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 statements (IDS) submitted on 05/03/2023, 09/18/2023 were considered by the examiner.
Claim Objections
Claims 3 and 19 objected to because of the following informalities:
Claim 3, line 1: –the– should be inserted before “machine”;
Claim 19, line 2: the first instance of “the” should be deleted.
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.
No limitations were interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 and 6-20 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.
In particular, claim 1 recites the following limitations:
[A1]: receiving data obtained from a sensor of an implanted joint implant;
[B1]: analyzing the data with a trained estimation model to simultaneously determine kinematic information of the joint in six degrees of freedom;
[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 implant with an inertial measurement unit or magnetic sensor, analyzing the measurements using prior knowledge 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]: a sensor of an implanted 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]: a sensor of an implanted 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 convention. For example, US 2007/0270722 A1 (Loeb) teaches that prior art includes a fully implanted sensor of a single joint angle based on the effect of a permanent magnet on a nearby Hall-effect sensor (¶ [0007]).
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) 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 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); ¶ [0059] and Fig. 1 of US 2005/0010301 A1 (Disilvestro 2005); 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). The plurality of disclosures depict 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 16-20 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 sensor is a Hall sensor and the joint implant further includes at least one magnet”. Claim 17 recites “the joint femoral component includes one or more magnets and the tibial component includes the Hall sensor”. These elements do not integrate the exception into a practical application of the exception or qualify as significantly more because the elements amount 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); ¶ [0059] and Fig. 1 of US 2005/0010301 A1 (Disilvestro 2005); 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). The plurality of disclosures depict 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) 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.
Claim 4 recites “the joint is a knee joint and the implanted joint implant includes femoral and tibial components”. Claim 14 recites “the implanted joint implant is any of a knee implant, shoulder implant, hip implant, and spine implant”. 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 convention. For example, the element is disclosed in at least ¶ [0022] and Fig. 3 of US 2006/0142670 A1 (DiSilvestro 2006); ¶ [0059] and Fig. 1 of US 2005/0010301 A1 (Disilvestro 2005); 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). The plurality of disclosures depict the well-understood, routine, and conventional nature of the element.
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 convention. 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); ¶¶ [0063]-[0064] of US 2022/0143820 A1 (Bashkirov); and ¶¶ [0067]-[0069] of US 2007/0239165 A1 (Amirouche).
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 convention as evidenced by US 2006/0071934 A1 (Sagar) 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 convention as evidenced by US 2022/0092240 A1 (Chi) 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 convention. For example, the elements are disclosed in at least ¶ [0139] of US 2021/0153778 A1 (Gupta); ¶ [0021] of US 2023/0218466 A1 (Seo); and ¶ [0020] of US 2008/0202233 A1 (Lan).
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 5 is not rejected under 35 U.S.C. §101. Specifically, claim 5 recites “the femoral component includes a plurality of magnets and the tibial component includes a Hall sensor”, which is not a well-understood, routine, or conventional element.
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 8-10 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 8 recites “the data” in line 1. Claim 1 recites “data obtained from a sensor” in line 3. Claim 7 recites “data from a prototype” in line 2. It is unclear whether the recitation of “the data” in claim 8 refers to the data of claim 1 or claim 7. For the purposes of examination, the recitation in claim 8 will be interpreted to be “the data from the prototype”. Claims 9 and 10 also recite “the data” which is unclear for similar reasons, so claims 9 and 10 are rejected on similar grounds.
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, and 14-20 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) in view of US 2024/0307125 A1 (Messinger).
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 sensor 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); 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).
The above combination is silent regarding whether the kinematic information of the joint includes six degrees of freedom.
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 (¶ [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). 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 determining kinematics in six degrees of freedom 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 and the joint implant further includes at least one magnet (Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami depict a femoral guiding pin encapsulating a permanent magnet and 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 are capable of determining an 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 and the implanted joint implant includes femoral and tibial components (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).
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 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 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 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 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; 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).
The above combination is silent regarding whether the kinematic information of the joint includes six degrees of freedom.
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 (¶ [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). 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 determining kinematics in six degrees of freedom 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 and the implanted joint implant includes femoral and tibial components (Figs. 1-2 and A. Sensor Configuration on Page 6272 of Arami depict a femoral part and a tibial part).
With regards to claim 17, the above combination teaches or suggests the femoral component includes one or more magnets and the tibial component includes the Hall sensor (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).
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 knee joint includes six degrees of freedom.
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 (¶ [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). 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 determining kinematics in six degrees of freedom as taught by Messinger. The motivation would have been to provide a more complete diagnostic analysis of the kinematics of the joint of Arami.
Claim 5 is 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) in view of US 2024/0307125 A1 (Messinger), as applied to claim 4 above, and further in view of US 2018/0116823 A1 (Johannaber).
With regards to claim 5, the above combination teaches or suggests the femoral component includes a magnet and the tibial component includes a Hall sensor (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).
The above combination is silent regarding a plurality of magnets.
In a system relevant to the problem of monitoring an orientation between joint implant parts, Johannaber teaches a component including a plurality of magnets (¶ [0025] and Fig. 1 depict a component including a plurality of magnets (106A, 106B) or a magnet ring). 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 Messinger to incorporate a plurality of magnets 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.
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) in view of US 2024/0307125 A1 (Messinger), as applied to claim 6 above, and further in view of US 2009/0030300 A1 (Ghaboussi).
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).
Conclusion
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/S.C.K./Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791