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 .
Allowable Subject Matter
Claims 5, and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Objections
Claims 2, 4, 6, 8, 12, 14, 16, and 18 are objected to because of the following informalities:
As per claim 2, “ each microservice”, ll 2 should be “the each microservice”.
As per claim 4, “ resources”, ll 2 should be “the resources. “the microservice for updating resources is selected randomly”, ll 2-3 should be “the microservice for updating the resources is selected randomly”.
As per claim 6, “resource allocation update”, ll 1-2 should be “the resource allocation update”.
As per claim 8, “resources”, ll 1 should be “the resources”.
As per claim 12, see objection on claim 2.
As per claim 14, see objection on claim 4.
As per claim 16, see objection on claim 6.
As per claim 18, see objection on claim 2.
Appropriate correction is required.
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.
the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
Claims 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 101.
As per claim 1, the claim recites a system, therefore is a machine.
“computing a mean of output processing rate . . . evaluating a state of each microservice of the microservices . . . selecting a single microservice from the pipeline for updating resources for an action that changes the state in the single microservice that is selected . . . performing resource allocation update for the selected microservice “ These limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. Thus, the claim recites a mental process.
“updating the state of the selected microservice...” is a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g). The claim is ineligible.
As discussed above, “updating the state of the selected microservice...” is a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g). The claim is ineligible.
As per claim 2, see rejection on claim 1. “wherein the evaluating of the state of each activation state comprising storing a best Q-value for each microservice“ is 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) and Berkheimer Memo. See Liu. The claim is ineligible.
As per claim 3, see rejection on claim 1. “wherein the evaluating of the state employs a Q-learning workflow of a reinforcement learning (RL) method“ is 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) and Berkheimer Memo. See Gottin. The claim is ineligible.
As per claim 4, see rejection on claim 1. “wherein in exploration mode to select the microservice for updating resources, the microservice for updating resources is selected randomly“ is 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) and Berkheimer Memo. See Rath. The claim is ineligible.
As per claim 6, see rejection on claim 1. “wherein performing resource allocation update includes computing an expected output processing rate“ is 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) and Berkheimer Memo. See Kansal/Jung. The claim is ineligible.
As per claim 7, see rejection on claim 1. “wherein the updating the state of the selected microservice with the resource allocation includes a reward computation and updating of a Q-value for the selected microservice using a Bellman's equation“ is 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) and Berkheimer Memo. See Bai. The claim is ineligible.
As per claim 8, see rejection on claim 1. “wherein resources are selected from nodes by availability of CPU cores and random access memory (RAM) availability“ is 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) and Berkheimer Memo. See Chalapathy. The claim is ineligible.
As per claim 9, see rejection on claim 1. “wherein the microservices are directed towards object identification from a video stream“ is 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) and Berkheimer Memo. See Kuo. The claim is ineligible.
As per claim 10, see rejection on claim 9. “wherein the object identification is facial recognition“ is 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) and Berkheimer Memo. See Kuo. The claim is ineligible.
As per claim 11, see rejection on claim 1.
As per claim 12, see rejection on claim 2.
As per claim 13, see rejection on claim 3.
As per claim 14, see rejection on claim 4.
As per claim 16, see rejection on claim 6.
As per claim 17, see rejection on claim 7.
As per claim 18, see rejection on claim 8.
As per claim 19, see rejection on claim 9.
As per claim 20, see rejection on claim 11.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 6, 11, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kansal et al. (US 2012/0317578 ) (hereinafter Kansal) in view of Jung et al (US 2023/0315518 ) (hereinafter Jung).
As per claim 1, Kansal teaches:
A computer implemented method for resource management of stream analytics at each individual node comprising:
evaluating a state of each microservice of the microservices in the pipeline (Kansal, [0007]—under BRI, evaluating a state of each microservice can be finding out priority+ newness + amount of resource allocation, etc.);
selecting a single microservice from the pipeline for updating resources for an action that changes the state in the single microservice that is selected (Kansal, [0007]—under BRI, selecting a single microservice can be selecting a high priority job to increase resource);
performing resource allocation update for the selected microservice (Kansal, [0007]—under BRI, performing resource allocation update can be increasing a resource allocation); and
updating the state of the selected microservice with a chosen resource allocation (Kansal, [0007]).
Kansal does not expressly teach:
computing a mean of output processing rate of microservices in pipeline;
However, Jung discloses:
computing a mean of output processing rate of microservices in pipeline (Jung, [0004]—under BRI, a mean of output processing rate of microservices can be average input-output operations per second (IOPS) for a storage system processor servicing a run workload);
Both Jung and Kansal pertain to the art of resource allocation.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Jung’s method to compute mean processing rate because it is well-known in the art that the mean (arithmetic average) is a key measure of central tendency in statistics, offering benefits like incorporating every data point for a comprehensive overview, ease of calculation, and utility in further statistical analyses.
As per claim 6, Kansal/Jung teaches:
The computer implemented method of claim 1 (See rejection on claim 1), wherein performing resource allocation update includes computing an expected output processing rate (Jung, [0004]—under BRI, an expected value can be average).
As per claim 11, see rejection on claim 1.
As per claim 16, see rejection on claim 6.
As per claim 20, see rejection on claim 1.
Claims 2, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kansal/Jung as applied above, and further in view of Liu et al (WO 2022/177566) (hereinafter Liu).
As per claim 2, Kansal/Jung teaches:
The computer implemented method of claim 1 (see rejection on claim 1).
Kansal/Jung does not expressly teach:
wherein the evaluating of the state of each activation state comprising storing a best Q-value for each microservice.
However, Liu discloses:
wherein the evaluating of the state of each activation state comprising storing a best Q-value for each microservice (Liu, [0084).
Both Liu and Kansal/Jung pertain to the art of data processing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Liu’s method to store Q value because it is well-known in the art that in reinforcement learning (RL), the Q-value (Action-Value) represents the expected cumulative future reward an agent will receive by taking a specific action in a particular state and following a subsequent optimal policy. It measures the "quality" of an action, allowing the agent to evaluate the long-term benefit of its decisions rather than just immediate rewards.
As per claim 12, see rejection on claim 2.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kansal/Jung as applied above, and further in view of Gottin et al ( US 2019/0325304) (hereinafter Gottin).
As per claim 3, Kansal/Jung teaches:
The computer implemented method of claim 1 (see rejection on claim 1).
Kansal/Jung does not expressly teach:
wherein the evaluating of the state employs a Q-learning workflow of a reinforcement learning (RL) method.
However, Gottin discloses:
wherein the evaluating of the state employs a Q-learning workflow of a reinforcement learning (RL) method (Gottin, [0057]).
Both Gottin and Kansal/Jung pertain to the art of data processing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Gottin’s method to use a Q-learning workflow because it is well-known in the art that Q-learning is a model-free, value-based reinforcement learning algorithm that enables an agent to learn the optimal policy for sequential decision-making tasks through trial-and-error, without requiring a pre-existing model of the environment.
As per claim 13, see rejection on claim 3.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kansal/Jung as applied above, and further in view of Rath et al (US 2015/0363124) (hereinafter Rath).
As per claim 4, Kansal/Jung teaches:
The computer implemented method of claim 1 (see rejection on claim 1).
Kansal/Jung does not expressly teach:
wherein in exploration mode to select the microservice for updating resources, the microservice for updating resources is selected randomly.
However, Rath discloses:
wherein in exploration mode to select the microservice for updating resources, the microservice for updating resources is selected randomly (Rath, [0090]).
Both Rath and Kansal/Jung pertain to the art of data processing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Rath’s method to randomly select services because it is well-known in the art that random selection (randomization) eliminates selection and allocation bias by giving every subject an equal chance of inclusion, ensuring balanced, representative groups.
As per claim 14, see rejection on claim 4.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kansal/Jung as applied above, and further in view of Bai et al (CN 112035949) (hereinafter Bai).
As per claim 7, Kansal/Jung teaches:
The computer implemented method of claim 1 (see rejection on claim 1).
Kansal/Jung does not expressly teach:
wherein the updating the state of the selected microservice with the resource allocation includes a reward computation and updating of a Q-value for the selected microservice using a Bellman's equation.
However, Bai discloses:
wherein the updating the state of the selected microservice with the resource allocation includes a reward computation and updating of a Q-value for the selected microservice using a Bellman's equation (Bai, Claim 1).
Both Bai and Kansal/Jung pertain to the art of data processing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Bai’s method to use a reward computation and update of a Q-value because it is well-known in the art that Q-value computation estimates the long-term benefit of taking action allowing agents to learn optimal policies by updating this value iteratively.
As per claim 17, see rejection on claim 7.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kansal/Jung as applied above, and further in view of Chalapathy et al (US 2024/0428085) (hereinafter Chalapathy).
As per claim 8, Kansal/Jung teaches:
The computer implemented method of claim 1 (See rejection on claim 1).
Kansal/Jung does not expressly teach:
wherein resources are selected from nodes by availability of CPU cores and random access memory (RAM) availability.
However, Chalapathy discloses:
wherein resources are selected from nodes by availability of CPU cores and random access memory (RAM) availability (Chalapathy, [0003]—under BRI, selecting from nodes by availability of CPU cores and random access memory (RAM) availability can be selecting resources based on central processing unit (CPU) and random access memory (RAM) availability ).
Both Chalapathy and Kansal/Jung pertain to the art of data processing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Chalapathy’s method to select based on availability because it is well-known in the art that resources have to be available to be assigned for processing tasks.
As per claim 18, see rejection on claim 8.
Claims 9-10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kansal/Jung as applied above, and further in view of Kuo et al (US 2024/0185098) (hereinafter Kuo).
As per claim 9, Kansal/Jung teaches:
The computer implemented method of claim 1 (see rejection on claim 1).
Kansal/Jung does not expressly teach:
wherein the microservices are directed towards object identification from a video stream.
However, Kuo discloses:
wherein the microservices are directed towards object identification from a video stream (Kuo, [0050]).
Both Kuo and Kansal/Jung pertain to the art of data processing.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Kuo’s method to use a service for object identification from a video stream because it is well-known in the art that object identification in video streams, often powered by AI and deep learning (such as YOLO or CNNs), enables computers to recognize and locate objects—ranging from people to vehicles—in real-time. This technology moves beyond simple motion detection to provide context-aware, automated analysis of video data. Key benefits of implementing object identification in video streams include: Operational Efficiency and Automation.
As per claim 10, Kansal/Jung/Kuo discloses:
The computer implemented method of claim 9 (See rejection on claim 9), wherein the object identification is facial recognition (Kuo, [0050]).
As per claim 19, see rejection on claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 11720397 teaches a method of computing average processing rates.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLIE SUN whose telephone number is (571)270-5100. The examiner can normally be reached 9AM-5PM.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at (571) 272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHARLIE SUN/Primary Examiner, Art Unit 2198