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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/01/2026 was considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 4 and 16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 4 recites “the first component is a femoral component and the second component is a tibial component” in lines 2-3. Claim 1 recites “a magnetic flux density sensor of a first component” in line 3 and “a plurality of magnets in a second component” in lines 4-5. Therefore, claim 4 indicates that the femoral component has a sensor and the tibial component has the magnets, which is new matter. The specification indicates that the femoral component includes the plurality of magnets and the tibial component includes a hall sensor (¶ [0028] of the published specification). However, the specification does not provide support for the claimed arrangement. Claim 16 recites a similar limitation, so it is rejected on similar grounds.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-4 and 6-16, 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 do not include additional elements that integrate the exception into a practical application of the exception or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, p. 50, January 7, 2019), and the 2024 Guidance Update on Patent Subject Matter Eligibility (Federal Register, Vol. 89, No. 137 p. 58128, July 17, 2024).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is directed to a process, which is a statutory category.
Step 2A - Prong 1: Claim 1 is directed to an abstract idea in the form of a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components and/or is directed to a mathematical algorithm.
Claim 1 recites the following limitations:
[A1]: receiving data;
[B1]: analyzing the data with a trained estimation model to simultaneously determine kinematic information of the joint in six degrees of freedom, the trained estimation model being a single model trained to determine all six degrees of freedom simultaneously from the variations in the magnetic field;
[C1]: outputting the kinematic information.
These elements [A1]-[C1] of claim 1 are directed to an abstract idea because they are processes that, under their broadest reasonable interpretation, are mere steps that are capable of being mentally performed with the aid of pen and paper. For example, a skilled artisan is capable of reading measurements from the implant with an inertial measurement unit or magnetic sensor, analyzing the measurements using prior knowledge and/or mathematical algorithms to determine movements in six-degrees of freedom, and communicating the movements.
Step 2A - Prong Two: Claim 1 does not recite additional elements that integrate the judicial exception into a practical application. Claim 1 recites the following additional elements:
[A2]: data obtained from a magnetic flux density sensor of a first component of an implanted joint implant, the data representing variations in a magnetic field generated by a plurality of magnets in a second component of the joint implant; and
[B2]: input from a machine learning module.
The elements [A2]-[B2] do not integrate the exception into a practical application of the exception.
The element [A2] does not integrate the exception into a practical application of the exception because the element amounts to merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well-known elements - see MPEP 2106.04(d); MPEP 2106.05(g).
The element [B2] does not integrate the exception into a practical application of the exception because the elements amount to mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea - See MPEP 2106.04(d) and MPEP 2106.05(f).
Accordingly, each of the additional elements do not integrate the abstract into a practical application because they do not impose any meaningful limitations on practicing the abstract idea.
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. Claim 1 recites the following additional elements:
[A2]: data obtained from a magnetic flux density sensor of a first component of an implanted joint implant, the data representing variations in a magnetic field generated by a plurality of magnets in a second component of the joint implant; and
[B2]: input from a machine learning module.
The elements [A2]-[B2] do not amount to significantly more than the judicial exception itself.
The element [A2] does not amount to significantly more than the judicial exception itself because the element amounts to merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well-known elements - see MPEP 2106.05(g). Additionally, the element is well-understood, routine and conventional. For example, US 10,500,071 B2 (Wang) teaches the element in Fig. 3, Col. 4, lines 27-47, and Col. 5, line 47 to Col. 6, line 11; US 2022/0354423 A1 (Horeman) teaches the element in Fig. 2 and ¶ [0024]; and US 2018/0116823 A1 (Johannaber) (previously cited) teaches the element in Fig. 1 and ¶¶ [0025]-[0027]. The plurality of disclosures depicts the well-understood, routine, and conventional nature of the element.
The element [B2] does not qualify as significantly more because the element is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (See MPEP 2106.05(d)(II); Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Additionally, an input from a machine learning module is well-understood, routine, and conventional activity as evidenced by ¶ [0064] of US 2021/0401324 A1 (Liao) (previously cited) which teaches that collected motion data can be classified or recognized using common pattern recognition methods, wherein the motion data may be classified or recognized by a linear discriminant analyzer, a secondary discriminant analyzer, a support vector machine, or a neural network.
In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Independent claims 15 and 20 recite similar limitations and are not patent eligible for substantially similar reasons.
The Examiner notes that claim 20 recites “the cooperation of a magnet of a femoral component and a Hall sensor of a tibial component”. However, this element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amounts to merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well known elements - see MPEP 2106.04(d); MPEP 2106.05(g). Additionally, the element is well-understood, routine, and conventional. For example, the element is disclosed in at least ¶ [0022] and Fig. 3 of US 2006/0142670 A1 (DiSilvestro 2006) (previously cited); ¶ [0059] and Fig. 1 of US 2005/0010301 A1 (Disilvestro 2005) (previously cited); Paragraph 1 of A. Sensor Configuration and Fig. 1 of “Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator” (Arami) (previously cited). The plurality of disclosures depicts the well-understood, routine, and conventional nature of the element.
Claims 2-4 and 6-14 depend from claim 1, and they recite the same abstract idea as claim 1. Claims 16-19 depend from claim 15, and they recite the same abstract idea as claim 15. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process) and/or append abstract ideas (that is, the claims only recite limitations that add further mental processes) except for the following limitations.
Claim 2 recites “the magnetic flux density sensor is a Hall sensor”. This element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amounts to merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well known elements - see MPEP 2106.04(d); MPEP 2106.05(g). Additionally, the element is well-understood, routine, and conventional. For example, the element is disclosed in at least ¶ [0022] and Fig. 3 of US 2006/0142670 A1 (DiSilvestro 2006) (previously cited); ¶ [0059] and Fig. 1 of US 2005/0010301 A1 (Disilvestro 2005) (previously cited); Paragraph 1 of A. Sensor Configuration and Fig. 1 of “Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator” (Arami) (Previously cited). The plurality of disclosures depicts the well-understood, routine, and conventional nature of the element.
Claim 3 recites “machine learning module includes any of a neural network and a regression network”. However the above element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry. See MPEP 2106.05(d)(II). Additionally, the element is well-understood, routine, and conventional as evidenced by ¶ [0064] of US 2021/0401324 A1 (Liao) (previously cited) which teaches that collected motion data can be classified or recognized using common pattern recognition methods, wherein the motion data may be classified or recognized by a linear discriminant analyzer, a secondary discriminant analyzer, a support vector machine, or a neural network.
Claims 4 and 16 recite “the joint is a knee joint and the first component is a femoral component and the second component is a tibial component”. However, the above elements do not integrate the exception into a practical application of the exception or qualify as significantly more because the elements amount to (A) merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well known elements as discussed in MPEP 2106.04(d), 2106.05(g); and/or (B) generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.04(d), 2106.05(h). Additionally, the element is well-understood, routine, and conventional. For example, US 10,500,071 B2 (Wang) teaches the element in Fig. 3, Col. 4, lines 27-47, and Col. 5, line 47 to Col. 6, line 11; and US 2018/0116823 A1 (Johannaber) (previously cited) teaches the element in Fig. 1 and ¶¶ [0025]-[0027].
Claims 7 and 11 recite “the step of training the estimation model includes obtaining data from a prototype”. Claim 8 recites “the data pertains to different poses from a prototype”. Claim 9 recites “the data is obtained through the use of a robot”. However, the above elements do not integrate the exception into a practical application of the exception or qualify as significantly more because the elements amount to (A) merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well known elements as discussed in MPEP 2106.04(d), 2106.05(g); and/or (B) generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.04(d), 2106.05(h). Additionally, the element is well-understood, routine, and conventional. For example, the elements are disclosed in at least Paragraph 1 of D. Validation 1 of “Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator” (Arami) (previously cited); ¶¶ [0063]-[0064] of US 2022/0143820 A1 (Bashkirov) (previously cited); and ¶¶ [0067]-[0069] of US 2007/0239165 A1 (Amirouche) (previously cited).
Claim 10 recites “the data is obtained through the use of video motion capture”. However, the above element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amounts to (A) merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well known elements as discussed in MPEP 2106.04(d), 2106.05(g); and/or (B) generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.04(d), 2106.05(h). Additionally, the element is well-understood, routine, and conventional as evidenced by US 2006/0071934 A1 (Sagar) (previously cited) which teaches that motion capture using optical systems is conventional in ¶ [0043].
Claim 11 recites “the step of training the estimation model includes creating a finite element analysis”. However, the above element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amounts to (A) merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well known elements as discussed in MPEP 2106.04(d), 2106.05(g); and/or (B) generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.04(d), 2106.05(h). Additionally, the element is well-understood, routine, and conventional as evidenced by US 2022/0092240 A1 (Chi) (previously cited) which teaches that, for initial training of a machine learning module, optimization starts with a standard finite element analysis in ¶ [0029].
Claim 18 recites “the outputting step includes providing a visual model of the kinematic information”. Claim 19 recites “the visual model is a graphical representation of the motion of bones of the joint”. However, the above elements do not integrate the exception into a practical application of the exception or qualify as significantly more because the elements amount to (A) merely adding insignificant extra-solution activity to the judicial exception as discussed in MPEP 2106.04(d), 2106.05(g). Additionally, the element is well-understood, routine, and conventional. For example, the elements are disclosed in at least ¶ [0139] of US 2021/0153778 A1 (Gupta) (previously cited); ¶ [0021] of US 2023/0218466 A1 (Seo) (previously cited); and ¶ [0020] of US 2008/0202233 A1 (Lan) (previously cited).
In view of the above, the additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 1-4, 6-10, 14-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over “Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator” (Arami) (previously cited) in view of US 2018/0116823 A1 (Johannaber) (previously cited) and US 2024/0307125 A1 (Messinger) (previously cited).
With regards to claim 1, Arami teaches a method of determining kinematic information of a joint (D. Validation on page 6275 depict testing the gait patterns of two subject’s walking and determining and flexion extension (FE) rotation angle kinematics) comprising the steps of: receiving data obtained from a magnetic flux density sensor of a first component of an implanted joint implant (D. Validation on page 6275 and C. Flexion-Extension Angle Estimators on pages 6273-6274 depict acquiring raw measurements of Hall-effect sensors; A. Sensor Configuration on page 6272 and Figs. 1-2 depict the sensors being of a knee prosthesis, wherein a polyethylene insert of a tibial part having two Hall-effect sensors), the data representing variations in a magnetic field generated by a magnet in a second component of the joint implant (Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami depict a femoral guiding pin encapsulating a permanent magnet); analyzing the data with a trained estimation model to simultaneously determine kinematic information of the joint in at least one degree of freedom using input from a machine learning module (D. Validation on page 6275 and C. Flexion-Extension Angle Estimators on pages 6273-6274 depict analyzing the raw measurements of the Hall-effect sensors using a locally linear neuro-fuzzy estimator (LLM) and a linear regression estimator to determine FE angles, wherein the LLM and linear regression estimator were trained using a machine learning module–see at least Figs. 5 and 6); and outputting the kinematic information (Figs. 4 and 7 depict the FE angle being output as a graph).
Arami is silent regarding whether there is a plurality of magnets for generating the variations in the magnetic field.
In a system relevant to the problem of monitoring an orientation between joint implant parts, Johannaber teaches a component including a plurality of magnets for generating a sensed magnetic field (¶¶ [0025]-[0026] and Fig. 1 depict a component including a plurality of magnets (106A, 106B) or a magnet ring, the plurality of magnets being used for generating a magnetic field sensed by the sensor 108, which may include a hall effect sensor). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the femoral component of Arami to incorporate a plurality of magnets for generating the variations in the magnetic field as taught by Johannaber. Because both a single magnet and a plurality of magnets can be used to determine an orientation of a component relative to another (¶ [0028] of Johannaber; Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami), it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
The above combination is silent regarding whether the kinematic information of the joint includes six degrees of freedom and the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from the variations in the magnetic field.
In a system relevant to the problem of determining kinematics of elements using machine learning and hall effect sensors, Messinger teaches using a trained estimation model to determine kinematic information of elements in six degrees of freedom, wherein the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from variations in a magnetic field (¶ [0062] discloses a deep learning algorithm for detecting a change in coordinates of a magnetic element in relation to the array of magnetic sensors, wherein the changes in coordinates include translation along the x-axis, the y-axis, and/or the z-axis, and/or rotation of the magnetic element about a longitudinal axis, a lateral axis, and/or a vertical axis thereof; ¶ [0063] discloses the medical tool may include one or more magnetic elements, which would alter the magnetic field). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the determination of the kinematics of Arami to incorporate using a trained estimation model to determine kinematic information of elements in six degrees of freedom, wherein the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from the variations in the magnetic field as taught by Messinger. The motivation would have been to provide a more complete diagnostic analysis of the kinematics of the joint of Arami.
With regards to claim 2, the above combination teaches or suggests the sensor is a Hall sensor (Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami depict two Hall-effect sensors).
With regards to claim 3, the above combination teaches or suggests the machine learning module (D. Validation on page 6275 of Arami and C. Flexion-Extension Angle Estimators on pages 6273-6274 of Arami depict a linear-regression estimator and a locally linear neuro-fuzzy estimator (LLM)). Arami further teaches potentially using a neural network (IV Discussion on page 6277 discusses other machine learning estimators such as multilayer neural networks and Gaussian linear regression can form nonlinear mapping).
The above combination is silent regarding whether the machine learning module includes any of a neural network and a regression network.
In a system relevant to the problem of determining kinematics of elements using machine learning and hall effect sensors, Messinger teaches a machine learning module for determining a spatial location and/or orientation of an element using magnetic sensors and magnetic elements includes a neural network (¶ [0095] teaches applying received signals to one or more deep learning algorithm which may include a convolution neural network algorithm, multilayer perceptron algorithm, XGBoost algorithm, recurrent neural network algorithm, and the like). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the machine learning module of the above combination with the neural network of Messinger. Because both elements can determine the orientation of a magnetic sensor in relation to a magnetic element (¶ [0095] of Messinger; D. Validation on page 6275 of Arami), it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
With regards to claim 4, the above combination teaches or suggests the joint is a knee joint which comprises a tibial component and a femoral component (Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami depict a femoral guiding pin and a tibial part).
The above combination is silent regarding whether the first component which comprises the magnetic flux density sensor is a femoral component and the second component which comprises the plurality of magnets is a tibial component. The difference between the prior art and the claimed invention amounts to a reversal of parts and/or a rearrangement of parts which, similar to In reGazda, 219 F.2d 449, 104 USPQ 400 (CCPA 1955) and In reJapikse, 181 F.2d 1019, 86 USPQ 70 (CCPA 1950), would not have modified the operation of the device and would have been obvious. See MPEP 2144.04(VI)(A) and 2144.04(VI)(C).
With regards to claim 6, the above combination teaches or suggests the step of training the estimation model (C. Flexion-Extension Angle Estimators on pages 6273-6274 of Arami depict training the linear regression estimator and the locally linear neuro-fuzzy estimator).
With regards to claim 7, the above combination teaches or suggests the step of training the estimation model includes obtaining data from a prototype (D. Validation on page 6275 of Arami teaches the polyethylene insert with configured Hall-effect sensors fixed into the robotic knee simulator for performing a squat movement used as the train set to build the linear regression and local linear neuro-fuzzy estimators).
With regards to claim 8, the above combination teaches or suggests the data from the prototype pertains to different poses of the prototype (D. Validation on page 6275 of Arami teaches the squat movement with an FE rotation from 14° to 61°).
With regards to claim 9, the above combination teaches or suggests the data from the prototype is obtained through the use of a robot (D. Validation on page 6275 of Arami teaches the robotic knee simulator).
With regards to claim 10, the above combination teaches or suggests the data from the prototype is obtained through the use of video motion capture (B. Robotic Knee Simulator on page 6274 and D. Validation on page 6275 of Arami depict a motion capture system consisting of four Mx3+ cameras for obtaining FE angles used for training the estimators).
With regards to claim 14, the above combination teaches or suggests the implanted joint implant is any of a knee implant, shoulder implant, hip implant, and spine implant the joint is a knee joint and the implanted joint implant includes femoral and tibial components (Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami depict a knee implant).
With regards to claim 15, Arami teaches a method of determining kinematic information of a joint (D. Validation on page 6275 depict testing the gait patterns of two subject’s walking and determining and flexion extension (FE) rotation angle kinematics) comprising the steps of: applying data obtained from a Hall sensor of a first component of an implanted joint implant to a trained estimation model to simultaneously determine kinematic information of the joint (D. Validation on page 6275 and C. Flexion-Extension Angle Estimators on pages 6273-6274 depict acquiring raw measurements of Hall-effect sensors of a tibial component; A. Sensor Configuration on page 6272 and Figs. 1-2 depict the sensors being of a knee prosthesis; D. Validation on page 6275 and C. Flexion-Extension Angle Estimators on pages 6273-6274 depict analyzing the raw measurements of the Hall-effect sensors using a locally linear neuro-fuzzy estimator (LLM) and a linear regression estimator to determine FE angles); and outputting the kinematic information (Figs. 4 and 7 depict the FE angle being output as a graph).
Arami is silent regarding whether there is a plurality of magnets for generating the variations in the magnetic field.
In a system relevant to the problem of monitoring an orientation between joint implant parts, Johannaber teaches a component including a plurality of magnets for generating a sensed magnetic field (¶¶ [0025]-[0026] and Fig. 1 depict a component including a plurality of magnets (106A, 106B) or a magnet ring, the plurality of magnets being used for generating a magnetic field sensed by the sensor 108, which may include a hall effect sensor). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the femoral component of Arami to incorporate a plurality of magnets for generating the variations in the magnetic field as taught by Johannaber. Because both a single magnet and a plurality of magnets can be used to determine an orientation of a component relative to another (¶ [0028] of Johannaber; Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami), it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
The above combination is silent regarding whether the kinematic information of the joint includes six degrees of freedom and the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from the variations in the magnetic field.
In a system relevant to the problem of determining kinematics of elements using machine learning and hall effect sensors, Messinger teaches using a trained estimation model to determine kinematic information of elements in six degrees of freedom, wherein the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from variations in a magnetic field (¶ [0062] discloses a deep learning algorithm for detecting a change in coordinates of a magnetic element in relation to the array of magnetic sensors, wherein the changes in coordinates include translation along the x-axis, the y-axis, and/or the z-axis, and/or rotation of the magnetic element about a longitudinal axis, a lateral axis, and/or a vertical axis thereof; ¶ [0063] discloses the medical tool may include one or more magnetic elements, which would alter the magnetic field). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the determination of the kinematics of Arami to incorporate using a trained estimation model to determine kinematic information of elements in six degrees of freedom, wherein the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from the variations in the magnetic field as taught by Messinger. The motivation would have been to provide a more complete diagnostic analysis of the kinematics of the joint of Arami.
With regards to claim 16, the above combination teaches or suggests the joint is a knee joint which comprises a tibial component and a femoral component (Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami depict a femoral guiding pin and a tibial part).
The above combination is silent regarding whether the first component which comprises the magnetic flux density sensor is a femoral component and the second component which comprises the plurality of magnets is a tibial component. The difference between the prior art and the claimed invention amounts to a reversal of parts and/or a rearrangement of parts which, similar to In reGazda, 219 F.2d 449, 104 USPQ 400 (CCPA 1955) and In reJapikse, 181 F.2d 1019, 86 USPQ 70 (CCPA 1950), would not have modified the operation of the device and would have been obvious. See MPEP 2144.04(VI)(A) and 2144.04(VI)(C).
With regards to claim 18, the above combination teaches or suggests the outputting step includes providing a visual model of the kinematic information (Figs. 4 and 7 of Arami depict the FE angle being output as a graph which amounts to a visual model).
With regards to claim 19, the above combination teaches or suggests the visual model is a graphical representation of the motion of bones of the joint (Figs. 4 and 7 of Arami depict the FE angle being output as a graph which is a representation of the flexion extension rotation of the bones of the knee joint).
With regards to claim 20, Arami teaches a method of determining kinematic information of a knee joint (D. Validation on page 6275 depict testing the gait patterns of two subject’s walking and determining and flexion extension (FE) rotation angle kinematics) comprising the steps of: applying data obtained from the cooperation of a magnet of a femoral component and a Hall sensor of a tibial component to a trained estimation model to determine kinematic information of the knee joint (D. Validation on page 6275 and C. Flexion-Extension Angle Estimators on pages 6273-6274 depict acquiring raw measurements of Hall-effect sensors; Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami depict a femoral guiding pin encapsulating a permanent magnet and a polyethylene insert of a tibial part having two Hall-effect sensors; D. Validation on page 6275 and C. Flexion-Extension Angle Estimators on pages 6273-6274 depict analyzing the raw measurements of the Hall-effect sensors using a locally linear neuro-fuzzy estimator (LLM) and a linear regression estimator to determine FE angles); and outputting the kinematic information as a visual representation depicting the movement of the femur and the tibia (Figs. 4 and 7 depict the FE angle being output as a graph).
The above combination is silent regarding whether the kinematic information of the joint includes six degrees of freedom and the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from the variations in the magnetic field.
In a system relevant to the problem of determining kinematics of elements using machine learning and hall effect sensors, Messinger teaches using a trained estimation model to determine kinematic information of elements in six degrees of freedom, wherein the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from variations in a magnetic field (¶ [0062] discloses a deep learning algorithm for detecting a change in coordinates of a magnetic element in relation to the array of magnetic sensors, wherein the changes in coordinates include translation along the x-axis, the y-axis, and/or the z-axis, and/or rotation of the magnetic element about a longitudinal axis, a lateral axis, and/or a vertical axis thereof; ¶ [0063] discloses the medical tool may include one or more magnetic elements, which would alter the magnetic field). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the determination of the kinematics of Arami to incorporate using a trained estimation model to determine kinematic information of elements in six degrees of freedom, wherein the trained estimation model is a single model trained to determine all six degrees of freedom simultaneously from the variations in the magnetic field as taught by Messinger. The motivation would have been to provide a more complete diagnostic analysis of the kinematics of the joint of Arami.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over “Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator” (Arami) (previously cited)in view of US 2018/0116823 A1 (Johannaber) (previously cited) and US 2024/0307125 A1 (Messinger) (previously cited), as applied to claim 6 above, and further in view of US 2009/0030300 A1 (Ghaboussi) (previously cited).
With regards to claim 11, the above combination is silent regarding whether the step of training the estimation model includes creating a finite element analysis.
In a system relevant to the problem of training machine learning models, Ghaboussi teaches training an estimation model includes creating a finite element analysis (¶ [0105] teaches an algorithm uses a partially-trained neural network (NN) in an iterative non-linear finite element (FE) analysis of the test structure in order to extract approximate, but gradually improving, stress-strain information with which to further train the neural network). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the training of the above combination to incorporate creating a finite element analysis as taught by Ghaboussi. The motivation would have been to improve the training of the machine learning model.
With regards to claim 12, the above combination teaches or suggests the step of training the estimation model further includes obtaining data from a prototype (D. Validation on page 6275 of Arami teaches the polyethylene insert with configured Hall-effect sensors fixed into the robotic knee simulator for performing a squat movement used as the train set to build the linear regression and local linear neuro-fuzzy estimators).
With regards to claim 13, the above combination teaches or suggests determining a model error (D. Validation on page 6275 of Arami teaches determining a root mean square of error).
Response to Arguments
Claim Objections
In view of the claim amendments filed 06/15/2026, the claim objections were withdrawn.
Rejections under 35 U.S.C. §112(b)
In view of the claim amendments filed 06/15/2026, the claim rejections under 35 U.S.C. §112(b) were withdrawn.
Rejections under 35 U.S.C. §101
Applicant's arguments filed 06/15/2026 have been fully considered but they are not persuasive.
The Applicant asserts that the Applicant has amended each of independent claims 1, 15, 20 to incorporate the subject matter of original claim 5.
However, none of the independent claims were amended to incorporate the subject matter of original claim 5. Although the claims were amended to recite a sensor of a first component and a plurality of magnets of a second component, there is no indication that the first component is a tibial component and the second component is a femoral component. Amended claims 4 and 16 do not include the feature either.
Prior Art Rejections
There are new grounds of rejections necessitated by the claim amendments filed 06/15/2026.
To the extent that the Applicant’s arguments are applicable to the current rejections, the Examiner makes the following comments.
Applicant's arguments filed 06/15/2026 have been fully considered but they are not persuasive.
On pages 6-7 of the response filed 06/15/2026, the Applicant asserts that Messinger is not directed to determining kinematic information of a joint from an implanted joint implant.
This argument is not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In this case, Arami teaches the determination of joint kinematics. Messinger teaches the determination of the 6-DOF kinematic information based on magnets and magnetic field sensors. One of ordinary skill would have been motivated to apply the methods for determining the 6-DOF kinematic information of Messinger to the joint kinematic determination of Arami because to provide a more complete diagnostic analysis of the kinematics of the joint of Arami.
In response to applicant's argument that Messinger is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Messinger is relevant to the problem of determining kinematics using relative movement of magnets and magnetic sensors. Therefore, Messinger is analogous art.
On page 7 of the response filed 06/15/2026, the Applicant asserts that the repeated “and/or” phrasing of paragraph [0062] of Messinger is “expressly permissive” and indicates Messinger does not disclose simultaneously determining movement in six degrees of freedom.
This argument is not persuasive. There is no indication that the disclosure is “expressly permissive” as the Applicant suggests. One of ordinary skill would read the disclosure and understand that the recitations of “the data sets may include a change in the coordinates of the magnetic element, such as, translation of the magnetic element along the x-axis, the y-axis, and/or the z-axis, and/or rotation of the magnetic element about a longitudinal axis, a lateral axis, and/or a vertical axis thereof” means that the deep learning algorithm is configured to determine movement along the listed axes and rotation along the listed axes.
On page 7 of the response filed 06/15/2026, the Applicant asserts that the motivation to provide a more complete diagnostic analysis of the kinematics of the joint of Arami is a conclusory statement that cannot sustain an obviousness rejection, and the only roadmap for selecting Messinger’s isolated training-set sentence and grafting it onto Arami’s single-angle estimator is the claims themselves, which is impermissible hindsight.
These arguments are not persuasive. The arguments amount to a general statement of nonobviousness without evidence of why the motivation is conclusory and why it cannot sustain an obviousness rejection. Additionally, in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). In this case, it is known in the art that determining additional parameters of movement of a joint are desirable. Therefore, the Examiner maintains that one of ordinary skill would have been motivated to determine additional movements along additional degrees of freedom to monitor all movements of the joint and have a more complete diagnostic analysis of the joint.
On page 7 of the response filed 06/15/2026, the Applicant asserts that the proposed modification would require a wholesale redesign of Arami’s deliberately minimal two-sensor configuration because Messinger’s architecture depends on an array of magnetic sensors external to the patient.
This argument is not persuasive.
In response to applicant's argument that the modification would require a redesign such that plurality of sensors are external to the patient, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). One of ordinary skill would have been able to use the teachings of determining the 6-DOF kinematic information of Messinger and applying them to the magnets and sensors of the joint of Arami. One of ordinary skill would not need to redesign the system of Arami such that the sensors are external to the body, and Messinger does not teach that the sensors must be external to the body. Additionally, Messinger does not require the use of more than two sensors to generate the kinematic data in the 6-DOF because the broadest reasonable interpretation of an array of sensors includes two sensors and Messinger does not teach that a minimum number of sensors is required.
On page 8 of the response filed 06/15/2026, the Applicant asserts that amending the trained estimation model such that it is a “single model trained to determine all six degrees of freedom simultaneously from the variations in the magnetic field” highlights the distinction.
This argument is not persuasive because Messinger teaches the determination of the 6-DOF kinematic information simultaneously using a single deep learning algorithm in at least ¶ [0062].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2024/0082032 A1 (Sebhki) teaches a motion tracking and/or localization system that comprises a set of permanent magnets, a magnetic sensor, and a trained neural network or trained machine learning operation for outputting a localization-associated measurement value of the magnetic sensor relative to the magnets (¶ [0006]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.C.K./Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791
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