DETAILED ACTION
This Office Action is in response to the Applicants' communication filed on March 30, 2026. Claims 28-47 are currently pending and have been examined.
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 arguments filed on March 30, 2026 have been fully considered.
Applicant argues in its Argument that cited arts fail to teach limitations of the amended independent Claim 28 and similar claimed Claims 46-47.
Examiner replies: In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
During patent examination, the pending claims must be given their broadest reasonable interpretation consistent with the specification. See MPEP § 2111. Further, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). See also MPEP § 2145(VI).
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Regarding limitations the Claim 28 of the instant case, “wherein the gesture recognition result is a gesture corresponding to the preset policy and the electronic device performs the task based on the preset policy”, Im as modified teaches fully to use multiple resolution images, motion, and etc. for gesture recognition, where the model and algorithms used for the gesture recognition explicitly follow rules or policies for the recognition. Unless specific set of policies are explicitly specified, these limitations are inherently by the operations of the any gesture recognition model/algorithm.
Therefore, it is reasonable to conclude that Im as modified teaches all limitations as claimed.
Conclusion: Examiner has shown the rejections set forth in the Office Action of January 16, 2026 are proper. In light of amended Claims, new grounds of rejection are set forth below. Since the new grounds of rejection are necessitated by Applicant's amendments to the claims, the present action is made final.
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 of this title, 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 28-30, 33-36 and 46-47 are rejected under 35 U.S.C. 103 as being unpatentable over US 20130050425 A1 (Im), in view of Liao, W., Xu, W., Kong, S., Ahmad, F. and Liu, W., 2019, March. A two-stage method for hand-raising gesture recognition in classroom. In Proceedings of the 2019 8th international conference on educational and information technology (pp. 38-44) (Liao) and in further view of Lee, H., Lim, S.Y., Lee, I., Cha, J., Cho, D.C. and Cho, S., 2013. Multi-modal user interaction method based on gaze tracking and gesture recognition. Signal Processing: Image Communication, 28(2), pp.114-126 (Lee) and Wang, H., 2021. Two stage continuous gesture recognition based on deep learning. Electronics, 10(5), p.534 (Wang).
Regarding Claims 28 and 46-47:
An air gesture recognition method, wherein the method is applied to an electronic device, and the method comprises: receiving a first operation performed by a user; enabling an air gesture function in response to the first operation, wherein after the air gesture function is enabled, a camera module of the electronic device is in a working state; receiving a second operation performed by the user; after the second operation, controlling the camera module to collect a plurality of frames of first images of a first size specification; analyzing the plurality of frames of first images; when an analysis result of the plurality of frames of first images meets a preset gesture trigger condition, controlling the camera module to collect a plurality of frames of second images of a second size specification, wherein the plurality of frames of second images comprise images with same content but different size specifications as the plurality of frames of first images; performing gesture recognition based on the plurality of frames of second images, to obtain a gesture recognition result; and responding based on a preset policy corresponding to the gesture recognition result to control the electronic device to perform a task, wherein the gesture recognition result is a gesture corresponding to the preset policy and the electronic device performs the task based on the preset policy, and wherein a resolution corresponding to the first size specification is less than a resolution corresponding to the second size specification (Im: Figs. 8-10, 13, and par. 137-148, a gesture recognition method that uses multiple resolution images; the camera controller 106 may control the image sensor 104 to output a depth image having a low resolution of QQVGA (160.times.120) to the image processor 108 in the first mode which does not require accurate gesture recognition. In contrast, the camera controller 106 may control the image sensor 104 to output a depth image having a high resolution of VGA (640.times.480) in the second mode requiring accurate gesture recognition. If the gesture recognition mode is divided into three modes according to program characteristics, it is possible to respectively set resolutions according to the modes).
Im does not teach explicitly on an analysis result of the plurality of frames of first images meets a preset gesture trigger condition, controlling the camera module to collect a plurality of frames of second images of a second size specification. However, Liao and Lee teach (Liao: Figs. 1-2, and Chapter 3, a two stage gesture recognition method that in stage1 to use low resolution image to conduct pose estimation, and in stage 2, use coarse-to-fine, i.e., fine resolution images to perform gesture classification; Lee: 2.2.3. and Fig. 8, the low resolution image is used to locate object region, and high resolution image is used to determine the position and size).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Im with an analysis result of the plurality of frames of first images meets a preset gesture trigger condition, controlling the camera module to collect a plurality of frames of second images of a second size specification as further taught by Liao and Lee. The advantage of doing so is to provide a mechanism for hand-raising gesture recognition with better accuracy (Liao: Abstract).
Im does not teach explicitly on using multiple images to recognize gesture. However, Wang teaches (Wang: e.g., 2. And Fig. 3, a method for continuous gesture recognition that uses temporal segmentation for both appearance info and hand motion info to classify video frames, where the model and algorithms used for the gesture recognition explicitly follow rules or policies for the recognition. Unless specific set of policies are explicitly specified, these limitations are inherently by the operations of the any gesture recognition model/algorithm).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Im with using multiple images to recognize gesture as further taught by Wang. The advantage of doing so is to provide a mechanism for gesture recognition using both appearance and motion info (Wang: Abstract).
Regarding Claim 29, Im as modified further teaches:
The method according to claim 28, wherein the method further comprises: after the controlling the camera module to collect a plurality of frames of first images of a first size specification, caching the plurality of frames of first images by using a first internal memory; and after the controlling the camera module to collect a plurality of frames of second images of a second size specification, caching at least a part of the plurality of frames of second images by using a second internal memory, wherein in a case in which a same image is stored, power consumption caused by the first internal memory is lower than power consumption caused by the second internal memory (Im: Figs. 8-10, capture low and high resolution images; where captured images need to be stored memory, and low resolution images requires less memory sizes than high resolution images; consequently, less power).
Regarding Claim 30, Im as modified further teaches:
The method according to claim 29, wherein the caching at least a part of the plurality of frames of second images by using a second internal memory comprises: storing all images of the plurality of frames of second images into the second internal memory (Im: Figs. 8-10, where it is noted that multi-stage memory structure are known practice in the field, e.g., cache vs. main storage vs. disk and etc.).
Regarding Claim 33, Im as modified further teaches:
The method according to claim 29, wherein the first internal memory is a tightly coupled memory (TCM) and the second internal memory is a double data rate synchronous dynamic random access memory (DDR SDRAM) (It is noted that TCM and DDR SDRAM are known commercial memory used in the field).
Regarding Claim 34, Im as modified further teaches:
he method according to claim 28, wherein the second size specification is QVGA, and a corresponding resolution is 320x240; and the first size specification is QQVGA, and a corresponding resolution is 160x 120 (Lee: 2.2.3., low and high resolution images, where choices of the resolution depend on design and application requirements).
Regarding Claim 35, Im as modified further teaches:
The method according claim 28, wherein the camera module is a front camera; and the controlling the camera module to collect a plurality of frames of first images of a first size specification comprises: controlling the front camera to collect images and output the plurality of frames of first images of the first size specification according to a first frame rate; and the controlling the camera module to collect a plurality of frames of second images of a second size specification comprises: controlling the front camera to collect images and output the plurality of frames of second images of the second size specification according to a second frame rate, wherein the second frame rate is greater than the first frame rate (Lee: 2.2.3., system captures image pyramid of multi-resolution that has both low and high resolution images on same target area, where Im: Fig. 15 illustrates that camera modules are controlled through a controller 606).
Regarding Claim 36, Im as modified further teaches:
The method according to claim 28, wherein the second operation is an air gesture operation, and the preset gesture trigger condition comprises that there is a hand feature and a start gesture in the plurality of frames of first images and a status of the start gesture changes (Wang: e.g., Figs. 1-3).
Claims 37-38 are rejected under 35 U.S.C. 103 as being unpatentable over US 20130050425 A1 (Im), in view of Liao, W., Xu, W., Kong, S., Ahmad, F. and Liu, W., 2019, March. A two-stage method for hand-raising gesture recognition in classroom. In Proceedings of the 2019 8th international conference on educational and information technology (pp. 38-44) (Liao) and in further view of Lee, H., Lim, S.Y., Lee, I., Cha, J., Cho, D.C. and Cho, S., 2013. Multi-modal user interaction method based on gaze tracking and gesture recognition. Signal Processing: Image Communication, 28(2), pp.114-126 (Lee) and Wang, H., 2021. Two stage continuous gesture recognition based on deep learning. Electronics, 10(5), p.534 (Wang) and Neverova, N., Wolf, C., Taylor, G.W. and Nebout, F., 2014, September. Multi-scale deep learning for gesture detection and localization. In European conference on computer vision (pp. 474-490). Cham: Springer International Publishing (Neverova).
Regarding Claim 37, Im as modified does not teach explicitly on multi-scale gesture analysis method. However, Neverova teaches: (Neverova: 3.-4., e.g., Figs. 1, 4, using multiple temporal scales corresponding to dynamics poses of 3 different durations).
The method according to claim 28, wherein the method further comprises: determining that the analysis result of the plurality of frames of first images meets the preset gesture trigger condition; and the determining that the analysis result of the plurality of frames of first images meets the preset gesture trigger condition comprises: obtaining N consecutive frames of images from the plurality of frames of first images based on a preset first sampling period, to analyze whether there is the hand feature in the plurality of frames of first images; after it is determined that there is the hand feature in the plurality of frames of first images, obtaining M consecutive frames of images from the plurality of frames of first images based on a preset second sampling period, to analyze whether there is the start gesture in the plurality of frames of first images; and if it is determined, for S consecutive times, that there is the start gesture in the plurality of frames of first images and the status of the start gesture changes, determining that the analysis result of the plurality of frames of first images meets the preset gesture trigger condition (Neverova: 3.-4., e.g., Figs. 1, 4, using multiple temporal scales corresponding to dynamics poses of 3 different durations).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Im as modified with multi-scale gesture analysis method as further taught by Neverova. The advantage of doing so is to provide a mechanism for a progressive learning procedure enabling our method to scale to a higher number of data modalities (Neverova: Intro).
Regarding Claim 38, Im as modified further teaches:
The method according to claim 28, wherein the performing gesture recognition based on the plurality of frames of second images, to obtain a gesture recognition result comprises: obtaining T consecutive frames of images from the plurality of frames of second images based on a preset third sampling period, to identify a specific gesture in the plurality of frames of first images; identifying the specific gesture within preset duration; and when it is determined that the specific gesture is one of a plurality of preset gestures, generating the gesture recognition result based on the specific gesture (e.g., Wang: Fig. 3 and Neverova: Figs. 1 and 4).
Claims 40-41 are rejected under 35 U.S.C. 103 as being unpatentable over US 20130050425 A1 (Im), in view of Liao, W., Xu, W., Kong, S., Ahmad, F. and Liu, W., 2019, March. A two-stage method for hand-raising gesture recognition in classroom. In Proceedings of the 2019 8th international conference on educational and information technology (pp. 38-44) (Liao) and in further view of Lee, H., Lim, S.Y., Lee, I., Cha, J., Cho, D.C. and Cho, S., 2013. Multi-modal user interaction method based on gaze tracking and gesture recognition. Signal Processing: Image Communication, 28(2), pp.114-126 (Lee) and Wang, H., 2021. Two stage continuous gesture recognition based on deep learning. Electronics, 10(5), p.534 (Wang) and Always-on (Sony).
Regarding Claim 40, Im as modified does not teach explicitly on using Always-on platform. However, Sony teaches:
The method according to claim 28, wherein the method is applied to a system architecture of the electronic device, and the system architecture comprises a first algorithm platform and a second algorithm platform; and the second algorithm platform is a framework that is provided by a native chip and that supports an always-on camera AON algorithm; the first algorithm platform is an AON algorithm integration framework that supports a plurality of services and that is created based on the second algorithm platform; a service supported by the first algorithm platform for processing comprises an air gesture service; and a service supported by the second algorithm platform for processing comprises a face unlock screen service (It is noted that AON is known technologies in mobile and IoT, e.g., Sony).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Im as modified with multi-scale gesture analysis method as further taught by Sony. The advantage of doing so is to adopt known features and technologies in marketplace to enhance product features.
Regarding Claim 41, Im as modified further teaches:
The method according to claim 40, wherein the method further comprises: analyzing the plurality of frames of first images by using the first algorithm platform; and determining, by using the first algorithm platform, whether the analysis result of the plurality of frames of first images meets the preset gesture trigger condition; and performing the gesture recognition based on the plurality of frames of second images by using the first algorithm platform, to obtain the gesture recognition result (e.g., Liao: Fig. 1, Lee: 2.2.3., Wang: Fig. 1).
Allowable Subject Matter
The Claims 31, 39 and 42 are objected to as being dependent upon a rejected base claim, but are potentially allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 32, 43-45 depend on Claims 31 and 42 respectively, therefore, are objected for the same reasons as Claims 31 and 42.
Conclusion
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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHITONG CHEN whose telephone number is (571)270-1936. The examiner can normally be reached on M-F 9:30am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yuwen Pan can be reached on 571-272-7855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZHITONG CHEN/
Primary Examiner, Art Unit 2649
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