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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-19 is/are directed to the abstract idea of a mathematical concept and a mental process. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
The claim(s) recite(s) generating map and IMU data, reducing that data, and updating data. The rejected dependent claims only supply additional steps (mathematical calculations, and mental processes) that a processor must perform. All of these concepts relate to the abstract idea of certain methods of mathematical concepts and mental processes. The concept described in claims 1-19 is/are not meaningfully different than those methods of mathematical concepts and mental processes found by the courts to be abstract ideas. As such, the description in claims 1-19 is an abstract idea.
This judicial exception is not integrated into a practical application because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claim(s) recite(s) the additional limitations of providing an output via a user interface which appears to be nothing more than pose-solution activity. The hardware is recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of generic computer components that perform the generic functions of [e.g. "transmitting information", "generating information"] common to electronics and computer systems does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves another technology or technical field. Their collective functions merely provide conventional computer implementation (i.e. mere instructions to implement the abstract idea on a generic computing system).
Claims 1-19 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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)(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.
Claims 1-5, 7-14, 16, 17, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lavy et. al. (US Patent Publication 2020/0309541)
Regarding claims 1, 8, and 17, Lavy discloses a system for simultaneous localization and mapping in underground environments, the system comprising AND a computer-implemented method of simultaneous localization and mapping in underground environments, the method comprising: (abstract; ¶290, 293; “up to tunnels where ego motion/dead reckoning is used, and then applying the same method used to extract the features” (emphasis added) Lavy discloses use of the invention for mapping inside tunnels and because a tunnel is explicitly defined as “an artificial underground passage” the usage of Lavy for mapping tunnels is “mapping in underground environments”)
generating mapping data of the underground environment (¶148, 156, 161-162; “The full loop of SLAM includes … mapping …; namely, (i)updating maps“ “detections are projected to a map by the following procedure.“ “bounding box detection and the transformation to map domain according to ego motion and camera calibration, in accordance with an embodiment of the present invention“ “Once a local map is generated“)
and inertial measurement data of an agent traversing the underground environment; (¶182, 184-186, 293; “An inertial sensor may be used in the SLAM model“)
generating, by the agent, reduced mapping data by performing data reduction of the generated mapping data; (¶181; “… on client maps, size is minimized … This is achieved in the training process of a SLAM model, … it may be pre-processed for entire geographical areas and reduce map size at a given zoom level by about 2-3 orders of magnitude“)
updating, by the agent, a local map based on correlated features in the reduced mapping data; (¶181, “This can be combined with update logic, whereby client and server communicate in real time and fetch updates over a cellular network.”)
updating, by the agent, a localized position of the agent with reference to the updated local map; and (¶66, 148; “the method of FIG. 2 also includes correcting localization error” “The full loop of SLAM includes both localization and mapping at the same time“)
providing, by the agent via a user interface, a localization and mapping output including at least one of the updated local map and the updated localized position of the agent. (¶55; “a low-computational SLAM model is distributed for real-time use in a smartphone 120 application or in firmware of embedded dashboard camera 110.“ “localization is performed in real time such that navigation instructions are more accurate and context aware. Further, wrong tum/exit or other aviation errors are quickly recognized and a new route calculation is triggered. Yet further, when a driver follows an instruction, repeated instructions are avoided.”
Regarding claim 2, Lavy further discloses wherein the mapping data includes any combination of range data, bearing data and elevation data of the underground environment with reference to the agent. (¶40, 180; “the system of FIG. 1 includes lidar or radar transmitters embedded in vehicle 100,“ “shows how other visible features are added, such as building height in logarithmic scale, in accordance with an embodiment is the present invention. FIG. 12 shows how building height is used in a red channel, train routes are yellow, pedestrian routes are blue, and car routes are purple.“)
Regarding claim 3, Lavy further discloses wherein the mapping data includes at least one of i) image data generated by one or more cameras and ii) point cloud data generated by one or more Light Detection and Ranging (LiDAR) sensors. (¶39, 40)
Regarding claims 4, and 13, Lavy further discloses wherein the inertial measurement data includes linear acceleration (Accelerometer) and angular velocity (gyroscope) of the agent along three mutually perpendicular axes. (¶185, 186)
Regarding claim 5, Lavy further discloses wherein the reduced mapping data is generated by inputting the mapping data into one or more machine learning models trained to project the mapping data to a lower dimensional latent space. (Triplet Network (a type of neural network, which is a type of machine learning) of ¶190-201)
Regarding claims 7, and 16, Lavy further discloses wherein the localization and mapping output further includes an insight or recommendation for improving the simultaneous localization and mapping. (¶122; “Embodiments of the present invention provide a method for improving data collected from nodes“)
Regarding claim 9, Lavy further discloses wherein at least one of the inertial sensor and the mapping sensor includes multiple sensors collocated on a single substrate. (¶64; “Additionally, the IMU sensors include one or more of a mobile phone”)
Regarding claim 10, Lavy further discloses wherein the processor is collocated on a same substrate as at least one of the inertial sensor and the mapping sensor. (¶64; “Additionally, the IMU sensors include one or more of a mobile phone”)
Regarding claim 11, Lavy further discloses wherein the mapping sensor includes one or more cameras, and the mapping data includes image data generated by the one or more cameras. (¶39, 40)
Regarding claim 12, Lavy further discloses wherein the mapping sensor includes one or more Light Detection and Ranging (LiDAR) sensors and the mapping data includes point cloud data generated by the one or more LiDAR sensors. (¶39, 40)
Regarding claim 14, Lavy further discloses wherein the processor is configured to generate the reduced mapping data by inputting the mapping data into one or more machine learning models trained to project the mapping data to a lower dimensional latent space. (Triplet Network (a type of neural network where models are trained to generalize effectively from limited examples, which is a type of machine learning) of ¶190-201)
Regarding claim 19, Lavy further discloses updating a mapping confidence level associated with a region of the underground environment based on a time duration (i.e. freshness) since previous scan of the region. (¶71; “the method of FIG. 2 estimates freshness of a map tile … using pixel-level confidence intervals.” )
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 6, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lavy as applied to claims 5, and 14 above.
Regarding claims 6, and 15, Lavy discloses training the machine learning model in underground tunnels but does not appear to disclose training data customized for underground mining environments. Nevertheless, the limitation would have been obvious to a person of ordinary skill in the art at the time of filing because Lavy discloses “When running localization on client maps, size is minimized so as to allow local storage on the client for oflline work, and to reduce bandwidth data consumption. Another benefit is reducing compute time by about 30%. This is achieved in the training process of a SLAM model,” (¶181) and “Using a SLAM method to generate pixel level top views of the areas hidden, up to tunnels where ego motion/dead reckoning is used, and then applying the same method used to extract the features from standard top view/aerial imagery.” (¶293) That is to say that Lavy discloses using the invention in underground tunnels and a person of ordinary skill in the art at the time of filing would have found it obvious to train specifically for a mining environment so as to save bandwidth and computing time.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Lavy as applied to claim 17 above, and further in view of Kroepfl et. al. (US Patent Publication 2023/0357076).
Regarding claim 18, Kroepfl teaches wherein the central server is implemented by dynamically assigning central server functionalities dynamically between the multiple agents. (¶290, 291 “Any of these various functions may be distributed over multiple locations from central or core servers” “The client device(s) may include at least some of the components, features, and functionality of the example computing device(s) 1600 described herein with respect to FIG. 16.”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN D HUTCHINSON whose telephone number is (571)272-8413. The examiner can normally be reached 7-5 Mon-Thur.
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/ALAN D HUTCHINSON/Primary Examiner, Art Unit 3669