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.
Terminal Disclaimer
The terminal disclaimer filed on 05/04/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Patent No. 11,309,079 has been reviewed and is accepted. The terminal disclaimer has been recorded.
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 Martin et al. (US20100057646A1 hereinafter Martin)
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);
Moore does not explicitly disclose machine learning algorithm for medical recommendation, but Martin disclose the recommendation module may use that information to improve the recommendation algorithms used in the future. In this way, the recommendation engine may become more likely to present a user's preferred dashboard(s) (646; Para 0140).
displaying, by the dashboard computer, the medical recommendation for the patient on the display (‘646; Paras 0015 through Para 0032: Figs 2 through19);
receiving, by the dashboard computer through the interface, an outcome of the medical recommendation for the patient (‘646; Para 0162: feedback from more senior doctors, experienced nurses, department leaders, etc. may have a greater effect on the outcome of the analysis than feedback from more junior doctors, less-experienced nurses, etc. ); and
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, including a reduction in mortality rate.(‘646; Para 0163: after analyzing the data, the process uses the statistical information to modify (at 1760) the dashboard selection criteria, normalization operations, rule definition, and/or other system algorithms and/or parameters. For instance, if a number of users prefer a modified dashboard to a default dashboard, the recommendation algorithm may become more likely to present the modified dashboard, or even make the modified dashboard the default. In some embodiments, the updates may be reviewed by a system administrator or other user before being implemented. Some embodiments may apply aspects of machine learning to all users (e.g., when a majority of users prefer a particular dashboard, that dashboard may become more highly recommended). Other embodiments may apply aspects of machine learning only to individual users or patients (e.g., if a particular user never chooses a certain dashboard, that dashboard may become less likely to be recommended to that particular user but may not affect the recommendations offered to other users)
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
intelligent dashboards as taught by Martin and the motivation is to provide patient’s dashboard relevant to medical treatment for the patients using machine learning algorithm.
Moore in view of Martin discloses evidence-based medicine provides a means for getting this data to healthcare providers in order to provide a means for continuous learning and for improving care. However, many healthcare providers may have problems acquiring the skills needed to conduct appropriate searches and review the relevant literature or to consult databases within the context of their daily work. Thus, there is wide variation in the delivery of medical care and the quality of the care delivered. Greater access to clinical information should result in reduced morbidity and mortality within healthcare institutions. Syndication technologies may provide opportunities for providing evidence-based data, and many other types of healthcare data, to healthcare providers in order to assist continually improving healthcare delivery (‘754; Para 0319).
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 05/04/2026, with respect to the rejection(s) of claim(s) 1, 8, 15 under 35USC103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Martin.
Conclusion
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686
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