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 .
Response to Arguments
Rejection Under 102
Applicant's arguments filed 01/30/2026 have been fully considered.
Applicant argues that Hussami does not disclose or suggest the combination of recitations of the independent claim 1. Hussami does not teach reconstructing additional estimated sensor data using a machine learning model based on the time series of measurements. The claim recites obtaining a time series of measurements from at least one mobile sensor carried by a portion of a body during motion of the at least one mobile sensor and reconstructing additional estimated sensor data using a machine learning model based on the time series of measurements. Hussami only predicts body state information but doesn’t reconstruct additional estimated sensor data.
In response to Applicant, Hussami discloses gathering data over time and where the data is sensor data, such as user movements, construed as measurements that are obtained from the mobile sensor. (See [0105], [0107]). The mobile sensors are discussed in [0105] to be wearable. Therefore as the wearable sensor obtains the time series measurements when the body is in motion. This is construed to read on the claim language. Additionally, Hussami discloses reconstructing a visual representation of a body part of the user where the state of the body is based on the trained model using the mobile sensor data. (See [0115]). For extra clarification, this trained model is discussed in the paragraph above in reference to the machine learning algorithm, which is used to train the model. (See [0114]). Therefore, this also reads on the claimed limitation. Thus, the rejection is maintained.
Independent claim 13 is the non-transitory computer readable medium claim that recites similar limitations as claim 1 and for analogous reasons is patentable over Hussami.
In response to Applicant, see Response to Argument “A”. For similar reasons given above, the rejection is maintained.
Rejection Under 101
Applicant's arguments filed 01/30/2026 have been fully considered.
Applicant argues that the claims recite a practical implementation of a system involving the use of a carried mobile sensor and displaying measurements and other practical predicted values. The technology improves the operation of computing systems to display measurements and/or biomarkers. The specification at [0079], [0099] describe how models may increase the accuracy of predictions, and parameters are more accurately predicted using technology.
In response to Applicant, the claims do not amount to a practical application since the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. See the updated rejection below for clarification in light of the amendment.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-12 are drawn to a method, which is within the four statutory categories (i.e. process). Claims 13-20 are drawn to a computer system, which is within the four statutory categories (i.e. apparatus).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, claim 1 (and substantially similar with independent claim 13) recites:
A method comprises:
obtaining a time series of measurements from at least one mobile sensor carried by a portion of a body during motion of the at least one mobile sensor;
reconstructing additional estimated sensor data using a machine learning model based on the time series of measurements;
analyzing the additional estimated sensor data together with the time series of measurements to predict a physiological measurement, a biomarker, or combinations thereof; and
display the physiological measurement, the biomarker, or combinations thereof.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behaviors or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the mobile sensor, machine learning model, processor, non-transitory computer-readable storage medium storing instructions, the limitations in the context of this claim encompass an automation of organizing collected user information to predict a physiological measurement and/or biomarker. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behaviors or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-12 and 14-20 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 1 (and substantially similar with independent claim 13) recites:
A method comprises:
obtaining a time series of measurements from at least one mobile sensor carried by a portion of a body during motion of the at least one mobile sensor; (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
reconstructing additional estimated sensor data using a machine learning model (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) based on the time series of measurements;
analyzing the additional estimated sensor data together with the time series of measurements to predict a physiological measurement, a biomarker, or combinations thereof; and
display the physiological measurement, the biomarker, or combinations thereof. (merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g))
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the mobile sensor, machine learning model, processor, non-transitory computer-readable storage medium storing instructions, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0051], [0057], [0060], [0131], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of displaying measurement or biomarker information amounts to insignificant extrasolution activity, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-12, 14-20 recite additional limitations that further the abstract idea; claims 2-5, 8, 10, 12 and 14-20 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea, and claims 2-12, 14-20 additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the mobile sensor, machine learning model, processor, non-transitory computer-readable storage medium storing instructions, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0051], [0057], [0060], [0131]); using the mobile sensor, machine learning model, processor, non-transitory computer-readable storage medium storing instructions, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application. The following represent examples that courts have identified as insignificant extrasolution activities (e.g. see MPEP 2106.05(g)): displaying measurement or biomarker information, e.g., outputting or providing access to the information, Symantec, 838 F.3d at 1321 and MPEP 2106.05(g)(3)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea and are generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5-11, 13-14, 17, 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hussami et al. (US 2022/0269346).
Regarding claim 1, Hussami discloses a method comprises:
obtaining a time series of measurements from at least one mobile sensor carried by a portion of a body during motion of the at least one mobile sensor; (Hussami [0096] A predicted movement may be associated with one or more segments of a musculoskeletal representation of the portion of the body of the user. Such predicted movements may include linear/angular velocities and/or linear/angular accelerations of one or more segments of the musculoskeletal representation. The linear velocities and/or the angular velocities may be absolute (e.g., measured with respect to a fixed frame of reference) or relative (e.g., measured with respect to a frame of reference associated with another segment or body part). [0105] FIG. 1 illustrates a system 100 in accordance with embodiments of the present disclosure. The system 100 may include a plurality of sensors 102 configured to record signals resulting from the movement of portions of a human body. [0107] the IMU is attached and information derived from the sensed data (e.g., position and/or orientation information) may be tracked as the user moves over time. For example, one or more IMUs may be used to track movements of portions of a user's body proximal to the user's torso (e.g., arms, legs) as the user moves over time)
reconstructing additional estimated sensor data using a machine learning model based on the time series of measurements; and (Hussami [0115] System 100 may include a display device 108 configured to display a visual representation of a body state (e.g., a visual representation of a hand). As discussed in more detail below, processor 101 may use one or more trained inferential models 104 configured to predict body state information based, at least in part, on signals recorded by sensors [0246] As a result of the training, the statistical model implicitly represents the statistics of motion of the articulated rigid body under defined movement constraints. The output of the trained statistical model may be used to generate a computer-based musculo-skeletal representation of at least a portion of the user's body, which in turn can be used for applications such as rendering a representation of the user's body in a virtual environment)
analyzing the additional estimated sensor data together with the time series of measurements to predict a physiological measurement, a biomarker, or combinations thereof. (Hussami [0153] After the trained inferential model receives the sensor data as a set of input parameters, the predicted musculoskeletal position information may be output from the trained inferential model. As discussed above, in some examples, the predicted musculoskeletal position information may include a set of musculoskeletal position information values (e.g., a set of joint angles) for a multi-segment articulated rigid body model representing at least a portion of the user's body. In some examples, the musculoskeletal position information may include a set of probabilities that the user is performing one or more movements from a set of possible movements)
display the physiological measurement, the biomarker, or combinations thereof (Hussami [0089] An important feature of computer applications that generate musculoskeletal representations of the human body is low latency between a movement of the user's body and the representation of that movement by the computer application (e.g., displaying a visual representation to the user) [0844] neuromuscular signals are processed to produce a visualization… visualization may be projected over a body part of the user, such as an arm of the user, to provide the user with feedback information that may involve the body part. For instance, in one implementation, the projection may include a visual indication that shows muscle-group activations and/or degrees of joint angles within the projected feedback information).
Regarding claim 2, Hussami discloses the method of claim 1, and further discloses wherein said analyzing further comprises selecting a subset of the additional estimated sensor data together with the time series of measurements, wherein the selected subset of the additional estimated sensor data is related to the physiological measurement, the biomarker, or combinations thereof. (Hussami [0102] In some examples, an appropriate temporal shift may be identified by generating multiple training datasets with multiple temporal shifts. In some examples, the temporal shifts may be different respective time intervals).
Regarding claim 5, Hussami discloses the method of claim 1 further comprises training the machine learning model using a plurality of users during a first time period. (Hussami [0135] In some examples, a user-independent inferential model may be generated based on training data corresponding to the recorded signals from multiple users, and as the system is used by a user, the inferential model may be trained based on recorded sensor data such that the inferential model learns the user-dependent characteristics to refine the prediction capabilities of the system and increase the prediction accuracy for the particular user.)
Regarding claim 6, Hussami discloses the method of claim 5, wherein said obtaining, said reconstructing, and said analyzing are associated with a single user during a second time period. (Hussami [0135] In some examples, a user-independent inferential model may be generated based on training data corresponding to the recorded signals from multiple users, and as the system is used by a user, the inferential model may be trained based on recorded sensor data such that the inferential model learns the user-dependent characteristics to refine the prediction capabilities of the system and increase the prediction accuracy for the particular user.) [0137] As discussed above, the sensor data obtained at operation 502 may be obtained by recording sensor signals as each of one or multiple users perform each of one or more tasks one or more times.
Regarding claim 7, Hussami discloses the method of claim 6, wherein an identity of the single user differs from identities of each user of the plurality of users. (Hussami [0135] In some examples, a user-independent inferential model may be generated based on training data corresponding to the recorded signals from multiple users, and as the system is used by a user, the inferential model may be trained based on recorded sensor data such that the inferential model learns the user-dependent characteristics to refine the prediction capabilities of the system and increase the prediction accuracy for the particular user.)
Regarding claim 8, Hussami discloses the method of claim 1 further comprises training the machine learning model using a user during a first time period, a first environmental setting, or combinations thereof. (Hussami [0170] At operation 930, method 900 may include generating one or more training datasets by time-shifting at least a portion of the neuromuscular activity data over the first time series relative to the second time series,
Regarding claim 9, Hussami discloses the method of claim 8, wherein said obtaining, said reconstructing, and said analyzing are associated with the user during a second time period, a second environmental setting, or combinations thereof. (Hussami [0169] At operation 920, method 900 may include receiving ground truth data from a second, different sensor that indicates a body part state of a body part of the user over a second time series.
Regarding claim 10, Hussami discloses the method of claim 1, wherein the at least one mobile sensor comprises inertial measurement units. (Hussami [0107] Autonomous sensors may include one or more Inertial Measurement Units (IMUs), which may measure a combination of physical aspects of motion, using, for example, an accelerometer, a gyroscope, a magnetometer, or a combination thereof).
Regarding claim 11, Hussami discloses the method of claim 1, wherein the at least one mobile sensor is embedded in a wearable device. (Hussami [0092] neuromuscular sensors (e.g., neuromuscular sensors on a wearable device donned by the user).
Regarding claim 13, the claim recites substantially similar limitations as those recited in the rejection of claim 1, and, as such, is rejected for similar reasons as given above. Additionally, Hussami further discloses a computing system comprises: a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the computing system to perform operations comprising: ([0174] In this respect, it should be appreciated that one implementation of the embodiments of the present invention includes at least one non-transitory computer-readable storage medium (e.g., a computer memory, a portable memory, a compact disk, etc.) encoded with a computer program (e.g., a plurality of instructions), which, when executed on a processor)
Regarding claim 14, the claim recites substantially similar limitations as those recited in the rejection of claim 1, and, as such, is rejected for similar reasons as given above. Additionally, Hussami further discloses wherein the time sequence model increases an accuracy of the reconstructed additional estimated sensor data. (Hussami [0104] In some examples, an appropriate delay time interval may be determined using a known electromechanical delay time for a body part, a user, and/or a category of users. For example, when the known electromechanical delay associated with the body part is 40 ms, the time intervals may be selected ranging from 20 to 60 ms. Prediction accuracies may be generated for inferential models trained using time-shifted training datasets generated using the selected time intervals. One or more of the inferential models may be selected for use in predicting body part state using the generated prediction accuracies).
Regarding claim 17, Hussami discloses the computing system of claim 13 further comprises a display screen to display the physiological measurement, the biomarker, or combinations thereof. (Hussami [0089] An important feature of computer applications that generate musculoskeletal representations of the human body is low latency between a movement of the user's body and the representation of that movement by the computer application (e.g., displaying a visual representation to the user).
Regarding claim 19, Hussami discloses the computing system of claim 13 further comprises an interface, wherein the interface communicatively couples the computing system to the at least one mobile sensor. (Hussami [0465] Other embodiments are directed to a computerized system configured to calibrate performance of one or more statistical models used to generate a musculoskeletal representation. The system comprises a user interface configured to instruct a user to perform at least one gesture while wearing a wearable device having a plurality of neuromuscular sensors arranged thereon, and at least one computer processor.
Regarding claim 20, Hussami discloses the computer system of claim 13, wherein the at least one mobile sensor comprises a thermocouple, a thermistor, a resistance temperature detector, a pressure sensor, a light sensor, a motion sensor, a proximity sensor, a gas sensor, an air quality sensor, a pH sensor, a humidity sensor, a magnetic sensor, a biometric sensor, inertial measurement units, or combinations thereof. (Hussami [0109] Each of the autonomous sensors may include one or more sensing components configured to sense information about a user. In the case of IMUs, the sensing components may include one or more accelerometers, gyroscopes, magnetometers, or any combination thereof, to measure characteristics of body motion).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-4, 12, 15-16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hussami et al. (US 2022/0269346) in view of Wang et al. ("Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders," 18 Feb. 2019).
Regarding claim 3, Hussami discloses the method of claim 1, but does not appear to disclose the following, however, Wang teaches it is old and well known in the art of data processing to determine at least one higher-dimensional body movement compared to the time series of measurements from the at least one mobile sensor carried by the portion of the body. (Wang section 2.2. sensor data from smartphone embedded sensors and the corresponding stride count, stride-length, and cumulative walking-distance from foot-mounted module attached to the instep of the right foot of the pedestrian).
Therefore, it would have been obvious to one of ordinary skill in the art of data processing, before the effective filing date of the claimed invention, to modify Hussami to incorporate determining at least one higher-dimensional body movement compared to the time series of measurements from the at least one mobile sensor carried by the portion of the body, as taught by Wang, in order to achieve superior step-count accuracy and to estimate stride lengths properly for each person. See Wang pg. 4 last paragraph.
Regarding claim 4, Hussami discloses the method of claim 1, but does not appear to disclose the following, however, Wang teaches it is old and well known in the art of data processing wherein the machine learning model comprises a sequential model for encoding time sequences followed by a decoder network mapping an output of the sequential model to a final output. (Wang section 2.4.2. Due to the fact the raw sensor readings from accelerometers and gyroscopes inevitably contain noise, it is necessary to perform a filtering operation. Unlike the traditional filtering techniques (e.g., low-pass or median filter), the denoising autoencoders approach is highly efficient in learning signal features and predicting stride-length with much better accuracy. In order to force the hidden layer to extract more robust features and prevent it from simply learning the identity, we trained a DAE (Denoising Autoencoders) to reconstruct the merged feature from a corrupted version of it. As shown in Figure 8, the DAE contains three parts: a Dropout, an Encoder and a Decoder. The network output maps the hidden representation of corrupted h back to a reconstruction h^). The motivation to combine the references was discussed above and is incorporated herein.
Regarding claim 12, Hussami discloses the method of claim 1, but does not appear to disclose the following, however, Wang teaches it is old and well known in the art of data processing wherein the physiological measurement, the biomarker, or combinations thereof comprise a stride length. (Wang section 1 pg. 4 We trained the proposed model with walking information from a smartphone, and the ground truth of stride-length from a foot-mounted IMU module, to predict an adaptive stride-length). The motivation to combine the references was discussed above and is incorporated herein.
Regarding claim 15, Hussami discloses the computing system of claim 13, but does not appear to disclose the following, however, Wang teaches it is old and well known in the art of data processing wherein a user comprises the portion of the body, and wherein the instructions, when executed by the processor, cause the computing system to perform operations comprising: map biomechanical measurements during motion of the portion of the body; and train the machine learning model to reconstruct the additional estimated sensor data for the user based on the time series of measurements from the at least one mobile sensor. (Wang section 2.4.2. Due to the fact the raw sensor readings from accelerometers and gyroscopes inevitably contain noise, it is necessary to perform a filtering operation. Unlike the traditional filtering techniques (e.g., low-pass or median filter), the denoising autoencoders approach is highly efficient in learning signal features and predicting stride-length with much better accuracy. In order to force the hidden layer to extract more robust features and prevent it from simply learning the identity, we trained a DAE (Denoising Autoencoders) to reconstruct the merged feature from a corrupted version of it. As shown in Figure 8, the DAE contains three parts: a Dropout, an Encoder and a Decoder. The network output maps the hidden representation of corrupted h back to a reconstruction h^; Section 2.4.3. The algorithm takes a set of training samples with the corresponding actual stride-length as input to train the network. The accelerometer readings and gyroscope readings are divided into segments by stride event. The fixed length of stride data is fed to LSTM-DAE network. The actual stride-length is used to train the regression layer on the top of the network. Once the training is done, the LSTM-DAE model will be used to predict the stride-length of pedestrian). The motivation to combine the references was discussed above and is incorporated herein.
Regarding claim 16, the claim recites substantially similar limitations as those already addressed in the rejection of claim 15, and, as such, is rejected for similar reasons given above. Additionally, Hussami further discloses analyzing the additional estimated sensor data together with the time series of measurements to predict the physiological measurement, the biomarker, or combinations thereof of another user, wherein an identity of the other user differs from an identity of each user of the plurality of users, and wherein the other user comprises the portion of the body. (Hussami [0135] In some examples, a user-independent inferential model may be generated based on training data corresponding to the recorded signals from multiple users, and as the system is used by a user, the inferential model may be trained based on recorded sensor data such that the inferential model learns the user-dependent characteristics to refine the prediction capabilities of the system and increase the prediction accuracy for the particular user). The motivation to combine the references was discussed above and is incorporated herein.
Regarding claim 18, Hussami discloses the computing system of claim 13, but does not appear to disclose the following, however, Wang teaches it is old and well known in the art of data processing comprises a mobile electronic device, and wherein the mobile electronic device comprises the at least one mobile sensor. (Wang section 1 pg. 4 Training data was generated from smartphone). The motivation to combine the references was discussed above and is incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
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