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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/20/2026 has been entered.
Response to Arguments
Rejection Under 101:
Applicant's arguments filed 05/20/2026 have been fully considered. Applicant argues that the amended claims (see Remarks pg. 9) recite an improvement to the technical field of surgical data processing and machine learning reliability. The ML model confirms irregular datasets and updates the master dataset to improve accuracy of predictions (see Remarks pg. 9 for specification paragraphs). Thus, the claims integrate the judicial exception into a practical application, such that the claims are not directed to an abstract idea. In response to Applicant’s argument, the amendment is directed toward the abstract idea. The use of the machine learning model is considered an additional element that invokes the use of a computer to carry out the abstract idea and does not amount to an improvement. See the updated rejection below for further clarification.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 8-12, 20-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-5, 20-22 are drawn to a system, which is within the four statutory categories (i.e. apparatus). Claims 8-12, 23-25 are drawn to a method, which is within the four statutory categories (i.e. process).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, claim 1 (and substantially similar with independent claim 8) recites:
A surgical computing system comprising a processor configured to:
obtain a first set of surgical data associated with a first surgical procedure, wherein the first set of surgical data is generated by a surgical instrument during the first surgical procedure;
obtain a master set of surgical data from a surgical database that is communicatively coupled with the processor, wherein the master set of surgical data comprises verified surgical data associated with historic surgical procedures;
parse the first set of surgical data into a first plurality of sub-groups;
process, using a machine learning model trained on the master set of surgical data, each of the plurality of sub-groups to generate a plurality of corresponding data patterns;
identify a discrepancy among the plurality of corresponding data patterns;
determine that at least a first portion of the first set of surgical data is problematic based on the identified discrepancy;
analyze a sub-group of the first plurality of sub-groups associated with the identified discrepancy to identify one or more datapoints that cause the identified discrepancy;
parse the first set of surgical data into a second plurality of sub-groups;
process, using the machine learning model, each of the second plurality of sub-groups to confirm that the one or more identified datapoints are a cause of the identified discrepancy;
determine a data type associated with the problematic first portion of the first set of surgical data;
generate substitute surgical data based on the first set of surgical data, the master set of surgical data, and the determined data type associated with the problematic first portion of the first set of surgical data;
generate a revised first set of surgical data comprising at least a second portion of the first set of surgical data and the substitute surgical data; and
generate a revised master set of surgical data based on the revised first set of surgical data, wherein the revised master set of surgical data is used in association with training the machine learning model to increase accuracy of the machine learning model in future processing of surgical data.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the processor and computing system, surgical database coupled with the processor, machine learning model, training the machine learning model, the limitations in the context of this claim encompass following rules to process and revise data after the determination of problematic data of a surgical procedure. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-7, 9-12, 20-25 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 1 (and substantially similar with independent claim 8) recites:
A surgical computing system comprising a processor configured to: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
obtain a first set of surgical data associated with a first surgical procedure, wherein the first set of surgical data is generated by a surgical instrument during the first surgical procedure;
obtain a master set of surgical data from a surgical database that is communicatively coupled with the processor (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), wherein the master set of surgical data comprises verified surgical data associated with historic surgical procedures;
parse the first set of surgical data into a first plurality of sub-groups;
process, using a machine learning model trained on the master set of surgical data (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), each of the plurality of sub-groups to generate a plurality of corresponding data patterns;
identify a discrepancy among the plurality of corresponding data patterns;
determine that at least a first portion of the first set of surgical data is problematic based on the identified discrepancy;
analyze a sub-group of the first plurality of sub-groups associated with the identified discrepancy to identify one or more datapoints that cause the identified discrepancy;
parse the first set of surgical data into a second plurality of sub-groups;
process, using the machine learning model (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), each of the second plurality of sub-groups to confirm that the one or more identified datapoints are a cause of the identified discrepancy;
determine a data type associated with the problematic first portion of the first set of surgical data;
generate substitute surgical data based on the first set of surgical data, the master set of surgical data, and the determined data type associated with the problematic first portion of the first set of surgical data;
generate a revised first set of surgical data comprising at least a second portion of the first set of surgical data and the substitute surgical data; and
generate a revised master set of surgical data based on the revised first set of surgical data, wherein the revised master set of surgical data is used in association with training the machine learning model (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) to increase accuracy of the machine learning model (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) in future processing of surgical data.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of processor and computing system, surgical database coupled with the processor, machine learning model, training the machine learning model, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0009], [0058], [0061]-[0062], [0134], [0161]-[0162], [00177], see MPEP 2106.05(f))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim 2, 9 recites the problematic data comprises incomplete or irregular data, which amounts to furthering the abstract idea; claim 3, 10 recites inserting the revised set to the master set, which amounts to furthering the abstract idea; claim 4, 11 recites determining the master set needs to be revised based on the revised first set and generating a revised master set, which amounts to furthering the abstract idea; claim 5, 12 recites using a verification set of data to make sure the revised set is valid, which amounts to furthering the abstract idea; claim 20, 23 recites obtaining data from a second procedure, determining problematic data, and combining the non-problematic portion of data, which furthers the abstract idea; claim 21, 24 recites the second set of data is associated with a related outcome or procedure constraint, which furthers the abstract idea; claim 22, 25 recites the portion of the first set of surgical data is associated with privacy classification and having a privacy threshold for the substitute data, which furthers the abstract idea; and claims 2-7, 9-12, 20-25 additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using processor and computing system, surgical database coupled with the processor, machine learning model, training the machine learning model, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0009], [0058], [0061]-[0062], [0134], [0161]-[0162], [00177]); using a processor and computing system, database coupled with the processor, machine learning model, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to furthering the abstract idea and generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
Subject Matter Free of Prior Art
Claims 1-5, 8-12, 20-25 are free of prior art over Bishop et al. (US 2022/0286687) in view of Shelton, IV et al. (US 2019/0205441). The prior art references, or reasonable combination thereof, could not be found to disclose, or suggest all of the limitations found in the independent claims. The closest prior art is Bishop et al. (US 2022/0286687), which teaches a surgical robotic system for reducing a data volume based on the identifying features of an event in the surgical procedure. Shelton, IV et al. (US 2019/0205441) teaches a medical hub for communicating and validating data during medical procedures. The references taken solely, or in combination, fail to provide the required limitations of the amended claims, and modification of any complementary combination of the references of record would be impermissible hindsight and not provide any advantages over their present application. The dependent claims are also free of prior art due to their corresponding dependency from the independent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA R COVINGTON whose telephone number is (303)297-4604. The examiner can normally be reached Monday - Friday, 10 - 5 MT.
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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