Detailed Action
This application was filed on 08/15/2024 which is CIP of 17/551436 (now US patent #12067766) filed on 12/15/2021.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-18 are pending.
Claims 1-18 are rejected.
Information Disclosure Statement
The information disclosure Statement (IDS) submitted on 08/15/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS statements are being considered by the examiner.
Drawings
The drawing submitted on 08/15/2024 are accepted.
Claim Objections
Claim 1, lines 16 recites “the master”. There is insufficient antecedent basis for this limitation in the claim.
Claims 7 and 13 include similar limitations as noted above for claim 1 and are objected to for similar rational as set forth above.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The term “desirable” in claims 1, 7, and 13 is a relative term which renders the claims indefinite. The term “desirable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
At least due to dependency, dependent claims 2-6, 8-2, 14-18 are rejected under the same rational as set forth for claims 1, 7, and 13.
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 is rejected under 35 U.S.C. 101 as being directed to abstract idea without significantly more.
Representative claim 1 is directed to a machine-learning-based real-time task scheduling method, applied to an Apache Storm cluster, wherein the Apache Storm cluster includes a master node and a plurality of worker nodes, comprising:
for a worker node of the plurality of worker nodes, executing a training task distributed by the master node;
collecting latency time lengths of each of a plurality of machine learning models under different CPU (central processing unit) utilization and memory usage;
calculating a mean squared error of the latency time lengths of each of the plurality of machine learning models;
comparing the plurality of machine learning models according to mean squared errors of latency time lengths corresponding to the plurality of machine learning models to select a desirable machine learning model; and
installing the desirable machine learning model on the worker node;
providing an API (application programming interface) for the worker node configured for communication between the master node and the worker node;
when receiving a task by the master node, requesting the worker node to predict a latency time length according to current CPU utilization and current memory usage of the worker node; and
returning the predicted latency time length to the master via the API of the worker node; and
after the master node collects predicted latency time lengths of the plurality of worker nodes, assigning the task to a corresponding worker node with a lowest predicted latency time length.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely,
Mental processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper (see, October 2019 Patent Eligibility Guidance Update, 84 Fed. Reg. 55,942, hereinafter “PEG”).
Mathematical Concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations
Certain Methods Of Organizing Human Activity including fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
For instance, humans can mentally and/or via aid of pen/paper perform a real-time task scheduling method… , [that] includes a master node and a plurality of worker nodes (e.g. manager mentally and/or via paper distributing/organizing tasks/human activities to workers/persons), comprising: mentally and/or via pe paper for a worker node of the plurality of worker nodes, executing a training task distributed by the master node; mentally and/or using mathematical concepts for calculating a mean squared error of the latency time lengths of each of the plurality of machine learning models; mentally and/or via pen/paper comparing the plurality of machine learning models according to mean squared errors of latency time lengths corresponding to the plurality of machine learning models to mentally select a desirable machine learning model; and… mentally and/or via pen/paper to predict a latency time length according to current CPU utilization and current memory usage of the worker node; and mentally and/or via pen paper assigning the task to a corresponding worker node with a lowest predicted latency time length (e.g. mentally assigning assignments workers which is also falls within certain methods of organizing activities).
Per prong 2, Step 2A, the additional non-emphasized elements as noted above, are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception; are merely adding words “apply it” (or an equivalent) with the judicial exception/mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP2106.05 (d, f, g, h). For instance,
“machine-learning-based… applied to an Apache Storm cluster, wherein the Apache Storm cluster” – is merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(f, h)
“collecting latency time lengths of each of a plurality of machine learning models under different CPU (central processing unit) utilization and memory usage” - are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
“installing the desirable machine learning model on the worker node” – is merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(f, h)
“providing an API (application programming interface) for the worker node configured for communication between the master node and the worker node” – is merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(f, h)
“when receiving a task by the master node, requesting the worker node…” - are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
“returning the predicted latency time length to the master via the API of the worker node” - are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
“after the master node collects predicted latency time lengths of the plurality of worker nodes” are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
Additionally, the recited claim limitations do not improve the functionality of the electronic device or achieve improved technical results and does not sufficiently tie any limitations or combination of limitations to any improvement (if any) to the functionality of the electronic device or achieving improved technical results.
Per Step 2B, the additional non-emphasized elements as noted above, are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception; are merely adding words “apply it” (or an equivalent) with the judicial exception/mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(d, f, g, h). . For instance,
“machine-learning-based… applied to an Apache Storm cluster, wherein the Apache Storm cluster” – is merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(f, h)
“collecting latency time lengths of each of a plurality of machine learning models under different CPU (central processing unit) utilization and memory usage” - are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
“installing the desirable machine learning model on the worker node” – is merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(f, h)
“providing an API (application programming interface) for the worker node configured for communication between the master node and the worker node” – is merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and/or Generally linking the use of the judicial exception to a particular technological environment or field of use and/or Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception – see MPEP 2106.05(d, f, h)
“when receiving a task by the master node, requesting the worker node…” - are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
“returning the predicted latency time length to the master via the API of the worker node” - are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
“after the master node collects predicted latency time lengths of the plurality of worker nodes” are mere data gathering/insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
Additionally, the recited claim limitations do not improve the functionality of the electronic device or achieve improved technical results.
Accordingly, claim 1 is rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Independent claims 7, and 13 are system and medium claims corresponding to method claim 1 and are of substantially same scope.
Accordingly, claims 7, and 13 are rejected under the same rational as set forth for claim 1.
Dependent claims 2-6, 8-12, and 14-18 when considered individually or in combination per steps as noted above are rejected under the same rational as set forth above for claims 1, 7, and 13, and the recited claim limitations do not improve the functionality of the electronic device or achieve improved technical results. In particular,
As per claim 2, the rejection of claim 1 further incorporated, further recites wherein: the latency time length is an average frame processing time in a minute.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception, are merely adding words “apply it” (or an equivalent) with the judicial exception, and/or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 3, the rejection of claim 1 further incorporated, further recites wherein: the plurality of machine learning models includes Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Deep Belief Networks (DBN).
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception, are merely adding words “apply it” (or an equivalent) with the judicial exception, and/ or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 4, the rejection of claim 1 further incorporated, further recites wherein: the training task includes one of objection detection, location tracking, event tagging, target navigation, and model reconstruction.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception, are merely adding words “apply it” (or an equivalent) with the judicial exception, and/or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 5, the rejection of claim 1 further incorporated, further recites wherein: the worker node manages one or more worker processes capable of running a plurality of tasks in parallel.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception, are merely adding words “apply it” (or an equivalent) with the judicial exception, and/or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claim 6, the rejection of claim 1 further incorporated, further recites wherein: the Apache Storm cluster is a heterogeneous distributed stream processing system.
Per prong 1, Step 2A, the above emphasized element/concepts are not meaningfully different than those concepts found by the courts to be abstract, namely, Mental Processes including concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or humans using pen and paper.
Per prong 2, Step 2A and 2B, the additional elements (e.g. non-emphasized elements) are mere data gathering/sending steps/insignificant extra-solution activity to the judicial exception, are merely adding words “apply it” (or an equivalent) with the judicial exception, and/or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(d, f, g, h).
As per claims 8-12, and 14-18:
Claims 8-12 and 14-18 are system and medium claims corresponding to method claims 2-6 and are of substantially same scope.
Accordingly, claims 8-12, and 14-18 are rejected under the same rational as set forth for claims 2-6.
Examiner Notes
Claims 1-18 are allowable if the above noted objections and rejections are overcome via amendments and/or arguments. Reasons for allowance will be held in abeyance until all matters in the prosecution are closed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
COGNITIVE SCHEDULER FOR KUBERNETES
DOCUMENT ID
US 20220405127 A1
DATE PUBLISHED
2022-12-22
Abstract
Embodiments are directed to deploying a workload on the best/highest performance node. Nodes configured to accommodate a request for a workload are selected. Information is collected on each of the selected nodes and the workload. Predicted response times expected for the workload running on each of the selected nodes are determined. The workload is deployed on a node of the selected nodes, the node having a corresponding predicted response time for the workload, the workload being deployed on the node based at least in part on the corresponding predicted response time.
PIXEL-BASED LOAD BALANCING
DOCUMENT ID
US 20190052706 A1
DATE PUBLISHED
2019-02-14
Abstract
Disclosed are methods, systems, and apparatus for load-balancing image-processing jobs based on the number of pixels in the images and/or the nature of the processing that is requested on those pixels. For example, a set of machines may run software to provide various types of image processing services, such as format conversion, recompression, resizing, cropping, among others. These are referred to as image servers. In accordance with the teachings hereof, the load on each image server can be calculated based on the number of pixels in the images that are waiting to be processed in the image server's processing queue, adjusted by the type of processing that is requested on each image. The adjustment typically reflects and adjusts for the relative time needed to perform various types of processing. Load scores can be further adjusted based on the processing capabilities of each image server, in some embodiments.
Zhang et al. “Hetero-Edge: Orchestration of Real-time Vision Applications on Heterogeneous Edge Clouds”, 2019, pages 1270-1278, retrieved from URL:< https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8737478>
Abstract
Disclosed Running computer vision algorithms on images or videos collected by mobile devices represent a new class of latency-sensitive applications that expect to benefit from edge cloud computing. These applications often demand real-time responses (e.g.,>100ms), which can not be satisfied by traditional cloud computing. However, the edge cloud architecture is inher ently distributed and heterogeneous, requiring new approaches to resource allocation and orchestration. This paper presents the design and evaluation of a latency-aware edge computing platform, aiming to minimize the end-to-end latency for edge applications.
The proposed platform is built on Apache Storm, and consists of multiple edge servers with heterogeneous computation (includ ing both GPUs and CPUs) and networking resources. Central to our platform is an orchestration framework that breaks down an edge application into Storm tasks as defined by a directed acyclic graph (DAG) and then maps these tasks onto heterogeneous edge servers for efficient execution. An experimental proof-of-concept testbed is used to demonstrate that the proposed platform can indeed achieve low end-to-end latency: considering a real-time 3D scene reconstruction application, it is shown that the testbed can support up to 30 concurrent streams with an average per frame latency of 32ms, and can achieve 40% latency reduction relative to the baseline Storm scheduling approach.
TECHNIQUES FOR MODIFYING CLUSTER COMPUTING ENVIRONMENTS
DOCUMENT ID
US 20220147390 A1
DATE PUBLISHED
2022-05-12
Abstract
Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.
INTELLIGENT RESOURCE MANAGEMENT
DOCUMENT ID
US 20220413931 A1
DATE PUBLISHED
2022-12-29
Abstract
A system and method for distributing resources in a computing system is disclosed. The resources include hardware components in a hardware pool, a management infrastructure, and an application. A telemetry system is coupled to the resources to collect operational data from the operation of the resources. A data analytics system is coupled to the telemetry subsystem to predict a future operational data value based on the collected operational data. A policy engine is coupled to the data analytics system to determine a configuration to allocate the resources based on the future operational data value.
Wu et al., “Machine Learning-Based Real-time Task Scheduling for Apache Storm”, 2024, total pages: 9, retrieved from URL: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13062/130620I/Machine-learning-based-real-time-task-scheduling-for-Apache-Storm/10.1117/12.3021842.pdf
Abstract
data processing. How ever, the existing task scheduling algorithms for Apache Storm do not adequately take into account the het erogeneity and dynamics of node computing resources and task demands, leading to high processing latency and suboptimal performance. In this thesis, we propose an innovative machine learning-based task scheduling scheme tailored for Apache Storm. The scheme leverages machine learning models to predict task performance and assigns a task to the computation node with the lowest predicted processing latency. In our design, each node operates a machine learning-based monitoring mechanism. When the master node schedules a new task, it queries the computation nodes obtains their available resources, and processes latency predictions to make the optimal assignment decision. We explored three machine learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Deep Belief Networks (DBN). Our experiments showed that LSTM achieved the most accurate latency predictions. The evaluation results demonstrate that Apache Storm with the proposed LSTM-based scheduling scheme significantly improves the task processing delay and resource utilization, compared to the existing algorithms.
Machine Learning Assisted Quality Of Service (QoS) For Solid State Drives
DOCUMENT ID
US 20220374169 A1
DATE PUBLISHED
2022-11-24
Abstract
A method for meeting quality of service (QoS) requirements in a flash controller that includes one or more instruction queues and a neural network engine. A configuration file for a QoS neural network is loaded into the neural network engine. A current command is received at the instruction queue(s). Feature values corresponding to commands in the instruction queue(s) are identified and are loaded into the neural network engine. A neural network operation of the QoS neural network is performed using as input the identified feature values to predict latency of the current command. The predicted latency is compared to a first latency threshold. When the predicted latency exceeds the first latency threshold one or more of the commands in the instruction queue(s) are modified. The commands are not modified when the predicted latency does not exceed the latency threshold. A next command in the instruction queue(s) is then performed.
SHARING OF COMPUTE RESOURCES BETWEEN THE VIRTUALIZED RADIO ACCESS NETWORK (VRAN) AND OTHER WORKLOADS
DOCUMENT ID
US 20220035665 A1
DATE PUBLISHED
2022-02-03
Abstract
The present disclosure relates to systems and methods for sharing compute resources. The systems and methods may include identifying a plurality of workloads to complete by a deadline. The systems and methods may include generating a performance prediction for each workload of the plurality of workloads. The systems and methods may use the performance prediction to calculate a number of compute resources required for the plurality of workloads to complete by the deadline. The systems and methods may schedule the plurality of workloads across the number of compute resources.
Capacity Planning For Data Center Services
DOCUMENT ID
US 8250198 B2
DATE PUBLISHED
2012-08-21
Abstract
Techniques and systems for providing capacity planning for data center services are disclosed herein. In some aspects, a multi-tier service is analyzed to determine a query response time (QRT) that can be used for resource planning or other service management reasons. The QRT may include a front end (FE) response time of FE servers and a back end (BE) response time of BE servers, where the BE servers are partitioned servers, and where the response times are based on various request rates from users. In various aspects, the QRT may include a network transmission time of data transmitted between an end user and each tier. In further aspects, the FE response time may be modeled using a single server queuing model while the BE response time may be modeled using a simulation or regression analysis.
See form 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA A AMIN whose telephone number is (571)270-3181. The examiner can normally be reached on Monday-Friday from 8:00 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kevin Young, can be reached on 571-270-3180. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/MUSTAFA A AMIN/ Primary Examiner, Art Unit 2194
Prosecution Insights