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 .
Election/Restrictions
Applicant’s election of claims 1-7 with traverse in the reply filed on 12/10/2025 is acknowledged. Applicant’s arguments that the Inventions of Groups I, II, and III are related to head-mounted devices configured to estimate body posture is not found persuasive. Inventions of Groups I, II, and III feature materially different modes of operation or are capable of being used in materially different processes and would require an additional search burden. Therefore, the restriction requirement is maintained.
Claims 8-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.
Claims 1-7 are hereby under examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/28/2022, 03/25/2024, and 05/15/2024 have been considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 2 and 3 are objected to because of the following informalities:
Claim 2 recites “the body posture” in line 1. This should read “the estimated body posture”.
Claim 3 recites “The head-mounted display” in line 1. This should read “The head-mounted device”.
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.
Claims 1-7 are 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 1, the claim recites the limitation "the received inertial measurements" in line 8. There is insufficient antecedent basis for this limitation in the claim.
For the purposes of examination, the claim limitation is interpreted as “the received inertial measurement data”.
Regarding claim 3, the claim recites the limitation "the artificial neural network" in line 2. There is insufficient antecedent basis for this limitation in the claim.
For the purposes of examination, the claim limitation is interpreted as being dependent on claim 2.
Regarding claim 4, the claim recites the limitation "the neural network" in line 1. There is insufficient antecedent basis for this limitation in the claim.
For the purposes of examination, claim 4 is interpreted as being dependent from claim 2, and “the neural network” is interpreted as “the artificial neural network”.
Further regarding claim 4, the claim recites “wherein estimating the body posture comprises: computing a loss value using a loss function; and adjusting the trained neural network based on the computed loss value” in lines 2-5. It is unclear how the functional limitation of “estimating the body posture” can comprise adjusting the trained neural network. Adjusting neural networks based on a loss value is a common method of training a neural network, therefore it is unclear if this step comprises the training of the neural network, a further training of the trained neural network, or an implementation of the neural network. Clarification is requested.
For the purposes of examination, the claim is interpreted as “wherein the training of the neural network comprises: computing a loss value using a loss function; and adjusting the neural network based on the computed loss value”.
Regarding claim 7, the claim recites the limitation "the presented information" in line 1. There is insufficient antecedent basis for this limitation in the claim.
For the purposes of examination, claim 7 is interpreted as being dependent from claim 6, and the limitation is interpreted as “the information”.
All claims not explicitly addressed above are rejected under 35 U.S.C. 112(b) are rejected by virtue of their dependency on a rejected base claim.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Step 1
Regarding claim 1, the claim recites a device configured to perform a steps or acts, including inputting a received inertial measurements into a machine learning model. Thus, the claim is directed to a machine, which is one of the statutory categories of invention.
Step 2A, Prong One
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of inputting a received inertial measurements into a machine learning model sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
Step 2A, Prong Two
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites the limitations of a display, a plurality of sensors comprising an inertial measurement unit, and a controller comprising instructions. Each of these components are generic components that do not amount to a particular machine. It is noted that a display, controller and inertial measurement unit are capable be mounted to a head, and the claim contains no details pertaining to the how the components are mounted on a head or the structure of the head mounting device for holding the recited elements. Further, claim 1 recites the functional limitations of receiving an estimated body posture from the machine learning model, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The receiving of the estimated body posture does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the estimated body posture, nor does the method use a particular machine to perform the Abstract Idea. Further according to section 2106.05(f) of the MPEP, merely using a computer (i.e., controller) as a tool to perform an Abstract Idea does not integrate the Abstract Idea into a practical application.
Step 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites functional limitations of receiving inertial measurement data from the inertial measurement unit and receiving an estimated body posture from the machine learning model. Receiving data and receiving an output from a machine learning model are well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the receiving limitation is recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as display, inertial measurement unit, and controller do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)). The additional elements of the display, inertial measurement unit, and controller are considered generic sensors and components, and do not add to the claim such that the claim amounts to significantly more than the exception.
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Dependent claims 2-6 also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data gathering and the display of data. The receiving and inputting steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
It is noted that claim 7 is not rejected under 35 U.S.C. 101 as the claim recites the limitation of displaying information comprising recommendations on corrective posture actions, thereby effecting a particular treatment for the estimated body posture.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Patent Publication 2023/0139626 by Berliner et al., hereinafter “Berliner”.
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 plurality of sensors comprising an inertial measurement unit ([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.); and a controller comprising instructions executable to control the head-mounted device to (Figs. 2,4, [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 ([0120]; Sensors communication module may receive data from different sensors to determine a status of a user.) input the received inertial measurements 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 receive an estimated body posture from the machine learning model (the output of the machine learning model is the position, orientation, pose, and/or angle of the head of the wearer).
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 2 is rejected under 35 U.S.C. 103 as being unpatentable over Berliner, as applied to claim 1, in view of The Wearable Sensor System (2016)… by Lee et al., hereinafter “Lee”.
Berliner 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 estimate 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 estimate 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 physiological 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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Berliner in view of Lee, as applied to claim 2, in view of US Patent Publication 2022/0265205 by Takahashi et al., hereinafter “Takahashi”.
Berliner in view of Lee teaches the head-mounted display of claim 2, wherein the artificial neural network has been trained to estimate the body posture using image data from one or more cameras (Berliner, [0182]; The camera is used in conjunction with the integrated IMU to determine the position, orientation, pose, and/or angle of the head of the wearer.) but does not teach wherein the plurality of sensors further comprises one or more cameras.
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 Lee such that the plurality of sensors further comprises one or more cameras, in order to estimate the spinal alignment without using a large-scale device, as taught by Takahashi ([0094]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Berliner in view of Lee, as applied to claim 2, in view of US Patent Publication 2024/0321447 by Selvaraj et al., hereinafter “Selvaraj”.
Berliner in view of Lee teaches the head-mounted device of claim 2, but does not teach wherein the neural network has been trained based on an average human population, and wherein estimating the body posture comprises: computing a loss value using a loss function; and adjusting the trained neural network based on the computed loss value.
Selvaraj teaches a machine learning model for the prediction of a physiological 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 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 Lee such that the neural network has been trained based on an average human population, and wherein estimating the body posture comprises: computing a loss value using a loss function; and adjusting the trained neural network 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 Lee in view Selvaraj, as applied to claim 4, in view of US Patent Publication 2019/0171280 by Son et al., hereinafter “Son”.
The combination of Berliner, Lee, 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 a user indicating accuracy of the estimated body posture.
Son teaches a device and method of supervised machine learning wherein the machine learning model is trained to predict a physiological 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 physiological condition, and train the machine learning model based on supervised learning using the user input compared to the model’s estimation of the physiological 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, Lee, and Selvaraj such that computing the loss value using the loss function comprises a supervised learning process that includes receiving an input from a user indicating accuracy of the estimated body posture, as this method of supervised learning trains the model against an explicit correct answer ([0007]), improving accuracy of the model.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Berliner, as applied to claim 1, in view of US Patent Publication 2013/0184611 by Nichols, hereinafter “Nichols”.
Regarding claims 6 and 7, Berliner teaches the head-mounted device of claim 1, but does not teach wherein the controller further comprises 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.
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]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication 2024/0070955 by Li et al. teaches a head-mounted device comprising: a display; a plurality of sensors comprising an inertial measurement; and a controller comprising instructions executable to control the head-mounted device to : receive inertial measurement data from the inertial measurement unit; input the received inertial measurements into a machine learning model; and receive an estimated body posture from the machine learning model.
US Patent Publication 2023/0012278 by Vule et al. teaches a neck evaluation device comprising a plurality of sensors including an inertial measurement unit configured to determine a neck posture of the user.
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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791