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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/22/2025 has been entered.
Claims 1-2, 4-10, 12-19, 22, 24-29 and 32-58 remain pending in this application.
The objection to the Abstract has been withdrawn in light of the amendment made to the Abstract of the application.
Claim Objections
Claims 1 and 32 are objected to because of the following informalities: In particular, claim1 1 and 32 recite “displaying the dashboard the dashboard on a display screen;” on lines 7 and 8 respectively, and Examiner considers that there is a typographical error and claims should recite “displaying the dashboard ”.
Claim 5 is objected to because of the following informalities: Claim 5 is not ending with a period.
MPEP 608.01(m) recites:
The claim or claims must commence on a separate physical sheet or electronic page and should appear after the detailed description of the invention. Any sheet including a claim or portion of a claim may not contain any other parts of the application or other material. While there is no set statutory form for claims, the present Office practice is to insist that each claim must be the object of a sentence starting with "I (or we) claim," "The invention claimed is" (or the equivalent). If, at the time of allowance, the quoted terminology is not present, it is inserted by the Office of Data Management. Each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations. See Fressola v. Manbeck, 36 USPQ2d 1211 (D.D.C. 1995). Where a claim sets forth a plurality of elements or steps, each element or step of the claim should be separated by a line indentation, 37 CFR 1.75(i).
Appropriate correction is required.
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 1-2, 4-10, 12-19, 22, 24-29 and 32-58 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-2, 4-10, 12-19, 22, 24-29 and 40-58 are drawn to a method which is within the four statutory categories (i.e. process). Claims 32-39 are drawn to a device (system) which is within the four statutory categories (i.e. machine).
Step 2A, Prong 1:
Claims 1 and 32 have been amended to recite:
“retrieving patient-related data from at least one data source;
generating, based on the patient-related data, a dashboard comprising a plurality of panels that present different categories of medical information for a patient, including the retrieved patient-related data;
displaying the dashboard the dashboard on a display screen;
presenting, on the same display screen and without replacing the dashboard, an ordering interface panel for entering order information, wherein the ordering interface panel is displayed simultaneously with at least a portion of the dashboard such that both the ordering interface panel and said portion of the dashboard remain visible and available for user interaction;
populating order information in the ordering interface panel with order information for a medical order;
executing by a rules engine, one or more rules to detect any patterns, errors, and anomalies between the order information and the patient-related medical information displayed on the dashboard, the one or more rules including comparing the order information to at least one of clinical guidelines, local or national coverage determinations, compliance with regulations, preferred practice patterns, prescription medication guidelines, insurance requirements, clinical research study information, clinical decision support information, co-management information, information from a patient portal, information from artificial intelligence engines, and data accuracy;
in response to detected patterns, errors, and anomalies based on the execution of the rules, generating an alert indicating the detected patterns, errors, and anomalies;
displaying the alert on the display screen simultaneously with the dashboard and the ordering interface panel, wherein the ordering interface panel and said portion of the dashboard remain in view and interactive without requiring navigation away from either the dashboard or the ordering interface panel;
finalizing the medical order via the ordering interface panel; and
inputting a finalized order into the at least one data source.”
The limitations of “retrieving patient-related data…;…detect any patterns, error, and anomalies between the order information and the patient-related medical information…comparing the order information to at least one of clinical guidelines, local or national coverage determinations, compliance with regulations, preferred practice patterns, prescription medication guidelines, insurance requirements, clinical research study information, clinical decision support information, co-management information,…;…finalizing the medical order…; inputting the finalized order in at least one data source” correspond to “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 processor does not take the claims out of the methods of organizing human interactions grouping. Thus, the claims recite an abstract idea.
The limitations of “detecting an inconsistency between the order information and the medical information” also correspond to an abstract idea of “mental processes”, since detecting (or determining) an inconsistency covers performance of the limitation in mind (or user using pen and paper) for the recitation of generic computer components (generic processor and memory).
The limitation of “executing by a rules engine one or more rules to detect any patterns, errors and anomalies…” corresponds to mathematical relationships, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
The dependent claims also correspond to “mental process”, such as, claim 50 recites “…detecting a change in patient related data from the first medical practice, from a second medical practice, or from the patient via a patient portal;…”, since detecting (or determining) an inconsistency covers performance of the limitation in mind (or user using pen and paper) for the recitation of generic computer components (generic processor and memory).
The dependent claims also correspond to mathematical relationships, such as, claim 35 recites “the rules engine comprises a natural language processing model trained on at least one of clinical guidelines, clinical decision support guidelines, financial guidelines, clinical data, and clinical documentation interpreting guidelines, clinical data, and clinical documentation, and wherein detecting patterns, errors, and anomalies between the order information and medical information and wherein the rule engine is configured to generate an alert by performing operations comprising: processing interpreted data through an inference engine comparing clinical data and clinical notes to guidelines; identifying commonalities and discrepancies between patient related data and guidelines, plan or order that is inconsistent with at least one of clinical guidelines, clinical decision support guidelines, financial guidelines, clinical data, and clinical documentation interpreting guidelines, clinical data, and clinical documentation; …”, claim 43 recites “…applying natural language processing and rules-based parsing to align guideline content by at least one of condition, treatment, and timing attributes;…”, claim 44 recites “…the computer includes a cognitive engine that performs pattern recognition on structured clinical data and unstructured provider notes to detect inconsistencies in at least one of patient history, examination findings, or orders, flag potential insurance submission issues or authorization mismatches,…”, claim 45 recites “…parsing unstructured clinical documentation using a natural language processing engine trained on structured and unstructured clinical, financial, and regulatory data, wherein the engine identifies potential diagnostic or procedural codes not present in existing structured data and displays discrepancies and recommended codes to a user”, claim 46 recites “…the rules engine comprises a natural language processing model trained on structured and unstructured clinical, financial, and regulatory data, and wherein the model is configured to: parse and codify clinical notes, examination findings, and diagnostic information; apply an inference engine to compare the codified data to at least one of clinical guidelines, insurance rules, and practice patterns; identify discrepancies between proposed orders and patient medical information;…”, claim 47 recites “…the rules engine comprises a natural language processing model trained on at least one of clinical guidelines, clinical decision support guidelines, financial guidelines, clinical data, and clinical documentation interpreting guidelines, clinical data, and clinical documentation; wherein detecting patterns, errors, and anomalies between the order information and medical information and generating an alert comprises: processing interpreted data through an inference engine comparing clinical data and clinical notes to guidelines; identifying commonalities and discrepancies between patient related data and guidelines, plan or order that is inconsistent with at least one of clinical guidelines, clinical decision support guidelines, financial guidelines, clinical data, and clinical documentation interpreting guidelines, clinical data, and clinical documentation;…”, claim 56 recites “…the rules engine utilizes cognitive system enhanced clinical decision support to find at least one of discrepancies in claims data, payments, to alert physicians about inconsistent medical documentation or improper orders, to speed up a process of complying with regulations, or to alert the physician that a plan or order is inconsistent with a preferred practice plan or clinical decision support for a patient”, claim 58 recites “…applying natural language processing and rules-based parsing to align guideline content by at least one of condition, treatment, and timing attributes, and generating a summary outlining similarities and discrepancies among the guidelines in association with the alert”, therefore these limitations fall within the “mathematical concept” grouping of abstract ideas.
The processor recited in the claims is described in the current specification as a generic computing component. In particular, the current specification recites “In some embodiments, the system 30 can include at least one computing device, including one or more processors 32. Some processors 32 can include processors 32 residing in one or more conventional server platforms….” In [0217] and “…Further, in some embodiments, one or more components of the network 39a, 39b can include a number of client devices which can be personal computers 40 including for example desktop computers 40d, laptop computers 40a, 40e, digital assistants and/or personal digital assistants (shown as 40c), cellular phones or mobile phones or smart phones (shown as 40b), or smart watch, pagers, digital tablets, internet appliances, and other processor-based devices…” in [0221].
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.
Claims 2, 4-10, 12-19, 22, 24-29 and 33-58 are ultimately dependent from claims 1, 32 and include all the limitations of claims 1, 32. Therefore, claims 2, 4-10, 12-19, 22, 24-29 and 33-58 recite the same abstract idea. Claims 2, 4-10, 12-19, 22, 24-29 and 33-58 describe a further limitation regarding the basis for detecting inconsistency between the order information and medical information. These are all just further describing the abstract idea recited in claims 1, 32, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements shown below in “bolded” style:
Claim 1. A computer implemented method of creating medical orders, the method comprising:
retrieving patient-related data from at least one data source;
generating, based on the patient-related data, a dashboard comprising a plurality of panels that present different categories of medical information for a patient, including the retrieved patient-related data;
displaying the dashboard the dashboard on a display screen;
presenting, on the same display screen and without replacing the dashboard, an ordering interface panel for entering order information, wherein the ordering interface panel is displayed simultaneously with at least a portion of the dashboard such that both the ordering interface panel and said portion of the dashboard remain visible and available for user interaction;
populating order information in the ordering interface panel with order information for a medical order;
executing by a rules engine, one or more rules to detect any patterns, errors, and anomalies between the order information and the patient-related medical information displayed on the dashboard, the one or more rules including comparing the order information to at least one of clinical guidelines, local or national coverage determinations, compliance with regulations, preferred practice patterns, prescription medication guidelines, insurance requirements, clinical research study information, clinical decision support information, co-management information, information from a patient portal, information from artificial intelligence engines, and data accuracy;
in response to detected patterns, errors, and anomalies based on the execution of the rules, generating an alert indicating the detected patterns, errors, and anomalies;
displaying the alert on the display screen simultaneously with the dashboard and the ordering interface panel, wherein the ordering interface panel and said portion of the dashboard remain in view and interactive without requiring navigation away from either the dashboard or the ordering interface panel;
finalizing the medical order via the ordering interface panel; and
inputting a finalized order into the at least one data source.
Claim 2. The method of claim 1 wherein the alert comprises an alternative order suggestion.
Claim 4. The method of claim 1 wherein the at least one data source includes one or more of an electronic medical record system, and at least one of practice management system and at least one clearing house, prescription medication software, insurance regulations data source, payor data source, patient portal, insurance requirements data source, an insurance verification, referral, preauthorization system, authorizations data source, laboratory system, clinical research system, co-management system, image management system, health information exchange system, picture archiving and communication system, diagnostic equipment, preferred practice patterns data source, clinical decision support system, or artificial intelligence engine.
Claim 5. The method of claim 1 wherein the dashboard shows medical information displayed on a same axis over time to provide a visualization of medical information over time
Claim 6. The method of claim 1 wherein the patient-related data includes at least three of clinical data, examination data, symptoms, medications, diagnostic test, images, injections, procedures, claim information, and authorizations.
Claim 7. The method of claim 1 wherein the alert comprises at least one of highlighting displayed information selected from the group consisting of medical services, clinical data, examination data, symptoms, medicine, diagnostic test, images, injections, procedure, claim information, insurance regulations, and authorizations
Claim 8. The method of claim 1 wherein the alert is generated in response to the order information and medical information that includes data from at least two separate data sources, one of which is an electronic health records system and at least one practice management system, patient portal, comangement system, clinical research system, healthcare information technology system, a health information exchange, picture archiving and communication system, claims-based system, insurance company system, laboratory system, prescription medication software, image management system, practice management system.
Claim 9. The method of claim 1 and further comprising linking to at least four of healthcare information technology system, electronic health records system, a health information exchange, insurance regulations data source, clearinghouse, payor data source, patient portal, comanagement system, clinical research system, picture archiving and communication system, insurance companies system, insurance verification, referral, preauthorization system, laboratory system, prescription medication software system, image management system, practice management system preferred practice patterns data source, clinical decision support system, and artificial intelligence engine.
Claim 10. The method of claim 1 wherein detection of patterns, errors, and anomalies includes detecting an order inconsistent with at least one preferred practice plan, insurance regulations, clinical research study, clinical decision support, compliance regulatory rules, and authorization status.
Claim 12. The method of claim 1 and further comprising displaying at least one past patient related order.
Claim 13. The method of claim 1 wherein detecting patterns, errors, and anomalies includes at least one of checking the order information for compliance with regulations, comparing the order information to options based on preferred practice plans and clinical decision support, comparing the order information to a information in a clinical research system checking the order information for mistakes, checking the order information for contradictory documentation, checking the order information for errors, checking the order information for discrepancies, or checking the order information for compliance insurance regulations, or checking the order information for an authorization, or checking the order information for cost-effective and medically equivalent alternatives evaluating costs and pricing.
Claim 14. The method of claim 1 wherein orders generated by user interaction are displayed onto the dashboard for visualization by the user of the order with medical information over time.
Claim 15. The method of claim 1, wherein at least one instance of the medical information is represented in at least one data field by an icon, indicator, graphical marker, visual cue, or displayed visual representation.
Claim 16. The method of claim 15, wherein the icon, indicator, graphical marker, visual cue, or displayed visual representation visually changes based on a status of underlying information, including improvement, worsening or no change of a problem, disease, symptom, condition, test, outcome or diagnosis, insurance related information, clinical research system, clinical research study, insurance verification, authorizations, claim status or warning.
Claim 17. The method of claim 15, wherein the medical information is represented in the at least one data field includes an icon, indicator, graphical marker, visual cue, or displayed visual representation identifying an existence of underlying patient data.
Claim 18. The method of claim 17, wherein at least one data field includes a link to underlying data when hovered, clicked or interacted with the link to underlying data generates a visual representation of the underlying data.
Claim 19. The method of claim 1 wherein the rules engine is configured to highlight multiple instances of medical information or to provide suggestions to guide interaction by a user with the ordering interface.
Claim 22. The method of claim 1 and further comprising populating orders into at least one electronic health records system, practice management system, patient portal, co-management system, claims-based system, insurance company system, laboratory clinical research system, diagnostic test ordering systems, image management system, and prescription medication software and further scheduling an appointment or appointment reminder for at least one of a medical service, procedure, injection, medication, diagnostic test, image studies, or office visit.
Claim 24. The method of claim 1 wherein the ordering interface is an ordering panel that is generated onto the dashboard.
Claim 25. The method of claim 1 wherein the alert includes information corresponding to at least one of missed appointments, incorrectly scheduled follow up appointments or future appointments, diagnostic test not performed in a certain period of time, an injection or procedure performed or scheduled in an incorrect period of time on a wrong part of a body, or a drug, injection or procedure not authorized, a change inpatient insurance, a change in insurance of clinical guidelines, clinical research study, unfulfilled prescription, and discrepancies in data.
Claim 26. The method of claim 1 wherein the patient related data includes data from a first user from a first medical practice and also includes data from at least one of an additional user from a different medical practice that provides medical service to the patient and user who is the patient.
Claim 27. The method of claim 1 and further comprising: preprocessing the medical information prior to retrieving the medical information; and generating an object optimized for displaying the medical information.
Claim 28. The method of claim 27 wherein the ordering interface is populated with the data by retrieving the medical information from the generated object optimized for displaying the medical information to minimize time required for retrieval of the medical information.
Claim 29. The method of claim 27 and further comprising using proximity request storage to store the object optimized for displaying the data.
Claim 32. A device comprising:
a processor; and
a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising:
retrieving patient-related data from at least one data source;
generating, based on the patient-related data, a dashboard comprising a plurality of panels that present different categories of medical information for a patient, including the retrieved patient-related data;
displaying the dashboard the dashboard on a display screen;
presenting, on the same display screen and without replacing the dashboard, an ordering interface panel for entering order information, wherein the ordering interface panel is displayed simultaneously with at least a portion of the dashboard such that both the ordering interface panel and said portion of the dashboard remain visible and available for user interaction;
populating order information in the ordering interface panel with order information for a medical order;
executing by a rules engine, one or more rules to detect any patterns, errors, and anomalies between the order information and the patient-related medical information displayed on the dashboard, the one or more rules including comparing the order information to at least one of clinical guidelines, local or national coverage determinations, compliance with regulations, preferred practice patterns, prescription medication guidelines, insurance requirements, clinical research study information, clinical decision support information, co-management information, information from a patient portal, information from artificial intelligence engines, and data accuracy;
in response to detected patterns, errors, and anomalies based on the execution of the rules, generating an alert indicating the detected patterns, errors, and anomalies;
displaying the alert on the display screen simultaneously with the dashboard and the ordering interface panel, wherein the ordering interface panel and said portion of the dashboard remain in view and interactive without requiring navigation away from either the dashboard or the ordering interface panel;
finalizing the medical order via the ordering interface panel; and
inputting a finalized order into the at least one data source.
Claim 33. The device of claim 32 wherein the dashboard includes medical information displayed on a same axis over time to provide a visualization of medical information over time.
Claim 34. The device of claim 32 wherein the alert comprises at least one of an alternative order suggestion medically equivalent alternatives evaluating costs and pricing, alternative based on insurance guidelines, alternatives based on authorization, highlighting displayed information selected from the group consisting of medical services, clinical data, examination data, symptoms, medicine, diagnostic test, images, injections, procedure, claim information, insurance regulations and authorizations.
Claim 35. The device of claim 32 wherein the rules engine comprises a natural language processing model trained on at least one of clinical guidelines, clinical decision support guidelines, financial guidelines, clinical data, and clinical documentation interpreting guidelines, clinical data, and clinical documentation, and wherein detecting patterns, errors, and anomalies between the order information and medical information and wherein the rule engine is configured to generate an alert by performing operations comprising: processing interpreted data through an inference engine comparing clinical data and clinical notes to guidelines; identifying commonalities and discrepancies between patient related data and guidelines, plan or order that is inconsistent with at least one of clinical guidelines, clinical decision support guidelines, financial guidelines, clinical data, and clinical documentation interpreting guidelines, clinical data, and clinical documentation; and generating alerts and alternative orders based on the commonalities and discrepancies.
Claim 36. The device of claim 32 wherein the at least one data source includes one or more of an electronic medical record system, and at least one of a practice management system, clearing house, prescription medication software, insurance regulations data source, payor data source, patient portal, insurance requirements data source, an insurance verification, referral, preauthorization system, authorizations data source, laboratory system, clinical research system, comanagement system, image management system, health information exchange system, picture archiving and communication system, diagnostic equipment, preferred practice patterns data source, clinical decision support system, or artificial intelligence engine.
Claim 37. The device of claim 32 wherein the alert is generated in response to the order information and medical information that includes data from at least two separate data sources, one of which is an electronic health records system and at least one practice management system, patient portal, comanagement system, clinical research system, healthcare information technology system, a health information exchange, picture archiving and communication system, claims-based system, insurance company system, laboratory system, prescription medication software, image management system, practice management system.
Claim 38. The device of claim 32 wherein the patient-related data includes data from a first user from a first medical practice and also includes data from at least one of an additional user from a different medical practice that provides medical service to the patient and user who is the patient.
Claim 39. The device of claim 32 wherein orders generated by user interaction are displayed on the dashboard for visualization by the user of the order with medical information.
Claim 40. The method of claim 1 and further comprising displaying orders generated by user interaction on the dashboard for visualization by the user during a current visit or a future visit that the order is an intended order by the user.
Claim 41. The method of claim 1 and further comprising displaying future appointments or scheduled medical services for the patient.
Claim 42 The method of claim 1 wherein the dashboard or the ordering interface is specific to a medical specialty.
Claim 43. The method of claim 1 and further comprising: accessing a plurality of clinical guidelines from disparate sources; applying natural language processing and rules-based parsing to align guideline content by at least one of condition, treatment, and timing attributes; and generating a summary outlining similarities and discrepancies among the guidelines.
Claim 44. The method of claim 1, wherein the computer includes a cognitive engine that performs pattern recognition on structured clinical data and unstructured provider notes to detect inconsistencies in at least one of patient history, examination findings, or orders, flag potential insurance submission issues or authorization mismatches, and generate alert notifications.
Claim 45. The method of claim 1, further comprising parsing unstructured clinical documentation using a natural language processing engine trained on structured and unstructured clinical, financial, and regulatory data, wherein the engine identifies potential diagnostic or procedural codes not present in existing structured data and displays discrepancies and recommended codes to a user.
Claim 46. The method of claim 1, wherein the rules engine comprises a natural language processing model trained on structured and unstructured clinical, financial, and regulatory data, and wherein the model is configured to: parse and codify clinical notes, examination findings, and diagnostic information; apply an inference engine to compare the codified data to at least one of clinical guidelines, insurance rules, and practice patterns; identify discrepancies between proposed orders and patient medical information; and generate at least one of alerts and alternative order suggestions in response to discrepancies.
Claim 47. The method of claim 1 wherein the rules engine comprises a natural language processing model trained on at least one of clinical guidelines, clinical decision support guidelines, financial guidelines, clinical data, and clinical documentation interpreting guidelines, clinical data, and clinical documentation; wherein detecting patterns, errors, and anomalies between the order information and medical information and generating an alert comprises: processing interpreted data through an inference engine comparing clinical data and clinical notes to guidelines; identifying commonalities and discrepancies between patient related data and guidelines, plan or order that is inconsistent with at least one of clinical guidelines, clinical decision support guidelines, financial guidelines, clinical data, and clinical documentation interpreting guidelines, clinical data, and clinical documentation; and generating alerts and alternative orders based on the commonalities and discrepancies.
Claim 48. The method of claim 18 wherein the underlying information accessed includes at least one of PDF document, exam document, medical provider note, plan, diagnostic image, chart structured data, or graphs in the at least one data source.
Claim 49. The method of claim 22 where an appointment or appointment reminder is schedulable via the dashboard.
Claim 50. The method of claim 26 and further comprising: detecting a change in patient related data from the first medical practice, from a second medical practice, or from the patient via a patient portal; and triggering an alert via the rules engine based on the change in patient related data.
Claim 51. The method of claim 26, and further comprising creating, sending, or receiving a message or alert to a user.
Claim 52. The method of claim 26 and further comprising alerting one of the first user or second user to a modification of the medical information by a user.
Claim 53. The method of claim 52 and further comprising setting an alert generation threshold on selected medical information.
Claim 54. The method of claim 53 and further comprising alerting the at least one user in response to a parameter of the selected medical information meeting or passing the threshold.
Claim 55. The method of claim 1 wherein the rules engine is based on artificial intelligence, adaptive learning engine techniques, natural language processing programs or conventional business logic.
Claim 56. The method of claim 1 wherein the rules engine utilizes cognitive system enhanced clinical decision support to find at least one of discrepancies in claims data, payments, to alert physicians about inconsistent medical documentation or improper orders, to speed up a process of complying with regulations, or to alert the physician that a plan or order is inconsistent with a preferred practice plan or clinical decision support for a patient.
Claim 57. The method of claim 1 and further comprising pre-processing the patient-related data prior to retrieving the patient-related data and generating an object optimized for displaying the patient-related data, wherein the ordering interface panel is populated with the order information by retrieving the patient-related data from the object optimized for displaying the patient-related data to minimize time required for retrieval of the patient-related data with the dashboard and ordering interface panel are displayed simultaneously.
Claim 58. The method of claim 1 and further comprising accessing a plurality of clinical guidelines from disparate sources, applying natural language processing and rules-based parsing to align guideline content by at least one of condition, treatment, and timing attributes, and generating a summary outlining similarities and discrepancies among the guidelines in association with the alert.
These additional elements are 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)).
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 processor to perform detecting an inconsistency step amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims recite a rules engine and “the rules engine is based on artificial intelligence, adaptive learning engine techniques, natural language processing programs or conventional business logic”. The current specification recites “Those skilled in the art will appreciate that the inference engine may implement conventional artificial intelligence techniques such as those provided commercially by Watson Health and Truven Health Analytics, Inc. to process received data in connection with data repositories to provide diagnostic feedback and the like.” in [0324]. Accordingly, using rules engine to detect an inconsistency between the order information and medical information process is a well-understood, routine and conventional activity known in the industry and claims are directed to mere instruction to apply an exception.
The claims recite “displaying the alert on the display screen simultaneously with the dashboard and the ordering interface panel, wherein the ordering interface panel and said portion of the dashboard remain in view and interactive without requiring navigation away from either the dashboard or the ordering interface panel” and the feature of displaying a screen simultaneously with the dashboard and interface panel and portion of the dashboard remain in view and interactive without requiring navigation away from either the dashboard or the interface panel correspond to a well-understood, routine and conventional activity known in the filed as evidenced by Henderson et al. (hereinafter Henderson) (US 9582838 B1). In particular, Henderson discloses “…a mini-screen dashboard that can be used with the larger known dashboard methods and systems. The mini-screen dashboard does not replace the monitoring of all status on larger dashboards, such as the physiological conditions and statuses of individual patients. Instead, it uses the same information that are used for the patients being monitored on these larger dashboards and collects the information together in a new and different way so that there can be a very small mini-screen dashboard that stays in the foreground of the computer screens that caregivers and other healthcare clinicians may use for a variety of tasks that may take them away from monitoring one of the larger standard dashboards.” in col. 3, lines 36-51, “…allowing the mini-screen dashboard to always be shown on the foreground of the monitor screen without being obtrusive. This foreground display causes the mini screen dashboard to remain visible on the screen or screens where the system users perform the majority of their job requirements…” in col. 4, lines 46-55, “…when an alert first appears, the system can also add an alert mini screen proximate to the mini-screen dashboard…” in col 7, lines 50-52.
The claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 12/22/2025 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 the amended claims now require that the dashboard and ordering interface panel “remain visible and available for user interaction” and “remain in view and interactive”, which provide a technical improvement.
In response, Examiner submits that the feature of displaying a screen simultaneously with the dashboard and interface panel and portion of the dashboard remain in view and interactive without requiring navigation away from either the dashboard or the interface panel correspond to a well-understood, routine and conventional activity known in the filed as evidenced by Henderson et al. (hereinafter Henderson) (US 9582838 B1). In particular, Henderson discloses “…a mini-screen dashboard that can be used with the larger known dashboard methods and systems. The mini-screen dashboard does not replace the monitoring of all status on larger dashboards, such as the physiological conditions and statuses of individual patients. Instead, it uses the same information that are used for the patients being monitored on these larger dashboards and collects the information together in a new and different way so that there can be a very small mini-screen dashboard that stays in the foreground of the computer screens that caregivers and other healthcare clinicians may use for a variety of tasks that may take them away from monitoring one of the larger standard dashboards.” in col. 3, lines 36-51, “…allowing the mini-screen dashboard to always be shown on the foreground of the monitor screen without being obtrusive. This foreground display causes the mini screen dashboard to remain visible on the screen or screens where the system users perform the majority of their job requirements…” in col. 4, lines 46-55, “…when an alert first appears, the system can also add an alert mini screen proximate to the mini-screen dashboard…” in col 7, lines 50-52.
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
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.
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687