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 Arguments
Applicant’s arguments, see pages 10-13, filed 08/24/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 102 and 35 USC 103 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 Therefore, the rejection has been withdrawn.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 the amendment of 08/24/2026.
Response to Amendment
The amendment of 08/24/2026 has been accepted.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Freytag (US 2025/0014344 A1), and in further view of Wolf (US 2020/0273548 A1).
Consider claims 1, 12, and 20, Freytag discloses a computer system/method for using machine learning to analyze a surgical video and assess performance of a surgeon conducting a surgical procedure (¶6; “a method for giving feedback on a surgery and a corresponding feedback system”), comprising:
[Claim 20: A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor (¶36, 101, 182, FIG. 1), cause a computer to:]
one or more processors (¶36); and
a memory comprising instructions, that when executed (¶36, 182), cause the computer system to:
receive surgical video data including one or more images capturing at least a portion of an ophthalmic surgical procedure from a user device (¶197, 200; “For providing a feedback to a surgeon, who has conducted a surgery, the device 2 may upload video data to the processing device 6 directly or via the data base 4. The video data may… comprise multiple still images, i.e., frames, from the surgery.”);
preprocessing, by the one or more processors, the surgical video data using one or more trained processing machine learning models including one or more of a trained semantic segmentation machine learning model or a trained video editing machine learning model (¶19, 200-207; “step S2, temporal and/or spatial semantic video segmentation, object detection, object tracking and/or anomaly detection may be performed”; “analyzing the video data S2 and/or evaluating the analyzed video data S3 can be carried out using a machine learning algorithm… machine learning algorithms may be implemented for example using neural networks and/or may be implemented as self-learning algorithms so that they can be trained, or fine-tuned, continuously during the analysis and/or evaluation of video data);
process the surgical video data using one or more trained assessment machine learning models to generate one or more assessment metrics (¶202-207; “Evaluating the analyzed video data includes detecting at least one event of interest within the video data and/or deriving at least one score from the at least one event of interest… analyzing the video data S2 and/or evaluating the analyzed video data S3 can be carried out using a machine learning algorithm.”; “step S3, this information can be used for evaluating the analyzed video data, for example for providing any information to a user regarding an assessment of the corresponding surgery”), wherein:
the one or more trained assessment machine learning models include one or more of a trained surgical instrument assessment machine learning model, a trained surgical phase assessment machine learning model, or a trained anterior capsulotomy assessment machine learning model (¶37, 60-73; “it may be beneficial to derive the scores not from the analyzed data, but to directly derive the scores from the video data, e.g., by using a machine learning algorithm.”; ¶77; “Further score values … may be for example, without limitation: maximum length of a surgery phase; minimum length of a surgery phase; consistency of a phase length over a period of time; length of idle phases; focus during the surgery with regard to patient eye; centration of the operational microscope with regard to patient iris during the surgery; number of incision attempts; quality of instrument handling (e.g., estimated tool velocity, lack of unnecessary movements, . . . ); rhexis quality (size of rhexis, circularity of rhexis, number of grabs and releases); number of enters/exits of instruments in the eye; smoothness of instrument movements in the eye (depending on surgery phase); centration of movements in the eye (depending on surgery phase); tilting of the eye when an instrument is inserted during incision and paracentesis; tilting of the tool tip (e.g. not going too deep), analysis of complications (length of complication, phase, . . . ); size, adjustment and consistency of incisions (e.g. with regard bleeding); cleanness of pupil after irrigation/aspiration, capsule polishing; quality of surgical scene preparation (e.g., draping, . . . ), etc.”);
the one or more trained assessment machine learning models are trained using historical ophthalmic surgery data (¶207; “For example, video data, analysis results and/or evaluation results from previous surgeries may be used as training data sets.”); and
the one or more assessment metrics include one or more of a surgical instrument metric (¶77, 206; “number of enters/exits of instruments in the eye”), a surgical phase metric (¶77, 205-206; “an event of interest may be for example a specific surgery phase”; length of phase), or an anterior capsulotomy metric (¶37, 77, 95; capsulorrhexis, incision attempts);
generate a performance assessment of the surgeon based upon at least the one or more assessment metrics (¶78, 206, 208; “After the evaluation, an evaluation result may be output in step S4”; ¶215; “the evaluation result may be used for different purposes, all of them giving the surgeon or user a feedback on a surgery.”); and
provide the performance assessment of the surgeon to the user device (¶208; “After the evaluation, an evaluation result may be output in step S4 and may be for example displayed on the display unit 12.”; ¶198; “display unit 12 may be… user input device, such as a touchpad, and/or may be any kind of user end device, such as a tablet or smartphone.).
In related art, Wolf further supports receive surgical video data including one or more images capturing at least a portion of an ophthalmic surgical procedure from a user device (Wolf ¶419-425);
preprocessing, by the one or more processors, the surgical video data using one or more trained processing machine learning models including (Wolf ¶82-83; “analyzing the image data to obtain a preprocessed image data, and subsequently analyzing the image data and/or the preprocessed image data to obtain the desired outcome”; ¶419-429);
process the surgical video data using one or more trained assessment machine learning models to generate one or more assessment metrics (Wolf ¶425-429; “the image-based information may be derived from the surgical footage using any suitable trained machine-learning model (or other image recognition algorithms) for identifying events, phases of surgical procedures … once identified may be used to populate a surgical report.”), wherein:
the one or more trained assessment machine learning models include one or more of a trained surgical instrument assessment machine learning model, a trained surgical phase assessment machine learning model, or a trained anterior capsulotomy assessment machine learning model (Wolf ¶425-429; “the image-based information may be derived from the surgical footage using any suitable trained machine-learning model (or other image recognition algorithms) for identifying events, phases of surgical procedures, surgical tools, anatomical structures within the surgical footage, and the like, … once identified may be used to populate a surgical report.”);
the one or more trained assessment machine learning models are trained using historical ophthalmic surgery data (Wolf ¶425-429, 436; “a machine learning model may be trained using historical surgical footage including known events that begin the surgical phase.”).
Freytag states, “it may be beneficial to derive the scores not from the analyzed data, but to directly derive the scores from the video data, e.g., by using a machine learning algorithm (Freytag ¶73).” Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the machine learning techniques of Wolf into the surgical video analysis of Freytag to predictably yield the claimed system for using machine learning to analyze a surgical video and assess performance of a surgeon conducting a surgical procedure.
Consider claims 2 and 13, Freytag, as modified by Wolf, discloses the claimed invention wherein the historical ophthalmic surgery data includes one or more images indicating one or more of an instrument presence, an instrument identification (Freytag ¶50), an instrument color, an instrument material (Wolf ¶217, 229), a surgical step identification, a surgical phase identification, a capsulorrhexis identification (Freytag ¶37), a limbus identification, a pupil identification (Freytag ¶38, 77), a purkinje image identification, an anatomical landmark identification (Freytag ¶38), or an anatomical change identification (Freytag ¶203-207).
Consider claim 3, Freytag, as modified by Wolf, discloses the claimed invention wherein the surgical instrument metric includes one or more of instrument ordering, instrument location, or instrument duration (Freytag ¶203-207; Wolf ¶209, 217, 229, 263, 352).
Consider claims 4 and 14, Freytag, as modified by Wolf, discloses the claimed invention wherein:
the surgical instrument metric includes one or more of instrument ordering, instrument location, or instrument duration (Freytag ¶203-207; Wolf ¶209, 217, 229, 263, 352); and
the memory comprises further instructions that, when executed (Freytag ¶179-182), cause the system to:
generate the surgical instrument metric using the trained surgical instrument assessment machine learning model, the trained surgical instrument assessment machine learning model trained using one or more of the historical ophthalmic surgery data or surgical instrument subset data, wherein the surgical instrument subset data includes one or more images indicating one or more of an instrument presence, an instrument identification, an instrument color, or an instrument material (Freytag ¶17-18, 37-38, 62-63, 77, 95, 203-207; Wolf ¶209, 217, 229, 263, 352, 419-429).
Consider claim 5, Freytag, as modified by Wolf, discloses the claimed invention wherein the surgical phase metric includes one or more of a surgical step order or surgical step duration (Freytag ¶77, 130, 141, 147, 203-207; Wolf ¶209, 229, 263, 523).
Consider claims 6 and 15, Freytag, as modified by Wolf, discloses the claimed invention wherein: the surgical phase metric includes one or more of a surgical step order or surgical step duration (Freytag ¶77, 130, 141, 147, 203-207; Wolf ¶209, 229, 263, 523); and
the memory comprises further instructions that, when executed (Freytag ¶179-182), cause the system to:
generate the surgical phase metric using the trained surgical phase assessment machine learning model, the trained surgical phase assessment machine learning model trained using one or more of the historical ophthalmic surgery data or phase subset data, wherein the phase subset data includes one or more images indicating one or more of a surgical step identification or a surgical phase identification (Freytag ¶17-18, 46, 60, 71, 77, 92, 105, 130, 143, 205-207; Wolf ¶419-429, 82-83).
Consider claim 7, Freytag, as modified by Wolf, discloses the claimed invention wherein the anterior capsulotomy metric includes one or more of a capsulorrhexis size, a capsulorrhexis centration, a capsulorrhexis eccentricity, a capsulorrhexis circularity, a capsulorrhexis smoothness, or a fluidity of a rhexis formation (Freytag ¶37, 77, 102; Wolf ¶419-429).
Consider claims 8 and 16, Freytag, as modified by Wolf, discloses the claimed invention wherein:
the anterior capsulotomy metric includes one or more of a capsulorrhexis size, a capsulorrhexis centration, a capsulorrhexis eccentricity, a capsulorrhexis circularity, a capsulorrhexis smoothness, or a fluidity of a rhexis formation (Freytag ¶37, 60-73, 77, 102; Wolf ¶419-429, 656, 660); and
the memory comprising further instructions that, when executed (Freytag ¶179-182), cause the system to:
generate the anterior capsulotomy metric using the trained anterior capsulotomy assessment machine learning model, the trained anterior capsulotomy assessment machine learning model trained using one or more of the historical ophthalmic surgery data or capsulotomy subset data, wherein the capsulotomy subset data includes one or more images indicating one or more of a capsulorrhexis identification, a limbus location, a purkinje image location, an anatomical landmark, or an anatomical change (Freytag ¶17-18, 37-38, 62-63, 73, 77, 92, 95, 102, 203-207; Wolf ¶82-83, 209, 217, 229, 263, 419-429).
Consider claims 9 and 17, Freytag, as modified by Wolf, discloses the claimed invention, the memory comprising further instructions that, when executed, cause the system to:
process the surgical video data using the trained semantic segmentation machine learning model to generate a semantic segmentation subset data including one or more images indicating a semantic segmentation of a capsulorrhexis, the trained semantic segmentation machine learning model trained using historical semantic segmentation data including at least one or more images of a classified capsulorrhexis (Freytag ¶37, 60-73, 77, 82-83, 95, 203-207; Wolf ¶209, 217, 229, 263, 419-429); and
process one or more of the semantic segmentation subset data or the surgical video data using the one or more trained assessment machine learning models to generate one or more assessment metrics (Freytag ¶73, 77, 92, 95, 102, 203-207; Wolfe ¶419-429).
Consider claims 10 and 18, Freytag, as modified by Wolf, discloses the claimed invention wherein the performance assessment includes one or more of a skill level assessment, a phase of surgery duration assessment, a surgical quality assessment, a skill progression assessment, an anterior capsulotomy assessment, a board certification assessment, a credentialing assessment, or a pay-for-performance assessment (Freytag ¶66, 77, 203-207, 216-217; Wolfe ¶167, 400-402, 419-429).
Consider claims 11 and 19, Freytag, as modified by Wolf, discloses the claimed invention, the memory comprising further instructions that, when executed, cause the system to:
process the surgical video data using the trained video editing machine learning model to generate an edited surgical video data (Freytag ¶45-46, 203-207; Wolf ¶419-429), wherein:
the trained video editing machine learning model is trained using historical surgical phase activity data, wherein the historical surgical phase activity data includes one or more images indicating one or more of a paracentesis, a medication injection, a viscoelastic insertion, a main wound, a capsulorrhexis initiation, a capsulorrhexis completion, a hydrodissection, a phacoemulsification, a cortical removal, a lens insertion, a viscoelastic removal, or a wound closure (Freytag ¶37-38, 203-207; Wolfe ¶150, 419-429, 679); and
the edited surgical video data removes one or more images(Freytag ¶45-46; Wolf ¶150, 198-199, 679);
one or more of:
provide the edited surgical video data to the user device (Freytag ¶155); or
process the edited surgical video data using the one or more trained assessment machine learning models to generate the one or more assessment metrics (Freytag ¶92, 140-149, 203-207; Wolf ¶419-429).
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2023/0172684 A1 discloses a video-based surgery analytics and quality/skills assessment system.
Kim (‘Objective assessment of intraoperative technical skill in capsulorhexis using videos of cataract surgery.’)
Yu (‘Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques’, from 05/22/2024 IDS).
Yeh (‘PhacoTrainer: A Multicenter Study of Deep Learning for Activity Recognition in Cataract Surgical Videos’, from 05/22/2024 IDS).
Matton (‘Analysis of Cataract Surgery Instrument Identification Performance of Convolutional and Recurrent Neural Network Ensembles Leveraging BigCat’, from 05/22/2024 IDS).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY HYTREK whose telephone number is (703)756-4562. The examiner can normally be reached M-F 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steve Koziol can be reached at (408)918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY HYTREK/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
