DETAILED ACTION
This action is responsive to claim amendments and Applicant’s Remarks filed 22 October 2025. The Examiner acknowledges the amendments to claims 1-9 and 12-15, as well as the cancellation of claims 10-11. Claims 1-9 and 12-15 are pending.
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 .
Claim Objections
Claim(s) 1 and 13-14 is/are objected to because of the following informalities:
Claims 1, 13, and 14 should each read “referring to the external knee adduction moment conversion table” [line 12 in claim 1, line 13 in claim 13, line 12 in claim 14].
Claims 13 and 14 should each read “[[the]] a external knee adduction moment conversion table stored in the memory” [lines 13-14 in claim 13, lines 12-13 in claim 14].
Appropriate correction is required.
Claim Interpretation
Examiner Notes: currently, NO limitation invokes interpretation under § 112(f).
Intended Use: The Examiner notes that the limitation “display the external knee adduction moment value and the prognosis of knee osteoarthritis on a display as information for assisting a physician to diagnose the condition of the knee joint” [see emphasized portion] in each of claims 1 [lines 19-21], 13 [lines 20-22], and 14 [lines 19-21] is considered to define an intended use of the invention [See also Rowe v. Dror, 112 F.3d 473, 478, 42 USPQ2d 1550, 1553 (Fed. Cir. 1997) ("where a patentee defines a structurally complete invention in the claim body and uses the preamble only to state a purpose or intended use for the invention, the preamble is not a claim limitation"), wherein the Examiner notes that the use of “for” in the identified limitation is considered to define an intended use limitation, even though the identified limitation is not in the preamble, such that the language following “for…” is not considered to be a positive recitation of assisting a physician or diagnosing the condition of the knee joint; To satisfy an intended use limitation which is limiting, a prior art structure which is capable of performing the intended use as recited in the preamble meets the claim (MPEP § 2111.02(II))], such that any prior art under § 102 or § 103 that teaches the claimed structure of the display to “display the external knee adduction moment value and the prognosis of knee osteoarthritis… as information” is considered to teach the identified limitation.
Claim Rejections - 35 USC § 112
The previously presented Examiner’s Note Regarding Machine Learning on p. 2-3 of the Non-Final Rejection dated 28 July 2025 is maintained.
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.
Claim(s) 1-9 and 12-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Representative claim(s) 1 [representing all independent claims] recite(s):
A processing device comprising:
an input/output interface configured to receive an acceleration detected, in a wired or wireless manner, by a single sensor which is attached to or around a single knee of a leg of a human and is configured to detect the acceleration of the human during exercise;
a memory configured to store computer readable instructions, the received acceleration, a predetermined instruction command, and an external knee adduction moment conversion table; and
a processor configured to execute the computer readable instructions, by executing the predetermined instruction command stored in the memory, so as to:
estimate an external knee adduction moment value, using the acceleration and referring the external knee adduction moment conversion table, based on a trained estimation model for estimating the external knee adduction moment value;
estimate a condition of a knee joint of the single knee based on the external knee adduction moment value, the condition of the knee joint indicating a prognosis of knee osteoarthritis; and
display the external knee adduction moment value and the prognosis of knee osteoarthritis on a display as information for assisting a physician to diagnose the condition of the knee joint.
(Emphasis added: abstract idea, additional element)
Step 2A Prong 1
Representative claim(s) 1 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper:
“estimate an external knee adduction moment value, using the acceleration and referring the external knee adduction moment conversion table” – may be performed by merely applying known or derived mathematical formulas on known or collected limited data under no particular time constraints [Applicant’s Specification ¶0054]
“estimate a condition of a knee joint of the single knee based on the external knee adduction moment value, the condition of the knee joint indicating a prognosis of knee osteoarthritis” – may be performed by merely observing known or collected data or information and drawing conclusions therefrom based on known correlations or relationships [Applicant’s Specification ¶0051]
If a claim, under BRI, covers performance of the limitations in the mind but for the mere recitation of extra-solutionary activity (and otherwise generic computer elements) then the claim falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1 of the Mayo framework as set forth in the 2019 PEG.
No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
Alternatively or additionally, these steps describe the concept of using implicit mathematical formula(s) [i.e., “estimate an external knee adduction moment value, using the acceleration and referring the external knee adduction moment conversion table”] to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts [Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)]. The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
The dependent claims merely include limitations that either further define the abstract idea [e.g. limitations relating to the data gathered or particular steps which are entirely embodied in the mental process] and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data [Int. Ventures v. Cap One Financial], collecting information, analyzing it, and displaying certain results of the collection and analysis [Electric Power Group], collection, storage, and recognition of data [Smart Systems Innovations].
Step 2A Prong 2
The judicial exception is not integrated into a practical application.
Representative claim 1 only recites additional elements of extra-solutionary activity – in particular, generic computer function [the Examiner notes that claims 1-15 fail to positively recite any step of data gathering using the recited sensor] – without further sufficient detail that would tie the abstract portions of the claim into a specific practical application (2019 PEG p. 55 – the instant claim, for example does not tie into a particular machine, a sufficiently particular form of data or signal collection – via the claimed generic computer function, or a sufficiently particular form of display or computing architecture/structure).
Dependent claim(s) 2-8 and 12 merely add detail to the abstract portions of the claim but do not otherwise encompass any additional elements which tie the claim(s) into a particular application/integration [the dependent claim(s) recite generic ‘steps’ which encompass mere computer instructions to carry out an otherwise wholly abstract idea].
Dependent claim(s) 9 encounter substantially the same issues as the independent claim(s) from which they depend in that they encompass further generic extra-solutionary activity [generic data gathering] and/or generic computer elements [storage, memory per se].
Accordingly, the claim(s) are not integrated into a practical application under Step 2A Prong 2.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1 and 13-14 as individual wholes fail to amount to significantly more than the judicial exception at Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of extra-solutionary activity [i.e., generic computer function] and generic computer elements cannot amount to significantly more than an abstract idea [MPEP § 2106.05(f)] and is further considered to merely implement an abstract idea on a generic computer [MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality].
For the independent claim portions and dependent claims which provide additional elements of extra-solutionary data gathering, MPEP § 2106.05(g) establishes that mere data gathering for determining a result does not amount to significantly more. The extra-solutionary activity of processor steps of acquiring, storing, and processing signals as presently recited, cannot provide an inventive concept which amounts to significantly more than the recited abstract idea.
For the independent claims as well as the dependent claims merely reciting generic computer elements and functions [generic computer elements (input/output interface, processor, memory) and respective functions therein], MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality.
Accordingly, the generic computer elements and functions, as presently limited, cannot provide an inventive concept since they fall under a generic structure and/or function that does not add a meaningful additional feature to the judicial exception(s) of the claim(s).
Claim(s) 1, 9, and 13-14 recite(s) a “trained estimation model of the estimating external knee adduction moment”, wherein claim 9 further recites training the estimation model using a correct answer label. Such a trained estimation model of the estimating external knee adduction moment is considered well-understood, routine, and conventional, as known by at least:
Hu (“Intelligent Sensor Networks”, previously presented) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Hu, Page 5)]
Huang (“Kernel Based Algorithms for Mining Huge Data Sets”, previously presented) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Huang, Page 1)]
Mitchell (“The Discipline of Machine Learning”, previously presented) [For example, we now have a variety of algorithms for supervised learning of classification and regression functions; that is, for learning some initially unknown function f : X [Calibri font/0xE0] Y given a set of labeled training examples {xi; yi} of inputs xi and outputs yi = f(xi) (Mitchell, Pages 3-4)]
Claim(s) 1, 6-8, and 13-15 recite(s) “a single sensor which is attached to or around a single knee of a leg of a human and is configured to detect the acceleration of the human during exercise” [claims 1, 13-14], “detecting exercise of the leg in a vertical direction by using the single sensor” [claim 6], “the single sensor detects the acceleration including both an acceleration in a horizontal direction and an acceleration in the vertical direction” [claims 7-8], and “a detection device including the single sensor” [claim 15]. While the Examiner notes that claims 1, 6-8, and 13-14 fail to positively recite the claimed sensor as comprising the claimed processing device, for the sake of compact prosecution, such a sensor/detection device is considered well-understood, routine, and conventional, as known by at least:
Applicant’s disclosure is not particular regarding the particular structure of the generically claimed sensor/detection device, and recites the claimed sensor/detection device at a high level of generality [see Applicant’s Specification ¶¶0027, 0042, 0105]. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the medical technology arts. Thus, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the field of movement detection. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional element because it describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) [see Berkheimer memo from April 19, 2018, Page 3, (III)(A)(1), not attached]. Adding hardware that performs “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible [TLI Communications].
Singh (US-20100245091-A1, previously presented) [a means for motion detection (not shown) such as, but not limited to a 3-axis accelerometer or the like, the identification and implementation of which is apparent to one of ordinary skill in the art (Singh ¶0076)]
Regarding the recitation of a particular treatment or prophylaxis to integrate the judicial exception as a practical application of the judicial exception or allow the claim(s) as whole(s) to amount to significantly more than the judicial exception, the Examiner notes that while claims 1 and 13-14 recite subject matter regarding “estimate a condition of the knee joint of the single knee based on the external knee adduction moment value, the condition of the knee joint indicating a prognosis of knee osteoarthritis [lines 15-18 of claim 1, lines 16-18 of claim 13, lines 15-17 of claim 14], and to “display the external knee adduction moment value and the prognosis of knee osteoarthritis on a display as information for assisting a physician to diagnose the condition of the knee joint” [lines 19-21 of claim 1, lines 20-22 of claim 13, lines 19-21 of claim 14] and further updating the external knee adduction moment value and the prognosis of knee osteoarthritis [lines 2-4 of claim 12], the identified subject matter is not considered to recite a particular treatment or prophylaxis, as the subject matter of claims 1 and 12-24 do not positively recite any treatment or prophylaxis being performed or applied to the knee joint [merely indicating a prognosis is not considered to define a treatment or prophylaxis, and merely displaying information for assisting a physician to make a diagnosis does not positively recite a diagnosis being made (intended use) or any treatment or prophylaxis being applied based on any diagnosis].
Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-9 and 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheung (US-20200397384-A1, previously presented) in view of Herr (US-20170042467-A1, previously presented).
Regarding claim 1, Cheung teaches
A processing device comprising:
an input/output interface configured to receive an acceleration rate detected, in a wired or wireless manner, by a single sensor [The sensor 12 may be communicatively connectable to a use's portable electronic device 16 via Bluetooth or other similar wired or wireless communication connection for data transfer (Cheung ¶0043); The data obtained by the user's portable electronic device (for example a mobile phone 16) from the sensor 12 can then be transferred via a telecommunication network to a remote server 20 where it is further processed as described below in more detail (Cheung ¶0044)] which is configured to detect the acceleration of a human during exercise [FIG. 2b depicts an exemplary locations for the sensor(s) which are located away from the knee joint for which the KAM is being predicted. In the view depicted a wearable sensor 12 is located proximal to the ankle on the lateral side of both feet (although only one foot is visible). Preferably, a sensor may be located at the lateral malleolus level of each leg-preferably on the lateral side although it has been determined that it is also possible to locate the sensors on the medial side (Cheung ¶0061, Fig. 2b); Each sensor 12 can measure the angular velocity and acceleration of an object in three dimensions and can calculate the object's orientation in 3D space (Cheung ¶0063)];
a memory configured to store computer readable instructions, the received acceleration rate, a predetermined instruction command [As depicted, the remote server 20 may perform sensor data calibration and real-time gait cycle segmentation for data processing 22 (Cheung ¶0046), wherein server 20 defining a computing system is considered to read on a memory storing computer-readable instructions], and an external knee adduction moment conversion table [The processed data is inputted into the trained machine learning model on the server 20 (or in an optional embodiment (not shown) on the portable electronic device 16) to predict the KAM for the specific subject during their recent gait cycle(s) (Cheung ¶0049), wherein a list defined by known outputs given certain inputs (defined by the trained model) is considered to define a table based at least one Applicant’s Fig. 7C-D, wherein a table is defined by a list of inputs and known outputs]; and
a processor configured to execute the computer readable instructions, by executing the predetermined instruction command stored in the memory [Cheung ¶0046], so as to:
estimate an external knee adduction moment value, using the acceleration and referring the external knee adduction moment conversion table, based on a trained estimation model for estimating the external knee adduction moment value [The processed data is inputted into the trained machine learning model on the server 20 (or in an optional embodiment (not shown) on the portable electronic device 16) to predict the KAM for the specific subject during their recent gait cycle(s) (Cheung ¶0049)], and
estimate a condition of a knee joint of the single knee based on the external knee adduction moment value, the condition of the knee joint indicating a prognosis of knee osteoarthritis [An alert may be issued to the user where the predicted KAM exceeds a predetermined threshold for one or more gait cycles (e.g. visual, haptic, sound or other means for alerting the user). This alert may then enable the user to subtly adjust their walking to reduce the KAM in the next gait cycle (Cheung ¶0049); the information may be utilised to issue an alert to the user when the predicted KAM for one or more gait cycles exceeds a predetermined threshold, enabling the user to correct/adjust their gait, preferably prior to the next gait cycle (Cheung ¶0117); Subjects provided conventionally measured clinical KAM together with sensor data for the purposes of training have diagnosis of knee osteoarthritis (Cheung ¶0056), wherein an incorrect/improper knee adduction moment based on training data of subjects with knee osteoarthritis is considered to be indicative of a prognostic condition of knee osteoarthritis]; and
display the external knee adduction moment value and the prognosis of knee osteoarthritis as information for assisting a physician to diagnose the condition of the knee joint [a Sensor Data Display module for visualising predicted KAM and a Server Data transfer module for transferring data to a portable electronic device (Cheung ¶0047); The predicted KAM can then be displayed at the use's portable electronic device in real time through an application or similar, optionally as a raw value or as a graphical representation. An alert may be issued to the user where the predicted KAM exceeds a predetermined threshold for one or more gait cycles (e.g. visual, haptic, sound or other means for alerting the user). This alert may then enable the user to subtly adjust their walking to reduce the KAM in the next gait cycle (Cheung ¶0049); wherein based on the Examiner’s note above regarding intended use, Cheung is considered to teach the limitation to “display…”].
However, Cheung fails to explicitly disclose wherein the single sensor is attached to or around a single knee of a leg of the human.
Herr discloses systems for monitoring knee adduction moment of a subject, wherein accelerometers for measuring data to be used in calculating a knee adduction moment are positioned around a knee of a leg of a human [A set of sensors is used to track the relative orientations and positions of segments of the tibias and femurs, and derivatives of these orientations and positions (angular and translational velocity, angular and translational acceleration, etc.). Any sensors or combinations of sensors capable of providing information towards these measurements may be used, as long as they are portable; examples include but are not limited to accelerometers (Herr ¶0043, Fig. 3); The above sensors communicate with one or several microprocessors. The function of these processors may include, for instance: reading and filtering sensor data, loading stored information about the user, calculating any signals of interest including knee adduction moment (Herr ¶0047)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Cheung to receive an acceleration detected from a single sensor which is attached to or around a knee of a leg of a human, as sensors positioned around a knee are considered to provide relevant data regarding the estimation of a corresponding knee adduction moment [Herr ¶0047].
Regarding claim 2, Cheung in view of Herr teaches
The processing device according to claim 1, wherein the processor estimates the external knee adduction moment value based on the acceleration including an acceleration after landing of the leg [Numbers 71, 73, 75, 77 indicate four peaks, in two gait cycles. As is known in the art, peaks 71 and 75 indicate heel-strike while peaks 73 and 77 represent the toe-off part of a subject's gait cycle. The segment 78a between number 71 and 73, as well as the segment 78b between 75 and 77 is the stance phase of each gait cycle and it is the phase which is useful for KAM calculation (Cheung ¶0084, Fig. 3b); The algorithm adopted feeds all 6 axis IMU sensor data (3-axis accelerometer data and 3-axis gyroscope data) into the prediction module/the trained neural network (Cheung ¶0085), wherein the Examiner notes that while the data depicted in Fig. 3b is gyroscope data, segment 78b is considered to define a relevant time segment after landing of the leg (heel strike) for estimating the knee adduction moment, wherein ¶0085 discloses that both gyroscope and accelerometer data is input into the model].
Regarding claim 3, Cheung in view of Herr teaches
The processing device according to claim 1, wherein the processor estimates the external knee adduction moment value based on the acceleration including a peak value of an acceleration detected after landing of the leg [Cheung ¶¶0063, 0084-0085, Fig. 3b, wherein the segment 78a is defined as after heel-strike (after landing of the leg), which is considered to include any peak value of acceleration].
Regarding claim 4, Cheung in view of Herr teaches
The processing device according to claim 1, wherein the processor estimates the external knee adduction moment value based on the acceleration including a number of peaks of an acceleration detected after landing of the leg [Cheung ¶¶0063, 0084-0085, Fig. 3b, wherein the segment 78a is defined as after heel-strike (after landing of the leg), which is considered to include any number of peaks of acceleration].
Regarding claim 5, Cheung in view of Herr teaches
The processing device according to claim 1, wherein the processor estimates the external knee adduction moment value based on the acceleration including a peak value of an acceleration detected after landing of the leg and time taken from landing of the leg until the peak value is detected [Cheung ¶¶0063, 0084-0085, Fig. 3b, wherein the segment 78a is defined as time after heel-strike (after landing of the leg), which is considered to include any peak value of acceleration].
Regarding claim 6, Cheung in view of Herr teaches
The processing device according to claim 1, wherein the acceleration after landing of the leg is specified by detecting exercise of the leg in a vertical direction by using the single sensor [Cheung ¶¶0063, 0084].
Regarding claim 7, Cheung in view of Herr teaches
The processing device according to claim 1, wherein
the single sensor detect the acceleration including both an acceleration in a horizontal direction and an acceleration in the vertical direction [Cheung ¶0063], and
the exercise of the leg in the vertical direction is based on the acceleration in the vertical direction [Cheung ¶0085].
Regarding claim 8, Cheung in view of Herr teaches
The processing device according to claim 1, wherein
the single sensor detect the acceleration including both an acceleration in a horizontal direction and an acceleration in the vertical direction [Cheung ¶0063], and
the processor estimates the external knee adduction moment value based on the acceleration in the horizontal direction [Cheung ¶0085].
Regarding claim 9, Cheung in view of Herr teaches
The processing device according to claim 1, wherein the trained estimation model is obtained through learning using the acceleration and a learning external knee adduction moment value prepared in advance as a correct answer label [The model is trained based on using the Subject's personal information (such as age, gender, height, body mass, knee width and ankle width), IMU sensor data and measured KAM (Cheung ¶0100)].
Regarding claim 12, Cheung in view of Herr teaches
The processing device according to claim 1, wherein the processor is further configured to update the external knee adduction moment value and the prognosis of knee osteoarthritis at a predetermined timing [the predicted KAM for a subject for a gait cycle is outputted from the neural network before a next gait cycle of the subject (Cheung ¶0011)].
Regarding claim 13, Cheung teaches
A non-transitory computer-readable storage medium for embodying computer readable instructions for causing a computer to execute a process by a processor so as to perform the steps of:
receiving, via an input/output interface in a wired or wireless manner, an acceleration detected by a single sensor [The sensor 12 may be communicatively connectable to a use's portable electronic device 16 via Bluetooth or other similar wired or wireless communication connection for data transfer (Cheung ¶0043); The data obtained by the user's portable electronic device (for example a mobile phone 16) from the sensor 12 can then be transferred via a telecommunication network to a remote server 20 where it is further processed as described below in more detail (Cheung ¶0044)] configured to detect the acceleration of a human during exercise [FIG. 2b depicts an exemplary locations for the sensor(s) which are located away from the knee joint for which the KAM is being predicted. In the view depicted a wearable sensor 12 is located proximal to the ankle on the lateral side of both feet (although only one foot is visible). Preferably, a sensor may be located at the lateral malleolus level of each leg-preferably on the lateral side although it has been determined that it is also possible to locate the sensors on the medial side (Cheung ¶0061, Fig. 2b); Each sensor 12 can measure the angular velocity and acceleration of an object in three dimensions and can calculate the object's orientation in 3D space (Cheung ¶0063)], the received acceleration being stored in a memory of the computer [As depicted, the remote server 20 may perform sensor data calibration and real-time gait cycle segmentation for data processing 22 (Cheung ¶0046), wherein server 20 defining a computing system is considered to read on a memory storing computer-readable instructions];
estimating an external knee adduction moment value, using the acceleration and referring the external knee adduction moment conversion table stored in the memory, based on a trained estimation model for estimating the external knee adduction moment value [The processed data is inputted into the trained machine learning model on the server 20 (or in an optional embodiment (not shown) on the portable electronic device 16) to predict the KAM for the specific subject during their recent gait cycle(s) (Cheung ¶0049), wherein a list defined by known outputs given certain inputs (defined by the trained model) is considered to define a table based at least one Applicant’s Fig. 7C-D, wherein a table is defined by a list of inputs and known outputs];
estimating a condition of a knee joint of the single knee based on the external knee adduction moment value, the condition of the knee joint indicating a prognosis of knee osteoarthritis [An alert may be issued to the user where the predicted KAM exceeds a predetermined threshold for one or more gait cycles (e.g. visual, haptic, sound or other means for alerting the user). This alert may then enable the user to subtly adjust their walking to reduce the KAM in the next gait cycle (Cheung ¶0049); the information may be utilised to issue an alert to the user when the predicted KAM for one or more gait cycles exceeds a predetermined threshold, enabling the user to correct/adjust their gait, preferably prior to the next gait cycle (Cheung ¶0117); Subjects provided conventionally measured clinical KAM together with sensor data for the purposes of training have diagnosis of knee osteoarthritis (Cheung ¶0056), wherein an incorrect/improper knee adduction moment based on training data of subjects with knee osteoarthritis is considered to be indicative of a prognostic condition of knee osteoarthritis]; and
displaying the external knee adduction moment value and the prognosis of knee osteoarthritis on a display as information for assisting a physician to diagnose the condition of the knee joint [a Sensor Data Display module for visualising predicted KAM and a Server Data transfer module for transferring data to a portable electronic device (Cheung ¶0047); The predicted KAM can then be displayed at the use's portable electronic device in real time through an application or similar, optionally as a raw value or as a graphical representation. An alert may be issued to the user where the predicted KAM exceeds a predetermined threshold for one or more gait cycles (e.g. visual, haptic, sound or other means for alerting the user). This alert may then enable the user to subtly adjust their walking to reduce the KAM in the next gait cycle (Cheung ¶0049); wherein based on the Examiner’s note above regarding intended use, Cheung is considered to teach the limitation of “displaying…”].
However, Cheung fails to explicitly disclose wherein the single sensor is attached to or around a single knee of a leg of the human.
Herr discloses systems for monitoring knee adduction moment of a subject, wherein accelerometers for measuring data to be used in calculating a knee adduction moment are positioned around a knee of a leg of a human [A set of sensors is used to track the relative orientations and positions of segments of the tibias and femurs, and derivatives of these orientations and positions (angular and translational velocity, angular and translational acceleration, etc.). Any sensors or combinations of sensors capable of providing information towards these measurements may be used, as long as they are portable; examples include but are not limited to accelerometers (Herr ¶0043, Fig. 3); The above sensors communicate with one or several microprocessors. The function of these processors may include, for instance: reading and filtering sensor data, loading stored information about the user, calculating any signals of interest including knee adduction moment (Herr ¶0047)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the non-transitory computer-readable storage medium of Cheung to receive an acceleration detected from a single sensor which is attached to or around a knee of a leg of a human, as sensors positioned around a knee are considered to provide relevant data regarding the estimation of a corresponding knee adduction moment [Herr ¶0047].
Regarding claim 14, Cheung teaches
A method for causing a processor to execute a process, the method comprising executing on the processor the steps of:
receiving, via an input/output interface in a wired or wireless manner, an acceleration detected by a single sensor [The sensor 12 may be communicatively connectable to a use's portable electronic device 16 via Bluetooth or other similar wired or wireless communication connection for data transfer (Cheung ¶0043); The data obtained by the user's portable electronic device (for example a mobile phone 16) from the sensor 12 can then be transferred via a telecommunication network to a remote server 20 where it is further processed as described below in more detail (Cheung ¶0044)] configured to detect the acceleration of a human during exercise [FIG. 2b depicts an exemplary locations for the sensor(s) which are located away from the knee joint for which the KAM is being predicted. In the view depicted a wearable sensor 12 is located proximal to the ankle on the lateral side of both feet (although only one foot is visible). Preferably, a sensor may be located at the lateral malleolus level of each leg-preferably on the lateral side although it has been determined that it is also possible to locate the sensors on the medial side (Cheung ¶0061, Fig. 2b); Each sensor 12 can measure the angular velocity and acceleration of an object in three dimensions and can calculate the object's orientation in 3D space (Cheung ¶0063)], the received acceleration being stored in a memory [As depicted, the remote server 20 may perform sensor data calibration and real-time gait cycle segmentation for data processing 22 (Cheung ¶0046), wherein server 20 defining a computing system is considered to read on a memory storing computer-readable instructions];
estimating an external knee adduction moment value, using the acceleration and referring the external knee adduction moment conversion table stored in the memory, based on a trained estimation model for estimating the external knee adduction moment value [The processed data is inputted into the trained machine learning model on the server 20 (or in an optional embodiment (not shown) on the portable electronic device 16) to predict the KAM for the specific subject during their recent gait cycle(s) (Cheung ¶0049), wherein a list defined by known outputs given certain inputs (defined by the trained model) is considered to define a table based at least one Applicant’s Fig. 7C-D, wherein a table is defined by a list of inputs and known outputs];
estimating a condition of a knee joint of the single knee based on the external knee adduction moment value, the condition of the knee joint indicating a prognosis of knee osteoarthritis [An alert may be issued to the user where the predicted KAM exceeds a predetermined threshold for one or more gait cycles (e.g. visual, haptic, sound or other means for alerting the user). This alert may then enable the user to subtly adjust their walking to reduce the KAM in the next gait cycle (Cheung ¶0049); the information may be utilised to issue an alert to the user when the predicted KAM for one or more gait cycles exceeds a predetermined threshold, enabling the user to correct/adjust their gait, preferably prior to the next gait cycle (Cheung ¶0117); Subjects provided conventionally measured clinical KAM together with sensor data for the purposes of training have diagnosis of knee osteoarthritis (Cheung ¶0056), wherein an incorrect/improper knee adduction moment based on training data of subjects with knee osteoarthritis is considered to be indicative of a prognostic condition of knee osteoarthritis]; and
displaying the external knee adduction moment value and the prognosis of knee osteoarthritis on a display as information for assisting a physician to diagnose the condition of the knee joint [a Sensor Data Display module for visualising predicted KAM and a Server Data transfer module for transferring data to a portable electronic device (Cheung ¶0047); The predicted KAM can then be displayed at the use's portable electronic device in real time through an application or similar, optionally as a raw value or as a graphical representation. An alert may be issued to the user where the predicted KAM exceeds a predetermined threshold for one or more gait cycles (e.g. visual, haptic, sound or other means for alerting the user). This alert may then enable the user to subtly adjust their walking to reduce the KAM in the next gait cycle (Cheung ¶0049); wherein based on the Examiner’s note above regarding intended use, Cheung is considered to teach the limitation of “displaying…”].
However, Cheung fails to explicitly disclose wherein the single sensor is attached to or around a single knee of a leg of the human.
Herr discloses systems for monitoring knee adduction moment of a subject, wherein accelerometers for measuring data to be used in calculating a knee adduction moment are positioned around a knee of a leg of a human [A set of sensors is used to track the relative orientations and positions of segments of the tibias and femurs, and derivatives of these orientations and positions (angular and translational velocity, angular and translational acceleration, etc.). Any sensors or combinations of sensors capable of providing information towards these measurements may be used, as long as they are portable; examples include but are not limited to accelerometers (Herr ¶0043, Fig. 3); The above sensors communicate with one or several microprocessors. The function of these processors may include, for instance: reading and filtering sensor data, loading stored information about the user, calculating any signals of interest including knee adduction moment (Herr ¶0047)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Cheung to receive an acceleration detected from a single sensor which is attached to or around a knee of a leg of a human, as sensors positioned around a knee are considered to provide relevant data regarding the estimation of a corresponding knee adduction moment [Herr ¶0047].
Regarding claim 15, Cheung in view of Herr teaches
A processing system comprising:
the processing device according to claim 1; and
a detection device including the single sensor [see § 103 modification of claim 1 above; Herr ¶¶0043, 0047].
Response to Arguments
Applicant’s arguments, see Applicant’s Remarks p. 8, filed 22 October 2025, with respect to the previously presented claim objections have been fully considered and are persuasive. The claim objections of claims 1 and 13-14 have been withdrawn.
Applicant’s arguments, see Applicant’s Remarks p. 8, with respect to the previously applied claim rejections under § 112(b) have been fully considered and are persuasive. The rejections of claims 2-5, 9, 13-15, and those dependent therefrom been withdrawn.
Applicant's arguments, see Applicant’s Remarks p. 9-12, with respect to the previously applied claim rejections under § 101 have been fully considered but they are not persuasive.
The Applicant asserts that the amendment to claims 1 and 13-14 to incorporate the subject matter of claims 10-11 allow claims 1 and 13-14 to recite features that are significantly more than an abstract idea and integrate the invention into a practical application. However, the Examiner notes that merely indicating a prognosis of knee osteoarthritis and displaying the external knee adduction moment value and prognosis on a display as information for assisting a physician to diagnose the condition of the knee joint is not considered to recite any subject matter that may be considered a particular treatment or prophylaxis, and is considered to recite subject matter that merely further limits the identified abstract idea [indicating a prognosis…] and is considered a generic computer element and function [displaying…].
The Applicant also asserts that the invention of claim 1 cannot be performed by pen-and-paper because it uses a trained estimation model, estimates a knee joint of the single knee based on the external knee adduction moment value (the condition of the knee joint indicating a prognosis of knee osteoarthritis), and displays the external knee adduction moment value and the prognosis of knee osteoarthritis on a display as information for assisting a physician to diagnose the condition of the knee joint. However, while the Examiner does not specifically disagree that the limitations directed towards use of a trained estimation model and displaying the external knee adduction moment value and the prognosis of knee osteoarthritis on a display as information for assisting a physician to diagnose the condition of the knee joint cannot be performed by pen-and-paper, the Examiner does disagree with the Applicant’s argument that the limitation(s) directed towards estimating a knee joint of the single knee based on the external knee adduction moment value (the condition of the knee joint indicating a prognosis of knee osteoarthritis) cannot be performed by pen-and-paper, as the Examiner notes that the estimation may be performed in the mind or by hand by merely applying known or derived mathematical formulas on known or collected limited data under no particular time constraints [Note that, in the present disclosure, any of the following two values can be used as the “KAM value”. There is a two-dimensional curve (KAM value curve) in which time is plotted on the horizontal axis and KAM values calculated at each time are plotted on the vertical axis. At this time, the highest peak value (KAM peak value) detected during the stance phase can be used as the first KAM value. It is possible to reflect a value at a moment when the largest force is applied to the knee joint during the stance phase, on this KAM peak value. As the second KAM value, an area value (KAM area value) between a KAM value curve and the horizontal axis (straight line) during the stance phase can be used. It is possible to reflect a value of a whole load applied to the knee joint during the stance phase, on this KAM area value. In fact, the KAM peak value is used for evaluating a degree or a prognosis of knee osteoarthritis in document 1 listed above, and the KAM area value is used for evaluating a degree or a prognosis of knee osteoarthritis in documents 2 and 3 listed above (Applicant’s Specification ¶0054, see emphasized portions)]. Regarding the limitations directed towards use of a trained estimation model and displaying the external knee adduction moment value and the prognosis of knee osteoarthritis on a display as information for assisting a physician to diagnose the condition of the knee joint, the Examiner notes that the argued limitations were not identified as being directed towards a judicial exception at Step 2A Prong 1 and were thus further analyzed at Step 2A Prong 2 and Step 2B, wherein the Examiner identified the limitations as being directed towards well understood, routine, and conventional subject matter and generic computer elements and functions, respectively.
The Applicant further asserts that the claimed features performed by a process should be categorized as either (1) Applying the judicial exception with, or by use of, a particular machine [MPEP § 2106.05(b)], or (2) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception [MPEP § 2106.05(e)], as the Applicant notes that claim 1 is clearly directed to a “machine”, which the Applicant notes is subject matter eligible under § 101. The Applicant also notes that each of claims 13-14 recite additional elements that integrate the alleged judicial exception into a practical application. However, the Examiner disagrees with the Applicant’s arguments, as the claimed device is considered to only recite generic computer elements and functions, such that (1) the judicial exception is NOT applied with, or by use of, a particular machine. Furthermore, the Examiner notes that the judicial exception (2) is not applied or used in a meaningful way, as the identified additional elements are merely considered to refer to extra-solutionary activity [generic computer elements/function] or well-understood, routine, and conventional subject matter. The mere recitation of the claim(s) being directed towards a machine does not preclude a rejection under § 101.
Applicant's arguments, see Applicant’s Remarks p. 12-14, with respect to the previously applied claim rejections under § 103 have been fully considered but they are not persuasive.
The Applicant asserts that the previously presented prior art fails to disclose or suggest all elements recited in amended claim 1, as the Applicant notes that Cheung merely describes detecting a KAM value by at least one sensor attached at an ankle, not a knee, and wherein Herr uses a plurality of sensors to detect a KAM value. However, the Examiner notes that 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). Cheung was not specifically applied to teach detecting a KAM value at a knee, and was further modified by Herr, as Herr discloses that data to be used in calculating a knee adduction moment may be obtained from sensors that are positioned around a knee of a leg of a human [A set of sensors is used to track the relative orientations and positions of segments of the tibias and femurs, and derivatives of these orientations and positions (angular and translational velocity, angular and translational acceleration, etc.). Any sensors or combinations of sensors capable of providing information towards these measurements may be used, as long as they are portable; examples include but are not limited to accelerometers (Herr ¶0043, Fig. 3); The above sensors communicate with one or several microprocessors. The function of these processors may include, for instance: reading and filtering sensor data, loading stored information about the user, calculating any signals of interest including knee adduction moment (Herr ¶0047)]. Herr was not specifically applied to employ the plurality of sensors as disclosed by Herr.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHARLES A MARMOR II/Supervisory Patent Examiner
Art Unit 3791
/S.P.L./Examiner, Art Unit 3791