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 .
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/02/2026 has been entered.
Status of Claims
This action is in response to applicant arguments filled on 03/02/2026 for application 18/579919.
Claim 1, 11, and 12 have been amended.
Claims 1-20 are currently pending and have been examined.
Detailed Action
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.1
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102 (a)(1)as being anticipated by over Lucas et al. (US 10957433 B2) in view of Min et al. (US 2021/0334961 A1).
In claim 1, a computer-implemented method for supporting data-based clinical decision-making comprising:
Lucas teaches:
a. extracting medical concepts and relations between medical concepts contained in a first structured medical report and/or a first template for such reports, the first template and/or the structured medical report comprising a data structure whose elements representing said medical concepts and relations between the medical concepts, the first structured medical report and/or first template having been created by a first user prior to extraction (col. 1 last Para. And Col. 2 lines 3-15 wherein “determining a first concept from a text of a medical record from an electronic health record system… identifying a match to the first concepts in a first list of concepts…identifying a match to a second concept in a second list of concepts… and providing the second concept as an identifier of the patient’s medical record” is taught);
b. integrating the data elements of the data structure representing the extracted medical concepts and relations between the medical concepts as nodes and edges into a computer-implemented graph database, thereby constructing or expanding the graph database from the first structured medical report and/or first template (Col. 38 lines 14-20 and Fig. 6 wherein “return value may include the graph distance of the path, which provides a qualifier for how many "is a" nodes are in the path between the descendant and the queried code.);
c. weighting the medical concepts and relations between these medical concepts of the graph database according to their relevance (col. 16 lines 30-35 and lines 40-52 wherein “A rule set may be based upon the number of occurrences of the feature, the scaled weights of the features, or other qualitative and quantitative assessments
of features encoded in logic known to those of ordinary skill in the art. In other MLA, features may be organized in a binary tree structure. For example, key features which distinguish between the most classifications may exist as the root of the binary tree and each subsequent branch in the tree until a classification may be awarded based upon reaching a terminal node of the tree”); and
d. based on the weights of the medical concepts and relations between these medical concepts stored in the graph database (Col. 23 lines 1-25 wherein recommendations are presented to a user. The recommendations are based on weights as seen above).
Lucas does not explicitly teach however Min teaches:
inferring and presenting to a second user, via a user interface of a clinical reporting system on a computer in real-time during composition of a second structured medical report and/or a second report template, one or more recommendations for actions the second user may take when composing the second report template and/or the second structured medical reports (Para. 397 wherein “The system re-calculates any measurements derived from the changes contours in real time, or near real time. Also, the changes made in one panel on one view are displayed correspondingly in the other views/panels.” i.e. changes are tracked and shared across different users in real time. Para. 486 teaches wherein treatment recommendations from medical data is taught. See also Para. 919 wherein medical reports can be shared across different suers is taught).
It would have been obvious to one of ordinary skill in the art at the time of filling to combine the clinical concept identification, extraction, and prediction system that utilizes trees and weights as taught in Lucas with the sharing of medical information in real time across different users as taught in MIN. The well-known elements described are merely a combination of old elements, and in combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Lucas teaches the method according to claim 1,further comprising extracting the medical concepts and the relations between the medical concepts, the annotations being based on a medical ontology (col. 45 lines 40-45).
As per claim 3, Lucas teaches the method according to claim 1 wherein the data structure representing medical concepts and relations between these medical concepts comprised in the template and/or the structured medical report is chosen to have a tree structure (fig. 3).
As per claim 4, Lucas teaches the method according to claim 1 wherein extracting further comprises extracting attributes of the medical concepts and/or relations between the medical concepts and/or meta data (col. 12 lines 37-42).
As per claim 5, Lucas teaches the method according to claim 1 wherein weighting the elements of the graph database comprises weighting them according to the frequency of their occurrence within the templates for the structured medical reports and/or the corresponding structured medical reports (Col. 16 lines 30-35).
As per claim 6, Lucas teaches the method according to claim wherein weighting the elements of the graph database comprises weighting them according to one of the modality or type of examination, the template author, the country, region or language of the report or its template, and/or clinical discipline (Col. 21 lines 15-20).
As per claim 7, Lucas teaches the method according to claim 1 wherein weighting the elements of the graph database comprises training the graph database based on user input data using a machine learning algorithm (col. 21 1st para).
As per claim 8, Lucas teaches the method according to any of the preceding claim comprising selecting the weights on which the recommendations are inferred based on the input parameters of the patient (col. 23 lines 1-10 and col. 28 lines 1-10).
As per claim 9, Lucas teaches the method according to claim 1 wherein inferring and presenting to the user one or more recommendations comprises inferring a recommendation for adding, replacing and/or removing one or more report elements in the template for structured medical reports (col. 28 lines 1-10).
As per claim 10, Lucas teaches the method according to claim 1 wherein inferring and presenting to the user one or more recommendations comprises inferring a recommendation for adding, replacing and/or removing one or more report elements in the structured medical report, for including certain further data in the structured medical report, for carrying out certain steps of data or image processing, carrying out further patient- specific or case-specific actions, and/or consulting background information and/or recommendations for actions (col. 28 lines 1-10 and col. 24 lines 10-15).
Claims 11-20 recite substantially similar limitations as seen above and hence are rejected or similar rationale as noted above.
Response to Arguments
The Applicant argues the art rejection. The Applicant states that Lucas extracts medical concepts from unstructured documents and before any generation of structured record. Thus the extraction step of Lucas happens before any structured data is present, not after the creation of the structured data. The Examiner respectfully disagrees. Lucas teaches “other types of records could be used instead of patient records to create a structured set of data from unstructured information.”; i.e. Lucas structures the unstructured data in order to extract structured field values. In order for any data to be extracted/analyzed/linked/weighted, the data has to be structured in some form or another.
The Applicant argues that Lucas does not teach weighting the concepts and relations in the graph based on relevance in a clinical context. The weighting features in Lucas call for a classification model. Lucas feature weights optimize discrimination for AI decisions rather than signifying clinical importance of concepts or relations. The Examiner respectfully disagrees. Lucas teaches “the training date may weight the occurrence of words in medical texts. While the word “beast” may rarely occur in an EMR/EHR, (e.g., patient was mauled by unknown beast), “breast” may occur more frequently (e.g., patient expresses concern re: lump in breast, breast cancer, stage iv breast cancer, patient's breast recovered from surgery, etc.), giving “breast” a much higher probability of occurrence weighting than “beast.” As a result, the preprocessing stage 220 may replace “beast” with “breast,” terminate pre-processing, and indicate that the resulting text is reasonable.” Applicant specification teaches “the concepts and relations in the graph database can be weighted 703 based on the frequency of occurrence of the individual annotated concepts within the templates for the structured medical reports and/or the corresponding structured medical reports created using those templates. For example, it can be counted how many times a concept or relation has been used in a report and/or report template and this number can be compared to the total number of all structured reports and/or report templates used so far for data extraction.”
Applicant other arguments are moot in view of new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAROUN P KANAAN whose telephone number is (571)270-1497. The examiner can normally be reached Monday-Friday 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid 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.
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MAROUN P. KANAAN
Primary Examiner
Art Unit 3687
/MAROUN P KANAAN/Primary Examiner, Art Unit 3687
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