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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 22 January 2026 has been entered.
The Examiner acknowledges the amendments to claims 1, 12, and 14, as well as the cancelation of claim 15. Claims 1-14 and 16-20 are pending.
Claim Interpretation
Examiner Notes: currently, NO limitation invokes interpretation under § 112(f).
Claim Rejections - 35 USC § 112
Examiner’s Note Regarding Machine Learning: the Examiner’s note regarding the claimed machine learning model/neural network of the Non-Final Rejection dated 30 June 2025 [p. 2] 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) 1-14 and 16-20 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) 12 [representing all independent claims] recite(s):
A system for determining cycling power, the system comprising:
a plurality of force sensors positionable underfoot, the plurality of force sensors being disposed on a wearable device worn by a user;
one or more processors communicatively coupled to the plurality of force sensors; and
a non-transitory storage memory storing a machine learning model trained to predict mechanical cycling power;
wherein the one or more processors are configured to:
identify at least one revolution associated with the plurality of force sensors;
obtain a plurality of force sensor readings from the plurality of force sensors during the at least one revolution;
determine force values for the at least one revolution using aggregate data based on the plurality of force sensor readings;
determine a user mass associated with the plurality of force sensor readings;
for each revolution, determine a cycling cadence, a foot velocity, and a foot angle; and
determine a mechanical cycling power associated with the plurality of force sensor readings by inputting the force values, the user mass, the cycling cadence, the foot velocity and the foot angle to a machine learning model trained to predict the mechanical cycling power, wherein crank arm length is not used as an input to the machine learning model;
output an output dataset, wherein the output dataset comprises the mechanical cycling power;
use the output dataset as an input to a game; and
generate, based on the output dataset, at least one of a haptic signal, and an audio signal.
(Emphasis added: abstract idea, additional element)
Step 2A Prong 1
Representative claim(s) 12 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper:
“identify at least one revolution associated with the plurality of force sensors” – may be performed by merely visually observing at least a limited amount of data under no particular time constraints and drawing mental conclusions therefrom [Applicant’s Specification ¶¶0197, 0199]
“obtain a plurality of force sensor readings from the plurality of force sensors during the at least one revolution” – may be performed by merely observing already known or previously collected data [Applicant’s Specification ¶0198]
“determine force values for the at least one revolution using aggregate data based on the plurality of force sensor readings” – may be performed by merely applying known mathematical calculations on already known or previously collected data for at least a limited amount of data under no particular time constraint [the aggregate force data for a given revolution may be determined as a sum of the individual force sensor values received from each force sensor (Applicant’s Specification ¶0201); For instance, each force value can be determined as a sum of the individual force sensor values received from each force sensor at the corresponding time step. Alternatively, discrete force values such as a peak force value or a mean force value may be determined (Applicant’s Specification ¶0202)]
“determine a user mass associated with the plurality of force sensor readings” – may be performed by merely observing already known or previously collected information or by applying known mathematical calculations on already known or previously collected data for at least a limited amount of data under no particular time constraints [Typically, the user mass can be determined prior to the cycling activity. For example, user mass may be measured using the force sensors underfoot of a user. Alternatively, mass may be measured using a separate mass-measurement device such as a scale. In some cases, mass may be input manually, e.g. by a user interacting with an application on processing device 108. In some cases, determining the user mass may require converting the user's measured weight to a mass value (Applicant’s Specification ¶0191)]
“for each revolution, determine a cycling cadence, a foot velocity, and a foot angle” – may be performed by merely observing already known or previously collected information [Applicant’s Specification ¶¶0205] or by applying known mathematical calculations on already known or previously collected data for at least a limited amount of data under no particular time constraints [Applicant’s Specification ¶¶0206-0208, 0210-0211, 0213-0214]
“determine a mechanical cycling power associated with the plurality of force sensor readings by inputting the force values, the user mass, the cycling cadence, the foot velocity and the foot angle…, wherein crank arm length is not used as an input” – may be performed by merely observing already known or previously collected data/information and applying known or derived relationships to the data/information to draw mental conclusions therefrom [Applicant’s Specification ¶¶0221-0222]
“output an output dataset, wherein the output dataset comprises the mechanical cycling power” – may be performed by merely writing down or taking a mental note of the determined mechanical cycling power
“use the output dataset as an input to a game” – may be performed by merely using the determined mechanical cycling power to influence any non-specific verbal or physical game as a mere method of organizing human activity [MPEP § 2106.04(a)(2)(II)]
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., “determine force values for the at least one revolution using aggregate data based on the force sensor readings”, “determine a user mass associated with the plurality of force sensor readings”, “for each revolution, determine a cycling cadence, a foot velocity, and a foot angle”, “determine a mechanical cycling power associated with the plurality of force sensor readings by inputting the force values, the user mass, the cycling cadence, the foot velocity and the foot angle…, wherein crank arm length is not used as an input”] 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 12 only recites additional elements of extra-solutionary activity – in particular, extra-solution activity [generic computer function, data gathering] – 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 [generic computer function, data gathering], or a sufficiently particular form of display or computing architecture/structure).
Dependent claim(s) 3-8 and 11 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].
Dependent claim(s) 2, 14, and 16-20 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 12 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, data gathering] 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, transmitting, outputting, generating 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 [generic computer elements of one or more processors, non-transitory storage memory, and corresponding functions recited therein, each recited at a high level of generality], 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 generically recited 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, 5, 7, 9-10, 12, and 18 recite a “machine learning model”, wherein claim 9 further specifies that the machine learning model is “a neural network”. Such a “machine learning model”/“neural network” 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)]
Claim 1, 12-13 and 20 recites “a plurality of force sensors positionable underfoot, the plurality of force sensors being disposed on a wearable device worn by a user”, “wherein the plurality of force sensors is disposed on an insole, a shoe, a compression-fit garment, or a sock”, and “wherein the plurality of force sensors comprise force-sensing resistors”, respectively. Such a “plurality of force sensors” 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 force sensors/force-sensing resistors, and recites the force sensors/force-sensing resistors at a high level of generality [Various types of force sensors may be used, such as force sensing resistors (also referred to as "sensels" or sensing elements), pressure sensors, piezoelectric tactile sensors, elasto-resistive sensors, capacitive sensors or more generally any type of force sensor that can be integrated into a wearable device or fitness equipment capable of collecting force data underfoot (Applicant’s Specification ¶0149)]. 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. 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].
Schneider (US-20160351771-A1, previously presented) [may comprise footwear which may include one or more sensors, including but not limited to those disclosed herein and/or known in the art. FIG. 3 illustrates one example embodiment of a sensor system 302 providing one or more sensor assemblies 304. Assembly 304 may comprise one or more sensors, such as for example, an accelerometer, gyroscope, location-determining components, force sensors and/or or any other sensor disclosed herein or known in the art. In the illustrated embodiment, assembly 304 incorporates a plurality of sensors, which may include force-sensitive resistor (FSR) sensors 306; however, other sensor(s) may be utilized (Schneider ¶0076)]
Esposito (US-20160367191-A1, previously presented) [In some embodiments, FSR (Force Sensitive Resistor) and/or piezo-resistive sensors may be used. One type of piezoresistive force sensor that has been used previously in footwear pressure sensing applications, known as the FLEXIFORCE® sensor, can be made in a variety of shapes and sizes, and measures resistance, which is inversely proportional to applied force (Esposito ¶0015)]
Morris Bamberg (US-20090235739-A1, previously presented) [More specifically, the insole can include a plurality of force sensitive resistors in the heel and toe of the insole, and a tri-axial accelerometer in an arch section of the insole (Bamberg ¶0023)]
Claim 16-17 and 19 recite “at least one inertial measurement unit associated with the plurality of force sensors” configured to inertial measurement data, gyroscope data, and accelerometer data, respectively. Such an inertial measurement unit 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 inertial measurement unit, and recites the inertial measurement unit at a high level of generality [IMU 112 can include one or more sensors for measuring the position and/or motion of the user's foot (e.g. via a carrier unit). For example, IMU 112 may include sensors such as a gyroscope, accelerometer (e.g., a three-axis accelerometer), magnetometer, orientation sensors (for measuring orientation and/or changes in orientation), angular velocity sensors, or inclination sensors. Generally, IMU 112 includes at least an accelerometer. The IMU 112 also typically includes a gyroscope (Applicant’s Specification ¶0138)]. 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 motion sensing. 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].
Strausser (US-20150045703-A1, previously presented) [Inertial measurement units (IMUs) could be coupled to the leg support 212. An inertial measurement unit is generally composed of an accelerometer and a gyroscope and sometimes a magnetometer as well; in many modern sensors these devices are MEMS (Mico electromechanical systems) that have measurement in all three orthogonal axes on one or more microchips. The behavior of IMUs is well understood in the art (IMUs being used for applications from missile guidance to robotics to cell phones to hobbyist toys); they typically provide measurement of angular orientation with respect to gravity, as well as measurement of angular velocity with respect to earth and linear acceleration, all in three axes (Strausser ¶0025)]
Examiner’s Note Regarding Particular Treatment or Prophylaxis: Claim(s) 1 and 12 recite subject matter regarding “generate, based on the output dataset, at least one of a haptic signal, and an audio signal” [claims 1, 12], 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 [MPEP § 2106.04(d)(2)]. The identified limitations are considered to fail to apply the recited exception(s), as there is no positive recitation of the haptic or audio signal being used for any purpose [the haptic/audio signal(s) are not output in any physical or audible form], and are considered non-particular [any generic haptic/audio signal(s)].
Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B.
Subject Matter Not Taught By Prior Art
The closest prior art of record regarding claims 1 and 12 is Mahmoud (US-10004946-B2, previously presented) and Dugan (US-20070197274-A1, previously presented), which in combination is considered to teach almost each and every limitation of claims 1 and 12 [see Non-Final Rejection dated 30 June 2025 p. 14-19 and 21-24 regarding the specific subject matter considered to be taught by Mahmoud and Mahmoud in view of Dugan (original claims 1 and 12, and dependent claims 2 and 14, which include subject matter that is presently recited in claims 1 and 12 regarding the use of the output dataset as an input to a game)], except for the limitation “wherein crank arm length is not used as an input to the machine learning model”. As Mahmoud in view of Dugan specifically requires crank arm length as an input to the machine learning model [Mahmoud Col 9:25-36, 10:47-48], it would not have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mahmoud or Dugan, alone or in combination, to incorporate or employ the subject matter “wherein crank arm length is not used as an input to the machine learning mode” without the benefit of hindsight. As such, claims 1, 12, and those dependent therefrom are not considered to be taught by any prior art reference.
Response to Arguments
Applicant’s arguments, see Applicant’s Remarks p. 9, filed 22 January 2026, with respect to the previously applied rejections under § 112(b) and § 112(d) have been fully considered and are persuasive. The rejections of claim 14 under § 112(b) and § 112(d) have been withdrawn.
Applicant's arguments, see Applicant’s Remarks p. 9-10, have been fully considered but they are not persuasive.
The Applicant asserts that the amendments to claims 1 and 12 regarding “generating, based on the output dataset, at least one of a haptic signal, and an audio signal” ties the claimed method and system to a specific, practical application which produces a real-world physical effect and cures any alleged deficiencies under § 101. However, while the Examiner acknowledges that the generation of at least one of a haptic signal and an audio signal based on the output dataset is analyzed at Step 2A Prong 1 as being an additional element, further analysis at Steps 2A Prong 2 and Step 2B shows that the generation of a signal is considered to be a generic computer function; and as the signal is merely generated without anything being done to output the signal, the signal itself is not considered to be a particular treatment or prophylaxis that may apply the identified judicial exception(s). As such, the recitation of generating a signal fails to integrate the judicial exception(s) into a practical application at Step 2A Prong 2 and allow the claim(s) as wholes to amount to significantly more at Step 2B.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEVERO ANTONIO P LOPEZ whose telephone number is (571)272-7378. The examiner can normally be reached M-F 9-6 EST.
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/SEVERO ANTONIO P LOPEZ/Examiner, Art Unit 3791