DETAILED ACTION
This office action is in response to the application filed on February 20, 2025. Claims 1 – 18 are pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on February 20, 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words. The form and legal phraseology often used in patent claims, such as "means" and "said," should be avoided. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, "The disclosure concerns," "The disclosure defined by this invention," "The disclosure describes," etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because has more than 150 words. Correction is required. See MPEP § 608.01(b).
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 1-5, 7-15, 17 and 18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 - 10 of copending Application No. 19/105,323. Although the claims at issue are not identical, they are not patentably distinct from each other. See below:
Instant Application- 19/105,318
Instant Application- 19/105,323
Claim 1. An obstacle proximity detection device comprising a processor configured to execute operations comprising: acquiring, sequentially, three-dimensional point cloud data representing a scanned outdoor structure; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area around the first point cloud data on the basis of the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and generating and presenting, based on a predetermined condition, an alert, wherein the alert indicates proximity between the object and the obstacle.9. (New) The obstacle proximity detection device according to claim 1, wherein the predetermined condition comprises a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data.
Claim 1. An obstacle proximity detection device comprising: an acquisition unit configured to sequentially acquire three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner; a specifying unit configured to specify first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; a setting unit configured to set a first detection area that is an area set in advance by a user and is an area around the first point cloud data; a moving unit configured to move, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and an output unit configured to output an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.
Claim 1. An obstacle proximity detection device comprising a processor configured to execute operations comprising: acquiring, sequentially, three-dimensional point cloud data representing a scanned outdoor structure; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area around the first point cloud data on the basis of the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and generating and presenting, based on a predetermined condition, an alert, wherein the alert indicates proximity between the object and the obstacle.10. (New) The obstacle proximity detection device according to claim 1, wherein the predetermined condition comprises a case where a number of pieces of point data of the first point cloud data existing in a second detection area is equal to or larger than a predetermined threshold, and the second detection area corresponds to an area around the second point cloud data.
Claim 1. An obstacle proximity detection device comprising: an acquisition unit configured to sequentially acquire three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner; a specifying unit configured to specify first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; a setting unit configured to set a first detection area that is an area set in advance by a user and is an area around the first point cloud data; a moving unit configured to move, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and an output unit configured to output an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.
Claim 2. The obstacle proximity detection device according to claim 1, wherein the object is a utility pole under construction, and the obstacle represents either one of a cable or a wall surface existing between existing utility poles.
Claim 2. The obstacle proximity detection device according to claim 1, wherein the object is a utility pole under construction, and the obstacle is a cable or a wall surface existing between existing utility poles.
Claim 3. The obstacle proximity detection device according to claim 1, wherein the specifying further comprises: rotating data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle with respect to an xy plane, projecting the rotated data of the predetermined region with respect to the xy plane, and specifying the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular.
Claim 3. The obstacle proximity detection device according to claim 1, wherein, in specifying the first point cloud data, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle with respect to an xy plane, projects the rotated data of the predetermined region with respect to the xy plane, and specifies the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular.
Claim 4. The obstacle proximity detection device according to claim 2, wherein the specifying further comprises: rotating data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis, projecting the rotated data of the predetermined region on a yz plane, and specifying the data of the predetermined region as the second point cloud data representing the cable in a case where the data of the predetermined region projected on the yz plane is circular.
Claim 4. The obstacle proximity detection device according to claim 2, wherein, in specifying second point cloud data representing the cable, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis, projects the rotated data of the predetermined region on a yz plane, and specifies the data of the predetermined region as the second point cloud data representing the cable in a case where the data of the predetermined region projected on the yz plane is circular.
Claim 5. The obstacle proximity detection device according to claim 2, wherein the specifying further comprises: projecting data of a predetermined region included in the three-dimensional point cloud data on an xy plane, and specifying the data of the predetermined region as the second point cloud data representing the wall surface in a case where the data of the predetermined region projected on the xy plane has a linear shape.
Claim 5. The obstacle proximity detection device according to claim 2, wherein, in specifying second point cloud data representing the wall surface, the specifying unit projects data of a predetermined region included in the three-dimensional point cloud data on an xy plane, and specifies the data of the predetermined region as the second point cloud data representing the wall surface in a case where the data of the predetermined region projected on the xy plane has a linear shape.
Claim 7. A computer implemented method comprising: sequentially acquiring three-dimensional point cloud data representing a scanned outdoor structure; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area around the first point cloud data on the basis of the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and generating and presenting, based on a predetermined condition, an alert, wherein the alert indicates proximity between the object and the obstacle.Claim 11. The obstacle proximity detection device according to claim 1, wherein the predetermined condition comprises a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data.
Claim 6. An obstacle proximity detection method in which a computer executes processing of: sequentially acquiring three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area set in advance by a user and is an area around the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and outputting an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.
Claim 7. A computer implemented method comprising: sequentially acquiring three-dimensional point cloud data representing a scanned outdoor structure; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area around the first point cloud data on the basis of the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and generating and presenting, based on a predetermined condition, an alert, wherein the alert indicates proximity between the object and the obstacle.Claim 12. The obstacle proximity detection device according to claim 1, wherein the predetermined condition comprises a case where a number of pieces of point data of the first point cloud data existing in a second detection area is equal to or larger than a predetermined threshold, and the second detection area corresponds to an area around the second point cloud data.
Claim 6. An obstacle proximity detection method in which a computer executes processing of: sequentially acquiring three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area set in advance by a user and is an area around the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and outputting an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.
Claim 13. The computer implemented method according to claim 7, wherein the object is a utility pole under construction, and the obstacle represents either one of a cable or a wall surface existing between existing utility poles.
Claim 8. The obstacle proximity detection method according to claim 6, wherein the object is a utility pole under construction, and the obstacle is a cable or a wall surface existing between existing utility poles.
Claim 14. The computer implemented method according to claim 7, wherein the specifying further comprises: rotating data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle with respect to an xy plane, projecting the rotated data of the predetermined region with respect to the xy plane, and specifying the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular.
Claim 9. The obstacle proximity detection method according to claim 6 wherein, in specifying the first point cloud data, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle with respect to an xy plane, projects the rotated data of the predetermined region with respect to the xy plane, and specifies the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular.
Claim 15. The obstacle proximity detection method according to claim 13, wherein the specifying further comprises: rotating data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis, projecting the rotated data of the predetermined region on a yz plane, and specifying the data of the predetermined region as the second point cloud data representing the cable in a case where the data of the predetermined region projected on the yz plane is circular.
Claim 10. The obstacle proximity detection method according to claim 8, wherein, in specifying second point cloud data representing the cable, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis, projects the rotated data of the predetermined region on a yz plane, and specifies the data of the predetermined region as the second point cloud data representing the cable in a case where the data of the predetermined region projected on the yz plane is circular.
Claim 8. A computer readable non-transitory recoding medium storing a computer executable program instruction that when executed by a processor cause a computer to execute operations comprising: acquiring sequentially three-dimensional point cloud data representing a scanned outdoor structure; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area around the first point cloud data on the basis of the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and generating and presenting, based on a predetermined condition, an alert, wherein the alert indicates proximity between the object and the obstacle.Claim 17. The computer readable non-transitory recoding medium according to claim 8, wherein the predetermined condition comprises a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data.
Claim 7.A computer readable non-transitory recoding medium storing a computer executable program instruction that when executed by a processor cause a computer to execute an obstacle proximity detection program to execute processing of: sequentially acquiring three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area set in advance by a user and is an area around the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and outputting an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.
Claim 8. A computer readable non-transitory recoding medium storing a computer executable program instruction that when executed by a processor cause a computer to execute operations comprising: acquiring sequentially three-dimensional point cloud data representing a scanned outdoor structure; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area around the first point cloud data on the basis of the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and generating and presenting, based on a predetermined condition, an alert, wherein the alert indicates proximity between the object and the obstacle.Claim 18. The computer readable non-transitory recoding medium according to claim 8, wherein the predetermined condition comprises a case where a number of pieces of point data of the first point cloud data existing in a second detection area is equal to or larger than a predetermined threshold, and the second detection area corresponds to an area around the second point cloud data.
Claim 7. A computer readable non-transitory recoding medium storing a computer executable program instruction that when executed by a processor cause a computer to execute an obstacle proximity detection program to execute processing of: sequentially acquiring three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner; specifying first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; setting a first detection area that is an area set in advance by a user and is an area around the first point cloud data; moving, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and outputting an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.
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 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.
Claims 1, 7, 8, 9 – 12, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dong-liang ZHANG (CN 114299443 A) referred to as ZHANG hereinafter, and in view of HWANG et al. (US 2015/0371110 A1) referred to as HWANG hereinafter.
Regarding Claim 1, ZHANG teaches an obstacle proximity detection device (Abstract, Fig. 1, monitoring system) comprising execute operations (Abstract, a pole tower real-time anti-collision monitoring method ) comprising:
acquiring, sequentially, three-dimensional point cloud data (Page 3 step 2: using the depth camera to collect (i.e. acquiring) the surrounding environment, obtaining the image data and point cloud data in the scene. Depth map collected by the depth camera needs registration, to generate registration depth map (i.e. three-dimensional), namely point cloud data, Page 2, 2nd paragraph, industrial application of three-dimensional target detection) representing a scanned outdoor structure (Page 3, step 1: the depth camera is installed on the pole tower, for real-time monitoring (i.e. sequentially) tower local and its surrounding environment. Depth camera is installed at the height of 3 m to 6 m above the pole tower (see FIG. 1), the visual angle of the camera towards the external object collision pole tower possibility of the largest possibility, and the front lower part of the pole tower (i.e. a scanned outdoor structure) falls in the view of the camera);
specifying first point cloud data representing an object (Page 2, step 2 in the initial state, namely the camera view, based on the point cloud data collected by the depth camera to identify the pole tower and the ground, respectively the point cloud data of the pole tower and the point cloud data of the ground (i.e. first point cloud data of objects)) and second point cloud data representing an obstacle from the three-dimensional point cloud data (Page 2, step 2 a moving object in the camera field of view, the recognition of the moving object, and obtaining the point cloud data of the moving object (i.e. second point cloud data of obstacle));
setting a first detection area that is an area around the first point cloud data on the basis of the first point cloud data (Page 2, step 2 when there is a moving object in the camera field of view (i.e. set a first detection area));
moving, on the basis of a feature point, the first point cloud data and the first detection area according to movement of the feature point (Page 4, step 4, recognition the type (i.e. feature) of the moving object, according to the area corresponding relation of the moving object in the image, comparing the real time point cloud data with the background point cloud data to obtain the difference point cloud data, namely obtaining the point cloud data of the moving object); and
generating and presenting (Page 2, 3rd paragraph, calculates the distance between the mobile object and the pole tower according to the depth information, and sends an alarm to the outside, and feeds back the monitoring information to the monitoring center), based on a predetermined condition, an alert, wherein the alert indicates proximity between the object and the obstacle (Page 3, 1st paragraph, feeding back the type of the mobile object and the obtained minimum distance information to the monitoring system, if the type of the mobile object is preset easy to cause damage (i.e. predetermined condition) of the object type, and the minimum distance (i.e. predetermined condition) is in the preset distance range (i.e. proximity between object and obstacle), then the monitoring system controls to send out alarm information (i.e. generate and present)).
ZHANG does not specifically teach a processor or extracting feature point from point cloud data. Therefore ZHANG fails to explicitly teach a processor configured to execute operations and a feature point extracted from the point cloud data.
However, HWANG teaches a processor configured to execute operations (Par. [0050] The feature region extraction apparatus may execute a program implemented in software and stored in a memory, using a processor and a memory) and a feature point extracted from the point cloud data (Par. [0126] Referring to FIG. 20, The receiver 2010 may receive a point cloud. The divider 2020 may divide the point cloud into a plurality of regions. The generator 2030 may generate a hierarchical structure in which the regions are arranged in a hierarchy. The extractor 2040 may extract at least one feature region by exploring the regions based on the hierarchical structure. Par. [0127] the feature region extraction apparatus 2000 may further include a detector 2060 that extracts a feature point from the extracted feature region. Furthermore, the feature region extraction apparatus 2000 may further include a recognizer 2070 that recognizes an object corresponding to the point cloud based on the extracted feature point).
References ZHANG and HWANG are considered to be analogous art because they relate to analysis of point cloud data. Therefore, it would have been obvious that one of ordinary skill in the art, before the effective filing date of the claimed invention, would recognize the advantage of further specifying extracting a feature as suggested by HWANG in the invention of ZHANG in order be used for 3D matching, 3D object recognition, and the like (see HWANG, Par. [0053]).
Regarding Claim 9, ZHANG in view of HWANG teaches Claim 1. ZHANG further teaches wherein the predetermined condition comprises a case where a part of the first detection area (Page 2, step 2- the camera field of view (i.e. the first detection area) overlaps with a part of a second detection area (Page 2, step 2- when there is a moving object in the field of view (i.e. overlaps)) that is an area around the second point cloud data (Page 2, step 2- obtaining point cloud data of the pole tower part (i.e. second detection area)).
Regarding Claim 10, ZHANG in view of HWANG teaches Claim 1. ZHANG further teaches wherein the predetermined condition comprises a case where a number of pieces of point data of the first point cloud data existing in a second detection area is equal to or larger than a predetermined threshold (Page 3, step 3 - calculating the distance (i.e. predetermined condition) between the moving object and the pole tower, using bounding box method to reduce the point cloud of the pole tower (i.e. second detection area). The working principle of the method for surrounding the box is as follows: constructing a bounding box containing all point cloud data, and decomposing it into several small bounding box with uniform size, selecting the point closest to the center (i.e. predetermined threshold) to replace all points of the small bounding box to achieve the purpose of simple point cloud data, and controlling the simplified result by controlling the size of the small bounding box), and the second detection area corresponds to an area around the second point cloud data (Page 3, step 3 - obtaining point cloud data of the pole tower part (i.e. second detection area)).
Method claim 7 is drawn to the method of using the corresponding apparatus claimed in claim 1. Therefore method claim 7 corresponds to apparatus claim 1 and is rejected for the same reasons of obviousness as used above.
Computer-readable recording medium Claim 8 is drawn to using the corresponding apparatus claimed in Claim 1. Therefore computer-readable recording medium Claim 8 corresponds to apparatus Claim 1 and is rejected for the same reasons of obviousness as used above.
Method claims 11 and 12 are drawn to the method of using the corresponding apparatus claimed in claims 9 and 10. Therefore method claims 11 and 12 correspond to apparatus claims 9 and 10 and are rejected for the same reasons of obviousness as used above.
Computer-readable recording medium Claims 17 and 18 are drawn to using the corresponding apparatus claimed in Claims 9 and 10. Therefore computer-readable recording medium Claims 17 and 18 correspond to apparatus Claims 9 and 10 and are rejected for the same reasons of obviousness as used above.
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over ZHANG (CN 114299443 A), in view of HWANG et al. (US 2015/0371110 A1), and in further view of van Cruyningen et al. (US 2018/0032088 A1) referred to as van Cruyningen hereinafter.
Regarding Claim 2, ZHANG in view of HWANG teaches Claim 1. ZHANG further teaches wherein the object is a utility pole under construction (Page 2, step 2- a pole tower real-time anti-collision monitoring, in the initial state, namely the camera view, based on the point cloud data collected by the depth camera to identify the pole tower and the ground, respectively the point cloud data of the pole tower), and the obstacle represents existing between existing utility poles (Page 4, step 4- when there is a moving object in the field of view, real-time recognition moving the type of the object, and obtaining the point cloud data of the moving object. can adopt the method of Faster R-CNN recognition the type of the object. If the type of the mobile object is easy to cause damage (i.e. obstacle) of the object, and the distance between it and the tower is less than a certain degree, then sending the alarm information).
ZHANG in view of HWANG does not specifically teach the obstacle is a cable or wall surface. Therefore, ZHANG in view of HWANG fails to explicitly teach the obstacle represents either one of a cable or a wall surface existing between existing utility poles.
However, van Cruyningen teaches the obstacle represents either one of a cable or a wall surface existing between existing utility poles (Fig. 1, Par. [0060] a LiDAR scan implementation is very easy to understand. A LiDAR scanner is mounted on base station 30 and is positioned near tower 10 so that it is as close as possible to record strong reflections, while still keeping all the objects of interest in the LiDAR scanner field of view. The LiDAR scanner emits pulses of light and measures the time for a reflection to come back. Given the known speed of light, it can determine the distance to the reflector to within a few centimeters. It repeats this for many, many pulses in different directions so that it can accurately determine the distance to any point on tower 10, phase conductors 12, insulators 14, shield wires 16, and any obstacles in the field of view. The result of the LiDAR scan is a point cloud representing the tower and obstacles).
References ZHANG, HWANG and van Cruyningen are considered to be analogous art because they relate to analysis of point cloud data. Therefore, it would have been obvious that one of ordinary skill in the art, before the effective filing date of the claimed invention, would recognize the advantage of further specifying obstacles from point cloud data as suggested by van Cruyningen in the inventions of ZHANG and HWANG in order to build up a point cloud representing of a tower, other objects of interest (insulators, phase conductors, and shield wires), and potential obstacles (see van Cruyningen, Par. [0085]).
Method claim 13 is drawn to the method of using the corresponding apparatus claimed in claim 2. Therefore method claim 13 corresponds to apparatus claim 2 and is rejected for the same reasons of obviousness as used above.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over ZHANG (CN 114299443 A), in view of HWANG et al. (US 2015/0371110 A1), and in further view of Richards, JR. (US 2013/0127851 A1) referred to as Richards hereinafter.
Regarding Claim 3, ZHANG in view of HWANG teaches Claim 1. ZHANG in view of HWANG does not specifically teach rotating the point cloud data. Therefore, ZHANG in view of HWANG fails to explicitly teach rotating data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle with respect to an xy plane, projecting the rotated data of the predetermined region with respect to the xy plane, and specifying the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular.
However, Richards teaches rotating data of a predetermined region (Par. [0026] The extracted information can be used to identify structures and/or structural features from the point cloud, for example, building facades, domes, ground planes, fences, trees, and/or terrain tangent planes. The technology advantageously enables the extraction of information (e.g., location of a structure, shape of structure, etc.) from a point cloud via rotation and projection techniques) included in the three-dimensional point cloud data (Par. [0005] The surface discovery module is configured to rotate the 3-dimensional image data around an axis in 3-dimensional space of the 3-dimensional image data) by a predetermined angle (Par. [0035] The structure discovery system 110 rotates the point cloud (212e to 214e to 216e) into a projected line on a coordinate axis to form the linear condensation 218e. The structure discovery system 110 can determine a rotation angle, .phi. utilizing the techniques described herein via a regression line fitting through the subcloud. In some examples, the .phi. is determined when the variance (or standard deviation) is minimum for the subcloud) with respect to an xy plane (Par. [0026] rotates the 3-dimensional image data around an axis (e.g., x-axis, y-axis, z-axis, or any in between axis) to align points of a structure (e.g., plane, cylinder, sphere, etc.) for extraction of information from the 3-dimensional image data (e.g., linear condensations, structures, etc.) Par. [0031] rotation of a point cloud 200c Fig. 2C), projecting the rotated data of the predetermined region with respect to the xy plane (Par. [0015] the rotating step (b) further includes (b-1) projecting isometrically the 3-dimensional image data onto an observation plane. Par. [0061] the surface discovery module 312 projects (425) isometrically the 3-dimensional image data onto an observation plane (e.g., viewing plane from image acquisition system, viewing plane from camera, etc.). In other examples, the surface discovery module 312 selects (423) the axis based on a plane of interest), and specifying the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular (Par. [0026] rotates the 3-dimensional image data around an axis (e.g., x-axis, y-axis, z-axis, or any in between axis) to align points of a structure (e.g., plane, cylinder, sphere, etc.) for extraction of information from the 3-dimensional image data (e.g., linear condensations, structures, etc.). The rotation of the 3-dimensional image data enables projection of the structure onto a coordinate plane to form linear condensations of the structure (e.g., structure becomes a straight line, structure becomes a circle, etc.). The extracted information can be used to identify structures and/or structural features from the point cloud. Par. [0060] The linear condensation identification module 313 identifies (430) linear condensations (e.g., lines, circles, etc.) in the rotated 3-dimensional image data).
References ZHANG, HWANG and Richards are considered to be analogous art because they relate to analysis of point cloud data. Therefore, it would have been obvious that one of ordinary skill in the art, before the effective filing date of the claimed invention, would recognize the advantage of further specifying rotating and projecting point cloud data as suggested by Richards in the inventions of ZHANG and HWANG in order to increase the efficiency of discovering and correctly identifying structures (see Richards, Par. [0018]).
Method claim 14 is drawn to the method of using the corresponding apparatus claimed in claim 3. Therefore method claim 14 corresponds to apparatus claim 3 and is rejected for the same reasons of obviousness as used above.
Claims 4, 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over ZHANG (CN 114299443 A), in view of HWANG et al. (US 2015/0371110 A1), in view of van Cruyningen et al. (US 2015/0371110 A1), and in further view of Richards, JR. (US 2013/0127851 A1).
Regarding Claim 4, ZHANG in combination with HWANG and van Cruyningen teaches Claim 2. While van Cruyningen teaches point cloud data representing the cable in Fig. 1 and Par. [0060], ZHANG in combination with HWANG and van Cruyningen does not specifically teach rotating the point cloud data. Therefore, ZHANG in combination with HWANG and van Cruyningen fails to explicitly teach rotating data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis, projecting the rotated data of the predetermined region on a yz plane, and specifying the data of the predetermined region as the second point cloud data in a case where the data of the predetermined region projected on the yz plane is circular.
However, Richards teaches rotating data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis (Par. [0026]-[0036] The extracted information can be used to identify structures and/or structural features from the point cloud. The structure identification can be, for example, utilized in various applications to identify specific buildings within a city, track changes within a landscape, and/or utilize specific features on a target to track the target within a landscape. FIG. 2C is a diagram illustrating an exemplary rotation of a point cloud 200 C. First, the structure discovery system 110 rotates a point cloud clockwise through angle Θ around a vertical z-axis 210 C. The first rotation can orient facades of structures into a line projection. Second, the structure discovery system 110 rotates the point cloud clockwise about a y-axis 220 c through angle Ф), projecting the rotated data of the predetermined region on a yz plane (Par. [0053] The surface discovery module 312 rotates (e.g., 90° counter-clockwise, 45° clockwise, etc.) the 3-dimensional image data around an axis (e.g., z-axis, y-axis, etc.) in 3-dimensional space of the 3-dimensional image data. Par. [0031] The structure discovery system 110 rotates a point cloud clockwise through angle θ around a vertical z-axis 210 C. Par [0015] The rotating step (b) further includes (b-1) projecting isometrically the 3-dimensional image data onto an observation plane. Par. [0061] In some examples, the surface discovery module 312 projects (425) isometrically the 3-dimensional image data onto an observation plane (e.g., viewing plane from image acquisition system, viewing plane from camera, etc.). In other examples, the surface discovery module 312 selects (423) the axis based on a plane of interest. Par. [0036] the structure discovery system 110 projects the planar condensations, for a given structure, onto the x-z plane and the y-z plane), and specifying the data of the predetermined region as the second point cloud data representing the cable in a case where the data of the predetermined region projected on the yz plane is circular (Par. [0026] The technology, generally, rotates the 3-dimensional image data around an axis (e.g., x-axis, y-axis, z-axis, or any in between axis) to align points of a structure (e.g., plane, cylinder, sphere, etc.) for extraction of information from the 3-dimensional image data (e.g., linear condensations, structures, etc.). The rotation of the 3-dimensional image data enables projection of the structure onto a coordinate plane to form linear condensations of the structure (e.g., structure becomes a straight line, structure becomes a circle, etc.). The extracted information can be used to identify structures and/or structural features from the point cloud. Par. [0053] The linear condensation identification module 313 identifies (430) linear condensations (e.g., lines, circles, etc.) in the rotated 3-dimensional image data).
References ZHANG, HWANG, van Cruyningen and Richards are considered to be analogous art because they relate to analysis of point cloud data. Therefore, it would have been obvious that one of ordinary skill in the art, before the effective filing date of the claimed invention, would recognize the advantage of further specifying rotating and projecting point cloud data as suggested by Richards in the inventions of ZHANG, HWANG and van Cruyningen in order to increase the efficiency of discovering and correctly identifying structures (see Richards, Par. [0018]).
Regarding Claim 5, ZHANG in combination with HWANG and van Cruyningen teaches Claim 2. ZHANG in combination with HWANG and van Cruyningen does not specifically teach projecting the point cloud data. Therefore, ZHANG in combination with HWANG and van Cruyningen fails to explicitly teach projecting data of a predetermined region included in the three-dimensional point cloud data on an xy plane, and specifying the data of the predetermined region as the second point cloud data representing the wall surface in a case where the data of the predetermined region projected on the xy plane has a linear shape.
However, Richards teaches projecting data of a predetermined region included in the three-dimensional point cloud data on an xy plane (Par. [0026], The extracted information can be used to identify structures and/or structural features from the point cloud. Par. [0027] The structure discovery system 110 processes the image data to generate one or more structures (e.g., building, tank, wall, etc.). Par. [0028] Although FIG. 1 illustrates the plurality of buildings 132, 134, and 136, the structure discovery system 110 can be utilized to discovery any type of structure (e.g., wall, dome, vehicle, etc.). Par. [0029] FIG. 2A illustrates a bird's eye view (i.e., vertical towards nadir and the x-y plane) view of an urban scene with several buildings 220 as observed by the cameras 210. Par.[0034] The structure discovery system 110 can identify where each subcloud will project onto the X and y axes as the point cloud 200 d is rotated about the origin of the specified Cartesian coordinate system since the structure discovery system 110 has depth measurements of the structures (e.g., input by a user, determined by an image device, etc.) and/or the structure discovery system 110 controls the rotation of the point cloud 200 d. Par. [0030] The rotated point cloud 200 b enables the structure discovery system 110 to extract the facades (e.g., rotated façade 242 b) as projected onto the x-axis and the y-axis), and specifying the data of the predetermined region as the second point cloud data representing the wall surface in a case where the data of the predetermined region projected on the xy plane has a linear shape (Par. [0053] The structure generation module 314 generates a structure (e.g., a building in a wireframe, a tank in a wireframe, etc.) based on the linear condensations and the rotated 3-dimensional image data. The generated structure can be utilized to identify structural elements within a scene associated with the 3-dimensional image data (e.g., streets and buildings within a city, doorways on a building, etc.). Par. [0060] The linear condensation identification module 313 identifies (430) linear condensations (e.g., lines, circles, etc.) in the rotated 3-dimensional image data. The structure generation module 314 generates (440) a structure (e.g., building, car, etc.) based on the linear condensations and the rotated 3-dimensional image data. Par. [0030] The rotated point cloud 200 b enables the structure discovery system 110 to extract the facades (e.g., rotated façade 242 b) as projected onto the x-axis and the y-axis. Par. [0027] The structure discovery system 110 processes the image data to generate one or more structures (e.g., building, tank, wall, etc.)).
References ZHANG, HWANG, van Cruyningen and Richards are considered to be analogous art because they relate to analysis of point cloud data. Therefore, it would have been obvious that one of ordinary skill in the art, before the effective filing date of the claimed invention, would recognize the advantage of further specifying projecting point cloud data as suggested by Richards in the inventions of ZHANG, HWANG and van Cruyningen in order to increase the efficiency of discovering and correctly identifying structures (see Richards, Par. [0018]).
Method claim 15 is drawn to the method of using the corresponding apparatus claimed in claim 4. Therefore method claim 15 corresponds to apparatus claim 4 and is rejected for the same reasons of obviousness as used above.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dong-liang ZHANG (CN 114299443 A), in view of HWANG et al. (US 2015/0371110 A1), and in further view of GOTO et al. (US 2021/0025696 A1) referred to as GOTO hereinafter.
Regarding Claim 6, ZHANG in view of HWANG teaches Claim 1. ZHANG further teaches setting the area around in which a distance is a preset distance as the first detection area (Page 4, step 5: comparing the point cloud data of the moving object with the precision cloud point cloud of the pole tower, calculating the distance from each point of the pole tower point cloud to the moving object point cloud, calculating the minimum distance between the moving object and the pole tower. Step 6: Feeding back the type and distance information of the mobile object to the monitoring system. If the type of the mobile object is easy to cause damage of the object, and the distance between it and the tower is less than a certain degree (i.e. preset distance), then sending the alarm information).
While ZHANG teaches calculating the minimum distance between the moving object and the pole tower, ZHANG in view of HWANG does not specifically teach the central axis of the pole tower. Therefore, ZHANG in view of HWANG fails to explicitly teach wherein the setting further comprises detecting at least two circles from the first point cloud data and calculating a central axis of the first point cloud data by connecting centers of the at least two circles.
However, GOTO teaches wherein the setting further comprises: detecting at least two circles from the first point cloud data (Par. [0053] the detection device acquires three-dimensional point cloud data representing three-dimensional coordinates of points on the surfaces of columnar objects. Par. [0060] The 3D model extracting unit 13a creates 3D columnar model data acquired by performing 3D modeling of columnar objects from the point cloud data. When the columnar object is a cylindrical object, the acquisition unit 13b may acquire an array of radii (i.e. at least two circles) for every predetermined height of the cylindrical object from the 3D columnar model data), calculating a central axis of the first point cloud data by connecting centers of the at least two circles (Par. [0063] The acquisition unit 13b acquires central axis data as an array of central axis coordinates illustrated in FIG. 4. The central axis data is data defining a central axis (C in FIG. 4) having a radius r as an array of coordinate values (x, y, z) of a center point for each of predetermined heights of the columnar model for a 3D columnar model).
References ZHANG, HWANG and GOTO are considered to be analogous art because they relate to analysis of point cloud data. Therefore, it would have been obvious that one of ordinary skill in the art, before the effective filing date of the claimed invention, would recognize the advantage of further specifying a central axis of the pole as suggested by GOTO in the inventions of ZHANG and HWANG in order to easily acquiring a state of a columnar object with high accuracy without using new point cloud data or image data (see GOTO, Par. [0030]).
Conclusion
Any inquiry concerning this communication should be directed to SUSAN E HODGES whose telephone number is (571)270-0498. The Examiner can normally be reached on Monday - Friday from 8:00 am (EST) to 4:00 pm (EST).
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor, Brian T. Pendleton, can be reached on (571) . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Susan E. Hodges/Primary Examiner, Art Unit 2425
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