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 .
Priority
Acknowledgement is made of the applicant’s claim for Foreign priority to Application of PCT International Application No. PCT/CN2022/072913, and the PCT International application claims priority to Chinese Patent Application No. CN202110099097.6, filed on January 25, 2021.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-20 are within the four statutory categories (a process, machine, manufacture or composition of matter).
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Claims 1-11 are directed to a method consisting of a series of steps, meaning that it is directed to the statutory category of process. Claims 12-20 are directed to storage mediums and processors which are machines.
Regarding claim 1, the following claim elements are abstract ideas:
detecting a parameter optimization request initiated by a target user (This is an abstract idea of a mental process. It involves observing that a request has been made and recognizing, based on the content of the request, that it is a parameter optimization request. A person could, through observation and judgement, identify that a user is requesting optimization of parameters based on the provided information or instructions. This type of observation and evaluation can be performed in the human mind and therefore constitutes an abstract idea.);
determining a parameter sampling algorithm matching the target user (This is an abstract idea of a mental process. The limitation recites selecting a parameter sampling algorithm based on the target user. A person could review user information or a request and, through observation and judgement, select an appropriate method for generating parameter values. See MPEP 2106.04(a)(2)(III).);
invoking, in response to a sample acquisition request initiated by the target user, the parameter sampling algorithm to generate a test sample (This is an abstract idea of a mental process. The limitation recites responding to a request and applying a selected method to generate a test sample. A person could review a request and, through observation and judgement, apply a chosen approach to produce a candidate value. The recitation of “invoking” the algorithm is merely an instruction to apply the abstract idea. See MPEP 2106.04(a)(2)(III) and 2106.05(f).);
determining a simulation result for the test sample based on a preset objective function (This is an abstract idea of a mental process. The limitation recites evaluating a test sample using an objective function to determine a result. A person could apply a rule or calculation to a candidate value and, through observation and judgement, determine the resulting outcome.);
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
outputting the simulation result for the test sample for the target user (This step of “outputting the simulation result” recites transmitting and/or displaying data. Receiving or transmitting data over a network has been recognized by the courts as well-understood, routine, and conventional activity. Further, presenting the result of the abstract idea is merely insignificant extra-solution activity.).
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract ideas:
objective function to calculate and obtain the simulation result corresponding to the test sample (This is an abstract idea of a mental process. The limitation recites applying an objective function to a test sample to calculate and obtain a result. A person could apply a rule or calculation to a candidate value and, through evaluation and judgement, determine the resulting outcome with the aid of computational tools such as a calculator.);
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
sending the test sample to a user end of the target user for the user end to input the test sample to the preset objective function (The step of “sending the test sample” recites transmitting data to another device. Receiving or transmitting data over a network has been recognized by the courts as well-understood, routine and conventional activity. Further, sending data for use in performing the abstract idea is merely insignificant extra-solution activity.)
receiving the simulation result corresponding to the test sample sent by the user end (The step of “receiving the simulation result” recites receiving data from another device. Receiving or transmitting data over a network has been recognized by the courts as well-understood, routine, and conventional activity. Further, receiving data as part of presenting or using the result of the abstract idea is merely insignificant extra-solution activity.).
Regarding claim 3, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract ideas:
determining a target node for starting the parameter sampling algorithm (This is an abstract idea of a mental process. The limitation recites selecting a target node to start the parameter sampling algorithm. A person could, through observation and judgement, select an appropriate location or resource to perform the process.);
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
sending, in response to the sample acquisition request, the sample acquisition request to the target node starting the parameter sampling algorithm (The4 step of “sending the sample” recites transmitting data to another device. Receiving or transmitting data over a network has been recognized by the courts as well-understood, routine, and conventional activity. Further, sending data as part of initiating performance of an abstract idea is merely insignificant extra-solution activity.);
acquiring, through the target node, the test sample generated by the parameter sampling algorithm (This step of “acquiring the test sample” recites receiving data from another device. Receiving or transmitting data over a network has been recognized as well-understood, routine, and conventional activity. Further, obtaining data for use in performing the abstract idea is merely insignificant extra-solution activity.).
Regarding claim 4, the rejection of claim 1 is incorporated herein. Further, claim 4 recites the following additional elements, which taken alone or in combination with other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
inputting the test sample to the preset objective function to calculate and obtain the simulation result for the test sample (This amounts to mere instructions to apply the abstract idea by providing data for use in performing the abstract idea. Further, providing input data for use in performing the abstract idea is insignificant extra-solution activity.).
Regarding claim 5, the rejection of claim 4 is incorporated herein. Further, claim 5 recites the following abstract ideas:
establishing a simulation module for the objective function (This is an abstract idea of a mental process. The limitation recites setting up a simulation module to perform evaluation using the objective function. A person could, through observation and judgement, decide to use a particular program or environment to perform the evaluation.);
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
inputting the test sample to the simulation module to calculate and obtain the simulation result for the test sample (This amounts to mere instructions to apply the abstract idea by providing data for use in performing the abstract idea. Further, providing input data for use in performing the abstract idea is insignificant extra-solution activity.).
Regarding claim 6, the rejection of claim 1 is incorporated herein. Further, claim 6 recites the following additional elements, which taken alone or in combination with other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
receiving a simulation trial identifier sent by the target user (Receiving data from another device has been recognized by the courts as a generic computer function that is well-understood, routine, and conventional activity.); and
storing the simulation result for the test sample for the target user based on the simulation trial identifier (Storing information in memory has been recognized by the courts as a generic computer function that is well-understood, routine, and conventional activity.).
Regarding claim 7, the rejection of claim 1 is incorporated herein. Further, claim 7 recites the following abstract ideas:
determining, upon detecting a presence of a storage operation for the request service information in a data storage component, the parameter sampling algorithm for the target user to request service in the request service information (This is an abstract idea of a mental process. The limitation recites evaluating stored request service information and determining a parameter sampling algorithm based on that information. A person could, through observation and judgement, review stored information and select an appropriate method for generating parameter values.).
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
receiving the parameter optimization request sent by the target user, wherein the parameter optimization request comprises request service information for the parameter sampling algorithm (Receiving data from another device has been recognized by the courts as a generic computer function that is well-understood, routine, and conventional activity. Further, the recitation that the request comprises service information merely describes the content of the data and is insignificant extra-solution activity.);
Regarding claim 8, the rejection of claim 7 is incorporated herein. Further, claim 8 recites the following abstract ideas:
verifying the parameter optimization request of the target user based on the verification information in the request service information to obtain a verification result (This is an abstract idea of a mental process. The limitation recites evaluating the parameter optimization request using verification information to determine a verification result. A person could, through observation and judgement, review the request and corresponding verification information to determine whether the request satisfies the verification criteria.); and
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
storing the request service information to the data storage component in response to the verification result being a successful verification (Storing information in memory has been recognized by the courts as a generic computer function that is well-understood, routine, and conventional activity.).
Regarding claim 9, the rejection of claim 7 is incorporated herein. Further, claim 9 recites the following abstract ideas:
determining, based on the storage notification message, the parameter sampling algorithm for the target user to request service in the request service information (This is an abstract idea of a mental process. The limitation recites evaluating a storage notification message and determining a parameter sampling algorithm based on that information. A person could, through observation and judgement, review the information and select an appropriate method for generating parameter values.).
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
acquiring, via a resource controller, a storage notification message initiated by the data storage component when storing the request service information (Receiving data from another device has been recognized by the courts as a generic computer function that is well-understood, routine, and conventional activity.);
Regarding claim 10, the rejection of claim 1 is incorporated herein. Further, claim 10 recites the following abstract ideas:
acquiring a judgment result as to whether the test sample satisfies a preset convergence condition (This is an abstract idea of a mental process. The limitation recites evaluating whether a test sample satisfies a convergence condition to obtain a result. A person could, through observation and judgement, assess whether a candidate value meets a condition and determining a corresponding result.); and
determining a target sample based on the test sample in response to the judgment result being that the test sample satisfies the convergence condition (This is an abstract idea of a mental process. The limitation recites evaluating a test sample and determining a target sample based on whether a convergence condition is satisfied. A person could, through observation and judgement, assess whether a candidate value meets a condition and selecting a resulting value based on the assessment.); or
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
returning, in response to the judgment result being that the test sample does not satisfy the convergence condition, to the operation of invoking, in response to the sample acquisition request initiated by the target user, the parameter sampling algorithm to generate the test sample to continue execution (This amounts to mere instructions to apply the abstract idea by directing that the process be repeated when a condition is not satisfied. Further, directing continuation of the process does not add any meaningful limitation and is insignificant extra-solution activity.).
Regarding claim 11, the rejection of claim 1 is incorporated herein. Further, claim 11 recites the following abstract ideas:
detecting a black box optimization request initiated by the target user, and determining the parameter sampling algorithm matching the target user (This is an abstract idea of a mental process. The limitation recites observing a request and determining an appropriate parameter sampling algorithm based on the target user. A person could, through observation and judgement, recognize that a user is requesting optimization and select a suitable method for generating parameter values.).
Regarding claim 12, the following claim elements are abstract ideas:
detecting a parameter optimization request initiated by a target user (This is an abstract idea of a mental process. It involves observing that a request has been made and recognizing, based on the content of the request, that it is a parameter optimization request. A person could, through observation and judgement, identify that a user is requesting optimization of parameters based on the provided information or instructions. This type of observation and evaluation can be performed in the human mind and therefore constitutes an abstract idea.);
determining a parameter sampling algorithm matching the target user (This is an abstract idea of a mental process. The limitation recites selecting a parameter sampling algorithm based on the target user. A person could review user information or a request and, through observation and judgement, select an appropriate method for generating parameter values. See MPEP 2106.04(a)(2)(III).);
invoking, in response to a sample acquisition request initiated by the target user, the parameter sampling algorithm to generate a test sample (This is an abstract idea of a mental process. The limitation recites responding to a request and applying a selected method to generate a test sample. A person could review a request and, through observation and judgement, apply a chosen approach to produce a candidate value. The recitation of “invoking” the algorithm is merely an instruction to apply the abstract idea. See MPEP 2106.04(a)(2)(III) and 2106.05(f).);
determining a simulation result for the test sample based on a preset objective function (This is an abstract idea of a mental process. The limitation recites evaluating a test sample using an objective function to determine a result. A person could apply a rule or calculation to a candidate value and, through observation and judgement, determine the resulting outcome.);
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
a memory (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
one or more processors (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
outputting the simulation result for the test sample for the target user (This step of “outputting the simulation result” recites transmitting and/or displaying data. Receiving or transmitting data over a network has been recognized by the courts as well-understood, routine, and conventional activity. Further, presenting the result of the abstract idea is merely insignificant extra-solution activity.).
Regarding claim 13, the rejection of claim 12 is incorporated herein. The claim recites similar limitations corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 13.
Therefore, claim 13 is ineligible.
Regarding claim 14, the rejection of claim 12 is incorporated herein. The claim recites similar limitations corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 14.
Therefore, claim 14 is ineligible.
Regarding claim 15, the rejection of claim 12 is incorporated herein. The claim recites similar limitations corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 15.
Therefore, claim 15 is ineligible.
Regarding claim 16, the rejection of claim 15 is incorporated herein. The claim recites similar limitations corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 16.
Therefore, claim 16 is ineligible.
Regarding claim 17, the rejection of claim 12 is incorporated herein. The claim recites similar limitations corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 17.
Therefore, claim 17 is ineligible.
Regarding claim 18, the rejection of claim 12 is incorporated herein. The claim recites similar limitations corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding claim 19, the rejection of claim 18 is incorporated herein. The claim recites similar limitations corresponding to claim 8. Therefore, the same subject matter analysis that was utilized for claim 8, as described above, is equally applicable to claim 19.
Therefore, claim 19 is ineligible.
Regarding claim 20, the following claim elements are abstract ideas:
detecting a parameter optimization request initiated by a target user (This is an abstract idea of a mental process. It involves observing that a request has been made and recognizing, based on the content of the request, that it is a parameter optimization request. A person could, through observation and judgement, identify that a user is requesting optimization of parameters based on the provided information or instructions. This type of observation and evaluation can be performed in the human mind and therefore constitutes an abstract idea.);
determining a parameter sampling algorithm matching the target user (This is an abstract idea of a mental process. The limitation recites selecting a parameter sampling algorithm based on the target user. A person could review user information or a request and, through observation and judgement, select an appropriate method for generating parameter values. See MPEP 2106.04(a)(2)(III).);
invoking, in response to a sample acquisition request initiated by the target user, the parameter sampling algorithm to generate a test sample (This is an abstract idea of a mental process. The limitation recites responding to a request and applying a selected method to generate a test sample. A person could review a request and, through observation and judgement, apply a chosen approach to produce a candidate value. The recitation of “invoking” the algorithm is merely an instruction to apply the abstract idea. See MPEP 2106.04(a)(2)(III) and 2106.05(f).);
determining a simulation result for the test sample based on a preset objective function (This is an abstract idea of a mental process. The limitation recites evaluating a test sample using an objective function to determine a result. A person could apply a rule or calculation to a candidate value and, through observation and judgement, determine the resulting outcome.);
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A non-transitory computer-readable storage medium (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
one or more processors (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
outputting the simulation result for the test sample for the target user (This step of “outputting the simulation result” recites transmitting and/or displaying data. Receiving or transmitting data over a network has been recognized by the courts as well-understood, routine, and conventional activity. Further, presenting the result of the abstract idea is merely insignificant extra-solution activity.).
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 1-4, and 6-8, 10-15 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Golovin et al., (Pub. No.: US 20200097853 A1 (Filed: 2017)).
Regarding claim 1, Golovin discloses:
An information processing method, comprising: detecting a parameter optimization request initiated by a target user (Golovin, paragraph [0010] “ The one or more trials for the study respectively include one or more sets of values for one or more adjustable parameters associated with the study. The result for each trial includes an evaluation of the corresponding set of values for the one or more adjustable parameters. [0194] “ The parameter optimization system of the present disclosure can allow the user or other authorized processes to request one or more particular Trials to be evaluated.” – under the broadest reasonable interpretation, a request to perform parameter optimization, and the system receiving that request corresponds to detecting the parameter optimization, and the system receiving that request corresponds to detecting the parameter optimization request.);
determining a parameter sampling algorithm matching the target user (Golovin, paragraph [0158] “The use of a default option can allow the service to dynamically select a recommended black-box algorithm along with low-level settings based on the study configuration.” [0129] “ To provide another example, the adjustable parameters of a user interface (e.g., font, color, etc.) can be optimized relative to a specific subset of users (e.g., engineers that live in Pittsburgh, Pa.). Thus, in some implementations, the parameter optimization system can be used to perform personalized or otherwise specialized optimization of systems such as products or processes.” – under BRI, a parameter sampling algorithm includes a black-box optimization algorithm used to generate parameter values. Golovin teaches dynamically selecting a recommended black-box algorithm based on the study configuration. Golovin further teaches performing optimization relative to a specific subset of users. Selecting an algorithm corresponds to determining a parameter sampling algorithm, and performing optimization relative to a specific subset of users corresponds to matching the parameter sampling algorithm to the target user.);
invoking, in response to a sample acquisition request initiated by the target user, the parameter sampling algorithm to generate a test sample (Golovin, paragraph [0010] “The operations include performing one or more black-box optimization techniques to generate a suggested trial based at least in part on the one or more results and the one or more sets of values respectively associated with the one or more results.” [0181] “SuggestTrials: This method can take a “worker handle” as input, and return a globally unique handle for a “long-running operation” that can represent the work of generating Trial suggestions. The user can then poll the API periodically to check the status of the operation. Once the operation is completed, it can contain the suggested Trials.” [0194] “The parameter optimization system of the present disclosure can allow the user or other authorized processes to request one or more particular Trials to be evaluated.” – under BRI, invoking a parameter sampling algorithm includes using a black-box optimization algorithm to generate candidate parameter values. Golovin teaches that a user requests Trials to be evaluated and that the system generates suggested Trials using optimization techniques. Golovin further teaches a SuggestTrials method that is invoked to generate suggested Trials. Generating a suggested Trial corresponds to generating a test sample, and invoking the SuggestTrials method in response to the user request corresponds to generating a test sample, and invoking the SuggestTrials method in response to the user request corresponds to invoking the parameter sampling algorithm in response to the sample acquisition request.),
determining a simulation result for the test sample based on a preset objective function; and outputting the simulation result for the test sample for the target user (Golovin, paragraph [0010] “The result for each trial includes an evaluation of the corresponding set of values for the one or more adjustable parameters.” [0409] “The evaluation worker computing device(s) 130 can evaluate a suggested set of parameters and, in response, provide a result. For example, the result can be an evaluation of an objective function for a suggested set of parameters. In some implementations, the evaluation worker computing device(s) 130 can be provided and/or owned by the user.” – under BRI, a test sample includes a set of parameter values to be evaluated, and a simulation result includes a result obtained by evaluating the parameter values using an objective function. Golovin teaches evaluating a suggested set of parameter values to provide a result, where the result is an evaluation of an objective function. The suggested set of parameter values corresponds to the test sample., and the evaluation of the objective function corresponds to determining a simulation result for the test sample. Golovin further teaches providing the result in response to the evaluation, which corresponds to outputting the simulation result for the test sample for the target user.).
Regarding claim 2, Golovin discloses:
The method of claim 1, wherein determining the simulation result for the test sample based on the preset objective function comprises: sending the test sample to a user end of the target user for the user end to input the test sample to the preset objective function to calculate and obtain the simulation result corresponding to the test sample; and receiving the simulation result corresponding to the test sample sent by the user end (Golovin, paragraph [0166] “Given this configuration, in some implementations, basic use of the system (with each trial being evaluated by a single process) can be implemented as follows:” [0167]:
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[0167] “In some instances, a “client” can refer to or include a communications path to the parameter optimization system and a “worker” can refer to a process that evaluates a Trial. In some instances, each worker has or is a client.” [0168] “Further, as used in the above example pseudocode, RunTrial is the problem—specific evaluation of the objective function ƒ.” – under BRI, a “test sample” includes a Trial, and a “user end” includes a client or worker that communicates with the system. Golovin teaches that the system provides a Trial to the client via “trial = client.GetSuggestion()”, which corresponds to sending the test sample to a user end. Golovin further teaches that the client/worker evaluates the Trial via “metrics = RunTrial(trial)”, where RunTrial is the evaluation of the objective function, corresponding to inputting the test sample to the preset objective function to calculate and obtain the simulation result. Golovin further teaches reporting the results via “client.CompleteTrial(trial, metrics)”, which corresponds to receiving the simulation result corresponding to the test sample sent by the end user.).
Regarding claim 3, Golovin discloses:
The method of claim 1, wherein after detecting the parameter optimization request initiated by the target user and determining the parameter sampling algorithm matching the target user, the method further comprises: determining a target node for starting the parameter sampling algorithm (Golovin, paragraph [0167] “In some instances, a “client” can refer to or include a communications path to the parameter optimization system and a “worker” can refer to a process that evaluates a Trial. In some instances, each worker has or is a client.” [0181] “SuggestTrials: This method can take a “worker handle” as input, and return a globally unique handle for a “long-running operation” that can represent the work of generating Trial suggestions.” [0186] “ In particular, the main components include (1) a Dangling Work Finder that restarts work lost to preemptions; (2) a Persistent Database that holds the current state of all Studies; (3) a Suggestion Service that creates new Trials; (4) an Early Stopping Service that helps terminate a Trial early; (5) a System API that can perform, for example, JSON, validation, multiplexing, etc.; and (6) Evaluation Workers.” – under Bri, a target node includes a selected worker or service that performs generation of parameter values. Golovin teaches that the `SuggestTrials` method takes a worker handle as input for the work of generating Trial suggestions, indicating selection of a worker for performing the operation. Golovin further teaches that the Suggestion Service creates new Trials. Providing a worker handle for generating Trial suggestions corresponds to determining a target node, and generating Trial suggestions corresponds to starting the parameter sampling algorithm.); and
wherein invoking, in response to the sample acquisition request initiated by the target user, the parameter sampling algorithm to generate the test sample comprises: sending, in response to the sample acquisition request, the sample acquisition request to the target node starting the parameter sampling algorithm; and acquiring, through the target node, the test sample generated by the parameter sampling algorithm (Golovin, paragraph [0178] “Workers and end users can make calls to the parameter optimization system of the present disclosure using, for example, a REST API or using an internal RPC protocol” [0181] “SuggestTrials: This method can take a “worker handle” as input, and return a globally unique handle for a “long-running operation” that can represent the work of generating Trial suggestions. The user can then poll the API periodically to check the status of the operation. Once the operation is completed, it can contain the suggested Trials.” [0186] “In particular, the main components include (1) a Dangling Work Finder that restarts work lost to preemptions; (2) a Persistent Database that holds the current state of all Studies; (3) a Suggestion Service that creates new Trials; (4) an Early Stopping Service that helps terminate a Trial early; (5) a System API that can perform, for example, JSON, validation, multiplexing, etc.; and (6) Evaluation Workers.” – under BRI, a sample acquisition request includes a request to obtain parameter values, a target node includes a worker or service identified by a worker handle, and a test sample includes a Trial. Golovin teaches workers and end users make calls to the system using a REST API or RPC protocol, which corresponds to sending the sample acquisition request. Golovin further teaches that the SuggestTrials method is invoked and takes a worker handle as input, indicating the request is directed to a specific worker or service, corresponding to the target node starting the parameter sampling algorithm. Golovin further teaches that the SuggestTrials method generates Trial suggestions via a Suggestion Service and returns a handle that allows the user to poll for and obtain the suggested Trials. Obtaining the suggested Trials via the handle corresponds to acquiring, through a target node, the test sample generated by the parameter sampling algorithm.).
Regarding claim 4, Golovin discloses:
The method of claim 1, wherein determining the simulation result for the test sample based on the preset objective function comprises: inputting the test sample to the preset objective function to calculate and obtain the simulation result for the test sample (Golovin, paragraph [0143] “A Trial is a list of parameter values, x, that will lead to a single evaluation of ƒ(x). A trial can be “Completed”, which means that it has been evaluated and the objective value ƒ(x) has been assigned to it, otherwise it is “Pending”. Thus, a trial can correspond to an evaluation that provides an associated measure of performance of a system given a particular set of parameter values.” [0168] “Further, as used in the above example pseudocode, RunTrial is the problem—specific evaluation of the objective function ƒ.” [0409] “The evaluation worker computing device(s) 130 can evaluate a suggested set of parameters and, in response, provide a result. For example, the result can be an evaluation of an objective function for a suggested set of parameters.” – under BRI, a test sample includes a Trial or a set of parameter values, and a simulation result includes the output of an objective function. Golovin teaches that a Trial is a list of parameter values x that leads to a single evaluation of
f
(
x
)
, where
f
is the objective function. Passing the parameter values
x
into a function
f
to obtain
f
(
x
)
corresponds to inputting a test sample to a preset objective function to calculate and obtain the simulation result. Golovin further teaches that the result is an evaluation of the objective function for the parameter values, corresponding to the simulation result for the test sample.).
Regarding claim 6, Golovin discloses:
The method of claim 1, further comprising: receiving a simulation trial identifier sent by the target user; and storing the simulation result for the test sample for the target user based on the simulation trial identifier (Golovin, paragraph [0160] “Finally, it is desirable to allow multiple trials to be evaluated in parallel, and allow for the possibility that each trial evaluation could itself be a distributed process. To this end Workers can be defined, which can be responsible for evaluating suggestions, and can be identified by a persistent name (a worker_handle) that persists across process preemptions or crashes.” [0180] “CreateStudy: Given a Study configuration, this can create an optimization Study and return a globally unique identifier (“guid”) which can be used for all future system calls. If a Study with a matching name exists, the guid for that Study is returned. This can allow parallel workers to call this method and all register with the same Study.” [0399] “The database 104 can store a full state of one or more Trials and/or Studies along with any other information associated with a Trial or a Study.” – under BRI, a simulation trial identifier includes a persistent or unique identifier associated with a user, process, or trial, and storing simulation results based on the identifier includes associating and storing data with that identifier. Golovin teaches that workers are identified by a persistent name (worker_handle) and that the study is assigned a globally unique identifier used for system calls. Golovin further teaches a database that stores information associated with trials and studies. Storing information associated with trials using identifiers corresponds to storing the simulation result for the test sample based on the simulation trial identifier.).
Regarding claim 7, Golovin discloses:
The method of claim 1, wherein detecting the parameter optimization request initiated by the target user and determining the parameter sampling algorithm matching the target user comprises: receiving the parameter optimization request sent by the target user, wherein the parameter optimization request comprises request service information for the parameter sampling algorithm; and determining, upon detecting a presence of a storage operation for the request service information in a data storage component, the parameter sampling algorithm for the target user to request service in the request service information (Golovin, paragraph [0108] “ the parameter optimization system can continuously or periodically consider which of a plurality of available black-box optimization techniques is best suited for performance of the next round of suggestion, given the current status of the study (e.g., number of trials, number of parameters, shape of data and previous trials, feasible parameter space) and any other information including user-provided guidance about processing time/expenditure or other tradeoffs.” [0162] “A developer may use one of the client libraries of the parameter optimization system of the present disclosure implemented in multiple programming languages (e.g. C++, Python, Golang, etc.), which can generate service requests encoded as protocol buffers… Users can specify a study configuration indicating:” [0163] “Identifying characteristics of the study (e.g. name, owner, permissions)” [0164] “The set of parameters along with feasible sets for each…” [0165] “Whether the goal is minimization or maximization of the objective function.” [0399] “The database 104 can store a full state of one or more Trials and/or Studies along with any other information associated with a Trial or a Study.” – under BRI, a parameter optimization request includes a service request comprising configuration information for selecting an algorithm, and determining a parameter sampling algorithm based on stored request information includes selecting an algorithm based on stored configuration data. Golovin teaches that service requests are generated and include study configuration information specified by a user. Golovin further teaches that such information is stored in a database associated with trials and studies and that the system selects a black-box optimization technique based on current status of the study and user-provided information. Using the configuration information from the request, storing that information, and selecting an algorithm based on the stored information corresponds to receiving the parameter optimization request and determining the parameter sampling algorithm based on stored request service information.).
Regarding claim 8, Golovin discloses:
The method of claim 7, wherein the request service information further comprises verification information; and after receiving the parameter optimization request sent by the target user, the method further comprises: verifying the parameter optimization request of the target user based on the verification information in the request service information to obtain a verification result; and storing the request service information to the data storage component in response to the verification result being a successful verification (Golovin, paragraph [0171] “To configure a study, the user can provide a study name, owner, optional access permissions, an optimization goal from MAXIMIZE, MINIMIZE, and specify the feasible region X via a set of ParameterConfigs, each of which specifies a parameter name along with its feasible values.” [0186] “(5) a System API that can perform, for example, JSON, validation, multiplexing, etc.” [0399] “The database 104 can store a full state of one or more Trials and/or Studies along with any other information associated with a Trial or a Study.” – under BRI, verifying a parameter optimization request includes validating request information such as identity or permission information to determine whether the request is acceptable, thereby obtaining a verification result indicating whether the request is valid. Storing the request service information in response to successful verification includes storing the configuration data after the request has been validated. Golovin teaches a System API that performs validation of requests. Golovin further teaches that users provide configuration information including permissions and parameter configurations, and that such information is stored in a database associated with studies. Validating the request using the System API inherently produces a result indicating whether the request is valid, and storing the configuration information in the database corresponds to storing the request service information in response to successful verification.).
Regarding claim 10, Golovin discloses:
The method of claim 1, wherein after determining the simulation result for the test sample based on the preset objective function, the method further comprises: acquiring a judgment result as to whether the test sample satisfies a preset convergence condition; and determining a target sample based on the test sample in response to the judgment result being that the test sample satisfies the convergence condition; or returning, in response to the judgment result being that the test sample does not satisfy the convergence condition, to the operation of invoking, in response to the sample acquisition request initiated by the target user, the parameter sampling algorithm to generate the test sample to continue execution (Golovin, paragraph [0231] “ Given good estimates of the final objective value, together with confidence intervals enables the termination of unpromising trials, e.g., those whose probability of exceeding the best trial result yet found is below a set threshold.” [0232] “This method also allows for automatic convergence detection: If the confidence interval of the final objective value both contains the more recent intermediate result (possibly after smoothing) and/or is smaller in width than a fixed threshold, then the trial can be declared as converged and terminated.” – under BRI, a judgement result includes an evaluation used to determine whether a condition is satisfied, and a present convergence condition includes a threshold-based condition applied to evaluation data. Golovin teaches determining whether a trial satisfies a condition based on evaluation results, such as whether a confidence interval satisfies the threshold corresponds to acquiring a judgement result as to whether the test sample satisfies a preset convergence condition. Golovin further teaches that when the condition is satisfied, the trial is declared “converged and terminated,” which corresponds to determining a target sample based on the test sample. Golovin also teaches terminating trials based on evaluation results relative to thresholds, and such threshold-based evaluation inherently distinguishes between trials that satisfy the condition and those that do not. Continuing evaluation of trials that do not meet the termination or convergence thresholds corresponds to returning to invoke the parameter sampling algorithm to generate the test samples to continue execution.).
Regarding claim 11, Golovin discloses:
The method of claim 1, wherein the objective function is a black box algorithm; and detecting the parameter optimization request initiated by the target user and determining the parameter sampling algorithm matching the target user comprises: detecting a black box optimization request initiated by the target user, and determining the parameter sampling algorithm matching the target user (Golovin, paragraph [0095] “ In one particular example application, the parameter optimization system can be employed to optimize the parameters of a machine-learned model such as, for example, a deep neural network. For example, the adjustable parameters of the model can include hyperparameters such as, for example, learning rate, number of layers, number of nodes in each layer, etc. Through the use of black-box optimization technique(s), the parameter optimization system can iteratively suggest new sets of values for the model parameters” [0108] “the parameter optimization system can continuously or periodically consider which of a plurality of available black-box optimization techniques is best suited for performance of the next round of suggestion, given the current status of the study (e.g., number of trials, number of parameters, shape of data and previous trials, feasible parameter space) and any other information including user-provided guidance about processing time/expenditure or other tradeoffs.” [0162] “A developer may use one of the client libraries of the parameter optimization system of the present disclosure implemented in multiple programming languages (e.g. C++, Python, Golang, etc.), which can generate service requests encoded as protocol buffers” – under BRI, an objective function that is a black box algorithm includes an optimization process using black box techniques. Golovin teaches using “black box optimization techniques” to iteratively suggest parameter values and evaluate model performance, which corresponds to the objective function being a black box algorithm. Under BRI, detecting a parameter sampling algorithm matching the target user includes selecting an optimization technique based on user-related information. Golovin teaches that a user generates service requests to the system, which corresponds to detecting a black box optimization request initiated by the target user. Golovin further teaches selecting from a plurality of black box optimization techniques based on system information and user-provided guidance, which corresponds to determining the parameter sampling algorithm matching the target user.).
Regarding claim 12, Golovin discloses:
An electronic device, comprising: a memory configured to store one or more computer instructions; and one or more processors configured to run the one or more computer instructions stored in the memory, to execute operations comprising (Golovin, paragraph [0400] “The manager computing device(s) 102 can include one or more processors 112 and a memory… The memory 114 can store data 116 and instructions 118 which are executed by the processor(s) 112 to cause the computing system 102 to perform operations.):
detecting a parameter optimization request initiated by a target user (Golovin, paragraph [0010] “ The one or more trials for the study respectively include one or more sets of values for one or more adjustable parameters associated with the study. The result for each trial includes an evaluation of the corresponding set of values for the one or more adjustable parameters. [0194] “ The parameter optimization system of the present disclosure can allow the user or other authorized processes to request one or more particular Trials to be evaluated.” – under the broadest reasonable interpretation, a request to perform parameter optimization, and the system receiving that request corresponds to detecting the parameter optimization, and the system receiving that request corresponds to detecting the parameter optimization request.);
determining a parameter sampling algorithm matching the target user (Golovin, paragraph [0158] “The use of a default option can allow the service to dynamically select a recommended black-box algorithm along with low-level settings based on the study configuration.” [0129] “ To provide another example, the adjustable parameters of a user interface (e.g., font, color, etc.) can be optimized relative to a specific subset of users (e.g., engineers that live in Pittsburgh, Pa.). Thus, in some implementations, the parameter optimization system can be used to perform personalized or otherwise specialized optimization of systems such as products or processes.” – under BRI, a parameter sampling algorithm includes a black-box optimization algorithm used to generate parameter values. Golovin teaches dynamically selecting a recommended black-box algorithm based on the study configuration. Golovin further teaches performing optimization relative to a specific subset of users. Selecting an algorithm corresponds to determining a parameter sampling algorithm, and performing optimization relative to a specific subset of users corresponds to matching the parameter sampling algorithm to the target user.);
invoking, in response to a sample acquisition request initiated by the target user, the parameter sampling algorithm to generate a test sample (Golovin, paragraph [0010] “The operations include performing one or more black-box optimization techniques to generate a suggested trial based at least in part on the one or more results and the one or more sets of values respectively associated with the one or more results.” [0181] “SuggestTrials: This method can take a “worker handle” as input, and return a globally unique handle for a “long-running operation” that can represent the work of generating Trial suggestions. The user can then poll the API periodically to check the status of the operation. Once the operation is completed, it can contain the suggested Trials.” [0194] “The parameter optimization system of the present disclosure can allow the user or other authorized processes to request one or more particular Trials to be evaluated.” – under BRI, invoking a parameter sampling algorithm includes using a black-box optimization algorithm to generate candidate parameter values. Golovin teaches that a user requests Trials to be evaluated and that the system generates suggested Trials using optimization techniques. Golovin further teaches a SuggestTrials method that is invoked to generate suggested Trials. Generating a suggested Trial corresponds to generating a test sample, and invoking the SuggestTrials method in response to the user request corresponds to generating a test sample, and invoking the SuggestTrials method in response to the user request corresponds to invoking the parameter sampling algorithm in response to the sample acquisition request.),
determining a simulation result for the test sample based on a preset objective function; and outputting the simulation result for the test sample for the target user (Golovin, paragraph [0010] “The result for each trial includes an evaluation of the corresponding set of values for the one or more adjustable parameters.” [0409] “The evaluation worker computing device(s) 130 can evaluate a suggested set of parameters and, in response, provide a result. For example, the result can be an evaluation of an objective function for a suggested set of parameters. In some implementations, the evaluation worker computing device(s) 130 can be provided and/or owned by the user.” – under BRI, a test sample includes a set of parameter values to be evaluated, and a simulation result includes a result obtained by evaluating the parameter values using an objective function. Golovin teaches evaluating a suggested set of parameter values to provide a result, where the result is an evaluation of an objective function. The suggested set of parameter values corresponds to the test sample., and the evaluation of the objective function corresponds to determining a simulation result for the test sample. Golovin further teaches providing the result in response to the evaluation, which corresponds to outputting the simulation result for the test sample for the target user.).
Regarding claim 13, Golovin teaches all the elements of claim 12, therefore is rejected for the same reasons as those presented for claim 12. The claim recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 14, Golovin teaches all the elements of claim 12, therefore is rejected for the same reasons as those presented for claim 12. The claim recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 15, Golovin teaches all the elements of claim 12, therefore is rejected for the same reasons as those presented for claim 12. The claim recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding claim 17, Golovin teaches all the elements of claim 12, therefore is rejected for the same reasons as those presented for claim 12. The claim recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding claim 18, Golovin teaches all the elements of claim 12, therefore is rejected for the same reasons as those presented for claim 12. The claim recites similar limitations corresponding to claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale.
Regarding claim 19, Golovin teaches all the elements of claim 18, therefore is rejected for the same reasons as those presented for claim 18. The claim recites similar limitations corresponding to claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Regarding claim 20, Golovin discloses:
A non-transitory computer-readable storage medium storing a set of instructions that are executable by one or more processors of a device to cause the device to perform operations comprising (Golovin, paragraph [0010] “The computer system includes one or more processors and one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computer system to perform operations.”):
detecting a parameter optimization request initiated by a target user (Golovin, paragraph [0010] “ The one or more trials for the study respectively include one or more sets of values for one or more adjustable parameters associated with the study. The result for each trial includes an evaluation of the corresponding set of values for the one or more adjustable parameters. [0194] “ The parameter optimization system of the present disclosure can allow the user or other authorized processes to request one or more particular Trials to be evaluated.” – under the broadest reasonable interpretation, a request to perform parameter optimization, and the system receiving that request corresponds to detecting the parameter optimization, and the system receiving that request corresponds to detecting the parameter optimization request.);
determining a parameter sampling algorithm matching the target user (Golovin, paragraph [0158] “The use of a default option can allow the service to dynamically select a recommended black-box algorithm along with low-level settings based on the study configuration.” [0129] “ To provide another example, the adjustable parameters of a user interface (e.g., font, color, etc.) can be optimized relative to a specific subset of users (e.g., engineers that live in Pittsburgh, Pa.). Thus, in some implementations, the parameter optimization system can be used to perform personalized or otherwise specialized optimization of systems such as products or processes.” – under BRI, a parameter sampling algorithm includes a black-box optimization algorithm used to generate parameter values. Golovin teaches dynamically selecting a recommended black-box algorithm based on the study configuration. Golovin further teaches performing optimization relative to a specific subset of users. Selecting an algorithm corresponds to determining a parameter sampling algorithm, and performing optimization relative to a specific subset of users corresponds to matching the parameter sampling algorithm to the target user.);
invoking, in response to a sample acquisition request initiated by the target user, the parameter sampling algorithm to generate a test sample (Golovin, paragraph [0010] “The operations include performing one or more black-box optimization techniques to generate a suggested trial based at least in part on the one or more results and the one or more sets of values respectively associated with the one or more results.” [0181] “SuggestTrials: This method can take a “worker handle” as input, and return a globally unique handle for a “long-running operation” that can represent the work of generating Trial suggestions. The user can then poll the API periodically to check the status of the operation. Once the operation is completed, it can contain the suggested Trials.” [0194] “The parameter optimization system of the present disclosure can allow the user or other authorized processes to request one or more particular Trials to be evaluated.” – under BRI, invoking a parameter sampling algorithm includes using a black-box optimization algorithm to generate candidate parameter values. Golovin teaches that a user requests Trials to be evaluated and that the system generates suggested Trials using optimization techniques. Golovin further teaches a SuggestTrials method that is invoked to generate suggested Trials. Generating a suggested Trial corresponds to generating a test sample, and invoking the SuggestTrials method in response to the user request corresponds to generating a test sample, and invoking the SuggestTrials method in response to the user request corresponds to invoking the parameter sampling algorithm in response to the sample acquisition request.),
determining a simulation result for the test sample based on a preset objective function; and outputting the simulation result for the test sample for the target user (Golovin, paragraph [0010] “The result for each trial includes an evaluation of the corresponding set of values for the one or more adjustable parameters.” [0409] “The evaluation worker computing device(s) 130 can evaluate a suggested set of parameters and, in response, provide a result. For example, the result can be an evaluation of an objective function for a suggested set of parameters. In some implementations, the evaluation worker computing device(s) 130 can be provided and/or owned by the user.” – under BRI, a test sample includes a set of parameter values to be evaluated, and a simulation result includes a result obtained by evaluating the parameter values using an objective function. Golovin teaches evaluating a suggested set of parameter values to provide a result, where the result is an evaluation of an objective function. The suggested set of parameter values corresponds to the test sample., and the evaluation of the objective function corresponds to determining a simulation result for the test sample. Golovin further teaches providing the result in response to the evaluation, which corresponds to outputting the simulation result for the test sample for the target user.).
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 5, 9, and 16 are rejected under the 35 U.S.C. 103 as being unpatentable over Golovin et al., (Pub. No.: US 20200097853 A1 (Filed: 2017) in view of Wang et al., (Pub. No.: US 20220358268 A1 (Filed: 2019)).
Regarding claim 5, Golovin teaches all the elements of claim 4, therefore is rejected for the same reasons as those presented for claim 4. However, Golovin does not teach but Golovin in view of Wang teaches the following limitations:
establishing a simulation module for the objective function; and inputting the test sample to the simulation module to calculate and obtain the simulation result for the test sample (Wang, paragraph [0060] “ In some embodiments, various algorithm functions of the integrated energy service are closely interrelated, and can run independently, interact with each other, and operate collaboratively to complete a variety of energy management tasks, wherein, for example, component modules related to integrated energy, a big data analysis module, a system module, an optimization module, a simulation module, etc., are all based on the container technology, support distributed layout, support cross-platform interoperability, and support cloud platforms.” Golovin, paragraph [0143] “A Trial is a list of parameter values, x, that will lead to a single evaluation of ƒ(x).” [0168] “ RunTrial is the problem—specific evaluation of the objective function ƒ.” [0409] “ the result can be an evaluation of an objective function for a suggested set of parameters.” – under BRI, a simulation module includes a containerized compute module configured to execute algorithm functions, and a test sample includes a Trial. Wang teaches a simulation module implemented using container technology to perform various algorithm functions. Golovin teaches that a Trial is a set of parameter values x that is evaluated using an objective function f, where RunTrial performs the evaluation and produces a result. Evaluating the Trial using the objective function within the simulation module corresponds to inputting the test sample to the simulation module to calculate and obtain the simulation result for the test sample.).
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having a combination of Golovin and Wang before them, to implement the evaluation of the objective function of Golovin with a simulation module as taught by Wang. One would have been motivated to make such a combination in order to execute algorithm functions, such as objective functions, within defined system modules that can run independently and interact with other components. This would allow the evaluation of parameter values to be performed within a dedicated module of the system.
Regarding claim 9, Golovin teaches all the elements of claim 7, therefore is rejected for the same reasons as those presented for claim 7. However, Golovin does not teach but Golovin in view of Wang teaches the following limitations:
acquiring, via a resource controller, a storage notification message initiated by the data storage component when storing the request service information; and determining, based on the storage notification message, the parameter sampling algorithm for the target user to request service in the request service information (Wang, paragraph [0060] “The IEMS adopts MYSQL database and uses RABBITMQ to form message and data distribution bus technology, which is used for message transmission, data transmission and task distribution between algorithms and between algorithms and the front end.” [0061] “common PaaS-layer services related to information and communication include: message reception and distribution service, load balancing service, data management service, etc.” Golovin, paragraph [0108] “the parameter optimization system can continuously or periodically consider which of a plurality of available black-box optimization techniques is best suited for performance of the next round of suggestion, given the current status of the study (e.g., number of trials, number of parameters, shape of data and previous trials, feasible parameter space) and any other information including user-provided guidance about processing time/expenditure or other tradeoffs. Thus, a partnership between a human user and the parameter optimization system can guide selection of the appropriate black-box optimization technique at each instance of suggestion.” – under BRI, a resource controller includes a service or module that receives and processes messages or tasks, and a storage notification message includes a system message used in connection with system operations involving data handling. Wang teaches a message and data distribution bus using RABBITMQ for message transmission and task distribution between system components and further teaches message reception and distribution services, loading balancing services, and data management services that receive and process such messages. Receiving messages via these message reception and distribution services corresponds to acquiring a message via a resource controller, where the services function as system components that manage message reception and processing. Golovin teaches determining a parameter sampling algorithm based on system information. Using the received message to trigger processing corresponds to determining the parameter sampling algorithm based on the message.).
Regarding claim 16, Golovin teaches all the elements of claim 15, therefore is rejected for the same reasons as those presented for claim 15. The claim recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Conclusion
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/Daravanh Phakousonh/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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