DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed 11/30/2016 has been entered. Claims 1-6 have been canceled. Claims 7-18 are presented for examination.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8, 10-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zou (CN 112965530 translation) in view of Manouchehri (US 2024/0411314).
Regarding claim 1, Zou teaches a method, comprising:
receiving, by a controller having one or more processors, input data (pages 3-4, 1, there may be a master drone and multiple slave drones overseen by the master; the master drone performs the operations of the invention; the master drone receives information e.g., from the slave drones; inherently, the master drone has a controller/processor);
activating, with the controller, an active mode selected from two or more modes for at least a first agent of the agents (a mode can be understood as any state that is associated with various actions; Fig. 1, pages 1-2, 3-5, for example, the system may select between different search methods e.g., an adaptive variable-scale dynamic method used searching a designated target position and a dynamic swarm method used for a general target search; see also the steps of Fig. 1, which indicate that actions may be taken in response to particular conditions/states/modes; Figs. 1-2, pages 3-4, the master generates a grid representing an area, divided into cells; each cell is assigned a probability, thereby forming a probability map, each indicating the probability that a target is in that cell; the master receives information from each drone e.g., heading angle and location coordinates, and then updates the probability map; based on the updated probability map, the system commands drones to perform associated actions e.g., perform a search and/or move; see translation of Fig. 1);
wherein each of the modes include actions that the first agent may perform during the mission and control algorithms for executing each of the actions (Figs. 1-2, pages 3-4, the drones can be commanded to move or search in a variety of ways; one or more steps of the operation e.g., as seen in Fig. 1, can be understood as control algorithms; also, as noted in page 5, under different conditions, different algorithms may be used e.g., adaptive variable-scale dynamic target search and particle swarm algorithm); and
directing, with the one or more processors of the controller, at least the first agent during the mission by:
initiating a current action selected from the actions of the active mode based on the control algorithms of the active mode in response to activation of the active mode (Figs. 1-2, pages 3-4, 1-2, a drone may be commanded to search using an adaptive variable-scale dynamic method e.g., in response to the updating of the probability map, the searching of a region without finding the target etc., as discussed in Figs. 1-2 and page 4; the method shown in Fig. 1 and described in pages 3-4 effectively describes algorithms/methods for determining actions of drones based on current conditions and the current probability map);
receiving and processing sensor data and/or communication data received from one or more of the agents and/or other systems monitoring the operational area in response to
completion of the current action (Fig. 1, pages 3-4, after drones have searched and arrived at locations, the system collects information from the drones e.g., location coordinates and heading; based on the collected information, the probability map is updated);
updating a hybrid state estimator based on the sensor data and/or communication data in response to receiving and processing the sensor data and/or communication data (Fig. 1, pages 3-4, after drones have searched and arrived at locations, the system collects information from the drones e.g., location coordinates and heading; based on the collected information, the probability map is updated);
generating an estimation of a current state of the mission with the hybrid state estimator in response to completion of the hybrid state estimator update (Fig. 1, pages 3-4, after drones have searched and arrived at locations, the system collects information from the drones e.g., location coordinates and heading; based on the collected information, the probability map is updated; storage of the received data and the updated probability map reflect an estimation of the current state)
wherein the estimation includes discrete elements and continuous elements (Zhou teaches an estimation feature/process with various discrete and continuous elements; for example, Zhou teaches a grid with distinct probability values i.e., discrete elements; on the other hand, the grid itself continuously remains and is updated e.g., a continuous element. (See Zhou page 1); Zhou further teaches multiple continuous elements as part of its estimation, which is not limited to the grid; by way of example, Zhou teaches a probability map that is continuously updated in real time (Zhou page 1), continuously involves the use of particular search particle swarm and other algorithms (Zhou pages 1-2, 4), continuously monitors GPS data and measures speed (Zhou page 2), continuously iterates an algorithm to perform a search estimation (Zhou page 2), continuously updates a location estimation (Zhou page 5) etc.); and
activating one of the two or more modes as the active mode for at least the first agent in accordance with confidence-based transition logic based on the estimation generated by the hybrid state estimator in response to completion of generation of the estimation by the hybrid
state estimator (Figs. 1-2, pages 3-4, the master generates a grid representing an area, divided into cells; each cell is assigned a probability, thereby forming a probability map, each indicating the probability that a target is in that cell; the master receives information from each drone e.g., heading angle and location coordinates, and then updates the probability map; based on the updated probability map, the system commands drones to perform associated actions e.g., perform a search and/or move; for example, as noted in Fig. 1, pages 3-4, if a drone detects the target during a search, the probability map is updated, and drones are commanded to move to a corresponding target location and search using a particle swarm algorithm).
However, Zou does not expressly disclose the input data indicative of one or more objectives of a mission, wherein the mission includes agents performing actions in an operational area.
In the same field of endeavor Manouchehri teaches the input data indicative of one or more objectives of a mission, wherein the mission includes agents performing actions in an operational area ([0049, 0050, 0003], it is known for a drone to receive an assignment from a server to perform various operations, based on its role; a leader drone will receive leader oriented operations, such as how to instruct other drones of how to complete a mission).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the input data indicative of one or more objectives of a mission, wherein the mission includes agents performing actions in an operational area as suggested in Manouchehri into Zou because Zou and Manouchehri pertain to analogous fields of technology. Both Zou and Manouchehri pertain to a master and slave drones the carry out a mission and perform operations. In Manouchehri, the master drone can receive an assignment to perform operation in accordance with its leadership role. It would be desirable to incorporate this feature into Zou to indicate a method by which the master drone learns of its objectives and operations e.g., see Manouchehri [0049, 0050, 0003].
Regarding claim 2, the combination of Zou and Manouchehri teaches the invention as claimed in claim 1. The combination of Zou and Manouchehri also teaches wherein initiating the current action includes:
determining which of the actions of the active mode to initiate as the current action based on the control algorithms of the active mode;
transmitting a message to the agents indicative of the current action; and
actuating a physical controller of the first agent to perform the current action (Zou Fig. 1, pages 3-5, the system can be in a variety of states/modes, which lead to particular actions; for example, the system may be using a particle swarm algorithm for a general search; or using a variable scale algorithm, to search a designated position; additionally, different actions are dictated by the probability map being in different modes/states; page 1, the master drone then controls each drone to carry out an action e.g., a particular movement or search; inherently, this means the master drone transmits a message to each of the slave drones).
Regarding claim 3, the combination of Zou and Manouchehri teaches the invention as claimed in claim 1. The combination of Zou and Manouchehri also teaches
producing the two or more modes, the transition logic for changing between the two or more modes, the hybrid state estimator for generating the estimation of the current state of the mission, and the control algorithms for executing the actions of each of the two or more modes (Zou Fig. 1, pages 3-5, the system can be in a variety of states/modes, which lead to particular actions; for example, the system may be using a particle swarm algorithm for a general search; or using a variable scale algorithm, to search a designated position; additionally, different actions are dictated by the probability map being in different modes/states; page 1, the master drone then controls each drone to carry out an action e.g., a particular movement or search; inherently, this means the master drone transmits a message to each of the slave drones; as seen in Fig. 1, pages 3-5, the system has algorithms/methods for transitioning between different modes e.g., different search algorithms, probability maps etc.).
Regarding claim 4, the combination of Zou and Manouchehri teaches the invention as claimed in claim 1. The combination of Zou and Manouchehri also teaches wherein updating the hybrid state estimator includes monitoring a physical state of the agents in the operational area (Zou pages 3-4, the master drone receives information e.g., location coordinates and heading, from the slave drones and updates the probability map).
Regarding claim 5, the combination of Zou and Manouchehri teaches the invention as claimed in claim 1. The combination of Zou and Manouchehri teaches wherein updating the hybrid state estimator includes generating a target model that predicts motion of one or more targets in the operational area (Fig. 1, pages 4-5, when the target has been detected, the system has a method/model for predicting the designated position of the target; the prediction can use the adaptive variable scale algorithm and take into account the maximum moving speed of the target).
Regarding claim 6, the combination of Zou and Manouchehri teaches the invention as claimed in claim 5. The combination of Zou and Manouchehri also teaches wherein the target model includes a hierarchical prediction of where in the operational area detection of potential undetected targets may occur (Figs. 1-2, page 3, the probability grid is divided into multiple cells, each cell associated with a probability value that a target is there; this is hierarchical in various ways e.g., some cells have higher probabilities or importance than others; some cells are positioned higher in the grid than others etc.)
Regarding claim 7, the combination of Zou and Manouchehri teaches the invention as claimed in claim 1. The combination of Zou and Manouchehri also teaches wherein the mission includes searching for one or more targets located within two or more regions of the operational area, wherein the control algorithms include a search algorithm driven by a probabilistic target state estimate (Figs. 1-2, pages 3-5, as indicated in Fig. 1, the system commands drones to search multiple regions based on a probability map that can be repeatedly updated based on prior searches; the regions are represented by cells in the probability map grid, where each cell is associated with a probability that a target is at the corresponding region).
Regarding claim 8, the combination of Zou and Manouchehri teaches the invention as claimed in claim 1. The combination of Zou and Manouchehri also teaches wherein the mission includes searching for one or more targets located within two or more regions of the operational area, wherein the control algorithms include a nonmyopic information theoretic search algorithm (Figs. 1-2, pages 3-5, as indicated in Fig. 1, the system commands drones to search multiple regions based on a probability map that can be repeatedly updated based on prior searches; that the regions are represented by cells in the probability map grid, where each cell is associated with a probability that a target is at the corresponding region; that is, the system is constantly adaptive and dynamic, adjusting to changing conditions i.e., non-myopic; the updating of the probability is based on collection of information from slave drones i.e., information theoretic)
Regarding claim 10, the combination of Zou and Manouchehri teaches the invention as claimed in claim 1. However, Zou does not expressly disclose with the one or more processors of the controller: predicting that the first agent will fail a first objective of the one or more objectives of the mission based on the estimation generated by the hybrid state estimator prior to failure of the first objective; and requesting or assigning a second agent of the agents to assist the first agent to avoid failure of the first objective.
In the same field of endeavor, Moucherhri teaches with the one or more processors of the controller:
predicting that the first agent will fail a first objective of the one or more objectives of the mission based on the estimation generated by the hybrid state estimator prior to failure of the first objective; and
requesting or assigning a second agent of the agents to assist the first agent to avoid failure of the first objective (Fig. 4C, [0025, 0043], claim 10, drones may be performing mission tasks; the system may detect that a particular drone is distressed the system can detect that a drone is distressed; the system may then replace the distressed drone with a new drone who takes over the corresponding task; it would be obvious to modify Zou to incorporate this feature, in the context/mission where drones are performing a search task, based on the probability map e.g., see Zou Figs. 1-2, pages 3-5).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated with the one or more processors of the controller: predicting that the first agent will fail a first objective of the one or more objectives of the mission based on the estimation generated by the hybrid state estimator prior to failure of the first objective; and requesting or assigning a second agent of the agents to assist the first agent to avoid failure of the first objective as suggested in Manouchehri into Zou because Zou and Manouchehri pertain to analogous fields of technology. Both Zou and Manouchehri relate to the sending of drones on missions, where each drone performs a particular, assigned task. In Manouchehri, when the system detects a drone is distressed and perhaps cannot perform its assigned task, the system assigns a new drone to take its place and duties. It would be desirable to incorporate this feature into Zou, so that the drones described in Zou could be flexibly replaced when necessary, leading to a greater chance of satisfying mission objectives e.g., see Manouchehri Fig. 4C, [0025, 0043], claim 10.
Regarding claim 11, the claim corresponds to claim 1 and is rejected for the same reasons. The combination of Zou and Manouchehri also teaches a system, comprising:
two or more agents configured to operate within an operational area during a mission (Zou Figs. 1-2, page 3 describes a system involving multiple drones searching an area for a target).
Regarding claim 12, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. Claim 12 corresponds to claim 2 and is rejected for the same reasons.
Regarding claim 13, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. The combination of Zou and Manouchehri also teaches wherein the controller is disposed on board the first agent (Zou pages 1, 3, there can be master drone that performs the operations of the invention and commands slave drones to perform tasks; see also Manouchehri [0043], which contemplates leader and follower drones).
Regarding claim 14, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. The combination of Zou and Manouchehri also teaches wherein the controller is disposed within a remote system geographically separate from the two or more agents and in operable communication therewith (Zou pages 1, 3, there can be master drone that performs the operations of the invention and commands slave drones to perform tasks; see also Manouchehri [0043], which contemplates leader and follower drones; the drones communicate and can be geographically separate).
Regarding claim 15, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. Claim 15 also corresponds to claim 4 and is rejected for the same reasons.
Regarding claim 16, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. Claim 16 also corresponds to claim 5 and is rejected for the same reasons.
Regarding claim 17, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. Claim 17 also corresponds to claim 7 and is rejected for the same reasons.
Regarding claim 18, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. Claim 18 also corresponds to claim 8 and is rejected for the same reasons.
Regarding claim 20, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. Claim 20 also corresponds to claim 10 and is rejected for the same reasons.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zou and Manouchehri, as applied in claim 1, and further in view of Fonseca (US 2022/0161822).
Regarding claim 9, the combination of Zou and Manouchehri teaches the invention as claimed in claim 1. The combination of Zou and Manouchehri also teaches
wherein the mission includes searching for one or more targets located within two or more regions of the operational area,
wherein the estimation generated by the hybrid state estimator includes a dynamic map for each of the two or more regions segmented into areas,
wherein the map indicates an estimated certainty as to whether undetected targets are located within each of the areas (Zuo Figs. 1-2, pages 3-5, as indicated in Fig. 1, the system commands drones to search multiple regions based on a probability map that can be repeatedly updated based on prior searches; that the regions are represented by cells in the probability map grid, where each cell is associated with a probability that a target is at the corresponding region; that is, the system is constantly adaptive and dynamic, adjusting to changing conditions),
wherein searching each of the areas increases the estimated certainty for the corresponding area that there are not any undetected targets in the corresponding area (Zuo Fig. 2, page 4, when a drone visits a particular cell in the map e.g., cell 3,3 in Fig. 2, when no target is found, the probability of a target being there drops from 0.135 to .0024)
wherein the estimated certainty for each of the areas of the map decrease over time after being searched (Zuo Figs. 1-2, pages 3-4, the sum of all probabilities in the probability map is 1; as a result, consider an example where a first cell is searched as noted above, and then later another, different second cell is searched; the probability associated with the second cell will drop, and the probabilities of all other unsearched cells will increase; as indicated in Fig. 1, searches are made multiple times and the probability map is repeatedly updated).
However, the combination of Zou and Manouchehri does not expressly disclose the map is a heatmap.
In the same field of endeavor, Fonseca teaches the map is a heatmap (Abstract, [0013, 0022, 0026, 0044, 0068], it is known to represent an uncertainty model as multiple cells in a heatmap, where the heatmap represents a probability of a cell begin occupied; it is further known to determine a route or operation based on the heatmap).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have incorporated the map is a heatmap as suggested in Fonseca into Zou, because Zou and Fonseca pertain to analogous fields of technology. Both Zou and Fonseca relate to a map where different regions or cells in the map correspond to different likelihoods as to whether something occupies each cell. In Fonseca, this map is represented as a heatmap. It would be desirable to incorporate this feature into Zou so the invention of Zou can represent its probability map in a variety of known ways e.g., see Fonseca Abstract, [0013, 0022, 0026, 0044, 0068].
Regarding claim 19, the combination of Zou and Manouchehri teaches the invention as claimed in claim 11. Claim 19 corresponds to claim 9 and is rejected for the same reasons.
Response to Arguments
The Examiner acknowledges the Applicant's amendments to claims 1 and 11.
Regarding claims 1 and 11, Applicant alleges that the cited prior art does not teach the amended limitation, “wherein the estimation includes discrete elements and continuous elements.” More specifically, on pages 1-2 of the reply, Applicant alleges that Zou does not read on the amended limitation, because it does not teach a hybrid state estimator that tracks continuous dynamic variables (e.g., physical equations of motion) and discrete executive mode states.
Examiner respectfully disagrees. Although Applicant describes features of a hybrid state estimator and alleges that the Zhou reference does not teach them, such features have not been incorporated into the claim language. Examiner has reviewed the specification and did not find specific definitions for “hybrid state estimator”, “discrete element” or “continuous element” that are required for all embodiments of the claimed invention. As a result, such terms are given their broadest reasonable interpretation. In the view of the Examiner, the amended limitation only requires an estimation that includes any continuous or discrete element/feature. Zhou appears to teach an estimation feature/process with a variety of discrete and continuous elements. For example, Zhou teaches a grid with distinct probability values i.e., discrete elements. However, the grid itself continuously remains and is updated e.g., a continuous element. (See Zhou page 1). Zhou further teaches multiple continuous elements as part of its estimation, which is not limited to the grid. By way of example, Zhou teaches a probability map that is continuously updated in real time (Zhou page 1), continuously involves the use of particular search particle swarm and other algorithms (Zhou pages 1-2, 4), continuously monitors GPS data and measures speed (Zhou page 2), continuously iterates an algorithm to perform a search estimation (Zhou page 2), continuously updates a location estimation (Zhou page 5) etc. For at least the above reasons, Examiner believes Zhou teaches “generating an estimation of a current state of the mission … wherein the estimation includes discrete elements and continuous elements.”
Applicant alleges that claims 2-10 and 12-20 are allowable in view of their dependency on claims 1 and 11. Claims 2-10 and 12-20 are rejected as being taught by Zhou, Manouchehri and/or Fonseca.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ucar (US 2025/0247760) teaches that it is known for a drone to continuously monitor a region using a sensor e.g., see Uear [0059].
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC YOON whose telephone number is (408)918-7581. The examiner can normally be reached on 9 am to 5 pm ET Monday through Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman, can be reached at telephone number 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC J YOON/Primary Examiner, Art Unit 2118
