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 .
Claims Pending
Applicant's arguments, filed 12/23/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 12/23/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Applicant’s previous cancellation of claims 2, 11, and 20 and addition of claims 21-23 is acknowledged.
Claims 1, 3-10, 12-19, and 21-23 are the current claims hereby under examination.
Claim Objections – Previously Withdrawn
Applicant’s amendments, filed 1/25/2024, have been fully considered, and the previous objection withdrawn.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 15: The claim limitation “wherein the processor is configured to…” “…presenting the 3D rendering to the user thereby illustrating the range of motion” has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses a generic placeholder “processor” coupled with functional language “presenting the 3D rendering to the user thereby illustrating the range of motion” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier that has a known structural meaning before the phrase “processor”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation:
A display, or equivalents thereof, as described in Par. 89 of the disclosure filed on 06/29/2021.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112 - Withdrawn
Applicant’s amendments, filed 12/23/2025, have been fully considered, and the previous rejection withdrawn. The added amendment’s to claims 21, 22, and 23 provide sufficient support in regards to the second RNN model and classifier.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-6, 8, 10, 12-15, 17, 19, and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards a judicial exception without significantly more. These claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception or that are sufficient to amount to significantly more than the judicial exception.
Step 1 of the subject matter eligibility test
Claim 1, 10, and 19 are directed towards a method, system, and system, respectively, which describes one of the four statutory categories of patentable subject matter.
Step 2A of the subject matter eligibility test
Prong 1: Claims 1, 10, and 19 recite the abstract idea of a mental process as follows:
“capturing acceleration data and orientation data for a limb of a user in real time”, “determining…” “…based on a user-selected calibration posture, estimated positions of joints of the limb in real time by processing only the acceleration data and the orientation data generated…” “…through a first Recurrent Neural Network (RNN) model, wherein the estimated positions are relative to a coordinate system”, “tracking motion of the limb in real time based on the estimated positions determined at different times as the user moves the limb based on the acceleration data and the orientation data”(Only Claims 1 and 10), “tracking motion of the limb based on the estimated positions determined at different times as the user moves the limb based on the acceleration data and the orientation data”(Only Claim 19), “the first RNN model includes an initial, fully connected layer, followed by a gated recurrent unit, followed by a subsequent fully connected layer”, “placing the gated recurrent unit in a stateless mode, shuffling segments of IMU acceleration and orientation data, and processing the segments as shuffled through the first RNN model”, “the initial, fully connected layer is trained to generate initial states for the gated recurrent unit from the segments as shuffled, and wherein for each segment processed, the initial state of the gated recurrent unit is set by the initial, fully connected layer”, and “at runtime, subsequent to a one time initial state setting of the gated recurrent unit, the gated recurrent unit operates in a stateful mode in which final states of the gated recurrent unit are passed to next time frames as initial states”.
The capturing acceleration data and orientation data for a limb of a user in real time, determining based on a user-selected calibration posture, estimated positions of joints of the limb in real time by processing only the acceleration data and the orientation data generated through a first Recurrent Neural Network (RNN) model, wherein the estimated positions are relative to a coordinate system, tracking motion of the limb in real time based on the estimated positions determined at different times as the user moves the limb based on the acceleration data and the orientation data, tracking motion of the limb based on the estimated positions determined at different times as the user moves the limb based on the acceleration data and the orientation data, the first RNN model includes an initial, fully connected layer, followed by a gated recurrent unit, followed by a subsequent fully connected layer, placing the gated recurrent unit in a stateless mode, shuffling segments of IMU acceleration and orientation data, and processing the segments as shuffled through the first RNN model, the initial, fully connected layer is trained to generate initial states for the gated recurrent unit from the segments as shuffled, and wherein for each segment processed, the initial state of the gated recurrent unit is set by the initial, fully connected layer, and at runtime, subsequent to a one time initial state setting of the gated recurrent unit, the gated recurrent unit operates in a stateful mode in which final states of the gated recurrent unit are passed to next time frames as initial states can be practically performed by the human mind, with the aid of a pen and paper, but for performance on a generic processor, in a computer environment, or merely using the computer as a tool to perform the steps.
A person of ordinary skill receive a piece of paper with acceleration and orientation data. A person of ordinary skill in the art could reasonably determine estimated positions relative to a coordinate system of joints of a real in real time by processing acceleration data and orientation data through an RNN using a generic computer based on having a piece of paper with acceleration and orientation data that includes a starting position. A person of ordinary skill in the art could reasonably track the motion of a limb in real time based on having a piece of paper with acceleration and orientation data with a generic computer. A person of ordinary skill in the art could reasonably track the motion of a limb at different times based on having a piece of paper with acceleration and orientation data with a generic computer. A person of ordinary skill in the art could reasonably modify an RNN to include an initial, fully connected layer, followed by a gated recurrent unit, followed by a subsequent fully connected layer with a generic computer. A person of ordinary skill in the art could reasonably place a gated recurrent unit in a stateless mode, shuffle segments of acceleration and orientation data, and process shuffled segments through a first RNN model using a generic computer based on having a piece of paper with acceleration and orientation data. A person of ordinary skill in the art could reasonably train an initial layer to generate states for a gated recurrent unit for each segment of data processed using a generic computer based on having a piece of paper with segments of data. A person of ordinary skill in the art could reasonably operate a gated recurrent in a stateful mode in which final states of the gated recurrent unit are passed to next time frames as initial states with a generic computer based on having data with different time frames. There is currently nothing to suggest an undue level of complexity in the receiving, determining, tracking, including, placing, shuffling, processing, training, and operating steps. Therefore, a person would be able to practically be able to perform the determining, tracking, including, placing, shuffling, processing, training, and operating steps mentally or with the aid of pen and paper.
Prong Two: Claims 1, 10, and 19 do not recite additional elements that integrate the mental process into a practical application. Therefore, the claims are “directed to” the mental process. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g. computing hardware (Claim 1), a processor (Claims 10 and 19), one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable by a processor (Claim 19),) and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data-gathering components (e.g. “a first IMU sensor” and “a first IMU sensor disposed on an end of the limb”).
For claims 1, 10, and 19. The additional elements merely serve to gather data to be used by the abstract idea. The IMU sensor is merely used as a pre-solution step of necessary data gathering to be used by the abstract idea. The IMU sensor is merely used as additional types data gathering. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing that is performed remains in the abstract realm, i.e. the gathered data is not used for a treatment or meaningful purpose. Additionally, there is no overall improvement to existing technology present. The mental process merely functions on generic computer elements that do not change the functionality of the device itself. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test for Claims 1, 10, and 19 .
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example,
An IMU sensor attached to the limb of a user as disclosed by Jiang et al (“Development of a real-time hand gesture recognition wristband based on sEMG and IMU sensing,” 2016) (cited 199 times) hereinafter Jiang, “Wristband prototype (a) contains four sEMG sensors and one IMU module and (b) is worn just below the distal ends of the radius” (Fig. 1) and Diez, et al, (“Signal processing requirements for step detection using wrist-worn IMU,” 2015) (Cited 7 times) hereinafter Diez, “This paper studies how the accuracy of the step detection algorithm of a pedestrian dead-reckoning (PDR) system is affected by the sampling frequency and the filtering of the data gathered from a wrist-worn inertial measurement unit (IMU).” (Abstract),
A processor, computer hardware, and one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable by a processor as disclosed by Liao (US Pub. No. 20170337682) hereinafter Liao “well-known computer processors, memory units, storage devices, computer software, and other components. A high-level block diagram of such a computer is illustrated in FIG. 23. Computer 2302 contains a processor 2304....” (Par. 150) and Horak (US Pub. No. 20110213278) hereinafter Horak “Certain well-known details often associated with computing, firmware, and software technology are not set forth in the following disclosure to avoid unnecessarily obscuring the various disclosed embodiments. Further, those of ordinary skill in the relevant art will understand that they can practice other embodiments without one or more of the details described below. Aspects of the disclosed embodiments may be implemented in the general context of computer-executable instructions, such as program modules, being executed by a computer, computer server, or device containing a processor. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Aspects of the disclosed embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote storage media including memory storage devices…” (Par. 117).
are all well-understood, routine, and conventional.
Claims 3-6, 8, 12-15, 17, and 21-23 do not include additional elements, alone or in combination that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) as all of the elements are directed to the further describing of the abstract idea, pre-solution activities, and computer implementation.
The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely further describe the abstract idea:
translating the estimated positions from the coordinate system to a user coordinate system (Claims 3 and 12),
presenting results of the tracking to the user in the user coordinate system (Claims 3 and 12) (Examiner's Note: A person of ordinary skill in the art could reasonably verbally present results based on having a piece of paper with tracking data),
translating the coordinate system to a user-specific coordinate system based on a user calibration (Claims 4 and 13) (Examiner's Note: a person of ordinary skill in the art could reasonably translate a coordinate system to another with a generic computer based on having a piece of paper with calibration data),
measuring a range of motion of the limb based on the tracking of the motion of the limb of the user in real time (Claims 5 and 14) (Examiner's Note: A person of ordinary skill in the art could reasonably measure a range of motion of a limb based on receiving a piece of paper with limb tracking information in real time with a generic computer),
generating a real-time 3D rendering of the motion of the limb as tracked (Claims 6 and 15) (Examiner's Note: A person of ordinary skill in the art could reasonably generate a real-time 3D rendering based on receiving motion of a limb with a generic computer),
presenting to the user (Claims 6 and 15) ((Examiner's Note: A person of ordinary skill in the art could reasonably verbally present information to a user),
providing a real time notification to the user (Claims 8 and 17) (Examiners Note: a person of ordinary skill in the art could reasonably provide a notification in real time verbally),
detecting whether torso motion occurs during the tracking of motion of the limb by estimating head rotation and head displacement of the user by processing data from one or more second IMU sensors disposed on a head of the user through a second RNN model and classifying, by a classifier, the head rotation and the head displacement as pure head rotation of the user or as torso motion of the user, wherein the second RNN model and the classifier are trained based on camera generated and labeled ground-truth data (Claims 21, 22, and 23),
in response to determining that torso motion of the user occurs during the tracking of motion of the limb, pausing the tracking (Claims 21, 22, and 23).
Further describe the pre-solution activity (or structure used for such activity):
A 3D display (Claims 6 and 15),
A processor (Claims 12, 14, and 15),
A Head worn IMU sensor (Claims 21, 22, 23).
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example,
An in-ear (and head worn) IMU sensor as disclosed by Ferlini et al. (Ferlini et al., “Head Motion Tracking Through in-Ear Wearables.” 2020) (Cited 36 times) hereinafter Ferlini “The left earbud has a 6-axis Inertial Measurement Unit (IMU) with accelerometer and gyroscope and a Bluetooth Low Energy (BLE) interface which is used to stream data and to send periodic beacons that can be used to detect proximity to nearby devices” (Page 9, Col. 2, eSense platform) and Min et al. (“Exploring Audio and Kinetic Sensing on Earable Devices”, 2018) (Cited 50 times) hereinafter Min, “For the study, we prototyped earbud devices with a 6-axis inertial measurement unit and a microphone.” (abstract),
A processor as disclosed by Liao and Horak Above.
A 3D display as disclosed by Song (US Pub. No. 20180321348) hereinafter Song, “a 3-dimensional (3D) display, a transparent display, or any one of other various output devices that are well known to one of ordinary skill in the art.” (Par. 174) and Courtemanche (US Pub. No. 20180035886) hereinafter Courtemanche “the display 142 may be implemented as a conventional monitor displaying static images or videos in 2D and/or in 3D.” (Par. 73).
are all well-understood, routine, and conventional.
Taken alone or in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, data gathering, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements improves the functioning of a mobile device, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102 – Previously Withdrawn
Applicant’s amendments, filed 1/25/2024, have been fully considered, and the previous rejection withdrawn.
Claim Rejections - 35 USC § 103 – Previously Withdrawn
Applicant’s amendments, filed 06/17/2025, have been fully considered, and the previous rejection withdrawn.
The Closest prior at of record includes Kaifosh (US Pub. No. 20180020978) hereinafter Kaifosh,
Colburn (US Pub. No. 20210378529) hereinafter Colburn, Yang (US Pub. No. 20180373985) hereinafter Yang, He (US Pub. No. 20200320303) hereinafter He, Vanne (US Pub. No. 20220329965) hereinafter Vanne, Moon (US Pub. No. 20110066010) hereinafter Moon, and Olveira Santos (US Pub. No. 20220215925) hereinafter Santos.
Kaifosh discloses A method (Abstract (method)), comprising:
capturing acceleration data and orientation data for a limb of a user in real time by a first IMU sensor (Par. 83 (single arm/wrist-worn device)) (Par. 113, act – 952) disposed on an end of the limb of the user (Par. 83 (single arm/wrist-worn device)) (Par. 71, “one or more Inertial Measurement Units (IMUs), which measure a combination of physical aspects of motion, using, for example, an accelerometer and a gyroscope”) (Par. 73, “In the case of IMUs, the movement sensing components may include one or more accelerometers, gyroscopes, magnetometers, or any combination thereof to measure characteristics of body motion, examples of which include, but are not limited to, acceleration, angular velocity, and sensed magnetic field around the body”), wherein the first IMU sensor (Par. 83 (single arm/wrist-worn device)) is an only sensor disposed on the limb providing acceleration data and orientation data for the limb (Par. 71, “one or more Inertial Measurement Units (IMUs), which measure a combination of physical aspects of motion, using, for example, an accelerometer and a gyroscope”) (Par. 73, “In the case of IMUs, the movement sensing components may include one or more accelerometers, gyroscopes, magnetometers, or any combination thereof to measure characteristics of body motion, examples of which include, but are not limited to, acceleration, angular velocity, and sensed magnetic field around the body”);
determining, with computing hardware and based on a user-selected calibration posture (Par. 79, “processor(s) 812 may be configured to communicate with one or more of autonomous movement sensors 810…” “… calibration may involve changing the offset(s) of one or more accelerometers, gyroscopes, or magnetometers.”), estimated positions of joints of the limb in real time by processing only the acceleration data and the orientation data generated by the first IMU sensor (Par. 80, 83, 118 (real-time)) (Par. 71, “As used herein, the term “autonomous movement sensors” refers to sensors…” “… an accelerometer and a gyroscope.”) (Fig. 9B, Par. 112 (using single autonomous sensor)) (Par. 73 (acceleration and angular velocity data)) (Par. 113, “the autonomous movement sensor(s) comprise one or more accelerometers, one or more gyroscopes”) through a first Recurrent Neural Network (RNN) model (Par. 116, Fig. 9B, act – 956, “Process 950 then proceeds to act 956, where the autonomous sensor signals are provided as input to a statistical model (e.g., a neural network) trained using one or more of the techniques described above in connection with process 900.”) (Par. 110 (recurrent neural network)), wherein the estimated positions are relative to a coordinate system (Par. 118, Fig. 9B, act 958-960 (computer based musculoskeletal representation)) (Par. 118, “A set of joint angles between connected rigid body segments in the musculo-skeletal model may define the orientation of each of the connected rigid body segments relative to each other and a reference frame, such as the torso of the body.”); and
tracking, with the computing hardware (Par. 134 (processors)), motion of the limb based on the estimated positions determined at different times as the user moves the limb based on the acceleration data and the orientation data (Par. 117, act -958 (generated spatial information)) (Par. 122, “the generated spatial information may be used to track the user's movements over time”) (Par. 118 (real-time updating)) from only the first IMU sensor (Par. 71, “As used herein, the term “autonomous movement sensors” refers to sensors…” “… an accelerometer and a gyroscope.”) (Fig. 9B, Par. 112 (using single autonomous sensor)) (Par. 73 (acceleration and angular velocity data)) (Par. 113, “the autonomous movement sensor(s) comprise one or more accelerometers, one or more gyroscopes”).
Kaifosh fails to explicitly disclose wherein the first RNN model includes an initial, fully connected layer, followed by a gated recurrent unit, followed by a subsequent fully connected layer.
Kaifosh does teach wherein the RNN is an LSTM (Par. 64).
However, Colburn teaches wherein the first RNN model includes an initial, fully connected layer (Par. 209 (Input layer)), followed by a gated recurrent unit (Par. 209 (GRU layer)), followed by a subsequent fully connected layer (Par. 209 (output layer)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Kaifosh with that of Colburn to include wherein the first RNN model includes an initial, fully connected layer, followed by a gated recurrent unit, followed by a subsequent fully connected layer through the combination of references as differing neural network architectures are known in the art (Colburn (Par. 209,211)) and it would have yielded the same or similar results as that of Kaifosh.
Modified Kaifosh fails to explicitly disclose wherein the first RNN model is trained by placing the gated recurrent unit in a stateless mode, shuffling segments of IMU acceleration and orientation data, and processing the segments as shuffled through the first RNN model, wherein the initial, fully connected layer is trained to generate initial states for the gated recurrent unit from the segments as shuffled,
and wherein, for each segment processed, the initial state of the gated recurrent unit is set by the initial, fully connected layer; and
wherein, at runtime, subsequent to a one time initial state setting of the gated recurrent unit, the gated recurrent unit operates in a stateful mode in which final states of the gated recurrent unit are passed to next time frames as initial states.
Yang does teach a hidden to hidden state and an input to hidden state for a CNN (Par. 48, “a prominent feature shared by LSTM and GRU is the additive nature in updating the hidden state from t to t+1, i.e., keep the existing state and add changes on top of the existing state through the use of gating functions. Incrementally updating the hidden state helps each hidden state unit to remember the existence of a specific feature for a long series of steps, and more importantly, to create shortcut paths to allow the error to be back-propagated easily through multiple steps without vanishing too quickly. The gating functions of LSTM and GRU may also be accommodated when a non-recurrent layer is replaced with a PreRNN layer 135 or 175. Each gating function may be split into two components and the pre-trained feedforward (non-recurrent) layer may be fused into the components.”) (Par. 49-51).
He teaches the passing of states from fully connected layers to a GRU and updating a loss value with iterations (Fig. 7, Par. 132-138, “constantly updating the loss value of the current iterative step and the current position, the loss values may be progressively aggregated, and upon reaching the iterative termination condition, the final aggregate loss value is obtained.”) and using a GRU for training (Par. 150, “Besides, in the policy generating model shown in FIG. 8, the policy generating network may likewise include a gated recurrent unit 802a and a first fully-connected layer 802b. Optionally, the policy generating model shown in FIG. 8 may also include classifier connected to the output of the first fully-connected layer 802b (not shown in the figure).”) (Par. 154, “Correspondingly, when training the initial policy generating model using the aggregate loss value obtained based on the preset loss function to thereby obtain the trained policy generating model, parameters (θ.sub.π) of the gated recurrent unit, the first fully-connected layer, and the fully-connected unit, and the parameter (θ.sub.v) of the second fully-connected layer may be adjusted based on backpropagation of the aggregate loss value determined from the first component, and parameters (θ.sub.v) of the gated recurrent unit, the second fully-connected layer, and the fully-connected unit may also be adjusted based on the backpropagation of the aggregate loss value determined from the second component.”).
However, the prior of record fails to explicitly disclose the entirety of the limitations wherein the first RNN model is trained by placing the gated recurrent unit in a stateless mode, shuffling segments of IMU acceleration and orientation data, and processing the segments as shuffled through the first RNN model, wherein the initial, fully connected layer is trained to generate initial states for the gated recurrent unit from the segments as shuffled, and wherein, for each segment processed, the initial state of the gated recurrent unit is set by the initial, fully connected layer; and wherein, at runtime, subsequent to a one time initial state setting of the gated recurrent unit, the gated recurrent unit operates in a stateful mode in which final states of the gated recurrent unit are passed to next time frames as initial states. As such, claims 1, 3-10, 12-19, and 21-23 have overcome the prior art.
Allowable Subject Matter
Claims 7, 9, 16, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments, filed 12/23/2025, regarding the previous 101 rejection have been fully considered and are deemed as not persuasive.
The applicant’s arguments that the independent claims do not recite a judicial exception, focusing on the structure of the first RNN model, have been fully considered and deemed as not persuasive. As indicated in the 101 rejection above, “A person of ordinary skill in the art could reasonably modify an RNN to include an initial, fully connected layer, followed by a gated recurrent unit, followed by a subsequent fully connected layer with a generic computer. A person of ordinary skill in the art could reasonably place a gated recurrent unit in a stateless mode, shuffle segments of acceleration and orientation data, and process shuffled segments through a first RNN model using a generic computer based on having a piece of paper with acceleration and orientation data. A person of ordinary skill in the art could reasonably train an initial layer to generate states for a gated recurrent unit for each segment of data processed using a generic computer based on having a piece of paper with segments of data. A person of ordinary skill in the art could reasonably operate a gated recurrent in a stateful mode in which final states of the gated recurrent unit are passed to next time frames as initial states with a generic computer based on having data with different time frame.” The indicated operations of the first RNN model can be practically performed by the human mind, with the aid of a pen and paper, but for performance on a generic processor, in a computer environment, or merely using the computer as a tool to perform the steps. As such, the applicant’s arguments are deemed as not persuasive.
The applicant’s arguments, that claims recite an improvement to technology, have been fully considered and deemed as not persuasive. The additional elements merely serve to gather data to be used by the abstract idea. The IMU sensor is merely used as a pre-solution step of necessary data gathering to be used by the abstract idea. The IMU sensor is merely used as additional types data gathering. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing that is performed remains in the abstract realm, i.e. the gathered data is not used for a treatment or meaningful purpose. Additionally, there is no overall improvement to existing technology present. The mental process merely functions on generic computer elements that do not change the functionality of the device itself. As such, the applicant’s arguments are deemed as not persuasive.
The applicant’s arguments, that the additional elements amount to significantly more than the judicial exception have been fully considered and deemed as not persuasive. As indicated in the 101 rejection above, per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. As such, the applicant’s arguments are deemed as not persuasive.
The applicant’s arguments, regarding the amendments to the claims have been fully considered and deemed as not persuasive. As the limitations were not previously addressed, the limitations have been addressed in the 101 rejection as indicated above.
(Examiner's Note: Based on the applicant’s arguments regarding real time tracking in each of the independent claims, and grouping of independent Claim 19 with independent Claims 1 and 10 in said arguments, it appears that the applicant may have intended for Claim 19 to be amended to recite “tracking motion of the limb in real time”. However, no such amendment was made).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARI SINGH KANE PADDA whose telephone number is (571)272-7228. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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/ARI S PADDA/ Examiner, Art Unit 3791
/JASON M SIMS/ Supervisory Patent Examiner, Art Unit 3791
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