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
Applicant’s amendment and arguments filed 6/10/2026, with respect to the 35 USC 101 rejection of the claims have been fully considered and are persuasive. Applicant has incorporated additional elements that amount to integration of the abstract idea into a practical application, namely device control in response to RBD detection. The 35 USC 101 rejection of the claims has been withdrawn.
Applicant's amendment and arguments filed 6/10/2026 regarding the 35 USC 112(a) rejection of claims 3, 6 and 15 have been fully considered but they are not persuasive. Applicant argues that par. [0260] of the published application discloses models 11-15 (pressure data model 11, displacement data model 12, eye movement data model 13, etc.); that par. [0261-0265] expressly identify the outputs of each model; and that par. [0273] discloses that the model 21 is a deep neural network that receives the outputs of the hub device’s model as inputs and produces sleep state information, which is then used to determine RBD occurrence. This is not found persuasive in that par. [0260] states: “The first machine learning model 11 may be pre-trained to extract a feature from pressure data collected by the pressure sensor.” This language is repeated for each model. However, the specification does not indicate how the models are pre-trained or even what particular features are being extracted. A machine learning model is defined by how it is trained and what data output it is producing. Without either of those clearly or explicitly defined, the algorithm for producing said results is not adequately disclosed. The models are so broadly described that they amount to intended functional results without any clear indication on how those results are achieved or what those results even are other than “a feature”. Par. [0273] does not remedy this issue in that this section escribes using the various feature outputs of the various models to feed into another model that has bene trained to use these features to make a prediction. This model 21 has likewise not been described in sufficient detail. There is no indication on how this model is trained or on what particular features it is trained to produce the intended result. This is a “black box” algorithm without sufficient detail to satisfy the written description requirement. The rejection is maintained.
Applicant’s amendment and arguments filed 6/10/2026 has been considered but does not overcome the applied art of Chuang et al. (2023/0337971) in view of Burton (2021/0169417). Burton discloses connecting to smart home systems, such as lighting systems to adjust lighting color to assist in sleep quality improvement (see Burton, par. [0452]). The rejection has been update to reflect the claim language.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 3, 6 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
First, as noted in MPEP §2161.01, Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.”
The claims require obtaining information by inputting processed data into machine learning models. Applicant’s specification repeats the functional result of obtaining information by inputting processed data into machine learning models (see par. [0261-0265] of PGPUB 2025/0099027, which is the publication of the present application. Applicant has not provided any details regarding how each model is specifically trained; what specific type of information is being produced from the model; and/or how that information is used to identify RBD. Applicant has claimed a functional result on the use of a machine learning model without setting forth any steps that the model takes to provide said results steps taken to particular train a model to produce said results. The claimed models are essentially “black box” algorithms as currently claimed and described.
Lastly, the Examiner notes “It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015)”, see MPEP §2161.01.
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 (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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 4, 7, 8, 13, 14 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chuang et al. (2023/0337971) in view of Burton (2021/0169417).
Regarding Claims 1, 7, 8, 13, 16 and 18, Chuang discloses a plurality of sensors (such as heart rate sensors and movement sensors, e.g. inertial measurement units, see par. [006]); and a user device such as a control unit 160 (par. [0006]) in smartwatch or smart band that receives heart rate data and movement data from the heart rate sensors and inertial measurement units, respectively (par. [0015]). The user device then determines sleep state information using the heart rate data and body motion data to then determine whether Rapid Eye Movement Sleep Behavior Disorder (RBD) of a user occurs (see Fig. 1, steps S110-S190). Chuang discloses the computing can be distributed to other devices such as a server (which can be considered a “hub”), see par. [0037, 0041]). Chuang fails to disclose performing any operation or action upon detection of RBD.
However, in the same field of endeavor of tracking RBD using a smartwatch, Burton discloses performing actions, such as issuing an alert, upon detection of RBD in order to allow a user to seek treatment for recurrent instances of RBD (par. [3053-3054]). Burton also discloses the system can be connected wirelessly to smart home systems such a slighting systems that change the color of the lighting for the purpose of improving sleep quality (par. [0452]). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device in the Chuang reference to include issuing an alert upon the detection of RBD and also control lighting color through a smart home system, as taught and suggested by Burton, for the purpose of improving sleep quality.
Regarding Claims 3, 4, 14 and 17, Chuang discloses the server can use machine learning models to process the sensor data (par. [0042-0043]) and then discloses determining from the processed data that a movement is above a movement threshold during REM sleep (par. [0035]).
Claims 1, 2, 5, 6 and 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over Mushtaq et al. (2023/0218225) in view of Chuang et al. (2023/0337971), further in view of Burton (2021/0169417).
Regarding Claims 1 and 13, Mushtaq discloses a hub device that is in communication with sensors of a sensor array to obtain at least two sets of sensing data, such as lighting, sound, CO2 concentrations, motion etc. (par. [0005, 0011, 0014, 0114]). Mushtaq further discloses that a user interface, such as device 310 (Fig. 3) can receive sensed data and also discloses other devices that have central controllers for processing can be included in the system (see par. [0184]). Mushtaq discloses the central controllers provide analysis of health information to produce indications of REM sleep disorders (par. [0057]) and issuing commands with the hub, such as issuing recommendations to adjust CO2 levels in the environment (par. [0057]). Mushtaq discloses the REM disorder determinations can be based on motion (Claim 17) but fails to disclose the details of such a detection algorithm. However, in the same field of endeavor of REM sleep disorder determination using a distributed network of devices, Chuang discloses a plurality of sensors (such as heart rate sensors and movement sensors, e.g. inertial measurement units, see par. [006]); and a user device such as a control unit 160 (par. [0006]) in smartwatch or smart band that receives heart rate data and movement data from the heart rate sensors and inertial measurement units, respectively (par. [0015]). The user device then determines sleep state information using the heart rate data and body motion data to then determine whether Rapid Eye Movement Sleep Behavior Disorder (RBD) of a user occurs (see Fig. 1, steps S110-S190). This provides the benefit of simple and convenient real-time detection of REM sleep disorders without the need for polysomnography and clinician review (par. [0004, 0007]). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device in the Mushtaq reference to include obtaining sleep stage information and body motion data, as taught and suggested by Chuang, for the purpose of providing simple and convenient real-time detection of REM sleep disorders without the need for polysomnography and clinician review.
Mushtaq and Chuang do not disclose controlling a device to output lighting having a specific color. However, in the same field of endeavor of sleep management, Burton discloses connecting a sleep monitoring system wirelessly to smart home systems such as lighting systems that change the color of the lighting for the purpose of improving sleep quality (par. [0452]). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device in the Mushtaq and Chuang combination to include controlling lighting color through a smart home system, as taught and suggested by Burton, for the purpose of improving sleep quality.
In regard to Claim 2, Mushtaq discloses the sensors can include radar sensors (par. [0014]) and accelerometers for the motion sensing (par. [0332]).
With regard to Claim 5, the combination of Mushtaq and Cheung incorporate contact sensors (heart rate sensors, accelerometers, see Mushtaq, [0014, 0332] and Cheung par. [0038, 0039]) and non-contact sensors (like radar sensors, Mushtaq par. [0014]).
In regard to Claims 6 and 14 , the combination of Mushtaq and Cheung disclose processing sensor data with machine learning models and determining RBD occurs when movement is greater than a threshold (Cheung, par. [0035, 0042, 0043]).
Regarding Claims 11 and 12, Mushtaq discloses the hub is configured to identify RBD and can control home appliances accordingly, such as adjusting ambient light, issuing alerts/alarms, wakeup alarms, etc. as well as alert medical personnel (par. [0007-0009, 0057, 0149]).
With regard to Claim 15, the combination of Mushtaq and Cheung incorporate contact sensors (heart rate sensors, accelerometers, see Mushtaq, [0014, 0332] and Cheung par. [0038, 0039]) and non-contact sensors (like radar sensors, Mushtaq par. [0014]). The combination of Mushtaq and Cheung disclose processing sensor data with machine learning models and determining RBD occurs when movement is greater than a threshold (Cheung, par. [0035, 0042, 0043]).
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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/ALLEN PORTER/Primary Examiner, Art Unit 3796
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