Detailed Notice
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 .
Status of Claims
Claims 21-40 are currently pending.
Claims 21-23, 25-30, 32, 35, and 38-39 are amended.
Claims 1-20 are canceled.
Claims 21-40 are rejected.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 21-40 are directed toward a computer-implemented method (i.e. a process). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 21, 27, and 35 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A computer-implemented method for optimizing a future surgical preoperative plan, the method comprising: training, by a computing device, a machine learning system on aggregated data associated with a plurality of prior preoperative plans, wherein the aggregated data includes at least one revision made to at least one prior preoperative plan of the plurality of prior preoperative plans; determining, using the machine learning system, a preoperative plan for a procedure to be performed on a patient, wherein the preoperative plan is based at least in part on the aggregated data including the at least one revision made to the at least one prior preoperative plan; generating a virtual model representing an application of the preoperative plan on an anatomy of the patient; displaying the virtual model within an electronic display; receiving one or more changes to the preoperative plan; generating, based on the one or more changes to the preoperative plan, an updated virtual model; displaying the updated virtual model within the electronic display: storing the one or more changes to the preoperative plan in the aggregated data: and training, by the computing device, the machine learning system on the aggregated data including the one or more changes to the preoperative plan for use in determining a future preoperative plan”.
The limitations of determining, a preoperative plan for a procedure to be performed on a patient, wherein the preoperative plan is based at least in part on the aggregated data including the at least one revision made to the at least one prior preoperative plan; receiving one or more changes to the preoperative plan; storing the one or more changes to the preoperative plan in the aggregated data: determining a future preoperative plan, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of determining, receiving, storing, and determining, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Additionally, claim 27 recites: “A computer-implemented method for optimizing a future surgical operative plan, the method comprising: determining an operative plan for a procedure to be performed on a patient; generating a virtual model representing an application of the operative plan on an anatomy of the patient; displaying the virtual model within an electronic display, including displaying an augmented reality of the application of the operative plan on the anatomy of the patient; receiving one or more changes to the operative plan; generating, based on the one or more changes to the operative plan, a first updated operative plan and a first updated virtual model representing an application of the first updated operative plan on the anatomy of the patient; displaying the first updated virtual model within the electronic display, including displaying an augmented reality of the application of the first updated operative plan on the anatomy of the patient; receiving intraoperative data generated during the procedure; generating, based on the intraoperative data, a second updated operative plan and a second updated virtual model representing an application of the second updated operative plan on the anatomy of the patient; displaying the second updated virtual model within the electronic display, including displaying an augmented reality of the application of the second updated operative plan on the anatomy of the patient; and training, by a computing device, a machine learning system on aggregated data including the first and second updated operative plans for use in determining a future operative plan”.
The limitations of determining an operative plan for a procedure to be performed on a patient; receiving one or more changes to the operative plan; generating, based on the one or more changes to the operative plan, a first updated operative plan and representing an application of the first updated operative plan on the anatomy of the patient; receiving intraoperative data generated during the procedure; generating, based on the intraoperative data, a second updated operative plan representing an application of the second updated operative plan on the anatomy of the patient; including displaying the first and second updated operative plans for use in determining a future operative plan, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of determining, receiving, generating, receiving, generating, displaying, and determining, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model or machine learning, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Additionally, claim 35 recites: “A computer-implemented method for optimizing a future surgical plan, the method comprising: training, by a computing device, a machine learning system on aggregated data associated with a plurality of prior operative plans, wherein the aggregated data includes at least one change made to at least one prior operative plan of the plurality of operative plans; determining, using the machine learning system, an operative plan for a procedure to be performed on a patient, wherein the operative plan is based at least in part on the aggregated data including the at least one change made to the at least one prior operative plan; executing the operative plan, wherein is at least partially executed by a surgical robot; receiving intraoperative information generated during the procedure, the intraoperative information including information related to movement or control of the surgical robot during execution of the operative plan; storing the intraoperative information in the aggregated data: and training, by the computing device, the machine learning system on the aggregated data including the intraoperative information for use in determining a future operative plan”.
The limitations of determining, an operative plan for a procedure to be performed on a patient, wherein the operative plan is based at least in part on the aggregated data including the at least one change made to the at least one prior operative plan; executing the operative plan; receiving intraoperative information generated during the procedure, the intraoperative information including information during execution of the operative plan; storing the intraoperative information in the aggregated data: determining a future operative plan, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of determining, executing, receiving, storing, and determining, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model or machine learning, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Dependent claims 22-26, 28-34, and 36-40 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 21, 27, and 35. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 21-40 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “computing device”, “machine learning system”, “virtual model”, “electronic display”, “updated virtual model”, and “surgical robot”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0014]-[0015], and [0023], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “training, by a computing device, a machine learning system on aggregated data associated with a plurality of prior preoperative plans, wherein the aggregated data includes at least one revision made to at least one prior preoperative plan of the plurality of prior preoperative plans”, “using the machine learning system”, “generating a virtual model representing an application of the preoperative plan on an anatomy of the patient; displaying the virtual model within an electronic display”, “generating, based on the one or more changes to the preoperative plan, an updated virtual model; displaying the updated virtual model within the electronic display”, “and training, by the computing device, the machine learning system on the aggregated data including the one or more changes to the preoperative plan for use in”, “generating a virtual model representing an application of the operative plan on an anatomy of the patient; displaying the virtual model within an electronic display, including displaying an augmented reality of the application of the operative plan on the anatomy of the patient”, “a first updated virtual model”, “displaying the first updated virtual model within the electronic display, including displaying an augmented reality of the application of the first updated operative plan on the anatomy of the patient”, “and a second updated virtual model”, “displaying the second updated virtual model within the electronic display”, “an augmented reality of the application of the second updated operative plan on the anatomy of the patient; and training, by a computing device, a machine learning system on aggregated data including”, “training, by a computing device, a machine learning system on aggregated data associated with a plurality of prior operative plans, wherein the aggregated data includes at least one change made to at least one prior operative plan of the plurality of operative plans”, “using the machine learning system”, “wherein is at least partially executed by a surgical robot”, “related to movement or control of the surgical robot”, and “training, by the computing device, the machine learning system on the aggregated data including the intraoperative information for use in”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving intraoperative data generated during the procedure”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 22-26, 28-34, and 36-40 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 21, 27, and 35, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 22-26, 28-34, and 36-40 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 21, 27, and 35, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving intraoperative data generated during the procedure”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 21-40 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 11/25/2025 have been fully considered. Regarding the 35 U.S.C. 101 Rejection, Applicant argues any alleged abstract idea is integrated into a practical application. More specifically, Applicant argues the specification presents a technical explanation of an asserted improvement of providing better preoperative plans that can be aggregated and used to create or design future preoperative plans by providing a surgical procedure planning system and method with multiple feedback loops for optimizing future surgical preoperative plans. Applicant also argues the amendments to the claims reflect the improvement. Examiner respectfully disagrees. The amendments of training a machine learning model or virtual model is recited at a high level of generality such that they amount to computer tools and merely apply the abstract idea to the additional elements (see MPEP 2106.05(f) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)”). Additionally, the improvement identified by the Applicant is an improvement to the abstract idea (optimizing future surgical preoperative plans). MPEP 2106.05(a) recites “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. in addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))” and “Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology”. Therefore, the 35 U.S.C. 101 Rejection is maintained.
Regarding the 35 U.S.C. 102 Rejection, Examiner finds the amendments to the claims persuasive in overcoming the Quaid reference. Quaid does not disclose “training, by a computing device, a machine learning system on aggregated data associated with a plurality of prior operative plans, wherein the aggregated data includes at least one change made to at least one prior operative plan of the plurality of operative plans; determining, using the machine learning system, an operative plan for a procedure to be performed on a patient, wherein the operative plan is based at least in part on the aggregated data including the at least one change made to the at least one prior operative plan”, “training, by the computing device, the machine learning system on the aggregated data including the intraoperative information for use in determining a future operative plan”, “displaying the virtual model within an electronic display, including displaying an augmented reality of the application of the operative plan on the anatomy of the patient; receiving one or more changes to the operative plan; generating, based on the one or more changes to the operative plan, a first updated operative plan and a first updated virtual model representing an application of the first updated operative plan on the anatomy of the patient; displaying the first updated virtual model within the electronic display, including displaying an augmented reality of the application of the first updated operative plan on the anatomy of the patient”, and “including displaying an augmented reality of the application of the second updated operative plan on the anatomy of the patient; and training, by a computing device, a machine learning system on aggregated data including the first and second updated operative plans for use in determining a future operative plan” in independent claims 21 and 27. Quaid also does not disclose “displaying the virtual model within an electronic display, including displaying an augmented reality of the application of the operative plan on the anatomy of the patient; receiving one or more changes to the operative plan; generating, based on the one or more changes to the operative plan, a first updated operative plan and a first updated virtual model representing an application of the first updated operative plan on the anatomy of the patient; displaying the first updated virtual model within the electronic display, including displaying an augmented reality of the application of the first updated operative plan on the anatomy of the patient” and “including displaying an augmented reality of the application of the second updated operative plan on the anatomy of the patient; and training, by a computing device, a machine learning system on aggregated data including the first and second updated operative plans for use in determining a future operative plan” in independent claim 35. Therefore, the 35 U.S.C. 102 Rejection is withdrawn.
Regarding the Non-Statutory Double Patenting Rejection, the amendments to the claims are different than the claims of U.S. Patent No. 11,158,415. Therefore, the Non-Statutory Double Patenting Rejection is withdrawn.
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.
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
