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 .
DETAILED ACTION
Claims 1-30 are pending and examined.
Claim Objection
Claims 1, 7-11, 17-21, and 27-30 are rejected because of the following informalities: ML should be spelt out in full as machine learning. Appropriate correction is needed.
Drawing
The drawings are objected to because Fig. 2D does not show “isometric views of an autonomous mobile robot (AMR) system that is controllable by the autonomous mobile robot process” [0014]. Fig. 2D seems to show there are different AMRs. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-30 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of copending Application No. 18298032.
Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-30 are rejected under 35 U.S.C. §103 as being unpatentable over claims 1-21 of copending Application No. 18298032 in view of Zass et al., US 2020/0410425 (A1).
As to claims 1, 11, and 21, copending Application No. 18298032 teaches a computer implemented method, executed on a computing device (“A computer implemented method, executed on a computing device, comprising”, copending Application No. 18298032 preamble), comprising:
navigating an autonomous mobile robot (AMR) within a defined space (“navigating an autonomous mobile robot (AMR) within a defined space”, copending Application No. 18298032 claim 1);
acquiring imagery at one or more defined locations within the defined space (“acquiring time-lapsed imagery at a plurality of defined locations within the defined space over an extended period of time”, copending Application No. 18298032 claim 1); and
Claims of copending Application No. 18298032 do not explicitly teach:
processing the imagery using an ML model to define a completion percentage for the one or more defined locations within the defined space; and
reporting the completion percentage of the one or more defined locations within the defined space to a user.
However, Zass, in the same field of endeavor, teaches:
processing the imagery using an ML model to define a completion percentage for the one or more defined locations within the defined space (Zass: Figs. 11-12 and related text: “update the at least one electronic record by updating the at least one progress record, for example by updating at least one progress status corresponding to at least one task in the at least one progress record. For example, Step 1120 may analyze image data to determine that a task was completed or a current percent of completion of the task”, ¶191; using machine learning to compare between image and electronic record (“Step 930 may analyze image data captured from a construction site (such as image data captured from the construction site using at least one image sensor and obtained by Step 710) to identify at least one discrepancy between at least one electronic record associated with the construction site (such as the at least one electronic record obtained by Step 920) and the construction site. In some examples, Step 930 may analyze the at least one electronic record and/or the image data using a machine learning model trained using training examples to identify discrepancies between the at least one electronic record and the construction site. For example, a training example may comprise an electronic record and image data with a corresponding label detailing discrepancies between the electronic record and the construction site. In some examples, Step 930 may analyze the at least one electronic record and the image data using an artificial neural network configured to identify discrepancies between the at least one electronic record and the construction site”, ¶147). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the images of construction site as taught by Zass, with a reasonable expectation of success, to include processing the imagery using an ML model to identify discrepancies between the at least one electronic record and the construction site, ¶147).
reporting the completion percentage of the one or more defined locations within the defined space to a user (Zass: “at least one electronic record may comprise at least one progress record associated with the construction site (such as progress record 630), and Step 1130 may update the at least one electronic record by updating the at least one progress record, for example by updating at least one progress status corresponding to at least one task in the at least one progress record. For example, Step 1120 may analyze image data to determine that a task was completed or a current percent of completion of the task, and Step 1130 may update at least one progress status corresponding to the task in the at least one progress record according to the determination. In another example, Step 1120 may analyze image data to determine that a task was not progressed or completed as specified in an electronic record (for example not progressed or completed as planned according to project schedule 620, not progressed or completed as reported according to progress records 630, etc.), and in response Step 1130 may record a delay in the at least one progress record according to the determination”, ¶191; “at least one electronic record updated by Step 1130, the at least one electronic record obtained by Step 920, etc.) may comprise information based on at least one image captured from at least one additional construction site. For example, the at least one electronic record may comprise information derived from image data captured from a plurality of construction sites. Moreover, the information about the plurality of construction sites may be aggregated, and statistics from the plurality of construction sites may be generated. Further, information from one construction site may be compared with information from other construction sites. In some examples, such statistics and/or comparisons may be provided to the user.”, ¶197).
As to claims 2, 12, and 22, Zass teaches the computer implemented method wherein the defined space is a construction site (claims 2, 9, 16 of copending 18298032 application is rejected by Zass “generating tasks from images of construction sites are provided”, abs).
As to claims 3, 13, and 23, Zass teaches the computer implemented method wherein the imagery includes one or more of: flat images; 360° images; and videos (claims 3, 10, 17 of copending 18298032 application is rejected by Zass “one or more image sensors 260 may be configured to capture visual information by converting light to: images; sequence of images; videos; 3D images; sequence of 3D images; 3D videos; and so forth”, ¶75).
As to claims 4, 14, and 24, Zass teaches the computer implemented method wherein navigating an autonomous mobile robot (AMR) within a defined space includes one or more of: navigating an autonomous mobile robot (AMR) within a defined space via a predefined navigation path; navigating an autonomous mobile robot (AMR) within a defined space via GPS coordinates; and navigating an autonomous mobile robot (AMR) within a defined space via a machine vision system (claims 3, 10, 17 of copending 18298032 application is rejected by Zass GPS, ¶78; “LIDAR using image sensors 260 and light sources 265”, ¶77).
As to claims 5, 15, and 25, Zass teaches the computer implemented method wherein the machine vision system includes one or more of: a LIDAR system; and a plurality of discrete machine vision cameras (claims 4, 11, 18 of copending 18298032 application is rejected by Zass “LIDAR using image sensors 260 and light sources 265”, ¶77).
As to claims 6, 16, and 26, Zass teaches the computer implemented method wherein the plurality of defined locations include one or more of: at least one human defined location; and at least one machine defined location (claims 3, 10, 17 of copending 18298032 application is rejected by Zass Figs. 10A-B show robot navigation locations).
As to claims 7, 17, and 21, Zass teaches the computer implemented method wherein processing the imagery using an ML model to define a completion percentage for the one or more defined locations within the defined space includes: comparing the imagery to visual training data to define the completion percentage for the one or more defined locations within the defined space (Figs. 17A-D and related text teaches comparison of electronic records and acquired images for consistency).
As to claims 8, 18, and 28, Zass teaches the computer implemented method wherein processing the imagery using an ML model to define a completion percentage for the one or more defined locations within the defined space includes: comparing the imagery to user’s defined completion content to define the completion percentage for the one or more defined locations within the defined space (Figs. 17A-D and related text teaches comparison of electronic records and acquired images for consistency).
As to claim 9, 19, and 29, Zass teaches the computer implemented method further comprising:
training the ML model using visual training data that identifies construction projects or portions thereof in various levels of completion so that the ML model may associate various completion percentages with visual imagery (“Step 930 may analyze image data captured from a construction site (such as image data captured from the construction site using at least one image sensor and obtained by Step 710) to identify at least one discrepancy between at least one electronic record associated with the construction site (such as the at least one electronic record obtained by Step 920) and the construction site. In some examples, Step 930 may analyze the at least one electronic record and/or the image data using a machine learning model trained using training examples to identify discrepancies between the at least one electronic record and the construction site. For example, a training example may comprise an electronic record and image data with a corresponding label detailing discrepancies between the electronic record and the construction site. In some examples, Step 930 may analyze the at least one electronic record and the image data using an artificial neural network configured to identify discrepancies between the at least one electronic record and the construction site”, ¶147); Figs. 11-12 and related text: “update the at least one electronic record by updating the at least one progress record, for example by updating at least one progress status corresponding to at least one task in the at least one progress record. For example, Step 1120 may analyze image data to determine that a task was completed or a current percent of completion of the task”, ¶191; see claim 1 above for reasons to combine, rationale and motivation).
As to claim 10, 20, and 30, Zass teaches the computer implemented method wherein training the ML model using visual training data that identifies construction projects or portions thereof in various percentages of completion includes: having the ML model make an initial estimate concerning the completion percentage of a specific visual image within the visual training data; and providing the specific visual image and the initial estimate to a human trainer for confirmation and/or adjustment (“Step 930 may analyze image data captured from a construction site (such as image data captured from the construction site using at least one image sensor and obtained by Step 710) to identify at least one discrepancy between at least one electronic record associated with the construction site (such as the at least one electronic record obtained by Step 920) and the construction site. In some examples, Step 930 may analyze the at least one electronic record and/or the image data using a machine learning model trained using training examples to identify discrepancies between the at least one electronic record and the construction site. For example, a training example may comprise an electronic record and image data with a corresponding label detailing discrepancies between the electronic record and the construction site. In some examples, Step 930 may analyze the at least one electronic record and the image data using an artificial neural network configured to identify discrepancies between the at least one electronic record and the construction site”, ¶147); Figs. 11-12 and related text: “update the at least one electronic record by updating the at least one progress record, for example by updating at least one progress status corresponding to at least one task in the at least one progress record. For example, Step 1120 may analyze image data to determine that a task was completed or a current percent of completion of the task”, ¶191; “the user interface may be configured to enable the user to provide an indication of a construction stage”, 318; see claim 1 above for reasons to combine, rationale and motivation).
This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Furthermore, there is no apparent reason why applicant would be prevented from presenting claims corresponding to those of the instant application in the other copending application.
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Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 11-20 are rejected under 35 U.S.C. §101 because the claimed invention is not directed to patent eligible subject matter.
101 Analysis
Based upon consideration of all of the relevant factors with respect to the claim as a whole, the claim is determined to be directed to an abstract idea. The rationale for this determination is explained below:
When considering subject matter eligibility under 35 U.S.C. § 101 under the 2019 Revised Patent Subject Matter Eligibility Guidance, the Office is charged with determining whether the scope of the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1).
If the claim falls within one of the statutory categories (Step 1), the Office must then determine the two-prong inquiry for Step 2A whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) (Step 2A Prong 1), and if so, whether the claim is integrated into a practical application of the exception (Step 2A Prong 2), and if so, re-evaluate whether the inventive concept is more than what is well-understood, routine, conventional activity in the field (Step 2B).
Claims 11-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1: statutory category
Claims 11-20 are rejected under 35 USC §101 because the claimed invention is directed to a computer program product residing on a computer readable medium. MPEP 2106.03 II states “ the BRI of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves).” Therefore, the claimed 11-20 are not one of statutory categories of invention (Step 1: Yes).
Conclusion:
Claims 11-20 are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter and are not patent eligible.
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.
The following is a quotation of 35 U.S.C. §112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9-10, 19-20, and 29-30 are rejected under 35 U.S.C. §112(b) or 35 U.S.C. §112(pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 9, 19, and 29 recite “… training the ML model using visual training data that identifies construction projects or portions thereof in various levels of completion so that the ML model may associate various completion percentages with visual imagery …” It is indefinite and unclear whether there is any association between the ML model and the imagery. Appropriate correction is required.
Claims 10, 20, and 20 are rejected based on dependency on claims 9, 19, and 20 respectively.
Notice re prior art available under both pre-AIA and AIA
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 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.
Claim Rejections - 35 USC §103
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 of this title, 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.
Claims 1-12 are rejected under 35 U.S.C. §103 as being unpatentable over Zass et al., US 2020/0410425 (A1).
As to claim 1, Zass teaches a computer implemented method, executed on a computing device (“sensor data may be applied to a machine learning model … Locations within the physical environment may be determined from the set of boundary points represented by the sensor data, and the vehicle may be controlled through the physical environment within the drivable free-space using the locations and the class labels”, abs), comprising:
navigating an autonomous mobile robot (AMR) within a defined space (“provide the information to the image acquisition robot, to an external system controlling (directly or indirectly) the image acquisition robot, to a different process controlling (directly or indirectly) the image acquisition robot, and so forth. For example, the provided information may include one or more of an indication of at least one capturing parameter, an indication of the particular area of the construction site, an indication of a planned capturing time, an indication of a planned capturing position, an indication of a planned capturing angle, navigation data to the planned capturing position, and so forth. In one example, Step 1440 may comprise causing the image acquisition robot to move to a particular position in the construction site and to capture the at least one image from the particular position.”, ¶235);
acquiring imagery at one or more defined locations within the defined space (“provide the information to the image acquisition robot, to an external system controlling (directly or indirectly) the image acquisition robot, to a different process controlling (directly or indirectly) the image acquisition robot, and so forth. For example, the provided information may include one or more of an indication of at least one capturing parameter, an indication of the particular area of the construction site, an indication of a planned capturing time, an indication of a planned capturing position, an indication of a planned capturing angle, navigation data to the planned capturing position, and so forth. In one example, Step 1440 may comprise causing the image acquisition robot to move to a particular position in the construction site and to capture the at least one image from the particular position.”, ¶235); and
reporting the completion percentage of the one or more defined locations within the defined space to a user (“at least one electronic record may comprise at least one progress record associated with the construction site (such as progress record 630), and Step 1130 may update the at least one electronic record by updating the at least one progress record, for example by updating at least one progress status corresponding to at least one task in the at least one progress record. For example, Step 1120 may analyze image data to determine that a task was completed or a current percent of completion of the task, and Step 1130 may update at least one progress status corresponding to the task in the at least one progress record according to the determination. In another example, Step 1120 may analyze image data to determine that a task was not progressed or completed as specified in an electronic record (for example not progressed or completed as planned according to project schedule 620, not progressed or completed as reported according to progress records 630, etc.), and in response Step 1130 may record a delay in the at least one progress record according to the determination”, ¶191; “at least one electronic record updated by Step 1130, the at least one electronic record obtained by Step 920, etc.) may comprise information based on at least one image captured from at least one additional construction site. For example, the at least one electronic record may comprise information derived from image data captured from a plurality of construction sites. Moreover, the information about the plurality of construction sites may be aggregated, and statistics from the plurality of construction sites may be generated. Further, information from one construction site may be compared with information from other construction sites. In some examples, such statistics and/or comparisons may be provided to the user.”, ¶197).
Zass teaches processing the imagery to define a completion percentage for the one or more defined locations within the defined space (Figs. 11-12 and related text: “update the at least one electronic record by updating the at least one progress record, for example by updating at least one progress status corresponding to at least one task in the at least one progress record. For example, Step 1120 may analyze image data to determine that a task was completed or a current percent of completion of the task”, ¶191). Zass does not specifically teach the method:
processing the imagery using an ML model to define a completion percentage for the one or more defined locations within the defined space.
However, Zass teaches using machine learning to compare between image and electronic record (“Step 930 may analyze image data captured from a construction site (such as image data captured from the construction site using at least one image sensor and obtained by Step 710) to identify at least one discrepancy between at least one electronic record associated with the construction site (such as the at least one electronic record obtained by Step 920) and the construction site. In some examples, Step 930 may analyze the at least one electronic record and/or the image data using a machine learning model trained using training examples to identify discrepancies between the at least one electronic record and the construction site. For example, a training example may comprise an electronic record and image data with a corresponding label detailing discrepancies between the electronic record and the construction site. In some examples, Step 930 may analyze the at least one electronic record and the image data using an artificial neural network configured to identify discrepancies between the at least one electronic record and the construction site”, ¶147). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the images of construction site as taught by Zass, with a reasonable expectation of success, to include processing the imagery using an ML model to identify discrepancies between the at least one electronic record and the construction site, ¶147).
As to claim 2, Zass teaches the computer implemented method wherein the defined space is a construction site (“generating tasks from images of construction sites are provided”, abs).
As to claim 3, Zass teaches the computer implemented method wherein the imagery includes one or more of: flat images; 360° images; and videos (“one or more image sensors 260 may be configured to capture visual information by converting light to: images; sequence of images; videos; 3D images; sequence of 3D images; 3D videos; and so forth”, ¶75).
As to claim 4, Zass teaches the computer implemented method wherein navigating an autonomous mobile robot (AMR) within a defined space includes one or more of: navigating an autonomous mobile robot (AMR) within a defined space via a predefined navigation path; navigating an autonomous mobile robot (AMR) within a defined space via GPS coordinates; and navigating an autonomous mobile robot (AMR) within a defined space via a machine vision system (GPS, ¶78; “LIDAR using image sensors 260 and light sources 265”, ¶77).
As to claim 5, Zass teaches the computer implemented method wherein the machine vision system includes one or more of: a LIDAR system; and a plurality of discrete machine vision cameras (“LIDAR using image sensors 260 and light sources 265”, ¶77).
As to claim 6, Zass teaches the computer implemented method wherein the plurality of defined locations include one or more of: at least one human defined location; and at least one machine defined location (Figs. 10A-B show robot navigation locations).
As to claim 7, Zass teaches the computer implemented method wherein processing the imagery using an ML model to define a completion percentage for the one or more defined locations within the defined space includes: comparing the imagery to visual training data to define the completion percentage for the one or more defined locations within the defined space (Figs. 17A-D and related text teaches comparison of electronic records and acquired images for consistency).
As to claim 8, Zass teaches the computer implemented method wherein processing the imagery using an ML model to define a completion percentage for the one or more defined locations within the defined space includes: comparing the imagery to user’s defined completion content to define the completion percentage for the one or more defined locations within the defined space (Figs. 17A-D and related text teaches comparison of electronic records and acquired images for consistency).
As to claim 9, Zass teaches the computer implemented method further comprising:
training the ML model using visual training data that identifies construction projects or portions thereof in various levels of completion so that the ML model may associate various completion percentages with visual imagery (“Step 930 may analyze image data captured from a construction site (such as image data captured from the construction site using at least one image sensor and obtained by Step 710) to identify at least one discrepancy between at least one electronic record associated with the construction site (such as the at least one electronic record obtained by Step 920) and the construction site. In some examples, Step 930 may analyze the at least one electronic record and/or the image data using a machine learning model trained using training examples to identify discrepancies between the at least one electronic record and the construction site. For example, a training example may comprise an electronic record and image data with a corresponding label detailing discrepancies between the electronic record and the construction site. In some examples, Step 930 may analyze the at least one electronic record and the image data using an artificial neural network configured to identify discrepancies between the at least one electronic record and the construction site”, ¶147); Figs. 11-12 and related text: “update the at least one electronic record by updating the at least one progress record, for example by updating at least one progress status corresponding to at least one task in the at least one progress record. For example, Step 1120 may analyze image data to determine that a task was completed or a current percent of completion of the task”, ¶191; see claim 1 above for reasons to combine, rationale and motivation).
As to claim 10, Zass teaches the computer implemented method wherein training the ML model using visual training data that identifies construction projects or portions thereof in various percentages of completion includes: having the ML model make an initial estimate concerning the completion percentage of a specific visual image within the visual training data; and providing the specific visual image and the initial estimate to a human trainer for confirmation and/or adjustment (“Step 930 may analyze image data captured from a construction site (such as image data captured from the construction site using at least one image sensor and obtained by Step 710) to identify at least one discrepancy between at least one electronic record associated with the construction site (such as the at least one electronic record obtained by Step 920) and the construction site. In some examples, Step 930 may analyze the at least one electronic record and/or the image data using a machine learning model trained using training examples to identify discrepancies between the at least one electronic record and the construction site. For example, a training example may comprise an electronic record and image data with a corresponding label detailing discrepancies between the electronic record and the construction site. In some examples, Step 930 may analyze the at least one electronic record and the image data using an artificial neural network configured to identify discrepancies between the at least one electronic record and the construction site”, ¶147); Figs. 11-12 and related text: “update the at least one electronic record by updating the at least one progress record, for example by updating at least one progress status corresponding to at least one task in the at least one progress record. For example, Step 1120 may analyze image data to determine that a task was completed or a current percent of completion of the task”, ¶191; “the user interface may be configured to enable the user to provide an indication of a construction stage”, 318; see claim 1 above for reasons to combine, rationale and motivation).
As to claims 11, 12,13, 14, 15, 16, 17, 18, 19, and 20, they are claims that recite substantially the same limitations as the corresponding method claims 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. As such, claims 11, 12,13, 14, 15, 16, 17, 18, 19, and 20 are rejected for substantially the same reasons given for the corresponding method claims 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 and are incorporated herein.
As to claims 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30, they are apparatus claims that recite substantially the same limitations as the corresponding method claims 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. As such, claims 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30 are rejected for substantially the same reasons given for the corresponding method claims 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 and are incorporated herein.
Examiner’s Note
The examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Applicant should consider the entire prior art as applicable as to the limitations of the claims. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Examiner’s Request
The examiner requests, in response to this office action, support must be shown for language added to any original claims on amendment and any new claims. That is, the applicant is requested to indicate support for amended claim language and newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). (MPEP 2163 I. B. New or Amended Claims). This will assist the examiner in prosecuting the application. When responding to this office action, applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. In amending in reply to a rejection of claims in an application or patent under reexamination, the applicant or patent owner must clearly point out the patentable novelty which he or she thinks the claims present in view the state of the art disclosed by the references cited or the objections made. The applicant or patent owner must also show how the amendments avoid such references or objections.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUEN WONG whose telephone number is (313)446-4851. The examiner can normally be reached on M-F 9-5:30 EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris Almatrahi, can be reached on (313)446-4821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YUEN WONG/ Primary Examiner, Art Unit 3667