Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The Action is responsive to the Amendments and Remarks filed on 12/19/2025. Claims 1-4, 6-14, and 16-20 are pending claims. Claims 1 and 11 are written in independent form.
Claim Interpretation
Claims 1 and 11 recite the phrase “to enable updating” which is being understood as the intent to enable updating, but is not actively performing any enabling or updating step/limitation. Examiner suggests to amend the claim limitations to recite all of the steps in a positive manner.
Claims 1 and 11 recite the limitation “based on the information, maximizing a total area of coverages of the information sampled from the edge nodes to enable updating a most outdates states in the global environment that are relevant to perform tasks and updating a global map, which is covered by the edge nodes of the global environment” which is being interpreted to have a scope of “based on the information, maximizing a total area of coverages of the information sampled from the edge nodes”. However, for the purpose of compact prosecution, the limitation is being addressed herein as if all of the steps are recited in a positive manner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yadav et al. (U.S. Pre-Grant Publication No. 2016/0359872, hereinafter referred to as Yadav) and further in view of Seyfi et al. (U.S. Pre-Grant Publication No. 2023/0376558, hereinafter referred to as Seyfi).
Regarding Claim 1:
Yadav teaches a method, comprising:
Performing, in a global environment that includes a central node and edge nodes that communicate with each other, by the central node, operations comprising:
Yadav teaches “Sensors can send their data to a primary collector and a secondary collector, both of which collect and report the data from the sensors at all times. A centralized system can receive the data from the primary and secondary collectors and identify any duplicates in order to de-duplicate the data before sending it to the pipeline.” (Para. [0110]) thereby teaching performing operations in a global environment that includes a centralized system/node and sensors/edge nodes that are in communication with each other.
Sampling information from the edge nodes concerning a state of the global environment, the information comprising a position of each edge node and an area covered by each edge node in the global environment;
Yadav teaches “Because it can be overwhelming for a system to capture every packet in a network, in some example embodiments, sensors 104 can be configured to capture only a representative sample of packets (e.g., every 1,000th packet or other suitable sample rate)” (Para. [0018]) thereby teaching the sensor data as information, the sensors as edge nodes, and the sensor data concerning a state of the environment captured by the sensor.
Yadav further teaches “As sensors 104 capture communications, they can continuously send network traffic data to collectors 108. The network traffic data can relate to a packet, a collection of packets, a flow, a group of flows, etc. The network traffic data can also include other details such as the VM BIOS ID, sensor ID, associated process ID, associated process name, process user name, sensor private key, geo-location of a sensor, environmental details, etc.” (Para. [0017]) where “Sensors 104 can change where they send their network traffic data if their environments change, such as if a certain collector experiences failure or if a sensor is migrated to a new location and becomes closer to a different collector” (Para. [0019]). Therefore, Yadav teaches the information comprising position and area coverage information by each sensor/edge node in the global environment.
Mapping the information to additional context for the information, wherein the additional context includes an indication of quality for the information, said quality being based on noise or loss associated with the information.
Yadav teaches “When a sensor is identified as being compromised, the flows from the sensor can be annotated to indicate such data is not reliable. The annotation can ensure that the collector does not rely on the data and statistics from the compromised sensor, or otherwise performs a verification procedure.” (Para. [0105]) thereby teaching mapping the information to additional context related to compromised source that is an indication of quality being questionable.Yadav further teaches detecting a quality issue by teaching “A network will often experience different amounts of packet loss at different points within the path of a flow. It is important to identify the amount of packet loss at each point to fine tune and improve the network” (Para. [0304]) and “The pipeline can identify packet loss at each point by comparing data or packets captured and reported by sensors at each point. This comparison can be performed per flow, per link, or on a host basis. Moreover, the pipeline can perform the comparison for data captured within a specific time window. For example, the pipeline can compare data from each point within a 30 minute time window. The pipeline can then identify packet loss at each point and determine if there is a problem at a specific point within the link, path, or flow.” (Para. [0308]).
Based on the information, and the additional context, maximizing a total area of coverages of the information sampled from the edge nodes to enable:
Yadav teaches using sampled information to maximize a total area of coverages of the information sampled from edge nodes by teaching “collectors 108 can retain a complete dataset describing one period (e.g., the past minute or other suitable period of time), with a smaller dataset of another period (e.g., the previous 2-10 minutes or other suitable period of time), and progressively consolidate network traffic flow data of other periods of time (e.g., day, week, month, year, etc.)” (Para. [0021]) and “a system that takes a small sampling of reports from various nodes and select the reports that include the most “packets seen.” In other words, records from a node that has seen the most traffic from a specific host can be preserved. The system can initially capture all reports of traffic flows and then discard duplicate reports.” (Para. [0076]). The total area of coverage is maximized by selecting reports that includes the most “packets seen” and discarding duplicate reports.Yadav further teaches using the annotated reliability context by teaching “When a sensor is identified as being compromised, the flows from the sensor can be annotated to indicate such data is not reliable. The annotation can ensure that the collector does not rely on the data and statistics from the compromised sensor, or otherwise performs a verification procedure” (Para. [0105]).
updating a most outdated states in the global environment that are relevant to perform tasks and
Yadav further teaches updating most outdated states in the global environment relevant to perform tasks by teaching “the present disclosure provides a centralized mechanism which tracks collector information, such as status, location, and collector-to-sensor mappings, as well as sensor information, such as location of specific sensors, and updates the configuration settings of sensors as necessary to maintain accurate and up-to-date collector-to-sensor mappings.” (Para. [0081]) and “This disclosure can detect current collector and sensor status, conditions, and updates to dynamically update and maintain proper collector-to-sensor mappings from a centralized location.” (Para. [0082]) and “a centralized mechanism which tracks collector information, such as status, location, and collector-to-sensor mappings, as well as sensor information, such as location of specific sensors, and updates the configuration settings of sensors as necessary to maintain accurate and up-to-date collector-to-sensor mappings” (Para. [0083]).
updating a global map, which is covered by the edge nodes, of the global environment;
Yadav also teaches teaches “the present disclosure provides a centralized mechanism which tracks collector information, such as status, location, and collector-to-sensor mappings, as well as sensor information, such as location of specific sensors, and updates the configuration settings of sensors as necessary to maintain accurate and up-to-date collector-to-sensor mappings.” (Para. [0081]) thereby teaching updating a global map of the global environment based on collected information about sensors to maintain an accurate and up-to-date global mapping. Yadav further teaches the global map being covered by the edge nodes/sensors by teaching “As sensors 104 capture communications, they can continuously send network traffic data to collectors 108. The network traffic data can relate to a packet, a collection of packets, a flow, a group of flows, etc. The network traffic data can also include other details such as the VM BIOS ID, sensor ID, associated process ID, associated process name, process user name, sensor private key, geo-location of a sensor, environmental details, etc.” (Para. [0017]).
Based on the information, updating an information retrieval cost;
Yadav teaches “the placement of collectors 108 can be optimized according to various priorities such as network capacity, cost, and system responsiveness” and “Alternatively, collectors 108 can utilize solid state drives, disk drives, magnetic tape drives, or a combination of the foregoing according to cost, responsiveness, and size requirements” (Para. [0020]) thereby teaching determining a current information retrieval cost for the current environment.
Orchestrating placement of a group of edge nodes, which has generated the set of next information; and
Yadav teaches “Sensors 104 can change where they send their network traffic data if their environments change, such as if a certain collector experiences failure or if a sensor is migrated to a new location and becomes closer to a different collector. In some example embodiments, sensors 104 can send different types of network traffic data to different collectors. For example, sensors 104 can send network traffic data related to one type of process to one collector and network traffic data related to another type of process to another collector.” (Para. [0019]) thereby teaching orchestrating placement of different groups of edge nodes/sensors grouped by reporting/generating data for particular collectors and generating next information with the groups of edge nodes/sensors.
Executing, by the group of edge nodes, the tasks.
Yadav teaches “the trigger to switch from a collector to another collector can be based on health, where health can include memory usage, CPU utilization, bandwidth, or errors” (Para. [0084]) thereby teaching using the collected health information to perform switching tasks or actions in the global environment.
Yadav further teaches “network traffic monitoring system 100 can include a wide bandwidth connection between collectors 108 and analytics module 110. Analytics module 110 can include application dependency (ADM) module 160, reputation module 162, vulnerability module 164, malware detection module 166, etc., to accomplish various tasks with respect to the flow data collected by sensors 104 and stored in collectors 108.” (Para. [0023]) thereby teaching a plurality of tasks.
Yadav explicitly teaches all of the elements of the claimed invention as recited above except:
Using the updated global map, the information retrieval cost, tasks and actions to update an attention mechanism, which selects a set of next information by maximizing an area of coverage based on the information and removing a portion of the information, which has an information gain less than an expected information gain of the set of next information;
However, in the related field of endeavor of applications of mixed integer programming, Seyfi in combination with Yadav teaches:
Using the updated global map, the information retrieval cost, tasks and actions to update an attention mechanism, which selects a set of next information by maximizing an area of coverage based on the information and removing a portion of the information, which has an information gain less than an expected information gain of the set of next information;
Yadav teaches “known connections (i.e., known server ports in these connections) can be used to train machine learned classifiers using features derived from these connections.” (Para. [0635]).
Seyfi teaches “Variable embeddings for the variable features, constraint embeddings for constraint features and edge embeddings for edge features are generated for each mixed integer linear program (MILP) sample in a dataset” (Abstract) where “a number of MILP samples are obtained by extracting features, making branching decisions and optionally determining a label or class for via a softmax output layer. The results are stored as training data…The goal of the training is to train a neural network to make the same choices at each branching node using the same feature set so that it can be used with other MILP instances” (Para. [0116]). Seyfi further teaches “The weights of the neural network 600 are updated via a gradient decent algorithm until the loss is minimized, i.e. the weights of one or more of the GAT 504, GAT 508 or GRU 602 are updated via a gradient decent algorithm until the loss is minimized.” where a GAT is a Graphical Attention Network (Para. [0118]).
Yadav teaches “collectors 108 can retain a complete dataset describing one period (e.g., the past minute or other suitable period of time), with a smaller dataset of another period (e.g., the previous 2-10 minutes or other suitable period of time), and progressively consolidate network traffic flow data of other periods of time (e.g., day, week, month, year, etc.)” (Para. [0021]) and “a system that takes a small sampling of reports from various nodes and select the reports that include the most “packets seen.” In other words, records from a node that has seen the most traffic from a specific host can be preserved. The system can initially capture all reports of traffic flows and then discard duplicate reports.” (Para. [0076]).Therefore, Yadav in combination with Seyfi teaches the attention mechanism of trained machine learned classifiers operable to select information that maximizes an area of coverage by sampling “reports from various nodes” and selecting reports “that include the most ‘packets seen’” and removing a portion of the information including duplicate reports which provides no information gain, thus being less than the expectation that collecting data will provide some level of information gain, otherwise the data would not be collected.
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Seyfi and Yadav at the time that the claimed invention was effectively filed, to have modified the systems and methods for collecting and managing sensor packet logs, as taught by Yadav, with the use of graph attention networks (GATs), as taught by Seyfi.
One would have been motivated to make such modification because Seyfi teaches “use an attention mechanism to extract information associated with the interplay between neighboring nodes. By using attention, the freedom to prioritize each node according to its neighborhood structure and embedding features is provided by the model” (Para. [0079]) and it would have been obvious to a person having ordinary skill in the art that using the attention mechanism taught by Seyfi would improve the analysis of Yadav’s teaching of analyzing logical entity’s neighboring logical entities (Para. [0546]).
Regarding Claim 2:
Seyfi and Yadav further teach:
Wherein the information comprises one or more messages generated by one or more edge nodes from which the information was retrieved.
Yadav teaches “The sensor can then send the packet log to the collector (step 306). In some embodiments, the sensor sends the packet log to the appropriate collector as configured by the analytics module in step 301. It should be understood that the various sensors in example method 300 can perform steps 302-306 independently and related to different packets, packet logs, and collectors where appropriate.” (Para. [0046]).
Regarding Claim 3:
Seyfi and Yadav further teach:
Wherein the tasks and actions are executable by one or more edge nodes.
Yadav teaches “the trigger to switch from a collector to another collector can be based on health, where health can include memory usage, CPU utilization, bandwidth, or errors” (Para. [0084]) thereby teaching using the collected health information to perform a switching task or action in the global environment.
Yadav further teaches “a centralized mechanism which tracks collector information, such as status, location, and collector-to-sensor mappings, as well as sensor information, such as location of specific sensors, and updates the configuration settings of sensors as necessary to maintain accurate and up-to-date collector-to-sensor mappings” (Para. [0083]) thereby teaching reconfiguring the sensors to send packet logs to a new collector when the collector is switched based on health.
Regarding Claim 4:
Seyfi and Yadav further teach:
Wherein the edge nodes comprises respective agents, which interact with the global environment.
Yadav teaches “each software package installed on a sensor of a computing node can contain a real software packet and a control engine…configured to operate on various operating systems and communicate with an upgrade server to control installation, launch, or uninstallation of the software packet on a corresponding sensor” (Para. [0066]) thereby teaching edge nodes with agents interacting with the global environment.
Regarding Claim 6:
Seyfi and Yadav further teach:
Wherein the information comprises the information gain relative to a state of the global environment before the global map was updated.
Yadav teaches “collectors 108 can retain a complete dataset describing one period (e.g., the past minute or other suitable period of time), with a smaller dataset of another period (e.g., the previous 2-10 minutes or other suitable period of time), and progressively consolidate network traffic flow data of other periods of time (e.g., day, week, month, year, etc.)” (Para. [0021]) thereby teaching maximization of information gain relative to a state before the global map was updated.
Regarding Claim 7:
Seyfi and Yadav further teach:
Wherein a task is executed in the global environment and the operations are performed in real time as that task is executed.
Yadav teaches “centralized system can then determine which collector should be assigned to that sensor, and dynamically update the configuration settings of the sensor to point the sensor to the new collector. In this way, the centralized system can maintain the accuracy of the configuration settings of the sensors and ensure that the sensors are always connected to a collector even when an assigned collector goes down or the sensor otherwise experiences problems contacting its assigned collector.” (Para. [0083]) thereby teaching executing the task dynamically while operations are performed in real time as that task is executing.
Regarding Claim 8:
Seyfi and Yadav further teach:
Wherein the edge nodes comprise a combination of static sensors and mobile sensors.
Yadav teaches “sensors 104 can reside on nodes of a data center network (e.g., virtual partition, hypervisor, physical server, switch, router, gateway, other network device, other electronic device, etc.)…[and] can monitor communications to and from the nodes and report on environmental data related to the nodes” (Para. [0016]). Yadav further teaches “The sensor could read an externally maintained configuration file to figure out if it is deployed on a virtual machine or hypervisor or physical switch. Use of an external file to solve this problem requires either a person to update the configuration file each time new sensors are deployed or the same sensor moves to a different virtual machine” (Para. [0234]) thereby teaching that the sensors can be static or mobile.
Regarding Claim 9:
Seyfi and Yadav further teach:
Wherein the set of next information is retrieved from the group of edge nodes based upon the expected information gain of the set of next information relative to the state of the global environment.
Yadav teaches “In some example embodiments, serving layer 118 can also request raw data from a sensor or collector” (Para. [0033]) and “creating summary statistics related to the datacenter, identifying components or hosts that are at capacity, identifying components or hosts that are under-utilized or incapacitated, comparing current activity to historical or expected activity, etc.” (Para. [0049]) thereby teaching requesting expected information from a sensor or collector based on historical or expected activity.
Regarding Claim 10:
Seyfi and Yadav further teach:
Wherein the set of next information
includes updated information of most outdated states in the global environment that are relevant to perform any tasks that are executed in the global environment at a time when the set of next information is obtained, and
Yadav teaches “Automatically detecting the environment in which such a sensor is placed by collectively analyzing the data reported by all of the sensors is the new technique presented in this disclosure.” (Para. [0235]) thereby teaching that the set of information includes updated information from most outdated states in the global environment when “data reported by all of the sensors” is analyzed”. Yadav further teaches “the trigger to switch from a collector to another collector can be based on health, where health can include memory usage, CPU utilization, bandwidth, or errors” (Para. [0084]) thereby teaching using the collected health information to perform switching tasks or actions in the global environment.
It is noted that the limitation merely recites that the next information happens to include updated information of most outdates states instead of intentionally targeting most outdated states for generating/obtaining updated information and that the information is “…relevant to perform any tasks…”.
maximizes the area of coverage based on the set of next information sampled from the edge nodes.
Yadav teaches “Because it can be overwhelming for a system to capture every packet in a network, in some example embodiments, sensors 104 can be configured to capture only a representative sample of packets (e.g., every 1,000th packet or other suitable sample rate)” (Para. [0018]). thereby teaching the sensor data as information, the sensors as edge nodes, and the sensor data concerning a state of the environment captured by the sensor.
Yadav further teaches maximizing the area of coverage based on the collected sensor data by teaching “collectors 108 can retain a complete dataset describing one period (e.g., the past minute or other suitable period of time), with a smaller dataset of another period (e.g., the previous 2-10 minutes or other suitable period of time), and progressively consolidate network traffic flow data of other periods of time (e.g., day, week, month, year, etc.)” (Para. [0021]) and “a system that takes a small sampling of reports from various nodes and select the reports that include the most “packets seen.” In other words, records from a node that has seen the most traffic from a specific host can be preserved. The system can initially capture all reports of traffic flows and then discard duplicate reports.” (Para. [0076]).Therefore, Yadav teaches maximizing an area of coverage by sampling “reports from various nodes” and selecting reports “that include the most ‘packets seen’” which maximizes the area of sampled reports from various sensors using “the most ‘packets seen’” which are flowing from different sources across the geographic area.
Regarding Claim 11:
All of the limitations herein are similar to some or all of the limitations of Claim 1.
Seyfi and Yadav further teach:
A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors (Yadav – Para. [0061]).
Regarding Claim 12:
All of the limitations herein are similar to some or all of the limitations of Claim 2.
Regarding Claim 13:
All of the limitations herein are similar to some or all of the limitations of Claim 3.
Regarding Claim 14:
All of the limitations herein are similar to some or all of the limitations of Claim 4.
Regarding Claim 15:
All of the limitations herein are similar to some or all of the limitations of Claim 5.
Regarding Claim 16:
All of the limitations herein are similar to some or all of the limitations of Claim 6.
Regarding Claim 17:
All of the limitations herein are similar to some or all of the limitations of Claim 7.
Regarding Claim 18:
All of the limitations herein are similar to some or all of the limitations of Claim 8.
Regarding Claim 19:
All of the limitations herein are similar to some or all of the limitations of Claim 9.
Regarding Claim 20:
All of the limitations herein are similar to some or all of the limitations of Claim 10.
Response to Amendment
Applicant’s Amendments, filed on 12/19/2025, are acknowledged and accepted.
Response to Arguments
On page 7 of the Remarks filed on 12/19/2025, Applicant argues that the “[previously] cited combination of art is entirely silent with respect to” the amended language performing a mapping operation “required to map the information to additional context for the information. The additional context includes an indication of quality for the message, where that quality is based on noise or loss associated with the information”.After spending further time for consideration and search, Applicant’s amendment and corresponding remark does not appear to overcome the previously cited prior art. The amended limitation(s) is/are addressed in the rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tripathy et al. (U.S. Pre-Grant Publication No. 2023/0071442) teaches adaptive learning based systems and methods for optimization of unsupervised clustering. The embodiments of present disclosure herein address unresolved problem of involving manual intervention in data preparation, annotating or labelling training data to train classifiers, and taking a number of clusters directly as an input from the users for data classification. The method of the present disclosure provides a fully unsupervised optimized approach for auto clustering of input data that automatically determines the number of clusters for the input data by leveraging concepts of graph theory and maximizing a cost function. The method of present disclosure is capable of handling a new data by continuously and incrementally improving the clusters. The method of present disclosure is domain agnostic, scalable, provides expected level of accuracy for real-world data, and helps in minimizing utilization of powerful processors leading to reduced overall cost.
Del Pero et al. (U.S. Pre-Grant Publication No. 2024/0362808) teaches a computing system that is operable to (i) receive image data and corresponding secondary sensor data, (ii) generate a reconstruction of a map from the image data, wherein the reconstruction comprises sequential pose information, (iii) determine constraints from the secondary sensor data, and (iv) validate the reconstruction of the map by applying the determined constraints from the secondary sensor data to the determined sequential pose information from the reconstruction of the map and determining whether the sequential pose information fails to satisfy any of the constraints determined from the secondary sensor data.
Huang (U.S. Pre-Grant Publication No. 2023/0275834) teaches a service traffic processing method and apparatus. The method includes: receiving service traffic of a service function chain (SFC) computing network; determining an atomic function identifier of the service traffic; determining, according to a pre-stored SFC forwarding table, a first atomic function instance corresponding to the atomic function identifier, wherein the SFC forwarding table comprises a mapping relationship between an atomic function identifier and a plurality of equivalent atomic function instances; and forwarding the service traffic to the first atomic function instance, wherein the first atomic function instance is used for processing the service traffic.
Bao (U.S. Pre-Grant Publication No. 2023/0237064) teaches receiving, by using a transceiver component, collection indicator data sent by an edge cluster; performing pre-aggregation processing on the collection indicator data to obtain pre-aggregated indicator data, and sending the pre-aggregated indicator data to a coordinated write component; converting, by the coordinated write component, the pre-aggregated indicator data into conversion indicator data that has a target storage format, and performing merging processing on the conversion indicator data to obtain storage indicator data; writing the storage indicator data into a database component; and writing, by the database component, the storage indicator data into a storage disk.
Montemerlo et al. (U.S. Pre-Grant Publication No. 2022/0082408) teaches processing an initial representation of an environment to generate an updated representation that includes representations of one or more objects there were obscured in the initial representation. One of the methods includes obtaining initial surfel data comprising a plurality of surfels; determining a plurality of non-static surfels from the plurality of surfels; obtaining guidance data that characterizes a prediction for static surfaces in one or more first regions of the environment that were obscured by the plurality of non-static surfels in the initial surfel data; and processing the guidance data and the initial surfel data to generate final surfel data that comprises, for each first region of the environment, a plurality of predicted surfels that represent static surfaces in the first region that are partially or wholly obscured by objects represented by respective non-static surfels.
Zhang et al. (U.S. Pre-Grant Publication No. 2022/0329650) teaches a control node divides a plurality of edge nodes included in an edge node cluster into N edge node sets, where each edge node set includes at least two edge nodes. The control node selects M second edge nodes from a first edge node set, where the first edge node set is one of the N edge node sets. The control node sends a control rule to the M second edge nodes, so that the second edge node performs fault detection on a first edge node according to the control rule. Therefore, when the control node cannot perform fault detection on the first edge node, the second edge node can perform fault detection on the first edge node.
Hunter et al. (U.S. Pre-Grant Publication No. 2023/0117081) teaches a user-centric application comprising a plurality of service modules, the method comprising identifying available resources associated with each of a plurality of edge devices in the local network, identifying a first device of the local network, the first device offering a user input service, identifying a second device of the local network, the second device to receive an output event and present the output event for consumption by the user, for each service module, each service module to provide a portion of a functionality of the user-centric application and having an associated resource request, deploying the service module to an edge device based on the associated resource request and the identified available resources, and configuring data flows between the deployed service modules, the first device, and the second device to realize the user-centric application.
Sohail et al. (U.S. Pre-Grant Publication No. 2022/0129426) teaches receiving data from source at edge node (102), preparing data for the cloud (104), collaborating with the cloud (106), chunking the data into chunks (108), fingerprinting the chunks (110) and writing the fingerprint to recipe (116), and updating the edge node (118).
Levinson et al. (U.S. Pre-Grant Publication No. 2017/0248963) teaches autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide map data for autonomous vehicles. In particular, a method may include accessing subsets of multiple types of sensor data, aligning subsets of sensor data relative to a global coordinate system based on the multiple types of sensor data to form aligned sensor data, and generating datasets of three-dimensional map data. The method further includes detecting a change in data relative to at least two datasets of the three-dimensional map data and applying the change in data to form updated three-dimensional map data. The change in data may be representative of a state change of an environment at which the sensor data is sensed. The state change of the environment may be related to the presence or absences of an object located therein.
Guim Bernat et al. (U.S. Pre-Grant Publication No. 2021/0144517) teaches multi-entity (e.g., multi-tenant) edge computing deployments are disclosed. Among other examples, various configurations and features enable the management of resources (e.g., controlling and orchestrating hardware, acceleration, network, processing resource usage), security (e.g., secure execution and communication, isolation, conflicts), and service management (e.g., orchestration, connectivity, workload coordination), in edge computing deployments, such as by a plurality of edge nodes of an edge computing environment configured for executing workloads from among multiple tenants.
Lauterbach et al. (U.S. Pre-Grant Publication No. 2022/0204019) teaches sensor calibration with environment map. In some implementations, a three-dimensional surfel representation of a real-world environment is obtained. One or more surfels of the surfel representation having a particular classification of the different classifications are selected. Input sensor data from one or more sensors installed on an autonomous or semi-autonomous vehicle are received. The input sensor data is compared to the surfel representation to identify one or more differences between the observation and the surfel representation. At least one sensor of the one or more sensors is calibrated using the one or more differences between the observation and the surfel representation.
Non-Patent Literature Seong Jun Kim and Sung Ha Kang and Haomin Zhou, Optimal Sensor Positioning (OSP); A Probability Perspective Study, 19 April 2016, arXiv:1604.05391 (Year: 2016) teaches “a method to optimally position a sensor system, which consists of multiple sensors, each has limited range and viewing angle, and they may fail with a certain failure rate. The goal is to find the optimal locations as well as the viewing directions of all the sensors and achieve the maximal surveillance of the known environment. We setup the problem using the level set framework. Both the environment and the viewing range of the sensors are represented by level set functions. Then we solve a system of ordinary differential equations (ODEs) to find the optimal viewing directions and locations of all sensors together. Furthermore, we use the intermittent diffusion, which converts the ODEs into stochastic differential equations (SDEs), to find the global maximum of the total surveillance area. The numerical examples include various failure rates of sensors, different rate of importance of surveillance region, and 3-D setups. They show the effectiveness of the proposed method.” (Abstract)
Bartholemew et al. (U.S. Pre-Grant Publication No. 2023/0291794) teaches administering a distributed edge computing system using a computing platform are disclosed. Exemplary implementations may: identify a plurality of computing clusters running at least one workload; collect data from the plurality of computing clusters; aggregate the data from the plurality of computing clusters; access a model; reconcile, based at least in part on accessing the model, one or more of the data from the data store and state data for the at least one workload to create reconciled cluster data; receive one or more messages from a user device; and in response to receiving the one or more messages from the user device, provide at least a portion of the reconciled cluster data to the user device.
Chinese Patent Publication No. CN116368355A teaches realization method of a sensor based on the Internet of things, and also provides a calibration method of the sensor based on deep learning and a system thereof, wherein the method comprises the steps that the sensor acquires historical data according to time sequence; acquiring at least part of corresponding values of historical data through a standard sensor; providing the history data and the numerical values to a transducer model; the transducer model trains historical data and numerical values to obtain an original model; performing multistage compression optimization on the original model through deep learning pruning or knowledge distillation to obtain a model after multistage compression optimization; and calibrating the original data acquired after the sensor according to the original model or the model after the multi-stage compression optimization. The sensor calibration method and the system thereof have the capability of being deployed in the sensor terminal equipment, the base station and the cloud server respectively, so that the application in the multi-type sensor equipment and the multi-scene thereof is realized, and the multi-stage collaborative calibration also realizes the simple and efficient inspection of the calibration result.
Chinese Patent Publication No. CN110537078A teaches effectively utilize local and remote computing resource and communication bandwidth using multiple equipment equipped with movable sensor to provide distributed environment mapping. According in a first aspect, a kind of determining method for being marked on the global position in global map one or morely is provided, method includes the following steps: determining that the one or more between the sequential sensor data captured by one or more mobile devices is poor; One or more relative positioning landmark locations are determined about one or more mobile devices; It is poor based on the one or more between sequential sensor data, determine relative device pose relatedly with one or more relative positioning landmark locations; And the correlation between determining each equipment pose and one or more relative positioning landmark locations.
Ahmed et al. (U.S. Pre-Grant Publication No. 2020/0370920) teaches feedback for map information is based on an integrated navigation solution for a device within a moving platform using obtained motion sensor data from a sensor assembly of the device, obtained radar measurements for the platform and obtained map information for an environment encompassing the platform. An integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique that uses a nonlinear measurement model for radar measurements. The map information is assessed based at least in part on the integrated navigation solution and radar measurements so that feedback for the map information can be provided.
Andrews, Jr. (U.S. Patent No. 7,895,021) teaches a process is provided for disposing a sensor in an environment for optimally obtaining characteristic measurements. The process includes modeling covariant sets of environment elements that correspond to conditions of the environment and of sensor elements that correspond to characteristic functionalities of the sensor. The process further includes covariantly coupling the environmental elements with the sensor elements to produce a third set of configuration elements; and combining the configuration elements to obtain a fitness function parameter. The process may additionally include adjusting the environment elements and the sensor elements; repeating operations for covariantly coupling and combining until obtaining the fitness function parameter over a defined region of the environment within a set of fitness function parameters. Also, the process may include determining an extreme value within the set of fitness function parameters, which may also include optimizing the fitness function parameter as an optimum value with a genetic algorithm over the set of fitness function parameters.
Stilwell (U.S. Pre-Grant Publication No. 2020/0030965) teaches designating a first robot a lead robot and assigning a first task in a task area to the lead robot. Broadcasting a work query in the task area seeks the presence of subordinate robots configured to perform tasks. Receiving a work confirmation signal from a subordinate robot in the task area answers the work query with an affirmation that the subordinate robot is in the task area to perform tasks. Transmitting a task command to the subordinate robot in response to the work confirmation signal comprises a directive to perform the first task. Receiving a task confirmation signal informs the lead robot of the subordinate robot electronic characteristics comprising processing capabilities, transmit signal profile, receive signal profile, and storage device capabilities. Processing confirms whether the subordinate robot can collaborate with the lead robot to do the first task.The reference further teaches “The sequence of sub-tasks can be optimized to maximize use of lead robot, subordinate robot and second subordinate robot aggregated processing capabilities” (Par. [0077]) where “The squad leader robot SQL—to cloud—to HOL assembly 20 architecture permits the real-time building of decision engine software and supports the cohesive use of: Virtual mapping of robot locations, actions, and situations;…[and] Virtual mapping from sensors to systems;” (Paras. [0082]-[0085]).
Lection et al. (U.S. Pre-Grant Publication No. 2016/0314149) teaches sampling crowd sourced data. The approach selects an sampling node from a set of crowd nodes. The sampling node receives a data acquisition request from a data collector and receives data from the set of crowd nodes with the data being responsive to the data acquisition request. The received data is processed by the sampling node to reduce redundant data as defined by the data acquisition request. An acquired data message block is generated and transmitted from the sampling node to the data collector.The reference further teaches “The data collected from the crowd nodes and the amount of abstraction is determined by data acquisition message block 530 that was received from the data collector and defines the data that is being collected. The data acquisition message block defines the data items of interest (e.g., video of a particular parade, etc.), the range of data considered redundant, the period of collection, the maximum collection geographic area, and the collection radius. The collection radius defines the overall collection area (e.g., the area of the city where the parade is occurring, etc.) and the maximum collection geographic area is used to define a collection area within the overall collection area (e.g., a city block on one side of the street, etc.).” (Para. [0043])
Sanden et al. (U.S. Pre-Grant Publication No. 2023/0213494) teaches improved analysis of geological samples through the coordinated use of multiple sensors. Among other advantages, the invention offers significant advances in the accuracy, ease, and speed with which substances found in single samples, and/or across multiple samples, can be identified, mapped, and otherwise analyzed.The reference further teaches “setting the at least one adjustable operating parameter comprises causing the sensor to collect data associated with a known reference sample, and setting one or more of the adjustable operating parameters to optimize desired data collection characteristics of the sensor.” (Para. [0015]) and “at 630, the controller 102 can cause the system 1000 to conduct an analysis of one or more regions 302 of one or more samples 300. Optionally, controller 102 can parse all command data records of an SID&CDS read at 615 to generate an optimized analysis path for either or both of sample table 202 and sensors 120, based on sensors selected by the operator 90, sample region(s) 302 to be analyzed, and the geometry(ies) of samples to be analyzed, in order for example to reduce overall scanning time, avoid component collisions, and/or maximize surface areas or regions 302 being scanned within a given time frame.” (Para. [0145]).
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/ROBERT F MAY/Examiner, Art Unit 2154 1/22/2026
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154