DETAILED ACTION
Response to Amendment
This is a Final Office Action on the merits in response to communications on 2025/12/05. Claims 1, 3, 5, 9, 11, and 13 – 16 are amended. Claims 1 – 16 are pending and are addressed below.
Response to Arguments
Applicant’s amendments have overcome the 112(b) rejection regarding a insufficient antecedent basis for “skeleton section”, but however does not overcome the 112(b) rejection regarding “puck width”. Applicant’s amendments have overcome the 101 rejection. Applicant’s amendments have not overcome the 102 nor the 103 rejections. In response to Applicant’s remarks on 112(b) rejection, Applicant argues that “As indicated on the attached response from ChatGPT, the “‘puck width’ on a nozzle refers to the width of the spray pattern directly at the nozzle's exit.”” (Applicant Remarks, Page 1). ChatGPT is not considered to be an authority in the art, and is furthermore known to provide false information. By comparison, examiner has found no connection between “puck width” and nozzle through convention search channels, much less specifically defining “puck width” as “the width of a spray pattern directly at the nozzle’s exit”. Furthermore, a comparable commonly used AI, Gemini, found that that “puck width” is not a standard industry term in regards to nozzles and spray patterns (see attached Gemini query). Since ChatGPT is considered to be factually unreliable and examiner finds no other evidence in the industry connecting puck width to nozzles and spray patterns, examiner maintains the term is indefinite under 112(b).
Note that even if the ChatGPT response is found to be consistent with use in the industry, the claim requires “a nozzle having a puck width”. According to ChatGPT, the “puck width” refers to “the width of the spray pattern directly at the nozzle’s exit”. Therefore, examiner concludes that per ChatGPT, every nozzle has a spray pattern that can be considered the “puck width” of that nozzle, and thus, the limitation would not further limit the nozzle.
The amendments are further addressed in the body of the Final Rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 9 are rejected under 35 U.S.C. 112(b), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. “Puck width” is not a term in the art, and therefore there is no specified range by the applicant for the “nozzle”.
Claims 7 and 15 are rejected under 35 U.S.C. 112(b), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. “Puck width” is not a term in the art, therefore there is no specified width by the applicant for the “line”.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 6, 8 – 14 and 16 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Hendricks, SR., et. al. (US 20220349132 A1), hereinafter referred to as Hendricks.
Examiner notes that Claims 1, 7, 9, and 15 are being interpreted in light of the 112b rejection above.
Regarding Claim 1:
A method executed by a processor to program a robot to autonomously fill cracks in a pavement, wherein the robot fills the cracks using a nozzle having a puck width, the method comprising the steps of:
obtaining, by a camera, an image of the pavement;
Hendricks discloses “…vision equipment may be used to create a composite image…” (Hendricks, [0064]).
identifying one or more crack regions in the image of the pavement;
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect… objects” (Hendricks, [0064]) and “The road feature identified by the optical system may include a crack in a surface of the road.” (Hendricks, [0064]).
eroding each of the one or more crack regions into lines representing a skeleton section;
Hendricks discloses “the vision equipment may be used to create a composite image and representation of distances from the camera. The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect cracks and or objects to avoid on a ground surface.” (Hendricks, [0064]).
converting each of the one or more skeleton sections into a plurality of points;
Hendricks discloses “The optical system may further include an optical mapping module configured to map the crack” (Hendricks, [0063]) and “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot.” (Hendricks, [0063]).
for each of the plurality of points, determining a crack cross section through each of the plurality of points on the skeleton to opposite edges of the crack region;
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment, allowing the crack sealer or sealant distal arm tool to dispense a corresponding volume of sealant. The volume of sealant may be changed by altering the rate of material flow or varying the speed of the RMV. In certain examples, the computer program may ignore previously sealed regions and the AI may find additional cracks that the computer program may not detect. Both the computer program and the AI may contain logic to connect nearby segments.” (Hendricks, [0064]).
generating a path to fill the one or more crack regions;
Hendricks discloses “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot. The multi-axis robot may be configured to convey the sealant from the maintenance module to the crack mapped by the optical mapping module.” (Hendricks, [0063]).
determining a volume of sealant to fill the one or more crack regions along the path based on the crack cross sections;
Hendricks discloses “measurements may include a width of the crack, a length of a crack, a depth of the crack, and a volume open space within the crack” (Hendricks, [0063]).
generating instructions to fill the one or more crack regions using the path and the volume of the sealant; and sending the instructions to the robot.
Hendricks discloses “the control system may be configured to selectively instruct the multi-axis robot which individual cracks to seal based on the measurements from the optical mapping module” (Hendricks, [0065]).
Regarding Claim 2:
The method of claim 1, wherein the step of identifying the one or more crack regions comprises the steps of:
preprocessing the image to generate a pre-processed image;
Hendricks discloses “the vision equipment may be used to create a composite image and representation of distances from the camera” (Hendricks, [0064]).
applying an AI deep learning model to the pre-processed image to obtain a segmented image; and
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment, allowing the crack sealer or sealant distal arm tool to dispense a corresponding volume of sealant… Both the computer program and the AI may contain logic to connect nearby segments” (Hendricks, [0064]).
postprocessing the segmented image to identify the one or more crack regions.
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect cracks and or objects to avoid on a ground surface” (Hendricks, [0064]).
Regarding Claim 3:
The method of claim 2, further comprising the steps of:
identifying an obstacle in the image of the pavement; and
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect… objects” (Hendricks, [0064]) and “The road feature identified by the optical system may include a crack in a surface of the road.” (Hendricks, [0064]).
ensuring that the robot avoids filling the obstacle with sealant.
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect… objects to avoid on a ground surface… The computer program, AI, or combinations thereof may assign values based on the width of the segment, allowing the crack sealer or sealant distal arm tool to dispense a corresponding volume of sealant.” (Hendricks, [0064]).
Regarding Claim 4:
The method of claim 3, wherein the step of ensuring that the robot avoids filling the obstacle with sealant comprises the steps of:
determining whether the obstacle intersects the path; and
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect cracks and or objects to avoid on a ground surface.” (Hendricks, [0064]).
if it is determined that the obstacle intersects the path, instructing the robot to lift the nozzle to avoid the obstacle.
Hendricks discloses “In certain examples, the computer program may ignore previously sealed regions…” (Hendricks, [0095]). Hendricks discloses “the autonomous control system may allow the distal arm tool to follow a contour of the road as the distal arm tool moves across a surface of the road.” (Hendricks, [0085]).
Regarding Claim 5:
The method of claim 2, wherein the step of generating the path comprises the steps of:
for each skeleton section:
pruning small branches off the skeleton section before converting the skeleton section into the plurality of points;
Hendricks discloses “the vision equipment may be used to create a composite image and representation of distances from the camera. The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect cracks and or objects to avoid on a ground surface… In certain examples, the computer program may ignore previously sealed regions and the AI may find additional cracks that the computer program may not detect. Both the computer program and the AI may contain logic to connect nearby segments.” (Hendricks, [0064]).
generating X and Y coordinates for each of the plurality of points; and
Hendricks discloses “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot.” (Hendricks, [0063]).
categorizing each of the plurality of points as a starting point, an ending point or a continuation point.
Hendricks discloses “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot. The multi-axis robot may be configured to convey the sealant from the maintenance module to the crack mapped by the optical mapping module.” (Hendricks, [0063]). Hendricks also discloses “the vision equipment may be used to create a composite image and representation of distances from the camera.” (Hendricks, [0063]).
Regarding Claim 6:
The method of claim 5, wherein the step of generating the path comprises the steps of:
dividing each of the one or more crack regions into a plurality of segments;
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment” (Hendricks, [0064]).
determining a cross-sectional value for each of the plurality of segments; and
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment” (Hendricks, [0064]).
classifying each of the plurality of segments based on the cross-sectional value.
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment, allowing the crack sealer or sealant distal arm tool to dispense a corresponding volume of sealant. The volume of sealant may be changed by altering the rate of material flow or varying the speed of the RMV. In certain examples, the computer program may ignore previously sealed regions and the AI may find additional cracks that the computer program may not detect. Both the computer program and the AI may contain logic to connect nearby segments.” (Hendricks, [0064]).
Regarding Claim 8:
The method of claim 2, wherein the step of generating the path comprises the steps of:
receiving a last known position of the robot; and
Hendricks discloses “the vision equipment may be used to create a composite image and representation of distances from the camera” (Hendricks, [0064]).
for each of the one or more crack regions:
determining a centroid for each of the crack regions;
Hendricks discloses “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot.” (Hendricks, [0063]).
determining which of the centroids is closest to the last known position of the robot; and
Hendricks discloses “computer program may ignore previously sealed regions and the AI may find additional cracks that the computer program may not detect” (Hendricks, [0064]).
ordering the one or more crack regions based on the distance between the corresponding centroid and the last known position of the robot.
Hendricks discloses “Both the computer program and the AI may contain logic to connect nearby segments. The logic may preferably have a decreased connection distance variable to reduce aberrations in the composite image, which may conserve sealant or promote efficiency” (Hendricks, [0064]).
Regarding Claim 9:
A system for programming a robot to autonomously fill cracks in a pavement, wherein the robot fills the cracks using a nozzle having a puck width, the system comprising:
a camera configured to obtain an image of the pavement; and
Hendricks discloses “…vision equipment may be used to create a composite image…” (Hendricks, [0064]).
a processor configured to:
identify one or more crack regions in the image of the pavement from the image;
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect… objects” (Hendricks, [0064]) and “The road feature identified by the optical system may include a crack in a surface of the road.” (Hendricks, [0064]).
erode each of the one or more crack regions into lines representing a skeleton section;
Hendricks discloses “the vision equipment may be used to create a composite image and representation of distances from the camera. The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect cracks and or objects to avoid on a ground surface.” (Hendricks, [0064]).
convert each of the one or more skeleton sections into a plurality of points;
Hendricks discloses “The optical system may further include an optical mapping module configured to map the crack” (Hendricks, [0063]) and “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot.” (Hendricks, [0063]).
for each of the plurality of points, determine a crack cross section through each of the plurality of points on the skeleton to opposite edges of the crack region;
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment, allowing the crack sealer or sealant distal arm tool to dispense a corresponding volume of sealant. The volume of sealant may be changed by altering the rate of material flow or varying the speed of the RMV. In certain examples, the computer program may ignore previously sealed regions and the AI may find additional cracks that the computer program may not detect. Both the computer program and the AI may contain logic to connect nearby segments.” (Hendricks, [0064]).
generate a path to fill the one or more crack regions;
Hendricks discloses “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot. The multi-axis robot may be configured to convey the sealant from the maintenance module to the crack mapped by the optical mapping module.” (Hendricks, [0063]).
determine a volume of sealant to fill the one or more crack regions along the path based on the crack cross sections;
Hendricks discloses “measurements may include a width of the crack, a length of a crack, a depth of the crack, and a volume open space within the crack” (Hendricks, [0063]).
generate instructions to fill the one or more crack regions using the path and the volume of the sealant; and send the instructions to the robot.
Hendricks discloses “the control system may be configured to selectively instruct the multi-axis robot which individual cracks to seal based on the measurements from the optical mapping module” (Hendricks, [0065]).
Regarding Claim 10:
The system of claim 9, wherein to identify the one or more crack regions, the processor is configured to:
preprocess the image to generate a pre-processed image;
Hendricks discloses “the vision equipment may be used to create a composite image and representation of distances from the camera” (Hendricks, [0064]).
apply an AI deep learning model to the pre-processed image to obtain a segmented image; and
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment, allowing the crack sealer or sealant distal arm tool to dispense a corresponding volume of sealant… Both the computer program and the AI may contain logic to connect nearby segments” (Hendricks, [0064]).
postprocess the segmented image to identify the one or more crack regions.
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect cracks and or objects to avoid on a ground surface” (Hendricks, [0064]).
Regarding Claim 11:
The system of claim 10, wherein the processor is configured to:
identify an obstacle in the image of the pavement; and
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect… objects” (Hendricks, [0064]) and “The road feature identified by the optical system may include a crack in a surface of the road.” (Hendricks, [0064]).
ensure that the robot avoids filling the obstacle with sealant.
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect… objects to avoid on a ground surface… The computer program, AI, or combinations thereof may assign values based on the width of the segment, allowing the crack sealer or sealant distal arm tool to dispense a corresponding volume of sealant.” (Hendricks, [0064]).
Regarding Claim 12:
The system of claim 11, wherein to ensure that the robot avoids filling the obstacle with sealant, the processor is configured to:
determine whether the obstacle intersects the path; and
Hendricks discloses “The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect cracks and or objects to avoid on a ground surface.” (Hendricks, [0064]).
if the processor determines that the obstacle intersects the path, the processor is configured to instruct the robot to lift the nozzle to avoid the obstacle.
Hendricks discloses “In certain examples, the computer program may ignore previously sealed regions…” (Hendricks, [0095]). Hendricks discloses “the autonomous control system may allow the distal arm tool to follow a contour of the road as the distal arm tool moves across a surface of the road.” (Hendricks, [0085]).
Regarding Claim 13:
The system of claim 10, wherein to generate the path, the processor is configured to:
for each skeleton section, the processor is configured to:
prune small branches off the skeleton section before converting the skeleton section into the plurality of points;
Hendricks discloses “the vision equipment may be used to create a composite image and representation of distances from the camera. The composite image and the representations of distances from the camera may be processed by a computer program, AI, or combinations thereof to detect cracks and or objects to avoid on a ground surface… In certain examples, the computer program may ignore previously sealed regions and the AI may find additional cracks that the computer program may not detect. Both the computer program and the AI may contain logic to connect nearby segments.” (Hendricks, [0064]).
generate X and Y coordinates for each of the plurality of points; and
Hendricks discloses “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot.” (Hendricks, [0063]).
categorize each of the plurality of points as a starting point, an ending point or a continuation point.
Hendricks discloses “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot. The multi-axis robot may be configured to convey the sealant from the maintenance module to the crack mapped by the optical mapping module.” (Hendricks, [0063]). Hendricks also discloses “the vision equipment may be used to create a composite image and representation of distances from the camera.” (Hendricks, [0063]).
Regarding Claim 14:
The system of claim 13, wherein to generate the path, the processor is configured to:
divide each of the one or more crack regions into a plurality of segments; determine a cross-sectional value for each of the plurality of segments; and
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment” (Hendricks, [0064]).
classify each of the plurality of segments based on the cross-sectional value.
Hendricks discloses “The computer program, AI, or combinations thereof may assign values based on the width of the segment, allowing the crack sealer or sealant distal arm tool to dispense a corresponding volume of sealant. The volume of sealant may be changed by altering the rate of material flow or varying the speed of the RMV. In certain examples, the computer program may ignore previously sealed regions and the AI may find additional cracks that the computer program may not detect. Both the computer program and the AI may contain logic to connect nearby segments.” (Hendricks, [0064]).
Regarding Claim 16:
The system of claim 10, wherein to generate the path, the processor is configured to:
receive a last known position of the robot; and
Hendricks discloses “the vision equipment may be used to create a composite image and representation of distances from the camera” (Hendricks, [0064]).
for each of the one or more crack regions, the processor is configured to:
determine a centroid for each of the crack regions;
Hendricks discloses “The location translator may be configured to relay the location, the orientation, and the measurements of the crack from the optical mapping module to the multi-axis robot.” (Hendricks, [0063]).
determine which of the centroids is closest to the last known position of the robot; and
Hendricks discloses “computer program may ignore previously sealed regions and the AI may find additional cracks that the computer program may not detect” (Hendricks, [0064]).
order the one or more crack regions based on the distance between the corresponding centroid and the last known position of the robot.
Hendricks discloses “Both the computer program and the AI may contain logic to connect nearby segments. The logic may preferably have a decreased connection distance variable to reduce aberrations in the composite image, which may conserve sealant or promote efficiency” (Hendricks, [0064]).
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 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Hendricks, SR., et. al. (US 20220349132 A1), hereinafter referred to as Hendricks, in view of Ebrahimi Afrouzi, et. al. (US 20200225673 A1), hereinafter referred to as Afrouzi.
Regarding Claim 7:
The method of claim 6, further comprising the steps of:
for each skeleton section:
drawing a line having a width equal to the puck width from one of the plurality of points to another of the plurality of points;
Afrouzi discloses “the processor may fit a line to the subset of data” (Afrouzi, [0249]).
determining if all of the plurality of points between the one point and the other point lie along the line; and
Afrouzi discloses “the processor may consider data points falling within column four and may determine if the data points belong with the line fitted to the data points” (Afrouzi, [0249]).
if it is determined that all of the plurality of points lie along the line, replacing all of the plurality of points with the one and the other of the plurality of points.
Afrouzi discloses “In some embodiments, the processor may fit a line to the subset of data using, for example, least square method. FIG. 62B illustrates a line fit to data points falling within columns one to three” (Afrouzi, [0249]).
Regarding Claim 7, Hendricks discloses the system of Claim 6, but fails to disclose the limitations of Claim 7.
Afrouzi discloses “the processor may fit a line to the subset of data” (Afrouzi, [0249]) and “the processor may consider data points falling within column four and may determine if the data points belong with the line fitted to the data points” (Afrouzi, [0249]).
It would have been obvious to one having ordinary skill in the art at the time of the applicant’s effective filing date to combine the “drawing a line” between points and “determining if… [the] points lie along the line” taught by Afrouzi with the system of Hendricks because analyzing data points in the disclosed manner would give the system an additional method by which to clean up the captured image of the cracks.
Afrouzi additionally discloses “the processor may fit a line to the subset of data using, for example, least square method. FIG. 62B illustrates a line fit to data points” (Afrouzi, [0249]).
It would have been obvious to one having ordinary skill in the art at the time of the applicant’s effective filing date to combine the method of consolidating multiple points down to a single data point because it would decrease the amount of data that the main processor would need to manage, making the overall system more efficient.
Examiner notes that Claim 15 is being interpreted in light of the 112b rejection above.
Regarding Claim 15:
The system of claim 13, wherein for each skeleton section, the processor is configured to:
draw a line having a width equal to the puck width from one of the plurality of points to another of the plurality of points;
Afrouzi discloses “the processor may fit a line to the subset of data” (Afrouzi, [0249]).
determine if all of the plurality of points between the one point and the other point lie along the line; and
Afrouzi discloses “the processor may consider data points falling within column four and may determine if the data points belong with the line fitted to the data points” (Afrouzi, [0249]).
if the processor determines that all of the plurality of points lie along the line, the processor is configured to replace all of the plurality of points with the one and the other of the plurality of points.
Afrouzi discloses “In some embodiments, the processor may fit a line to the subset of data using, for example, least square method. FIG. 62B illustrates a line fit to data points falling within columns one to three” (Afrouzi, [0249]).
Regarding Claim 15, Hendricks discloses the system of Claim 13, but fails to disclose the limitations of Claim 15.
Afrouzi discloses “the processor may fit a line to the subset of data” (Afrouzi, [0249]) and “the processor may consider data points falling within column four and may determine if the data points belong with the line fitted to the data points” (Afrouzi, [0249]).
It would have been obvious to one having ordinary skill in the art at the time of the applicant’s effective filing date to combine the “drawing a line” between points and “determining if… [the] points lie along the line” taught by Afrouzi with the system of Hendricks because analyzing data points in the disclosed manner would give the system an additional method by which to clean up the captured image of the cracks.
Afrouzi additionally discloses “the processor may fit a line to the subset of data using, for example, least square method. FIG. 62B illustrates a line fit to data points” (Afrouzi, [0249]).
It would have been obvious to one having ordinary skill in the art at the time of the applicant’s effective filing date to combine the method of consolidating multiple points down to a single data point because it would decrease the amount of data that the main processor would need to manage, making the overall system more efficient.
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 JAMES B CHIN whose telephone number is (571)272-4634. The examiner can normally be reached Monday - Friday | 9:00 AM to 5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.B.C./
Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656