DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/03/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-4, 8-11, 16-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 8-11, 16-18 respectively of U.S. Patent No. 11734882. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 is an obvious variant and broader recitation of claim 1 of U.S. Patent No. 11734882.
Current Application 18/754477
U.S. Patent No. 11734882
Claim 1. A method comprising:
receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through a portion of a building;
identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building; and
modifying an interface to include a difference between an identified object and an object expected at a location within the portion of the building, the identified object and the expected object having a same object type.
Claim 1. (Original) A method comprising: accessing a model of a portion of a building, the model indicating locations of one or more expected objects within the portion of the building; receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through the portion of the building;
identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building; determining, for one or more locations within the portion of the building, a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames; and
modifying an interface displayed to a user to present, at each of the one or more locations within the portion of the building, the determined count difference.
Claim 8. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, cause the hardware process to perform steps comprising: receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through a portion of a building; identifying one or more objects in the plurality of image at one or more locations within the portion of the building; and modifying an interface to include a difference between an identified object and an object expected at a location within the portion of the building, the identified object and the expected object having a same object type.
Claim 8. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, cause the hardware process to perform steps comprising: accessing a model of a portion of a building, the mode indicating locations of one or more expected objects within the portion of the building; receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through the portion of the building; identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building; determining, for one or more locations within the portion of the building, a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames; and modifying an interface displayed to a user to present, at each of the one or more locations within the portion of the building, the determined count difference.
Claim 15. A system comprising: a hardware processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising: receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through a portion of a building; identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building; and modifying an interface to include a difference between an identified object and an object expected at a location within the portion of the building, the identified object and the expected object having a same object type.
Claim 15. A system comprising: a hardware processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising: accessing a model of a portion of a building, the mode indicating locations of one or more expected objects within the portion of the building; receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through the portion of the building; identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building; determining, for one or more locations within the portion of the building, a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames; and modifying an interface displayed to a user to present, at each of the one or more locations within the portion of the building, the determined count difference.
2. The method of claim 1, further comprising: accessing a model of the portion of the building, the model indicating locations of one or more expected objects within the portion of the building, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
2. (Original) The method of claim 1, wherein the 3D model is generated based on 3D information captured by a lidar system.
3. The method of claim 2, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
3. The method of claim 1, wherein each of the objects is associated with an object type, and wherein the determining a count difference at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
4. The method of claim 1, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
4. The method of claim 1, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine a location for each object.
9. The non-transitory computer-readable storage medium of claim 8, wherein the steps further comprise: accessing a model of the portion of the building, the model indicating locations of one or more expected objects within the portion of the building, the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
9. The non-transitory computer-readable storage medium of claim 8, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
10. The non-transitory computer-readable storage medium of claim 9, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
10. The non-transitory computer-readable storage medium of claim 8, wherein each of the objects is associated with an object type, and wherein the determining a count difference at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
11. The non-transitory computer-readable storage medium of claim 8, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
11. The non-transitory computer-readable storage medium of claim 8, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine a location for each object.
16. The system of claim 15, wherein the steps further comprise: accessing a model of the portion of the building, the model indicating locations of one or more expected objects within the portion of the building, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
16. The system of claim 15, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
17. The system of claim 16, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames
17. The system of claim 15, wherein each of the objects is associated with an object type, and wherein the determining a count difference at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
18. The system of claim 15, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
18. The system of claim 15, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine a location for each object.
Claims 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11436812. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 is an obvious variant and broader recitation of claim 1 of U.S. Patent No. 11436812.
Current Application
US Patent 11436812 B2
Claim 1. A method comprising:
receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through a portion of a building;
identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building; and
modifying an interface to include a difference between an identified object and an object expected at a location within the portion of the building, the identified object and the expected object having a same object type.
Claim 1. A method comprising: accessing a floorplan of a portion of a building, the floorplan identifying locations of one or more expected objects within the portion of the building, wherein the one or more expected objects are associated with one or more object types; determining, for each object type, one or more expected objects of the object type in the portion of the building based on the accessed floorplan; receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through the portion of the building;
identifying one or more objects in the plurality of image frames, each of the one or more objects associated with an object type and a location within the portion of the building where the object is disposed; generating a 3-dimensional (3D) model of the portion of the building from the plurality of image frames; modifying, for each of the one or more identified objects, a region of the 3D model to include the identified object, the region corresponding to the location within the portion of the building where the identified object is disposed; determining, for each of the one or more identified objects, a probability of the identified object being located at the location within the portion of the building based on the modified 3D model and the accessed floorplan; determining, for each object type, a count difference between a number of the one or more expected objects and a number of one or more identified objects associated with a probability greater than a predetermined threshold; and modifying an interface displayed to a user to present, for each object type, the determined count difference.
Claims 1-3, 4, 8-10,11, 15-17, 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 7, 8-10, 14, 15-17, 20 respectively of U.S. Patent No. 12045936 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 is an obvious variant and broader recitation of claim 1 of U.S. Patent No. 12045936.
Current Application
US Patent 12045936 B2
Claim 1. A method comprising:
receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through a portion of a building; identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building; and
modifying an interface to include a difference between an identified object and an object expected at a location within the portion of the building, the identified object and the expected object having a same object type.
Claim 1. A method comprising: accessing a model of a portion of a building, the model indicating locations of one or more expected objects within the portion of the building; identifying one or more objects in the plurality of image frames from a video captured by a camera system as the camera system is moved through the portion of the building, the identified one or more objects at one or more locations within the portion of the building, wherein each expected object and each identified object are associated with an object type; and modifying an interface to include, for each object type, a count difference between a number of the one or more expected objects of the object type and a number of the identified one or more objects of the object type identified in the plurality of image frames.
Claim 8. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, cause the hardware process to perform steps comprising: receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through a portion of a building; identifying one or more objects in the plurality of image at one or more locations within the portion of the building; and modifying an interface to include a difference between an identified object and an object expected at a location within the portion of the building, the identified object and the expected object having a same object type.
Claim 8. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, cause the hardware process to perform steps comprising: accessing a model of a portion of a building, the model indicating locations of one or more expected objects within the portion of the building; identifying one or more objects in the plurality of image frames from a video captured by a camera system as the camera system is moved through the portion of the building, the identified one or more objects at one or more locations within the portion of the building, wherein each expected object and each identified object are associated with an object type; and modifying an interface to include, for each object type, a count difference between a number of the one or more expected objects of the object type and a number of the identified one or more objects of the object type identified in the plurality of image frames.
Claim 15. A system comprising: a hardware processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising: receiving, from a camera system, a video comprising a plurality of image frames captured as the camera system is moved through a portion of a building; identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building; and modifying an interface to include a difference between an identified object and an object expected at a location within the portion of the building, the identified object and the expected object having a same object type.
Claim 15. A system comprising: a hardware processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising: accessing a model of a portion of a building, the model indicating locations of one or more expected objects within the portion of the building; identifying one or more objects in the plurality of image frames from a video captured by a camera system as the camera system is moved through the portion of the building, the identified one or more objects at one or more locations within the portion of the building, wherein each expected object and each identified object are associated with an object type; and modifying an interface to include, for each object type, a count difference between a number of the one or more expected objects of the object type and a number of the identified one or more objects of the object type identified in the plurality of image frames.
2. The method of claim 1, further comprising: accessing a model of the portion of the building, the model indicating locations of one or more expected objects within the portion of the building, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
2. The method of claim 1, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
3. The method of claim 2, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
3. The method of claim 1, wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, the count difference between the number of the one or more expected objects of the object type from the accessed model and the number of the one or more objects of the object type identified in the plurality of image frames.
4. The method of claim 1, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
7. The method of claim 1, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine a location for each object.
9. The non-transitory computer-readable storage medium of claim 8, wherein the steps further comprise: accessing a model of the portion of the building, the model indicating locations of one or more expected objects within the portion of the building, the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
9. The non-transitory computer-readable storage medium of claim 8, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
10. The non-transitory computer-readable storage medium of claim 9, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
10. The non-transitory computer-readable storage medium of claim 8, wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, the count difference between the number of the one or more expected objects of the object type from the accessed model and the number of the one or more objects of the object type identified in the plurality of image frames.
11. The non-transitory computer-readable storage medium of claim 8, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
14. The non-transitory computer-readable storage medium of claim 8, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine a location for each object.
16. The system of claim 15, wherein the steps further comprise: accessing a model of the portion of the building, the model indicating locations of one or more expected objects within the portion of the building, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
16. The system of claim 15, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model.
17. The system of claim 16, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames
17. The system of claim 15, wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, the count difference between the number of the one or more expected objects of the object type from the accessed model and the number of the one or more objects of the object type identified in the plurality of image frames.
18. The system of claim 15, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
20. The system of claim 15, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine a location for each object.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 8, 9, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fleischman et al. (US 20190005719 A1) hereinafter Fleischman, in view of Stenger et al. (US 2020/0074668 A1) hereinafter referred to as Stenger.
Claim 1. A method comprising:
receiving, from a camera system (FLEISCHMAN receives a sequence of images from an image capture (camera) system, para [0067] & claim 1), a video comprising a plurality of image frames captured as the camera system is moved through a portion of a building (FLEISCHMAN receives a sequence of images from an image capture (camera) system, the images in the sequence are captured as the image capture system is moved through an environment, e.g., a floor of a construction site, along a camera path, para [0067] & claim 1); and
FLEISCHMAN fails to disclose identifying one or more objects in the plurality of image frames at one or more locations within the portion of the building, modifying an interface to include a difference between an identified object and an object expected at a location within the portion of the building, the identified object and the expected object having a same object type.
Stenger discloses identifying one or more objects in the plurality of image frames (detecting objects and their locations in each of the panoramic images; detecting objects within the panoramic and floorplan images, Abstract & para [0184]), each of the one or more objects associated with an object type and a location within the portion of the building where the object is disposed (detecting objects and their locations in each of the panoramic images, acquiring a floorplan image, detecting objects and their locations in the floorplan image; detecting objects within the panoramic and floorplan images, Abstract & para [0184]); modifying an interface to include a difference between an identified object and an object (Stenger discloses determining, for each object type, a difference between the one or more expected objects and one or more identified objects associated with a probability greater than a predetermined threshold. In Stenger, the most likely object is used as the determined object; thus, if reference character 1406 has a 98.0% probability of being a door and a 2.0% probability of being a window, the computer system will interpret the object as being a door; a different threshold when determining objects; for example, if an object only had a 65% probability of being a particular object, the object may be ignored, or further analysis of the object could be performed to increase the likelihood of correctly identifying the object, para [0077]) expected at a location within the portion of the building, the identified object and the expected object having a same object type (Stenger, objects can also be detected in each individual panoramic image; panoramic image can be cropped or otherwise edited (modified) so that a standard field of view is shown in the virtual tour instead of the wide field of view of the panoramic image; digital image allows for computer editing of the panoramic image; panoramic image can be cropped or otherwise adjusted so that a smaller and more natural field of view is shown to the user; view at specified location, para [0010], [0011], [0048], [0175], [0176]).
It would have been obvious to one ordinary skilled in the art before the filing of the claimed invention to combine the teachings of Fleischman with the teachings of Stenger since they are both analogous in spatial location identifying related field.
One ordinary skilled in the art before the filing of the claimed invention would have been motivated to combine the teachings of Fleischman with the teachings of Stenger in order to increase likelihood of correctly identifying the object.Claims 8 and 15 essentially recite the same limitations as claim 1. Therefore, the rejection of claim 1 is applied to claims 8 and 15.Claims 2, 9 and 16. The method of claim 1, further comprising: accessing a model of the portion of the building, the model indicating locations of one or more expected objects within the portion of the building, wherein the accessed model comprises one or more of: a 2D model, a floor plan, a 3D model, a point cloud, a lidar-generated model, and a SLAM model (Fleischman, [0002], [0005] [0038] floorplan, SLAM). Same rationale as claim 1
Allowable Subject Matter
Claims 3-7, 10-14 and 17-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 3, no prior art teaches alone or in combination the features “The method of claim 2, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
Claim 4, no prior art teaches alone or in combination the features “The method of claim 1, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
Claims 5-7 depend on allowable claim 3.
Claims 10, no prior art teaches alone or in combination the features “The non-transitory computer-readable storage medium of claim 9, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
Claim 11, no prior art teaches alone or in combination the features “The non-transitory computer-readable storage medium of claim 8, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
Claims 12-14 depend on allowable claim 10 and are therefore allowable for the same reasons as claim 10.
Claim 17, no prior art teaches alone or in combination the features “The system of claim 16, wherein each of the objects is associated with the object type, and wherein determining a count difference between a number of the one or more expected objects from the accessed model and a number of one or more objects identified in the plurality of image frames at a location comprises determining, for each object type, a count difference between a number of the one or more expected objects of the object type from the accessed model and a number of one or more objects of the object type identified in the plurality of image frames.
Claim 18, no prior art teaches alone or in combination the features “The system of claim 15, wherein identifying the one or more objects in the plurality of image frames comprises, for each image frame, applying a machine learning model to the image frame, the machine learning model configured to classify pixels in the image frame as one or more objects and to determine the location for each object.
Claims 19 and 20 depend on allowable claim 17 and are therefore allowable for the same reasons as claim 17.
Relevant prior art:US 20190243928 A1 a computer-implemented method for determining a function configured to determine a semantic segmentation of a 2D floor plan representing a layout of a building. The method comprises providing a dataset comprising 2D floor plans each associated to a respective semantic segmentation. The method also comprises learning the function based on the dataset. Such a method provides an improved solution for processing a 2D floor plan.
Conclusion
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/MARTIN MUSHAMBO/Primary Examiner, Art Unit 2615 03/07/2026