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 08/30/2024 has been considered and is in compliance with the provisions of 37 CFR 1.97.
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 (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 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.
Claims 1-18 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Drinkard et al. (US 2022/0291692; hereinafter Drinkard).
Regarding Claim 1:
Drinkard discloses a method to generate an image-based localization model for mobile robot navigation, the method comprising: performing data collection at a plurality of different service locations, to which a fleet of mobile robots is deployable, to generate collected data (Drinkard, Para. [0034-0037], Drinkard discloses collecting environmental information for a plurality of locations within which a fleet of autonomous robots perform operations);
performing a data retention operation with respect to the collected data, the data retention operation being performed based on a data retention policy (Drinkard, Para. [0058-0060], Drinkard discloses performing updating of the collected environmental data based on the quality of the map information (i.e. data retention policy));
generating a first image-based localization model for a first service location of the plurality of different service locations, using the collected data (Drinkard, Para. [0039-0042], Drinkard discloses the plurality of robots perform SLAM self-localization operations using the collected data at the robots’ respective location);
generating a second image-based localization model for a second service location of the plurality of different service locations, using the collected data (Drinkard, Para. [0039-0042], Drinkard discloses the plurality of robots perform SLAM self-localization operations using the collected data at the robots’ respective location);
deploying the first image-based localization model to a first mobile robot of the fleet of mobile robots, the first mobile robot being deployed at the first service location of the plurality of different service locations, the first mobile robot to use the first image-based localization model to navigate the first service location (Drinkard, Para. [0091], Drinkard discloses deploying the updated map information to the robot for the robot’s current location within the environment, with the updated map information being based on the SLAM self-localization); and
deploying a second image-based localization model to a second mobile robot of the fleet of mobile robots, the second mobile robot being deployed at the second service location of the plurality of different service locations, the second mobile robot to use the second image-based localization model to navigate the second service location (Drinkard, Para. [0091], Drinkard discloses deploying the updated map information to the robot for the robot’s current location within the environment, with the updated map information being based on the SLAM self-localization).
Regarding Claim 2:
Drinkard discloses the method of claim 1.
Drinkard further discloses wherein the collected data comprises location- specific collected data for each of the plurality of different service locations (Drinkard, Para. [0053], Drinkard discloses the collected environmental data is collected from the individual robots operating within the environment).
Regarding Claim 3:
Drinkard discloses the method of claim 2.
Drinkard further discloses wherein the location-specific collected data is collected by at least one mobile robot deployed at a specific one of the plurality of different service locations (Drinkard, Para. [0053], Drinkard discloses the collected environmental data is collected from the individual robots operating within the environment).
Regarding Claim 4:
Drinkard discloses the method of claim 1.
Drinkard further discloses wherein the collected data comprises image data and pose data (Drinkard, Para. [0026], [0039], Drinkard discloses the collected data from the autonomous robot includes pose data and camera data, ultrasonic sensor data, LIDAR, laser scanner data).
Regarding Claim 5:
Drinkard discloses the method of claim 2.
Drinkard further discloses wherein the data retention policy is a volume- based data retention policy (Drinkard, Para. [0053], Drinkard discloses the robot performs data retention based on the amount of data collected).
Regarding Claim 6:
Drinkard discloses the method of claim 5.
Drinkard further discloses wherein the volume-based data retention policy is to retain a uniform amount of data for each instance of the location-specific collected data for each of the plurality of different service locations (Drinkard, Para. [0184-0185], Drinkard discloses the autonomous robots collected the same data for every location within the operating environment).
Regarding Claim 7:
Drinkard discloses the method of claim 2.
Drinkard further discloses wherein the data retention policy is an age-based data retention policy, in terms of which location-specific collected data for a specific one of the plurality of different service locations is retained for a determinable time period (Drinkard, Para. [0052], Drinkard discloses the map information may be considered older and will be updated based on the determined deterioration of the old map information, see at least Para. [0063], [0154]).
Regarding Claim 8:
Drinkard discloses the method of claim 2.
Drinkard further discloses wherein generating the first image-based localization model for the first service location of the plurality of different service locations further comprises: retrieving location data specific to the first service location from the collected data (Drinkard, Para. [0034-0037], Drinkard discloses collecting environmental information for a location which the autonomous robot is operating);
obtaining map data pertaining to the first service location (Drinkard, Para. [0034-0037], Drinkard discloses the autonomous robot obtaining static or historic map for the location which the robot is operating);
generating a map of the first service location based on the map data (Drinkard, Para. [0034-0037], Drinkard discloses the autonomous robot updates or receives updated map information for the location which the robot is operating);
dividing the map of the first service location into a plurality of map cells (Drinkard, Para. [0101-0102], Fig. 11A, Drinkard discloses the updated map information includes the map divided into a hierarchical grip map);
assigning a portion of the location data to each of the plurality of map cells to generate assigned data (Drinkard, Para. [0117], Drinkard discloses assigning data to each grid cell); and
generating the first image-based localization model for the first service location, using the assigned data (Drinkard, Para. [0039-0042], Drinkard discloses the plurality of robots perform SLAM self-localization operations using the collected data at the robots’ respective location).
Regarding Claim 9:
Drinkard discloses the method of claim 8.
Drinkard further discloses wherein the plurality of map cells comprises a grid of map cells (Drinkard, Para. [0101-0102], Fig. 11A, Drinkard discloses the updated map information includes the map divided into a hierarchical grip map).
Regarding Claim 10:
Drinkard discloses the method of claim 8.
Drinkard further discloses wherein the location data comprises pose data and image data, a pose in the pose data being associated with a pose timestamp, an image in the image data being associated with an image timestamp, the pose timestamp to match the image timestamp (Drinkard, Para. [0026], [0039], [0100], [0108], Drinkard discloses the collected data from the autonomous robot includes sensor data time steps for pose data and camera data, ultrasonic sensor data, LIDAR, laser scanner data).
Regarding Claim 11:
Drinkard discloses the method of claim 2.
Drinkard further discloses wherein generating the first image-based localization model for the first service location of the plurality of different service locations further comprises: retrieving location-specific collected data related to the first service location from the collected data (Drinkard, Para. [0053], Drinkard discloses receiving collected data for locations within the working environment);
generating a plurality of online model performance metrics based on the location-specific collected data related to a current version of the image- based localization model (Drinkard, Para. [0053], Drinkard discloses generating an update of the map based on the collected environmental information for the location at which the autonomous robot is operating);
using a portion of the location-specific collected data, performing an offline evaluation of the current version of the image-based localization model (Drinkard, Para. [0059], Drinkard discloses determining if the updated map information meets the quality requirements to update the static/historic map); and
automatically generating a new version of the image-based localization model for the first service location, based on the offline evaluation (Drinkard, Para. [0060], Drinkard discloses generating and updating the map information based on the quality determination).
Regarding Claim 12:
Drinkard discloses the method of claim 11.
Drinkard further discloses wherein the plurality of online model performance metrics is reported by a navigation stack of the first mobile robot (Drinkard, Para. [0053], Drinkard discloses generating an update of the map based on the collected environmental information for the location at which the autonomous robot is operating).
Regarding Claim 13:
Drinkard discloses the method of claim 12.
Drinkard further discloses retraining the first image-based localization model using the plurality of online model performance metrics (Drinkard, Para. [0060], Drinkard discloses generating and updating the map information based on the collected location data).
Regarding Claim 14:
Drinkard discloses the method of claim 1.
Drinkard further discloses at the first mobile robot, performing a reboot operation (Drinkard, Para. [0038], Drinkard discloses the autonomous robot stops operations in order to update their local static map information);
responsive to the reboot operation and at the first mobile robot, automatically checking remote storage to determine that a new image-based localization model has been generated and stored at the remote storage (Drinkard, Para. [0038], Drinkard discloses the autonomous robot stops operations in order to update their local static map information, unless the current version is the most updated version of the map);
responsive to determining that the new image-based localization model has been generated and is stored at the remote storage, storing the new image-based localization model to local memory at the first mobile robot (Drinkard, Para. [0038], Drinkard discloses the autonomous robots updating the local static map); and
at the first mobile robot, serving image-based localization responses to localization requests at the first mobile robot (Drinkard, Para. [0063], Drinkard discloses updating the map information dependent on requested update to the robots).
Regarding Claim 15:
Drinkard discloses the method of claim 14.
Drinkard further discloses wherein the automatic checking of the remote storage to determine that the new image-based localization model has been generated and stored at the remote storage comprises checking that a retrained version of a current image-based localization model has been generated (Drinkard, Para. [0038], Drinkard discloses the autonomous robot stops operations in order to update their local static map information, unless the current version is the most updated version of the map).
Regarding Claim 16:
Drinkard discloses the method of claim 15.
Drinkard further discloses wherein in the automatic checking of the remote storage to determine that the new image-based localization model has been generated and stored at the remote storage comprises checking that a new image-based localization model type has been generated (Drinkard, Para. [0038], Drinkard discloses the autonomous robot stops operations in order to update their local static map information, unless the current version is the most updated version of the map).
Regarding Claim 17:
Drinkard discloses the method of claim 1.
Drinkard further discloses at a cloud storage, maintaining a plurality of image-based localization model types, and a plurality of versions of each of the plurality of image-based localization model types (Drinkard, Para. [0102], Drinkard discloses storing the collected environmental information and current version of the map on a remote server such as a cloud network, see also Para. [0178-0183]); and
at the first mobile robot, implementing fallback logic to allow the first mobile robot to use the plurality of image-based localization model types and the plurality of versions of each of the plurality of image-based localization model types (Drinkard, Para. [0130], Drinkard discloses performing self-localization operations in an offline manner, using historical map information for working environment, see also Para. [0138-0140]).
Regarding Claim 18:
Drinkard discloses the method of claim 17.
Drinkard further discloses wherein the maintaining comprises maintaining a directory file structure to store the plurality of image-based localization model types and the plurality of versions within the cloud storage (Drinkard, Para. [0102], Drinkard discloses storing the collected environmental information and current version of the map on a remote server such as a cloud network, see also Para. [0178-0183]).
Regarding Claim 20:
The claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise.
Allowable Subject Matter
Claim 19 is 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.
Claim 19 recites the claim limitation “the fallback logic is included at robotics stack of the first mobile robot and accesses the directory file structure in order to access at least one of the plurality of image-based localization model types of the plurality of version within the cloud storage”, which the examiner has found to not be explicitly disclosed by the cited art above of Drinkard, and not explicitly disclosed by the pertinent art cited below.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lu et al. (US 2021/0373161) – discloses autonomous driving vehicle which perform LiDAR localization and generates a mapped environment, as well as performing autonomous navigation within the mapped environment.
Ben Shalom et al. (USP 11,520,344) – discloses a control system and method for controlling and operating a fleet of mobile robots within a warehouse through image localization.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZACHARY JOSEPH WALLACE whose telephone number is (469)295-9087. The examiner can normally be reached 7:00 am - 5:00 pm, Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Z.J.W./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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