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 .
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.
Claim Rejections - 35 USC § 101
Regarding 35 USC 101, it is the position of the examiner, the claims are eligible.
The groups of claims are clearly directed to a practical application. The mapping of input coordinate tensors to scene feature tensors having a dimensionality greater than the input tensors is clearly mathematical or data analysis, but this lacks any purpose if navigation or characterization of a scene were to be performed mentally. It is of use when a machine (computer) is performing SLAM in the process of mapping a scene as it is captured by an object as it navigates the environment, which is recited. The Applicant explains how this higher-dimensionality is useful for AI and to store the scene as parameters of a neural network, rather than as 3d coordinates, which is an improvement of the way in which machines can process the data, and would not be useful to a human attempting to characterize a scene (par. [0029]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-2, 9-10, 12,15, 25-26 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Kutliroff (10,719,759) in view of Chen et al (12,140,696)
Regarding claims 1 and 26 Kutliroff discloses,
A differentiable mapping engine (SLAM system 100, include map builder fig. 1, block 101, examiner interprets as differentiable mapping engine) to receive image data comprising a sequence of images captured using one or more camera devices of an object as it navigates an environment (note fig. 1, block 102, map builder receive image data from mobile device, IMU, 2D camera image data, or 3D camera data and col. 4 lines 33-35, map building module receive sensor data from mobile device) ; and
A neural network scene representation comprising a neural network architecture trained to map input coordinate indicating at least a point location in three-dimensional space to scene feature having a dimensionality greater than the input (note fig. 1 block 110 and 116 and col. 5 lines 32-65 and col. 6 lines 1-25, fig. 1 shows sensor data from 3D camera and lines cite neural network architecture and operations)
the neural network scene representation being communicatively coupled to the differentiable mapping engine (fig. 1 block 110 and 104 artificial neural network module communicates with map builder), wherein the differentiable mapping engine (map builder fig. 1, block 104) is configured to use the neural network scene representation as a mapping of the environment during operation of the differentiable mapping engine (map builder uses neural network results as data ,col. 4 lines 36-46, as described in the updating process of neural network). Kutliroff does not clearly disclose neural network architecture trained map input coordinate tensors indicating at least a point location in three-dimensional space to scene feature tensors having a dimensionality greater than the input tensors. Chen disclose neural network architecture trained map input coordinate tensors indicating at least a point location in three-dimensional space to scene feature tensors having a dimensionality greater than the input tensors (note col. 52 lines 15-31, dimension reduction and col. 53 lines 1-5, feature tensors flatten the dimensions). Kutliroff and Chen are combinable because they are from the same field of endeavor. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute neural network architecture trained map input coordinate tensors indicating at least a point location in three-dimensional space to scene feature tensors having a dimensionality greater than the input tensors in the system of Kutliroff as evidenced by Chen the suggestion/motivation for doing so provides improvements overcoming issues performed under certain conditions (note col. 1 lines 24-33). It would have been obvious to combine Chen with Kutliroff to obtain the invention as specified by claim 1.
Regarding claim 2 Kutliroff discloses,
wherein the differentiable mapping engine (SLAM system 100, include map builder fig. 1, block 101, examiner interprets as differentiable mapping engine)
comprises one or more neural networks, and wherein the neural network scene representation and the differentiable mapping engine are trained end-to-end using an optimization function (SLAM system 100 include neural network module).
Regarding claim 9 Kutliroff discloses,
Wherein parameters for the neural network architecture of the neural network scene representation are determined for a plurality of landmarks within the environment, each landmark representing a different scene of the environment (note col. 6 lines 14-34, lines cite parameters for the neural network architecture).
Regarding claim 10 Kutlifoff discloses,
A place recognition engine to determine if a current object location is a known object location based on data generated by one or more of the neural network scene representation and the differentiable mapping engine (note col. 8 lines 53-60, key recognition, object location, position locator note col. 5 lines 19-26).
Regarding claim 12 Kutliroff discloses,
a three-dimensional model generator communicatively coupled to the neural network scene representation, the three-dimensional model generator using an output of the neural network scene representation to generate a three-dimensional model of the environment (note fig. 1, block 116 , 3D camera).
Regarding claim 15 Kutliroff discloses,
A model-to-scene converter to train parameters for the neural network architecture of the neural network scene representation based on a supplied three-dimensional model of a modelled environment (note fig. 4, block 402 and col. 8 lines 14-24, transformation node).
Regarding claim 25 Kutliroff discloses,
A scene comparator to receive scene feature tensors for two or more instantiations of the neural network scene representation and to determine a set of differences between the two instantiations (note col. 11 lines 20-34, lines cite comparing image data).
Regarding claim 33 Kutliroff discloses.
Obtaining trained parameter values for the neural network scene representation, the trained parameter values representing a neural map of at least a portion of the environment (note col. 6 lines 22-28, cites parameter for neural network); and
Determining a set of updates for the trained parameter values while tracking the object within the environment using the differentiable mapping engine, wherein the set of updates comprise an update for the neural map (note col. 4 lines 36-45, cites updating parameters).
Claim Rejections - 35 USC § 102
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)(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 38 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chen et al (12,140,696).
Regarding claim 38 Chen discloses,
obtaining trained parameters for a neural network scene representation comprising a neural network architecture, the neural network architecture being trained to map input coordinate tensors indicating at least a point location in three-dimensional space to scene feature tensors having a dimensionality greater than the input tensors (note col. 52 lines 15-31, dimension reduction and col. 53 lines 1-5, feature tensors flatten the dimensions)
Obtaining training data comprising a sequence of images captured using one or more camera devices of an object during navigation of an environment and a corresponding sequence of poses of the object determined during the navigation (note col. 30 lines 22-35 and 45-60, radar processor scene based input from camera and receiver include neural network); and
Using the training data to train the system for simultaneous localization and mapping,
wherein during an inference mode the differentiable mapping engine is configured to determine pose data from input image data and the neural network scene representation is configured to map pose data to projected image data, the system being trained by optimizing a photometric error loss function between the input image data and the projected image data (note fig. 32, block 3208 and col. 44 lines 41-54, loss function to minimize means square, examiner interprets as optimal loss function).
Allowable Subject Matter
Claims 3, 5, 7-8, 13, 17, 19, 28, 30 and 35 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 for dependent claim 3. Prior art could not be found for the features wherein the differentiable mapping engine comprises: an image feature extractor comprising one or more neural networks to map an input image to an image feature tensor, wherein the differentiable mapping engine is configured to determine correspondences between image feature tensors over time to determine one or more poses of the object. These features in combination with other features could not be found in the prior art. Claim 5 depend on claim 3. Therefore, are also objected.
Regarding claim 7, prior art could not be found for the features synthetic view generator to: generate one or more input feature tensors for the neural network scene representation that are indicative of a synthetic pose of the object, supply the one or more input feature tensors to the neural network scene representation, and generate a rendered view from the synthetic pose using the output scene feature tensors of the neural network scene representation; wherein the differentiable mapping engine is configured to use a set of rendered views output by the synthetic view generator to track the object within the environment. These feature/s in combination with other features could not be found in the prior art. Claim 8 depend on claim 7. Therefore, are also objected.
Regarding claim 13 Kutliroff prior art could not be found for the features wherein the three-dimensional model comprises a point cloud model with geometric structures represented using coordinates within a three-dimensional frame of reference, wherein the three-dimensional model represents geometric structures using point coordinates within a three-dimensional frame of reference and metadata associated with the point coordinates, and wherein the three-dimensional model generator is configured to map scene feature tensors output by the neural network scene representation for determined point coordinates to said metadata. These features in combination with other features could not be found in the prior art.
Regarding claim 17, prior art could not be found for the features a training engine to update the trained parameters of the neural network architecture during navigation of the modelled environment based on received image data from the one or more camera devices of the object; a comparator to determine differences between the supplied three-dimensional model and a representation of the environment generated using the updated parameters of neural network scene representation. These features in combination with other features could not be found in the prior art. Claim 19 depend on claim 17. Therefore, are also objected.
Regarding claim 28, prior art could not be found for the feature determining a sequence of transformations from successive sets of image data obtained over time from the one or more camera devices, the sequence of transformations defining a set of poses of the object over time; and optimizing the sequence of poses, wherein the neural network scene representation is used to determine image data observable from a supplied pose for one or more of the determining and the optimizing, the image data representing a projection of the mapping of the environment onto an image plane of the pose. These features in combination with other features could not be found in the prior art. Claims 30 depend on claim 28. Therefore are also objected.
Regarding claim 35, prior art could not be found for the feature updating the trained parameter values while tracking the object within the environment using the differentiable mapping engine; using the updated trained parameter values and the neural network scene representation to generate an updated version of the three-dimensional model of the environment; comparing the initial version of the three-dimensional model and the updated version of the three-dimensional model; and outputting a set of changes to the three-dimensional model based on the comparing. These features in combination with other features could not be found in the prior art.
Related Prior Art
McCormac et al (10,915,731) A differentiable mapping engine (SLAM engine, examiner interprets as differentiable mapping engine, note fig. 4, block 465, slam system) to receive image data comprising a sequence of images captured using one or more camera devices of an object as it navigates an ,environment (note video acquisition interface (405) and frames of video data (415); and A neural network scene representation comprising a neural network architecture (note image classifier fig. 4, block 465, and col. 15 lines 58-67).
Xu et al (11,030,476) A differentiable mapping engine (SLAM system fig. 3 block 324, examiner interprets as differentiable mapping engine, note fig. 4, block 465, slam system) to receive image data comprising a sequence of images captured using one or more camera devices of an object as it navigates an environment (note col. 12 lines 10-25).
Curtis et al (12,333,802) a neural network architecture trained to map input coordinate tensors indicating at least a point location in three-dimensional space to scene feature tensors having a dimensionality greater than the input tensors (note col. 10 lines 34-40, neural network operated according to an equation, I is image tensor).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY M DESIRE whose telephone number is (571)272-7449. The examiner can normally be reached Monday-Friday 6:30am-3:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
G.D.
March 20, 2026
/GREGORY M DESIRE/Primary Examiner, Art Unit 2676