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 .
Information Disclosure Statement
The information disclosure statement submitted on 07/31/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The Amendment filed on 07/23/2026 has been entered. Claims 3, 4 and 14 are canceled. Claims 1-2, 5-13 and 15-23 remain pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 18 recites the limitation "the fourth edge device". There is insufficient antecedent basis for this limitation in the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 5-13 and 15-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-2, 5-11 and 22-23 are directed to a method and claims 12-21 are directed to a system. Therefore, the claims are eligible under Step 1 for being directed to a process and a machine respectively.
Independent claims 1, 12 and 22:
Step 2A Prong 1:
Claims recite:
analyzing the first set of edge data using one or more computing models to determine a first object detected in the first set of edge data - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
analyzing the second set of edge data using the one or more computing models to determine a second object detected in the second set of edge data - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper; and
determining whether the first object and the second object are a same object based upon one or more object parameters - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
receiving a first set of edge data from a first edge device of the plurality of edge devices - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
A method for sensor correlation by a plurality of edge devices, wherein the method is performed using one or more processors - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
transmitting the edge instruction to the second edge device - the steps recited at a high level of generality, and amounts to mere data transmitting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device - the steps recited at a high level of generality, and amounts to mere data modifying which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
receiving a first set of edge data from a first edge device of the plurality of edge devices - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
A method for sensor correlation by a plurality of edge devices, wherein the method is performed using one or more processors - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
transmitting the edge instruction to the second edge device - the steps recited at a high level of generality, and amounts to mere data transmitting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device - the steps recited at a high level of generality, and amounts to mere data modifying which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 2 and 13:
Step 2A Prong 1:
Claims recite:
wherein the edge instruction is a first edge instruction, wherein the method further comprises: generating a second edge instruction based at least in part upon the determination of whether the first object and second object are a same object, the first set of edge data, and the second set of edge data - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
transmitting the second edge instruction to the first edge device - the steps recited at a high level of generality, and amounts to mere data transmitting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
transmitting the second edge instruction to the first edge device - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 5 and 15:
Step 2A Prong 1:
Claims recite:
in response to the first object and the second object being determined to the same object, generating a calibration based on the first edge data, the second set of edge data, and the determined same object - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
providing the calibration to a third edge device to cause the calibration to be applied to a third set of edge data collected by the third edge device, the third edge device being different from the first edge device, the third edge device being different from the second edge device - These additional elements are recited at a high level of generality and merely invokes a generic computer machinery as a tool to perform the underlying abstract ideas and thus fails to integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception and generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 6 and 16:
Step 2A Prong 1:
Claims recite:
analyzing the third set of edge data to determine an operation parameter of the third edge device - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
generating a third edge instruction based at least in part upon the determined first object, the determined operation parameter, and the third set of edge data - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
receiving a third set of edge data from a third edge device, the third edge device being different from the first edge device, the third edge device being different from the second edge device - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
transmitting the third edge instruction to the third edge device - the steps recited at a high level of generality, and amounts to mere data transmitting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
receiving a third set of edge data from a third edge device, the third edge device being different from the first edge device, the third edge device being different from the second edge device - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
transmitting the third edge instruction to the third edge device - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 7 and 17:
Step 2A Prong 1: The claims recite the abstract ideas of claims 1 and 12.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
storing the first set of edge data in a processing memory; storing the second set of edge data in the processing memory; and storing the one or more object parameters in the processing memory - the steps recited at a high level of generality, and amounts to mere data storing which is well known which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
storing the first set of edge data in a processing memory; storing the second set of edge data in the processing memory; and storing the one or more object parameters in the processing memory - which is a well-understood, routine, conventional activity similar to Storing and retrieving information in memory described in MPEP 2106.05(d)(II).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 8 and 18:
Step 2A Prong 1:
Claims recite:
generating a first processing instruction, the first processing instruction includes an indication of a second computing device becoming a processing device, the second computing device being different from the first edge device, the third edge device being different from the second edge device - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
transmitting the first processing instruction to the second edge device - the steps recited at a high level of generality, and amounts to mere data transmitting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
transmitting the first processing instruction to the second edge device - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 9 and 19:
Step 2A Prong 1: The claims recite the abstract ideas of claims 8 and 18.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
providing access to the processing memory to the second computing device - These additional elements are recited at a high level of generality and merely invokes a generic computer machinery as a tool to perform the underlying abstract ideas. See MPEP 2106.05(f).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
providing access to the processing memory to the second computing device - These additional elements are recited at a high level of generality and merely invokes a generic computer machinery as a tool to perform the underlying abstract ideas. See MPEP 2106.05(f).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 10 and 20:
Step 2A Prong 1: The claims recite the abstract ideas of claims 1 and 12.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the first set of edge data includes a set of raw sensor data collected by a first sensor associated with the first edge device;
wherein the second set of edge data includes a set of processed sensor data,
wherein the set of processed sensor data is generated based on a second set of the sensor data collected by a second sensor associated with the second edge device; wherein the set of processed sensor data is smaller in size than the second set of the sensor data - the steps recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)) and amounts to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the first set of edge data includes a set of raw sensor data collected by a first sensor associated with the first edge device;
wherein the second set of edge data includes a set of processed sensor data,
wherein the set of processed sensor data is generated based on a second set of the sensor data collected by a second sensor associated with the second edge device; wherein the set of processed sensor data is smaller in size than the second set of the sensor data - the steps recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)) and amounts to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 11, 21 and 23:
Step 2A Prong 1: The claims recite the abstract ideas of claims 1, 12 and 22.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the one or more computing models include a large language model - the step recited at a high level of generality, and amounts to merely indicating a field of use or technological environment in which the judicial exception is performed (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception and generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Independent claim 22:
Step 2A Prong 1:
Claims recite:
analyzing the first set of edge data using one or more computing models to determine a first object detected in the first set of edge data and a first confidence parameter associated with the first object - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
analyzing the second set of edge data using the one or more computing models to determine a second object detected in the second set of edge data and a second confidence parameter associated with the second object - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
determining whether the first confidence parameter and the second confidence parameter are both at or above a confidence threshold - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and judging, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper; and
determining whether the first object and the second object are a same object based upon one or more object parameters - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
receiving a first set of edge data from a first edge device of the plurality of edge devices - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
A method for sensor correlation by a plurality of edge devices, wherein the method is performed using one or more processors - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
transmitting the edge instruction to the second edge device - the steps recited at a high level of generality, and amounts to mere data transmitting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device - the steps recited at a high level of generality, and amounts to mere data modifying which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
receiving a first set of edge data from a first edge device of the plurality of edge devices - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
A method for sensor correlation by a plurality of edge devices, wherein the method is performed using one or more processors - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
transmitting the edge instruction to the second edge device - the steps recited at a high level of generality, and amounts to mere data transmitting which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device - the steps recited at a high level of generality, and amounts to mere data modifying which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-13 and 15-23 are rejected under 35 U.S.C. 103 as being unpatentable over Fougnies et al. (hereinafter Fougnies), US 20190265002 A1, in view of Pretto et al. (hereinafter Pretto), "Building an Aerial–Ground Robotics System for Precision Farming: An Adaptable Solution," and this reference is included in the IDS filed 05/29/2024.
Regarding independent claim 1, Fougnies teaches a method for sensor correlation by a plurality of edge devices ([0029] provide for devices having network-connected scopes which are designed to hone in on the same target, which may be a still or moving target. In a first embodiment involving two scopes, a “lead scope” identifies a target and communicates location data regarding the target to a “follower scope” which uses the location data from the lead scope and its own location and orientation data to hone in the target; Fig. 1A; [0063] FIG. 1A shows a system view wherein a plurality of devices 10 (device1-devicen) and non-device/non-scope nodes 12 (node1-noden) are in communication with a network server 16 via wireless communication and an electronic network 18; [0067] FIG. 2 shows elements of a sample device 10, which may include (or may be) either a lead scope or a follower scope (i.e. edge devices)), the method comprising:
receiving a first set of edge data from a first edge device of the plurality of edge devices ([0143] FIG. 7 is a flowchart of a process for tracking a single presumed target by a plurality of scopes located remotely from one another and being moved by separate scope operators, wherein each of the scopes include a plurality of measurement devices configured to provide current target position data, and each of the scopes are in electronic communication with a network server, and the current target position data have error values. In one preferred embodiment the process is implemented by at least the following steps: [0144] 700: Identify current target position data regarding a presumed target that is located by an operator of the first scope, using the plurality of measurement devices in the first scope; [0145] 702: The first scope electronically communicates to the network server the current target position data regarding the presumed target identified by the operator of the first scope);
receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device ([0146] 704. The network server communicates to the remaining scopes the current target position data regarding the presumed target identified by the operator of the first scope. [0147] 706: Each of the remaining scopes use the current target position data regarding the presumed target identified by the operator of the first scope to locate the presumed target. [0148] 708: Upon locating the presumed target, each of the remaining scopes electronically communicate to the network server the current target position data regarding the presumed target, the current target position data being identified by the respective remaining scopes using the plurality of measurement devices in the respective remaining scopes);
analyzing the first set of edge data using one or more computing models to determine a first object detected in the first set of edge data ([0089] the target is represented by a one-dimensional object on a display screen, such as a dot. In an alternative embodiment, the target is represented by a simulated two-dimensional or three-dimensional image on the display screen. If a digital image is captured and transmitted, the actual image of the target may be displayed on the screen; [0090] 1. A lead scope identifies a deer (target) that is a quarter-mile away and is facing the device head-on; [0091] 2. The target position of the deer and a physical image of the deer is captured by the scope and communicated to the network server; [0092] 3. The IAMS in the network server or remotely accessed via the Internet identifies key visual features within the image and compares these features with known objects to categorize the target as a front view of the deer and retrieves a simulated image of a deer from its database; [0149] 710: The network server calculates updated current target position data upon receiving current target position data from any one of the remaining scopes by amalgamating the current target position data from each scope that located the presumed target, the updated current target position data having reduced error values compared to the error values of the current target position data identified by only the first scope);
analyzing the second set of edge data using the one or more computing models to determine a second object detected in the second set of edge data ([0089] the target is represented by a one-dimensional object on a display screen, such as a dot. In an alternative embodiment, the target is represented by a simulated two-dimensional or three-dimensional image on the display screen. If a digital image is captured and transmitted, the actual image of the target may be displayed on the screen; [0090] A lead scope identifies a deer (target) that is a quarter-mile away and is facing the device head-on; [0091] The target position of the deer and a physical image of the deer is captured by the scope and communicated to the network server; [0092] 3. The IAMS in the network server or remotely accessed via the Internet identifies key visual features within the image and compares these features with known objects to categorize the target as a front view of the deer and retrieves a simulated image of a deer from its database; [0149] 710: The network server calculates updated current target position data upon receiving current target position data from any one of the remaining scopes by amalgamating the current target position data from each scope that located the presumed target, the updated current target position data having reduced error values compared to the error values of the current target position data identified by only the first scope); and
wherein the method is performed using one or more processors ([0078] FIG. 3 shows elements of the network server 16, including a processor 52, memory 54, image analysis and manipulation software (IAMS) 56 which can implemented using artificial intelligence software, and a network interface 58 in communication with a wired or wireless communication transceiver 60; [0079] The processor functions of the individual devices 10 and the network server 16 depend upon the system architecture and the distribution of computing functions. As described herein, some of these functions can be performed at either processor 30 or 52, whereas other functions may be performed by the network server's processor 52).
Fougnies does not explicitly teach determining whether the first object and the second object are a same object based upon one or more object parameters; in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data; transmitting the edge instruction to the second edge device; and causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device.
However, in the same field of endeavor, Pretto teaches
determining whether the first object and the second object are a same object based upon one or more object parameters (Fig. 1; page 30 a UAV allows for rapid inspections of large areas, e.g., mapping weed distributions or crop nutrition-status indicators. This information can then be shared with a UGV, which can perform targeted actions, e.g., selective weed treatment or fertilizer applications on required areas; page 31; Section “Crop and Weed Detection”, In the Flourish project, we focus on vision-based approaches for plant classification and use machine learning techniques to effectively cope with the large variety of different crops and weeds as well as with changing environmental conditions. Figure 4(a)–(c) illustrates the results obtained by our plant-classification systems for both the UGV and UAV platforms; page 33; We propose a semisupervised online approach [17] that exploits additional arrangement information about the crops to adapt the visual classifier. We also successfully tested approaches that operated on image sequences obtained along crop rows, enabling the classifier to learn features that describe the plant arrangement [see Figure 5(a) and (b)] [16]. The image sequence reveals that crops grow along the row and have similar spacing, whereas the weeds appear randomly in the field strip. We show that incorporating this geometric information boosts the classification performance and generalization capabilities of the plant classifiers);
in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data (page 36; Section “Positioning and Environment Modeling” in a multirobot setup as in the Flourish project, the UAV and the UGV should be able to cooperatively build a shared model of the environment. The following presents the main contributions we proposed for localizing and modeling cultivated fields using a UAV (the “UAV Localization and Mapping” section) and a UGV (the “UGV Global Positioning and Mapping” section) as well as in terms of fusing this information among robots (the “Cooperative UAV–UGV Environment Modeling” section) and across time (the “Long-Term Temporal Map Registration” section; Fig. 7; Section “UAV Localization and Mapping”; pages 36-37 The key challenges for on-field vision-based localization are the homogeneous appearance of crops and the accuracy of the GPS which, when used alone, is not sufficient to construct maps for defining paths for UGV intervention. To address this, we developed an onboard state-estimation system that combines data from a synchronized VI sensor, a GPS sensor, the UAV inertial measurement unit (IMU), and, optionally, a laser altimeter to estimate the six degree-of freedom (6-DoF) pose. Figure 7(a) provides an overview of the system; page 30; Left column, 1st paragraph, a UAV allows for rapid inspections of large areas, e.g., mapping weed distributions or crop nutrition-status indicators. This information can then be shared with a UGV, which can perform targeted actions, e.g., selective weed treatment or fertilizer applications on required areas, with relatively high operating times and pay load capacities; page 44, Section “Weed Tracking”, The inputs were the images and the coordinates of the targets given by the classifier (see the “Crop and Weed Detection” section) in the images of the detection camera (see Figure 3). The outputs were the trigger time and position for the actuators; page 38, Section “Cooperative UAV–UGV Environment Modeling” Building a shared map of the environment is an essential but challenging task: the UAV can provide a coarse recon struction of large areas, which should be updated with more detailed information collected by the UGV. We introduced Aerial–Ground Collaborative 3D Mapping for Precision Farming (AgriColMap) [29], an effective map registration pipeline that registers heterogeneous maps built by the UGVs and UAVs);
transmitting the edge instruction to the second edge device (page 30; Left column, 1st paragraph, a UAV allows for rapid inspections of large areas, e.g., mapping weed distributions or crop nutrition-status indicators. This information can then be shared with a UGV, which can perform targeted actions, e.g., selective weed treatment or fertilizer applications on required areas, with relatively high operating times and pay load capacities; page 44, Section “Weed Tracking”, The inputs were the images and the coordinates of the targets given by the classifier (see the “Crop and Weed Detection” section) in the images of the detection camera (see Figure 3). The outputs were the trigger time and position for the actuators); and causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device (pages 42-43, section “UGV Position Tracking and Navigation”, For autonomous navigation on fields, the BoniRob UGV needs to accurately steer along the crop rows without crushing any of the value crops … The BoniRob can change its track width by adjusting the angles of the lever arms to which the wheels are attached, as shown in Figure 2(a). Thus, whether it can pass through a narrow gap or over an obstacle depends on the wheel positions [see Figure 14(d)]. We developed a path planner that considers the lever angles explicitly [35] by including the arm angles in the state space and adding actions that allow the planner to change them; pages 44-45, Section “Weed Tracking”, After receiving the delayed classification results and scene structures, the object initializer and updater module creates the templates of the received objects, propagates their updated poses, and accumulates their labels … After repeated intracamera tracking, updating, and inter camera tracking, the weed finally approaches the end effector, where the control algorithm predicts the trigger time and position of actuation for intervention).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of aerial and ground-based systems developed to monitor crop density, weed pressure, and crop nitrogen (N)-nutrition status and to accurately classify and locate weeds as suggested in Pretto into Fougnies’s system because both of these systems are addressing tracking a target by multiple devices. This modification would have been motivated by the desire for resource-use efficiency and optimization of human effort and yield (Pretto, page 29; Right Column).
Regarding dependent claim 2, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Pretto further teaches further comprising:
wherein the edge instruction is a first edge instruction, wherein the method further comprises:
generating a second edge instruction based at least in part upon the determination of whether the first object and second object are a same object, the first set of edge data, and the second set of edge data (page 38, Section “Cooperative UAV–UGV Environment Modeling” Building a shared map of the environment is an essential but challenging task: the UAV can provide a coarse reconstruction of large areas, which should be updated with more detailed information collected by the UGV); and
transmitting the second edge instruction to the first edge device (page 43, Section “UAV–UGV Mission Coordination” To unlock the potential of the Flourish robotics system, the ability to run coordinated missions between the robots is essential. Because both robots share information via Wi-Fi, this information needs to be kept at a minimal level, and the coordination needs to be ensured even when communication is lost. The only data exchanged are the UAV and UGV poses, the coordinates of the areas of interest, the requests from one robot to the other, and their status messages. Because of the lossy communication, exchanging requests and status is a reliable way to ensure that a message sent by one robot is indeed received by the other).
Regarding dependent claim 5, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Fougnies further teaches further comprising:
in response to the first object and the second object being determined to the same object,
generating a calibration based on the first edge data, the second set of edge data, and the determined same object ([0149] 710: The network server calculates updated current target position data upon receiving current target position data from any one of the remaining scopes by amalgamating the current target position data from each scope that located the presumed target, the updated current target position data having reduced error values compared to the error values of the current target position data identified by only the first scope); and
providing the calibration to a third edge device to cause the calibration to be applied to a third set of edge data collected by the third edge device, the third edge device being different from the first edge device, the third edge device being different from the second edge device ([0150] 712: The network server electronically communicates the updated current target position data regarding the presumed target to the remaining scopes that have not yet located the presumed target; [0151] 714: The remaining scopes that have not yet located the presumed target use the updated current target position data, instead of any previously received current target position data, for locating the presumed target).
Regarding dependent claim 6, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Fougnies further teaches further comprising:
receiving a third set of edge data from a third edge device, the third edge device being different from the first edge device, the third edge device being different from the second edge device ([0143] 708: Upon locating the presumed target, each of the remaining scopes electronically communicate to the network server the current target position data regarding the presumed target, the current target position data being identified by the respective remaining scopes using the plurality of measurement devices in the respective remaining scopes);
analyzing the third set of edge data to determine an operation parameter of the third edge device ([0143] 710: The network server calculates updated current target position data upon receiving current target position data from any one of the remaining scopes by amalgamating the current target position data from each scope that located the presumed target, the updated current target position data having reduced error values compared to the error values of the current target position data identified by only the first scope);
generating a third edge instruction based at least in part upon the determined first object, the determined operation parameter, and the third set of edge data ([0143] 712: The network server electronically communicates the updated current target position data regarding the presumed target to the remaining scopes that have not yet located the presumed target); and
transmitting the third edge instruction to the third edge device ([0143] 712: The network server electronically communicates the updated current target position data regarding the presumed target to the remaining scopes that have not yet located the presumed target).
Regarding dependent claim 7, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 6 that is incorporated. Fougnies further teaches further comprising:
storing the first set of edge data in a processing memory ([0051] If the system includes a network server and the network server receives the raw data from the measurement devices transmitted by the lead scope, it calculates the target position and stores the data);
storing the second set of edge data in the processing memory ([0051] If the system includes a network server and the network server receives the raw data from the measurement devices transmitted by the lead scope, it calculates the target position and stores the data); and
storing the one or more object parameters in the processing memory ([0051] If the network server receives the calculated target position, it stores this data and forwards it to other scopes).
Regarding dependent claim 8, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 7 that is incorporated. Fougnies further teaches further comprising:
generating a first processing instruction, the first processing instruction includes an indication of a second computing device becoming a processing device, the second computing device being different from the first edge device, the third edge device being different from the second edge device ([0143] 700: Identify current target position data regarding a presumed target that is located by an operator of the first scope, using the plurality of measurement devices in the first scope; 702: The first scope electronically communicates to the network server the current target position data regarding the presumed target identified by the operator of the first scope); and
transmitting the first processing instruction to the second edge device ([0143] 704. The network server communicates to the remaining scopes the current target position data regarding the presumed target identified by the operator of the first scope).
Regarding dependent claim 9, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 8 that is incorporated. Fougnies further teaches further comprising:
providing access to the processing memory to the second computing device ([0218] Consider an example wherein the target is a person or animal (a “person” is used in the following description for convenience of explanation), and it is necessary for the second scope to see facial details of the person so as to track the person and/or perform facial recognition of the person; [0219] As is well-known in the art, facial recognition typically involves collecting dozens of facial features (often referred to in the art as “facial landmarks”) of the person of interest, and then using an algorithm to create a facial signature for the person. The facial signature is then compared to a database of known faces to potentially identify the person, assuming that their facial signature is in the database. Alternatively, once the facial signature is obtained from the first scope, the second scope may use the facial signature to confirm that they are viewing the same person, or vice-versa, regardless of whether or not the person is identified in a database of known faces).
Regarding dependent claim 10, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Fougnies further teaches wherein the first set of edge data includes a set of raw sensor data collected by a first sensor associated with the first edge device ([0051] If the system includes a network server and the network server receives the raw data from the measurement devices transmitted by the lead scope, it calculates the target position and stores the data; [0137] 600: Identify current target position data regarding a presumed target that is located by an operator of the first scope, using the plurality of measurement devices in the first scope);
wherein the second set of edge data includes a set of processed sensor data ([0051] If the network server receives the calculated target position, it stores this data and forwards it to other scopes),
wherein the set of processed sensor data is generated based on a second set of the sensor data collected by a second sensor associated with the second edge device ([0137] 604: The second scope identifies its current target position data of the second scope's current target position using its plurality of measurement devices. 606: Calculate in a processor of the second scope, using its current target position data and the current target position data received from the first scope, position movements that are required to move the second scope from its current target position to the target position of the presumed target identified by the first scope);
wherein the set of processed sensor data is smaller in size than the second set of the sensor data ([0044] Data from these measurement devices are used to calculate the position of the target, which may be expressed in GPS coordinates or the like).
Regarding dependent claim 11, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Fougnies teaches wherein the one or more computing models include a large language model ([0126] The IAMS can be used to support the operator of a lead scope by identifying potential targets within the current field-of-view through object classification. Prior art processes exist to analyze image frames and identify objects in the image frame. For example, the GOOGLE® Cloud Vision API provides image analytics capabilities that allows applications to see and understand the content within the images. The service enables customers to detect a broad set of entities within an image from everyday objects (e.g., “sailboat”, “lion”, “Eiffel Tower”) to faces and product logos. Software applications of this type may be used for identifying potential targets within the current field-of-view through object classification).
Regarding independent claim 12, it is a system claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Fougnies further teaches a system for sensor correlation by a plurality of edge devices (Fig. 3; [0078]; Fig. 1A; [0063]), the system comprising:
one or more memories having instructions stored therein (Fig. 3, 54; [0078]); and
one or more processors configured to execute the instructions and perform operations (Fig. 3, 52; [0078]-[0079]).
Regarding dependent claim 13, it is a system claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Regarding dependent claim 14, it is a system claim that corresponding to the method of claims 3 and 4. Therefore, it is rejected for the same reason as claims 3 and 4 above.
Regarding dependent claim 15, it is a system claim that corresponding to the method of claim 5. Therefore, it is rejected for the same reason as claim 5 above.
Regarding dependent claim 16, it is a system claim that corresponding to the method of claim 6. Therefore, it is rejected for the same reason as claim 6 above.
Regarding dependent claim 17, it is a system claim that corresponding to the method of claim 7. Therefore, it is rejected for the same reason as claim 7 above.
Regarding dependent claim 18, it is a system claim that corresponding to the method of claim 8. Therefore, it is rejected for the same reason as claim 8 above.
Regarding dependent claim 19, it is a system claim that corresponding to the method of claim 9. Therefore, it is rejected for the same reason as claim 9 above.
Regarding dependent claim 20, it is a system claim that corresponding to the method of claim 10. Therefore, it is rejected for the same reason as claim 10 above.
Regarding dependent claim 21, it is a system claim that corresponding to the method of claim 11. Therefore, it is rejected for the same reason as claim 11 above.
Regarding independent claim 22, Fougnies teaches a method for sensor correlation by a plurality of edge devices ([0029] provide for devices having network-connected scopes which are designed to hone in on the same target, which may be a still or moving target. In a first embodiment involving two scopes, a “lead scope” identifies a target and communicates location data regarding the target to a “follower scope” which uses the location data from the lead scope and its own location and orientation data to hone in the target; Fig. 1A; [0063] FIG. 1A shows a system view wherein a plurality of devices 10 (device1-devicen) and non-device/non-scope nodes 12 (node1-noden) are in communication with a network server 16 via wireless communication and an electronic network 18; [0067] FIG. 2 shows elements of a sample device 10, which may include (or may be) either a lead scope or a follower scope (i.e. edge devices)), the method comprising:
receiving a first set of edge data from a first edge device of the plurality of edge devices ([0143] FIG. 7 is a flowchart of a process for tracking a single presumed target by a plurality of scopes located remotely from one another and being moved by separate scope operators, wherein each of the scopes include a plurality of measurement devices configured to provide current target position data, and each of the scopes are in electronic communication with a network server, and the current target position data have error values. In one preferred embodiment the process is implemented by at least the following steps: [0144] 700: Identify current target position data regarding a presumed target that is located by an operator of the first scope, using the plurality of measurement devices in the first scope; [0145] 702: The first scope electronically communicates to the network server the current target position data regarding the presumed target identified by the operator of the first scope);
receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device ([0146] 704. The network server communicates to the remaining scopes the current target position data regarding the presumed target identified by the operator of the first scope. [0147] 706: Each of the remaining scopes use the current target position data regarding the presumed target identified by the operator of the first scope to locate the presumed target. [0148] 708: Upon locating the presumed target, each of the remaining scopes electronically communicate to the network server the current target position data regarding the presumed target, the current target position data being identified by the respective remaining scopes using the plurality of measurement devices in the respective remaining scopes);
analyzing the first set of edge data using one or more computing models to determine a first object detected in the first set of edge data and a first confidence parameter associated with the first object ([0089] the target is represented by a one-dimensional object on a display screen, such as a dot. In an alternative embodiment, the target is represented by a simulated two-dimensional or three-dimensional image on the display screen. If a digital image is captured and transmitted, the actual image of the target may be displayed on the screen; [0090] 1. A lead scope identifies a deer (target) that is a quarter-mile away and is facing the device head-on; [0091] 2. The target position of the deer and a physical image of the deer is captured by the scope and communicated to the network server; [0092] 3. The IAMS in the network server or remotely accessed via the Internet identifies key visual features within the image and compares these features with known objects to categorize the target as a front view of the deer and retrieves a simulated image of a deer from its database; [0149] 710: The network server calculates updated current target position data upon receiving current target position data from any one of the remaining scopes by amalgamating the current target position data from each scope that located the presumed target, the updated current target position data having reduced error values compared to the error values of the current target position data identified by only the first scope; [0081] When calculating a presumed target position from GPS data and the other measurement devices, there are known, quantifiable errors introduced by the lead scope and follower scope(s), which can be represented by discrete values (e.g., +/−20 cm). Certain types of errors will be consistent from scope to scope based on inherent limitations of the measurement devices. Other types of errors may depend upon signal strength, such as the strength of a GPS signal or number of satellites used to calculate the position of the lead scope. For each calculated target position, the lead scope, follower scope and/or network server identifies the error value. When amalgamating and accumulating target positions from multiple scopes to calculate an updated target position, the error values may be used to weight the strength given to each target position (i.e. confidence parameter));
analyzing the second set of edge data using the one or more computing models to determine a second object detected in the second set of edge data and a second confidence parameter associated with the second object ([0089] the target is represented by a one-dimensional object on a display screen, such as a dot. In an alternative embodiment, the target is represented by a simulated two-dimensional or three-dimensional image on the display screen. If a digital image is captured and transmitted, the actual image of the target may be displayed on the screen; [0090] A lead scope identifies a deer (target) that is a quarter-mile away and is facing the device head-on; [0091] The target position of the deer and a physical image of the deer is captured by the scope and communicated to the network server; [0092] 3. The IAMS in the network server or remotely accessed via the Internet identifies key visual features within the image and compares these features with known objects to categorize the target as a front view of the deer and retrieves a simulated image of a deer from its database; [0149] 710: The network server calculates updated current target position data upon receiving current target position data from any one of the remaining scopes by amalgamating the current target position data from each scope that located the presumed target, the updated current target position data having reduced error values compared to the error values of the current target position data identified by only the first scope; [0081] When calculating a presumed target position from GPS data and the other measurement devices, there are known, quantifiable errors introduced by the lead scope and follower scope(s), which can be represented by discrete values (e.g., +/−20 cm). Certain types of errors will be consistent from scope to scope based on inherent limitations of the measurement devices. Other types of errors may depend upon signal strength, such as the strength of a GPS signal or number of satellites used to calculate the position of the lead scope. For each calculated target position, the lead scope, follower scope and/or network server identifies the error value. When amalgamating and accumulating target positions from multiple scopes to calculate an updated target position, the error values may be used to weight the strength given to each target position (i.e. confidence parameter)));
determining whether the first confidence parameter and the second confidence parameter are both at or above a confidence threshold ([0082] Various algorithms may be used to process the target positions. For example, target positions with the lowest error values may be more highly weighted. Alternatively, a target position with a very high error values compared to other target position error values may be deleted from the calculation);
wherein the method is performed using one or more processors ([0078] FIG. 3 shows elements of the network server 16, including a processor 52, memory 54, image analysis and manipulation software (IAMS) 56 which can implemented using artificial intelligence software, and a network interface 58 in communication with a wired or wireless communication transceiver 60; [0079] The processor functions of the individual devices 10 and the network server 16 depend upon the system architecture and the distribution of computing functions. As described herein, some of these functions can be performed at either processor 30 or 52, whereas other functions may be performed by the network server's processor 52).
Fougnies does not explicitly teach determining whether the first object and the second object are a same object based upon one or more object parameters; in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data; transmitting the edge instruction to the second edge device; and causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device.
However, in the same field of endeavor, Pretto teaches
determining whether the first object and the second object are a same object based upon one or more object parameters (Fig. 1; page 30 a UAV allows for rapid inspections of large areas, e.g., mapping weed distributions or crop nutrition-status indicators. This information can then be shared with a UGV, which can perform targeted actions, e.g., selective weed treatment or fertilizer applications on required areas; page 31; Section “Crop and Weed Detection”, In the Flourish project, we focus on vision-based approaches for plant classification and use machine learning techniques to effectively cope with the large variety of different crops and weeds as well as with changing environmental conditions. Figure 4(a)–(c) illustrates the results obtained by our plant-classification systems for both the UGV and UAV platforms; page 33; We propose a semisupervised online approach [17] that exploits additional arrangement information about the crops to adapt the visual classifier. We also successfully tested approaches that operated on image sequences obtained along crop rows, enabling the classifier to learn features that describe the plant arrangement [see Figure 5(a) and (b)] [16]. The image sequence reveals that crops grow along the row and have similar spacing, whereas the weeds appear randomly in the field strip. We show that incorporating this geometric information boosts the classification performance and generalization capabilities of the plant classifiers);
in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data (page 36; Section “Positioning and Environment Modeling” in a multirobot setup as in the Flourish project, the UAV and the UGV should be able to cooperatively build a shared model of the environment. The following presents the main contributions we proposed for localizing and modeling cultivated fields using a UAV (the “UAV Localization and Mapping” section) and a UGV (the “UGV Global Positioning and Mapping” section) as well as in terms of fusing this information among robots (the “Cooperative UAV–UGV Environment Modeling” section) and across time (the “Long-Term Temporal Map Registration” section; Fig. 7; Section “UAV Localization and Mapping”; pages 36-37 The key challenges for on-field vision-based localization are the homogeneous appearance of crops and the accuracy of the GPS which, when used alone, is not sufficient to construct maps for defining paths for UGV intervention. To address this, we developed an onboard state-estimation system that combines data from a synchronized VI sensor, a GPS sensor, the UAV inertial measurement unit (IMU), and, optionally, a laser altimeter to estimate the six degree-of freedom (6-DoF) pose. Figure 7(a) provides an overview of the system; page 30; Left column, 1st paragraph, a UAV allows for rapid inspections of large areas, e.g., mapping weed distributions or crop nutrition-status indicators. This information can then be shared with a UGV, which can perform targeted actions, e.g., selective weed treatment or fertilizer applications on required areas, with relatively high operating times and pay load capacities; page 44, Section “Weed Tracking”, The inputs were the images and the coordinates of the targets given by the classifier (see the “Crop and Weed Detection” section) in the images of the detection camera (see Figure 3). The outputs were the trigger time and position for the actuators; page 38, Section “Cooperative UAV–UGV Environment Modeling” Building a shared map of the environment is an essential but challenging task: the UAV can provide a coarse recon struction of large areas, which should be updated with more detailed information collected by the UGV. We introduced Aerial–Ground Collaborative 3D Mapping for Precision Farming (AgriColMap) [29], an effective map registration pipeline that registers heterogeneous maps built by the UGVs and UAVs);
transmitting the edge instruction to the second edge device (page 30; Left column, 1st paragraph, a UAV allows for rapid inspections of large areas, e.g., mapping weed distributions or crop nutrition-status indicators. This information can then be shared with a UGV, which can perform targeted actions, e.g., selective weed treatment or fertilizer applications on required areas, with relatively high operating times and pay load capacities; page 44, Section “Weed Tracking”, The inputs were the images and the coordinates of the targets given by the classifier (see the “Crop and Weed Detection” section) in the images of the detection camera (see Figure 3). The outputs were the trigger time and position for the actuators); and causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device (pages 42-43, section “UGV Position Tracking and Navigation”, For autonomous navigation on fields, the BoniRob UGV needs to accurately steer along the crop rows without crushing any of the value crops … The BoniRob can change its track width by adjusting the angles of the lever arms to which the wheels are attached, as shown in Figure 2(a). Thus, whether it can pass through a narrow gap or over an obstacle depends on the wheel positions [see Figure 14(d)]. We developed a path planner that considers the lever angles explicitly [35] by including the arm angles in the state space and adding actions that allow the planner to change them; pages 44-45, Section “Weed Tracking”, After receiving the delayed classification results and scene structures, the object initializer and updater module creates the templates of the received objects, propagates their updated poses, and accumulates their labels … After repeated intracamera tracking, updating, and inter camera tracking, the weed finally approaches the end effector, where the control algorithm predicts the trigger time and position of actuation for intervention).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of aerial and ground-based systems developed to monitor crop density, weed pressure, and crop nitrogen (N)-nutrition status and to accurately classify and locate weeds as suggested in Pretto into Fougnies’s system because both of these systems are addressing tracking a target by multiple devices. This modification would have been motivated by the desire for resource-use efficiency and optimization of human effort and yield (Pretto, page 29; Right Column).
Regarding dependent claim 23, the combination of Fougnies and Pretto teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Fougnies teaches wherein the one or more computing models include a large language model ([0126] The IAMS can be used to support the operator of a lead scope by identifying potential targets within the current field-of-view through object classification. Prior art processes exist to analyze image frames and identify objects in the image frame. For example, the GOOGLE® Cloud Vision API provides image analytics capabilities that allows applications to see and understand the content within the images. The service enables customers to detect a broad set of entities within an image from everyday objects (e.g., “sailboat”, “lion”, “Eiffel Tower”) to faces and product logos. Software applications of this type may be used for identifying potential targets within the current field-of-view through object classification).
Response to Arguments
Applicant's arguments filed 07/23/2026 have been fully considered. Each of applicant’s remarks is set forth, followed by examiner’s response.
(1) The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejections to Claim 8 is respectfully withdrawn in response to Applicant's amendment to the claim.
(2) Regarding rejections under 35 U.S.C. § 101 as being directed to an abstract idea without significantly more, Applicant alleges Step 2A, Prong One, the claims are not directed towards mental processes because the claimed steps cannot be practically performed in the human mind, such as "in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, he first set of edge data, and the second set of edge data; transmitting the edge instruction to the second edge device; and causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device" cannot be practically performed in human's mind, with or without physical aid. In other words, the human mind is not equipped to perform the steps. Therefore, claim 1 does not fall into the mental process grouping of abstract ideas as asserted in the Office Action.
As to point (2), Examiner respectfully disagrees. According to MPEP § 2106.04(II)(A)(1), in Step 2A, Prong One, this part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The claim recites “in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data; transmitting the edge instruction to the second edge device”. The claim does not put any limits on how an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data is generated. Thus, the step of “in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data” encompasses performing evaluation, judgment, and opinion to make a determination about generating an edge instruction. Under its broadest reasonable interpretation when read in light of the specification, the “in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data" encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. The claim recites limitations that fall within the mental process grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection III.
(3) Regarding Step 2A, Prong Two, Applicant argues claim 1 recites specific improvements to the technical field of sensor correlations to gather better sensor data, such that claim 1 provides a technical solution to improve the sensing and computer technology. For example, amended claim 1 recites limitations of "receiving a first set of edge data from a first edge device of the plurality of edge devices; receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device; analyzing the first set of edge data using one or more computing models to determine a first object detected in the first set of edge data; analyzing the second set of edge data using the one or more computing models to determine a second object detected in the second set of edge data; determining whether the first object and the second object are a same object based upon one or more object parameters; in response to the first object and the second object being determined to the same object, generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data; transmitting the edge instruction to the second edge device; and causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device."
As to point (3), Examiner respectfully disagrees. This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements: “receiving a first set of edge data from a first edge device of the plurality of edge devices; receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device” recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)). “A method for sensor correlation by a plurality of edge devices, wherein the method is performed using one or more processors” amounts to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)). “transmitting the edge instruction to the second edge device; causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device” recited at a high level of generality, and amounts to mere data modifying which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a). The consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. While the disclosure states that “In some embodiments, benefits include significant improvements, including, for example, improved accuracy in determining whether two objects as detected by the sensors are a same object based upon one or more object parameters. In certain embodiments, benefits include improved sensor data quality associated with an object after determining whether objects detected by sensors and/or edge devices are the same object. In some embodiments, benefits include improved sensor data effectiveness by determining whether certain devices are capable of detecting a target object and/or a target area such that only the devices with better accuracy in object detection is relied upon for receiving sensor data. In certain embodiments, benefits include improved performance by the edge devices and sensors thereof by sending edge instructions to the edge devices such that, in response to the received edge instructions, the edge devices can adjust sensor operation parameters (e.g., configurations, configuration parameters) and/or edge operation parameters, for example, to better collect sensor data associated with a target object and/or a target area” ([0019]) , there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea rather than to any technology. See MPEP 2106.05(a). Any purported improvements are provided by the judicial exception alone, i.e. mental process, thus the claim as a whole does not integrate the judicial exception into a practical application nor provide significantly more than the judicial exception. Thus, the claims are patent ineligible and are rejected under 35 U.S.C. 101 as detailed in the rejections set forth above.
(4) Regarding claim rejections under 35 U.S.C. 103, Applicant alleges the cited references do not disclose, collectively or individually, at least "generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data; transmitting the edge instruction to the second edge device;" as recited in claim 1. Additionally, the cited references do not disclose, collectively or individually, at least "causing to perform the edge instruction to change a sensor parameter for a sensor by the second edge device" as recited in claim 1.
As to point (4), Examiner respectfully disagrees. Pretto teaches autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multipurpose unmanned ground vehicles (UGVs) are used to cooperatively build a shared model of the environment. Targeted actions are generated by an onboard state-estimation system that combines data from a synchronized VI sensor, a GPS sensor, the UAV inertial measurement unit (IMU). Building a shared map of the environment is an essential but challenging task: the UAV can provide a coarse reconstruction of large areas, which should be updated with more detailed information collected by the UGV. We introduced Aerial–Ground Collaborative 3D Mapping for Precision Farming (AgriColMap) [29], an effective map registration pipeline that registers heterogeneous maps built by the UGVs and UAVs (pages 36-38, Sections “UAV Localization and Mapping”, “UGV Global Positioning and Mapping”, “Cooperative UAV–UGV Environment Modeling”. Pretto further teaches a UAV allows for rapid inspections of large areas, e.g., mapping weed distributions or crop nutrition-status indicators. This information can then be shared with a UGV, which can perform targeted actions, e.g., selective weed treatment or fertilizer applications on required areas, with relatively high operating times and pay load capacities; page 44, Section “Weed Tracking”, The inputs were the images and the coordinates of the targets given by the classifier (see the “Crop and Weed Detection” section) in the images of the detection camera (see Figure 3). The outputs were the trigger time and position for the actuators (page 30)). Thus, Pretto is considered to teach "generating an edge instruction based upon the first object detected in the first set of edge data, the first set of edge data, and the second set of edge data; transmitting the edge instruction to the second edge device". The combination of Fougnies and Pretto is considered to teach claim 1.
Similar arguments have been presented for claims 12 and 22 and thus, Applicant’s arguments are not persuasive for the same reasons. The respective dependent claims are rejected by virtue of depending on their respective independent claims 1, 12 and 22.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Shaiket al. (US 11954180 B2) discloses use of a sensor fusion to build active areas of interest for efficient processing of deep learning models.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/AMY P HOANG/ Examiner, Art Unit 2143
/JENNIFER N WELCH/ Supervisory Patent Examiner, Art Unit 2143
