DETAILED ACTION
This Office Action for U.S. Patent Application 18/939,432 is responsive to communications filed on 1/30/26, in reply to the Non-Final Rejection of 12/3/25. Currently, claims 1-20 are pending.
Response to Amendment
Applicant’s amendments to claims 1, 10, and 18 are acknowledged.
Response to Arguments
Applicant's arguments filed 1/30/26 have been fully considered but they are not persuasive.
Regarding claim 1, Applicant argues on pages 7-8 of the Response that Shin and Gotoda do not teach “predicting current state prediction data of a target object”, as originally presented.
However, Shin teaches that the state estimation component 222 may determine whether the target is a new target in image 202 or the target is an existing target that was tracked before image 202 (i.e., “current”) was generated. In response to determining that the target was an existing target, state estimation component 222 may obtain prior target state data 254 for the target (e.g., from the data store) (Fig. 2; col. 14, lines 16-32). Also, the prior target state data 254 refers to target state data that was estimated (e.g., by state estimation component 22) for a target based on images generated prior to image 202 (i.e., “previous”) (Fig. 2; col. 14, lines 32-35).
In addition, Applicant argues that Shin does not teach “predicting current state prediction data” and “determining current state estimation data” of the target object in claim 1.
For “predicting current state prediction data” of claim 1, please see the paragraph directly above for teachings from Shin. As for “determining current state estimation data” of claim 1, Shin teaches, for example, that the state estimation component 222 may determine a new state associated with targets 304 and 308 in view of image 202B and may update the current target state 256 for each target based on the determined new state (Figs. 3A-3C; col. 16, line 63-col. 17, line 27).
Further, Applicant argues that Gotoda does not the “reliability” of claim 1.
However, Gotoda teaches a mobile object tracking (prediction) unit 34 that includes a reliability calculation unit 710, an error factor presence determination unit 711, an reliability changing unit 712. Specifically, the reliability calculation unit 710 executes a reliability calculation step for calculating the reliability of an object position in the world coordinates (para[0061]-[0063]). The calculated reliability of an object position in Gotoda is interpreted to read on the “reliability of the current target detection data” as the limitation appears in claim 1.
Therefore, Shin and Gotoda teach all of the limitations of claim 1. In addition, please see the below-stated rejection of claim 1.
Regarding claim 2, Applicant argues on page 8 of the Response that Gotoda does not teach “the current target detection data comprises instantaneous velocity data of the target object”.
However, Shin teaches that a target state may refer to a location, a position, a scale or size, a velocity, etc. associated with a target during a time period that an image 202 is generated (col. 13, lines 54-56). It is noted that, generally, the time period in which an image is generated is a very short time period (e.g., “instantaneous”).
Therefore, Shin and Gotoda teach all of the limitations of claim 2. In addition, please see the below-stated rejection of claim 2.
Regarding claims 3-5, 7, 9, 10, 13, 14, 16-18, and 20, please see the above-stated discussion for claim 1 and the below-stated rejection of the claims.
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.
Claims 1-5, 7, 9-10, 13-14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. (U.S. Patent No. 12,125,277) in view of Gotoda et al. (U.S. Pub. No. 2024/0135574).
In regard to claim 1, Shin teaches a method of estimating a state performed by one or more processors (i.e., computer system 800 may include processors) (Fig. 8; col. 28, lines 9-32), the method performed for an image sequence comprising a previous image frame and current image frame, wherein a current time corresponds to the current image frame and a previous time corresponds to the previous image frame, (i.e., Figs. 3A-3C depict example images 202A-202C generated by image source 104, according to at least one embodiment; Fig. 3A, image 202A depicts an example environment 302 including objects 304, 306, 308, and 310; image 202A may be a first video frame of a sequence of video frames depicting environment 302 (i.e., “previous”); Fig. 3B depicts one or more estimated locations of targets 304, 306, 308, and 310 in environment 302 at a time period after image 202A is generated (i.e., “current”)) (Figs. 3A and 3B; col. 9, lines 49-55; col. 16, lines 41-62); the method comprising:
predicting, by a neural network model (i.e., the system may obtain the state data associated with the target using…a machine learning mode (e.g., a recurrent neural network) (col. 3, lines 46-50), current state prediction data of a target object for the current time by using previous state estimation data (i.e., state estimation component 222 may determine the change in the one or more coordinates for the bounding box associated with the target by determining a distance between the one or more coordinates of the bounding box associated with image 202 and coordinates of a bounding box associated with the target depicted in one or more prior images; in some embodiments, state estimation component 222 may determine whether the target is a new target in image 202 or the target is an existing target that was tracked before image 202 was generated; in response to determining that the target was an existing target, state estimation component 222 may obtain prior target state data 254 for the target (e.g., from the data store)) (Fig. 2; col. 14, lines 16-32) for the previous image frame and previous time of the image sequence in which the target object is represented, the previous image frame previous to the current image frame in the image sequence (i.e., prior target state data 254 refers to target state data that was estimated (e.g., by state estimation component 22) for a target based on images generated prior to image 202) (Fig. 2; col. 14, lines 32-35);
acquiring current target detection data of the target object for the current image frame of the image sequence (i.e., an object tracker 218 may be configured to track a state of a respective target in an environment; state estimation component 222 may be configured to determine a current target state 256 based on state data associated with a target at the time an image 202 depicting the target is generated; the current target state 256 may also include one or more target features (e.g., extracted from the bounding box region of image 202, extracted from a correlation response region of image 202, etc.)) (Fig. 2; col. 13, line 52-col. 14, line 15);
determining current state estimation data of the target object of the current time and the current image frame by updating the predicted current state prediction data by using the current target detection data (i.e., object trackers 218 associated with targets 304 and 308 may provide state data associated with targets 304 and 308 to state estimation module 220 to updated the current states of targets 304 and 308 in view of image 202B; for example, state estimation component 222 may determine a new state associated with targets 304 and 308 in view of image 202B and may update the current target state 256 for each target based on the determined new state (i.e., “updating”); state estimation component 222 may store the state determined for targets 304 and 308 with respect to image 202A as prior state data 254 and may store the updated current target state 256 at data store 250, as described above) (Figs. 3A-3C; col. 16, line 63-col. 17, line 27).
However, Shin does not explicitly teach determining a detection reliability of the current target detection data nor does it teach by using the detection reliability of the current target detection data.
In the same field of endeavor, Gotoda teaches determining a detection reliability of the current target detection data (i.e., the mobile object tracking (prediction) unit 34 includes a reliability calculation unit 710, an error factor presence determination unit 711, an reliability changing unit 712; the reliability calculation unit 710 executes a reliability calculation step for calculating the reliability of an object position in the world coordinates; the reliability changing unit 712 changes the reliability of the object position (basic observation noise) according to the determination result from the error factor presence determination unit 711) (para[0061]-[0063]) and teaches by using a detection reliability of the current target detection data (i.e., the mobile object tracking (prediction) unit 34 includes a reliability calculation unit 710, an error factor presence determination unit 711, an reliability changing unit 712; the reliability calculation unit 710 executes a reliability calculation step for calculating the reliability of an object position in the world coordinates; the reliability changing unit 712 changes the reliability of the object position (basic observation noise) according to the determination result from the error factor presence determination unit 711) (para[0061]-[0063]).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Shin and Gotoda because Gotoda teaches object detection to accurately detect objects such as vehicles and motorcycles in order to improve the determination accuracy in road and traffic patterns (See, for example, para[0046] and [0129] of Gotoda). Therefore, it would have been obvious to combine the teachings of Shin with those of Gotoda.
In regard to claim 2, Shin and Gotoda teach all of the limitations of claim 1 as discussed above. In addition, Shin teaches wherein the current target detection data comprises instantaneous velocity data of the target object (i.e., a target state may refer to a location, a position, a scale or size, a velocity, etc. associated with a target during a time period that an image 202 is generated; note: a time period that an image is generated is a very short time period (e.g., “instantaneous”)) (col. 13, lines 54-56).
In regard to claim 3, Shin and Gotoda teach all of the limitations of claim 1 as discussed above. However, Shin does not explicitly teach further comprising:
determining an amount of measurement noise based on the detection reliability; and
determining a Kalman gain by using the measurement noise, and
wherein the determining the current state estimation data comprises:
updating the current state prediction data by using the current target detection data and the Kalman gain based on the detection reliability.
In the same field of endeavor, Gotoda teaches further comprising:
determining an amount of measurement noise based on the detection reliability (i.e., the reliability calculation unit 710 executes a reliability calculation step for calculating the reliability of an object position in the world coordinates; the reliability here is observation noise) (para[0062]); and
determining a Kalman gain by using the measurement noise (i.e., inputs in the Kalman filter processing are the detected position of the observation target vehicle in the world coordinates, the applied observation noise, and the detection time; this Kalman filter processing unit may employ the convention technology without any change; note: a Kalman filter’s response is widely known in the art to be the Kalman filter’s gain) (para[0100]-[0101]), and
wherein the determining the current state estimation data comprises:
updating the current state prediction data by using the current target detection data (i.e., the observation update unit updates the state estimation value and the state noise by using the detected position of the observation target vehicle (e.g., “current target detection data”) in the world coordinates and the applied observation noise) (para[0103]) and the Kalman gain based on the detection reliability (i.e., the Kalman filter outputs the state estimation values of this observation update unit (for example, a three-dimensional position and a three-dimensional speed of the observation target vehicle)) (para[0104]).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Shin and Gotoda for the same reasons as those discussed above for claim 1.
In regard to claim 4, Shin and Gotoda teach all of the limitations of claims 1 and 3 as discussed above. However, Shin does not explicitly teach wherein the detection reliability is correlated inversely to the measurement noise.
In the same field of endeavor, Gotoda teaches wherein the detection reliability is correlated inversely to the measurement noise (i.e., the reliability calculation unit 710 executes a reliability calculation step for calculating the reliability of an object position in the world coordinates; the reliability here is observation noise regardless of a position estimation error factor on the road surface; the observation noise calculated here will be referred to as basic observation noise; the reliability changing unit 712 changes the reliability of the object position (basic observation noise) according to the determination result from the error factor presence determination unit 711; note: the higher the noise, the lower the reliability (e.g., “inverse” relationship)) (para[0061]-[0063]).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Shin and Gotoda for the same reasons as those discussed above for claim 1.
In regard to claim 5, Shin and Gotoda teach all of the limitations of claim 1 as discussed above. In addition, Shin teaches further comprising:
…based on matching states, each matching state comprising an indication of the current state prediction data matches the current target detection data, during a predetermined interval (i.e., in some embodiments, similarity component 212 of object localization module 210 may generate a set of similarity metric values each indicating a similarity between a detected object 304, 306, 308, 310 from a current image or video frame and an existing target (e.g., associated with visual features extracted from one or more previous image or video frame); determining that a calculated similarity metric for the state of the object and a respective future state of the target satisfies a similarity criterion (e.g., exceeds a similarity metric threshold)) (col. 10, lines 40-57).
However, Shin does not explicitly teach determining current prediction noise.
In the same field of endeavor, Gotoda teaches determining current prediction noise (i.e., the reliability calculation unit 710 executes a reliability calculation step for calculating the reliability of an object position in the world coordinates; the reliability here is observation noise) (para[0062]).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Shin and Gotoda for the same reasons as those discussed above for claim 1.
In regard to claim 7, Shin and Gotoda teach all of the limitations of claims 1 and 5 as discussed above. In addition, Shin teaches wherein the determining the current prediction noise comprises:
determining the current prediction noise based on a mismatch score scoring mismatch between the state prediction data and the target detection data during the predetermined interval (i.e., in some embodiments, similarity component 212 of object localization module 210 may generate a set of similarity metric values each indicating a similarity between a detected object 304, 306, 308, 310 from a current image or video frame and an existing target (e.g., associated with visual features extracted from one or more previous image or video frame); determining that a calculated similarity metric for the state of the object and a respective future state of the target satisfies a similarity criterion (e.g., exceeds a similarity metric threshold); note: if similarity metric threshold is not met, then interpreted to be “mismatch”) (col. 10, lines 40-57).
In regard to claim 9, Shin and Gotoda teach all of the limitations of claim 1 as discussed above. However, Shin does not explicitly teach further comprising:
performing the predicting of the current state prediction data and the determining of the current state estimation data, based on a Kalman filter algorithm.
In the same field of endeavor, Gotoda teaches further comprising:
performing the predicting of the current state prediction data and the determining of the current state estimation data, based on a Kalman filter algorithm (i.e., inputs in the Kalman filter processing are the detected position of the observation target vehicle in the world coordinates, the applied observation noise, and the detection time; this Kalman filter processing unit may employ the convention technology without any change; note: a Kalman filter’s response is widely known in the art to be the Kalman filter’s gain; the Kalman filter outputs state estimation values of this observation update unit) (para[0100]-[0104]).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Shin and Gotoda for the same reasons as those discussed above for claim 1.
In regard to claim 10, Shin teaches a method of estimating a state of a target object represented in an image sequence (i.e., an object tracker 218 may be configured to track a state of a respective target in an environment) (Fig. 2; col. 13, lines 52-54) comprising a previous image frame and a current image frame, wherein a current time corresponds to the current image frame and a previous time corresponds to the previous image frame (i.e., Figs. 3A-3C depict example images 202A-202C generated by image source 104, according to at least one embodiment; Fig. 3A, image 202A depicts an example environment 302 including objects 304, 306, 308, and 310; image 202A may be a first video frame of a sequence of video frames depicting environment 302 (i.e., “previous”); Fig. 3B depicts one or more estimated locations of targets 304, 306, 308, and 310 in environment 302 at a time period after image 202A is generated (i.e., “current”)) (Figs. 3A and 3B; col. 9, lines 49-55; col. 16, lines 41-62), the method performed by one or more processors (i.e., computer system 800 may include processors) (Fig. 8; col. 28, lines 9-32) and comprising:
predicting, by a neural network model (i.e., the system may obtain the state data associated with the target using…a machine learning mode (e.g., a recurrent neural network) (col. 3, lines 46-50), current state prediction data of the target object for the current time by using previous state estimation data (i.e., state estimation component 222 may determine the change in the one or more coordinates for the bounding box associated with the target by determining a distance between the one or more coordinates of the bounding box associated with image 202 and coordinates of a bounding box associated with the target depicted in one or more prior images; in some embodiments, state estimation component 222 may determine whether the target is a new target in image 202 or the target is an existing target that was tracked before image 202 was generated; in response to determining that the target was an existing target, state estimation component 222 may obtain prior target state data 254 for the target (e.g., from the data store)) (Fig. 2; col. 14, lines 16-32) predicted by the neural network for the previous image frame and the previous time of the image sequence (i.e., prior target state data 254 refers to target state data that as estimated (e.g., by state estimation component 22) for a target based on images generated prior to image 202) (Fig. 2; col. 14, lines 32-35);
acquiring current target detection data of the target object for the current time and the current image frame of the image sequence (i.e., an object tracker 218 may be configured to track a state of a respective target in an environment; state estimation component 222 may be configured to determine a current target state 256 based on state data associated with a target at the time an image 202 depicting the target is generated; the current target state 256 may also include one or more target features (e.g., extracted from the bounding box region of image 202, extracted from a correlation response region of image 202, etc.)) (Fig. 2; col. 13, line 52-col. 14, line 15);
…for an interval of the image sequence corresponding to the current time based on a matching state between the current state prediction data and the current target detection data (i.e., in some embodiments, similarity component 212 of object localization module 210 may generate a set of similarity metric values each indicating a similarity between a detected object 304, 306, 308, 310 from a current image or video frame and an existing target (e.g., associated with visual features extracted from one or more previous image or video frame); determining that a calculated similarity metric for the state of the object and a respective future state of the target satisfies a similarity criterion (e.g., exceeds a similarity metric threshold)) (col. 10, lines 40-57); and
determining current state estimation data of the target object of the current time and the current image frame by updating the predicted current state prediction data by using the current target detection data (i.e., object trackers 218 associated with targets 304 and 308 may provide state data associated with targets 304 and 308 to state estimation module 220 to updated the current states of targets 304 and 308 in view of image 202B; for example, state estimation component 222 may determine a new state associated with targets 304 and 308 in view of image 202B and may update the current target state 256 for each target based on the determined new state (i.e., “updating”); state estimation component 222 may store the state determined for targets 304 and 308 with respect to image 202A as prior state data 254 and may store the updated current target state 256 at data store 250, as described above) (Figs. 3A-3C; col. 16, line 63-col. 17, line 27) and the current prediction noise responsive to the current state prediction data matching the current target detection data (i.e., object trackers 218 associated with targets 304 and 308 may provide state data associated with targets 304 and 308 to state estimation module 220 to updated the current states of targets 304 and 308 in view of image 202B; for example, state estimation component 222 may determine a new state associated with targets 304 and 308 in view of image 202B and may update the current target state 256 for each target based on the determined new state; state estimation component 222 may store the state determined for targets 304 and 308 with respect to image 202A as prior state data 254 and may store the updated current target state 256 at data store 250, as described above) (Figs. 3A-3C; col. 16, line 63-col. 17, line 27).
However, Shin does not explicitly teach determining a current prediction noise.
In the same field of endeavor, Gotoda teaches determining a current prediction noise (i.e., the reliability calculation unit 710 executes a reliability calculation step for calculating the reliability of an object position in the world coordinates; the reliability here is observation noise) (para[0062]).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Shin and Gotoda because Gotoda teaches object detection to accurately detect objects such as vehicles and motorcycles in order to improve the determination accuracy in road and traffic patterns (See, for example, para[0046] and [0129] of Gotoda). Therefore, it would have been obvious to combine the teachings of Shin with those of Gotoda.
In regard to claim 13, the claim recites analogous limitations to claim 3 above, and is therefore rejected on the same premise.
In regard to claim 14, the claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
In regard to claims 16 and 20, the claims recite analogous limitations to claim 9 above, and are therefore rejected on the same premise.
In regard to claim 17, Shin teaches a non-transitory computer-readable storage medium storing instructions that, when executed by a processor (i.e., a non-transitory computer readable storage medium) (Claim 15, for example), cause the processor to perform the method of claim 10 (please see the above-stated rejection of claim 10.
In regard to claim 18, Shin teaches an electronic device comprising:
one or more processors (i.e., computer system 800 may include processors) (Fig. 8; col. 28, lines 9-32); and
a memory storing instructions (i.e., processor 802 may also include a microcode (“ucode”) read only memory (“ROM”) that stores microcode for certain macro instructions) (col. 29, lines 10-13) configured to cause the one or more processors to:
predict, by a neural network model (i.e., the system may obtain the state data associated with the target using…a machine learning mode (e.g., a recurrent neural network) (col. 3, lines 46-50), current state prediction data of a target object for a current time by using previous state estimation data (i.e., state estimation component 222 may determine the change in the one or more coordinates for the bounding box associated with the target by determining a distance between the one or more coordinates of the bounding box associated with image 202 and coordinates of a bounding box associated with the target depicted in one or more prior images; in some embodiments, state estimation component 222 may determine whether the target is a new target in image 202 or the target is an existing target that was tracked before image 202 was generated; in response to determining that the target was an existing target, state estimation component 222 may obtain prior target state data 254 for the target (e.g., from the data store)) (Fig. 2; col. 14, lines 16-32) for a previous time and a previous image frame of an image sequence (i.e., prior target state data 254 refers to target state data that as estimated (e.g., by state estimation component 22) for a target based on images generated prior to image 202) (Fig. 2; col. 14, lines 32-35), the image sequence comprising the previous image frame and a current image frame, wherein the current time corresponds to the current image frame and the previous time corresponds to the previous image frame (i.e., Figs. 3A-3C depict example images 202A-202C generated by image source 104, according to at least one embodiment; Fig. 3A, image 202A depicts an example environment 302 including objects 304, 306, 308, and 310; image 202A may be a first video frame of a sequence of video frames depicting environment 302 (i.e., “previous”); Fig. 3B depicts one or more estimated locations of targets 304, 306, 308, and 310 in environment 302 at a time period after image 202A is generated (i.e., “current”)) (Figs. 3A and 3B; col. 9, lines 49-55; col. 16, lines 41-62),
acquire current target detection data of the target object of the current time and the current image frame of the image sequence (i.e., an object tracker 218 may be configured to track a state of a respective target in an environment; state estimation component 222 may be configured to determine a current target state 256 based on state data associated with a target at the time an image 202 depicting the target is generated; the current target state 256 may also include one or more target features (e.g., extracted from the bounding box region of image 202, extracted from a correlation response region of image 202, etc.)) (Fig. 2; col. 13, line 52-col. 14, line 15),
…for an interval of the image sequence corresponding to the current time based on a matching state between the current state prediction data and the current target detection data (i.e., in some embodiments, similarity component 212 of object localization module 210 may generate a set of similarity metric values each indicating a similarity between a detected object 304, 306, 308, 310 from a current image or video frame and an existing target (e.g., associated with visual features extracted from one or more previous image or video frame); determining that a calculated similarity metric for the state of the object and a respective future state of the target satisfies a similarity criterion (e.g., exceeds a similarity metric threshold)) (col. 10, lines 40-57), and
determine current state estimation data of the target object of the current time and current image frame by updating the predicted current state prediction data by using the detection reliability of the current target detection data, the current prediction noise (i.e., object trackers 218 associated with targets 304 and 308 may provide state data associated with targets 304 and 308 to state estimation module 220 to updated the current states of targets 304 and 308 in view of image 202B (i.e., “updating”); for example, state estimation component 222 may determine a new state associated with targets 304 and 308 in view of image 202B and may update the current target state 256 for each target based on the determined new state; state estimation component 222 may store the state determined for targets 304 and 308 with respect to image 202A as prior state data 254 and may store the updated current target state 256 at data store 250, as described above) (Figs. 3A-3C; col. 16, line 63-col. 17, line 27), and the current target detection data responsive to the current state prediction data matches the current target detection data (i.e., object trackers 218 associated with targets 304 and 308 may provide state data associated with targets 304 and 308 to state estimation module 220 to updated the current states of targets 304 and 308 in view of image 202B; for example, state estimation component 222 may determine a new state associated with targets 304 and 308 in view of image 202B and may update the current target state 256 for each target based on the determined new state; state estimation component 222 may store the state determined for targets 304 and 308 with respect to image 202A as prior state data 254 and may store the updated current target state 256 at data store 250, as described above) (Figs. 3A-3C; col. 16, line 63-col. 17, line 27).
However, Shin does not explicitly teach determine a current prediction noise.
In the same field of endeavor, Gotoda teaches determine a current prediction noise (i.e., the reliability calculation unit 710 executes a reliability calculation step for calculating the reliability of an object position in the world coordinates; the reliability here is observation noise) (para[0062]).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Shin and Gotoda because Gotoda teaches object detection to accurately detect objects such as vehicles and motorcycles in order to improve the determination accuracy in road and traffic patterns (See, for example, para[0046] and [0129] of Gotoda). Therefore, it would have been obvious to combine the teachings of Shin with those of Gotoda.
Allowable Subject Matter
Claims 6, 8, 11, 12, 15, and 19 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.
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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KRISTIN DOBBS
Examiner
Art Unit 2488
/KRISTIN DOBBS/Examiner, Art Unit 2488
/SATH V PERUNGAVOOR/Supervisory Patent Examiner, Art Unit 2488
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