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 .
DETAILED ACTION
This is the first Office action on the merits. Claims 1-6 are currently pending and addressed below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/11/2025 has been received. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 10/23/2025 has been received. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Under 35 U.S.C. § 101 a claim is directed to non-statutory subject matter if:
It does not fall within one of the four statutory categories of invention (Process, Machine, Manufacture, or Composition of Matter) or
Meets a three-prong test for determining that
The claim recites a judicial exception (such as: a law of nature, a natural phenomenon, an abstract idea)
Without integration into a practical application, and
Does not recite additional elements that provide significantly more than the recited judicial exception
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1: Statutory Category – Is the claim directed to one of the four statutory categories (a process, a machine, a manufacture, or a composition of matter)?
Claim 1 is directed to a device (i.e. a machine), claim 5 is directed to a method (i.e. a process), and claim 6 is directed to a non-transitory computer-readable recording medium (i.e. a machine). Therefore, claim(s) 1, 5, and 6 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I – Is the claim directed to a judicial exception?
The judicial exceptions are as follows:
Abstract ideas (mathematical concepts, mental processes, and certain methods of organizing human activity)
Laws of nature (e.g., naturally occurring correlations, scientific principles)
Natural phenomena (e.g., wind)
Products of nature (e.g., a plant found in the wild, minerals)
Regarding Prong I of the Step 2A analysis in the 2019 Patent Eligibility Guidance (PEG), the claims are to be analyzed to determine whether they recite subject matter that falls within one of the
follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human
activity, and/or c) mental processes.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can
be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III).
Independent claim 1 includes limitations that recite an abstract idea (emphasized below).
Claim 1 recites:
1. A management device comprising:
a memory configured to store map information for each area where a robot, which autonomously travels outdoors and indoors, travels; and
processing circuitry configured to:
collect external information;
receive a current position of the robot or a travel route of the robot from a control device controlling the robot;
detect an occurrence of an event and an event occurrence area where the event occurs, based on the external information;
identify the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot;
determine information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; and
recommend information about the new travel route to a terminal device.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “detect…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement and “determine…” in the context of this claim encompasses a person looking at known data and information and deciding a route which would avoid entering an undesirable area. Accordingly, the claim recites at least one abstract idea.
Independent claim 5 includes limitations that recite an abstract idea (emphasized below).
Claim 5 recites:
5. A management method executed by a management device, the management method comprising:
collecting external information;
receiving a current position of a robot or a travel route of the robot from a control device controlling the robot;
detecting an occurrence of an event and an event occurrence area where the event occurs based on the external information;
identifying the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot;
determining information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; and
recommending information about the new travel route to a terminal device.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “detecting…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement and “determining…” in the context of this claim encompasses a person looking at known data and information and deciding a route which would avoid entering an undesirable area. Accordingly, the claim recites at least one abstract idea.
Independent claim 6 includes limitations that recite an abstract idea (emphasized below).
Claim 6 recites:
6. A non-transitory computer-readable recording medium storing therein a management program that causes a computer to execute a process comprising:
collecting external information;
receiving a current position of a robot or a travel route of the robot from a control device controlling the robot;
detecting an occurrence of an event and an event occurrence area where the event occurs based on the external information;
identifying the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot;
determining information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; and
recommending information about the new travel route to a terminal device.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “detecting…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement and “determining…” in the context of this claim encompasses a person looking at known data and information and deciding a route which would avoid entering an undesirable area. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II – Does the claim, as a whole, integrate the abstract idea into a practical
application?
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
The guidelines provide the following (non-exhaustive) list of exemplary considerations which are indicative that an additional element (or combination of elements) may have integrated the judicial element into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
An additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition
An additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture which is integral to the claim;
An additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception
The following is a (non-exhaustive) list of examples in which a judicial exception has not been integrated into a practical application:
An additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
An additional element adds insignificant extra-solutionary activity to the judicial exception;
An additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
The Office submits that the foregoing underlined limitation(s) recite additional elements that do
not integrate the recited judicial exception into a practical application.
Independent claim 1 includes limitations that recite additional limitations (emphasized below).
Claim 1 recites:
1. A management device comprising:
a memory configured to store map information for each area where a robot, which autonomously travels outdoors and indoors, travels; and
processing circuitry configured to:
collect external information;
receive a current position of the robot or a travel route of the robot from a control device controlling the robot;
detect an occurrence of an event and an event occurrence area where the event occurs, based on the external information;
identify the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot;
determine information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; and
recommend information about the new travel route to a terminal device.
For the following reason(s), the examiner submits that the above identified additional
limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “collect external information” and “receive a current position” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processing circuitry) to perform the process. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering environmental data for use in the detecting and identifying steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Lastly, the “processing circuitry” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose robotic control environment. The processing circuitry is recited at a high level of generality and merely automates the detecting and identifying steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Independent claim 5 includes limitations that recite additional limitations (emphasized below).
Claim 5 recites:
5. A management method executed by a management device, the management method comprising:
collecting external information;
receiving a current position of a robot or a travel route of the robot from a control device controlling the robot;
detecting an occurrence of an event and an event occurrence area where the event occurs based on the external information;
identifying the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot;
determining information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; and
recommending information about the new travel route to a terminal device.
For the following reason(s), the examiner submits that the above identified additional
limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “collecting external information” and “receiving a current position” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (management device) to perform the process. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering environmental data for use in the detecting and identifying steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Lastly, the “management device” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose robotic control environment. The management device is recited at a high level of generality and merely automates the detecting and identifying steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Independent claim 6 includes limitations that recite additional limitations (emphasized below).
Claim 6 recites:
6. A non-transitory computer-readable recording medium storing therein a management program that causes a computer to execute a process comprising:
collecting external information;
receiving a current position of a robot or a travel route of the robot from a control device controlling the robot;
detecting an occurrence of an event and an event occurrence area where the event occurs based on the external information;
identifying the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot;
determining information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; and
recommending information about the new travel route to a terminal device.
For the following reason(s), the examiner submits that the above identified additional
limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “collecting external information” and “receiving a current position” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering environmental data for use in the detecting and identifying steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Lastly, the “management program” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose robotic control environment. The processing circuitry is recited at a high level of generality and merely automates the detecting and identifying steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B – Do the additional elements incorporate an inventive concept to the claim?
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Regarding independent claim 1:
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor/computer to perform the detecting and identifying amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “collect external information” and “receive a current position” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in
Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of “collect external information” and “receive a current position” are well-understood, routine, and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processing circuitry/computer is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Thus, the independent claim 1 as well as the dependent claims are directed toward an abstract idea, not integrated into a practical application, and do not comprise significantly more than the recited abstract idea.
Regarding independent claim 5:
Regarding Step 2B of the 2019 PEG, representative independent claim 5 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor/computer to perform the detecting and identifying amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “collecting external information” and “receiving a current position” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in
Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of “collecting external information” and “receiving a current position” are well-understood, routine, and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processing circuitry/computer is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Thus, the independent claim 5 as well as the dependent claims are directed toward an abstract idea, not integrated into a practical application, and do not comprise significantly more than the recited abstract idea.
Regarding independent claim 6:
Regarding Step 2B of the 2019 PEG, representative independent claim 6 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor/computer to perform the detecting and identifying amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “collecting external information” and “receiving a current position” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in
Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of “collecting external information” and “receiving a current position” are well-understood, routine, and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processing circuitry/computer is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Thus, the independent claim 6 as well as the dependent claims are directed toward an abstract idea, not integrated into a practical application, and do not comprise significantly more than the recited abstract idea.
101 Analysis – Dependent Claims and Conclusion
Dependent claim(s) 2-4 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-4 are not patent eligible under the same rationale as provided for in the rejection of claim(s) 1, 5, and 6.
Therefore, claim(s) 1-6 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ebrahimi Afrouzi et al. (US 20220187841 A1), hereinafter Ebrahimi Afrouzi in view of Panzica et al. (US 20220179418 A1), hereinafter Panzica.
Regarding claim 1, Ebrahimi Afrouzi teaches:
1. A management device comprising:
a memory (Paragraph 0792, "Some embodiments may provide a robot including communication, mobility, actuation, and processing elements. In some embodiments, the robot may include, but is not limited to include, one or more of a casing, a chassis including a set of wheels, a motor to drive the wheels, a receiver that acquires signals transmitted from, for example, a transmitting beacon, a transmitter for transmitting signals, a processor, a memory storing instructions that when executed by the processor effectuates robotic operations, a controller, a plurality of sensors (e.g., tactile sensor, obstacle sensor, temperature sensor, imaging sensor, light detection and ranging (LIDAR) sensor, camera, depth sensor, time-of-flight (TOF) sensor, TSSP sensor, optical tracking sensor, sonar sensor, ultrasound sensor, laser sensor, light emitting diode (LED) sensor, etc.), network or wireless communications, radio frequency (RF) communications, power management such as a rechargeable battery, solar panels, or fuel, and one or more clock or synchronizing devices. In some cases, the robot may include communication means such as Wi-Fi, Worldwide Interoperability for Microwave Access (WiMax), WiMax mobile, wireless, cellular, Bluetooth, RF, etc. In some cases, the robot may support the use of a 360 degrees LIDAR and a depth camera with limited field of view. In some cases, the robot may support proprioceptive sensors (e.g., independently or in fusion), odometry devices, optical tracking sensors, smart phone inertial measurement units (IMU), and gyroscopes. In some cases, the robot may include at least one cleaning tool (e.g., disinfectant sprayer, brush, mop, scrubber, steam mop, cleaning pad, ultraviolet (UV) sterilizer, etc.). The processor may, for example, receive and process data from internal or external sensors, execute commands based on data received, control motors such as wheel motors, map the environment, localize the robot, determine division of the environment into zones, and determine movement paths. In some cases, the robot may include a microcontroller on which computer code required for executing the methods and techniques described herein may be stored.") configured to store map information for each area (Paragraph 0798, "In some embodiments, Light Weight Real Time SLAM Navigational Stack may include a state machine portion, a control system portion, a local area monitor portion, and a pose and maps portion. FIG. 4 provides a visualization of an example of a Light Weight Real Time SLAM Navigational Stack algorithm. The state machine 1100 may determine current and next behaviors. At a high level, the state machine 1100 may include the behaviors reset, normal cleaning, random cleaning, and find the dock. The control system 1101 may determine normal kinematic driving, online navigation (i.e., real time navigation), and robust navigation (i.e., navigation in high obstacle density areas). The local area monitor 1102 may generate a high resolution map based on short range sensor measurements and control speed of the robot. The control system 301 may receive information from the local area monitor 1102 that may be used in navigation decisions. The pose and maps portion 1103 may include a coverage tracker 1104, a pose estimator 1105, SLAM 1106, and a SLAM updater 1107. The pose estimator 1105 may include an Extended Kalman Filter (EKF) that uses odometry, IMU, and LIDAR data. SLAM 1106 may build a map based on scan matching. The pose estimator 1105 and SLAM 1106 may pass information to one another in a feedback loop. The SLAM updated 1107 may estimate the pose of the robot. The coverage tracker 1104 may track internal coverage and exported coverage. The coverage tracker 1104 may receive information from the pose estimator 1105, SLAM 1106, and SLAM updated 1107 that it may use in tracking coverage. In one embodiment, the coverage tracker 1104 may run at 2.4 Hz. In other indoor embodiments, the coverage tracker may run at between 1-50 Hz. For outdoor robots, the frequency may increase depending on the speed of the robot and the speed of data collection. A person in the art would be able to calculate the frequency of data collection, data usage, and data transmission to control system. The control system 1101 may receive information from the pose and maps portion 1103 that may be used for navigation decisions.") where a robot, which autonomously travels outdoors and indoors, travels; (Paragraph 0004, "Robotic devices are increasingly used within commercial and consumer environments. Some examples include robotic lawn mowers, robotic surface cleaners, autonomous vehicles, robotic delivery devices, robotic shopping carts, etc. Since changes in the environment, such as the movement of dynamic obstacles (e.g., humans walking around), occurs in real time, a robotic device must interact (e.g., executing actions or making movements) in real time as well for the interaction to be meaningful." as well as Paragraph 0798 "In some embodiments, Light Weight Real Time SLAM Navigational Stack may include a state machine portion, a control system portion, a local area monitor portion, and a pose and maps portion. FIG. 4 provides a visualization of an example of a Light Weight Real Time SLAM Navigational Stack algorithm. The state machine 1100 may determine current and next behaviors. At a high level, the state machine 1100 may include the behaviors reset, normal cleaning, random cleaning, and find the dock. The control system 1101 may determine normal kinematic driving, online navigation (i.e., real time navigation), and robust navigation (i.e., navigation in high obstacle density areas). The local area monitor 1102 may generate a high resolution map based on short range sensor measurements and control speed of the robot. The control system 301 may receive information from the local area monitor 1102 that may be used in navigation decisions. The pose and maps portion 1103 may include a coverage tracker 1104, a pose estimator 1105, SLAM 1106, and a SLAM updater 1107. The pose estimator 1105 may include an Extended Kalman Filter (EKF) that uses odometry, IMU, and LIDAR data. SLAM 1106 may build a map based on scan matching. The pose estimator 1105 and SLAM 1106 may pass information to one another in a feedback loop. The SLAM updated 1107 may estimate the pose of the robot. The coverage tracker 1104 may track internal coverage and exported coverage. The coverage tracker 1104 may receive information from the pose estimator 1105, SLAM 1106, and SLAM updated 1107 that it may use in tracking coverage. In one embodiment, the coverage tracker 1104 may run at 2.4 Hz. In other indoor embodiments, the coverage tracker may run at between 1-50 Hz. For outdoor robots, the frequency may increase depending on the speed of the robot and the speed of data collection. A person in the art would be able to calculate the frequency of data collection, data usage, and data transmission to control system. The control system 1101 may receive information from the pose and maps portion 1103 that may be used for navigation decisions.". Please also see paragraph 1021) and
processing circuitry (Paragraph 0792, "Some embodiments may provide a robot including communication, mobility, actuation, and processing elements. In some embodiments, the robot may include, but is not limited to include, one or more of a casing, a chassis including a set of wheels, a motor to drive the wheels, a receiver that acquires signals transmitted from, for example, a transmitting beacon, a transmitter for transmitting signals, a processor, a memory storing instructions that when executed by the processor effectuates robotic operations, a controller, a plurality of sensors (e.g., tactile sensor, obstacle sensor, temperature sensor, imaging sensor, light detection and ranging (LIDAR) sensor, camera, depth sensor, time-of-flight (TOF) sensor, TSSP sensor, optical tracking sensor, sonar sensor, ultrasound sensor, laser sensor, light emitting diode (LED) sensor, etc.), network or wireless communications, radio frequency (RF) communications, power management such as a rechargeable battery, solar panels, or fuel, and one or more clock or synchronizing devices. In some cases, the robot may include communication means such as Wi-Fi, Worldwide Interoperability for Microwave Access (WiMax), WiMax mobile, wireless, cellular, Bluetooth, RF, etc. In some cases, the robot may support the use of a 360 degrees LIDAR and a depth camera with limited field of view. In some cases, the robot may support proprioceptive sensors (e.g., independently or in fusion), odometry devices, optical tracking sensors, smart phone inertial measurement units (IMU), and gyroscopes. In some cases, the robot may include at least one cleaning tool (e.g., disinfectant sprayer, brush, mop, scrubber, steam mop, cleaning pad, ultraviolet (UV) sterilizer, etc.). The processor may, for example, receive and process data from internal or external sensors, execute commands based on data received, control motors such as wheel motors, map the environment, localize the robot, determine division of the environment into zones, and determine movement paths. In some cases, the robot may include a microcontroller on which computer code required for executing the methods and techniques described herein may be stored.") configured to:
collect external information; (Paragraph 0793, "In some embodiments, at least a portion of the sensors of the robot are provided in a sensor array, wherein the at least a portion of sensors are coupled to a flexible, semi-flexible, or rigid frame. In some embodiments, the frame is fixed to a chassis or casing of the robot. In some embodiments, the sensors are positioned along the frame such that the field of view of the robot is maximized while the cross-talk or interference between sensors is minimized. In some cases, a component may be placed between adjacent sensors to minimize cross-talk or interference. In some embodiments, the robot may include sensors to detect or sense objects, acceleration, angular and linear movement, temperature, humidity, water, pollution, particles in the air, supplied power, proximity, external motion, device motion, sound signals, ultrasound signals, light signals, fire, smoke, carbon monoxide, global-positioning-satellite (GPS) signals, radio-frequency (RF) signals, other electromagnetic signals or fields, visual features, textures, optical character recognition (OCR) signals, spectrum meters, and the like. In some embodiments, a microprocessor or a microcontroller of the robot may poll a variety of sensors at intervals.")
receive a current position of the robot or a travel route of the robot from a control device controlling the robot; (Paragraph 0877, "In some embodiments, neural network may be advantageous for older, manually constructed features that are human understandable and, to some extent, in removing the human middleman from the process. In some embodiments, a neural network may be used to adjudicate depth sensing, extract movement (e.g., angular and linear) of the robot, combine iterations of sensor readings into a map, adjudicate location (i.e., localization), extract dynamic obstacles and separate them from structural points, and actuate the robot such that the trajectory of the robot better matches the planned path.")
detect an occurrence of an event and an event occurrence area where the event occurs, based on the external information; (Paragraph 01239, "In some embodiments, the processor of the robot determines areas of the environment to avoid based on certain conditions (e.g., human activity, cleanliness, weather, etc.). In some embodiments, the conditions are chosen by a user using the application of the communication device." Please also see paragraph 01247)
…
Ebrahimi Afrouzi does not specifically discuss identifying devices which are or are predicted to be in the event area and determining an updated trajectory for each device. However, Panzica, in the same field of endeavor of autonomous machines, teaches:
… identify the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot; (Paragraph 0052, "The systems, methods, and vehicles described herein have an additional technical effect and benefit of providing more efficient navigation while simultaneously enhancing the safety and security of autonomous vehicles, passengers and/or cargo. By providing a mechanism to obtain constraint data, autonomous vehicles can benefit from the knowledge of when and where potential problem areas may exist for travel routes. A vehicle computing system can determine optimized travel routes or update travel routes in an enhanced manner by evaluating map data relative to current constraint data in order to avoid exclusion areas. By planning ahead to avoid such areas, the potential for navigational back-tracking is reduced. In addition, by avoiding exclusion areas that are identified because of certain events (e.g., traffic accidents, protestor demonstrations, parades, bridge closures, etc.), and by enabling the autonomous vehicle to drive out of a depart constraint area, the safety of vehicles, passengers, and/or cargo can be increased.")
determine information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; (Paragraph 0049, "The systems, methods, and vehicles described herein may provide a number of technical effects and benefits. For instance, the vehicle computing system can locally (e.g., on-board the vehicle) obtain constraint data, evaluate map data relative to the constraint data, and determine a travel route for navigating the autonomous vehicle in compliance with the constraint data. By performing such operations on-board the autonomous vehicle, the vehicle computing system can avoid certain latency issues that arise by reliance on a remote computing system for off-board operations. For example, the vehicle computing system can be configured to initialize and update its travel route(s) based on obtained constraint data and accessible map data as opposed to waiting for determined travel routes to be approved or disapproved by a central command or other remote computing device/system. As such, map data can be evaluated relative to constraint data and travel routes can be determined with increased computational efficiency.") and
recommend information about the new travel route to a terminal device. (Paragraph 0133, "Controlling motion of a vehicle at 1116 can include providing data indicative of a motion plan to a vehicle controller to implement the motion plan for the autonomous vehicle 102. For instance, an autonomous vehicle 102 can include a vehicle controller 116 as depicted in FIG. 1 that is configured to translate the motion plan into instructions. By way of example, the vehicle controller 116 can translate a determined motion plan into instructions to adjust the steering of the autonomous vehicle 102 “X” degrees, apply 10% braking force, modulate a speed of the autonomous vehicle 102, etc. The vehicle controller 116 can send one or more control signals to components of the vehicle controls 108 (e.g., braking control component, steering control component, speed/throttle control component) to execute the instructions and implement the motion plan.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system and methods of operating autonomous/semi-autonomous machines to identify and avoid regions or sub-regions as taught by Ebrahimi Afrouzi with the ability to identify devices which are or are predicted to be in the region and to determine an updated path for travel as taught by Panzica. This would allow the devices to efficiently avoid regions which may cause delays in travel or damage to the system.
Regarding claim 2, where all the limitations of claim 1 are discussed above, Ebrahimi Afrouzi further teaches:
2. The management device according to claim 1, wherein
when the robot does not retain information about the travel route, the processing circuitry is further configured to identify the robot located in the event occurrence area based on the current position, (Paragraph 0877, "In some embodiments, neural network may be advantageous for older, manually constructed features that are human understandable and, to some extent, in removing the human middleman from the process. In some embodiments, a neural network may be used to adjudicate depth sensing, extract movement (e.g., angular and linear) of the robot, combine iterations of sensor readings into a map, adjudicate location (i.e., localization), extract dynamic obstacles and separate them from structural points, and actuate the robot such that the trajectory of the robot better matches the planned path.") and
when the robot retains information about the travel route, identify the robot that retains the travel route included in the event occurrence area. (Paragraph 0990, "In embodiments, regaining localization may be different for different data structures. While an image search performed in a featureless scene due lost localization may not yield desirable results, a depth search may quickly help the processor regain localization of the robot and vice versa. For example, depth readings impacted by short readings caused by dust, particles, human legs, pet legs, a feature that is located at a different height, or an angle, may remain reasonably intact within the timeframe in which the depth readings were unclear. When trying to relocalize the robot, the first guess of the processor may comprise where the processor predicts the location of the robot to be. Based on control commands issued to the robot to execute a planned path, the processor may predict the vicinity in which the robot is located. In some embodiments, a best guess of a location of the robot may include a last known localization. In some embodiments, determining a next best guess of the location of the robot may include a search of other last known places of the robot, otherwise known as rendezvous points (RP). In some embodiments, the processor may use various methods in parallel to determine or predict a location of the robot.")
Regarding claim 3, where all the limitations of claim 1 are discussed above, Ebrahimi Afrouzi further teaches:
3. The management device according to claim 1, wherein
the external information includes a result of event occurrence prediction based on one or more of (Paragraph 0793, "In some embodiments, at least a portion of the sensors of the robot are provided in a sensor array, wherein the at least a portion of sensors are coupled to a flexible, semi-flexible, or rigid frame. In some embodiments, the frame is fixed to a chassis or casing of the robot. In some embodiments, the sensors are positioned along the frame such that the field of view of the robot is maximized while the cross-talk or interference between sensors is minimized. In some cases, a component may be placed between adjacent sensors to minimize cross-talk or interference. In some embodiments, the robot may include sensors to detect or sense objects, acceleration, angular and linear movement, temperature, humidity, water, pollution, particles in the air, supplied power, proximity, external motion, device motion, sound signals, ultrasound signals, light signals, fire, smoke, carbon monoxide, global-positioning-satellite (GPS) signals, radio-frequency (RF) signals, other electromagnetic signals or fields, visual features, textures, optical character recognition (OCR) signals, spectrum meters, and the like. In some embodiments, a microprocessor or a microcontroller of the robot may poll a variety of sensors at intervals.") information about a disaster in each area, (Paragraph 1370, "In some embodiments, the processor may combine AR with SLAM techniques. In some embodiments, a SLAM enabled device (e.g., robot, smart watch, cell phone, smart glasses, etc.) may collect environmental sensor data and generate maps of the environment. In some embodiments, the environmental sensor data as well as the maps may be overlaid on top of an augmented reality representation of the environment, such as a video feed captured by a video sensor of the SLAM enabled device or another device all together. In some embodiments, the SLAM enabled device may be wearable (e.g., by a human, pet, robot, etc.) and may map the environment as the device is moved within the environment. In some embodiments, the SLAM enabled device may simultaneously transmit the map as its being built and useful environmental information as its being collect for overlay on the video feed of a camera. In some cases, the camera may be a camera of a different device or of the SLAM enabled device itself. For example, this capability may be useful in situations such as natural disaster aftermaths (e.g., earthquakes or hurricanes) where first responders may be provided environmental information such as area maps, temperature maps, oxygen level maps, etc. on their phone or headset camera. Examples of other use cases may include situations handled by police or fire fighting forces. For instance, an autonomous robot may be used to enter a dangerous environment to collect environmental data such as area maps, temperature maps, obstacle maps, etc. that may be overlaid with a video feed of a camera of the robot or a camera of another device. In some cases, the environmental data overlaid on the video feed may be transmitted to a communication device (e.g., of a police or fire fighter for analysis of the situation). Another example of a use case includes the mining industry as SLAM enabled devices are not required to rely on light to observe the environment. For example, a SLAM enabled device may generate a map using sensors such as LIDAR and sonar sensors that are functional in low lighting and may transmit the sensor data for overlay on a video feed of camera of a miner or construction worker. In some embodiments, a SLAM enabled device, such as a robot, may observe an environment and may simultaneously transmit a live video feed of its camera to an application of a communication device of a user. In some embodiments, the user may annotate directly on the video to guide the robot using the application. In some embodiments, the user may share the information with other users using the application. Since the SLAM enabled device uses SLAM to map the environment, in some embodiments, the processor of the SLAM enabled device may determine the location of newly added information within the map and display it in the correct location on the video feed. In some cases, the advantage of combined SLAM and AR is the combined information obtained from the video feed of the camera and the environmental sensor data and maps. For example, in AR, information may appear as an overlay of a video feed by tracking objects within the camera frame. However, as soon as the objects move beyond the camera frame, the tracking points of the objects and hence information on their location are lost. With combined SLAM and AR, location of objects observed by the camera may be saved within the map generated using SLAM techniques. This may be helpful in situations where areas may be off-limits, such as in construction sites. For example, a user may insert an off-limit area in a live video feed using an application displaying the live video feed. The off-limit area may then be saved to a map of the environment such that its position is known. In another example, a civil engineer may remotely insert notes associated with different areas of the environment as they are shown on the live video feed. These notes may be associated with the different areas on a corresponding map and may be accessed at a later time. In one example, a remote technician may draw circles to point out different components of a machine on a video feed from an onsite camera through an application and the onsite user may view the circles as overlays in 3D space. In some embodiments, based on SLAM data and/or map and other data sets, a processor may overlay various equipment and facilities related to the environment based on points of interest (e.g., electrical layout of a room or building, plumbing layout of a room or building, framing of a room or building, air flow circulation or temperature in a room or building, etc.") information acquired by the robot, (Paragraph 0006, "Some aspects include a method for operating a battery operated wheeled device actuated with electric motors, including: capturing, by a primary sensor coupled to the wheeled device, primary sensor data indicative of a plurality of radial distances from the primary sensor to objects within a maximum range of the primary sensor as the robot performs work within an environment; transforming, by a processor of the wheeled device, the plurality of radial distances from a perspective of the primary sensor to a perspective of the wheeled device based on a physical position of the primary sensor relative to a body of the wheeled device; generating, by the processor, a partial map of visible areas of the environment in real-time at a first position of the wheeled device based on the primary sensor data and at least some secondary sensor data, wherein: the partial map is a bird's eye view of the environment; and the processor iteratively completes a full map of the environment based on new sensor data captured by sensors as the wheeled device performs work within the environment and new areas become visible to the sensors; and executing, by the wheeled device, a movement path to a second position.") information about human flow, information about weather, (Paragraph 1239, "In some embodiments, the map of the area, including but not limited to doorways, sub areas, perimeter openings, and information such as coverage pattern, room tags, order of rooms, etc. is available to the user through a graphical user interface (GUI) such as a smartphone, computer, tablet, dedicated remote control, or any device that may display output data from the robot and receive inputs from a user. Through the GUI, a user may review, accept, decline, or make changes to, for example, the map of the environment and settings, functions and operations of the robot within the environment, which may include, but are not limited to, type of coverage algorithm of the entire area or each subarea, correcting or adjusting map boundaries and the location of doorways, creating or adjusting subareas, order of cleaning subareas, scheduled cleaning of the entire area or each subarea, and activating or deactivating tools such as UV light, disinfectant sprayer, and steam. User inputs are sent from the GUI to the robot for implementation. For example, the user may use the application to create boundary zones or virtual barriers and cleaning areas. In some embodiments, the user may use the application to also define a task associated with each zone (e.g., no entry, steam cleaning, UV cleaning). In some cases, the task within each zone may be scheduled using the application (e.g., UV cleaning hospital beds on floor 2 on Tuesdays at 10:00 AM and Friday at 8:00 PM). In some embodiments, the robot may avoid entering particular areas of the environment. In some embodiments, a user may use an application of a communication device (e.g., mobile device, laptop, tablet, smart watch, remote, etc.) and/or a graphical user interface (GUI) of the robot to access a map of the environment and select areas the robot is to avoid. In some embodiments, the processor of the robot determines areas of the environment to avoid based on certain conditions (e.g., human activity, cleanliness, weather, etc.). In some embodiments, the conditions are chosen by a user using the application of the communication device.") and information about traffic, (Paragraph 0757, "In some embodiments, the relation between components forms a service-client relationship. In one example, a cloud service may store and save all previous mapping information and localizations of a robot as history. This history may be provided as a service to clients (e.g., similar to history stored by a web browser), wherein a person may search for data from a specific work session of the robot. In some embodiments, the history may be sent to an application that sorts, organizes and/or analyzes the data. In some embodiments, a data mining client may collect data from the histories of multiple robots (e.g., 100, 1000, 300000, or all robots) over periods of time to deduce large-scale patterns or reasoning. For example, traffic data associated with a particular location historic number of robot accidents, historic battery use, etc. may be aggregated and used to improve performance of robots. In embodiments, such services do not need to be on a physical server and may be geographically spread out. There may be multiple services that are available to multiple clients. In some embodiments, clients may, in part, provide services to other clients and vice versa. In some embodiments, a service provider to one client may itself be a client of one or more services.") and
the processing circuitry (Paragraph 0792, "Some embodiments may provide a robot including communication, mobility, actuation, and processing elements. In some embodiments, the robot may include, but is not limited to include, one or more of a casing, a chassis including a set of wheels, a motor to drive the wheels, a receiver that acquires signals transmitted from, for example, a transmitting beacon, a transmitter for transmitting signals, a processor, a memory storing instructions that when executed by the processor effectuates robotic operations, a controller, a plurality of sensors (e.g., tactile sensor, obstacle sensor, temperature sensor, imaging sensor, light detection and ranging (LIDAR) sensor, camera, depth sensor, time-of-flight (TOF) sensor, TSSP sensor, optical tracking sensor, sonar sensor, ultrasound sensor, laser sensor, light emitting diode (LED) sensor, etc.), network or wireless communications, radio frequency (RF) communications, power management such as a rechargeable battery, solar panels, or fuel, and one or more clock or synchronizing devices. In some cases, the robot may include communication means such as Wi-Fi, Worldwide Interoperability for Microwave Access (WiMax), WiMax mobile, wireless, cellular, Bluetooth, RF, etc. In some cases, the robot may support the use of a 360 degrees LIDAR and a depth camera with limited field of view. In some cases, the robot may support proprioceptive sensors (e.g., independently or in fusion), odometry devices, optical tracking sensors, smart phone inertial measurement units (IMU), and gyroscopes. In some cases, the robot may include at least one cleaning tool (e.g., disinfectant sprayer, brush, mop, scrubber, steam mop, cleaning pad, ultraviolet (UV) sterilizer, etc.). The processor may, for example, receive and process data from internal or external sensors, execute commands based on data received, control motors such as wheel motors, map the environment, localize the robot, determine division of the environment into zones, and determine movement paths. In some cases, the robot may include a microcontroller on which computer code required for executing the methods and techniques described herein may be stored.") is further configured to … that avoids the event indicated by the result of the event occurrence prediction. (Paragraph 01239, "In some embodiments, the processor of the robot determines areas of the environment to avoid based on certain conditions (e.g., human activity, cleanliness, weather, etc.). In some embodiments, the conditions are chosen by a user using the application of the communication device." Please also see paragraph 01247)
Ebrahimi Afrouzi does not specifically discuss determining an updated trajectory for each device. However, Panzica, in the same field of endeavor of autonomous machines, teaches:
… determine information about the new travel route (Paragraph 0049, "The systems, methods, and vehicles described herein may provide a number of technical effects and benefits. For instance, the vehicle computing system can locally (e.g., on-board the vehicle) obtain constraint data, evaluate map data relative to the constraint data, and determine a travel route for navigating the autonomous vehicle in compliance with the constraint data. By performing such operations on-board the autonomous vehicle, the vehicle computing system can avoid certain latency issues that arise by reliance on a remote computing system for off-board operations. For example, the vehicle computing system can be configured to initialize and update its travel route(s) based on obtained constraint data and accessible map data as opposed to waiting for determined travel routes to be approved or disapproved by a central command or other remote computing device/system. As such, map data can be evaluated relative to constraint data and travel routes can be determined with increased computational efficiency.") …
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system and methods of operating autonomous/semi-autonomous machines to identify and avoid regions or sub-regions as taught by Ebrahimi Afrouzi with the ability to identify devices which are or are predicted to be in the region and to determine an updated path for travel as taught by Panzica. This would allow the devices to efficiently avoid regions which may cause delays in travel or damage to the system.
Regarding claim 4, where all the limitations of claim 1 are discussed above, Ebrahimi Afrouzi further teaches:
4. The management device according to claim 1, wherein the processing circuitry (Paragraph 0792, "Some embodiments may provide a robot including communication, mobility, actuation, and processing elements. In some embodiments, the robot may include, but is not limited to include, one or more of a casing, a chassis including a set of wheels, a motor to drive the wheels, a receiver that acquires signals transmitted from, for example, a transmitting beacon, a transmitter for transmitting signals, a processor, a memory storing instructions that when executed by the processor effectuates robotic operations, a controller, a plurality of sensors (e.g., tactile sensor, obstacle sensor, temperature sensor, imaging sensor, light detection and ranging (LIDAR) sensor, camera, depth sensor, time-of-flight (TOF) sensor, TSSP sensor, optical tracking sensor, sonar sensor, ultrasound sensor, laser sensor, light emitting diode (LED) sensor, etc.), network or wireless communications, radio frequency (RF) communications, power management such as a rechargeable battery, solar panels, or fuel, and one or more clock or synchronizing devices. In some cases, the robot may include communication means such as Wi-Fi, Worldwide Interoperability for Microwave Access (WiMax), WiMax mobile, wireless, cellular, Bluetooth, RF, etc. In some cases, the robot may support the use of a 360 degrees LIDAR and a depth camera with limited field of view. In some cases, the robot may support proprioceptive sensors (e.g., independently or in fusion), odometry devices, optical tracking sensors, smart phone inertial measurement units (IMU), and gyroscopes. In some cases, the robot may include at least one cleaning tool (e.g., disinfectant sprayer, brush, mop, scrubber, steam mop, cleaning pad, ultraviolet (UV) sterilizer, etc.). The processor may, for example, receive and process data from internal or external sensors, execute commands based on data received, control motors such as wheel motors, map the environment, localize the robot, determine division of the environment into zones, and determine movement paths. In some cases, the robot may include a microcontroller on which computer code required for executing the methods and techniques described herein may be stored." Please also see paragraph 1261) is further configured to …
Ebrahimi Afrouzi does not specifically discuss determining new trajectories for each device in the area or expected to be in the area. However, Panzica, in the same field of endeavor of autonomous machines, teaches:
… determine information about new travel routes (Paragraph 0049, "The systems, methods, and vehicles described herein may provide a number of technical effects and benefits. For instance, the vehicle computing system can locally (e.g., on-board the vehicle) obtain constraint data, evaluate map data relative to the constraint data, and determine a travel route for navigating the autonomous vehicle in compliance with the constraint data. By performing such operations on-board the autonomous vehicle, the vehicle computing system can avoid certain latency issues that arise by reliance on a remote computing system for off-board operations. For example, the vehicle computing system can be configured to initialize and update its travel route(s) based on obtained constraint data and accessible map data as opposed to waiting for determined travel routes to be approved or disapproved by a central command or other remote computing device/system. As such, map data can be evaluated relative to constraint data and travel routes can be determined with increased computational efficiency.") that differ among robots located in the event occurrence area. (Paragraph 0064, "Further sample embodiments include a computing device for controlling one or more autonomous vehicles. The computing device receives from one or more networks traffic flow constraint information from at least one of a central planning resource or a traffic monitoring resource. Based on the traffic flow constraint information, autonomy map constraints for autonomous vehicles operating in a given region are updated. The autonomy map includes constraint data defining constraints descriptive of one or more geographic areas or geographic identifiers for which associated navigational constraints are defined, and the constraint data includes a depart constraint that specifies an area that an autonomous vehicle may not enter but may exit if inside the area when the depart constraint is imposed. The data indicating the autonomy map constraints is transmitted over the one or more networks to the one or more autonomous vehicles. In sample embodiments, the data indicating the autonomy map constraints is selectively transmitted to the one or more autonomous vehicles based on the one or more autonomous vehicles each being on a current route converging towards or intersecting with a road segment closure defined by the autonomy map constraints." as well as Paragraphs 0095 and 0116)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system and methods of operating autonomous/semi-autonomous machines to identify and avoid regions or sub-regions as taught by Ebrahimi Afrouzi with the ability to identify devices which are or are predicted to be in the region and to provide the information to each device as well as to determine an updated path of travel for each device as taught by Panzica. This would allow the devices to efficiently avoid regions which may cause delays in travel or damage to the system.
Regarding claim 5, Ebrahimi Afrouzi further teaches:
5. A management method executed by a management device, the management method comprising:
collecting external information; (Paragraph 0793, "In some embodiments, at least a portion of the sensors of the robot are provided in a sensor array, wherein the at least a portion of sensors are coupled to a flexible, semi-flexible, or rigid frame. In some embodiments, the frame is fixed to a chassis or casing of the robot. In some embodiments, the sensors are positioned along the frame such that the field of view of the robot is maximized while the cross-talk or interference between sensors is minimized. In some cases, a component may be placed between adjacent sensors to minimize cross-talk or interference. In some embodiments, the robot may include sensors to detect or sense objects, acceleration, angular and linear movement, temperature, humidity, water, pollution, particles in the air, supplied power, proximity, external motion, device motion, sound signals, ultrasound signals, light signals, fire, smoke, carbon monoxide, global-positioning-satellite (GPS) signals, radio-frequency (RF) signals, other electromagnetic signals or fields, visual features, textures, optical character recognition (OCR) signals, spectrum meters, and the like. In some embodiments, a microprocessor or a microcontroller of the robot may poll a variety of sensors at intervals.")
receiving a current position of a robot or a travel route of the robot from a control device controlling the robot; (Paragraph 0877, "In some embodiments, neural network may be advantageous for older, manually constructed features that are human understandable and, to some extent, in removing the human middleman from the process. In some embodiments, a neural network may be used to adjudicate depth sensing, extract movement (e.g., angular and linear) of the robot, combine iterations of sensor readings into a map, adjudicate location (i.e., localization), extract dynamic obstacles and separate them from structural points, and actuate the robot such that the trajectory of the robot better matches the planned path.")
detecting an occurrence of an event and an event occurrence area where the event occurs based on the external information; (Paragraph 01239, "In some embodiments, the processor of the robot determines areas of the environment to avoid based on certain conditions (e.g., human activity, cleanliness, weather, etc.). In some embodiments, the conditions are chosen by a user using the application of the communication device." Please also see paragraph 01247)
…
Ebrahimi Afrouzi does not specifically discuss identifying devices which are or are predicted to be in the event area and determining an updated trajectory for each device. However, Panzica, in the same field of endeavor of autonomous machines, teaches:
… identifying the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot; (Paragraph 0052, "The systems, methods, and vehicles described herein have an additional technical effect and benefit of providing more efficient navigation while simultaneously enhancing the safety and security of autonomous vehicles, passengers and/or cargo. By providing a mechanism to obtain constraint data, autonomous vehicles can benefit from the knowledge of when and where potential problem areas may exist for travel routes. A vehicle computing system can determine optimized travel routes or update travel routes in an enhanced manner by evaluating map data relative to current constraint data in order to avoid exclusion areas. By planning ahead to avoid such areas, the potential for navigational back-tracking is reduced. In addition, by avoiding exclusion areas that are identified because of certain events (e.g., traffic accidents, protestor demonstrations, parades, bridge closures, etc.), and by enabling the autonomous vehicle to drive out of a depart constraint area, the safety of vehicles, passengers, and/or cargo can be increased.")
determining information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; and (Paragraph 0049, "The systems, methods, and vehicles described herein may provide a number of technical effects and benefits. For instance, the vehicle computing system can locally (e.g., on-board the vehicle) obtain constraint data, evaluate map data relative to the constraint data, and determine a travel route for navigating the autonomous vehicle in compliance with the constraint data. By performing such operations on-board the autonomous vehicle, the vehicle computing system can avoid certain latency issues that arise by reliance on a remote computing system for off-board operations. For example, the vehicle computing system can be configured to initialize and update its travel route(s) based on obtained constraint data and accessible map data as opposed to waiting for determined travel routes to be approved or disapproved by a central command or other remote computing device/system. As such, map data can be evaluated relative to constraint data and travel routes can be determined with increased computational efficiency.")
recommending information about the new travel route to a terminal device. (Paragraph 0133, "Controlling motion of a vehicle at 1116 can include providing data indicative of a motion plan to a vehicle controller to implement the motion plan for the autonomous vehicle 102. For instance, an autonomous vehicle 102 can include a vehicle controller 116 as depicted in FIG. 1 that is configured to translate the motion plan into instructions. By way of example, the vehicle controller 116 can translate a determined motion plan into instructions to adjust the steering of the autonomous vehicle 102 “X” degrees, apply 10% braking force, modulate a speed of the autonomous vehicle 102, etc. The vehicle controller 116 can send one or more control signals to components of the vehicle controls 108 (e.g., braking control component, steering control component, speed/throttle control component) to execute the instructions and implement the motion plan.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system and methods of operating autonomous/semi-autonomous machines to identify and avoid regions or sub-regions as taught by Ebrahimi Afrouzi with the ability to identify devices which are or are predicted to be in the region and to determine an updated path for travel as taught by Panzica. This would allow the devices to efficiently avoid regions which may cause delays in travel or damage to the system.
Regarding claim 5, Ebrahimi Afrouzi further teaches:
6. A non-transitory computer-readable recording medium storing therein a management program that causes a computer to execute a process comprising:
collecting external information; (Paragraph 0793, "In some embodiments, at least a portion of the sensors of the robot are provided in a sensor array, wherein the at least a portion of sensors are coupled to a flexible, semi-flexible, or rigid frame. In some embodiments, the frame is fixed to a chassis or casing of the robot. In some embodiments, the sensors are positioned along the frame such that the field of view of the robot is maximized while the cross-talk or interference between sensors is minimized. In some cases, a component may be placed between adjacent sensors to minimize cross-talk or interference. In some embodiments, the robot may include sensors to detect or sense objects, acceleration, angular and linear movement, temperature, humidity, water, pollution, particles in the air, supplied power, proximity, external motion, device motion, sound signals, ultrasound signals, light signals, fire, smoke, carbon monoxide, global-positioning-satellite (GPS) signals, radio-frequency (RF) signals, other electromagnetic signals or fields, visual features, textures, optical character recognition (OCR) signals, spectrum meters, and the like. In some embodiments, a microprocessor or a microcontroller of the robot may poll a variety of sensors at intervals.")
receiving a current position of a robot or a travel route of the robot from a control device controlling the robot; (Paragraph 0877, "In some embodiments, neural network may be advantageous for older, manually constructed features that are human understandable and, to some extent, in removing the human middleman from the process. In some embodiments, a neural network may be used to adjudicate depth sensing, extract movement (e.g., angular and linear) of the robot, combine iterations of sensor readings into a map, adjudicate location (i.e., localization), extract dynamic obstacles and separate them from structural points, and actuate the robot such that the trajectory of the robot better matches the planned path.")
detecting an occurrence of an event and an event occurrence area where the event occurs based on the external information; (Paragraph 01239, "In some embodiments, the processor of the robot determines areas of the environment to avoid based on certain conditions (e.g., human activity, cleanliness, weather, etc.). In some embodiments, the conditions are chosen by a user using the application of the communication device." Please also see paragraph 01247)
…
Ebrahimi Afrouzi does not specifically discuss identifying devices which are or are predicted to be in the event area and determining an updated trajectory for each device. However, Panzica, in the same field of endeavor of autonomous machines, teaches:
… identifying the robot located in the event occurrence area or the robot predicted to be located in the event occurrence area based on the current position of the robot or the travel route of the robot; (Paragraph 0052, "The systems, methods, and vehicles described herein have an additional technical effect and benefit of providing more efficient navigation while simultaneously enhancing the safety and security of autonomous vehicles, passengers and/or cargo. By providing a mechanism to obtain constraint data, autonomous vehicles can benefit from the knowledge of when and where potential problem areas may exist for travel routes. A vehicle computing system can determine optimized travel routes or update travel routes in an enhanced manner by evaluating map data relative to current constraint data in order to avoid exclusion areas. By planning ahead to avoid such areas, the potential for navigational back-tracking is reduced. In addition, by avoiding exclusion areas that are identified because of certain events (e.g., traffic accidents, protestor demonstrations, parades, bridge closures, etc.), and by enabling the autonomous vehicle to drive out of a depart constraint area, the safety of vehicles, passengers, and/or cargo can be increased.")
determining information about a new travel route for each robot located in the event occurrence area or each robot predicted to be located in the event occurrence area based on the external information and the map information; and (Paragraph 0049, "The systems, methods, and vehicles described herein may provide a number of technical effects and benefits. For instance, the vehicle computing system can locally (e.g., on-board the vehicle) obtain constraint data, evaluate map data relative to the constraint data, and determine a travel route for navigating the autonomous vehicle in compliance with the constraint data. By performing such operations on-board the autonomous vehicle, the vehicle computing system can avoid certain latency issues that arise by reliance on a remote computing system for off-board operations. For example, the vehicle computing system can be configured to initialize and update its travel route(s) based on obtained constraint data and accessible map data as opposed to waiting for determined travel routes to be approved or disapproved by a central command or other remote computing device/system. As such, map data can be evaluated relative to constraint data and travel routes can be determined with increased computational efficiency.")
recommending information about the new travel route to a terminal device. (Paragraph 0133, "Controlling motion of a vehicle at 1116 can include providing data indicative of a motion plan to a vehicle controller to implement the motion plan for the autonomous vehicle 102. For instance, an autonomous vehicle 102 can include a vehicle controller 116 as depicted in FIG. 1 that is configured to translate the motion plan into instructions. By way of example, the vehicle controller 116 can translate a determined motion plan into instructions to adjust the steering of the autonomous vehicle 102 “X” degrees, apply 10% braking force, modulate a speed of the autonomous vehicle 102, etc. The vehicle controller 116 can send one or more control signals to components of the vehicle controls 108 (e.g., braking control component, steering control component, speed/throttle control component) to execute the instructions and implement the motion plan.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system and methods of operating autonomous/semi-autonomous machines to identify and avoid regions or sub-regions as taught by Ebrahimi Afrouzi with the ability to identify devices which are or are predicted to be in the region and to determine an updated path for travel as taught by Panzica. This would allow the devices to efficiently avoid regions which may cause delays in travel or damage to the system.
Conclusion
The Examiner has cited particular paragraphs or columns and line numbers in the referencesapplied to the claims above for the convenience of the Applicant. Although the specified citations arerepresentative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested of the Applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2141.02 [R-07.2015] VI. A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed Invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851 (1984). See also MPEP §2123.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HEATHER KENIRY whose telephone number is (571)270-5468. The examiner can normally be reached M-F 7:30-5:30.
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/H.J.K./Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
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