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 the Claims
The status of the claims as of the response filed 7/23/2025 is as follows: Claims 1-26 are cancelled, and all previously given rejections for these claims are considered moot. Claims 27-46 are newly pending in the application and have been considered below.
Response to Arguments
Rejection Under 35 USC 103
On pages 10-12 of the response filed 7/23/2025 Applicant argues that Farley “fails to disclose whether the resection equipment has been programmed to cut only along a fixed plane, and whether the resection equipment is capable of being tracked independent of the end effector as claim 27 requires.” Applicant further alleges that because Fig. 1 shows a human user holding resection equipment 105B, there is a strong implication “that the surgical plan does not even include a cut plane.” Applicant’s arguments are fully considered, but are not persuasive. Examiner maintains that Farley sufficiently discloses this limitation of claim 27. Paras. [0101], [0138], & [0141]-[0142] describe how the surgical system is trained using actual surgical activity data that captures deviations of resection equipment location from the initial plan. Para. [0069] further notes that the surgical instrument utilized by the system can be a sagittal saw, while para. [0137] specifically notes that the surgical plan can include generation of “resection planes,” i.e. surgical saw cutting planes. Paras. [0072]-[0074] further describe how the robotic arm can operate in a mode that allows the end effector to be moved with varying degrees of freedom while providing resistance to or restricting movement in certain directions; when taken together, all of these disclosures show that the system may track positional deviations from a planned sagittal surgical saw cutting position such as a generated resection plane as in [0137], which is considered to include a fixed plane along which the saw can cut in any direction as constrained by an appropriate number of degrees of freedom as in [0072]-[0074]. Examiner notes that there is no specific definition in the claims or specification of what cutting in any direction “independent of the end effector” means, nor is there any positively recited limitation for tracking the resection equipment independent of the end effector as Applicant appears to assert; accordingly, the use of a sagittal saw attachment via a robotic arm constrained by various degrees of freedom as described above is considered to meet this claim language because the saw is not the same as (i.e. is “independent of”) the cutting plane.
On page 12 of the response Applicant argues that “Farley does not disclose using such post-operative feedback data for training a neural network model.” Applicant’s arguments are fully considered, but are not persuasive. First, Examiner notes that claim 27 does not include training of a neural network model, merely training of a machine learning model. Regardless, Examiner maintains that Farley does adequately meet the contested claim language. Paras. [0082], [0135]-[0142], & [0179] describe the use of historical surgical data collected and stored after completion of a procedure (i.e. post-operative data) and including deviations from the recommended surgical plan as in [0139] (which can include planned resection planes as in [0137], and thus deviations of resection equipment location as in [0101] & [0141] relative to the planned resection planes) to train and continuously update machine learning models, including neural networks (as in [0135] & [0142]), predictor equations, transfer functions, etc.
On page 12 of the response Applicant argues that none of the cited prior art discloses “first and second arms that define a cutting plane.” Applicant’s arguments are fully considered, but are not persuasive. Examiner submits that Lavallee discloses this feature in at least Fig. 8, [0118], & [0124], where a first arm 24b and a second arm 24c attach to a surgical tool 2 (e.g. a sagittal saw as in [0087] & [0161]) and operate to allow for rotation (i.e. pivoting) about the rotatable connections 25a and 25b while constraining the movement of the tool to a range of movement within a fixed cutting plane, i.e. a plane defined by the first and second arms.
Claim Objections
Claims 36-37 are objected to because of the following informalities: Claim 36 introduces “a neural network component” in line 3 that is then referred to alternately as “the neural network model” in line 7 and “the neural network component” in line 8. Applicant is advised to standardize the nomenclature of this feature, by changing all three instances to be either a “neural network component” or “neural network model” for consistency. Claim 37 is also objected to on this basis because it inherits the objectionable language due to its dependence on claim 36. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a feedback training component configured to adapt weights and/or firing thresholds… in claim 37.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. In the instant case, para. [0217] notes that blocks of the block diagrams (e.g. including feedback training component 1228 in Figs. 12-13) are implemented by computer program instructions that are performed by one or more computer circuits, and this component of claim 37 will be interpreted accordingly.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 27-40 and 46 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 27 recites the limitation "the end effector" in line 10. There is insufficient antecedent basis for this limitation because there is no previous introduction of an end effector, rendering the claim indefinite. For purposes of examination, “the end effector” will be interpreted as “an end effector” newly introduced by the claim. Claims 28-40 are also rejected on this basis because they inherit the indefinite language due to their dependence on claim 27.
Claim 46 recites the limitation "the neural network model" in line 2. There is insufficient antecedent basis for this limitation because there is no previous introduction of a neural network model, merely a machine learning model in parent claim 41. For purposes of examination, “the neural network model” will be interpreted as “the machine learning model” introduced by parent claim 41.
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 27-30 and 32-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 27-40 are directed to a system (i.e. a machine), and 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 claim 27 recites steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. Specifically, claim 27 recites:
A surgical guidance system for computer assisted navigation during surgery, the surgical guidance system including a computer processor configured to:
receive post-operative feedback data from a network computer regarding surgical outcomes for a plurality of patients, the post-operative data including:
data regarding the extent of following rehabilitation exercises for each of a plurality of patients;
data indicating deviation of a sagittal surgical saw cutting plane measured during surgery from the sagittal surgical saw cutting plane defined by a surgical plan, the cutting plane being a fixed plane along which the sagittal surgical saw is configured to cut in any direction in the plane independent of the end effector;
data indicating deviation of surgical saw motion measurements during surgery from the sagittal surgical saw motion defined by the surgical plan;
train a machine learning model based on the post-operative feedback data;
receive pre-operative data from the network computer characterizing a defined patient;
generate a surgical plan for the defined patient based on processing the pre-operative data through the trained machine learning model; and
provide the surgical plan to a display device for review by a user.
But for the recitation of generic computer components like a computer processor, a network computer, and a display device, the italicized functions, when considered as a whole, describe a surgical evaluation and planning operation that could be achieved by a human actor such as a clinician or other medical professional managing their personal behavior and/or interactions with others. For example, a clinician could receive post-operative feedback data about patients adhering to rehab exercises and deviations of a sagittal surgical saw cutting plane and motion metrics from a plan during surgery, e.g. by directly communicating with patients and surgeons. The clinician could then train or fit a model using the post-operative feedback data, receive pre-operative data characterizing a new patient (e.g. by directly communicating with the new patient or a clinician of the new patient), and use the trained/fitted model to generate a surgical plan for the new patient by inputting the received pre-operative data. The clinician could then provide the generated surgical plan for review by another user, e.g. by writing up a report or otherwise visually representing the plan for a colleague or other user. Accordingly, claim 27 recites an abstract idea in the form of a certain method of organizing human activity.
Dependent claims 28-40 inherit the limitations that recite an abstract idea from their dependence on claim 27, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 28-29, 32-35, and 38-40 recite additional limitations that further describe the abstract idea identified in the independent claims. Specifically, claims 28-29 describe additional data types that are identified in a generated surgical plan, each of which are data types that a clinician would be capable of processing pre-operative data to select. Claims 32-34 describe additional post-operative feedback data types that are used to train the model, each of which are types of data that a clinician would be capable of evaluating and using to fit a model. Claim 35 recites forming subsets of the post-operative feedback data having similarities that satisfy a defined rule, identifying correlations among at least some values of the subsets, and train the model based on the correlations, which are data processing and grouping steps that clinician would be capable of performing when generating training data for a model. Claims 38-40 describe types of pre-operative data that are analyzed by the model to generate the surgical plan, each of which are types of data that a clinician would be capable of evaluating and making surgical plan decisions about.
However, recitation of an abstract idea is not the end of the 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
The judicial exception is not integrated into a practical application. In particular, independent claim 27 does not include additional elements that integrate the abstract idea into a practical application. The additional elements of claim 27 include a computer processor, a network computer, specifying that the model is a machine learning model, and a display device. These additional elements, when considered in the context of each claim as a whole, merely serve to automate operations that could occur by and among human actors (as described above), and thus amount to instructions to “apply” the abstract idea using generic computer components (see MPEP 2106.05(f)). For example, a patient and one or more clinicians can communicate to share data about post-operative rehab adherence and sagittal surgical saw cutting plan and motion deviations as well as new surgical plans generated by a clinician, and use of a computer processor, a network computer, and a display device as tools with which to achieve these otherwise-abstract data sharing functions digitally does not provide integration into a practical application. Similarly, the training and use of a machine learning model invokes high-level machine learning as a tool with which to automate and/or digitize the otherwise-abstract functions of fitting and using a mathematical or predictive model to assist in generating surgical plans, such that this element also does not provide integration into a practical application. Accordingly, claim 27 as a whole is directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 28-30 and 32-40 is also not integrated into a practical application under a similar analysis as above. Claims 28-29, 32-35, and 38-40 are performed with the same additional elements introduced in the independent claim, without introducing any new additional elements of their own, and accordingly also amount to mere instructions to apply the abstract idea using these same additional elements. Claim 30 recites providing data indicating poses of resection plants to a computer platform that generates graphical representations of the poses of the resection planes displayed through the display device within an extended reality headset as an overlay on the defined patient, which amounts to insignificant extra-solution activity in the form of outputting data to a display (see MPEP 2106.05(g)) and thus does not provide integration into a practical application. Claims 36 and 37 specify that the machine learning model is a neural network with a layer- and node-based architecture with adaptable weights and/or firing thresholds between the nodes, which again merely describes a known type of machine learning model at a high level of generality (e.g. by describing what the architecture of a typical neural network is) such that it amounts to mere instructions to “apply” the abstract idea in a manner that digitizes and/or automates the otherwise-abstract surgical plan generation functions with this known type of computerized component.
Examiner notes that claim 31 does integrate the recited abstract idea into a practical application by providing data indicating the poses of the resection planes to at least one controller of a surgical robot to control a sequence of movements of a surgical saw attached to an arm of the surgical robot so a cutting plane of the surgical saw becomes sequentially aligned with the poses of the resection planes. Such operations positively control the movements of a surgical robot to align with the generated surgical plan data, thereby integrating the abstract idea (i.e. receiving and analyzing data to determine a surgical plan) into a practical application (i.e. physically controlling operation of a surgical robot in accordance with the determined plan).
Accordingly, the additional elements of claims 27-30 and 32-40 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 27-30 and 32-40 are directed to an abstract idea.
Step 2B
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 integration of the abstract idea into a practical application, the additional elements of a computer processor, network computer, machine learning model, and a display device for performing the receiving, training, generating, providing, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes paras. [0083] & [0217] of the specification, where the computers are described in terms of “general purpose” processing components, as well as paras. [0052] & [0082], where various high-level display devices such as tablets or other display screens are disclosed. These disclosures do not indicate that the elements of the invention are particular machines, and instead provide generic examples of computer hardware, such that one of ordinary skill in the art would understand that any generic computers and display device could be used. Further, paras. [0129] & [0141]-[0143] describe machine learning models at a high level of generality (e.g. by describing what machine learning or neural networks typically entail) such that one of ordinary skill in the art would understand that known machine learning models (e.g. neural networks) may be applied to this surgical planning use-case when trained with the surgical planning data.
Regarding providing data to an XR headset as in claim 30, this step amounts to insignificant extra-solution activity, as explained above. Examiner further notes that it is well-understood, routine, and conventional to provide surgical planning data to an XR headset for overlay on a patient, as evidenced by at least paras. [0052], [0088], & [0098] of Farley et al. (US 20210307833 A1); paras. [0071], [0081], & [0089] of Chappuis et al. (US 20210093396); and paras. [0030] & [0035] of Roh et al. (US 20190029757 A1).
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computer implementation, machine learning model, and display device in combination is to digitize and/or automate a surgical evaluation and planning operation that could otherwise be achieved as a certain method of organizing human activity. Thus, when considered as a whole and in combination, claims 27-30 and 32-40 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 27-39 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Farley et al. (US 20210307833 A1).
Claim 27
Farley teaches a surgical guidance system for computer assisted navigation during surgery, the surgical guidance system including a computer processor configured to (Farley abstract, Figs. 2A-3, [0163]-[0167], noting a computerized surgical system including a processor for executing software):
receive post-operative feedback data from a network computer regarding surgical outcomes for a plurality of patients (Farley [0082], [0104], [0134]-[0136], noting historical post-operative surgical outcome data from previous patients is collected (i.e. received) and stored at a central database), the post-operative data including:
data regarding the extent of following rehabilitation exercises for each of a plurality of patients (Farley [0104], noting collected post-operative data can include self-reported data from post-operative questionnaires, sensor data from wearable devices that collect data relevant to the surgery (e.g. knee positioning or flexibility monitors), or motion capture data to record the motion of a patient’s body segments during specified postoperative activities (i.e. during rehabilitation exercises). These types of data are considered equivalent to “data regarding the extent of following rehabilitation exercises” in accordance with para. [0210] of Applicant’s disclosure where post-operative data is collected by querying the patient to complete a post-operative questionnaire or by linking with a wearable device that measures patient activity and such data is used to “check if patient is following defined rehabilitation processes”);
data indicating deviation of a sagittal surgical saw cutting plane measured during surgery from the sagittal surgical saw cutting plane defined by a surgical plan, the cutting plane being a fixed plane along which the sagittal surgical saw is configured to cut in any direction in the plane independent of the end effector (Farley [0101], [0125], [0138], [0141]-[0142], noting the system is trained using actual surgical activity data that captures deviations of resection equipment location from the initial plan. See also [0069], noting the surgical instrument utilized by the surgical system can be a sagittal saw, as well as [0137], noting that the surgical plan can include generation of resection planes, i.e. surgical saw cutting planes. See further [0072]-[0074], noting the robotic arm can operate in a mode that allows the end effector to be moved with varying degrees of freedom while providing resistance to or restricting movement in certain directions; when taken together, these disclosures show that the system may track positional deviations from a planned sagittal surgical saw cutting position such as a generated resection plane as in [0137], which is considered to include a fixed plane along which the saw can cut in any direction independent of the end effector as constrained by an appropriate number of degrees of freedom as in [0072]-[0074]);
data indicating deviation of surgical saw motion measurements during surgery from the sagittal surgical saw motion defined by the surgical plan (Farley [0101], [0125], [0141]-[0142], noting the system is trained using actual surgical activity data that captures deviations of resection equipment location (i.e. surgical saw motion measurements) from the initial plan);
train a machine learning model based on the post-operative feedback data (Farley [0082], [0135]-[0136], [0141]-[0142], [0179], noting the historical post-operative data is used to train machine learning models, including neural networks, predictor equations, transfer functions, etc.);
receive pre-operative data from the network computer characterizing a defined patient (Farley [0086], [0127], noting the system acquires (i.e. receives) pre-operative data for a particular patient from a variety of computerized sources);
generate a surgical plan for the defined patient based on processing the pre-operative data through the trained machine learning model (Farley Fig. 4B, [0125], [0134]-[0137], [0139], noting a case plan including various surgical planning features is generated for a patient using the trained models); and
provide the surgical plan to a display device for review by a user (Farley [0052], [0093]-[0095], noting the generated surgical plan is displayed to a user at a display device such as a screen or augmented reality head mounted device).
Claim 28
Farley teaches the surgical guidance system of Claim 27, and further teaches wherein the machine learning model is configured to: process the pre-operative data to output the surgical plan identifying an implant device, a pose for implantation of the implant device in the defined patient, and a predicted post-operative performance metric for the defined patient following the implantation of the implant device (Farley [0061], [0117], [0137], noting the surgical plan output by the system can identify an ideal size and position of implant components; see further [0125], [0175], noting the trained system can provide predicted likelihood of success and other predicted orthopedic responses or outcomes, i.e. predicted post-operative performance metrics for the patient following the implantation of the implant device).
Claim 29
Farley teaches the surgical guidance system of Claim 28, and further teaches wherein the machine learning model is further configured to: generate the surgical plan with identification of poses of resection planes for the implantation of the implant device in the defined patient (Farley [0117], [0137], noting the surgical plan output by the system can identify appropriate resection planes).
Claim 30
Farley teaches the surgical guidance system of Claim 29 and further teaches the system configured to: provide data indicating the poses of the resection planes to a computer platform that generates graphical representations of the poses of the resection planes displayed though the display device within an extended reality (XR) headset as an overlay on the defined patient (Farley [0052], [0094], [0098], noting various patient data and aspects of the surgical plan (e.g. resection planes as in [0094] & [0137]) can be displayed on a display device, including as an overlay on a patient using an augmented reality head mounted device, i.e. an XR headset).
Claim 31
Farley teaches the surgical guidance system of Claim 29 and further teaches the system configured to: provide data indicating the poses of the resection planes to at least one controller of a surgical robot to control a sequence of movements of a surgical saw attached to an arm of the surgical robot so a cutting plane of the surgical saw becomes sequentially aligned with the poses of the resection planes (Farley [0074], [0091], noting the system sends control instructions to a robotic arm (that may include a cutting device such as a saw) to automatically align the arm with the surgical plan).
Claim 32
Farley teaches the surgical guidance system of Claim 27 and further teaches the system configured to train the machine learning model based on the post-operative feedback data comprising at least one of: joint kinematics measurements; soft tissue balance measurements; deformity correction measurements; joint line measurements; and patient reported outcome measures (Farley [0104], [0129], noting post-operative data used to train the model can include postoperative patient body motion (i.e. joint kinematics), soft-tissue balance achieved (i.e. soft tissue balance measurements), and self-reported information reported by patients (i.e. patient reported outcome measures)).
Claim 33
Farley teaches the surgical guidance system of Claim 27 and further teaches the system configured to train the machine learning model based on at least one of: data indicating deviation between joint kinematics measurements of the defined patient during pre-operative stage compared to during post-operative stage; data indicating deviation between tissue balance measurements of the defined patient during pre-operative stage compared to during post-operative stage; data indicating deviation between deformity correction planned for the defined patient during pre-operative stage compared to deformity correction measured for the defined patient during post-operative stage; and data indicating deviation between joint line measurements of the defined patient during pre-operative stage compared to during post-operative stage (Farley [0128], [0171], noting various pre-operative measurements including joint kinematics measurements like flexion/extension gap, mechanical axis, congruence angle, etc., see also [0104], [0129], [0136], & [0174]-[0175], noting collected post-operative measurements including joint kinematics measurements like range of motion, tibia/femur kinematics, etc. The system is trained using correlations of pre-operative and post-operative data as noted in at least Fig. 4B, [0009], [0082], & [0179], showing that differences in pre- and post-operative kinematics and other patient features can be used to train the surgical plan generation system. See also [0097], noting a module analyzing pre- and post-resection ligament/gap balancing to optimize a surgical plan, indicating training of the system using deviation between tissue balance measurements).
Claim 34
Farley teaches the surgical guidance system of Claim 27 and further teaches the system configured to train the machine learning model based on the post-operative feedback data comprising at least one of:62Attorney Docket No. ROBOT.088.0002 data indicating deviation of surgical saw motion measurements during surgery from surgical saw motion defined by a surgical plan; data indicating deviation of an implant device size that is implanted into a patient during surgery from an implant device size defined by a surgical plan; and data indicating deviation of implant device pose after implantation into a patient during surgery from an implant device pose defined by a surgical plan (Farley [0101], [0125], [0141]-[0142], noting the system is trained using actual surgical activity data that captures deviations of resection equipment location (i.e. surgical saw motion measurements) from the initial plan; see also [0129] & [0174], noting collected post-operative measurements can include size of implants used and position/orientation/alignment of implants used. The system is trained using correlations of pre-operative and post-operative data as noted in at least Fig. 4B, [0009], [0082], & [0179], showing that differences in pre- and post-operative patient features (e.g. implant device size and pose as in [0009] & [0129]) can be used to train the surgical plan generation system).
Claim 35
Farley teaches the surgical guidance system of Claim 27 and further teaches the system configured to: form subsets of the post-operative feedback data having similarities that satisfy a defined rule; within each of the subsets, identify correlations among at least some values of the post-operative feedback data; and train the machine learning model based on the correlations identified for each of the subsets (Farley [0033], [0116], [0139], noting system equations can be refined (i.e. trained) for a particular group of patients (e.g. those included in a subset of data matching a defined rule as in [0139]) and used to generate optimized surgical plans for a patient based on historical outcomes of similar patients).
Claim 36
Farley teaches the surgical guidance system of Claim 27, and further teaches wherein the machine learning model comprises: a neural network component including an input layer having input nodes, a sequence of hidden layers each having a plurality of combining nodes, and an output layer having output nodes; and at least one processing circuit configured to provide different entries of the pre-operative data to different ones of the input nodes of the neural network model, and to generate the surgical plan based on output of output nodes of the neural network component (Farley [0135]-[0136], noting the trained machine learning model can be a recurrent neural network or other form of artificial neural network comprising a series of learned nodes and connections that provide surgical plan outputs based on the provided pre-operative patient data inputs).
Claim 37
Farley teaches the surgical guidance system of Claim 36, and further teaches a feedback training component configured to: adapt weights and/or firing thresholds that are used by the combining nodes of the neural network component based on values of the post-operative feedback data (Farley [0136], [0179], noting the trained RNN can be continuously improved based on updated patient and outcome data becoming available, i.e. the model’s initial weights and/or firing thresholds can be adapted based on analysis of new or updated post-operative feedback data).
Claim 38
Farley teaches the surgical guidance system of Claim 27, and further teaches wherein the machine learning model is configured to generate the surgical plan based on processing the pre-operative data comprising at least one of: joint kinematics measurement for the defined patient; soft tissue balance measurement for the defined patient; deformity correction measurement for the defined patient; and joint line measurement for the defined patient (Farley [0127]-[0128], [0171], noting the pre-operative patient data from which a surgical plan is generated can include gait or biomechanical information, flexion/extension gap, mechanical axis, congruence angle, etc., i.e. joint kinematics measurements).
Claim 39
Farley teaches the surgical guidance system of Claim 27, and further teaches wherein the machine learning model is configured to generate the surgical plan based on processing the pre-operative data comprising at least one of: anatomical landmark locations of the defined patient; anterior reference points of the defined patient; and anatomical dimensions of the defined patient (Farley [0061], [0171], noting the surgical plan can be developed from preoperative inputs including anatomical landmarks, reference points like mechanical axes of the leg bones, and anatomical dimensions of the patient such as length of femur and patellar height as gleaned from radiographic assessment information).
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 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 40 is rejected under 35 U.S.C. 103 as being unpatentable over Farley.
Claim 40
Farley teaches the surgical guidance system of Claim 39, and further teaches wherein: the machine learning model is configured to use the anterior reference points of the defined patient; and the anterior reference points identify a proximal tibial mechanical axis point (Farley [0061], [0171], noting the surgical plan can be developed from preoperative inputs including reference points like mechanical axes of the leg bones).
In summary, Farley teaches a non-limiting variety of pre-operatively obtained anatomical landmarks, reference points, and dimensions for use in developing a surgical plan (see [0061]). However, the present combination fails to explicitly disclose each and every type of pre-operative reference point as recited in the claim; specifically, Farley fails to explicitly disclose that a pre-operative anterior reference point identifies a tibial plateau level. However, Farley does teach that some of these types of data may be obtained intra-operatively and used to update or refine the surgical plan (Farley Fig. 8, [0157], [0172], noting intra-operatively collected inputs such as low point on tibia plateau (i.e. tibia plateau level), and other anatomical landmarks, reference points, and/or dimensions are utilized as inputs for surgical planning). Further, [0061] of Farley appears to contemplate additional specific types of pre-operative planning inputs similar to those explicitly listed. It therefore would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to obtain more specific anatomical reference points such as proximal tibial mechanical axis point and tibial plateau level pre-operatively for use in surgical planning because Farley teaches that many types of reference points can be captured pre-operatively via imaging techniques (per at least [0052], [0083], [0086], [0171]) while showing examples of anatomical reference points obtainable via imaging such as proximal tibial mechanical axis point and tibial plateau level that are useful in surgical planning (per at least [0061], [0157], [0172]). Thus, modification of the combination to explicitly specify that such inputs can be obtained pre-operatively would provide the benefit of allowing more robust pre-operative planning by utilizing more precise pre-operative patient-specific data in formulating a more accurate surgical plan, thereby improving and optimizing overall patient care (as suggested by Farley [0083] & [0124]-[0125]).
Claims 41-46 are rejected under 35 U.S.C. 103 as being unpatentable over Farley in view of Lavallee et al. (US 20220047329 A1).
Claim 41
Farley teaches a surgical guidance system for computer assisted navigation during surgery (Farley abstract, Fig. 1, noting a computerized surgical system), the system comprising:
a robotic arm; an end effector adapted to be attached to the robotic arm (Farley Fig. 1, [0041], [0069]-[0071], noting the system includes a surgical robotic arm 105A that may be integrated with or hold an end effector 105B);
the surgical guidance system including a computer processor configured to (Farley abstract, Figs. 2A-3, [0163]-[0167], noting a computerized surgical system including a processor for executing software):
receive post-operative feedback data provided by distributed networked computers regarding surgical outcomes for a plurality of patients (Farley [0082], [0104], [0134]-[0136], noting historical post-operative surgical outcome data from previous patients is collected (i.e. received) and stored at a central database), the post-operative feedback data including:
data indicating deviation of the sagittal surgical saw cutting plane measured during surgery from the sagittal surgical saw cutting plane defined by a surgical plan, the cutting plane being a fixed plane along which the sagittal surgical saw is configured to cut in any direction in the plane independent of the end effector (Farley [0101], [0125], [0138], [0141]-[0142], noting the system is trained using actual surgical activity data that captures deviations of resection equipment location from the initial plan. See also [0069], noting the surgical instrument utilized by the surgical system can be a sagittal saw, as well as [0137], noting that the surgical plan can include generation of resection planes, i.e. surgical saw cutting planes. See further [0072]-[0074], noting the robotic arm can operate in a mode that allows the end effector to be moved with varying degrees of freedom while providing resistance to or restricting movement in certain directions; when taken together, these disclosures show that the system may track positional deviations from a planned sagittal surgical saw cutting position such as a generated resection plane as in [0137], which is considered to include a fixed plane along which the saw can cut in any direction independent of the end effector as constrained by an appropriate number of degrees of freedom as in [0072]-[0074]);
data indicating deviation of sagittal surgical saw motion measurements during surgery from the sagittal surgical saw motion defined by the surgical plan (Farley [0101], [0125], [0141]-[0142], noting the system is trained using actual surgical activity data that captures deviations of resection equipment location (i.e. surgical saw motion measurements) from the initial plan);
train a machine learning model based on the post-operative feedback data (Farley [0082], [0135]-[0136], [0141]-[0142], [0179], noting the historical post-operative data is used to train machine learning models, including neural networks, predictor equations, transfer functions, etc.);
receive pre-operative data from one of the distributed network computers characterizing a defined patient (Farley [0086], [0127], noting the system acquires (i.e. receives) pre-operative data for a particular patient from a variety of computerized sources);
generate a surgical plan for the defined patient based on processing the pre-operative data through the trained machine learning model (Farley Fig. 4B, [0125], [0134]-[0137], [0139], noting a case plan including various surgical planning features is generated for a patient using the trained models); and
provide the surgical plan to a display device for review by a user (Farley [0052], [0093]-[0095], noting the generated surgical plan is displayed to a user at a display device such as a screen or augmented reality head mounted device).
In summary, Farley teaches a computerized surgical guidance system that includes a robotic arm with an integrated end effector, and further notes that movements of an attached tool may be constrained to a planned area of resection via forces provided by the robotic arm and/or end effector (Farley [0072], [0089]). However, Farley fails to explicitly disclose the specific physical structure and arrangement of the end effector device as recited in the instant claim (see lined through portions above for specific structural limitations not disclosed by Farley). However, Lavallee teaches an analogous surgical system with a robotic arm coupled to an end effector (Lavallee Figs. 3-5, [0101]), including: first arm having a proximal end pivotally attached to the end effector; and a second arm having a proximal end pivotally attached to a distal end of the first arm and a distal end adapted to be attached to a sagittal surgical saw such that rotation of the sagittal surgical saw is limited to a fixed plane defined by the first and second arms (Lavallee Fig. 8, [0118], [0124], noting a first arm 24b that pivots around end effector base 24a and a second arm 24c that attaches to first arm 24b as well as a surgical tool 2 (e.g. a sagittal saw as in [0087] & [0161]); the arrangement of the segment arms allows for rotation (i.e. pivoting) about the rotatable connections 25a and 25b as well as constrains the movement of the tool attachment mechanism to a range of movement within a fixed cutting plane). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the surgical robotic arm and end effector of Farley to include the specific physical structure and arrangement disclosed by Lavallee in order to provide a surgical system that can guide an end effector to cut an anatomical structure of a patient that does not require any invasive attachment to the patient’s bone while facilitating precise control constraining of the position and orientation of the end effector to reach a specific target cutting plane (as suggested by Lavallee [0012] & [0118]).
Claim 42
Farley in view of Lavallee teaches the surgical guidance system of Claim 41, and the combination further teaches wherein the computer processor is configured to receive data regarding the extent of following rehabilitation exercises for each of a plurality of patients (Farley [0104], noting collected post-operative data can include self-reported data from post-operative questionnaires, sensor data from wearable devices that collect data relevant to the surgery (e.g. knee positioning or flexibility monitors), or motion capture data to record the motion of a patient’s body segments during specified postoperative activities (i.e. during rehabilitation exercises). These types of data are considered equivalent to “data regarding the extent of following rehabilitation exercises” in accordance with para. [0210] of Applicant’s disclosure where post-operative data is collected by querying the patient to complete a post-operative questionnaire or by linking with a wearable device that measures patient activity and such data is used to “check if patient is following defined rehabilitation processes”).
Claim 43
Farley in view of Lavallee teaches the surgical guidance system of Claim 41 and the combination further teaches wherein the computer processor is further configured to train the machine learning model based on the post-operative feedback data comprising all of the following: joint kinematics measurements; soft tissue balance measurements; deformity correction measurements; joint line measurements; and patient reported outcome measures (Farley [0104], [0129], noting post-operative data used to train the model can include postoperative patient body motion (i.e. joint kinematics), soft-tissue balance achieved (i.e. soft tissue balance measurements), joint flexibility (i.e. deformity correction measurements), knee positioning (i.e. joint line measurements), and self-reported information reported by patients (i.e. patient reported outcome measures)).
Claim 44
Farley in view of Lavallee teaches the surgical guidance system of Claim 41 and the combination further teaches wherein the computer processor is further configured to train the machine learning model based all of the following: data indicating deviation between joint kinematics measurements of the defined patient during pre-operative stage compared to during post-operative stage; data indicating deviation between tissue balance measurements of the defined patient during pre-operative stage compared to during post-operative stage; data indicating deviation between deformity correction planned for the defined patient during pre-operative stage compared to deformity correction measured for the defined patient during post-operative stage; and data indicating deviation between joint line measurements of the defined patient during pre-operative stage compared to during post-operative stage (Farley [0128], [0171], noting various pre-operative measurements including joint kinematics and joint line measurements like flexion/extension gap, mechanical axis, congruence angle, etc.; see also [0104], [0129], [0136], & [0174]-[0175], noting collected post-operative measurements including joint kinematics and joint line measurements like range of motion, tibia/femur kinematics, knee positioning, etc. The system is trained using correlations of pre-operative and post-operative data as noted in at least Fig. 4B, [0009], [0082], & [0179], showing that differences in pre- and post-operative kinematics, joint lines, and other patient features can be used to train the surgical plan generation system. See also [0097], noting a module analyzing pre- and post-resection ligament/gap balancing to optimize a surgical plan, indicating training of the system using deviation between tissue balance measurements. See also [0117]-[0120], noting various surgical parameters (e.g. implant position, resection depth, rotation, angles, etc.) amounting to deformity correction are pre-operatively planned for each patient; the system can be trained as noted above by analyzing differences between planned surgical parameters and actual outcomes such as joint positioning such that the system is considered to be trained using “data indicating deviation between deformity correction planned for the defined patient during pre-operative stage compared to deformity correction measured for the defined patient during post-operative stage, since “deformity correction” measures are never explicitly defined or outlined by the Applicant and may thus encompass a variety of parameters like joint positioning).
Claim 45
Farley in view of Lavallee teaches the surgical guidance system of Claim 41 and the combination further teaches wherein the computer processor is further configured to train the machine learning model based on the post-operative feedback data comprising data indicating deviation of implant device pose after implantation into a patient during surgery from an implant device pose defined by a surgical plan (Farley [0101], [0125], [0141]-[0142], noting the system is trained using actual surgical activity data that captures deviations of a performed procedure from the initial plan; see also [0129] & [0174], noting collected post-operative measurements can include size of implants used and position/orientation/ alignment of implants used. The system is trained using correlations of pre-operative and post-operative data as noted in at least Fig. 4B, [0009], [0082], & [0179], showing that differences in pre- and post-operative patient features (e.g. implant device size and pose as in [0009] & [0129]) can be used to train the surgical plan generation system).
Claim 46
Farley in view of Lavallee teaches the surgical guidance system of Claim 41 and the combination further teaches wherein the following is used as an input to train the neural network model: data indicating deviation between joint line measurements of the defined patient during pre-operative stage compared to during post-operative stage (Farley [0128], [0171], noting various pre-operative measurements including joint line measurements like flexion/extension gap, mechanical axis, congruence angle, etc.; see also [0104], [0129], [0136], & [0174]-[0175], noting collected post-operative measurements including joint line measurements like range of motion, knee positioning, etc. The system is trained using correlations of pre-operative and post-operative data as noted in at least Fig. 4B, [0009], [0082], & [0179], showing that differences in pre- and post-operative joint lines and other patient features can be used to train the surgical plan generation system).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Poltaretskyi et al. (US 20190380792 A1) describes a surgical planning system that can utilize deviations from an initial surgical plan to train machine learning models for making surgical plan recommendations. Otto et al. (US 20190356481 A1) describes a surgical system that may track the actual cutting plane used by a saw for comparison to a planned cutting plane to determine deviations between the two.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAREN A HRANEK whose telephone number is (571)272-1679. The examiner can normally be reached M-F 8:00-4:00 ET.
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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684