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 .
Response to Amendment
The amendments filed 05/07/2026 have been acknowledged.
Claims 1, 2, 3, 4, 5, 6, 8, 10, 11, 13, and 15 have been amended.
Claims 17, 18 and 19 are new.
Claim 16 is cancelled.
Response to Arguments
Applicant’s arguments with respect to claims 1-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-3, 5-8, 10, 11, 13-15, and 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shimizu et al Shimizu hereinafter WO 2018037954 A1 “MOVING OBJECT CONTROL DEVICE, MOVING OBJECT CONTROL METHOD, AND MOVING OBJECT”
As per claim 1
Shimizu teaches A user detection apparatus comprising (Figure 16): at least one memory storing instructions; and at least one processor configured to execute the instructions (DESCRIPTION-OF-EMBODIMENTS: “The series of processes described above can be executed by hardware or can be executed by software. When a series of processing is executed by software, a program constituting the software is installed in a computer (for example, various ECU processors) incorporated in dedicated hardware. The program executed by the computer may be a program that is processed in time series in the order described in this specification”) detect a predetermined landmark and a user candidate based on an image obtained by photographing a vicinity of a vehicle (Figure 16, Description of embodiments: “… the bus 201, cameras 221F1 to 221B are arranged as imaging units. Any of the cameras 221F1 to 221B may be a stereo camera including two or more cameras, and the distance between the subjects captured by the parallax of the two or more cameras may be measured…The images (outside image) captured by the cameras 221F1, 221F2, 221L1 to 221L4, the camera 221R, and the camera 221B are used for detecting any object outside the vehicle such as a white line or a pedestrian”) the predetermined landmark indicating a getting-on place (Figure 8, Figure 16 “…the outside monitoring unit 171 detects a stop in the traveling direction of the bus 201 based on the outside image…” “…the vehicle outside monitoring unit 171 holds in advance a pattern of a mark (for example, a stop or a bus company mark) indicated on a sign, a bus shelter, etc., and recognizes the outside of the vehicle by pattern recognition using the pattern. Detect stops in the image.) and detect a state of the user candidate (Description of embodiments: “the out-of-vehicle monitoring unit 171 excludes those who are estimated to be clearly not waiting for the bus from the recognized people. For example, a person 501d and a person 501e walking in a direction away from a stop and a person 501f riding a bicycle are excluded.”) and a distance between the user candidate and the predetermined landmark; (Description of embodiments; “when the set stop position is too far from the boarding candidate, for example, when the distance between the set stop position and the boarding candidate is equal to or greater than a predetermined threshold, the vehicle 11 May pass through without stopping.” ) determine whether the user candidate is a user of the vehicle or not based on the state of the user candidate (Description of embodiments: the out-of-vehicle monitoring unit 171 excludes those who are estimated to be clearly not waiting for the bus from the recognized people. For example, a person 501d and a person 501e walking in a direction away from a stop and a person 501f riding a bicycle are excluded.) and the distance between the user candidate and the predetermined landmark (Description of embodiments: “ the out-of-vehicle monitoring unit 171 excludes those who are estimated to be clearly not waiting for the bus from the recognized people. For example, a person 501d and a person 501e walking in a direction away from a stop and a person 501f riding a bicycle are excluded. “ Furthermore “when the set stop position is too far from the boarding candidate, for example, when the distance between the set stop position and the boarding candidate is equal to or greater than a predetermined threshold, the vehicle 11 May pass through without stopping.”)
As per claim 2
Shimizu teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches the user detection apparatus according to claim 1, wherein the at least one processor is further configured to count a total number of user candidates whose distance to the predetermined landmark is determined to be smaller than or equal to a predetermined value (“when using a score to determine whether or not the passenger is a boarding candidate, a higher score is given to a person in the priority recognition area A1a than a person in the priority recognition area A1b, and the priority recognition area A1b. You may make it give a higher score to the person who is inside than the person who is outside the priority recognition area … a high score is given to a person who is running toward a stop, and a next highest score is given to a person who is walking toward the stop. Further, for example, a high score is given to a person looking at the direction of the bus 201 …Next, the vehicle outside monitoring unit 171 gives a score based on the position, the direction of the line of sight, the action, etc. to each remaining person… the out-of-vehicle monitoring unit 171 identifies a person who is closest to a person whose total score is equal to or greater than a predetermined threshold or a person with the highest score as a boarding candidate” )and determine that all the user candidates are users of the vehicle in a case where the total number of user candidates is equal to or greater than a predetermined number.” Distance is an attribute of the score that is held against the threshold.) determine that all the user candidates are users of the vehicle in a case where the total number of user candidates is equal to or greater than a predetermined number. (“the outside monitoring unit 171 performs the recognition process of the boarding candidate by comparing the number of boarding persons with the number of boarding persons when the number of boarding reservation information includes the number of boarding persons” The number of boarding persons in the reservation information is the predetermined number. )
As per claim 3
Shimizu teaches all claim limitations rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches wherein the at least one processor is further configured to determine that the user candidate is the user in a case where the distance between the user candidate and the predetermined landmark is equal to or shorter than a predetermined value (Figure 16 Description of embodiments: “Next, the vehicle outside monitoring unit 171 gives a score based on the position, the direction of the line of sight, the action, etc. to each remaining person. For example, a high score is given to a person in the priority recognition area A1b, and a next highest score is given to a person in the priority recognition area A2b. Further, for example, a high score is given to a person who is running toward a stop, and a next highest score is given to a person who is walking toward the stop…”) and the user candidate stays in a same location as the state of the user candidate (Figure 16, Figure 17, “a camera may be provided in the waiting room, and a recognition process for a boarding candidate in the waiting room may be performed using an image captured by the camera.”).
As per claim 5
Shimizu teaches all claim limitations rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu wherein the at least one processor is further configured to determine that the user candidate is the user in a case where the distance between the user candidate and the predetermined landmark is equal to or shorter than a predetermined value (Figure 16 Description of embodiments: “Next, the vehicle outside monitoring unit 171 gives a score based on the position, the direction of the line of sight, the action, etc. to each remaining person. For example, a high score is given to a person in the priority recognition area A1b, and a next highest score is given to a person in the priority recognition area A2b. Further, for example, a high score is given to a person who is running toward a stop, and a next highest score is given to a person who is walking toward the stop…”) ) and a plurality of the user candidates lines up as the state of the user candidate. (Figure 12. 501a and 501c are in line)
As per claim 6
Shimizu teaches all claim limitations rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches wherein the at least one processor is further configured to determine that the user candidate is the user in a case where the distance between the user candidate and the predetermined landmark is equal to or shorter than a predetermined value (Figure 16 Description of embodiments: “Next, the vehicle outside monitoring unit 171 gives a score based on the position, the direction of the line of sight, the action, etc. to each remaining person. For example, a high score is given to a person in the priority recognition area A1b, and a next highest score is given to a person in the priority recognition area A2b. Further, for example, a high score is given to a person who is running toward a stop, and a next highest score is given to a person who is walking toward the stop…”) and the user candidate is performing a predetermined action as the state of the user candidate. (Figure 22, Figure 23, Figure 24).
As per claim 7
Shimizu teaches all claim limitations rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches the vehicle is a bus (Figure 5) and the at least one processor is further configured to detect a bus stop as the predetermined landmark (Figure 8, Figure 13, Figure 15, Figure 16)
As per claim 8
Shimizu teaches all claim limitations rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches wherein the vehicle is a vehicle driven by a control of a driver, and the at least one processor is configured to inform the driver of presence of the user in a case where the user candidate is determined as the user. (Description of embodiments “A mark 652 that alerts the driver is displayed in the upper right corner of the screen. A message 653 indicating that the boarding candidate has been recognized is displayed at the lower center of the screen. This allows the driver to reliably recognize that there is a boarding candidate.“)
As per claim 10
Shimizu teaches all claim limitations p[previously rejected In claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches wherein the at least one processor is configured to output positional information of the user to a self-driving control system in a case where the vehicle is a vehicle driven by a control of the self- driving control system (Description of embodiments “ the operation control ECU 51 is an ECU that realizes an ADAS (Advanced Driving Assistant System) function and an automatic driving (Self driving) function. The image recognition result from the front camera ECU 22, the position information from the position information acquisition unit 23, and the communication unit The driving (running) of the vehicle 11 is controlled based on various information such as surrounding vehicle information supplied from 25…ECU 22, the operation control ECU 51 further recognizes the locus of the position of the target object…a recognition process for a boarding candidate is performed for people around the stop reference position…the vehicle outside monitoring unit 171 gives a score based on the position, the direction of the line of sight, the action, etc. to each remaining person” Figure 16)
As per claim 11
Shimizu teaches all claim limitations p[previously rejected In claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches a distance between the vehicle and the predetermined landmark is estimated based on positional information of the vehicle, (Description of embodiments: “For example, the situation recognition unit 162 detects the distance to the next stop based on the current position of the vehicle 11”) and the at least one processor is configured to detect the predetermined landmark and determine whether the user candidate is a user of the vehicle or not in a case where an estimated distance is equal to or shorter than a predetermined distance. (Description of embodiments “This determination process is repeatedly executed at a predetermined timing until it is determined that the vehicle has approached the stop candidate point. Then, when the distance to the next stop is less than the predetermined threshold, the operation control unit 163 determines that the vehicle has approached the stop candidate point, and the process proceeds to step S2…In step S4, the outside monitoring unit 171 starts the recognition process for the boarding candidate. Here, the vehicle outside monitoring unit 171 performs a boarding candidate recognition process for a region near the stop candidate point” Once the vehicle is in proximity of the stop candidate point (equal to or shorter than the predetermined distance) user candidacy determination commences.)
As per claim 13
Shimizu teaches A user detection system comprising: an onboard device (Fig 5 and FIG. 21 ) and a server apparatus (Description of embodiments “The vehicle control unit 151 is realized by, for example, a front camera ECU 22, a side view camera ECU 30, an integrated ECU 31, a front view camera ECU 33, a rear view camera ECU 40, an in-vehicle camera ECU 43, and the like. The vehicle control unit 151 includes a monitoring unit 161, a situation recognition unit 162, a driving control unit 163, a stop position setting unit 164, an imaging control unit 165, and a UI (user interface) control unit 166.”) wherein the onboard device is configured to photograph an image of a vicinity of a vehicle (Figure 5, Description of embodiments “FIG. 5 schematically shows an arrangement example of the cameras in the bus 201 as the vehicle 11”) and the server apparatus is configured to detect a predetermined landmark and a user candidate based on an image obtained by photographing a vicinity of a vehicle (Figure 16. Description of embodiments “Next, the outside monitoring unit 171 recognizes a person near the stop by face recognition or the like based on the outside image”) the predetermined landmark indicating a getting-on place by image processing on the image (Description of embodiments “vehicle outside monitoring unit 171 holds in advance a pattern of a mark (for example, a stop or a bus company mark) indicated on a sign, a bus shelter, etc., and recognizes the outside of the vehicle by pattern recognition using the pattern. Detect stops in the image. Alternatively, for example, the outside monitoring unit 171 detects the stop by recognizing a character indicating the name of the next stop from the outside image by character recognition or the like.”) detect a state of the user candidate (Description of embodiments: “the out-of-vehicle monitoring unit 171 excludes those who are estimated to be clearly not waiting for the bus from the recognized people. For example, a person 501d and a person 501e walking in a direction away from a stop and a person 501f riding a bicycle are excluded.”) and a distance between the user candidate and the predetermined landmark; (Description of embodiments; “when the set stop position is too far from the boarding candidate, for example, when the distance between the set stop position and the boarding candidate is equal to or greater than a predetermined threshold, the vehicle 11 May pass through without stopping.” ) determine whether the user candidate is a user of the vehicle based on the state of the user candidate and the distance between the user candidate and at the predetermined landmark (Figure 16, Figure 21, Figure 22, Figure 23, Description of embodiments: “when the set stop position is too far from the boarding candidate, for example, when the distance between the set stop position and the boarding candidate is equal to or greater than a predetermined threshold, the vehicle 11 May pass through without stopping.”)
As per claim 14
Shimizu teaches all claim limitations previously rejected in claim 13’s 102 rejection. See claim 13’s 102 rejection,
Shimizu teaches wherein the onboard device includes a plurality of cameras respectively installed at front, right, and left sides of the vehicle (Figure 5)
As per claim 15
Shimizu teaches A user detection method comprising detecting a user candidate and a predetermined landmark based on an image of a vicinity of a vehicle (Figure 10, Figure 16,) the predetermined landmark indicating a getting-on place (Figure 13, Figure 15, Figure 16) detecting a state of the user candidate and a distance between the user candidate and the predetermined landmark (Figure 16, Figure 21, Figure 22, Figure 23, Description of embodiments: “when the set stop position is too far from the boarding candidate, for example, when the distance between the set stop position and the boarding candidate is equal to or greater than a predetermined threshold, the vehicle 11 May pass through without stopping.”) and determining whether the user candidate is a user of the vehicle or not based on the state of the user candidate and the distance between the user candidate and the predetermined landmark (Figure 16, Figure 21, Figure 22, Figure 23,)
As per claim 17
Shimizu teaches all claim limitations previously rejected in claim 1s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches wherein the at least one processor is further configured to: determine that the user candidate is the user in a case where the distance is greater than a predetermined value and a plurality of the user candidates lines up as the state of the user candidate. (Figure 16 user 501h is outside the predetermined value. He is greater than the predetermined value in distance yet is recognized. Users 421, 501b and 501c are standing in line.)
As per claim 18
Shimizu teaches all claim limitations previously rejected in claim 1s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches wherein the at least one processor is further configured to :determine that all user candidates are users of the vehicle in a case where one of the user candidates whose distance to the predetermined landmark is determined to be smaller than or equal to a predetermined value is positioned at a head of a line,(Figure 16 user 421 is equal to the predetermined value and is at the head of the line) and other user candidates are lined up in a straight line behind the one user candidate. (Users 421, 501b and 501c are standing in line behind user 421)
As per claim 19
Shimizu teaches all claim limitations previously rejected in claim 1s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches wherein the image of the vicinity of the vehicle includes a region on a sidewalk located in a traveling direction of the vehicle. (Figure 16, Figure 21 Figure 23 Figure 24)
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 4 is rejected under 35 U.S.C. 103 as being unpatentable over Shimizu et al (Shimizu hereinafter WO 2018037954 A1 “MOVING OBJECT CONTROL DEVICE, MOVING OBJECT CONTROL METHOD, AND MOVING OBJECT”) in view of Huang et al (Huang hereinafter US 5953055 A)
As per claim 4
Shimizu teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu teaches wherein the at least one processor is further configured to determine that the user candidate is the user in a case where the distance between the user candidate and the predetermined landmark is equal to or shorter than a predetermined value (Figure 16 Description of embodiments: “Next, the vehicle outside monitoring unit 171 gives a score based on the position, the direction of the line of sight, the action, etc. to each remaining person. For example, a high score is given to a person in the priority recognition area A1b, and a next highest score is given to a person in the priority recognition area A2b. Further, for example, a high score is given to a person who is running toward a stop, and a next highest score is given to a person who is walking toward the stop…”)
Shimizu teaches does not teach a number of candidates being equal to or more than a predetermined number as the state of the user candidate.
Huang teaches determine that the user candidate is the user in a case where the distance between the user candidate and the predetermined landmark as being equal to or shorter than a predetermined value (Huang discloses applying a predetermined spatial limit in the image through a defined queue zone and bounding box/ROI which is equivalent to a preset spatial boundary that functions as the “predetermined value” Column 3 line 41: “ Bounding Box: The bounding box describes the location of the queue zone 420 within the camera view.” and Column 5 line 33 “The region of interest 440 corresponds to a bounding box that encloses the one or more queue zones 420. The output of the operation is a smaller frame that contains only the region of the image that corresponds to the queue zone 420 being analyzed.” A person is treated as “in the queue” only if they fall within that predetermined bounding box/ROI which is equivalent to a “predetermined value” vicinity constraint relative to the landmark area being analyzed.) and a number of candidates being equal to or more than a predetermined number as the state of the user candidate (Column 1 line 14 “the present invention is able to detect and record the number of people in the queue, the length of time the people have been waiting in the queue, as well as other characteristics of the queue” and (Column 5 line 64 “To determine the number of people in the queue, the queue zone 420 may be subdivided into a set of slots 425…Each slot 425 may be created such that it is approximately the size of a customer waiting in the queue…the system determines the number of slots 425 occupied by a customer. The number of slots 425 occupied by a customer correspond to the number of customers in the queue.” Huang’s system also used for alerts when the queue reaches a threshold for size. Column 14 line 2 “system 130 may also be programmed with sound to perform real-time alerts, such as by sounding an alarm once the queue reaches a certain length” These attributes in Huang’s system shows the system detects users, counts people, compares the count against a predetermined value and triggers a behavior.)
Accordingly, a person of ordinary skill in the art at the time this invention was effectively filed would have found it obvious to modify Shimizu’s workflow with Huang’s idea of queue analysis functionality which includes detecting and recording a number of people in a queue and comparing that number to a predetermined threshold. Huang states that “present invention may be implemented at checkout lanes in a retail establishment, in a bank, at customer service desks, at self-service kiosks, at banks, or any other location where a queue (line) of people or other objects may form.” And a person of ordinary skill in the art is aware that a bus stop queue applies to this. A person of ordinary skill in the art would note that Huang expressly teaches counting people within a defined region and generating control actions when the number reaches a certain value. This improves Shimizu’s end of the modified workflow by augmenting user determination by enabling the system to distinguish true riders based on a collective waiting behavior of a plurality of users rather than isolated individual detections using routine computer vision counting techniques.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Shimizu et al (Shimizu hereinafter WO 2018037954 A1 “MOVING OBJECT CONTROL DEVICE, MOVING OBJECT CONTROL METHOD, AND MOVING OBJECT”) in view of Papineau et al (Papineau hereinafter US 11694464 B2)
As per claim 9
Shimizu teaches all claim limitations p[previously rejected In claim 8’s 102 rejection. See claim 8’s 102 rejection.
Shimizu does not teach The user detection apparatus according to claim 8, wherein a number of users is informed of the driver.
Papineau teaches a number of users is informed of the drive( Papineau shows number determination by facial count verification: Column 13 line 35 “…server determines the number of occupants that are present in the vehicle based upon the count of the number of facial signatures…” and Column 5 line 62 “facial count and verification where each participant registers their individual face “. Then this determination is communicated with the driver via the checkpoint of occupancy confirmation and can be seen in the flow of figure 3 (flow directionality between from label 134, 138, 140 and 136)
Accordingly, a person of ordinary skill in the art at the time this invention was effectively filed would have found it obvious to further modify the Shimizu’s workflow with Papineau’s concept of informing the driver of the number of users. A person of ordinary skill in the art would do this because the combination enables clearer driver decision making and compliance using facial count verification techniques improving reliability versus manual counting, improving overall efficiency.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Shimizu et al (Shimizu hereinafter WO 2018037954 A1 “MOVING OBJECT CONTROL DEVICE, MOVING OBJECT CONTROL METHOD, AND MOVING OBJECT”) in view of Park et al (Park hereinafter US 20230214654 A1)
As per claim 12
Shimizu teaches all claim limitations p[previously rejected In claim 1’s 102 rejection. See claim 1’s 102 rejection.
Shimizu does not teach wherein the at least one processor includes an Al (artificial intelligence) engine, the Al engine is configured to: learn an image of the predetermined landmark or an image of something similar to the predetermined landmark; and assist detection of the predetermined landmark.
Park teaches an AI engine configured to learn an image of the predetermined landmark or an image of something similar to a predetermined landmark. (Park teaches training a DNN on annotated landmark features in training datasets Paragraph [0006] “To train the DNN(s) for accurate prediction, ground truth data corresponding to control point locations for landmark features may be generated from polyline and/or polygon annotations corresponding to landmark features in training data sets” Figure 10, Figure 1) and assist detection of the predetermined landmark (Paragraph [0006] “ Once trained and deployed, the DNN(s) may accurately and precisely compute outputs indicating control point locations and semantic information corresponding thereto, and these outputs may be decoded and post-processed to determine curves corresponding to landmark features.”). Park M’s AI engine is also being executed through a processor is (Paragraph [0061] “process 600 of using a DNN for landmark detection…may be carried out by a processor executing instructions stored in memory)
Accordingly, a person of ordinary skill in the art would have found it obvious to modify Shimizu’s workflow to incorporate a trained AI landmark detection engine as taught by Park. Park expressly teaches training a DNN using annotated landmark training data and then deploying the trained DNN to identify a landmark location and further explains that reducing heavy post processing decreases latency and computing requirements for real time vehicle deployment. This combination improves accuracy of detecting the bus stop landmark under real world variation and supports a real time operation with reduced latency compared to a non-AI approach. . Shimizu also explains that “the operation control ECU 51 further recognizes the locus of the position of the target object. The recognition result may be transmitted to an external server via the communication unit 25. In such a case, for example, the server learns a deep neural network or the like, generates a necessary dictionary or the like, and transmits it to the vehicle 11. In the vehicle 11, the dictionary or the like obtained in this way is received by the communication unit 25, and the received dictionary or the like is used for various predictions in the operation control ECU 51.” With this disclosure by Shimizu, a person of ordinary skill in the art would have seen Park’s explicit pipeline as advantageous to Shimizu’s.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE WRENSFORD CODRINGTON whose telephone number is (571)272-8130. The examiner can normally be reached 8:00am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHANE WRENSFORD CODRINGTON/ Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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