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 Arguments
The rejection under 35 U.S.C. 101 has been withdrawn in light of the amended claims.
Applicant's arguments filed 3/12/2026 regarding the rejection under 35 U.S.C. 103 have been fully considered but they are not persuasive. Applicant on page 11 of Remarks states “Paragraph [0021] of Andrade discusses, in part, that "the classifier machine learning model may assign the following risk-related labels based on an analysis of the video data (e.g., where a '0' indicates a low risk, a '1' indicates a mild violation (a mild risk), a '2' indicates a severe violation (a high risk), and a '3' indicates a collision." However, a simple discussion about an assignment of risk-related labels (Andrade, paragraph [0021]) does not teach or suggest a determination of "a type of scene captured by each video fragment of the plurality of video fragments," as recited in claim 1. Moreover, and as indicated above, amended claim 1 reflects the proposed amendment discussed during the interview in which the Examiner indicated that incorporation would require further search and consideration”.
Andrade’s invention is assigning a number based on the severity of a vehicle collision. Through the broadest reasonable interpretation, this number is determining a type of scene in the video fragments, as a 0 can mean a scene had relatively low risk involved and a 2 can indicate a scene had a relatively high risk involved. Thus, the number, which depends on the severity of the collision, is indicative of the type of scene captured in the video fragments.
Claim Objections
Claim 21 recites “type of scent”. “scent” should be read as “scene”. Appropriate correction is required.
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, 4-5, 11, 14-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Golov (US 20200250901 A1) in view of Coimbra De Andrade (herein referred to as Andrade) (US 20240395081 A1) and Aguilar (US 20180174616 A1).
Regarding claim 1, Golov discloses a data processing system, comprising: one or more processors coupled with memory (Golov, paragraph [0059], “The computer system (131) of the vehicle (111) includes one or more processors (133), a data recorder (101), and memory (135) storing firmware (or software) (147), including the computer instructions and data models for ADAS (105)”), to:
identify a plurality of videos taken by at least one camera of a plurality of cameras of a vehicle, each video of the plurality of videos captured between a first time and a second time (Golov, paragraph [0021], "For example, the data record can receive a data stream containing live video data from various cameras mounted on the vehicle and various output signals from the other sensors, such as radar, lidar, ultrasound sonar, etc. The sensor data can be raw data from the sensors or compressed image/video data").
Golov does not teach “and each camera of the plurality of cameras turned in different directions”.
However, Coimbra DeAndrade teaches and each camera of the plurality of cameras turned in different directions (Coimbra De Andrade, paragraph [0001], "A video system may utilize machine learning models to classify driving events (e.g., tailgating, a collision, distraction, drowsiness, and/or the like) triggered by accelerometers, front facing cameras, driver facing cameras, and/or the like").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to additionally mount cameras on different sides of Golov’s vehicle, as taught by Andrade.
The suggestion/motivation for doing so would have been to allow a driver to see from all sides, enhancing safety and awareness.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Golov in view of Andrade discloses identify, for the plurality of videos, a plurality of video fragments, each video fragment of the plurality of video fragments corresponding to data of the vehicle at a time interval of a plurality of time intervals between the first time and the second time for each video of the plurality of videos (Golov, paragraph [0075-0076], "At block 203, the data recorder (101) buffers in the first cyclic buffer (161) a first sensor data stream of a first time duration (e.g., 30 seconds). At block 213, the data recorder (101) buffers in the second cyclic buffer (163) a second sensor data stream of a second time duration (e.g., 3 minutes).").
While Golov in view of Andrade discloses determining a type of scene captured by each video fragment of the plurality of video fragments (Golov, paragraph [0079], At block 207, the data recorder (101) determines whether the request is for recording an accident (e.g., a collision or near collision)"), they do not teach doing so “based on the plurality of video fragments input into a model trained on data of a plurality of scenes”.
However, Andrade additionally discloses based on the plurality of video fragments input into a model trained on data of a plurality of scenes (Coimbra de Andrade, paragraph [0021], "As an example, the classifier machine learning model may assign the following risk-related labels based on an analysis of the video data (e.g., where a “0” indicates a low risk, a “1” indicates a mild violation (a mild risk), a “2” indicates a severe violation (a high risk), and a “3” indicates a collision): a tailgating severity label (e.g., 0, 1, or 2), a stop sign violation severity label (e.g., 0, 1, 2, or 3), a minor severity confidence label (e.g., from 0 to 1), a moderate severity confidence label (e.g., from 0 to 1), a major severity confidence label (e.g., from 0 to 1), a critical severity confidence label (e.g., from 0 to 4), a presence of a vulnerable road user (VRU) label (e.g., 0, 1, or 2), and/or the like").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to implement a classifier model into Golov’s (in view of Andrade) to classify the video fragments, as additionally taught by Andrade.
The suggestion/motivation for doing so would have been to automate the process and reduce manual labor.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Golov in view of Andrade does not teach “select a set of video fragments, based on respective data and a respective type of scene that corresponds to respective video fragments of the plurality of video fragments, from the plurality of video fragments; and generate a composite video using the set of video fragments”.
However, Aguilar teaches select a set of video fragments, based on respective data and a respective type of scene that corresponds to respective video fragments of the plurality of video fragments, from the plurality of video fragments; and generate a composite video using the set of video fragments(Aguilar, paragraph [0028], "Video segments can be selected from the set of one or more source video clips, and the video segments can be combined to create a compiled video. Video segments may be selected so as to create a compiled video of a fixed duration. For example, if a user wishes to create a compiled video having a duration of one minute, video segments totaling one minute in duration may be selected, or video segments may be selected and edited so that the compiled video is one minute long. Video segments can be selected based on various video segment selection criteria. In certain embodiments, source video clips can be analyzed to automatically determine a common theme, and video segments can be selected based on the common theme. In various embodiments, themes may be automatically determined using machine learning techniques, such as object recognition and/or facial recognition. In other embodiments, a user may provide a user-specified theme, and video segments can be selected based on the user-specified theme. Once a plurality of video segments are selected, they can be combined into a compiled video").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to create a video montage using to assign severities of Golov’s (in view of Andrade) videos, as taught by Aguilar.
The suggestion/motivation for doing so would have been to allow viewers to be more knowledgeable about accidents and enhance precaution and safety.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golov in view of Andrade and Aguilar to obtain the invention as specified in claim 1.
Regarding claim 4, Golov in view of Andrade and Aguilar discloses the data processing system of claim 1, wherein the one or more processors are configured to: generate, for each video fragment of the plurality of video fragments, a score determined according to the data and the type of scene of a respective video fragment (Coimbra de Andrade, paragraph [0021], "As an example, the classifier machine learning model may assign the following risk-related labels based on an analysis of the video data (e.g., where a “0” indicates a low risk, a “1” indicates a mild violation (a mild risk), a “2” indicates a severe violation (a high risk), and a “3” indicates a collision): a tailgating severity label (e.g., 0, 1, or 2), a stop sign violation severity label (e.g., 0, 1, 2, or 3), a minor severity confidence label (e.g., from 0 to 1), a moderate severity confidence label (e.g., from 0 to 1), a major severity confidence label (e.g., from 0 to 1), a critical severity confidence label (e.g., from 0 to 4), a presence of a vulnerable road user (VRU) label (e.g., 0, 1, or 2), and/or the like", severity is a score),
and select the set of video fragments according to the score of each video fragment included in the set of video fragments (Aguilar, paragraph [0028], "Video segments can be selected from the set of one or more source video clips, and the video segments can be combined to create a compiled video. Video segments may be selected so as to create a compiled video of a fixed duration. For example, if a user wishes to create a compiled video having a duration of one minute, video segments totaling one minute in duration may be selected, or video segments may be selected and edited so that the compiled video is one minute long. Video segments can be selected based on various video segment selection criteria. In certain embodiments, source video clips can be analyzed to automatically determine a common theme, and video segments can be selected based on the common theme. In various embodiments, themes may be automatically determined using machine learning techniques, such as object recognition and/or facial recognition. In other embodiments, a user may provide a user-specified theme, and video segments can be selected based on the user-specified theme. Once a plurality of video segments are selected, they can be combined into a compiled video", based on the severity, a video compilation is generated, as shown in claim 1’s modification).
Regarding claim 5, Golov in view of Andrade and Aguilar discloses the data processing system of claim 1.
Golov in view of Andrade and Aguilar does not teach “wherein the one or more processors are configured to: select the set of video fragments based at least on each video fragment of the set of video fragments corresponding to at least a subset of the plurality of time intervals; and generate the composite video using the set of video fragments corresponding to at least the subset of the plurality of time intervals”
However, Aguilar additionally teaches wherein the one or more processors are configured to: select the set of video fragments based at least on each video fragment of the set of video fragments corresponding to at least a subset of the plurality of time intervals; and generate the composite video using the set of video fragments corresponding to at least the subset of the plurality of time intervals (Aguilar, paragraph [0028], "For example, if a user specifies that he or she would like a compiled video having a duration of 30-seconds, and there are six source video clips, a five-second video segment can be selected from each source video clip").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to create a video montage based on clips with similar lengths from Golov’s (in view of Andarde and Aguilar) livestream, as additionally taught by Aguilar.
The suggestion/motivation for doing so would have been to keep the video montage format organized and consistent with a certain video length.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golov in view of Andrade and Aguilar with the additional teachings of Aguilar to obtain the invention as specified in claim 5.
Claims 11, 14-15 corresponds to claims 1, 4-5 respectively. Thus, claims 11, 14-15 are rejected for the same reasons of obviousness as claims 1, 4-5 respectively.
Claim 20 corresponds to claim 1, additionally reciting a non-transitory computer-readable media having processor readable instructions (Golov, paragraph [0122], “Examples of computer-readable media include but are not limited to non-transitory, recordable and non-recordable type media such as volatile and non-volatile memory devices, read only memory (ROM), random access memory (RAM), flash memory devices, floppy and other removable disks, magnetic disk storage media, optical storage media (e.g., Compact Disk Read-Only Memory (CD ROM), Digital Versatile Disks (DVDs), etc.), among others”). Thus, claim 20 is rejected for the same reasons of obviousness as claim 1.
Claim(s) 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Golov (US 20200250901 A1) in view of Coimbra De Andrade (herein referred to as Andrade) (US 20240395081 A1), Aguilar (US 20180174616 A1), and in further view of Simmons (US 20190333409 A1).
Regarding claim 3, Golov in view of Andrade and Aguilar discloses the data processing system of claim 1.
Golov in view of Andrade and Aguilar does not teach “wherein the one or more processors are configured to: determine that a drive session is complete; and identify, responsive to determination that the drive session is complete, the plurality of videos captured by the plurality of cameras of the vehicle”.
However, Simmons teaches wherein the one or more processors are configured to: determine that a drive session is complete; and identify, responsive to determination that the drive session is complete, the plurality of videos captured by the plurality of cameras of the vehicle (Simmons, paragraph [0039], "In some embodiments, the SDMS app can automatically activate any of the cameras at the start of a driving session, deactivate the cameras at the end of the driving session, and upload the recorded video log to the SDMS 100.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to transmit videos when a drive session of Golov (in view of Andrade and Aguilar) is complete, as taught by Simmons.
The suggestion/motivation for doing so would have been to automate the process of transmission, reducing the manual need for a person to upload the videos.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golov in view of Andrade, Aguilar, and in further view of Simmons to obtain the invention as specified in claim 3.
Claim 13 corresponds to claim 3. Thus, claim 13 is rejected for the same reasons of obviousness as claim 3.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Golov (US 20200250901 A1) in view of Coimbra De Andrade (herein referred to as Andrade) (US 20240395081 A1), Aguilar (US 20180174616 A1), and in further view of CfTesla on Youtube (https://www.youtube.com/watch?v=TF1qdUcLCCM).
Regarding claim 6, Golov in view of Andrade and Aguilar discloses the data processing system of claim 1.
Golov in view of Andrade and Aguilar does not teach wherein each video fragment of the set of video fragments corresponds to a different time interval of the plurality of time intervals between the first time and the second time”.
However, CfTesla teaches wherein each video fragment of the set of video fragments corresponds to a different time interval of the plurality of time intervals between the first time and the second time (CfTesla, 4:36, screenshot below).
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to capture Golov’s (in view of Andrade and Aguilar) videos of different time intervals, as taught by CfTesla.
The suggestion/motivation for doing so would have been to have non-overlapping clips, resulting in better editing and compiling of the videos.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golov in view of Andrade, Aguilar, and in further view of CfTesla to obtain the invention as specified in claim 6.
Claim 16 corresponds to claim 6. Thus, claim 16 is rejected for the same reasons of obviousness as claim 6.
Claim(s) 7, 10, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Golov (US 20200250901 A1) in view of Coimbra De Andrade (herein referred to as Andrade) (US 20240395081 A1), Aguilar (US 20180174616 A1), and in further view of van Welzen (US 20220108727 A1).
Regarding claim 7, Golov in view of Andrade and Aguilar discloses the data processing system of claim 1.
Golov in view of Andrade and Aguilar does not teach “wherein the one or more processors are configured to: identify a feature of the composite video; determine, based at least on the composite video having been input into the model, a scene of the composite video corresponding to the feature; select, based on the scene of the composite video, content to insert into the composite video; and provide, for display, the composite video including the content”.
However, van Welzen teaches wherein the one or more processors are configured to: identify a feature of the composite video (van Welzen, paragraph [0032], “In some embodiments, video represented by the game data 202, video uploaded by a user, stored videos from prior game sessions, etc. may be analyzed by the event detector 114 using machine learning models, neural networks, and/or other artificial intelligence techniques to identify events from the video or image data”),
determine, based at least on the composite video having been input into the model, a scene of the composite video corresponding to the feature (van Welzen, paragraph [0031], “With reference to FIG. 2, the event detector 114 may analyze game data 202 (e.g., live game data from live game sessions, pre-recorded game data from previously played game sessions, video of game sessions, etc.) to create event logs 204 corresponding to particular event types the event detector 114 is programmed or trained to detect and/or to trigger the highlight generator 116 to trigger the recorder 118 to generate a highlight (e.g., a game video(s) 206) from the game data”),
select, based on the scene of the composite video, content to insert into the composite video (van Welzen, paragraph [0014], "The recipe may be configured such that, when executed, one or more input videos and event metadata corresponding to the input video (e.g., event logs corresponding to events of particular types—such as kills or deaths in first person shooter (FPS) style games, or goals, home runs, touchdowns, or other scoring plays in sports style games) may be used to generate a video montage script"),
and provide, for display, the composite video including the content (van Welzen, paragraph [0022], "The client devices 104 may include a game application 106, a display 108, a graphical user interface (GUI) 110, and/or an input device(s) 112").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to implement van Welzen’s method of finding a feature in Golov’s (in view of Andrade and Aguilar) video, using a model to acquire a scene, finding content to insert into Golov’s (in view of Andrade and Aguilar) video, and displaying it.
The suggestion/motivation for doing so would have been to acquire more scenes for video compilation, which further gives the viewer more clips to watch and further their understanding of vehicle safety.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golov in view of Andrade, Aguilar, and in further view of van Welzen to obtain the invention as specified in claim 7.
Regarding claim 10, Golov in view of Andrade, Aguilar, and van Welzen discloses the data processing system of claim 7, wherein the one or more processors are configured to: generate, based at least on the scene having been input into a second model, the content to insert into the composite video (van Welzen, paragraph [0014], "The recipe may be configured such that, when executed, one or more input videos and event metadata corresponding to the input video (e.g., event logs corresponding to events of particular types—such as kills or deaths in first person shooter (FPS) style games, or goals, home runs, touchdowns, or other scoring plays in sports style games) may be used to generate a video montage script"),
and select the content responsive to generation of the content (van Welzen, paragraph [0049], "For example, once a recipe, parameters, data sources, and/or other information for the video montage 214 are selected, the video preview may be generated for the user and displayed with the GUI 110").
Claim 17 corresponds to claim 7. Thus, claim 17 is rejected for the same reasons of obviousness as claim 7.
Claim(s) 8-9, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Golov (US 20200250901 A1) in view of Coimbra De Andrade (herein referred to as Andrade) (US 20240395081 A1), Aguilar (US 20180174616 A1), van Welzen (US 20220108727 A1), and in further view of Muramatsu (US 20130070096 A1).
Regarding claim 8, Golov in view of Andrade, Aguilar, and van Welzen discloses the data processing system of claim 7.
Golov in view of Andrade, Aguilar, and van Welzen does not teach “wherein the one or more processors are configured to: identify one or more sets of data corresponding to a fragment of the composite video; and select, based on the one or more sets of data and the scene of the composite video, the content to insert into the composite video”.
However, Muramatsu teaches wherein the one or more processors are configured to: identify one or more sets of data corresponding to a fragment of the composite video; and select, based on the one or more sets of data and the scene of the composite video, the content to insert into the composite video (Muramatsu, paragraph [0011], "An aspect of the present invention is an object detection device detecting an object near a vehicle from an input video image, the input video image being a video image of surroundings of the vehicle shot from the vehicle. This object detection device is provided with: a video image converting section converting the input video image to a characteristics video image into which image characteristics have been extracted from the input video image; a video images-classified-by-distance extracting/composing section extracting areas which differ according to distances from the characteristics video image as video images classified by distance, on the basis of the distance from the vehicle, and composing a composite video image using the video images classified by distance;").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to perform object detection using Golov’s (in view of Andrade, Aguilar, and van Welzen) video, and insert them into Golov’s (in view of Andrade, Aguilar, and van Welzen) montage video, as taught by Muramatsu.
The suggestion/motivation for doing so would have been to allow viewers to understand where certain objects/vehicle are during a crash, which furthers their understanding of the situation they are watching.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golov in view of Andrade, Aguilar, van Welzen, and in further view of Muramatsu to obtain the invention as specified in claim 8.
Regarding claim 9, Golov in view of Andrade, Aguilar, and van Welzen discloses the data processing system of claim 7.
Golov in view of Andrade, Aguilar, and van Welzen does not teach “wherein the one or more processors are configured to: identify a location of the feature in a frame of the composite video; and insert the content into the frame of the composite video according to the location of the feature”.
However, Muramatsu teaches wherein the one or more processors are configured to: identify a location of the feature in a frame of the composite video; and insert the content into the frame of the composite video according to the location of the feature (Muramatsu, paragraph [0011], "An aspect of the present invention is an object detection device detecting an object near a vehicle from an input video image, the input video image being a video image of surroundings of the vehicle shot from the vehicle. This object detection device is provided with: a video image converting section converting the input video image to a characteristics video image into which image characteristics have been extracted from the input video image; a video images-classified-by-distance extracting/composing section extracting areas which differ according to distances from the characteristics video image as video images classified by distance, on the basis of the distance from the vehicle, and composing a composite video image using the video images classified by distance;", object detection shows a feature location).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to perform object detection using Golov (in view of Andrade, Aguilar, and van Welzen) video, and inserting them into Golov’s (in view of Andrade, Aguilar, and van Welzen) montage video, as taught by Muramatsu.
The suggestion/motivation for doing so would have been to allow viewers to understand where certain objects/vehicle are during a crash, which furthers their understanding of the situation they are watching.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golov in view of Andrade, Aguilar, van Welzen, and in further view of Muramatsu to obtain the invention as specified in claim 9.
Claims 18-19 correspond to claims 8-9. Therefore, claims 18-19 are rejected for the same reasons of obviousness as claims 8-9.
Claim(s) 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Golov (US 20200250901 A1) in view of Coimbra De Andrade (herein referred to as Andrade) (US 20240395081 A1), Aguilar (US 20180174616 A1), and in further view of Wolzien (US 20140049556 A1).
Regarding claim 21, Golov in view of Andrade and Aguilar discloses the data processing system of claim 1.
Golov in view of Andrade and Aguilar does not teach “wherein the composite video includes at least one overlay of the type of scent for each video fragment of the set of video fragments”.
However, Wolzien teaches wherein the composite video includes at least one overlay of the type of scent for each video fragment of the set of video fragments (Wolzien, paragraph [0069], Fig. 4 below, "Caller info is immediately displayed in a predetermined position and font (with drop shadow option) on the incoming caller picture ("Image Setup" in FIG. 4).").
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to overlay Golov’s (in view of Andrade and Aguilar) image with text describing the video, as taught by Wolzien.
The suggestion/motivation for doing so would have been to easily identify what video is being played.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Golov in view of Andrade, Aguilar, and in further view of Wolzien to obtain the invention as specified in claim 21.
Claim 22 corresponds to claim 21. Thus, it is rejected for the same reasons of obviousness.
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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/WAYNE ZHANG/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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