Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Status of Claims
Claims 1-20 have been examined. Claims 1, 8 and 15 have been amended.
Specification
The amendment of specification submitted on 12/11/2025 has been reviewed and entered.
Terminal Disclaimer
The terminal disclaimer filed on 12/11/2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent No. 11,309,079 has been reviewed and is disapproved (The terminal disclaimer identifies the party who is not the applicant (for cases filed on/after 09/06/2012, CFR 1.321 specifies that the applicant can disclaim, and the terminal disclaimer must specify the extent of the applicant’s ownership). See Terminal Disclaimer Review decision dated 12/30/2025.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 16 of U.S. Patent No. 12,046,364. Although the claims at issue are not identical, they are not patentably distinct from each other because both claims recite method of displaying information in a patient setting for, storing, analyzing the collected data.
Claims 1 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 16 of U.S. Patent No. 11,309,079. Although the claims at issue are not identical, they are not patentably distinct from each other because both claims recite method of displaying information in a patient setting for, storing, analyzing the collected data.
Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 14 of U.S. Patent No. 11,309,079. Although the claims at issue are not identical, they are not patentably distinct from each other because both claims recite a dashboard system for displaying, analyzing the collected data.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Moore (US. 20070106754) in view of Ober et al. (US20070185739A1 hereinafter Ober).
With respect to claim 1, Moore teaches a computer-implemented method for providing measurable improvement in patient outcomes, the computer-implemented method comprising:
determining, by a dashboard computer having a processor, a memory, an interface, and a display, a medical recommendation for a patient in a medical care environment, the determining utilizing a combination of a machine learning algorithm, physician gestalt, evidence based guidelines, and clinical care bundles, wherein the physician gestalt is specific to the patient, wherein the evidence based guidelines are specific to the medical care environment, and wherein the clinical care bundles are associated with categories of treatment for the patient (‘754; Para 0325: by disclosure, Moore describes clinical practice guidelines may provide a healthcare institution, its physicians and other healthcare providers with information regarding the appropriate treatment of a wide variety of conditions. Practice guidelines incorporate the best scientific evidence with expert opinion and represent recommendations based on rigorous clinical research and soundly generated professional consensus. Guidelines may also be useful sources of comparative data if the guidelines are explicit and there is good scientific evidence to support the recommendations. For example, there is good evidence to suggest that certain therapies should be administered within the first six hours following a myocardial infarction. This is a rigorously studied guideline and is widely accepted. Syndicated data may be used to disseminate this information to, and within, a healthcare institution, as well as used to collect and disseminate information pertain to the institution's performance and conformance with the guideline. Accrediting institutions, researchers, and other interested parties may, in turn, aggregate this syndicated data across a clinical specialty, geographic region, and so forth to derive norms of care, comparative studies);
displaying, by the dashboard computer, the medical recommendation for the patient on the display (‘754; Para 0336: syndicated evidence-based information, such as medical research, clinical trial findings, case studies, peer-reviewed articles, academic presentations, and the like, may be plotted, displayed, analyzed, or the like and distributed to an RSS-enabled client; Para 1003: a health care environment dashboard may be provided, including a user interface for locating, subscribing to, filtering, and otherwise processing feeds from any of the devices described above. Other devices, such as databases, billing systems, calendars, and the like may also publish feeds that may be monitored using the health care environment dashboard. The dashboard may be user configurable, and may provide user tools for selecting, displaying, and manipulating various feeds within a hospital or other health care context.)
receiving, by the dashboard computer through the interface, an outcome of the medical recommendation for the patient (‘754; Para 0336); and
Ober teaches
updating, by the dashboard computer utilizing the outcome of the medical recommendation for the patient, the machine learning algorithm so as to improve recommendations made using the machine learning algorithm, wherein the recommendations thus improved provide a measurable improvement in patient outcomes. (‘739; Para 0049: by disclosure, Ober describes Dashboard: A display of Process Algorithm decisions and suggested next recommended steps designed to present relevant data and information concerning a given patient's progress in the care process. The dashboard data preferably is derived from data extracts from the Patient Record, and Workflow Engine. The dashboard may be displayed on standard personal computers screens, monitors, hand-held devices, a Web browser, and/or any other interface capable of presenting visual images.; Para 0054: Ober describes Process Algorithm (PA): A CCP system component that contains a preferably expert-derived, condition-specific rules set (one or more rules) that use clinical logic to monitor the status of clinical events (events orders, event begin time, event end time, and the like). Before proceeding to a subsequent event in a CCP, one or more criteria regarding the patient's event status typically must be satisfied. A Process Algorithm preferably uses relevant data extracted from the Patient Record, the Workflow Engine, and the CPM, and such data are analyzed, preferably in real-time, by the PA to produce a recommendation to proceed with a next planned step in the Guideline (or to otherwise recommend the completion of additional steps to satisfy the PA requirements and assure that it is appropriate to proceed with the Guideline as planned).)
It would have been obvious to one of ordinary skill in the art at the time
filing date of claimed invention to modify the system of Moore with the technique of
providing medical care of Ober and the motivation is to provide patient’s dashboard relevant to medical treatment for the patients using machine learning algorithm.
Claims 8 and 15 are rejected as the same reason with claim 1.
With respect to claim 2, the combined art teaches the computer-implemented method according to claim 1, further comprising: in a data repository, accumulating patient data from disparate sources; and analyzing the patient data in the data repository, wherein the analyzing comprises determining a first trend in the patient data based on historical data associated with the patient (‘754; Paras 0191, 0210).
Claims 9 and 16 are rejected as the same reason with claim 2.
With respect to claim 3, the combined art teaches the computer-implemented method according to claim 2, wherein the analyzing further comprises determining a second trend using, in addition to the historical data associated with the patient, historical values obtained from other patient data over time, such that the second trend includes correlated historical data associated with a plurality of patients (‘754; Para 0385).
Claims 10 and 17 are rejected as the same reason with claim 3.
With respect to claim 4, the combined art teaches the computer-implemented method according to claim 1, further comprising: as data associated with the patient is collected from disparate sources, filtering the data associated with the patient based at least on a type of the patient to determine which data or what information derived from the data is to be presented on the display, including determining pertinent positives and pertinent negatives appropriate for the type of the patient, wherein the medical recommendation for the patient is one of the pertinent positives and pertinent negatives that exceeds a threshold associated with the machine learning algorithm (‘754; Para 1037: The information from the sources of information E118A may be disparate information).
Claims 11 and 18 are rejected as the same reason with claim 4.
With respect to claim 5, the combined art teaches the computer-implemented method according to claim 4, further comprising: filtering the evidence based guidelines specific to the medical care environment using the data associated with the patient collected from the disparate sources (‘754; Para 1037).
Claims 12 and 19 are rejected as the same reason with claim 5.
With respect to claim 6, the combined art teaches the computer-implemented method according to claim 4, further comprising: based on the patient's changing location within the medical care environment: generating or discontinuing a location-specific guideline for the type of the patient; and updating the display to reflect the generating or discontinuing the location- specific guideline for the type of the patient (‘754; Para 1043: Information needed to personalize data, i.e., associate the data with a particular patient or group of patients, may be retained at a different location, such as a secure data repository. ).
Claims 13 and 20 are rejected as the same reason with claim 6.
With respect to claim 7, the combined art teaches the computer-implemented method according to claim 4, wherein the disparate sources include a Health Level-7 (HL7) message feed (‘754; Paras 0073, 0136, 0139, 0152, 0210, etc…).
Claim 14 is rejected as the same reason with claim 7.
Response to Arguments
Applicant’s arguments, see Remark, filed 12/11/2025, with respect to claim rejection under 35USC101 have been fully considered and are persuasive. The claim rejection under 35USC101 has been withdrawn.
In the Remark filed 12/11/2025, the Applicant argued that Ober do not explicitly mention "updating, by the dashboard computer utilizing the outcome of the medical recommendation for the patient, the machine learning algorithm so as to improve recommendations made using the machine learning algorithm, wherein the recommendations thus improved provide a measurable improvement in patient outcomes" as expressly recited in claim 1. Indeed, nothing in the cited [0049], [0054] of Ober suggests updating a machine learning algorithm utilizing the outcome of the medical recommendation for the patient. Thus, the cited [0049], [0054] of Ober do not teach the claim limitation at issue. Accordingly, motivation to combine Moore and Ober notwithstanding, at least the finding of "all the claimed elements were known in the prior art" cannot be made. Since at least the finding of "all the claimed elements were known in the prior art" cannot be made against claim 1, the Office Action's rationale cannot be used to support a conclusion that claim 1 would have been obvious to one of ordinary skill in the art.
In response to the Applicant’s argument, the Examiner respectfully gives the broadest reasonable interpretation of the recited claim. In fact, Ober describes Dashboard: A display of Process Algorithm decisions and suggested next recommended steps designed to present relevant data and information concerning a given patient's progress in the care process. The dashboard data preferably is derived from data extracts from the Patient Record, and Workflow Engine. The dashboard may be displayed on standard personal computers screens, monitors, hand-held devices, a Web browser, and/or any other interface capable of presenting visual images (‘739; Para 0049). Ober further describes Process Algorithm (PA): A CCP system component that contains a preferably expert-derived, condition-specific rules set (one or more rules) that use clinical logic to monitor the status of clinical events (events orders, event begin time, event end time, and the like). Before proceeding to a subsequent event in a CCP, one or more criteria regarding the patient's event status typically must be satisfied. A Process Algorithm preferably uses relevant data extracted from the Patient Record, the Workflow Engine, and the CPM, and such data are analyzed, preferably in real-time, by the PA to produce a recommendation to proceed with a next planned step in the Guideline (or to otherwise recommend the completion of additional steps to satisfy the PA requirements and assure that it is appropriate to proceed with the Guideline as planned) (Para 0054).
Given the broadest reasonable interpretation of the recited claim, it is submitted that the Process Algorithm as taught by Ober is in a form of machine learning algorithm as described in the invention.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686