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 .
Notice to Applicant
This communication is in response to application filed 4/4/2025. It is noted that application is a continuation of 17/540,966 filed 12/02/2021 (PAT 12272446) which is a CIP of 17/318,975 filed 05/12/2021 (PAT 11232868). Claims 1-26 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-26 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-31 of U.S. Patent No. 12,272,446 and 1-30 of U.S. Patent No. 11, 232,868. Although the claims at issue are not identical, they are not patentably distinct from each other.
Pending claim 1 is not patentably distinct from issued claim 1 of U.S. Patent No. 12,272,446. Both claims recite:
receiving data generated by at least one sensor forming part of a manually operable handheld surgical instrument;
constructing, by a first machine learning model, a force profile comprising a plurality of force patterns;
extracting a plurality of features from the received data;
determining one or more attributes characterizing surgeon skill using the constructed force profile and extracted features; and
providing data characterizing the determination.
The only differences, if any, are minor variations in claim wording that do not patentably distinguish the claimed invention. The claimed subject matter therefore constitutes no more than an obvious variation of issued claim 1 of U.S. Patent No. 12,272,446.
Dependent claims 2–24 recite additional limitations directed to machine-learning model architectures, preprocessing, filtering, feature extraction, normalization, neural network implementations, sensor configurations, RFID-based model selection, user feedback, cloud training, and secure transmission of extracted features. These limitations correspond directly to, or constitute obvious variations of, the limitations recited in issued claims 2–29 of U.S. Patent No. 12,272,446 and the corresponding claims of U.S. Patent No. 11,232,868. Accordingly, claims 2–24 do not define patentably distinct subject matter.
Independent claim 25 recites a system comprising a manually operable handheld surgical instrument and a computing device configured to:
receive sensor-generated data;
construct a force profile using a first machine learning model;
extract features;
determine surgeon skill using a second machine learning model; and
provide the resulting determination.
Issued claim 30 of U.S. Patent No. 12,272,446 recites substantially the same system and operations. The differences between the pending system claim and the patented system claim, if any, are merely obvious drafting variations and do not define a patentably distinct invention.
Independent claim 26 recites:
receiving a plurality of features generated by or derived from at least one sensor forming part of a surgical instrument;
determining surgeon skill by sequential operation of a first machine learning model followed by a second machine learning model based on the extracted features; and
providing data characterizing the determination.
Issued claim 31 of U.S. Patent No. 12,272,446 recites substantially identical subject matter, including receiving sensor-generated data from a surgical instrument, extracting features, determining surgeon skill by sequential operation of a first machine learning model followed by a second machine learning model, and providing the resulting determination.
To the extent pending claim 26 recites receiving "a plurality of features generated by or derived from" the sensor rather than expressly reciting receiving raw sensor data followed by feature extraction, such variation merely reflects an obvious implementation choice concerning where feature extraction occurs within the processing pipeline. One of ordinary skill in the art would have recognized that receiving pre-extracted features rather than extracting the features after receipt of the sensor data represents a routine design alternative that does not change the underlying machine-learning processing or the resulting characterization of surgeon skill. Accordingly, pending claim 26 defines no patentably distinct invention over issued claim 31 of U.S. Patent No. 12,272,446.
Potentially Allowable Subject Matter
Subject Matter Eligibility (35 U.S.C. §101)
The pending claims are directed to statutory subject matter under 35 U.S.C. §101. The claims are not directed to a mathematical concept, certain methods of organizing human activity, or a mental process that could practically be performed in the human mind.
Even assuming, arguendo, that the recited claims involve a judicial exception, the pending claims integrate any such exception into a practical application. Specifically, the independent claims recite a specific technological process operating on physical sensor data generated by a manually operable handheld surgical instrument. The claims require receiving sensor data from the surgical instrument, constructing, by a first machine learning model, a force profile comprising a plurality of force patterns, extracting features from the received sensor data, determining one or more attributes characterizing surgeon skill using the constructed force profile together with the extracted features, and providing the resulting determination.
The recited machine learning model is not claimed merely as a generic instruction to "apply artificial intelligence." Rather, the claims recite, in light of the Specification paras. [0085] – [0092], a particular application of machine learning to construct an intermediate force profile from physical sensor measurements, which is subsequently utilized together with extracted features to objectively characterize surgeon skill. The claimed processing pipeline constitutes a specific improvement in computerized analysis of sensor-equipped surgical instruments and provides a technical solution to the technical problem of objectively evaluating surgical instrument manipulation during a surgical procedure.
Therefore the recitations of the machine learning models to characterize the skill of a surgeon in the pending claims go beyond a general recitation of artificial intelligence or machine learning and does not amount to an “apply it” recitation and amounts to a practical application. This improvement in the surgical instrument technology integrates the judicial exception into a practical application that will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
Subject Matter free from Prior Art
Fitzsimons (2023/0165649) para. [0078], the closest domestic prior art of record, teaches machine learning techniques may be employed to refine and optimize such values and profiles wherein the training dataset utilized in the machine learning comprises force and trajectory measurements collected during actual or simulated surgeries. Fitzsimons further teaches the machine-learning derived algorithm, the values and profiles may be selected to minimize the difference between the actual and desired forces or to minimize the duration of the task while keeping forces below a threshold.
Farley (US Patent No. 12,127,791), teaches the CASS (computer-assisted surgical system) may provide data such as profile data or historical logs describing use of the system during surgery (Farley; Col. 30, lines 25-28). Farley, Col. 31, lines 15-23, further teaches a machine learning model is trained to predict one or more values based on the input data.
Shelton (2019/0125455) teaches a method of hub communication with surgical instrument systems (Shelton; para. [0624], [1055], [1056], [1079]).
Venkatararaman (2020/0082934) paras. [0012], [0013] teaches applying a first and second machine learning model to data related to a surgical tool.
Lee (KR101840833B1), the closest foreign reference of record, teaches a machine learning wearable robot control apparatus and system. Narayan (Naryan et. Al. “Developing a novel force forecasting technique for early prediction of critical events in robotics.” PLoS ONE 15.5: e0230009. Public Library of Science. May 7, 2020) the closest Non Patent Literature of record teaches developing a novel force forecasting technique for early prediction of critical events in robotics.
However, neither the individual references nor any reasonable combination of the closest prior art teaches or suggests the specific processing architectures recited in independent claims 1, 25, and 26.
With respect to independent claims 1 and 25, the prior art fails to teach or suggest:
receiving data generated by at least one sensor forming part of a manually operable handheld surgical instrument;
constructing, by a first machine learning model using the received sensor data, a force profile comprising a plurality of force patterns;
extracting a plurality of features from the received sensor data;
determining one or more attributes characterizing surgeon skill using the constructed force profile together with the extracted features; and
providing data characterizing the determination.
With respect to independent claim 26, although the prior art teaches machine-learning analysis of surgical data, force sensing, feature extraction, and automated assessment of surgical performance, the prior art fails to teach or reasonably suggest determining surgeon skill by a sequential operation of a first machine learning model followed by a second machine learning model operating on features generated by or derived from one or more sensors forming part of a surgical instrument. The closest references generally employ a single predictive machine-learning model or otherwise do not disclose or suggest the claimed sequential machine-learning architecture for determining surgeon skill from sensor-derived features.
Accordingly, the closest prior art fails to teach or suggest the specific ordered combination of sensor-derived feature acquisition, sequential machine-learning processing, and surgeon skill characterization recited by independent claims 1, 25, and 26. The examiner has not identified any teaching, suggestion, or motivation that would have led one of ordinary skill in the art to modify the cited references to arrive at the claimed inventions without impermissible hindsight.
The dependent claims further recite additional technical limitations directed to particular implementations of the machine-learning architecture, preprocessing, feature generation, model selection, data augmentation, feature normalization, instrument identification, secure model training, and user feedback, none of which are taught or suggested in combination with the allowable subject matter of the independent claims.
Accordingly, claims 1–26 are considered free from prior art.
No final decision on patentability has been made in light of pending rejections.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH GIANG MICHELLE LE whose telephone number is (571)272-8207. The examiner can normally be reached Mon- Fri 8:30am - 5:30pm PST.
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LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/Primary Examiner, Art Unit 3686 7/9/2026
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