DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 05/20/26 have been fully considered but they are not persuasive.
Regarding claim 1, Applicant states that the reference of Segev fails to teach of set of image segments comprising a reference object segment and a building feature segment (Applicants Remarks pages 6-7). Examiner disagrees with Applicant. Applicant states that Segev segmentation is limited to rooms on a floorplan (Applicants Remarks page 6), the floorplan is that of a building, disclosing “present disclosure relates generally to systems and methods for selecting and placing equipment for use in buildings. Disclosed systems and methods may involve automatically simulating equipment selection and placement locations in a floor plan” (paragraph 0002). The segmented walls and room contours of the floorplan are read as building feature segments, since the floorplan is that of a building.
Applicant states that the semantic designations that are over laid does not read on set of image segments comprising a reference object segment and a building feature segment (Applicants Remarks page 7). Examiner disagrees with Applicant. Segev teaches the semantic designation 2810 is overlaid onto the features of a segmented room of the floorplan (paragraph 0786). The semantic designation is a label, which can be read as an image, since it is overlaid onto another image, the image being the room feature, also see fig 28B in which the semantic designation are image labels that are overlaid onto an image, not typed, thereby making it an image and thereby reading on set of image segments comprising a reference object segment and a building feature segment, the reference object segment read as the semantic designation and the building feature segment read as the segmented rooms of the floorplan
Applicant states that the motivation to combine Zhang, Chen and Segev is not sufficient (Applicants Remarks pages 6-7). Examiner disagrees with Applicant. First, Examiner inadvertently wrote Thomas reference when in it should have recited Zheng. Secondly, Zheng captures images of building features (column 2, lines 49-67) by Zheng with the machine learning model of Segev, the images captured by Zheng can be segmented by architectural features and labeled and overlaid on to a floor plan. This is an efficient and cheaper because a user would not have to do this by hand, nor would an outside party or company have to be used and compensated to provide this service.
Regarding claim 2, Applicant states that the reference of Carter fails to teach of a database of objects, obtaining objects from such database and selecting objects that fit within a width of a building feature based on an estimated measurement (Applicants Remarks pages 7-8). Examiner agrees with Applicant.
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.
Claim(s) 1, 5, is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al US 8705893 in view of Chen et al US 20160314370 further in view Segev et al 20210073449.
Regarding claim 1, Zhang et al teaches a method, comprising:
receiving, by server, the image including a building feature and a reference object (Camera (user device) captures images of building features (walls, doors, etc.) as user moves through a building (column 2, lines 49-67). Note: Images are received by a processor/computer for analysis;
Zhang et al fails to teach identifying, by the server the reference object in the image as captured by the user device to retrieve a known measurement of the reference object;
estimating, by the server, a measurement of the building feature based on the building feature segment and in accordance with a relationship between the known measurement of the reference object and the reference object segment;
outputting, from the server to the user device, the estimated measurement of the building feature;
Chen et al teaches identifying, by the server the reference object in the image as captured by the user device to retrieve a known measurement of the reference object (The image server 104 may receive one or more images from the camera 108 or from the image database 106. The image may include at least a first object, e.g. a common object and a second object, e.g. the object of interest, such as a building or other structure. A common object may be an object with identifiable characteristics which is likely to appear in images captured by a user and which has been associated with a predetermined measurement assumption or predetermined measurement assumption range (paragraph 0032);
estimating, by the server, a measurement of the building feature based on the building feature segment and in accordance with a relationship between the known measurement of the reference object and the reference object segment (Measurements of objects of interest in an image may be determined based on measurement assumptions of common objects which appear in the same image. The estimates or assumptions of the measurements of common objects in the image may be used to determine the measurements of objects of interest. A UE may utilize the measurement assumptions of the common objects in the image to simultaneously calibrate camera parameters and measure object dimensions (paragraph 0028, 0040 and 0041).;
outputting, from the server to the user device, the estimated measurement of the building feature (the UE 102 or image server 104 may cause a display of the image on a user interface (paragraph 0042);
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Zhang et al’s server to include: identifying, by the server the reference object in the image as captured by the user device to retrieve a known measurement of the reference object;
estimating, by the server, a measurement of the building feature based on the building feature segment and in accordance with a relationship between the known measurement of the reference object and the reference object segment;
outputting, from the server to the user device, the estimated measurement of the building feature.
The reason of doing so would have created an efficient and accurate way of identifying measuring building features/reference objects of Zhang et al.
Zhang et al in view of Chen et al fails to teach segmenting, by the server, the image to form a segmented image using an image segmentation machine learning model, the segmented image comprising a set of image segments overlaid on the image as captured, the set of image segments comprising a reference object segment and a building feature segment;
Segev et al 20210073449 teaches segmenting, by the server, the image to form a segmented image using an image segmentation machine learning model (Machine learning may refer to artificial intelligence or machine learning models or algorithms as described herein. using artificial intelligence to segment walls and rooms from images of floor plans (paragraph 0669). a Unet or Mask RCNN model may be used to segment walls from the images of 2D floorplan (paragraph 0670). Identifying wall boundaries may include performing one of a variety of types of analysis on the floor plan to extract room features such as doors, windows, walls, as wall length, area and many other possible features. The extraction of these features may be based on lines within the floor plan (paragraph 0671), the segmented image comprising a set of image segments overlaid on the image as captured, the set of image segments comprising a reference object segment and a building feature segment (architectural features within region 2802, such as chair 2812, window 2814, door 2816, and area 2818. Based on these and other architectural features, the disclosed method may determine semantic designation 2810, which may specify the chairman's office as “Office 1.” Semantic designation 2810, as well as other semantic designations for rooms in region 2802 may be associated with floor plan 2800, as shown. These might be overlaid on an image of the floor plan itself, be constructed as a separate image layer (paragraph 0786) Note: the architectural features, which read on building features, are images that are overlaid onto an image of the floor plan 2800;
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Zhang et al in view of Chen et al’s server to include: estimating a measurement of the building feature based on the building feature segment and in accordance with a relationship between the known measurement of the reference object and the reference object segment.
The reason of doing so would have created an efficient and cheaper way of measuring building features/reference objects of Zheng.
Regarding claim 5, Zhang et al teaches estimating a dimension of the reference object from one or more survey responses (the camera 14 and/or system 10 is moved about an interior floor of a building or other like structure being studied. For example, a user wearing the backpack 12 walks or strolls at a normal pace (e.g., approximately 0.5 meters (m) per second (s)) down the hallways and/or corridors and/or through the various rooms of a given floor of the building. (column 6, lines 14-23)
Claim(s) 3, 4, 7, is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al US 8705893 in view of Chen et al US 20160314370 further in view Segev et al 20210073449 and further in view Cornelison et al US 11257132.
Regarding claim 3, Zhang et al in view of Chen et al further in view Segev et al does not teach retrieving a standard size of the reference object and estimating the measurement of the building feature based on the standard size of the reference object.
Cornelison et al teaches retrieving a standard size of the reference object (the standardized reference objects may be stored in a database along with their standard sizes. For example, the database may indicate that a standard size for an outlet or outlet plate or cover is 2.75″×4.5.″ (column 12, lines 63-67); and estimating the measurement of the building feature based on the standard size of the reference object (column 2, lines 30-35, standardized reference object may be used to identified a size of another object).
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Zhang et al in view of Chen et al further in view Segev et al to include: retrieving a standard size of the reference object and estimating the measurement of the building feature based on the standard size of the reference object.
The reason of doing so would have created an efficient and cheaper way of measuring building features/reference objects of Thomas.
Regarding claim 4, Zhang et al in view of Chen et al further in view Segev et al further in view of Cornelison et al teaches wherein the building feature and the reference object are on a same wall (Cornelison: If the image analysis and device control system 230 determines that the room is a front hallway, the plurality of standardized reference objects may be, for example, a key hole, a door handle, a door frame, a deadbolt, a door hinge, a stair, a railing, and the like. based on the room indication output determined at step 315, a plurality of standardized reference objects associated with the room. (column 12, lines 38-60). Note: the objects of reference can be found on the same wall of a room since they are all objects associated with a door, which is on one wall of a room
Regarding claim 7, Zhang et al in view of Chen et al further in view Segev et al does not teach determining, based on the estimated measurement, a standard-sized product and outputting an indication of the standard-sized product with the estimate measurement.
Cornelison et al teaches determining, based on the estimated measurement, a standard-sized product and outputting an indication of the standard-sized product with the estimate measurement (column 2, lines 30-35, an know dimension associated with the standardized reference object may be used to identified a size of another object; note: obviously if the size of the identified object is known, the identified object can be used as a reference object).
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Zhang et al in view of Chen et al further in view Segev et al to include: determining, based on the estimated measurement, a standard-sized product and outputting an indication of the standard-sized product with the estimate measurement.
The reason of doing so would have created an efficient and cheaper way of measuring building features/reference objects of Thomas.
Claim(s) 6, is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al US 8705893 in view of Chen et al US 20160314370 further in view Segev et al 20210073449 and further in view of Chen (US 2003/0147553).
Regarding claim 6: Zhang et al in view of Chen et al further in view Segev et al does not teach wherein the building feature in the image is partially occluded.
Chen teaches wherein the building feature in the image is partially occluded. (paragraph 0019, 0026)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Zhang et al in view of Chen et al further in view Segev et al to include: wherein the building feature in the image is partially occluded.
The reason of doing so would have allowed all features of building to be processed and more valuable data to users would be determined.
Allowable Subject Matter
Claims 8-14 and 21-26 are allowed.
Regarding claim 8, A server device, comprising:
a processor and a non-transitory, computer-readable memory storing instructions;
wherein the processor executes the instructions to:
receive an image as captured by a user device of a building feature;
locate and segment a reference object in the image as captured by the user device to form a first segmented image using an image segmentation machine learning model, the first segmented image comprising a first set of segments overlaid on the image as captured;
locate and segment the building feature in the image as captured by the user device to form a second segmented image using the image segmentation machine learning model, the second segmented image comprising a second set of segments overlaid on the image as captured;
estimate a measurement of the building feature based on the second segmented image and in accordance with a relationship between a known measurement of the reference object and the first segmented image; and
output the estimated measurement of the building feature to the user device.
The following is an Examiner’s statement of reasons for allowance:
Zhang et al US 8705893 discloses A system which generates a floor plan of a building includes: a camera which obtains a series of images as it is moved, each image represented by a first data set representing color and a second 3D data set representing depth. A processor generates the floor plan from the image data, defined by polylines that represent structures of the building and polygons that represent an area which has been observed by the camera. Suitably, the processor: performs a local matching sub-process which is operative to align two adjacent images with one another; performs a global matching sub-process in which key images and registered to one another; finds a 2D subset of points from the image data associated with each image, corresponding to a plane defined therethrough; determines the polylines based on said subset of points; and defines the polygons based on the polylines and a determined pose of the camera (abstract)
Chen et al US 20160314370 discloses A method, apparatus and computer program product are provided for determination of object measurements based on measurement assumption of one or more common objects in an image. A method is provided including receiving an input image comprising at least a first object and a second object, detecting at least the first and second objects, constructing one or more measurement assumptions based on the first object, and determining, using a processor, one or more measurements of the second object based on the measurement assumptions (abstract)
Segev et al 20210073449 disclose Systems and methods for selecting equipment for use in buildings are disclosed. The system may include at least one processor configured to perform operations that include accessing a floor plan demarcating a plurality of rooms. The operations further include accessing functional requirements associated with the rooms and accessing technical specifications associated with the functional requirements. The operations include performing floor plan analysis on the floor plan to ascertain room features associated with the functional requirements and technical specifications. The operations include generatively analyzing the room features to determine a customized equipment configuration for at least some of the rooms, and generating a manufacturer dataset including a room identifier, an equipment identifier, and the customized equipment configuration (abstract)
However, Zhang et al, Chen et al, Segev et al or no prior art cited alone or in combination provides the motivation to teach locate and segment the building feature in the image as captured by the user device to form a second segmented image using the image segmentation machine learning model, the second segmented image comprising a second set of segments overlaid on the image as captured;
estimate a measurement of the building feature based on the second segmented image and in accordance with a relationship between a known measurement of the reference object and the first segmented image; and
output the estimated measurement of the building feature to the user device.
It is inherent that claims 9-14 are allowed for depending on allowable independent claim 8
Regarding claim 21, A server-implemented method comprising:
receiving, by a server, an image as captured by a user device of a building feature;
locating and segmenting, by the server, a reference object in the image as captured by the user device to form a first segmented image using an image segmentation machine learning model, the first segmented image comprising a first set of segments overlaid on the image as captured;
locating and segmenting, by the server, the building feature in the image as captured by the user device to form a second segmented image using the image segmentation machine learning model, the second segmented image comprising a second set of segments overlaid on the image as captured;
estimating, by the server, a measurement of the building feature based on the second segmented image and in accordance with a relationship between a known measurement of the reference object and the first segmented image; and
outputting, by the server, the estimated measurement of the building feature to the user device.
The following is an Examiner’s statement of reasons for allowance:
Zhang et al US 8705893 discloses A system which generates a floor plan of a building includes: a camera which obtains a series of images as it is moved, each image represented by a first data set representing color and a second 3D data set representing depth. A processor generates the floor plan from the image data, defined by polylines that represent structures of the building and polygons that represent an area which has been observed by the camera. Suitably, the processor: performs a local matching sub-process which is operative to align two adjacent images with one another; performs a global matching sub-process in which key images and registered to one another; finds a 2D subset of points from the image data associated with each image, corresponding to a plane defined therethrough; determines the polylines based on said subset of points; and defines the polygons based on the polylines and a determined pose of the camera (abstract)
Chen et al US 20160314370 discloses A method, apparatus and computer program product are provided for determination of object measurements based on measurement assumption of one or more common objects in an image. A method is provided including receiving an input image comprising at least a first object and a second object, detecting at least the first and second objects, constructing one or more measurement assumptions based on the first object, and determining, using a processor, one or more measurements of the second object based on the measurement assumptions (abstract)
Segev et al 20210073449 disclose Systems and methods for selecting equipment for use in buildings are disclosed. The system may include at least one processor configured to perform operations that include accessing a floor plan demarcating a plurality of rooms. The operations further include accessing functional requirements associated with the rooms and accessing technical specifications associated with the functional requirements. The operations include performing floor plan analysis on the floor plan to ascertain room features associated with the functional requirements and technical specifications. The operations include generatively analyzing the room features to determine a customized equipment configuration for at least some of the rooms, and generating a manufacturer dataset including a room identifier, an equipment identifier, and the customized equipment configuration (abstract)
However, Zhang et al, Chen et al, Segev et al or no prior art cited alone or in combination provides the motivation to teach locating and segmenting, by the server, the building feature in the image as captured by the user device to form a second segmented image using the image segmentation machine learning model, the second segmented image comprising a second set of segments overlaid on the image as captured;
estimating, by the server, a measurement of the building feature based on the second segmented image and in accordance with a relationship between a known measurement of the reference object and the first segmented image; and
outputting, by the server, the estimated measurement of the building feature to the user device.
It is inherent that claims 22-26 are allowed for depending on allowable independent claim 21
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Michael Burleson
Patent Examiner
Art Unit 2681
Michael Burleson
July 21, 2026
/MICHAEL BURLESON/
/AKWASI M SARPONG/SPE, Art Unit 2681 7/27/26
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