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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 13 April 2026 has been entered.
The Examiner acknowledges the amendments to claims 1 and 12-14, as well as the cancelation of claim 9. Claims 1-8 and 12-15 are pending.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
“411” [Fig. 5]
“A1” [Fig. 7A]; “A2” [Fig. 7A]; “A3” [Fig. 7A]; “A4” [Fig. 7A]; “A5” [Fig. 7A]
“U2” [Fig. 7B]; U3” [Fig. 7B]; U4” [Fig. 7B]; U5” [Fig. 7B]
“N1” [Fig. 7B]; “N2” [Fig. 7B]; “N3” [Fig. 7B]; “N4” [Fig. 7B]; “N5” [Fig. 7B]
“P1” [Fig. 7B]; “P2” [Fig. 7B]; “P3” [Fig. 7B]; “P4” [Fig. 7B]; “P5” [Fig. 7B]
“W1” [Fig. 7B]; “W2” [Fig. 7B]; “W3” [Fig. 7B]; “W4” [Fig. 7B]; “W5” [Fig. 7B]
“M1” [Fig. 7B]; “M2” [Fig. 7B]; “M3” [Fig. 7B]; “M4” [Fig. 7B]; “M5” [Fig. 7B]
“L1” [Fig. 7B]; “L2” [Fig. 7B]; “L3” [Fig. 7B]; “L4” [Fig. 7B]; “L5” [Fig. 7B]
“w1” [Fig. 7C]; “w2” [Fig. 7C]; “w3” [Fig. 7C]; “w4” [Fig. 7C]; “w5” [Fig. 7C]; “w8” [Fig. 7C]
“m1” [Fig. 7C]; “m2” [Fig. 7C]; “m3” [Fig. 7C]; “m4” [Fig. 7C]; “m5” [Fig. 7C]; “m6” [Fig. 7C]; “m8” [Fig. 7C]; “m9” [Fig. 7C]
“O1” [Fig. 7E]; “O2” [Fig. 7E]; “O3” [Fig. 7E]; “O4” [Fig. 7E]; “O5” [Fig. 7E]
“B1” [Fig. 7E]; “B2” [Fig. 7E]; “B3” [Fig. 7E]; “B4” [Fig. 7E]; “B5” [Fig. 7E]
“C1” [Fig. 7E]; “C2” [Fig. 7E]; “C3” [Fig. 7E]; “C4” [Fig. 7E]; “C5” [Fig. 7E]
“I1” [Fig. 7F]; “I2” [Fig. 7F]; “I3” [Fig. 7F]; “I4” [Fig. 7F]; “I5” [Fig. 7F]
“E1” [Fig. 7F]; “E2” [Fig. 7F]; “E3” [Fig. 7F]; “E4” [Fig. 7F]; “E5” [Fig. 7F]
“S114” [Fig. 8]; “S115” [Fig. 8]; “S514” [Fig. 13]
“38” [Fig. 16; the Examiner notes that ¶0098 of the Applicant’s Specification recites that Fig. 16 depicts “back icon 33”, which should read “back icon 38”]
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 external knee adduction moment peak value or the external knee adduction moment area value on a display as information for assisting a physician to diagnose a condition of the knee joint” [see emphasized portion] in each of claims 1 [lines 23-26], 13 [lines 21-24], 14 [lines 20-23] 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"); 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 external knee adduction moment peak value or the external knee adduction moment area value on a display as information” is considered to teach the identified limitation.
35 USC § 112 Analysis
Examiner’s Note Regarding Machine Learning: the claimed “trained estimation model for estimating an external knee adduction moment value, the trained estimation model being obtained through learning using a learning acceleration and a learning external knee adduction moment value prepared in advance as a correct answer label” of claim(s) 1 and 13-14 was considered under § 112(a), wherein the Examiner notes that the disclosure of training the estimation model of ¶¶0097, 0101, 0110 of the Applicant’s Specification is considered to provide sufficient written description support for the “trained estimation model for estimating an external knee adduction moment value” as presently claimed for one of ordinary skill in the art to understand that the Applicant possessed the instant invention at the time of filing.
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) 1-8 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 continuously detect the acceleration of the single knee during exercise;
a memory configured to store computer readable instructions, the received acceleration, and a predetermined instruction command; and
a processor configured to execute the computer readable instructions, by executing the predetermined instruction command stored in the memory, so as to:
apply the received acceleration to a trained estimation model for estimating an external knee adduction moment value, the trained estimation model being obtained through learning using a learning acceleration and a learning external knee adduction moment value prepared in advance as a correct answer label;
estimate the external knee adduction moment value based on an application result of the trained estimation model;
estimate an external knee adduction moment peak value or an external knee adduction moment area value of a knee joint of the single knee based on the external knee adduction moment value; and
display the external knee adduction moment value, and the external knee adduction moment peak value or the external knee adduction moment area value on a display as information for assisting a physician to diagnose a 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:
“apply the received acceleration to a… model for estimating an external knee adduction moment value” – may be performed by merely inputting at least a limited amount of known or previously collected data into a known or previously defined model based on known or derived relationships [Applicant’s Specification ¶0057]
“estimate the external knee adduction moment value based on an application result of the… model” – may be performed by merely observing at least a limited amount of known or previously collected data and drawing mental conclusions therefrom based on known or derived relationships/mathematical equations [Applicant’s Specification ¶¶0051, 0054]
“estimate an external knee adduction moment peak value or an external knee adduction moment area value of a knee joint of the single knee based on the external knee adduction moment value” – be performed by merely observing at least a limited amount of known or previously collected data and drawing mental conclusions therefrom based on known or derived relationships/mathematical equations [Applicant’s Specification ¶0054]
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 peak value or an external knee adduction moment area value of a knee joint of the single knee based on the external knee adduction moment value”] 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, extra-solution activity [generic computer function; wherein the Examiner notes that claim 1 fails to positively recite a sensor or use of a sensor to gather data (the acceleration received is merely defined as having come from a single sensor as claimed, which is considered to only limit the type of data)] – 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 extra-solution activity identified above, 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 ‘units’ or ‘steps’ which encompass mere computer instructions to carry out an otherwise wholly abstract idea].
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 [acquiring, storing, outputting signals, etc.] 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 [input/output interface, memory, processor, display, each recited at a high level of generality and corresponding 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 corresponding 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 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 continuously detect the acceleration of the single knee 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 processing device/computer as comprising the claimed sensor [claims 1, 13], or any step of using the claimed sensor [claim 14], for the sake of compact prosecution, such a single sensor/detection device comprising the single sensor 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 single sensor/detection device including the single sensor, and recites the single sensor/detection device including the single sensor at a high level of generality [An acceleration sensor is typically used as the detection device 200, and detects an acceleration rate during exercise. However, not only the acceleration sensor but also any sensor capable of detecting the exercise of the user 10, in particular, movements including bending and stretching of the knee, such as a gyro sensor, a geomagnetic sensor, and an expansion/contraction sensor can be used, and it is also possible to use an output value corresponding thereto for estimating the condition of the exercise or assisting the estimation. In addition, a plurality of sensors such as an acceleration sensor and a gyro sensor can be used in combination (Applicant’s Specification ¶0027); As one example, an acceleration sensor is used as the sensor 212. The acceleration sensor detects a change ratio of a movement amount (velocity) per unit time. The types thereof include a capacitance type, a piezo type, and a heat detection type, and any of these can be suitably used… In addition, as the sensor 212, it is possible to use a gyro sensor in combination with an acceleration sensor… Note that, in addition to this example, sensors capable of detecting the exercise of the user 10, in particular, movements including bending and stretching or shaking of the knee, such as a geomagnetic sensor and an expansion/contraction sensor, can be appropriately used in combination (Applicant’s Specification ¶0042); in place of or in combination with the acceleration sensor, any sensor capable of detecting the exercise of the user 10, in particular, movements including bending and stretching of the knee, such as a gyro sensor, a geomagnetic sensor, and an expansion/contraction sensor can be used (Applicant’s Specification ¶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 detecting movement/motion. 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].
Claim 1 and 13-14 recite “a trained estimation model for estimating an external knee adduction moment value, the trained estimation model being obtained through leaning using a learning acceleration and a learning external knee adduction moment value prepared in advance as a correct answer label”. Such a “trained estimation model” is considered well-understood, routine, and conventional, as known by at least:
Hu (“Intelligent Sensor Networks”, NPL 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”, NPL 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”, NPL 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)]
Examiner’s Note Regarding Particular Treatment or Prophylaxis: Claim(s) 1 and 13-14 recite subject matter regarding “displaying the external knee adduction moment value, and the external knee adduction moment peak value or the external knee adduction moment area value on a display as information for assisting a physician to diagnose a condition of the knee joint” [lines 23-26 of claim 1; lines 21-24 of claim 13; lines 20-23 of claim 14], which the Examiner notes is not considered to be a particular treatment or prophylaxis, as none of the identified claims positively recite or include language that is considered to be a particular treatment or prophylaxis as an additional element to integrate the judicial exception into a practical application or allow the identified claims to amount to significantly more than the judicial exception [See Intended Use analysis above under Claim Interpretations; wherein the Examiner further notes that no diagnosis is positively recited as being made and no treatment or prophylaxis is recited as being applied based on any diagnosis] [MPEP § 2106.04(d)(2)].
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) 1-8 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 detected, in a wired or wireless manner [Preferably the sensor 12 may be an inertial measurement unit (IMU) which includes accelerometer and gyroscope which provide data during various positions in the gait cycle for a subject when he/she is walking or running. 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)], by a single sensor which is attached to or around a leg of a human and is configured to continuously detect the acceleration of the leg during exercise [the system and method of predicting KAM can enable a subject to receive predicted KAM arising from walking and other activities being performed even when outside the laboratory in real-time or almost real-time; so that the subject can make subtle adjustments to their gait pattern and thereby reduce KAM (Cheung ¶0040), wherein the system of Cheung being configured to provide real-time analysis during walking is considered to read on the sensor continuously detecting acceleration; 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, and 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
a processor configured to execute the computer readable instructions, by executing the predetermined instruction command stored in the memory [Cheung ¶0046], so as to:
apply the received acceleration to a trained estimation model for estimating an external knee adduction moment value, the trained estimation model being obtained through learning using a learning acceleration and a learning external knee adduction moment value prepared in advance as a correct answer label [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); As discussed, for a specific subject gait cycle in real time, the relationship between KAM and processed sensor data is able to be predicted using a trained machine learning model (Cheung ¶0089); 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); the trained model can then be used in real time for KAM prediction (Cheung ¶0103)];
estimate the external knee adduction moment value based on an application result of the trained estimation model [Cheung ¶¶0049, 0089, 0100, 0103];
estimate an external knee adduction moment peak value or an external knee adduction moment area value of a knee joint of the single knee based on the external knee adduction moment value [Cheung ¶¶0049, 0089, 0100, 0103, wherein Cheung Fig. 4 depicts that the estimated KAM includes an external knee adduction moment peak value]; and
display the external knee adduction moment value, and the external knee adduction moment peak value or the external knee adduction moment area value on a display as information for assisting a physician to diagnose a 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 using a display to display “information”].
However, Cheung fails to explicitly disclose wherein the single sensor is attached to or around a single knee of the leg of the human and is configured to continuously detect the acceleration of the single knee during exercise.
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)].
As Cheung discloses training the trained estimation model for estimating a knee adduction moment using sensor data relevant to determining a knee adduction moment, such that when the trained estimation model is used, relevant sensor data is input to estimate a knee adduction moment [Cheung ¶0100], 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 employ wherein the single sensor is attached to or around a single knee of the leg of the human and is configured to continuously detect the acceleration of the single knee during exercise, 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 are 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 2, 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 6, wherein
the single sensor detects 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 detected 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 detects the acceleration including both an acceleration in a horizontal direction and an acceleration in a 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 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 external knee adduction moment peak value or the external knee adduction moment area value 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 [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] so as to perform the steps of:
receiving, via an input/output interface in a wired or wireless manner [Preferably the sensor 12 may be an inertial measurement unit (IMU) which includes accelerometer and gyroscope which provide data during various positions in the gait cycle for a subject when he/she is walking or running. 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)], an acceleration detected by a single sensor which is attached to or around a leg of a human and is configured to continuously detect the acceleration of the leg during exercise, the received acceleration being stored in a memory of the computer [the system and method of predicting KAM can enable a subject to receive predicted KAM arising from walking and other activities being performed even when outside the laboratory in real-time or almost real-time; so that the subject can make subtle adjustments to their gait pattern and thereby reduce KAM (Cheung ¶0040), wherein the system of Cheung being configured to provide real-time analysis during walking is considered to read on the sensor continuously detecting acceleration; 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)];
applying the received acceleration to a trained estimation model for estimating an external knee adduction moment value, the trained estimation model being obtained through learning using a learning acceleration and a learning external knee adduction moment value prepared in advance as a correct answer label [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); As discussed, for a specific subject gait cycle in real time, the relationship between KAM and processed sensor data is able to be predicted using a trained machine learning model (Cheung ¶0089); 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); the trained model can then be used in real time for KAM prediction (Cheung ¶0103)];
estimating an external the external knee adduction moment value based on an application result of the trained estimation model [Cheung ¶¶0049, 0089, 0100, 0103];
estimating an external knee adduction moment peak value or an external knee adduction moment area value of a knee joint of the single knee based on the external knee adduction moment value [Cheung ¶¶0049, 0089, 0100, 0103, wherein Cheung Fig. 4 depicts that the estimated KAM includes an external knee adduction moment peak value]; and
displaying the external knee adduction moment value, and the external knee adduction moment peak value or the external knee adduction moment area value on a display as information for assisting a physician to diagnose a 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 using a display to display “information”].
However, Cheung fails to explicitly disclose wherein the single sensor is attached to or around a single knee of the leg of the human and is configured to continuously detect the acceleration of the single knee during exercise.
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)].
As Cheung discloses training the trained estimation model for estimating a knee adduction moment using sensor data relevant to determining a knee adduction moment, such that when the trained estimation model is used, relevant sensor data is input to estimate a knee adduction moment [Cheung ¶0100], 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 employ wherein the single sensor is attached to or around a single knee of the leg of the human and is configured to continuously detect the acceleration of the single knee during exercise, 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 [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] the steps of:
receiving, via an input/output interface in a wired or wireless manner [Preferably the sensor 12 may be an inertial measurement unit (IMU) which includes accelerometer and gyroscope which provide data during various positions in the gait cycle for a subject when he/she is walking or running. 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)], an acceleration detected by a single sensor which is attached to or around a leg of a human and is configured to continuously detect the acceleration of the leg during exercise, the received acceleration being stored in a memory [the system and method of predicting KAM can enable a subject to receive predicted KAM arising from walking and other activities being performed even when outside the laboratory in real-time or almost real-time; so that the subject can make subtle adjustments to their gait pattern and thereby reduce KAM (Cheung ¶0040), wherein the system of Cheung being configured to provide real-time analysis during walking is considered to read on the sensor continuously detecting acceleration; 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)];
applying the received acceleration to a trained estimation model for estimating an external knee adduction moment value, the trained estimation model being obtained through learning using a learning acceleration and a learning external knee adduction moment value prepared in advance as a correct answer label [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); As discussed, for a specific subject gait cycle in real time, the relationship between KAM and processed sensor data is able to be predicted using a trained machine learning model (Cheung ¶0089); 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); the trained model can then be used in real time for KAM prediction (Cheung ¶0103)];
estimating the external knee adduction moment value based on an application result of the trained estimation model [Cheung ¶¶0049, 0089, 0100, 0103];
estimating an external knee adduction moment peak value or an external knee adduction moment area value of a knee joint of the single knee based on the external knee adduction moment value [Cheung ¶¶0049, 0089, 0100, 0103, wherein Cheung Fig. 4 depicts that the estimated KAM includes an external knee adduction moment peak value]; and
displaying the external knee adduction moment value, and the external knee adduction moment peak value or the external knee adduction moment area value on a display as information for assisting a physician to diagnose a 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 using a display to display “information”].
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)].
As Cheung discloses training the trained estimation model for estimating a knee adduction moment using sensor data relevant to determining a knee adduction moment, such that when the trained estimation model is used, relevant sensor data is input to estimate a knee adduction moment [Cheung ¶0100], 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 employ wherein the single sensor is attached to or around a single knee of the leg of the human and is configured to continuously detect the acceleration of the single knee during exercise, 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 13 April 2026, with respect to the previously presented claim objections have been fully considered and are persuasive. The objections to claims 1 and 13-14 have been withdrawn.
Applicant’s remarks, see Applicant’s Remarks p. 8-9, with respect to the previously presented “Claim Rejections - 35 USC § 112” heading have been acknowledged and the corresponding Examiner’s analysis has an updated heading to read 35 USC § 112 Analysis to avoid confusion.
Applicant's arguments, see Applicant’s Remarks p. 9-11, with respect to the previously applied rejections under § 101 have been fully considered but they are not persuasive.
The Applicant asserts that the claimed invention cannot be performed using pen-and-paper because the invention uses various factors, relating to the limitations of “apply the received acceleration to a trained estimation model…” [lines 11-14 of claim 1], “estimate the external knee adduction moment value…” [lines 15-18 of claim 1], “estimate an external knee adduction moment peak value or an external knee adduction moment area value…” [lines 19-22 of claim 1], and “display the external knee adduction moment value, and the external knee adduction moment peak value or the external knee adduction moment area value…” [lines 23-26 of claim 1]; wherein the Applicant specifically recites in their argument that the “estimate an external knee adduction moment peak value or an external knee adduction moment area value…” [lines 19-22 of claim 1] is “by using a signal sensor” [Applicant’s Remarks p. 10]. However, the Examiner notes that in the § 101 Step 2A Prong 1 analysis above, the use of a trained estimation model and the step/function to display information on a display were not identified as abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper, and were instead considered additional elements that require additional consideration at Steps 2A Prong 2 and Step 2B, wherein the use of a trained estimation model and the use of a display to display information are considered well-understood, routine, and conventional. Furthermore, the steps/functions to perform estimations as claimed are considered to be directed towards abstract ideas that may be performed in the mind or by hand, as the estimations amount to mere observation of known or previously collected data/information and drawing mental conclusions therefrom, wherein the Examiner notes that the Applicant’s arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the identified abstract idea(s). The Examiner further notes that in response to applicant’s argument that the claimed invention cannot be performed by pen-and-paper, it is noted that the features upon which applicant relies (i.e., “estimate an external knee adduction moment peak value or an external knee adduction moment area value…” [corresponding to lines 19-22 of claim 1] “by using a signal sensor” [Applicant’s Remarks p. 10]) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The Applicant further asserts that the features argued above performed by a processor 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 to monopolize the exception [MPEP § 2106.05(e)], as the Applicant notes that claim 1 is clear directed to a “machine”, which is subject matter eligible under § 101 [the Applicant similar argues that claims 13 and 14 are directed towards a “machine” and “process”, respectively, and are similarly subject matter eligible under § 101]. However, the Examiner disagrees with the Applicant’s argument, as the Examiner notes that the identified judicial exception is applied with, or by use of, a particular machine, as while the Examiner acknowledges that claims 1/13 are directed towards a machine and claim 14 is directed towards a process of using a machine [“machine” considered to refer to the recited input/output interface, memory, processor, and display of claim 1; computer and processor of claim 13; and processor of claim 14], each of the structural limitations directed towards the claimed “machine” are considered to be generic computer elements and corresponding functions thereon [See Step 2A Prong 2 and Step 2B analysis above], such that the identified judicial exception is not applied with, or by use of, a particular machine. Furthermore, the Examiner disagrees with the Applicant’s argument that the judicial exception is applied 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 to monopolize the exception, as the additional elements identified above are considered to be well-understood, routine, and conventional, and the recitation of “information for assisting a physician to diagnose a condition of the knee joint” is identified as being directed towards an intended use of “information” and thus fails to positively recite any step of performing a diagnosis or applying any particular treatment or prophylaxis based on any diagnosis. The Examiner further notes that Applicant’s argument that the features argued above performed by a processor should be categorized as (2) fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them as 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 to monopolize the exception.
Applicant's arguments, see Applicant’s Remarks p. 11-13, with respect to the previously applied rejections under § 103 have been fully considered but they are not persuasive.
The Applicant notes that: the sensor unit (IMU) of Cheung is not attached to or around a single knee; Cheung requires several sensors in the IMU attached at around an ankle to obtain the desired data by using described processes; Herr notes that one sensor may be attached around a knee, but still requires several sensors attached at several positions around the knee to obtain the desired data by using described processes; and each of Chung and Herr requires the use of the IMU because their desired data can be obtained by using the IMU, such that complicated calculations are required in each of Cheung and Herr. The Applicant further directs attention Liu et al. and Khurelbaatar et al., which the Applicant notes discloses that a knee adduction moment value could not be technically obtained by calculation of accelerations from a single sensor, but could be technically obtained by calculation of accelerations from a plurality of sensors, such as an IMU. As such, the Applicant asserts that the combination of Cheung in view of Herr fails to teach claims 1, 13-14, and those dependent therefrom. However, the Examiner disagrees with the Applicant’s arguments. The Examiner notes that the Liu et al. and Khurelbaatar et al. references cited by the Applicant are not directed towards systems or methods for using a trained estimation model or equivalent machine learning/neural network model for estimating a knee adduction moment value and are instead each directed towards directly calculating knee moments using known or derived equations [Liu p. 127; Khurelbaatar p. 66]; whereas the instant invention and Cheung are both directed towards the use of a trained estimation model or equivalent machine learning/neural network model for estimating a knee adduction moment value, such that the Applicant’s citation of Liu et al. and Khurelbaatar et al. are not considered evidentiary in showing how Cheung would not be applicable in the previously applied prior art rejection. Furthermore, the Examiner notes that while Herr is directed towards the use of a plurality of sensors to calculate a knee adduction moment, including at least one sensor attached to or around a single knee of a leg of a human and is configured to continuously detect the acceleration of the single knee during exercise [Herr ¶¶0043, 0047, Fig. 3], as Cheung discloses training the trained estimation model for estimating a knee adduction moment using sensor data relevant to determining a knee adduction moment, such that when the trained estimation model is used, relevant sensor data is input to estimate a knee adduction moment [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)], the modification by Herr to employ the sensor data come from a sensor positioned as claimed is considered to be applicable, as the data as measured by a sensor positioned at the knee as disclosed by Herr is considered to provide data relevant to determining a knee adduction moment. Moreover, Cheung defines the IMU as recited as being a single sensing unit and refers to the IMU as a single sensor [Cheung ¶¶0042-0043] and Herr discloses that the sensor used to provide relevant data in determining a knee adduction moment may be a singular accelerometer [Herr ¶0043], wherein based on at least the broadest reasonable interpretation of the Applicant’s Specification defining the detecting device as comprising a combined acceleration sensor and gyro sensor [Applicant’s Specification ¶0027], Cheung in view of Herr is considered to read on the claimed “single sensor”.
Conclusion
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/SEVERO ANTONIO P LOPEZ/Examiner, Art Unit 3791
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