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 .
Response to Amendment
Applicant’s amendment was received on 10/10/25 and has been entered and made of record. Currently, claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed 10/10/25 have been fully considered but they are not persuasive.
The applicant asserts Sadeghi (US 2014/0365239) does not teach determining whether the medical report matches “a predetermined version of the plurality of versions of the protocol”, as recited in claim 1, and similarly in claims 8 and 15. The Examiner respectfully disagrees as Sadeghi does disclose the above-mentioned feature. Specifically, Sadeghi discloses a decision engine 270 may identify one or more guidelines that are applicable to the input medical report and/or verify whether the input medical report complies with one or more guidelines. The decision engine 270 may verify the input medical report against any suitable guideline or combination of guidelines (paras 63-64). Guidelines can be updated over time; such updates are analogous to “versions” (paras 35 and 41). As such, the determination made by decision engine 270 of verifying whether the input medical report complies with one or more guidelines would be to the updated version of the guidelines, thus matching a predetermined version of the plurality of versions of the protocol, the protocol in this instance being the guidelines. Therefore, Sadeghi discloses comparing the external designation to each machine learning system designation and, based on the comparison, determining whether the external designation matches a predetermined version of the plurality of versions of the protocol, as recited in claim 1, and similarly in claims 8 and 15.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Agaian (US 2014/0233826) in view of Sadeghi (US 2014/0365239).
Regarding claims 1, 8, and 15, Agaian discloses a non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic medical images, a system for processing electronic medical images, the system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations, and a computer-implemented method for processing electronic medical images comprising:
receiving one or more digital medical images of at least one pathology specimen, the pathology specimen being associated with a patient (see Fig. 18 and paras 271 and 346, medical images of a pathology specimen are acquired);
receiving an external designation of the one or more digital medical images (see Figs. 19 and 20 and paras 347-348, a pathologist can annotate an image and create a diagnostic report);
providing the one or more digital medical images to a plurality of machine learning systems, each of the plurality of machine learning systems having been trained to analyze medical images using one of a plurality of versions of a protocol (see paras 151-154, a plurality of machine learning algorithms are trained on a specific type of Gleason pattern and an implementer selects the appropriate machine learning algorithm);
determining, by each of the plurality of machine learning systems, a machine learning system designation for the one or more digital medical images (see paras 263-265 and 346-348, a computer-aided diagnosis (CAD) system creates a diagnostic report and annotates images, just as a pathologist does); and
comparing the external designation to the machine learning system designations (see paras 274 and 346-348, an automatic algorithm analyzes the level of agreement between the pathologist report and the CAD report).
Agaian does not disclose expressly determining whether the external designation matches a predetermined version of the plurality of versions of the protocol.
Sadeghi discloses providing the one or more digital medical images to a plurality of machine learning systems, each of the plurality of machine learning systems having been trained to analyze medical images using one of a plurality of versions of a protocol (see paras 35, 41, and 63-64, decision engine 270 may identify one or more guidelines that are applicable to the input medical report and/or verify whether the input medical report complies with one or more guidelines, can be updated over time, such updates are analogous to “versions”) and
comparing the external designation to each machine learning system designation and, based on the comparison, determining whether the external designation matches a predetermined version of the plurality of versions of the protocol (see paras 9, 35, 37, and 63-64, medical reports are analyzed to determine if they are in compliance with current guidelines).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the comparing of designations to determine compliance with a predetermined protocol, as described by Sadeghi, with the system of Agaian.
The suggestion/motivation for doing so would have been to decrease the risk to a patient by ensuring up-to-date guidelines are being followed.
Therefore, it would have been obvious to combine Sadeghi with Agaian to obtain the invention as specified in claims 1, 8, and 15.
Regarding claims 2, 9, and 16, Sadeghi further discloses wherein the predetermined version of the plurality of versions of protocol corresponds to a most recently determined protocol (see paras 41 and 70, guidelines are kept up-to-date).
Regarding claims 3, 10, and 17, Sadeghi further discloses outputting to one or more users that the predetermined version of the plurality of versions of protocol was used in the external designation (see paras 63-64 and 68, decision engine 270 may identify one or more guidelines that are applicable to the input medical report and/or verify whether the input medical report complies with one or more guidelines, the decision engine 270 can also provide alerts to a user).
Regarding claim 4, 11, and 18, Sadeghi further discloses outputting whether the external designation does not match any of the machine learning system designations (see paras 38, 40, 68, and 80-83, any incomplete, missing, or incorrect recommendations may be alerted to a user).
Regarding claims 5, 12, and 19, Sadeghi further discloses upon determining the external designation does not match the predetermined version of the plurality of versions of protocol, outputting the predetermined version of the plurality of versions of protocol to a user or external system (see paras 80-83 and 109, any incomplete, missing, or incorrect recommendations may be alerted to a user).
Regarding claims 6, 13, and 20, Agaian further discloses upon determining the external designation does not match the predetermined version of the plurality of versions of the protocol, outputting the version of the plurality of versions of the protocol in which the machine learning designation most closely matches the external designation (see paras 346-348, a plurality of machine learning algorithms are trained on a specific type of Gleason pattern and an implementer selects the appropriate machine learning algorithm for the Gleason pattern, a final report is output by integrating the pathologists and automatic expert systems to produce a more accurate cancer diagnosis based on the biopsy assessment).
Regarding claims 7 and 14, Sadeghi further discloses
Upon determining a new version of the protocol has been developed (see paras 41, 70, and 86-89, monitoring of new guidelines is performed),
updating the predetermined version of the plurality of versions of the protocol to be the new version of the protocol (see paras 41, 70, and 89, when a new guideline is found, the system is updating with the new guideline); and
training a new machine learning system based on the new version of the protocol (see paras 153, 155, and 165, training of a machine learning model includes information based on the most up-to-date guidelines).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK R MILIA whose telephone number is (571) 272-7408. The examiner can normally be reached Monday-Friday, 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached at 571-270-3438. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK R MILIA/ Primary Examiner, Art Unit 2681