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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/18/2026 has been entered.
Status of Claims
Claims 1, 5, 11-12, 17, 19, 21 have been previously canceled.
Claims 2, 9, and 16 have been amended.
Claims 2-4, 6-10, 13-16, 18, 20, and 22-27 are currently pending.
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.
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) 2-4, 6-10, 13-16, 18, 20, and 22-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Flanagan et al. (US 2012/0215559 A1) in view of Byron et al. (US 2015/0142418 A1).
As per claim 2, a computer-implemented method comprising:
Flanagan teaches:
receiving a free-form text transcription of a clinical encounter (Para. 19-20);
generating engine annotations based on free-form text transcription by applying a natural language understanding (NLU) engine to the free-form text transcription, the engine annotations including medical codes, identified by the NLU engine, as being associated with the clinical encounter (Para. 73 wherein “, the text narrative may be re-formatted prior to fact extraction to add, remove or correct one or more sentence boundaries within the text narrative. In some embodiments, this may involve altering the punctuation in at least one location within the text narrative”. The information includes medical codes as seen in Para. 102);
receiving user annotations derived manually from the free-form text transcription by one or more human users, the user annotations including at least one medical code, identified by the one or more human users, as being associated with the clinical encounter (Para. 55 wherein “, the text narrative may be re-formatted prior to fact extraction to add, remove or correct one or more sentence boundaries within the text narrative. In some embodiments, this may involve altering the punctuation in at least one location within the text narrative”. See also Para. 59 wherein “In some embodiments, as discussed above, a fact review system may allow a user to add, delete and/or modify (collectively referred to as "change") a clinical fact extracted from a free-form narration of a patient encounter provided by a clinician, resulting in a change to the set of extracted facts”); and
Flanagan does not explicitly teach however Byron teaches:
generating training data by merging the engine annotations with the user annotations to obtain merged annotations (Para. 59, 75, and 113 wherein “fact review component 106 may allow the user to specify a location in the text narrative where the generated text should be inserted, or may allow the user to correct the location initially determined automatically. In some embodiments, CLU engine 104 or another suitable component may be used to update the generated text in response to the user's indication of a new location at which to insert it in the text narrative”);
Flanagan does not explicitly teach however Byron teaches:
comparing the engine annotations with the user annotations to identify at least one redundant annotation (Para. 114 wherein a user can correct information that is extracted. Para. 117 teaches training the engine using the extracted information. Para. 123 teaches wherein the training/correcting involves identifying redundant facts in the document);
removing the at least one redundant annotation from the merged annotations to obtain the training data (Para. 123); and
Flanagan further teaches:
training the NLU engine to perform medical coding based on the training data
(Para. 75 and 102).
It would have been obvious to one of ordinary skill in the art at the time of filling to combine the method and apparatus for linking extracted clinical facts as taught in Flanagan with the error correction of a natural language document by finding redundant information as taught in Byron. 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 3, Flanagan teaches the computer-implemented method of claim 2, wherein generating the training data by merging the engine annotations with the user annotations comprises:
comparing an order of annotations between the engine annotations and the user annotations (Para. 46 and 73); and
generating information identifying differences in the order (Para. 46).
As per claim 4, Flanagan teaches the computer-implemented method of claim 2, wherein generating the training data by merging the engine annotations with the user annotations comprises: comparing the engine annotations with the user annotations and removing one or more redundant annotations (Para. 75).
As per claim 6, Flanagan teaches the computer-implemented method of claim 2, wherein the training data includes:
a first engine annotation identified as an error made by the NLU engine from the engine annotations generated (Para. 46 and 73); and
a first user annotation identified as a correction of the error made by the NLU engine (Para. 46 and 73).
As per claim 7, Flanagan teaches the computer-implemented method of claim 2, wherein generating the engine annotations comprises:
generating one or more engine annotations and one or more engine links based on the free-form text transcription by applying the NLU engine to the free-form text transcription, wherein each of the one or more engine links associates one of the one or more engine annotations with a corresponding portion of the free-form text transcription (Para. 76 and 91).
As per claim 8, Flanagan teaches the computer-implemented method of claim 7, wherein the user annotations include at least one of a rejection of an engine annotation, a replacement of an engine annotation, a replacement of an engine link, or a re-ordering of the engine annotations (Para. 104).
As per claim 26, Flanagan teaches the computer-implemented method of claim 2, wherein the at lest one medical code in the user annotations is excluded from the engine annotations (Para. 73-74 and 105).
As per claim 27, Flanagan teaches the computer-implemented method of claim 2, wherein the training data includes a list of medical codes, associated with the free-form text transcription, that includes the medical codes in the engine annotations and the at least one medical code in the user annotations (105-106).
Claims 9, 10, 1316, 18, 20, and 22-25 recite substantially similar limitations as seen above and hence are rejected for similar rationale as noted above.
Response to Arguments
Applicant's arguments with respect to the art rejection above have been considered but are moot in view of the new ground(s) of rejection. It is respectfully submitted that the Examiner has applied new passages and citations to the claims at the present time.
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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