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
Note: in the Office Action dated 10/30/2025 the cover sheet contained a typographical error, listing an IDS dated 1/24/2025 when the IDS was filed 1/24/2024. The date of the IDS has been corrected on the current cover sheet, and the IDS is being resent herewith.
Response to Arguments
Applicant's arguments filed 1/20/2026 have been fully considered but they are not persuasive.
Regarding the rejections of claims 103, 5, 8-10 and 12 under 35 USC 102(a)(1) in view of Tsubaki (Pub. No. US 2016/0057445; hereafter Tsubaki), Applicant argues that Tsubaki does not fairly disclose the claim limitations of “determin[ing], based on the second data, a prediction of a second state of the one or more motion sensors” as called for in claim 1. Examiner respectfully disagrees.
Tsubaki discloses determining the “difference between the camera shake and the optical image stabilization effect” and that this difference is “referred to as an image motion prediction value” (see Tsubaki paragraph [0155]. That is to say that, based on the second data (i.e. the camera shake data), the device makes a prediction (i.e. the image motion prediction value) of a second state of the sensor (i.e. the state of whether the shake, which is detected by the sensor, will be small enough so that the search range can be small, as in mode 1, or whether there will be lots of shake requiring modes 3-4).
Furthermore, Tsubaki discloses that “in Fig. 14, the search range is set by moving the reference coordinate of the search range on the basis of the position posture change information from the shake information obtaining unit” (see Tsubaki paragraph [0152]). This moving of the reference coordinate is the same as Applicant’s example that “the second state 170 may specify a second position of the gyroscopic sensor 164 different than the first position.” However, this interpretation is not required to meet the claim limitations, as noted, above.
Regarding Applicant’s argument with respect to claim 3, Applicant has not explained how the search range disclosed in Tsubaki does not qualify as a “spatial margin associated with electronic image stabilization.” The search range is a “spatial margin” as can be seen in Fig. 14, and it is “associated with electronic image stabilization” as it is part of the image stabilization control discussed in Tsubaki. Applicant’s bare assertion that the search range does not constitute a “spatial margin associated with electronic image stabilization” is therefore unpersuasive.
Regarding the rejections of claims 4, 6, 7, 11, 13, 14, and 20 in view of Tsubaki and Kang, Applicant argues that “Kang does not explicitly disclose” the claim limitations “as in claim 1” (see Applicant’s arguments p. 10). However, Kang is not relied upon to teach any of the limitations of claim 1, which are taught by Tsubaki. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant does not show how the combination of Tsubaki in view of Kang fails to disclose the limitations called for in the claims, and Applicant’s arguments are therefore unpersuasive.
Applicant further argues with respect to claim 6 that “Kang does not explicitly disclose” a weighted statistical measure of historical prediction values as called for in the claim. However, explicit recitation of a feature is not required when the disclosure reasonably conveys that feature to the ordinary workman in the art (e.g. an inherent feature). In this case, Kang discloses machine learning, and as Examiner explained in the previous Office Action, dated 10/30/2025, machine learning involves a “weighted statistical measure of historical prediction values.” As noted in the previous Office Action, machine learning, for example neural networks, function by assigning weights to neurons which are “historical prediction values” which are then updated when new data is applied, therefore disclosing a “weighted statistical measure of historical prediction values” as the output of a neural network specifically is a weighted statistical measure (some product of neurons with associated weights) of the historical prediction values (neurons). Therefore the disclosure in Kang of use of machine learning renders obvious using a weighted statistical measure of historical prediction values, in that, if not inherently required by machine learning, is at least an obvious implementation of machine learning to the ordinary workman in the art at the time the invention was filed. Applicant’s argument that since the prior art of Kang does not explicitly recite the feature means that Kang cannot reasonably render the feature obvious is therefore unpersuasive, and the rejection of claim 6 made in view of Tsubaki and Kang is therefore maintained.
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) 1-3, 5, 8-10, and 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tsubaki (Pub. No. US 2016/0057445 A1; hereafter Tsubaki).
Regarding claims 1, 8, and 18, Tsubaki discloses an apparatus, method, and non-transitory computer readable medium, comprising: a processing system including one or more processors (see Tsubaki Fig. 1, items 4, 6, 10) and one or more memories coupled to the one or more processors (see Tsubaki Fig. 1, item 7), the processing system configured to: receive first data from an image sensor (see Tsubaki Fig. 1, items 2 and 6); receive second data from one or more motion sensors, the second data associated with a first state of the one or more motion sensors (see Tsubaki Fig. 1, items 3 and 4); determine, based on the second data, a prediction of a second state of the one or more motion sensors (see Tsubaki Fig. 15, “absolute value of image motion prediction value”); and perform electronic image stabilization (EIS) associated with the first data based on the prediction of the second state (see Tsubaki Figs. 14 and 15, the search region is selected based on the predicted motion, as shown in Fig. 15. See also paragraph [0042] which discloses that either EIS or OIS can be used as the image stabilization).
Regarding claims 2, 9, 19, Tsubaki discloses the apparatus, method and non-transitory computer readable medium of claims 1, 8, and 18, respectively, wherein the first data includes first image data associated with a first time and further includes second image data associated with a second time after the first time (see Tsubaki abstract “A motion vector search unit searches for a motion vector of a reference image later input from an image pickup device with respect to a standard image input previously”), and wherein the processing system is further configured to: process the first image data according to a first spatial margin and according to the first state of the one or more motion sensors (see Tsubaki Fig. 14, “original search range”); and process the second image data according to a second spatial margin different than the first spatial margin and according to the prediction of the second state (see Tsubaki Fig. 14, “search range after position control”).
Regarding claims 3 and 10 Tsubaki discloses the apparatus and method of claims 1 and 8, respectively, wherein the processing system is further configured to select a spatial margin associated with the EIS based at least in part on the prediction of the second state (see Tsubaki Figs. 14 and 15, the spatial margins are associated with the modes, which are based on the predicted motion).
Regarding claims 5 and 12, Tsubaki discloses the apparatus and method of claims 1 and 8, respectively, wherein the one or more motion sensors include a gyroscopic sensor, wherein the first state indicates a first position of the gyroscopic sensor, and wherein the second state indicates a second position of the gyroscopic sensor (see Tsubaki paragraph [0044] “shake information obtaining unit 3 is a position-posture sensor, such as a gyro sensor.” See also paragraph [0152] which discloses that “the search range is set by moving the reference coordinate of the search range on the basis of the position-posture change information from the shake information obtaining unit 3”).
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.
Claim(s) 4, 6, 7, 11, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsubaki in view of Kang et al. (Pub. No. US 2020/0077023 A1; hereafter Kang).
Regarding claims 4, 11, and 20, Tsubaki discloses the apparatus, method, and non-transitory computer readable medium of claims 3, 10, and 18, respectively, but does not disclose that the second state is associated with a particular motion classification of a plurality of motion classifications respectively associated with a plurality of spatial margins, and wherein the processing system is further configured to select the spatial margin from the plurality of spatial margins based on the prediction of the second state; [claims 6, 13] wherein the prediction of the second state corresponds to a weighted statistical measure of historical prediction values associated with the one or more motion sensors, and wherein the processing system is further configured to access the historical prediction values from one or more prediction history queues; [claim 7] wherein the processing system is further configured to execute a machine learning (ML) engine to determine the prediction of the second state.
Tsubaki discloses using the motion data to separate the regions into different modes (see Tsubaki Fig. 15), however, the modes are predefined and static, and do not specifically classify the motion into different movement classes.
Kang discloses using machine learning (see Kang Fig. 3A, item 312) to distinguish between different types of motion classes (see Kang Figs. 8A-8D which disclose “slow, fast, stairs, and car” as different motion classes). Machine learning via neural-network is a method which operates by “weighted statistical measure of historical prediction values associated with the one or more motion sensors” (i.e. the weights of the neurons on the input are a statistical measure of past experience of the machine).
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify the region selection of Tsubaki with a machine learning classification system like that in Kang in order to all the device to learn, define, and refine motion classes and associated region selections in order to enable adaptive mode settings that improve over time, instead of static, pre-set modes like in Tsubaki.
Allowable Subject Matter
Claims 15-17 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Finality
THIS ACTION IS MADE FINAL. 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAM S REISNER whose telephone number is (571)270-7542. The examiner can normally be reached Monday-Friday 9:00AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, STEPHANIE BLOSS can be reached at 571-272-3555. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NOAM REISNER/ Primary Examiner, Art Unit 2852 3/27/2026
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