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 .
Claim Objections
Claims 2-14, 16-25 are objected to because of the following informalities:
Dependent claims 2-14, and 16-25, refer back to the base claim(s) using indefinite article (e.g. “A system according to claim 1” or, “A method according to claim 15”) rather than using a definite article (e.g. “The system according to claim 1”). In order to maintain full conformity with 35 USC § 112 (d), it is suggested that dependent claims *, refer back to the respective base claims using a definite article.
Claim 15 is objected to because of the following informalities:
Preamble “A method scanning…”, should be recited like “A method for scanning…”
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 5-6, 8-16, 19-20, 22-25 is/are rejected under 35 U.S.C. 102(a)(1) and/or 102(a)(2) as being anticipated by Zhang et al. (US 20190346271 A1, hereinafter Zhang).
Regarding claim 1, Zhang discloses a system for scanning a structural object (abstract, ¶0013-0014, ¶0054, fig. 1), comprising:
a mobile scanning device configured to scan the structural object from a plurality of successive locations to generate for each location a respective point cloud representing a respective portion of the structural object (In one general aspect, the present invention is directed to a mobile, computer-based mapping system that estimates changes in position over time (an odometer) and/or generates a three-dimensional map representation, such as a point cloud, of a three-dimensional space. The mapping system may include, without limitation, a plurality of sensors including an inertial measurement unit (IMU), a camera, and/or a 3D laser scanner. It also may comprise a computer system, having at least one processor, in communication with the plurality of sensors, configured to process the outputs from the sensors in order to estimate the change in position of the system over time and/or generate the map representation of the surrounding environment, ¶0054);
a calculating unit configured to:
(i) determine from the point clouds a pose graph comprising estimates of positions of the successive locations in relation to the structural object (There are several potential applications for the sensor, such as 3D modeling, scene mapping, and environment reasoning. The mapping system can provide point cloud maps for other algorithms that take point clouds as input for further processing. Further, the mapping system can work both indoors and outdoors. Such embodiments do not require external lighting and can operate in darkness. Embodiments that have a camera can handle rapid motion, and can colorize laser point clouds with images from the camera, although external lighting may be required. The SLAM system can build and maintain a point cloud in real time as a user is moving through an environment, such as when walking, biking, driving, flying, and combinations thereof. A map is constructed in real time as the mapper progresses through an environment. The SLAM system can track thousands of features as points. As the mapper moves, the points are tracked to allow estimation of motion. Thus, the SLAM system operates in real time and without dependence on external location technologies, such as GPS. In embodiments, a plurality (in most cases, a very large number) of features of an environment, such as objects, are used as points for triangulation, and the system performs and updates many location and orientation calculations in real time to maintain an accurate, current estimate of position and orientation as the SLAM system moves through an environment. In embodiments, relative motion of features within the environment can be used to differentiate fixed features (such as walls, doors, windows, furniture, fixtures and the like) from moving features (such as people, vehicles, and other moving items), so that the fixed features can be used for position and orientation calculations. Underwater SLAM systems may use blue-green lasers to reduce attenuation, ¶0057); and
(ii) calculate for each location a respective uncertainty value in the pose graph (In embodiments, a SLAM system may determine a level of confidence as to its current estimation of position, orientation, or the like. A level of confidence may be based on the density of points that are available in a scan, the orthogonality of points available in a scan, environmental geometries or other factors, or a combination thereof. The level of confidence may be ascribed to position and orientation estimates at each point along the route of a scan, so that segments of the scan can be referenced as low-confidence segments, high-confidence segments, or the like. Low-confidence segments can be highlighted for additional scanning, for use of other techniques (such as making adjustments based on external data), or the like, ¶0159);
and a display configured to display the point clouds and to provide a visual indication for each point cloud of the calculated uncertainty value of the corresponding location in the pose graph (The pixel selected for display may be the pixel that is closest to the center of the cube. A representative down-scaled display being generated during operation of the SLAM is shown below. As described, the decision to display a single pixel in a volume represents a binary result indicative of either the presence of one or more points in a point cloud occupying a spatial cube of defined dimensions or the absence of any such points. In other exemplary embodiments, the selected pixel may be attributed, such as with a value indicating the number of pixels inside the defined cube represented by the selected pixel. This attribute may be utilized when displaying the sub sampled point cloud such as by displaying each selected pixel utilizing color and/or intensity to reflect the value of the attribute, ¶0201).
Regarding claim 2, Zhang discloses the system according to claim 1 in which the calculating unit is configured to determine the pose graph by optimizing, with respect to the estimates of the positions of the successive locations, a maximum-a-posteriori loss function of the estimates of the positions of the successive locations (eqns. 1, 2, in ¶s 0082-0084, ¶0107).
Regarding claim 5, Zhang discloses a system according to claim 1, wherein the visual indication for each point cloud indicates whether the uncertainty value of the corresponding location is respectively above or below a predetermined acceptability threshold (¶0123).
Regarding claim 6, Zhang discloses a system according to claim 5 which is operative to receive a user selection of the acceptability threshold (¶0123).
Regarding claim 8, Zhang discloses a system according to claim 1, wherein the visual indication for each point cloud is a colour of displayed points of the displayed point cloud (¶0154-0156).
Regarding claim 9, Zhang discloses a system according to claim 1wherein the mobile scanning device comprises a Lidar sensor (¶0054, ¶0081, ¶0137).
Regarding claim 10, Zhang discloses a system according to claim 9 wherein the mobile scanning device further comprises an inertial measurement unit (IMU - ¶0054).
Regarding claim 11, Zhang discloses a system according to claim 1, further comprising an indication unit configured to indicate corrective action to be performed by a user to reduce the levels of uncertainty in the pose graph (Using these metrics enables automated testing to resolve model issues and offline model correction such as when utilizing a loop-closure tool as discussed elsewhere herein. Use of these metrics further enables alerting the user when matches are bad and possibly auto-pausing, throwing out low confidence data, and alerting the user when scanning. FIG. 19(a) illustrates a scan of a building floor performed at a relatively slow pace. FIG. 19(b) illustrates a scan of the same building floor performed at a relatively quicker pace. Note the prevalence of light fray when compared to the scan acquired from a slower scan pace arising, in part, from the speed at which the scan is conducted. FIG. 19(c) illustrates a display zoomed in on a potential trouble spot of relatively low confidence, ¶0181).
Regarding claim 12, Zhang discloses a system according to claim 11 wherein the corrective action comprises closing a loop in the pose graph (Using these metrics enables automated testing to resolve model issues and offline model correction such as when utilizing a loop-closure tool as discussed elsewhere herein. Use of these metrics further enables alerting the user when matches are bad and possibly auto-pausing, throwing out low confidence data, and alerting the user when scanning. FIG. 19(a) illustrates a scan of a building floor performed at a relatively slow pace. FIG. 19(b) illustrates a scan of the same building floor performed at a relatively quicker pace. Note the prevalence of light fray when compared to the scan acquired from a slower scan pace arising, in part, from the speed at which the scan is conducted. FIG. 19(c) illustrates a display zoomed in on a potential trouble spot of relatively low confidence, ¶0181).
Regarding claim 13, Zhang discloses a system according to claim 11 or claim 12, wherein the display displays an indication of the corrective action to be performed (Using these metrics enables automated testing to resolve model issues and offline model correction such as when utilizing a loop-closure tool as discussed elsewhere herein. Use of these metrics further enables alerting the user when matches are bad and possibly auto-pausing, throwing out low confidence data, and alerting the user when scanning. FIG. 19(a) illustrates a scan of a building floor performed at a relatively slow pace. FIG. 19(b) illustrates a scan of the same building floor performed at a relatively quicker pace. Note the prevalence of light fray when compared to the scan acquired from a slower scan pace arising, in part, from the speed at which the scan is conducted. FIG. 19(c) illustrates a display zoomed in on a potential trouble spot of relatively low confidence, ¶0181).
Regarding claim 14, Zhang discloses a system according to claim 1 in which the mobile scanning unit, calculating unit and display are provided within a common housing (figs. 17-18, ¶0176).
Regarding method claim(s) 15-16, 19-20, 22-25 although wording is different, the material is considered substantively equivalent to the system claim(s) 1-2, 5-6, 8, 11-13 as described above.
Allowable Subject Matter
Claims 3, 4, 7, 17, 18, and 21 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:
Prior arts of record taken alone or in combination fails to reasonably disclose or suggest,
Regarding claim 3, the optimization of the maximum-a-posteriori loss function comprises deriving for each location a respective covariance matrix, the uncertainty value for each location being based on a highest eigenvalue of the covariance matrix.
Claim 4 is allowable for being dependent on allowable claim 3.
Regarding claim 7, in which the user selection indicates a selection of a surveying standard, and the calculating unit is configured to determine the acceptability threshold based on a tolerance distance value associated with the selected standard.
Regarding claim 17, the optimization of the maximum-a-posteriori loss function comprises deriving for each location a respective covariance matrix, the uncertainty value for each location being based on a highest eigenvalue of the covariance matrix.
Claim 18 is allowable for being dependent on allowable claim 3.
Regarding claim 21, in which the user selection indicates a selection of a surveying standard, further comprising determining the acceptability threshold based on a tolerance distance value associated with the selected standard.
Conclusion
The prior and/or pertinent art(s) made of record and not relied upon is considered pertinent to applicant's disclosure, are –
Huber (US 12014533 B2),
Huber (US 11573325 B2),
Zhang et al. (US 11567201 B2),
Yuan et al. (US 11468690 B2),
Zhang et al. (US 10962370 B2)
– who disclose different methods of scanning an environment to obtain point cloud data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NURUN FLORA whose telephone number is (571)272-5742. The examiner can normally be reached M-F 9:30 am -5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Chan can be reached at (571) 272-3022. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NURUN FLORA/Primary Examiner, Art Unit 2619
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