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 .
Information Disclosure Statement
Acknowledgment is made of information disclosure statement filed 22 July 2025.
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.
Claim(s) 1 – 12 and 14 - 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GRUHLKE et al; (Publication number: US 2022/0124242 A1), hereafter GRUHKLE.
Regarding claim 1:
GRUHKLE discloses a computer-implemented method (GRUHKLE [0007] “aspects generally include a method, apparatus, system …”), comprising:
predicting a user state, wherein the user state is measurable via a plurality of different sensor sampling modes (GRUHKLE [0051] … the camera is configured for gaze analysis. For example, the camera may provide, in a high-power mode, images to the controller that can be analyzed to determine a gaze direction of the eyes of the user. Within the context of claim 1 there is no difference between “predicting” and “determining” wherein “a gaze direction” defines the user state.);
determining a level of uncertainty associated with the predicted user state (GRUHKLE [0055] … the motion analysis module detects eye activity associated with the user being able to optically interpret the media on the display …”; the determination of the user’s eye activity data is the level of uncertainty, as claimed. This includes the determination that the user is able to/not able to interpret media on the display); and
selecting, from the plurality of different sensor sampling modes, a sampling mode to measure the user state (GRUHKLE Figure 4B 440/450, [0053] … the motion analysis module detects eye activity associated with the user being unable to optically interpret…; The method judges based on the eye movement data (corresponds to the user state), if a user is able to visually perceive elements (corresponds to the level of uncertainty), and adjusts the power level of the sensors accordingly)., wherein selecting the sampling mode comprises:
selecting a first sampling mode in response to determining that the level of uncertainty is at or above a threshold (GRUHKLE [0054] … the controller deactivates the camera and/or reduces capability of the camera. For example, the controller may shut the camera down and/or reduce an amount of power being supplied to the camera … the controller may deactivate the gaze analysis module and/or cease to analyze a gaze of the user… ; [0049] camera is the sensor which measures user state, as claimed. The sampling mode, as claimed, is based on output of analysis module which determines whether user is able to interpret media on the display; [0055] [0058]); or
selecting a second sampling mode in response to determining that the level of uncertainty is below the threshold (GRUHKLE [0056] … the controller reactivates the camera and/or improves a capability of the camera; [0049] camera is the sensor which measures user state, as claimed. The sampling mode, as claimed, is based on output of analysis module which determines whether user is able/not able to interpret media on the display; [0055] [0058]).
Regarding claim 2:
GRUHKLE discloses the computer-implemented method of claim 1, wherein the user state comprises at least one of: a user behavior; a user biometric; or an environmental state relating to a user (GRUHKLE [0051]; user state includes a gaze analysis).
Regarding claim 3:
GRUHKLE discloses the computer-implemented method of claim 2, wherein the user behavior comprises a user ocular behavior (GRUHKLE [0051]).
Regarding claim 4:
GRUHKLE discloses the computer-implemented method of claim 3, wherein the user ocular behavior comprises movement of at least one of a user pupil position or a user gaze (GRUHKLE [0051] [0055] user movement may include saccade in which movement of pupil position and gaze is changed).
Regarding claim 5:
GRUHKLE discloses the computer-implemented method of claim 3, wherein: the first sampling mode comprises sampling the user ocular behavior using a first type of sensor; and the second sampling mode comprises sampling the user ocular behavior using a second type of sensor (GRUHKLE [0037 – 0040] vision sensor 305 and camera 315).
Regarding claim 6:
GRUHKLE discloses the computer-implemented method of claim 5, wherein: the first type of sensor is different than the second type of the sensor (GRUHKLE [0037 – 0040] vision sensor 305 is a first sensor and camera 315 is a second sensor); and at least one of the first type of sensor or the second type of sensor comprises at least one of: an ultrasound detector; a camera; a self-mixing interferometry sensor; or a scanning-based eye-tracking sensor (GRUHKLE [0037 – 0040]; camera 315).
Regarding claim 7:
GRUHKLE discloses the computer-implemented method of claim 6, wherein the camera comprises at least one of: a waveguide-based camera; an infrared camera; a near-infrared camera; a video-based eye tracking camera; or a stereo camera (GRUHKLE [0054 – 0056] motion analysis performed using the camera corresponds to video-based eye tracking camera, as claimed).
Regarding claim 8:
GRUHKLE discloses the computer-implemented method of claim 3, wherein: the first sampling mode comprises capturing image data at a high-frame rate that is high relative to a low-frame rate; and the second sampling mode comprises image data at the low-frame rate (GRUHKLE [0054 – 0055] camera reduces frame rate of the camera).
Regarding claim 9:
GRUHKLE discloses the computer-implemented method of claim 1, wherein the first sampling mode is associated with a power consumption requirement that is high relative to a power consumption requirement associated with the second sample mode (GRUHKLE [0037 -0040] first sampling mode corresponds to high power camera sensing while low sensing mode corresponds to low power vision sensing).
Regarding claim 10:
GRUHKLE discloses the computer-implemented method of claim 1, wherein: the first sampling mode comprises the use of at least one always-on sensor; and the second sampling mode comprises the use of at least one on-demand sensor (GRUHKLE [0039] [0041] vision sensor always-on; camera enabled based selectively or on-demand).
Regarding claim 11:
GRUHKLE does not disclose the computer-implemented method of claim 1, wherein at least one of the predicting the user state and the determining the level of uncertainty comprises using a model that is pretrained based on past user behaviors and sensor measurements (Miller [0009] machine learning model is trained with commands resulting from eye gazing techniques).
Regarding claim 12:
GRUHKLE discloses the computer-implemented method of claim 1, wherein the predicting the user state comprises determining a physical location of the user (GRUHKLE [0029] input unit include GPS positioning system).
Regarding claim 14:
GRUHKLE discloses the computer-implemented method of claim 12, wherein the determining the physical location of the user comprises determining a physical location of the user using geolocation (GRUHKLE [0029] input unit include GPS positioning system).
Regarding claim 15:
GRUHKLE discloses the computer-implemented method of claim 1, wherein the predicting, the determining, and the selecting steps are each performed by at least one computation location of a plurality of locations, the plurality of locations comprises: a headset; a user computing device; and a cloud-based network (GRUHKLE Figure 4C headset; [0045] the headset which is worn by the user and moveable provides a plurality of computation locations during use. The primary location being an instant location of the user)
Regarding claim 16:
GRUHKLE discloses the computer-implement method of claim 15, further comprising selecting, from the plurality of computation locations, a primary computation location for performing each of the predicting, the determining, and the selecting steps (GRUHKLE Figure 4C headset; [0045] the headset which is worn by the user and moveable provides a plurality of computation locations during use. The primary location being an instant location of the user).
Regarding claim 17:
GRUHKLE discloses a system (GRUHKLE [0007] “aspects generally include a method, apparatus, system …”), comprising:
a mode-selection subsystem configured to select sampling modes to measure user states (GRUHKLE Figure 4B/4C controller corresponds to claimed mode-selection subsystem), wherein the user states are each measurable via a plurality of different sensor sample modes (GRUHKLE Figure 6 630 and 660);
a headset comprising a plurality of sensors (GRUHKLE [0045] wearable user device include VR/AR headset);
at least one physical processor (GRUHKLE Figure 2 processor 210); and
physical memory comprising computer-executable instruction that, when executed by the physical processor (GRUHKLE [0027] memory 215 includes instructions), cause the physical processor to:
predict a user state (GRUHKLE [0051] … the camera is configured for gaze analysis. For example, the camera may provide, in a high-power mode, images to the controller that can be analyzed to determine a gaze direction of the eyes of the user. Within the context of claim 1 there is no difference between “predicting” and “determining” wherein “a gaze direction” defines the user state.);
determine a level of uncertainty associated with the predicted user state (GRUHKLE [0055] … the motion analysis module detects eye activity associated with the user being able to optically interpret the media on the display …”; the determination of the user’s eye activity data is the level of uncertainty, as claimed. This includes the determination that the user is able to/not able to interpret media on the display); and
select, from the plurality of different sensor sampling modes, a sampling mode to measure the user state (GRUHKLE Figure 4B 440/450, [0053] … the motion analysis module detects eye activity associated with the user being unable to optically interpret…; The method judges based on the eye movement data (corresponds to the user state), if a user is able to visually perceive elements (corresponds to the level of uncertainty), and adjusts the power level of the sensors accordingly), wherein the selecting the sampling mode comprises:
selecting a first sampling mode in response to determining that the level of uncertainty is at or above a first threshold (GRUHKLE [0054] … the controller deactivates the camera and/or reduces capability of the camera. For example, the controller may shut the camera down and/or reduce an amount of power being supplied to the camera … the controller may deactivate the gaze analysis module and/or cease to analyze a gaze of the user… ; [0049] camera is the sensor which measures user state, as claimed. The sampling mode, as claimed, is based on output of analysis module which determines whether user is able to interpret media on the display; [0055] [0058]); or
selecting a second sampling mode in response to determining that the level of uncertainty is blow the threshold (GRUHKLE [0056] … the controller reactivates the camera and/or improves a capability of the camera; [0049] camera is the sensor which measures user state, as claimed. The sampling mode, as claimed, is based on output of analysis module which determines whether user is able/not able to interpret media on the display; [0055] [0058]).
Regarding claim 18:
Claim 18 is similarly rejected for those reasons discussed above in claim 11.
Regarding claim 19:
Claim 19 is similarly rejected for those reasons discussed above in claim 6.
Regarding claim 20:
Claim 20 is similarly rejected for those reasons discussed above in claim 1 (and additionally [0027] memory with instructions).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over GRUHKLE in view of Vengroff et al; (publication number: US 2009/0005987 A1), hereafter Vengroff.
Regarding claim 13:
GRUHKLE does not disclose the computer-implemented method of claim 12, wherein the predicting the user state further comprises: determining that information related to the physical location is not currently stored in a reference database for the user; and obtaining additional information about the physical location from at least one external database.
However, Vengroff discloses determining locations of interest based on user visits. More particularly, Vengroff discloses:
determining that information related to the physical location is not currently stored in a reference database for the user (Vengroff [0052] anonymous location corresponds to determined locations for which corresponding point of interest has not been identified; see also Figure 11 memory 1145 includes interest identifiers 1152, 1154, 1156); and
obtaining additional information about the physical location from at least one external database (Vengroff [0014] “… if the determined location of interest is an anonymous location without a known identification (e.g., without an identification of one or more points of interest at that location), the techniques may in some embodiments include identifying one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) that are located at or otherwise correspond to the determined location of interest.”; [0087] … “ The system 1150 may also obtain other types of information of interest, such as commercial map database information or other location information about possible POIs, from various sources, such as data sources 1188 and/or the third-party computing systems 1190.”).
It would have been obvious to modify GRUHKLE to include: determining that information related to the physical location is not currently stored in a reference database for the user; and obtaining additional information about the physical location from at least one external database, as claimed. Those skilled in the art would appreciate the ability to provide location information from an otherwise sparse dataset.
Conclusion
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/MIHIR K RAYAN/ 11 May 2026Primary Examiner, Art Unit 2622
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