DETAILED ACTION
Status of Claims
Claims 1-20 are currently pending and have been examined in this application. This Non-final communication is the first action on the merits.
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 2/06/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are directed to a system or method, which is one of the statutory categories of invention. (Step 1: YES)
The examiner has identified system Claim 1 as the claim that represents the claimed invention for analysis and is similar to method Claim 11. Claim 1 recites the limitations of (additional elements emphasized in bold are considered to be parsed from the remaining abstract idea):
A navigation system for navigating a robot unit, comprising one or more remote sensors, in a scene or venue, the scene or venue comprising static features and dynamic features, the system comprising a controller and a storage available to the controller and in which information is stored relating to the static features and the dynamic features, wherein the controller is configured to: receive an output from the one or more remote sensors, determine, from the output, a plurality of features, recognize, between the determined features, one or more static features as a determined feature which: emits or reflects an amount of radiation exceeding a predetermined minimum intensity, or emits or reflects radiation at a predetermined wavelength, and determine a position of the robot unit vis-a-vis the position of one or more recognized static features.
which under its broadest reasonable interpretation, covers performance of the limitation(s) as a mental process (concept performed in the human mind) to receive an output, determine features, recognize static features, and determine a position of a robot unit. in relationship to those static features. One of ordinary skill in the art could receive an output of sensor data, determine which objects are features based on that data, recognize which features are static features, and then determine a position of a robot unit in relationship to those static features. Similarly, if a claim limitation under its BRI, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. (Claims can recite a mental process even if they are claimed as being performed on a computer Gottschalk v. Benson, 409 U.S. 63; “Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” Versata Dev. Group v. SAP Am., Inc., 793 F. 3d 1306, 1335, 115 USPQ2d 1681, 1702. (Fed. Cir. 2015.))
Accordingly, the claim recites an abstract idea (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A navigation system for navigating a robot unit, comprising one or more remote sensors, in a scene or venue, the scene or venue comprising static features and dynamic features, the system comprising a controller and a storage available to the controller and in which information is stored relating to the static features and the dynamic features, wherein the controller is configured to: receive an output from the one or more remote sensors, determine, from the output, a plurality of features, recognize, between the determined features, one or more static features as a determined feature which: emits or reflects an amount of radiation exceeding a predetermined minimum intensity, or emits or reflects radiation at a predetermined wavelength, and determine a position of the robot unit vis-a-vis the position of one or more recognized static features.
The robot unit, remote sensors, controller, and storage in Claim 1 are just using generic computer components. The computer hardware is recited at a high level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claim 1 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Mere instructions to implement an abstract idea on or with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claim 1 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
The dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the aforementioned claims are not patent-eligible.
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)(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, 3, 5-7, 9, 11, 13, 15-17, and 19 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Jones (US 20190213438 A1)
Claim 1:
Jones teaches the following limitations:
A navigation system for navigating a robot unit, comprising one or more remote sensors, in a scene or venue, (Jones – [0082) … The mobile robot 102 includes a navigation module 118 that enables the mobile robot 102 to navigate in an environment based on the map 116. The mobile robot 102 includes one or more cameras 120 that are configured to capture images of the surroundings, allowing the mobile robot 102 to recognize objects in the images.) the scene or venue comprising static features and dynamic features, the system comprising a controller and a storage available to the controller and in which information is stored relating to the static features and the dynamic features, wherein the controller is configured to: (Jones - [0008] The control module can update the map to store information about a plurality of locations, in which for each location, the map can store information about at least one of (i) whether there is a semi-permanent barrier at the location, (ii) whether there is an impermanent barrier at the location, or (iii) how frequent an impermanent barrier appears at the location.)
receive an output from the one or more remote sensors, determine, from the output, a plurality of features, (Jones – [0074] … If the mobile robot is near the dining table, the mobile robot captures images of the dining table using its own camera, and labels the object in those images as “dining table.” The mobile robot then uses the labeled images to train its neural network so that it will be able to recognize the dining table the next time the robot navigates to the dining room. ; [0075] … The mobile robot can be configured to recognize the appliances and fixtures that are already installed in the new house, such as the refrigerator, the dishwasher, the cabinets, and the island in the kitchen. The mobile robot can be configured to recognize the toilets, the shower stalls, tubs, and cabinets in the bathrooms. … ) recognize, between the determined features, one or more static features as a determined feature (Jones - [0007] The recognition module can be configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. The control module can be configured to operate the cleaning head to perform the cleaning tasks taking into account of whether each of the objects recognized by the recognition module is a semi-permanent barrier or an impermanent barrier.) which: emits or reflects an amount of radiation exceeding a predetermined minimum intensity, or emits or reflects radiation at a predetermined wavelength, and determine a position of the robot unit vis-a-vis the position of one or more recognized static features. (Jones [0126] … non-contact time of flight sensors, such as lasers, volumetric point cloud sensors, point line sensors (e.g., a time of flight line sensor such as those made by PIXART), IR proximity sensors, light detection and ranging (LIDAR) sensors, … When the controller 706 determines that these features have been detected, the controller 706 determines the pose of the mobile cleaning robot 102 on the map of the home 300 using the location and orientation of the mobile cleaning robot 102 relative to these detected features.)
Examiner’s Note:
Semi-permanent corresponds to Static
impermanent corresponds to Dynamic
Claim 3:
Jones teaches the following limitations:
The navigation system according to claim 1, wherein: one or more the remote sensors are configured to output information representing a visible characteristic of each feature and (Jones – [0119] … Next, the robot 102 uses the camera 120 to capture images of a dining table 312, a floor lamp 500, and wall art 502, and uses the recognition module 122 to determine that the objects in the images are a dining table, a floor lamp, and wall art, respectively. The robot 102 updates the map 116 to include the dining table 312, the floor lamp 500, and the wall art 502 at their respective locations.)
the controller is configured to recognize, as a static feature, a feature with one or more of a plurality of predetermined visible characteristics. (Jones - [0110] For example, the recognition module 122 is configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. …)
Claim 5:
Jones teaches the following limitations:
The navigation system according to claim 1, wherein information is stored, in the storage, relating to each category of a plurality of feature categories, wherein the controller is configured to: categorize a feature into a first category of the plurality of categories, and recognize a feature as a static feature if the information, in the storage, relating to the first category, reflects that features of the first category are static features.
(Jones – [0083] … In some implementations, the recognition module 122 includes a neural network 124 that is trained using images of scenes and objects that are common in homes. The neural network 124 can be, e.g., a convolutional neural network. The recognition module 122 can include multiple neural networks 124 trained to classify various categories of objects. For example, a first neural network can be trained to recognize scenes and determine which room the mobile robot 102 is located, a second neural network can be trained to recognize objects in a room, and a third neural network can be trained to recognize individuals and pets.)
Claim 6:
Jones teaches the following limitations:
The navigation system according to claim 1, wherein the one or more static features are walls, pillars, racks, fences, building structures, heavy furniture or elements, storage elements, columns, walls, buildings, bridges, stationary equipment, such as semi-permanent walls, shelving, fire- extinguishers, or safety equipment.
(Jones – [0110] For example, the recognition module 122 is configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. Semi-permanent barriers include, e.g., beds, walls, and couches that are not moved frequently. Impermanent barriers include, e.g., doors, items left on the floor, ottomans, and chairs that are moved frequently. The control module 110 is configured to operate the cleaning head 128 to perform the cleaning tasks taking into account of whether each of the objects recognized by the recognition module 122 is a semi-permanent barrier or an impermanent barrier.)
Claim 7:
Jones teaches the following limitations:
The navigation system according to claim 1, wherein the controller is further configured to determine a position of the robot unit in the scene or venue based on the information of the storage and the determined position of the robot unit vis-a-vis the one or more recognized static features, the storage comprising information representing a position of each of the one or more recognized static features, wherein the information of the storage represents a map of the scene or venue and comprising therein the positions of the one or more recognized static features.
(Jones - [0122] The controller 706 is also operable with a sensing system 708. The sensing system 708 includes sensors usable by the controller 706 to navigate about the home 300. The sensing system 708, for example, has sensors to generate signals for detecting obstacles within the home 300 and for generating the map of the home 300. The sensing system 708 can include obstacle detection sensors, such as a time-of-flight sensor to detect distances to obstacles, cliff detection sensors to detect a drop-off (e.g., a staircase), bump sensors associated with a bumper mounted on the mobile cleaning robot 102, and contact sensors. The controller 706 operates the drive system for the mobile cleaning robot 102 to move around obstacles when the obstacle detection sensors detect the obstacles. ; [0123] The controller 706 uses signals from its sensor system to generate a map of the home 300 by tracking and updating positions and orientations of the mobile cleaning robot 102 over time. The mapping sensors include, for example, simultaneous localization and mapping (SLAM) sensors, dead reckoning sensors, and obstacle detection and avoidance (ODOA) sensors. …)
Claim 9:
Jones teaches the following limitations:
The navigation system according to claim 7, wherein the determination of the position of the robot in the scene or venue is based on a search in the map for correspondence with features, using only the one or more recognized static features.
(Jones – [0126] … When the controller 706 determines that these features have been detected, the controller 706 determines the pose of the mobile cleaning robot 102 on the map of the home 300 using the location and orientation of the mobile cleaning robot 102 relative to these detected features. The controller 706 localizes the mobile cleaning robot 102 within the home 300, in particular by determining a current pose of the mobile cleaning robot 102 with reference to the features corresponding to objects within the home 300. …)
Claim 11:
Jones teaches the following limitations:
A method of operating a navigation system for navigating a robot unit in a scene or venue, the scene or venue comprising static features and dynamic features, the method comprising: one or more remote sensors of the robot unit (Jones – [0082) … The mobile robot 102 includes a navigation module 118 that enables the mobile robot 102 to navigate in an environment based on the map 116. The mobile robot 102 includes one or more cameras 120 that are configured to capture images of the surroundings, allowing the mobile robot 102 to recognize objects in the images.) outputting information representing surroundings of the robot unit, determining, from the information, a plurality of features, (Jones – [0074] … If the mobile robot is near the dining table, the mobile robot captures images of the dining table using its own camera, and labels the object in those images as “dining table.” The mobile robot then uses the labeled images to train its neural network so that it will be able to recognize the dining table the next time the robot navigates to the dining room. ; [0075] … The mobile robot can be configured to recognize the appliances and fixtures that are already installed in the new house, such as the refrigerator, the dishwasher, the cabinets, and the island in the kitchen. The mobile robot can be configured to recognize the toilets, the shower stalls, tubs, and cabinets in the bathrooms. … ) recognizing, between the determined features, one or more static features as a determined feature (Jones - [0007] The recognition module can be configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. The control module can be configured to operate the cleaning head to perform the cleaning tasks taking into account of whether each of the objects recognized by the recognition module is a semi-permanent barrier or an impermanent barrier.) which: emits or reflects an amount of radiation exceeding a predetermined minimum intensity, or emits or reflects radiation at a predetermined wavelength, and determining a position of the robot unit vis-a-vis one or more recognized static features. (Jones [0126] … non-contact time of flight sensors, such as lasers, volumetric point cloud sensors, point line sensors (e.g., a time of flight line sensor such as those made by PIXART), IR proximity sensors, light detection and ranging (LIDAR) sensors, … When the controller 706 determines that these features have been detected, the controller 706 determines the pose of the mobile cleaning robot 102 on the map of the home 300 using the location and orientation of the mobile cleaning robot 102 relative to these detected features.)
Examiner’s Note:
Semi-permanent corresponds to Static
impermanent corresponds to Dynamic
Claim 13:
Jones teaches the following limitations:
The method according to claim 11, wherein: one of the one or more remote sensors outputs information representing a visible characteristic of each feature, and (Jones – [0119] … Next, the robot 102 uses the camera 120 to capture images of a dining table 312, a floor lamp 500, and wall art 502, and uses the recognition module 122 to determine that the objects in the images are a dining table, a floor lamp, and wall art, respectively. The robot 102 updates the map 116 to include the dining table 312, the floor lamp 500, and the wall art 502 at their respective locations.) the recognizing step comprises recognizing, as static features, features with one or more of a plurality of predetermined visible characteristics. (Jones - [0110] For example, the recognition module 122 is configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. …)
Claim 15:
Jones teaches the following limitations:
The method according to claim 11, in which information is stored in a storage relating to each category of a plurality of feature categories, the method comprising: categorizing a feature into a first category of the plurality of categories, and recognizing a feature as a static feature if the information, in the storage, relating to the first category, reflects that features of the first category are static features.
(Jones – [0083] … In some implementations, the recognition module 122 includes a neural network 124 that is trained using images of scenes and objects that are common in homes. The neural network 124 can be, e.g., a convolutional neural network. The recognition module 122 can include multiple neural networks 124 trained to classify various categories of objects. For example, a first neural network can be trained to recognize scenes and determine which room the mobile robot 102 is located, a second neural network can be trained to recognize objects in a room, and a third neural network can be trained to recognize individuals and pets.)
Claim 16:
Jones teaches the following limitations:
The method according to claim 11, wherein the one or more static features are walls, pillars, racks, fences, building structures, heavy furniture or elements, storage elements, columns, walls, buildings, bridges, stationary equipment, such as semi-permanent walls, shelving, fire- extinguishers, or safety equipment.
(Jones – [0110] For example, the recognition module 122 is configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. Semi-permanent barriers include, e.g., beds, walls, and couches that are not moved frequently. Impermanent barriers include, e.g., doors, items left on the floor, ottomans, and chairs that are moved frequently. The control module 110 is configured to operate the cleaning head 128 to perform the cleaning tasks taking into account of whether each of the objects recognized by the recognition module 122 is a semi-permanent barrier or an impermanent barrier.)
Claim 17:
Jones teaches the following limitations:
The method according to claim 11, further comprising determining a position of the robot unit in the scene or venue based on the information of a storage of the navigation system and the determined position of the robot unit vis-a-vis the one or more recognized static features, the storage comprising information representing a position of each of a number of the static features, wherein the information of the storage represents a map of the scene or venue and comprises therein the positions of the one or more recognized static features.
(Jones - [0122] The controller 706 is also operable with a sensing system 708. The sensing system 708 includes sensors usable by the controller 706 to navigate about the home 300. The sensing system 708, for example, has sensors to generate signals for detecting obstacles within the home 300 and for generating the map of the home 300. The sensing system 708 can include obstacle detection sensors, such as a time-of-flight sensor to detect distances to obstacles, cliff detection sensors to detect a drop-off (e.g., a staircase), bump sensors associated with a bumper mounted on the mobile cleaning robot 102, and contact sensors. The controller 706 operates the drive system for the mobile cleaning robot 102 to move around obstacles when the obstacle detection sensors detect the obstacles. ; [0123] The controller 706 uses signals from its sensor system to generate a map of the home 300 by tracking and updating positions and orientations of the mobile cleaning robot 102 over time. The mapping sensors include, for example, simultaneous localization and mapping (SLAM) sensors, dead reckoning sensors, and obstacle detection and avoidance (ODOA) sensors. …)
Claim 19:
Jones teaches the following limitations:
The method according to claim 17, wherein the determination of the position of the robot in the scene or venue is based on a search in the map for correspondence with features, using only the one or more recognized static features.
(Jones – [0126] … When the controller 706 determines that these features have been detected, the controller 706 determines the pose of the mobile cleaning robot 102 on the map of the home 300 using the location and orientation of the mobile cleaning robot 102 relative to these detected features. The controller 706 localizes the mobile cleaning robot 102 within the home 300, in particular by determining a current pose of the mobile cleaning robot 102 with reference to the features corresponding to objects within the home 300. …)
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.
Claim(s) 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Jones (US 20190213438 A1) as modified by Sorenson (US 20210293548 A1)
Claim 2:
Jones teaches the following limitations:
The navigation system according to claim 1, wherein the controller is further configured to determine, from the output, one or more dynamic features and,
(Jones - [0110] For example, the recognition module 122 is configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. Semi-permanent barriers include, e.g., beds, walls, and couches that are not moved frequently. Impermanent barriers include, e.g., doors, items left on the floor, ottomans, and chairs that are moved frequently. …)
Jones does not explicitly teach the following limitations, however Sorenson teaches:
in the step of determining the position, determine the position only from the one or more recognized static features.
(Sorenson – [0006] … . Operations to perform localization may include, in a case that additional information is desired to establish the location of the autonomous vehicle within the environment, using the static score of the object to determine second information about the location of the autonomous vehicle within the environment. The second information may augment the first information to establish the location of the autonomous vehicle within the environment. …)
Examiner’s Note: The examiner is interpreting the limitation “recognized static features.” to mean objects that are determined to have an acceptable threshold of staticness as detailed in paragraph [0027] of the instant specification. Therefore the BRI of the limitation “only” means using only features which fall within the static score threshold.
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Jones to include a method of determining which objects will be recognized as static and then using that information to determine the robot position as taught in Sorenson. Having a system for scoring objects as static or not static allows the robot to base its location on a consistent stationary object which improves the accuracy of robot positioning.
Claim 12:
Jones teaches the following limitations:
The method according to claim 11, wherein: the step of determining the plurality of features comprises determining one or more dynamic features, and
(Jones - [0110] For example, the recognition module 122 is configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. Semi-permanent barriers include, e.g., beds, walls, and couches that are not moved frequently. Impermanent barriers include, e.g., doors, items left on the floor, ottomans, and chairs that are moved frequently. …)
Jones does not explicitly teach the following limitations, however Sorenson teaches:
the step of determining the position comprises determining the position only from the one or more recognized static features.
(Sorenson – [0006] … . Operations to perform localization may include, in a case that additional information is desired to establish the location of the autonomous vehicle within the environment, using the static score of the object to determine second information about the location of the autonomous vehicle within the environment. The second information may augment the first information to establish the location of the autonomous vehicle within the environment. …)
Examiner’s Note: The examiner is interpreting the limitation “recognized static features.” to mean objects that are determined to have an acceptable threshold of staticness as detailed in paragraph [0027] of the instant specification. Therefore the BRI of the limitation “only” means using only features which fall within the static score threshold.
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Jones to include a method of determining which objects will be recognized as static and then using that information to determine the robot position as taught in Sorenson. Having a system for scoring objects as static or not static allows the robot to base its location on a consistent stationary object which improves the accuracy of robot positioning.
Claim(s) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jones (US 20190213438 A1) as modified by Lindhe (US 20160302639 A1)
Claim 4:
Jones teaches the following limitations:
recognize the determined feature as a static feature if the comparison identifies a match.
(Jones - [0110] For example, the recognition module 122 is configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. …)
Jones does not explicitly teach the following limitations, however Lindhe teaches:
The navigation system according to claim 1, wherein: the one or more remote sensors are configured to detect one or more predetermined surface characteristics of a determined feature, and (Lindhe - [0026] The landmark may be a fixed object or obstacle in a building such as a counter, a staircase, a door, a kitchen stove, etc or a movable object or obstacle such as a piece of furniture. From such a landmark a landmark signature may be derived. The landmark signature may be based on any combination of position, shape, orientation or other characteristics of a surface, either for flat surfaces or surfaces having another shape.) the controller is configured to compare the surface characteristics of the determined feature to predetermined surface characteristics and (Lindhe – [0081] The robotic cleaning device 2 and the processing unit 20, respectively, stores the generated landmark signature in relation to its position within the surface 26, 26′ so that during an initial cleaning, positional data of the surface 26, 26′ is created and stored. Once the generated landmark signature is stored for example in a database on the storage medium 22 it becomes a predetermined landmark signature. … [0083] … From the above characteristics or features the robotic cleaning device 2 creates the generated and thus later on the predetermined landmark signature via the processing unit 20. …)
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Jones to include a method of recognizing objects based on surface characteristics as taught in Lindhe. Having the ability to determine the surface characteristics of a given object improves the probability that an object in the data base of features will be accurately and efficiently identified and matched to current sensor readings.
Claim 14:
Jones teaches the following limitations:
recognizing the determined feature as a static feature if the comparison identifies a match.
(Jones - [0110] For example, the recognition module 122 is configured to, for each of a plurality of objects that are barriers to movements of the robot in the environment, classify whether the object is a semi-permanent barrier that is moved infrequently or an impermanent barrier that is moved frequently based on images of the object captured over a period of time. …)
Jones does not explicitly teach the following limitations, however Lindhe teaches:
The method according to claim 11, wherein: the one or more remote sensors detect one or more predetermined surface characteristics of a determined feature, and (Lindhe - [0026] The landmark may be a fixed object or obstacle in a building such as a counter, a staircase, a door, a kitchen stove, etc or a movable object or obstacle such as a piece of furniture. From such a landmark a landmark signature may be derived. The landmark signature may be based on any combination of position, shape, orientation or other characteristics of a surface, either for flat surfaces or surfaces having another shape.) the recognizing step comprises comparing the surface characteristics of the determined feature to predetermined surface characteristics and (Lindhe – [0081] The robotic cleaning device 2 and the processing unit 20, respectively, stores the generated landmark signature in relation to its position within the surface 26, 26′ so that during an initial cleaning, positional data of the surface 26, 26′ is created and stored. Once the generated landmark signature is stored for example in a database on the storage medium 22 it becomes a predetermined landmark signature. … [0083] … From the above characteristics or features the robotic cleaning device 2 creates the generated and thus later on the predetermined landmark signature via the processing unit 20. …)
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Jones to include a method of recognizing objects based on surface characteristics as taught in Lindhe. Having the ability to determine the surface characteristics of a given object improves the probability that an object in the data base of features will be accurately and efficiently identified and matched to current sensor readings.
Claim(s) 8, 10, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jones (US 20190213438 A1) as modified by Troy (US 20190265721 A1)
Claim 8:
Jones does not explicitly teach the following limitations, however Troy teaches:
The navigation system according to claim 1, wherein the one or more recognized static features have a fluorescent or retroreflecting material or element provided thereon or applied thereto.
(Troy - [0027] Referring now to FIG. 1A, a system 2 for navigating a sensor-equipped mobile platform through an environment to a destination includes a plurality of imaging targets 4 at a plurality of locations and a sensor-equipped mobile platform 6. As shown in FIGS. 3A and 3B, each imaging target 4 includes a machine-readable code 8 and a passive marker 10. The imaging targets are each positioned on a surface of an object or otherwise positioned within the environment of the system. ; [0029] The passive markers 10 of the imaging targets 4 contain, for example, retro-reflective materials that are capable of reflecting light back to the source when displayed under a controlled light source. For example, the reflective portion of the passive markers comprises: retro-reflective tape, reflective fabric tape, or reflective tape including microspheres. …)
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Jones to include a method of identifying known objects with a tag, code, or retroreflective decal applied directly to the object as taught in Troy. The use of physically applied tags or decals to identify objects in the environment improves the chances of positively identifying known objects and also conserves computer resources that would otherwise be spent processing sensor data.
Claim 10:
Jones does not explicitly teach the following limitations, however Troy teaches:
The navigation system according to claim 1, wherein the position of the robot is determined based on a complete extent of the one or more recognized static features.
(Troy – [0034] … In one embodiment, multiple imaging targets (e.g. three or more imaging targets) are utilized to determine the relative position and orientation of the mobile platform. In an aspect, the three-dimensional localization software uses the imaging targets and the laser range meter measurements to determine the location (position and orientation) of the mobile platform relative to the imaging targets. In another embodiment, an estimate of relative position and orientation of the mobile platform can be acquired from a single target by using the internal registration marks in the target, such as those in a QRcode.)
Examiner’s Note: The examiner is interpreting the limitation “complete extent” to mean a set of stored data, representing an object, that is applied to identify an object that has been recognized with partial sensor data or by a decal or code, as detailed in paragraph [0075] of the instant specification. Therefore the BRI of the limitation “based on a complete extent” of an object means applying complete object data based on stored object information to determine the position the robot.
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Jones to include a method of applying complete object information based an identified code in order to determine the position of a robot as taught in Troy. Having the ability to determine the object extents based on partial information or a tag or code improves the probability that an object will be identified and in turn improves the determination of the robot position, while also conserving computing resources that would otherwise be spent on fully scanning an object.
Claim 18:
Jones does not explicitly teach the following limitations, however Troy teaches:
The method according to claim 11, wherein the one or more recognized static features have a fluorescent or retroreflecting material or element provided thereon or applied thereto.
(Troy - [0027] Referring now to FIG. 1A, a system 2 for navigating a sensor-equipped mobile platform through an environment to a destination includes a plurality of imaging targets 4 at a plurality of locations and a sensor-equipped mobile platform 6. As shown in FIGS. 3A and 3B, each imaging target 4 includes a machine-readable code 8 and a passive marker 10. The imaging targets are each positioned on a surface of an object or otherwise positioned within the environment of the system. ; [0029] The passive markers 10 of the imaging targets 4 contain, for example, retro-reflective materials that are capable of reflecting light back to the source when displayed under a controlled light source. For example, the reflective portion of the passive markers comprises: retro-reflective tape, reflective fabric tape, or reflective tape including microspheres. …)
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Jones to include a method of identifying known objects with a tag, code, or retroreflective decal applied directly to the object as taught in Troy. The use of physically applied tags or decals to identify objects in the environment improves the chances of positively identifying known objects and also conserves computer resources that would otherwise be spent processing sensor data.
Claim 20:
Jones does not explicitly teach the following limitations, however Troy teaches:
The method according to claim 11, wherein the position of the robot is determined based on a complete extent of the one or more recognized static features.
(Troy – [0034] … In one embodiment, multiple imaging targets (e.g. three or more imaging targets) are utilized to determine the relative position and orientation of the mobile platform. In an aspect, the three-dimensional localization software uses the imaging targets and the laser range meter measurements to determine the location (position and orientation) of the mobile platform relative to the imaging targets. In another embodiment, an estimate of relative position and orientation of the mobile platform can be acquired from a single target by using the internal registration marks in the target, such as those in a QRcode.)
Examiner’s Note: The examiner is interpreting the limitation “complete extent” to mean a set of stored data, representing an object, that is applied to identify an object that has been recognized with partial sensor data or by a decal or code, as detailed in paragraph [0075] of the instant specification. Therefore the BRI of the limitation “based on a complete extent” of an object means applying complete object data based on stored object information to determine the position the robot.
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Jones to include a method of applying complete object information based an identified code in order to determine the position of a robot as taught in Troy. Having the ability to determine the object extents based on partial information or a tag or code improves the probability that an object will be identified and in turn improves the determination of the robot position, while also conserving computing resources that would otherwise be spent on fully scanning an object.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Sommer(US 11410328 B1) describes maintaining a feature point map. The maintaining can include selectively updating feature points in the feature point map based on an assigned classification of the feature points. For example, when a feature points is assigned a first classification, the feature point is updated whenever information indicates that the feature point should be updated.
Salfity (US 20200334887 A1) describes a control system to obtain image data from a given region and perform image analysis on the image data to detect a set of objects in the given region. For each object of the set, the example Memory control system may classify each object as being one of multiple predefined classifications of object permanency, including (i) a fixed classification, (ii) a static and fixed classification, and/or (iii) a dynamic classification.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN LINDSAY OSTROW whose telephone number is (703)756-1854. The examiner can normally be reached M-F 8 - 5.
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, Adam Mott can be reached on (571) 270 5376. 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.
/ALAN LINDSAY OSTROW/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
Prosecution Insights