abstract idea grouping. The limitations of “applying the determined trends to the information for the first patient, and identifying one or more likely payer candidates as the insurance discovery data for the first patient based on the determined trends” correspond to an abstract idea of “certain methods of organizing human activity”.
Dependent claims also correspond to an abstract idea of “certain methods of organizing human activities”, such as claim 12 recites “identifying one or more likely payer candidates comprises identifying a default set of payers based on the determined trends and the information for the first patient”, claim 16 recites “receiving a patient insurance source with the information for the first patient, identifying the received patient insurance source as an incorrect source, and identifying an actual insurance source for the first patient from the one or more likely payer candidates”, claim 17 recites “determining a degree of confidence for the one or more likely payer candidates”, claim 18 recites “providing information for the first patient to at least one of the one or more likely payer candidates, receiving a response indicating active coverage verification for the first patient, and identifying the one or more likely payer candidates as the insurance discovery data for the first patient based on the active coverage verification”, Claim 20 recites “receiving updated and/or supplemented demographic information from the plurality of medical entities for the first patient based on a likelihood of a match between the incomplete demographic information and data from the data repository, converting the updated and/or supplemented demographic information from the standardized data format to the non-standardized format…”. These limitations also correspond to a method of managing interactions between people, such as user following rules and instructions.
Claims 3, 6, 8-22 and 31 are ultimately dependent from claim 1 and include all the limitations of claim 1. Therefore, claims 3, 6, 8-22 and 31 recite the same abstract idea. Claims 3, 6, 8-22 and 31 describe a further limitation regarding the basis for determining insurance coverage for the patient. These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements that are shown in bolded style below:
Claim 1 has been amended to recite a computer-implemented data integration method for data exchange across medical data formats, the method comprising:
receiving, at a predictive analytics and data integration platform, an insurance discovery query comprising information in a non-standardized data format from a medical records and billing interface associated with a first medical entity, the insurance discovery query comprising demographic information for a first patient, wherein the insurance discovery query indicates that at least a portion of insurance information for the first patient is unknown, wherein the predictive analytics and data integration platform is communicatively coupled to a data repository comprising a networked collection of non-transitory storage devices associated with a plurality of medical entities, and wherein the non-standardized data format is one of multiple different non-standardized data formats recognized by the predictive analytics and data integration platform and used across the plurality of medical entities,
converting, by the predictive analytics and data integration platform, the information in the insurance discovery query from the non- standardized data format to a standardized data format that is a common data format recognized by the plurality of medical entities, the standardized data format conforming to a communication standard comprising one of a Health Level 7 (HL7®) format or a Fast Healthcare Interoperability Resources (FHIR®) format,
generating a single sign-on (SSO) interface, using a set of SSO credentials, for accessing the information in the insurance discovery query having been converted to the standardized data format,
providing, by the predictive analytics and data integration platform and via the SSO interface, remote access to the plurality of medical entities to the information in the insurance discovery query having been converted to the standardized data format,
receiving a plurality of patient records based on the information in the insurance discovery query from the plurality of medical entities in the standardized data format,
generating, by the predictive analytics and data integration platform, insurance discovery data for the first patient in the standardized data format, the insurance discovery data comprising insurance information predicted based on the demographic information for the first patient and the plurality of patient records, and
transmitting, by the predictive analytics and data integration platform, the insurance discovery data to the medical records and billing interface associated with the first medical entity to resolve the insurance information for the first patient being unknown,
wherein the receiving of the insurance discovery query, the converting of the information in the insurance discovery query, the providing of the remote access, the generating of the insurance discovery data, and the transmitting of the insurance discovery data are performed in real-time.
Claim 3 has been amended to recite the method of claim 1, wherein the HL7@ format is an HL7@ 2.x format or an HL7@ 3 format.
Claim 6 has been amended to recite the method of claim 1, wherein the non-standardized data format comprises a comma separated values (CSV) format, an extensible markup language format (XML), or a JavaScript® Object Notation (JSON) format.
Claim 8 recites the method of claim 1, comprising receiving the insurance discovery query from and providing the insurance discovery data to the medical records and billing interface via an application programming interface (API).
Claim 9 recites the method of claim 1, comprising receiving the insurance discovery query from and providing the insurance discovery data to the medical records and billing interface via an Substitutable Medical Applications Reusable Technologies (SMART®) information technology integration protocol.
Claim 10 recites the method of claim 1, comprising receiving the insurance discovery query from and providing the insurance discovery data to a graphical user interface at a display associated with the medical records and billing interface.
Claim 11 recites the method of claim 1, wherein the generation of the insurance discovery data comprises:
executing a set of machine learning classifiers by the predictive analytics and data integration platform, each of the machine learning classifiers having been trained by:
applying predictive analytics to the data repository comprising information for a plurality of second patients and a plurality of insurance payers, and
determining trends in payer coverage based on one or more of geographic information, employer information, guarantor information, and age demographics associated with the data repository,
applying the determined trends to the information for the first patient, and
identifying one or more likely payer candidates as the insurance discovery data for the first patient based on the determined trends.
Claim 12 recites the method of claim 11, wherein identifying one or more likely payer candidates comprises identifying a default set of payers based on the determined trends and the information for the first patient.
Claim 13 recites the method of claim 11, wherein the plurality of insurance payers comprises one or more of medical insurance payers or liability insurance payers.
Claim 14 recites the method of claim 13, wherein the medical insurance payers comprise government insurance payers.
Claim 15 recites the method of claim 13, wherein the liability insurance payers comprise one or more of automotive liability insurance, homeowner insurance, worker compensation insurance, or business liability insurance.
Claim 16 recites the method of claim 11, comprising:
receiving a patient insurance source with the demographic information for the first patient, identifying the received patient insurance source as an incorrect source, and identifying an actual insurance source for the first patient from the one or more likely payer candidates.
Claim 17 recites the method of claim 11, comprising determining a degree of confidence for the one or more likely payer candidates.
Claim 18 recites the method of claim 11, comprising providing information for the first patient to at least one of the one or more likely payer candidates, receiving a response indicating active coverage verification for the first patient, and identifying the one or more likely payer candidates as the insurance discovery data for the first patient based on the active coverage verification.
Claim 19 recites the method of claim 18, comprising providing the information for the first patient to the at least one of the one or more likely payer candidates via an application programming interface (API).
Claim 20 has been amended to recite the method of claim 1,
wherein the demographic information for the first patient comprises incomplete demographic information,
the method comprising:
receiving updated and/or supplemented demographic information from the plurality of medical entities for the first patient based on a likelihood of a match between the incomplete demographic information and data from the data repository, and
providing the updated and/or supplemented demographic information to the medical records and billing interface.
Claim 21 recites the method of claim 20, wherein the demographic information comprises data fields including one or more of social security number, name, date of birth, and insurance subscriber number.
Claim 22 recites the method of claim 20, comprising updating, at the medical and billing interface, a record of the incomplete demographic information for the first patient with the updated and/or supplemented demographic information for the first patient.
Claim 31 recites the method of claim 20, wherein the medical records and billing interface is configured to store the updated and/or supplemented demographic information in local records.
These additional elements correspond to hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
The newly added feature of “generating a single sign-on (SSO) interface, using a set of SSO credentials, for accessing the information…” corresponds to a well-understood, routine and conventional activity known in the industry, as evidenced by Blumenthal (US 2019/0304582 A1) (listed in the “Notice of References Cited” of previous office action. In particular, Blumenthal discloses “The second step will be to perform data extraction and normalization, via AutoE 36, i.e. to ensure that data for each (structured) element is in the appropriate format, and the values within proper range, for the input Attributes Form. All normalized data will be checked for compliance with the HL7 FHIR STU 3 standard.” in [0140], “In addition to the security provided by the blockchain data structure, transactions on the blockchain, e.g. updates to the EMR, are registered in near real-time and instantly accessible to all permissioned parties involved with the patient's care.” in [0021]. The well-understood, routine and conventional activities are not sufficient to amount to significantly more than the judicial exception.
Claims also recite other additional limitations beyond abstract idea, including functions such as receiving data from/to a database, providing/transmitting data are insignificant extra-solution activities (see MPEP 2106.05 (g)), which do not provide a practical application for the abstract idea.
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. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic interface to convert data formats and a machine learning model to perform record matching steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
In addition, claims recite “executing a set of machine learning classifiers by the predictive analytics and data integration platform, each of the machine learning classifiers having been trained by: applying predictive analytics to the data repository comprising information for a plurality of second patients and a plurality of insurance payers, and determining trends in payer coverage based on one or more of geographic information, employer information, guarantor information, and age demographics associated with the data repository, applying the determined trends to the information for the first patient, and identifying one or more likely payer candidates as the insurance discovery data for the first patient based on the determined trends” and the current specification describes the machine learning classification as a well-understood, routine and conventional activity, such as [0087] of the current specification recites “The predictive analytics platform 102, for example, may include machine learning classifiers trained using historic data records to identify patterns in the data records of the data universe 104 based upon claims information such as the information supplied in the medical claim information 108, 109. In another example, the predictive analytics platform 102 may derive insights 154 from the data universe 104 through cluster analysis of data records by applying techniques such as, in some examples, centroid-based clustering, density-based clustering, and/or multi-dimensional clustering. The information accessed from the data universe 104 may be arranged in a variety of manners to apply the machine learning analysis and/or cluster analysis such as, in some examples, a convolutional neural network (CNN), deep neural network (DNN), clustering tree, and/or synaptic learning network. The arrangement of data and/or type of learning analysis applied may be based in part upon the type and depth of information accessed, the desired insights to draw from the data, storage limitations, and/or underlying hardware functionality of the predictive analytics platform 102.”.
Accordingly, machine learning classification and classifier trained to generate a likelihood of a matching process is a well-understood, routine and conventional activity known in the industry and claims are directed to mere instruction to apply an exception.
Therefore, claims 1, 3, 6, 8-22 and 31 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 03/10/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Applicant argues that claims provide a technical solution to the technical problem of enabling the real-time transfer of standardized insurance discovery data across different medical entities with the benefit of a single sign-on procedure.
In response, Examiner submits that the additional elements of "providing, by the predictive analytics and data integration platform and in real-time, remote access to the plurality of medical entities to the information in the insurance discovery query having been converted to the standardized data format"; "wherein the predictive analytics and data integration platform is communicatively coupled to a data repository comprising a networked collection of non-transitory storage devices associated with a plurality of medical entities"; and "wherein the non-standardized data format is one of multiple different standardized data formats recognized by the predictive analytics and data integration platform and used across the plurality of medical entities." correspond to additional elements that are directed to mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment. There is no indication of a technological improvement recited in the current claims nor in the current specification. In particular, converting patient data from non-standard form to a standard form corresponds to certain methods of organizing human activity, such as human user can make these conversions using generic computing components.
Additionally, as indicated in the rejection above, the newly added feature of “generating a single sign-on (SSO) interface, using a set of SSO credentials, for accessing the information…” corresponds to a well-understood, routine and conventional activity known in the industry, as evidenced by Blumenthal (US 2019/0304582 A1) (listed in the “Notice of References Cited” of previous office action. In particular, Blumenthal discloses “The second step will be to perform data extraction and normalization, via AutoE 36, i.e. to ensure that data for each (structured) element is in the appropriate format, and the values within proper range, for the input Attributes Form. All normalized data will be checked for compliance with the HL7 FHIR STU 3 standard.” in [0140], “In addition to the security provided by the blockchain data structure, transactions on the blockchain, e.g. updates to the EMR, are registered in near real-time and instantly accessible to all permissioned parties involved with the patient's care.” in [0021]. The well-understood, routine and conventional activities are not sufficient to amount to significantly more than the judicial exception.
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Obeid Mamon can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687