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 Accounting
Applicant's arguments, filed 04/30/2026, have been fully considered.
The following rejections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 04/30/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-7 have been amended.
Claims 8-20 are withdrawn in accordance with the response to the restriction requirement received on 12/10/2025.
Claims 1-7 are the current claims hereby under examination.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 recites “a camera” in line 4. This should read “a camera;”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 5, the claim recites "wherein computing the loss value using the loss function comprises… indicating accuracy of the estimated head posture” in lines 1-4. Claim 4, from which claim 5 depends, recites wherein the loss value is computed upon predicting the head posture. Therefore it is unclear how using the model is trained by using the estimated head posture upon predicting the head posture (i.e., obtaining the predicted head posture). Claim 1 sets forth that the estimated head posture is received from the predicted head posture. It is unclear if this occurs before the computation of the loss function, or after. Clarification is requested.
For the purposes of examination, the claim is interpreted as “indicating accuracy of the predicted head posture”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 2023/0139626 by Berliner et al. – previously cited, hereinafter “Berliner” in view of US Patent Publication 2013/0184611 by Nichols – previously cited, hereinafter “Nichols”.
Figs. 1 and 7 of Berliner teaches a head-mounted device for postural alignment correction ([0086]; wearable extended reality appliance 110), the head-mounted device comprising: a display (virtual screen 112); a camera ([0182]; An embodiment includes a motion sensor such as an integrated IMU and a camera that may operate together to track head motions of the wearer, such as the position, orientation, pose, and/or angle of the head of the wearer.); a plurality of sensors comprising an inertial measurement unit ([0182]; See above, an integrated IMU.); and a controller comprising instructions executable to control the head-mounted device to (Figs. 2 and 4, [0091, 0120]; XR Unit 204 is an example of wearable extended reality appliance 110, and comprises a memory 411 that may contain software modules to execute processes, via processing device 460, consistent with the present disclosure): receive inertial measurement data from the inertial measurement unit; receive image data from the camera; ([0120]; Sensors communication module may receive data from different sensors to determine a status of a user.) input the received inertial measurement data and the received image data into a machine learning model ([0139, 0182]; The processor within wearable extended reality appliance 110 may implement any methods described with a machine learning model. Therefore the determination of the position, orientation, pose, and/or angle of the head of the wearer can be determined via a machine learning model, and are based on the sensors (i.e., integrated IMU and camera). In order for the IMU and camera data to be used with the machine learning model, it must be input to the model); using the machine learning model, predict a head posture using the received inertial measurement data and the received image data (the output of the machine learning model is the position, orientation, pose, and/or angle of the head of the wearer), wherein the head posture is predicted based at least on the image data indicating a portion of a body of a user that is in view of the camera (See Fig. 7; The extended reality environment 620 includes the arms of the user, and therefore any body part, such as the arms or hands that are in view of the camera are also used in the prediction of the head posture); and receive an estimated head posture from the machine learning model based at least on the predicted head posture ([0207]; The estimation of the physical condition of the user can be facilitated using machine learning and is based on analysis, calculations and/or inference of measured data. Physical conditions such as head, and/or neck pain/posture (i.e., estimated head posture) can be based on their related calculated angles).
Berliner does not teach the controller comprising instructions executable to output information to the user using the display, wherein the information includes the estimated head posture and a recommendation on a corrective posture action.
Nichols teaches a system that provides biofeedback alerts for the posture estimated by wearable sensors. Nichols teaches a biofeedback alerts page that provides real-time notifications, via a display of the user’s posture. This page can alert the user of deviations from the targeted posture and provide recommendations of corrective posture actions on how to attain the target posture (Fig. 7A; [0064]). This biofeedback provides a means for training users to attain and maintain targeted posture, which can have significant benefits for health, safety and performance ([0003, 0009]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the controller taught by Berliner to comprise instructions executable to output information to a user using the display, wherein the information indicates the estimated body posture, or wherein the presented information includes recommendations on corrective posture actions, in order to provide feedback to users to attain and maintain targeted posture, which can have significant benefits for health, safety and performance, as taught by Nichols ([0003, 0009]).
Claims 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Berliner in view of Nichols, as applied to claim 1, in view of The Wearable Sensor System (2016)… by Lee et al. – previously cited, hereinafter “Lee”.
Regarding claim 2, Berliner in view of Nichols teaches the head-mounted device of claim 1, wherein the body posture comprises a head posture ([0182]; “the position, orientation, pose, and/or angle of the head of the wearer”), and wherein the machine learning model is an artificial neural network ([0139]; the machine learning model may comprise an artificial neural network). Berliner does not teach that the artificial neural network has been trained to predict the head posture by calculating a craniovertebral angle.
Lee teaches a head-mounted wearable device comprising an IMU (See Fig. 2) configured to calculate the craniovertebral angle, as this an existing method of quantifying forward head posture (FHP). Quantifying FHP is important because it is a posture that can lead to bothersome neck pain (I. Introduction, pg. 1-2).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the device of Berliner to predict head posture by calculating a craniovertebral angle, as this measure is an existing method of quantifying FHP, which can cause neck pain, as taught by Lee (I. Introduction, pg. 1-2).
It is noted that par. [0207] of Berliner teaches estimating a physical condition of the wearer of the wearable extended reality appliance, which can include head, back, and/or neck pain and posture. The craniovertebral angle as taught by Lee is a measure that quantifies, FHP, which can be indicative of and lead to neck and back pain, and therefore Berliner and Lee are considered to be in the same field of endeavor.
Regarding claim 7, the combination of Berliner, Nichols, and Lee, as applied to claim 2, teaches the head-mounted device of claim 1, wherein the machine learning model has been trained to predict the head posture by calculating an angle formed from a line crossing a cervical vertebra and a line joining a tragus of an ear of the user to the cervical vertebra (Lee; I. Introduction, pg. 2, the craniovertebral angle is formed by a straight line connecting the tragus and the 7th cervical vertebra).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Berliner in view of Nichols, as applied to claim 1, in view of US Patent Publication 2022/0265205 by Takahashi et al. – previously cited, hereinafter “Takahashi”.
Berliner in view of Nichols teaches the head-mounted display of claim 1, wherein the but does not teach wherein the camera comprises one or more of a downward-facing camera or a forward-facing camera.
Takahashi teaches a head-mounted device for determining the angles of the spine of the wearer. The head mounted device uses an integrated camera and an integrated IMU to estimate the angles of the spine of the user ([0059]). By using an integrated camera and IMU, the device is able to estimate the spinal alignment over time without using a large-scale device ([0094]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the device of Berliner in view of Nichols such that the camera was a camera integrated into the head-mounted device, in order to estimate the spinal alignment without using a large-scale device, as taught by Takahashi ([0094]). A camera integrated into the head-mounted device as taught by Takahashi, is capable of facing forward (if the user looks forward) and is capable of facing downward (if the user looks downward).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Berliner in view of Nichols, as applied to claim 1, in view of US Patent Publication 2024/0321447 by Selvaraj et al. – previously cited, hereinafter “Selvaraj”.
Berliner in view of Nichols teaches the head-mounted device of claim 2, but does not teach wherein the machine learning model has been trained based on an average human population, and wherein upon predicting the head posture, the machine learning model is further trained by: computing a loss value using a loss function; and adjusting the machine learning mode based on the computed loss value.
Selvaraj teaches a machine learning model for the prediction of a physical condition of an individual. The machine learning models may be based on a particular cohort of patients, or on a general population of patients ([0072]). The model being trained on the general population of patients allows for a model that is applicable to individuals that do not match previous patient pools ([0079]), thus making the general population model more widely applicable than models generate from previous patient pools. The machine learning models are trained on the basis of a loss function, where the loss function assesses the performance of the model and the network is adjusted to minimize the loss ([0112]). It is noted that the loss function updates the network’s weights in response to assessing the performance of the model, and therefore updates the weights during the use of the model ([0112]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the head-mounted device as taught by Berliner in view of Nichols such that the machine learning model has been trained based on an average human population, and wherein upon predicting the head posture, the machine learning model is further trained by: computing a loss value using a loss function; and adjusting the machine learning mode based on the computed loss value, to create a more accurate model that is widely applicable to a general population of users, as taught by Selvaraj ([0079]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Berliner in view of Nichols in view Selvaraj, as applied to claim 4, in view of US Patent Publication 2019/0171280 by Son et al. – previously cited, hereinafter “Son”.
The combination of Berliner, Nichols, and Selvaraj teaches the head-mounted device of claim 4, but does not teach wherein computing the loss value using the loss function comprises a supervised learning process that includes receiving an input from the user indicating accuracy of the estimated head posture.
Son teaches a device and method of supervised machine learning wherein the machine learning model is trained to predict a physical state of the user. A control unit of the device is configured to prompt the user for an input representative of the degree of the physical condition, and train the machine learning model based on supervised learning using the user input compared to the model’s estimation of the physical condition (i.e., indication of accuracy) ([0078]). This method of supervised learning trains the model against an explicit correct answer ([0007]), which can improve accuracy of the model.
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the device taught by Berliner, Nichols, and Selvaraj such that computing the loss value using the loss function comprises a supervised learning process that includes receiving an input from the user indicating accuracy of the estimated head posture, as this method of supervised learning trains the model against an explicit correct answer ([0007]), improving accuracy of the model.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Berliner in view of Nichols, as applied to claim 1, in view of US Patent Publication 2019/0092337 by Chua et al., hereinafter “Chua”.
Berliner teaches the head-mounted device of claim 1, but does not teach wherein the machine learning model has been trained to estimate a reclining position of the user using the image data.
Chua teaches a system for monitoring the physical state of a user that may employ a combination of optical cameras and inertial sensors ([0152]). Head and body dynamics of the user are used to estimate a physical state of the user, such as off-center posture (i.e., head/neck/back posture), reclining, or slumped shoulders, as this indicates fatigue of the user ([0154]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the machine learning model taught by Berliner in view of Nichols such that it has been trained to estimate a reclining position of the user using the image data, as taught by Chua. This combination would provide more classifications for head postures to determine the physical state (i.e., level of fatigue) of the user.
It is noted that Berliner teaches that the estimation of the head posture can be made in order to determine head, back and/or neck pain, posture, in addition to fatigue. Some positions that can be determined to be indicative of these physical conditions include slouching, while upright positions indicate alertness and focus ([0207]). Therefore, the modification in view of Chua provides more positions that may be indicative of head, back and/or neck pain, posture, or, fatigue. Further, Chua and Berliner are considered to be analogous art due to the shared objective of defining postures indicative of physical states.
Response to Arguments
Applicant’s arguments, filed 04/30/2026 have been fully considered.
The amendments to claims 2 and 3 overcome the objections of record, however the amendments to the claims necessitate an objection to claim 1.
The amendments to the claims overcome the rejections under 35 U.S.C. 112(b) of claims 1, 3, 4, and 7. However, the amendments to the claims necessitate the rejection of claim 5 under 35 U.S.C. 112(b).
The amendments to the claims overcome the rejections under 35 U.S.C. 101.
Applicant’s assertion regarding the rejection of claim 1 under 35 U.S.C. 102 is acknowledged. This assertion is moot as it is based on amendments to the claims not entered at the time of the previous Office action. The newly presented limitations are rejected on new grounds above.
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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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
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