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 .
Claim Status
Claims 1-20 are pending for examination.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5-6, 8-9, 12-13, 15-16 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen (Pub. No.: US 2010/0131440 A1).
Regarding claim 1, Chen teaches a computer-based method for dynamically tuning system configuration settings across multiple systems using hypothetical configuration analysis (abstract, “A computer implemented method employing experience transfer to improve the efficiencies of an exemplary configuration tuning in computing systems. The method employs a Bayesian network guided tuning algorithm to discover the optimal configuration setting. After the tuning has been completed, a Bayesian network is obtained that records the parameter dependencies in the original system. Such parameter dependency knowledge has been successfully embedded to accelerate the configuration searches in other systems. Experimental results have demonstrated that with the help of transferred experiences we can achieve significant time savings for the configuration tuning task.”. A method for applying a result of tuning to other systems), the method comprising:
gathering input data for a target system, the input data including configuration settings data (Fig. 7, para [0087], “The prefix of parameter name (`W.`, `A.`, or `D.`) denotes that the parameter comes from the web tier, application server or database tier. The minimum/maximum values of those configurations as well as their default settings are also presented in that table.”. Default settings), a series of configuration setting parameters (Fig. 7, para [0088], “Before tuning those configuration parameters however, we first define a metric to describe the system performance.”. Tunable configuration parameters), and telemetry data (Fig. 7, para [0088], throughput and response time from the webserver.);
generating, from the input data, a machine learning model configured to process network data and the configuration settings data from the target system (Fig. 7, paras [0036]-[0037], the method use a machine learning technique (e.g., Bayesian network) to generate a simulator for the system that is being tuned.) ;
determining dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model);
predicting, using the generated machine learning model, and based on the determined dependencies, performance outcomes under a tuneable range of the series of configuration setting parameters (Fig. 7 – Fig. 8, para [0037], “In order to facilitate the experience extraction and transferring, we employ a new configuration tuning algorithm based on the Bayesian network construction and sampling. Given a number of evaluated configuration samples, a Bayesian network is constructed to estimate the dependencies between configuration parameters, through which a non-deterministic induction is employed to infer the structure of configuration space, and to guide the generation of new samples towards the optimal region. Each time new samples have been evaluated, the Bayesian network is also updated to improve the model accuracy followed by the inference process to create the next generation of samples. Such iterative sample generation and inference makings will eventually converge to the best configuration setting of the system. More importantly, we also obtain a Bayesian network as the byproduct of configuration tuning, which records the dependencies between system configuration parameters.”. The method determines relationships / dependencies between various configuration parameters and performance metrics to optimize performance. As an example, a software upgrade increase performance.); and
generalizing the generated machine learning model across a plurality of secondary systems (para [0037], “The learned Bayesian network can serve as transferable experiences to benefit the configuration tuning in the other system S.sub.1. That is, we still use the Bayesian network based configuration tuning to search the optimal configuration in S.sub.1. However, rather than starting with empty knowledge about configuration dependencies, we use the dependency graph learned from S.sub.0 to drive the configuration search so that the configuration tuning in S.sub.1 can be significantly accelerated.”. The method transfers the tuned configuration to other systems).
Regarding claim 2, Chen teaches the computer-based method of claim 1, further comprising:
performing entity analysis on the gathered input data to determine a proportion of setting types (para [0085], “The emulator produces a varying number of concurrent client connections with each client simulating a session based on some common scenarios, which consists of a series of requests such as creating new accounts, searching by keywords, browsing for item details, updating user profiles, placing orders, and checking out.” and para [0037], the method determines the optimal configurations for the entirety of the system based on all the data collected from the system).
Regarding claim 5, Chen teaches the computer-based method of claim 1, further comprising:
performing statistical computations on the gathered input data to determine preliminary dependencies between the configuration setting parameters on the telemetry data (Fig. 9(a) -9(b) para [0037], “Each time new samples have been evaluated, the Bayesian network is also updated to improve the model accuracy followed by the inference process to create the next generation of samples. Such iterative sample generation and inference makings will eventually converge to the best configuration setting of the system.”. The method determines the dependences between configuration parameters and response time / throughput. The dependence becomes more accurate as the number of evaluations increases.).
Regarding claim 6, Chen teaches the computer-based method of claim 1, wherein determining dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model further comprises:
calculating a score reflecting the dependency between the given parameter and the given resource; and
normalizing the calculated score (para [0037], “In order to facilitate the experience extraction and transferring, we employ a new configuration tuning algorithm based on the Bayesian network construction and sampling. Given a number of evaluated configuration samples, a Bayesian network is constructed to estimate the dependencies between configuration parameters, through which a non-deterministic induction is employed to infer the structure of configuration space, and to guide the generation of new samples towards the optimal region. Each time new samples have been evaluated, the Bayesian network is also updated to improve the model accuracy followed by the inference process to create the next generation of samples. Such iterative sample generation and inference makings will eventually converge to the best configuration setting of the system. More importantly, we also obtain a Bayesian network as the byproduct of configuration tuning, which records the dependencies between system configuration parameters.”. The method determines the dependencies between configuration parameters and converge at an optimized configuration setting).
Regarding claim 8, recites a system configured to perform the method of claim 1. Therefore, it is rejected for the same reasons.
Chen further discloses the structures of the tuning system includes one or more processors and memories to perform the method. See para [0028].
Regarding claim 9, recites a system configured to perform the method of claim 2. Therefore, it is rejected for the same reasons.
Regarding claim 12, recites a system configured to perform the method of claim 5. Therefore, it is rejected for the same reasons.
Regarding claim 13, recites a system configured to perform the method of claim 6. Therefore, it is rejected for the same reasons.
Regarding claim 15, recites a computer program product configured to perform the method of claim 1. Therefore, it is rejected for the same reasons.
Regarding claim 16, recites a computer program product configured to perform the method of claim 2. Therefore, it is rejected for the same reasons.
Regarding claim 19, recites a computer program product configured to perform the method of claim 5. Therefore, it is rejected for the same reasons.
Regarding claim 20, recites a computer program product configured to perform the method of claim 6. Therefore, it is rejected for the same reasons.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (Pub. No.: US 2010/0131440 A1) in view of Selvahumar (Pub. No.: US 2020/0356838 A1).
Regarding claim 4, Chen teaches the computer-based method of claim 1, wherein the tuning method stores transaction requests, item details and order history using a database server (para [0085]), but fails to expressly teach further comprising: storing a chat history, using a chatbot backend server, within a storage component.
However, in the same field of purchase transaction, Selvahumar teaches a chatbot method and system configured to store chat history of the customer and order details. See abstract and Fig. 2.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Chen’s tuning system to use a chatbot to improve shopping experience.
Regarding claim 11, recites a system configured to perform the method of claim 4. Therefore, it is rejected for the same reasons.
Regarding claim 18, recites a computer program product configured to perform the method of claim 4. Therefore, it is rejected for the same reasons.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (Pub. No.: US 2010/0131440 A1) in view of Kato (Pat. No.: US 8,494,806 B2).
Regarding claim 7, Chen teaches the computer-based method of claim 1, but fails to teach further comprising:
outputting the predicted performance outcomes to a user via a user interface.
However, in the same field of configuration parameter optimization, Kato teaches a display interface configured to display optimal parameters determined by the optimization processes. Col. 5 line 60 – 64, “The output unit 230 displays, for the user, information necessary for optimizing a parameter set, an error message, an optimal parameter set selected in accordance with the optimization processing of the embodiment of the present invention, and the like.”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Chen’s tuning system with a display interface to display the optimized configuration parameters determined by the tuning system to improve user interaction.
Regarding claim 14, recites a system configured to perform the method of claim 7. Therefore, it is rejected for the same reasons.
Allowable Subject Matter
Claims 3, 10 and 17 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHEN Y WU whose telephone number is (571)272-5711. The examiner can normally be reached Monday-Friday, 10AM-6PM, EST.
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/ZHEN Y WU/Primary Examiner, Art Unit 2685