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 .
Status of Claims
This action is in response to the application filed on 05 June 2025.
Claims 1-20 are currently pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 62/801,257; 62/801,245; 62/864,663; 62/885,673; 62/939,946; and, PCT/US2020/016569 fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. For claims 1 & 14, the prior-filed applications do not provide support for training at least one neural network based on historical outcome data associated with a plurality of instances of a surgical procedure;
applying, by a processor, the at least one neural network to current patient data to generate a predictor equation, wherein the current patient data comprises at least anatomy data for an anatomy of a current patient and wherein the predictor equation functionally relates an implant to an estimated response for the anatomy of the current patient;
optimizing, by the processor, the predictor equation to generate one or more resection parameters for one or more resections required to achieve a position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure; and
updating the at least one neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical plan. Examiner cannot find disclosure for all of these claimed features in the prior filed applications. For claims 2 & 15, the prior-filed applications do not provide support for controlling, by the processor, actuation of a surgical tool to implement a resection of the surgical procedure according to the surgical plan. Examiner cannot find disclosure for this claimed feature in the prior filed applications. For claims 3, the prior-filed applications do not provide support for wherein the surgical procedure is an orthopedic procedure. Examiner cannot find disclosure for this claimed feature in the prior filed applications. For claims 4 & 16, the prior-filed applications do not provide support for wherein the controlling of the actuation by the processor is based on a size, position and orientation of the implant. Examiner cannot find disclosure for this claimed feature in the prior filed applications. For claims 5 & 17, the prior-filed applications do not provide support for wherein the training of the neural network is based on historical case log data sets. Examiner cannot find disclosure for this claimed feature in the prior filed applications. For claims 6 & 18, the prior-filed applications do not provide support for wherein the training of the neural network is based on historical outcome data correlated with one or more of historical patient data, historical implant data, or historical healthcare professional data associated with a plurality of instances of a surgical procedure. Examiner cannot find disclosure for this claimed feature in the prior filed applications. For claims 7 & 19, the prior-filed applications do not provide support for wherein the predictor equation functionally relates a size, position, and orientation of the implant to the estimated response for the anatomy of the current patient. Examiner cannot find disclosure for this claimed feature in the prior filed applications. For claims 8 & 20, the prior-filed applications do not provide support for wherein the optimizing of the predictor equation generates a size, position, and orientation of the implant. Examiner cannot find disclosure for all of these claimed features in the prior filed applications. For claim 9, the prior-filed applications do not provide support for wherein the at least one neural network comprises a plurality of input nodes and downstream nodes coupled by connections having associated weighting values. Examiner cannot find disclosure for all of these claimed features in the prior filed applications. For claim 10, the prior-filed applications do not provide support for wherein each of the weighting values comprises a predictor equation coefficient. Examiner cannot find disclosure for all of these claimed features in the prior filed applications. For claim 11, the prior-filed applications do not provide support for obtaining a sensitivity threshold value; and
applying the sensitivity threshold value to disregard one or more of the input nodes. Examiner cannot find disclosure for all of these claimed features in the prior filed applications. For claim 12, the prior-filed applications do not provide support for providing input data comprising signals that correspond with the input nodes to the neural network as seeding data, wherein the training of the neural network is based on historical case log data sets, and wherein the input data is extracted from the historical case log data sets. Examiner cannot find disclosure for all of these claimed features in the prior filed applications. For claim 13, the prior-filed applications do not provide support for altering the weighting values until the neural network is configured to provide a result that corresponds with the historical outcome data. Examiner cannot find disclosure for all of these claimed features in the prior filed applications.
Accordingly, claims 1-20 are not entitled to the benefit of the prior applications.
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 §§ 706.02(l)(1) - 706.02(l)(3) 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).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US12343085B2. Although the claims at issue are not identical, they are not patentably distinct from each other because recite substantially similar limitations.
This is a nonstatutory double patenting rejection.
The table/chart below exhibits the similarity* between the independent claims where claims 1 & 14 of the current application is a broader variation of the claims of the reference patent.
* Similarities highlighted in BOLD
App# 19/229,152
US12343085B2
Claim 1:
training at least one neural network based on historical outcome data associated with a plurality of instances of a surgical procedure;
applying, by a processor, the at least one neural network to current patient data to generate a predictor equation, wherein the current patient data comprises at least anatomy data for an anatomy of a current patient and wherein the predictor equation functionally relates an implant to an estimated response for the anatomy of the current patient;
optimizing, by the processor, the predictor equation to generate one or more resection parameters for one or more resections required to achieve a position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure; and
updating the at least one neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical plan.
Claim 1:
training at least one neural network based on historical case log data sets comprising historical outcome data correlated with one or more of historical patient data, historical implant data, or historical healthcare professional data associated with a plurality of instances of a surgical procedure;
applying, by a processor, the at least one neural network to current patient data for a current patient to generate a predictor equation, wherein the current patient data comprises at least anatomy data for an anatomy of the current patient, and wherein the predictor equation functionally relates a size, position, and orientation of an implant to an estimated response for the anatomy of the current patient;
optimizing, by the processor, the predictor equation to generate the size, position, and orientation of the implant, and one or more resection parameters for one or more resections required to achieve the position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure;
controlling, by the processor, actuation of a surgical tool to implement a resection of the surgical procedure according to the surgical plan, based on the size, position, and orientation of the implant; and
updating the at least one neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical procedure according to the surgical plan, wherein the surgical procedure is an orthopedic procedure
Claim 14:
train at least one neural network based on historical outcome data associated with a plurality of instances of a surgical procedure;
apply the at least one neural network to current patient data to generate a predictor equation, wherein the current patient data comprises at least anatomy data for an anatomy of the current patient and wherein the predictor equation functionally relates an implant to an estimated response for the anatomy of the current patient;
optimize the predictor equation to generate one or more resection parameters for one or more resections required to achieve a position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure; and
update the at least one neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical plan;
Claim 15:
train a neural network based on an artificial neural network and historical case log data sets comprising historical outcome data correlated with one or more of historical patient data, historical implant data, or historical healthcare professional data associated with a plurality of instances of a surgical procedure, wherein the artificial neural network comprises a plurality of input nodes and downstream nodes coupled by connections having associated weighting values;
apply the neural network to current patient data for a current patient to generate a predictor equation, wherein the current patient data comprises at least anatomy data for an anatomy of the current patient, and wherein the predictor equation functionally relates a size, position, and orientation of an implant to an estimated response for the anatomy of the current patient;
optimize the predictor equation to generate one or more of the size, the position, or the orientation of the implant, and one or more resection parameters for one or more resections required to achieve the position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure;
control actuation of a surgical tool to implement a resection of the surgical procedure according to the surgical plan, based on the size, position, and orientation of the implant; and
update the neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical procedure, wherein the surgical procedure is an orthopedic procedure
The subject matter of the present application amounts to claims which
are broader in scope. It has been held that a generic invention is “anticipated” by an invention within the scope of the generic claims. Dependent claims 2 and 15 are anticipated by claim 1 of US12343085B2 for reciting controlling, by the processor, actuation of a surgical tool to implement a resection of the surgical procedure according to the surgical plan. Dependent claim 3 is anticipated by claim 1 of US12343085B2 for reciting wherein the surgical procedure is an orthopedic procedure. Dependent claims 4 & 16 are anticipated by claim 1 of US12343085B2 for reciting wherein the controlling of the actuation by the processor is based on a size, position and orientation of the implant. Dependent claims 5 & 17 are anticipated by claim 1 of US12343085B2 for reciting wherein the training of the neural network is based on historical case log data sets. Dependent claims 6 & 18 are anticipated by claims 1 of US12343085B2 for reciting wherein the training of the neural network is based on historical outcome data correlated with one or more of historical patient data, historical implant data, or historical healthcare professional data associated with a plurality of instances of a surgical procedure. Dependent claims 7 & 19 are anticipated by claims 1 of US12343085B2 for reciting wherein the predictor equation functionally relates a size, position, and orientation of the implant to the estimated response for the anatomy of the current patient. Dependent claims 8 & 20 are anticipated by claims 1 of US12343085B2 for reciting wherein the optimizing of the predictor equation generates a size, position, and orientation of the implant. Dependent claim 9 is anticipated by claim 1 of US12343085B2 for reciting wherein the at least one neural network comprises a plurality of input nodes and downstream nodes coupled by connections having associated weighting values. Dependent claim 10 is anticipated by claim 3 of US12343085B2 for reciting wherein each of the weighting values comprises a predictor equation coefficient. Dependent claim 11 is anticipated by claim 4 of US12343085B2 for reciting obtaining a sensitivity threshold value; and applying the sensitivity threshold value to disregard one or more of the input nodes. Dependent claim 12 is anticipated by claim 5 of US12343085B2 for reciting providing input data comprising signals that correspond with the input nodes to the neural network as seeding data, wherein the training of the neural network is based on historical case log data sets, and wherein the input data is extracted from the historical case log data sets. Dependent claim 13 is anticipated by claim 5 of US12343085B2 for reciting providing input data comprising signals that correspond with the input nodes to the neural network as seeding data, wherein the training of the neural network is based on historical case log data sets, and wherein the input data is extracted from the historical case log data sets.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claims 1-13) and system (claims 14-20).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
training at least one neural network based on historical outcome data associated with a plurality of instances of a surgical procedure;
applying, by a processor, the at least one neural network to current patient data to generate a predictor equation, wherein the current patient data comprises at least anatomy data for an anatomy of a current patient and wherein the predictor equation functionally relates an implant to an estimated response for the anatomy of the current patient;
optimizing, by the processor, the predictor equation to generate one or more resection parameters for one or more resections required to achieve a position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure; and
updating the at least one neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical plan.
Claim 14 recites similar limitations as claim 1 but for the recitation of generic computer components.
These steps for surgical planning, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity but for recitation of generic computer components. That is nothing in the claim element precludes the italicized portions from managing personal behavior or relationships or interactions between people by organizing the activity around surgical planning for a surgical procedure. This could be analogized to considering historical usage information while inputting data. In addition, the italicized portions containing the recitation of training at least one neural network, generating a predictor equation, and optimizing the predictor equation have been treated as part of the abstract idea, specifically as mathematical calculations, which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. If a claim limitation, under its broadest reasonable interpretation, covers performance as organizing human activity and mathematical calculations but for the recitation of generic computer components, then it falls within the “Methods of Organizing Human Activity” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 1 and 14 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
applying, by a processor, the at least one neural network; and, memory comprising programmed instructions stored thereon for improved surgical planning and one or more processors coupled to the memory and configured to execute the stored programmed instructions amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f);
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical
application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) 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 limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as applying, by a processor, the at least one neural network, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, see Intellectual Ventures I LLC v. Capital One Bank, MPEP 2106.05(f); and, memory comprising programmed instructions stored thereon for improved surgical planning and one or more processors coupled to the memory and configured to execute the stored programmed instructions, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f)
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 generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-13 and 15-20 reciting particular aspects of surgical planning such as
[Claims 2 & 15] controlling, by the processor, actuation of a surgical tool to implement a resection of the surgical procedure according to the surgical plan;
[Claim 3] wherein the surgical procedure is an orthopedic procedure;
[Claims 4 & 16] wherein the controlling of the actuation by the processor is based on a size, position and orientation of the implant;
[Claims 5 & 17] wherein the training of the neural network is based on historical case log data sets;
[Claims 6 & 18] wherein the training of the neural network is based on historical outcome data correlated with one or more of historical patient data, historical implant data, or historical healthcare professional data associated with a plurality of instances of a surgical procedure;
[Claims 7 & 19] wherein the predictor equation functionally relates a size, position, and orientation of the implant to the estimated response for the anatomy of the current patient;
[Claims 8 & 20] wherein the optimizing of the predictor equation generates a size, position, and orientation of the implant;
[Claim 9] wherein the at least one neural network comprises a plurality of input nodes and downstream nodes coupled by connections having associated weighting values;
[Claim 10] wherein each of the weighting values comprises a predictor equation coefficient;
[Claim 11] obtaining a sensitivity threshold value; and
applying the sensitivity threshold value to disregard one or more of the input nodes;
[Claim 12] providing input data comprising signals that correspond with the input nodes to the neural network as seeding data, wherein the training of the neural network is based on historical case log data sets, and wherein the input data is extracted from the historical case log data sets; and,
[Claim 13] altering the weighting values until the neural network is configured to provide a result that corresponds with the historical outcome data;
these italicized portions are methods of organizing human activity but for the recitation of generic computer components since they merely describe types of data and determinations that can be performed by humans. Additionally, the italicized portions containing the recitations of functionally relating a size, position, and orientation of the implant to the estimated response for the anatomy of the current patient; generating a size, position, and orientation of the implant; and, altering the weighting values have been treated as part of the abstract idea, specifically as mathematical calculations, which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance).
Step 2A Prong 2
Dependent claims 2, 9, 11-12, and 15 recites additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claims 2 & 15 (controlling, by the processor, actuation of a surgical tool), claim 9 (wherein the at least one neural network comprises a plurality of input nodes and downstream nodes coupled by connections having associated weighting values), claim 11 (one or more of the input nodes), and claim 12 (the input nodes to the neural network) amount to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)); and, add insignificant extra-solution activity to the abstract idea such as recitation of claim 12 (obtaining a sensitivity threshold value) amounts to mere data gathering since it does not add meaningful limitation to the obtaining action performed, see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 2, 9, 11-12, and 15 recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, see Intellectual Ventures I LLC v. Capital One Bank, MPEP 2106.05(f). Also, see [0253] which provides examples of HMDs, [0298] which provides examples of memory devices, [0307] disclosing examples of various in-theater or mobile devices, and [0314] disclosing examples of various machine learning algorithms. Dependent claim 12 recites additional subject matter which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving the reference image set amounts; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Therefore, in consideration of all the facts, the present invention is not a patent-eligible invention under USC 101. Additionally, it is evident that the present claims monopolize the concept of using machine learning to plan bone resections (regardless of how it's done), restricting further innovation in this area without offering a specific, technical improvement to how the computer actually operates. Using AI tools is generally not enough to transform an abstract idea into patent-eligible subject matter if the core of the invention is still abstract; “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied 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.
Claim(s) 1, 5-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Laster et al. (US20170061375A1) in view of McKinnon et al. (US20130203031A1).
Regarding claim 1, Laster discloses training at least one neural network based on historical outcome data associated with a plurality of instances of a surgical procedure ([0069] “In some implementations, the predictive models may be trained using machine learning algorithms. […] Examples of types of machine learning models that may be used include […] artificial neural networks” [0040] “The predictive model has been trained using data indicating characteristics of other patients and items used in surgeries for the other patients.”)
applying, by a processor, the at least one neural network to current patient data to generate a predictor equation ([0069] “In some implementations, the predictive models may be trained using machine learning algorithms. […] Examples of types of machine learning models that may be used include […] artificial neural networks” [0280] “The processes and logic flows described in this specification may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output.” [0069] “The predictive models 112 may be implemented in any of a variety of forms. For example, the predictive models 112 may be a set of rules, for example, rules, equations, or other expressions determined through regression analysis.”)
wherein the current patient data comprises at least anatomy data for an anatomy of the current patient ([0082] “The surgeon profile may include a variety of other information […] a patient's anatomy is between two sizes of implants.”)
and updating the at least one neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical plan ([0132] “Further, the decision whether to generate or use a customized model may be determined by analysis of predictions made by one model relative to the actual outcomes observed, e.g., the implant components actually used.” [0036] “after the scheduled orthopaedic surgery is completed, receiving data indicating a size of the implant component that was implanted in the particular patient; based on the data indicating the size of the implant component that was implanted in the particular patient, altering parameters of the one or more models to change probabilities indicated by the one or more models for one or more sizes of the implant component”)
Laster does not explicitly disclose however McKinnon teaches and wherein the predictor equation functionally relates an implant to an estimated response for the anatomy of the current patient ([0125] “In the embodiment shown in FIGS. 1 a and b, step 118 utilizes a defined relationship or relationships between the orthopaedic factors and responses to identify an optimal biomechanic size for the orthopaedic implant as well as an optimal biomechanic position and orientation for the implant. These relationships may take a variety of forms.” [0126] “In one instance, the relationship between the orthopaedic factors and responses may be in the form of a series of mathematical equations, one for each orthopaedic response.”)
optimizing, by the processor, the predictor equation to generate one or more resection parameters for one or more resections required to achieve a position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure ([0280] “The processes and logic flows described in this specification may be performed by one or more programmable processors” [0085] “In this example, the method identifies an optimal implant for the patient's particular anatomy and biomechanics as well as an optimal position and orientation (e.g. in six degrees of freedom) for implantation of the implant. In this example, these optimized parameters are output in the form of data reflecting a recommended femoral component, tibial component” [0225] “data generated based on the patient characteristics for the particular patient and the information indicating the one or more characteristics of the surgical plan for the planned surgery for the particular patient”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Laster’s techniques for providing implants for surgery with McKinnon’s techniques for optimizing parameters. The motivation for the combination of Laster and McKinnon is to modify the system of Laster to incorporate a predictor equation that functionally relates an implant; and, optimizing the predictor equation as in McKinnon to optimize the biomechanic and anatomic fit of an orthopaedic implant for the surgical procedure (See McKinnon, Background).
Regarding claim 5, Laster discloses wherein the training of the neural network is based on historical case log data sets ([0069] “In some implementations, the predictive models may be trained using machine learning algorithms. […] Examples of types of machine learning models that may be used include […] artificial neural networks” [0040] “The predictive model has been trained using data indicating characteristics of other patients and items used in surgeries for the other patients.”)
Regarding claim 6, Laster discloses wherein the training of the neural network is based on historical outcome data correlated with one or more of historical patient data, historical implant data, or historical healthcare professional data associated with a plurality of instances of a surgical procedure ([0126] “In some implementations, surgeon preferences may be used to adjust the probability measures, such as the outputs 310 a-310 f of the predictive model 112 or the weighted scores 320. The surgeon preferences may be explicitly indicated by the surgeon or may be inferred from a history of procedures performed by the surgeon.”)
Regarding claim 7, Laster does not explicitly disclose however McKinnon teaches wherein the predictor equation functionally relates a size, position, and orientation of the implant to the estimated response for the anatomy of the current patient ([0125] “In the embodiment shown in FIGS. 1 a and b, step 118 utilizes a defined relationship or relationships between the orthopaedic factors and responses to identify an optimal biomechanic size for the orthopaedic implant as well as an optimal biomechanic position and orientation for the implant. These relationships may take a variety of forms.” [0126] “In one instance, the relationship between the orthopaedic factors and responses may be in the form of a series of mathematical equations, one for each orthopaedic response.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Laster’s techniques for providing implants for surgery with McKinnon’s techniques for optimizing parameters. The motivation for the combination of Laster and McKinnon is to modify the system of Laster to incorporate the predictor equation functionally relating a size, position, and orientation of the implant to the estimated response for the anatomy of the current patient as in McKinnon to optimize the biomechanic and anatomic fit of an orthopaedic implant for the surgical procedure (See McKinnon, Background).
Regarding claim 8, Laster does not explicitly disclose however McKinnon teaches wherein the optimizing of the predictor equation generates a size, position, and orientation of the implant ([0085] “In this example, the method identifies an optimal implant for the patient's particular anatomy and biomechanics as well as an optimal position and orientation (e.g. in six degrees of freedom) for implantation of the implant. In this example, these optimized parameters are output in the form of data reflecting a recommended femoral component, tibial component.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Laster’s techniques for providing implants for surgery with McKinnon’s techniques for optimizing parameters. The motivation for the combination of Laster and McKinnon is to modify the system of Laster to incorporate generating a size, position, and orientation of the implant as in McKinnon to optimize the biomechanic and anatomic fit of an orthopaedic implant for the surgical procedure (See McKinnon, Background).
Regarding claim 9, Laster discloses wherein the at least one neural network comprises […] having associated weighting values ([0142] “As another example, the predictive model 112 may be a machine learning classifier, an artificial neural network […]” [0119] “The predictive model 112 may naturally assign greatest weight to these parameters.”)
Laster does not explicitly disclose however McKinnon teaches […] a plurality of input nodes and downstream nodes coupled by connections […] ([0129] “FIG. 12 schematically illustrates a set of three trained neural networks providing relationships between the factors (inputs to the neural networks) and the responses (outputs of the neural networks) via a series of interlinked nodes.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Laster’s techniques for providing implants for surgery with McKinnon’s techniques for optimizing parameters. The motivation for the combination of Laster and McKinnon is to modify the system of Laster to incorporate a plurality of input nodes and downstream nodes coupled by connections as in McKinnon to optimize the biomechanic and anatomic fit of an orthopaedic implant for the surgical procedure (See McKinnon, Background).
Regarding claim 10, Laster discloses wherein each of the weighting values comprises a predictor equation coefficient ([0124] “In the example, the weighted scores 320 may represent fractional quantities to indicate that there is a relatively low likelihood that the item will be used. For example, a probability of use that is less than 15% may be assigned a weighted score of “0.3,” representing roughly one third of the item.”)
Regarding claim 11, Laster discloses obtaining a sensitivity threshold value; and applying the sensitivity threshold value to disregard […] ([0107] “To select from among various alternatives, the scheduler 110 may apply one or more thresholds so that appropriate items are selected. […] a set of items are selected whose combined likelihood scores meet a threshold”)
Laster does not explicitly disclose however McKinnon teaches […] one or more of the input nodes ([0129] “FIG. 12 schematically illustrates a set of three trained neural networks providing relationships between the factors (inputs to the neural networks) and the responses (outputs of the neural networks) via a series of interlinked nodes.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Laster’s techniques for providing implants for surgery with McKinnon’s techniques for optimizing parameters. The motivation for the combination of Laster and McKinnon is to modify the system of Laster to incorporate one or more of the input nodes as in McKinnon to optimize the biomechanic and anatomic fit of an orthopaedic implant for the surgical procedure (See McKinnon, Background).
Regarding claim 12, Laster discloses providing input data comprising signals that correspond with the input nodes to the neural network as seeding data, wherein the training of the neural network is based on historical case log data sets, and wherein the input data is extracted from the historical case log data sets ([0119] “The degree to which the inputs affect the output of the predictive model 112 may vary based on the data used to generate or train the predictive model 112 […] The parameters that define the predictive model 112 are learned or derived from the training data set, and so the predictive model 112 reflects the various relationships that exist in the training data set.” [0040] “The predictive model has been trained using data indicating characteristics of other patients and items used in surgeries for the other patients.”)
Regarding claim 13, Laster discloses altering the weighting values until the artificial neural network is configured to provide a result that corresponds with the historical outcome data ([0068] “The predictive models 112 can continue to be trained and updated over time.” [0145] “Training data that is generated at a location geographically near the medical facility where the medical procedure was performed can be given more weight than data for procedures performed farther away.”)
Regarding claim 14, Laster discloses memory comprising programmed instructions stored thereon for improved surgical planning and one or more processors coupled to the memory and configured to execute the stored programmed instructions ([0281] “Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.”)
train at least one neural network based on historical outcome data associated with a plurality of instances of a surgical procedure ([0069] “In some implementations, the predictive models may be trained using machine learning algorithms. […] Examples of types of machine learning models that may be used include […] artificial neural networks” [0040] “The predictive model has been trained using data indicating characteristics of other patients and items used in surgeries for the other patients.”)
apply the at least one neural network to current patient data to generate a predictor equation ([0069] “In some implementations, the predictive models may be trained using machine learning algorithms. […] Examples of types of machine learning models that may be used include […] artificial neural networks” [0280] “The processes and logic flows described in this specification may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output.” [0069] “The predictive models 112 may be implemented in any of a variety of forms. For example, the predictive models 112 may be a set of rules, for example, rules, equations, or other expressions determined through regression analysis.”)
wherein the current patient data comprises at least anatomy data for an anatomy of the current patient ([0082] “The surgeon profile may include a variety of other information […] a patient's anatomy is between two sizes of implants.”)
and update the at least one neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical plan ([0132] “Further, the decision whether to generate or use a customized model may be determined by analysis of predictions made by one model relative to the actual outcomes observed, e.g., the implant components actually used.” [0036] “after the scheduled orthopaedic surgery is completed, receiving data indicating a size of the implant component that was implanted in the particular patient; based on the data indicating the size of the implant component that was implanted in the particular patient, altering parameters of the one or more models to change probabilities indicated by the one or more models for one or more sizes of the implant component”)
Laster does not explicitly disclose however McKinnon teaches and wherein the predictor equation functionally relates an implant to an estimated response for the anatomy of the current patient ([0125] “In the embodiment shown in FIGS. 1 a and b, step 118 utilizes a defined relationship or relationships between the orthopaedic factors and responses to identify an optimal biomechanic size for the orthopaedic implant as well as an optimal biomechanic position and orientation for the implant. These relationships may take a variety of forms.” [0126] “In one instance, the relationship between the orthopaedic factors and responses may be in the form of a series of mathematical equations, one for each orthopaedic response.”)
optimize the predictor equation to generate one or more resection parameters for one or more resections required to achieve a position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure ([0280] “The processes and logic flows described in this specification may be performed by one or more programmable processors” [0085] “In this example, the method identifies an optimal implant for the patient's particular anatomy and biomechanics as well as an optimal position and orientation (e.g. in six degrees of freedom) for implantation of the implant. In this example, these optimized parameters are output in the form of data reflecting a recommended femoral component, tibial component” [0225] “data generated based on the patient characteristics for the particular patient and the information indicating the one or more characteristics of the surgical plan for the planned surgery for the particular patient”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Laster’s techniques for providing implants for surgery with McKinnon’s techniques for optimizing parameters. The motivation for the combination of Laster and McKinnon is to modify the system of Laster to incorporate a predictor equation that functionally relates an implant; and, optimizing the predictor equation as in McKinnon to optimize the biomechanic and anatomic fit of an orthopaedic implant for the surgical procedure (See McKinnon, Background).
Regarding claim 17, the limitations are rejected for the same reasons as stated above for claim 5.
Regarding claim 18, the limitations are rejected for the same reasons as stated above for claim 6.
Regarding claim 19, the limitations are rejected for the same reasons as stated above for claim 7.
Regarding claim 20, the limitations are rejected for the same reasons as stated above for claim 8.
Claim(s) 2-4 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Laster et al. (US20170061375A1) in view of McKinnon et al. (US20130203031A1) and further in view of Daley et al. (US20180233222A1).
Regarding claim 2, Laster in view of McKinnon does not explicitly disclose however Daley teaches controlling, by the processor, actuation of a surgical tool to implement a resection of the surgical procedure according to the surgical plan ([0059] “In some examples, the system or method may be used with a robotic surgical system in robotic assisted surgical procedures; […] For example, the system may include a surgical robot including a robotic arm, which may be operated by a surgeon, semi-autonomously, autonomously, or a combination thereof […] the operative plan during the surgical procedure based on intraoperative data collected during the surgical procedure.” [0067] “generate a preoperative plan for a tumor resection procedure.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Laster’s techniques for providing implants for surgery and McKinnon’s techniques for optimizing parameters with Daley’s techniques for surgical procedure planning. The motivation for the combination of Laster, McKinnon, and Daley is to modify the system of Laster and McKinnon to control actuation of a surgical tool as in Daley to execute a personalized workflow for the surgical procedure on the target patient (See Daley, Background).
Regarding claim 3, Laster discloses wherein the surgical procedure is an orthopedic procedure ([0173] “for example, the surgical plan for a patient indicates a left knee replacement is to be performed.”)
Regarding claim 4, Laster does not explicitly disclose however McKinnon teaches wherein the controlling of the actuation by the processor is based on a size, position and orientation of the implant ([0085] “In this example, the method identifies an optimal implant for the patient's particular anatomy and biomechanics as well as an optimal position and orientation (e.g. in six degrees of freedom) for implantation of the implant. In this example, these optimized parameters are output in the form of data reflecting a recommended femoral component, tibial component, and optionally patellar component, and custom cutting guides for implanting the components in the patient's joint.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Laster’s techniques for providing implants for surgery with McKinnon’s techniques for optimizing parameters. The motivation for the combination of Laster and McKinnon is to modify the system of Laster to incorporate on a size, position and orientation of the implant as in McKinnon to optimize the biomechanic and anatomic fit of an orthopaedic implant for the surgical procedure (See McKinnon, Background).
Regarding claim 15, the limitations are rejected for the same reasons as stated above for claim 2.
Regarding claim 16, the limitations are rejected for the same reasons as stated above for claim 4.
Prior Art Cited but Not Relied Upon
Balshi, S. F., Wolfinger, G. J., & Balshi, T. J. (2006). Surgical planning and prosthesis construction using computed tomography, CAD/CAM technology, and the Internet for immediate loading of dental implants. Journal of Esthetic and Restorative Dentistry, 18(6), 312-323.
This reference is relevant because it discloses surgical planning for a prosthesis.
Conclusion
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/WINSTON R FURTADO/Examiner, Art Unit 3687
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