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 .
This office action is in response to applicant’s arguments/remarks and amendments filed on 03/24/2026. Claims 1-13, and 15-16 have been amended. No Claims have been cancelled. No Claims have been newly added. Accordingly, claims 1-16 are currently pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14, and 16-17 of U.S. Patent No. 12,085,391 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, and 15-16 of the current application appear to be a broader version of claims 1, and 16-17. Claims 1, and 15-16 of the current application recite all the limitations recited in claims 1, and 16-17 of U.S. Patent No. 12,085,391 B2 except “wherein the estimated movement vector indicates a movement amount and a movement direction of a device”, “calculate a relative position of the device for the current time period with respect to a specified reference position in a real space”, “a display control unit configured to initiate display of a trajectory of the relative position of the device for the current time period with respect to the specified reference position in the real space”, and “in a plurality of previous time periods” (see detailed analysis in the table below of claims 1, and same analysis not shown applies to claims 15-16 vs 16-17). Claims 2-14 of the current application recite all the limitations recited in claims 2-14 U.S. Patent No. 12,085,391 B2.
18/793,972
U.S. Patent No. 12,085,391 B2
Claim 1: A positioning apparatus, comprising: an acceleration sensor configured to output an acceleration of the positioning apparatus; an angular velocity sensor configured to output an angular velocity of the positioning apparatus; and circuitry configured to estimate a movement vector in a current time period having a predetermined amount of time using a machine learning model based on the acceleration and the angular velocity retrieved from the acceleration sensor and the angular velocity sensor in the current time period; and to integrate the estimated movement vector as a relative position with respect to a reference position, wherein the machine learning model is trained in advance based on acceleration of one or more devices and an angular velocity of the one or more devices in a plurality of previous time periods, each time period having the predetermined amount of time.
Claim 1: A positioning apparatus, comprising: a movement vector estimator configured to estimate a movement vector using a machine learning model, wherein the estimated movement vector indicates a movement amount and a movement direction of a device in a predetermined time period, wherein the movement vector is estimated on a basis of input data to the machine learning model, the input data including acceleration of the device in the predetermined time period and an angular velocity of the device in the predetermined time period, wherein the acceleration of the device is detected by an acceleration sensor, and wherein the angular velocity of the device is detected by an angular velocity sensor; an integration section configured to integrate the estimated movement vector, and calculate a relative position of the device for the predetermined time period with respect to a specified reference position in a real space; and a display control unit configured to initiate display of a trajectory of the relative position of the device for the predetermined time period with respect to the specified reference position in the real space, wherein the machine learning model is trained in advance on a basis of acceleration of one or more devices in each of a plurality of prior predetermined time periods and an angular velocity of the one or more devices in each of the plurality of prior predetermined time periods, and wherein the movement vector estimator and the integration section are each implemented via at least one processor
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.
Claim(s) 1 and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwak KR 101301462 B1 (the examiner provided an English translation on 12/30/2025 and is relying upon, hence Kwak) in view of Gohl et al US 2019/0208101 A1 (hence Gohl).
In re claims 1, 15, and 16, Kwak discloses a pedestrian inertial navigation device and its navigation using a lowcost inertial sensor (Paragraph 0001) and teaches the following:
an acceleration sensor configured to output an acceleration of the positioning apparatus (Paragraph 0022 “an acceleration sensor (110)”);
an angular velocity sensor configured to output an angular velocity of the positioning apparatus (Paragraph 0022 “a gyroscope (1400”, and Paragraph 0057 “measured angular velocity”);
circuitry configured to estimate a movement vector in a current time period having a predetermined amount of time (Paragraph 0023 “A pedestrian inertial navigation device (100) tracks the position of a pedestrian using a lowcost acceleration sensor (110) and a gyroscope (140)”, Paragraph 0028 “The step detection module (120) is configured to detect a pedestrian's step using the measurement value of the acceleration sensor”, Paragraph 0029 “estimate the step length of a pedestrian using the steps detected by the step detection module (120)”, and Paragraph 0031 “estimate the attitude and heading angle of a pedestrian using the measurement values of the gyroscope (140)”, step length, attitude, and heading angle read on “vector”, and Paragraph 0033) using a machine learning model based on the acceleration retrieved from the acceleration sensor in the current time period (Paragraph 0072 “various techniques for estimating step length using an acceleration sensor (110)” and “an artificial neural network model”);
and integrate the estimated movement vector as a relative position with respect to a reference position (Paragraph 0076 “Coordinate transformation is required for rotation between the navigation frame of the acceleration sensor (110) and the gyroscope (140) and the body frame”, Paragraph 0100 “detects the pedestrian's step using the measurement value of the acceleration sensor (110)”, Paragraph 0101 “estimates the step length of the pedestrian”, and Paragraph 0102 “estimates the attitude and heading angle of the pedestrian using the measurement values of the gyroscope (140)”),
wherein the machine learning model is trained in advance based on acceleration of one or more devices in a plurality of previous time periods, each previous time period having the predetermined amount of time (Paragraph 0072 “various techniques for estimating step length using an acceleration sensor (110)” and “an artificial neural network model”)
However, Kwak discloses the machine learning model is trained using acceleration retrieved from the acceleration sensor in the current time period (Paragraph 0072 “various techniques for estimating step length using an acceleration sensor (110)” and “an artificial neural network model”), but doesn’t explicitly teach the following:
using a machine learning model based on the angular velocity retrieved from the angular velocity sensor in the current time period
Nevertheless, Gohl discloses an image stabilization device (Abstract) and teaches the following:
using a machine learning model based on the angular velocity retrieved from the angular velocity sensor in the current time period (Paragraph 0088 “The machine learning algorithm can be provided with a training data set that includes video sequences and corresponding handle motion profiles, rotational velocities, accelerations, and the like”)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Kwak reference to include training the machine learning model with angular velocity, as taught by Gohl, with a reasonable expectation of success, in order to detect movement of a device (Gohl, Paragraph 0090).
In re claim 14, Kwak teaches the following:
wherein the machine learning model has a configuration of a neural network (Paragraph 0072 “an artificial neural network model”)
Response to Arguments
Applicant's arguments filed on 03/24/2026 have been fully considered but they are not persuasive.
With respect to applicant’s arguments/remarks with respect to the rejection of claims 1-16 on the ground of non-statutory double patenting as allegedly being unpatentable over claims 1-14, 16, and 17 of U.S. Patent No. 12,085,391 and that the amended claims are believed to be patentably distinct from the combination of claims 1-14, 16, and 17 of U.S. Patent No. 12,085,391, the examiner respectfully disagrees with that statement. As stated and shown above, claims 1, and 15-16 of the current application appear to be a broader version of claims 1, and 16-17. Claims 1, and 15-16 of the current application recite all the limitations recited in claims 1, and 16-17 of U.S. Patent No. 12,085,391 B2 except “wherein the estimated movement vector indicates a movement amount and a movement direction of a device”, “calculate a relative position of the device for the current time period with respect to a specified reference position in a real space”, “a display control unit configured to initiate display of a trajectory of the relative position of the device for the current time period with respect to the specified reference position in the real space”, and “in a plurality of previous time periods”.
With respect to applicant’s arguments/remarks with respect to the rejection of claims 1 and 14-16 under 35 U.S.C.§103 as allegedly being unpatentable over Kwak in view of Golh and that Kwak doesn’t teach or suggest estimate a current movement vector during a predetermined amount of time, the examiner respectfully disagrees with that statement. While Kwak doesn’t recite the term vector, the BRI of the term is a quantity of movement from one point to another with direction and magnitude. As recited above, Kwak discloses in Paragraph 0033 that the pedestrian inertial navigation module (160) is configured to calculate a pedestrian's movement path using the stride length estimated by the stride length estimation module (130) and the posture and heading angle estimated by the posture and heading angle estimation module (150) and that reads on estimating a current movement vector during a predetermined amount of time.
With respect to applicant’s arguments/remarks with respect to the rejection of claims 1 and 14-16 under 35 U.S.C.§103 as allegedly being unpatentable over Kwak in view of Golh and that the combination of Kwak and Golh fails to teach or suggest estimate a movement vector in a current time period having a predetermined amount of time using a machine learning model based on the acceleration and the angular velocity retrieved from the acceleration sensor and the angular velocity sensor in the current time period, wherein the machine learning model is trained in advance based on acceleration of one or more devices and an angular velocity of the one or more devices in a plurality of previous time periods, each previous time period having the predetermined amount of time, the examiner respectfully disagrees with that statement. It appears that applicant’s argument is based on that neither Kwak nor Golh discloses estimate a movement vector in a current time period having a predetermined amount of time in order to fulfill the rest of the limitation. However, as discussed in the previous paragraph, Kwak discloses said limitation in at least Paragraph 0033.
With respect to applicant’s arguments/remarks that Kwak in view of Golh does not teach or suggest “estimate a movement vector in a current time period having a predetermined amount of time using a machine learning model based on the acceleration and the angular velocity retrieved from the acceleration sensor and the angular velocity sensor in the current time period” and “wherein the machine learning model is trained in advance based on acceleration of one or more devices and an angular velocity of the one or more devices in a plurality of previous time periods, each previous time period having the predetermined amount of time”, the examiner respectfully disagrees with that statement. Applicant should discuss the references applied against the claims, explaining how the claims avoid the references or distinguish from them. Accordingly, it appears that this argument is similar to the previous argument and is based on Kwak not teaching or suggesting “estimate a movement vector”. However, it has been shown above how Paragraph 0033 of Kwak reads on said limitation. Furthermore, the limitation current time period is the time period required for the user to traverse the path. Therefore, the calculation of the pedestrian’s movement path happens during the time period it takes the pedestrian to cover the path. Accordingly, the combination of Kwak in view of Golh discloses the claimed invention as recited above.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMI KHATIB whose telephone number is (571)270-1165. The examiner can normally be reached M-F: 9:00am-5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin M Piateski can be reached at 571-270 7429. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAMI KHATIB/Primary Examiner, Art Unit 3669
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