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 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 an abstract idea without significantly more.
Step One
The claims are directed to a method (claims 1 - 12) and a system with structural components (claims 13 - 20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claims 1,
Step 2A, Prong One
The claim recites in part:
processing, using a trained machine learning model, a set of inputs and a set of outputs associated with actual, simulated, or twinned performance of multiple processes of a complex system on one or more physical machines to generate objective functions for the respective multiple processes;
For example, a human can mentally reviews actual or simulated process data and determines objective functions, such as maximizing output, or minimizing cost, for multiple processes.
optimizing the set of inputs and the set of outputs of the multiple processes for meeting sustainability constraints for the complex system in view of waste metrics associated with the set of outputs and further for meeting process constraints for the respective multiple processes;
For example, a human mentally evaluates process data and selects inputs that satisfy specified constraints.
outputting a recommendation for actually operating the multiple processes on the one or more physical machines based on the optimized set of inputs and set of outputs, for avoiding a risk of failure to operate the multiple processes, while meeting the sustainability constraints for the complex system and the process constraints for the respective, multiple processes.
For example, a human can generate a recommendation that will reduce or eliminate the risk of failure.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim further recites a complex system and one or more physical machines which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
The claim further recites a complex system and one or more physical machines which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites in part:
optimizing an arrangement of the multiple processes with respect to one another within the complex system for meeting the sustainability constraints for the complex system, wherein the recommendation further includes the optimized arrangement for arranging the multiple processes in relation to one another for operating the multiple processes on the one or more physical machines.
For example, a human can mentally review actual or simulated process data and determines recommend the optimal arrangement for arranging the multiple processes
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 3,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the set of inputs are used by twin or simulation models of the respective, multiple processes as performed on the one or more physical machines to twin or simulate the configuration parameters of the multiple processes and the set of outputs result from application of the twin or the simulation models of the multiple processes using the set of inputs.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the set of inputs are used by twin or simulation models of the respective, multiple processes as performed on the one or more physical machines to twin or simulate the configuration parameters of the multiple processes and the set of outputs result from application of the twin or the simulation models of the multiple processes using the set of inputs.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim recites in part:
determining whether design samples that include an output of the output set that is the result of one or more inputs of the input set satisfy a condition;
For example, a human can mentally review design samples and determined if certain elements of the design are above a threshold.
if it is determined that the design samples do not satisfy the condition, causing acquisition of additional design samples.
For example, a human can mentally observe new samples and determine if certain elements of the observed design are above a threshold.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 5,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the set of inputs includes inherent inputs that represent configuration settings of the respective, multiple processes, and applied inputs that represent conditions to which the respective, multiple process are exposed, wherein outputs of the set of outputs result from applied inputs of the set of inputs for inherent inputs of the set of inputs
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites a data source which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the set of inputs includes inherent inputs that represent configuration settings of the respective, multiple processes, and applied inputs that represent conditions to which the respective, multiple process are exposed, wherein outputs of the set of outputs result from applied inputs of the set of inputs for inherent inputs of the set of inputs
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites in part:
wherein the set of outputs includes at least one desired
process output and at least one unwanted output, the unwanted output including a plurality of waste metrics resulting from actual, simulated, or twinned application of the multiple processes using the set of inputs, and the sustainability constraints correspond to the plurality of waste metrics,
wherein the optimizing the set of inputs and the set of outputs includes optimizing both the at least one desired process output and the at least one unwanted output.
For example, a human can mentally evaluate the desired process output and unwanted outputs (waste metrics), compare them to sustainability constraints, and decide which inputs should be adjusted to improve the desired output while reducing waste.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites in part:
deriving an objective function for each process of the multiple processes;
optimizing the objective function for each of the processes based on
sustainability constraints for the process to determine optimal and suboptimal states of the multiple processes, such that the number of optimal and suboptimal states exceeds the number of processes included in the multiple processes,
wherein the optimizing the set of inputs and the set of outputs includes optimizing the complex system by selecting from the optimal and suboptimal states for each of the processes of the multiple processes.
For example, a human mentally derives an objective for each process, compares the possible outcomes against sustainability constraints, and selects the combination of process states that best satisfy those constraints.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim recites in part:
deriving an objective function for each process of the multiple processes;
optimizing the objective function for each of the processes based on
sustainability constraints for the process to determine optimal and suboptimal states of the multiple processes, such that the number of optimal and suboptimal states exceeds the number of processes included in the multiple processes,
wherein the optimizing the set of inputs and the set of outputs includes optimizing the complex system by selecting from the optimal and suboptimal states for each of the processes of the multiple processes.
For example, a human mentally establishes different operating ranges (regimes) for a process, compares how the process would perform under each regime, and selects the most suitable regime.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim recites in part:
determining an optimal and one or more suboptimal operating points per at least one process of the multiple processes, and/or per regime of the two or more regimes of at least one of the respective processes, wherein the optimizing the set of inputs and the set of outputs of the multiple processes includes evaluation of the optimal and suboptimal operating points of the multiple processes.
For example, a human mentally identifies optimal and suboptimal operating points, compares them, and evaluates which operating point is preferable.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 10,
Step 2A, Prong One
The claim recites in part:
wherein at least one of establishing the regimes and determining the optimal and suboptimal points per process, per regime, includes performing a sensitivity analysis to quantify contribution of the set of inputs for the corresponding process operating at the corresponding regime to output variability related to the set of outputs for the corresponding regime.
For example, a human mentally identifies optimal and suboptimal operating points, compares them, and evaluates which operating point is preferable.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 11,
Step 2A, Prong One
The claim recites in part:
wherein optimizing the set of inputs and the set of outputs uses dynamic programming or genetic algorithm optimization
For example, a human mentally guides the optimizing toward better solutions through structured, iterative improvement.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 12,
Step 2A, Prong One
The claim recites in part:
wherein optimizing the set of inputs and the set of outputs solves an optimization problem having a number of dimensions associated with the set of inputs and/or the set of outputs, and the method further comprises selecting to use one of the dynamic programming or genetic algorithm optimization based on the number of dimensions.
For example, a human mentally determines the number of variables in an optimization problem and decides whether to use dynamic programming or a genetic algorithm based on that determination.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The claim further recites a memory, a processing device, a complex system and one or more physical machines which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 14 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 12. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1 - 5 and 13 - 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by BRYCH et al (US 2022/0382616)
As to claim 1, BRYCH et al teaches a method, comprising:
processing, using a trained machine learning model, a set of inputs and a set of
outputs associated with actual, simulated, or twinned performance of multiple processes
of a complex system on one or more physical machines to generate objective functions
for the respective multiple processes (paragraph [0316]…The example method depicted in FIG. 12 differs from the example methods described above in that the example method depicted in FIG. 12 also includes generating 1202, based on data describing the operation of a plurality of hardware components, a trained model that predicts remaining hardware life for a type of hardware component. In some embodiments, the trained model can be used to analyze the data to determine a remaining hardware life for each hardware component in the group of components. Generating 1202 a trained model that predicts remaining hardware life for a type of hardware component may be carried out, for example, using received data from a plurality of hardware components as input to one or more machine learning models. Such data can include, for example, as error data (e.g., GBB (grown bad blocks) data), P/E counts, data taken from hardware logs (e.g., kernel logs and firmware logs), sensor data (e.g., temperature and power consumption), lifecycle data, as well as other data received from the hardware components. The received data may be taken as input by one or more machine learning models to identify relationships between input data and the expected life of a hardware component, where such relationships may be expressed in one or more trained models that is used to predict the remaining hardware life for each particular type of hardware component. The trained model may use all of the received data or may determine that some received data affects the remaining hardware life for each hardware component while other received data has no impact on predicting the remaining hardware life for a particular hardware component or for all hardware components) (Examiner’s Note: “The received data may be taken as input by one or more machine learning models to identify relationships between input data and the expected life of a hardware component, where such relationships may be expressed in one or more trained models that is used to predict the remaining hardware life for each particular type of hardware component” reads on “processing, using a trained machine learning model, a set of inputs and a set of outputs associated with actual, simulated, or twinned performance of multiple processes of a complex system on one or more physical machines to generate objective functions for the respective multiple processes”; “hardware components” reads on “one or more physical machines” ; “identify relationships between input data and the expected life of a hardware component” reads on “to generate objective functions
for the respective multiple processes”);
optimizing the set of inputs and the set of outputs of the multiple processes for
meeting sustainability constraints for the complex system in view of waste metrics
associated with the set of outputs and further for meeting process constraints for the
respective multiple processes (paragraph [0185]…The storage systems described above may also implement AI platforms for delivering on the vision of self-driving storage. Such AI platforms may be configured to deliver global predictive intelligence by collecting and analyzing large amounts of storage system telemetry data points to enable effortless management, analytics and support. In fact, such storage systems may be capable of predicting both capacity and performance, as well as generating intelligent advice on workload deployment, interaction and optimization. Such AI platforms may be configured to scan all incoming storage system telemetry data against a library of issue fingerprints to predict and resolve incidents in real-time, before they impact customer environments, and captures hundreds of variables related to performance that are used to forecast performance load)(Examiner’s Note:” Such AI platforms may be configured to deliver global predictive intelligence by collecting and analyzing large amounts of storage system telemetry data points to enable effortless management, analytics and support. In fact, such storage systems may be capable of predicting both capacity and performance, as well as generating intelligent advice on workload deployment, interaction and optimization” reads on “optimizing the set of inputs and the set of outputs of the multiple processes for meeting sustainability constraints for the complex system in view of waste metrics associated with the set of outputs and further for meeting process constraints for the respective multiple processes” ; “capacity and performance” reads on “waste metrics”) ; and
outputting a recommendation for actually operating the multiple processes on the
one or more physical machines based on the optimized set of inputs and set of outputs,
for avoiding a risk of failure to operate the multiple processes, while meeting the
sustainability constraints for the complex system and the process constraints for the
respective, multiple processes (paragraph [0312]…analyzing the state of the hardware component may include using information about the component model, the historical number of read/writes, or other information. For example, a particular storage device model may have a specific TBW (total bytes or terabytes written) lifetime, or some other manufacturer specified lifetime. Alternatively, the TBW may be learned by one or more machine learning models analyzing historical data associated with the particular storage device model to determine the total bytes or terabytes that can typically be written the storage device before the storage device fails or otherwise becomes unsuitable for use. In such an example, the total number of bytes or terabytes that the storage device has already written may be used to determine the remaining bytes or terabytes written that the storage device can be expected to write prior to failing or becoming unsuitable for use (i.e., the remaining hardware life). Other characteristics of the hardware component may similarly be analyzed (e.g., by one or more machine learning models) to determine their impact on the remaining hardware life for a particular hardware component. For example, historical state information such as the ambient or operating temperature of the hardware component may, supply voltages, or other physical factors may be fed as input into one or more machine learning models and analyzed. Through such an analysis, it may be learned (as a hypothetical example) that if the ambient or operating temperature of the component is above a threshold, then the remaining hardware life is reduced in a stepwise or linear fashion, using a custom algorithm, or in some other predictable way. Readers will appreciate that in addition to using the machine learning models to predict the remaining life left, machine learning models may also be used (and trained models may be generated) to predict hardware components (e.g., storage devices) that are at a higher risk for early unexpected failure. In such an example, information associated with hardware components that have failed early may be fed into a model to see what other hardware components might be in danger of an early failure. As such, hardware components that are at risk for early failure may be proactively replaced or some other proactive measure may be taken (e.g., mirroring data stored on a drive that is a candidate for early failure))(Examiner’s Note: “machine learning models to predict the remaining life left, machine learning models may also be used (and trained models may be generated) to predict hardware components (e.g., storage devices) that are at a higher risk for early unexpected failure. In such an example, information associated with hardware components that have failed early may be fed into a model to see what other hardware components might be in danger of an early failure. As such, hardware components that are at risk for early failure may be proactively replaced or some other proactive measure may be taken” reads on “outputting a recommendation for actually operating the multiple processes on the one or more physical machines based on the optimized set of inputs and set of outputs, for avoiding a risk of failure to operate the multiple processes, while meeting the sustainability constraints for the complex system and the process constraints for the respective, multiple processes”).
As to claim 2, BRYCH et al teaches a method, further comprising optimizing an arrangement of the multiple processes with respect to one another within the complex system for meeting the sustainability constraints for the complex system, wherein the recommendation further includes the optimized arrangement for arranging the multiple processes in relation to one another for operating the multiple processes on the one or more physical machines (paragraph [0128]… Such telemetry data may describe various operating characteristics of the storage system 306 and may be analyzed for a vast array of purposes including, for example, to determine the health of the storage system 306, to identify workloads that are executing on the storage system 306, to predict when the storage system 306 will run out of various resources, to recommend configuration changes, hardware or software upgrades, workflow migrations, or other actions that may improve the operation of the storage system 306).
As to claim 3, BRYCH et al teaches a method, wherein the set of inputs are used by twin or simulation models of the respective, multiple processes as performed on the one or more physical machines to twin or simulate the configuration parameters of the multiple processes and the set of outputs result from application of the twin or the simulation models of the multiple processes using the set of inputs (paragraph [0195]… the storage systems described above may be configured to provide parallel storage, for example, through the use of a parallel file system such as BeeGFS. Such parallel files systems may include a distributed metadata architecture. For example, the parallel file system may include a plurality of metadata servers across which metadata is distributed, as well as components that include services for clients and storage servers).
As to claim 4, BRYCH et al teaches a method, wherein the method further comprises: determining whether design samples that include an output of the output set that is the result of one or more inputs of the input set satisfy a condition; and if it is determined that the design samples do not satisfy the condition, causing acquisition of additional design samples (paragraph [0241]… Storage resources may also include storage from external sources such as various combinations of block storage systems, file storage systems, and object storage systems. The storage resources 392-1 through 392-M may include any type(s) and/or configuration(s) of storage resources (e.g., any of the illustrative storage resources described above), and the container storage system 381 may be configured to determine the available storage resources in any suitable way, including based on a configuration file. For example, a configuration file may specify account and authentication information for cloud-based object storage 348 or for a cloud-based storage system 318. The container storage system 381 may also determine availability of one or more storage devices 356 or one or more storage systems. An aggregate amount of storage from one or more of storage device(s) 356, storage system(s), cloud-based storage system(s) 318, edge management services 366, cloud-based object storage 348, or any other storage resources, or any combination or sub-combination of such storage resources may be used to provide the storage pool 383. The storage pool 383 is used to provision storage for the one or more virtual volumes mounted on one or more of the nodes 390 within cluster 384) (Examiner’s Note: “The storage resources 392-1 through 392-M may include any type(s) and/or configuration(s) of storage resources (e.g., any of the illustrative storage resources described above), and the container storage system 381 may be configured to determine the available storage resources in any suitable way, including based on a configuration file” reads on “determining whether design samples that include an output of the output set that
is the result of one or more inputs of the input set satisfy a condition”).
As to claim 5, BRYCH et al teaches a method, wherein the set of inputs includes inherent inputs that represent configuration settings of the respective, multiple processes, and applied inputs that represent conditions to which the respective, multiple process are exposed, wherein outputs of the set of outputs result from applied inputs of the set of inputs for inherent inputs of the set of inputs (paragraph [0123]… [0123] The cloud services provider 302 depicted in FIG. 3A may be embodied, for example, as a system and computing environment that provides a vast array of services to users of the cloud services provider 302 through the sharing of computing resources via the data communications link 304. The cloud services provider 302 may provide on-demand access to a shared pool of configurable computing resources such as computer networks, servers, storage, applications and services, and so on).
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The claim further recites a memory, a processing device, a complex system and one or more physical machines which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 14 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 11, 12, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over BRYCH et al (US 2022/0382616) in view of de Nijs et al (US 2019/0279096).
As to claim 11, BRYCH et al teaches optimizing the set of inputs and the set of outputs.
BRYCH et al fails to explicitly show/teach wherein optimizing the set of inputs and the set of outputs uses dynamic programming or genetic algorithm optimization
However, de Nijs et al teaches optimizing the set of inputs and the set of outputs uses dynamic programming or genetic algorithm optimization (paragraph [0061]…The optimal policy π* (i.e., the policy which obtains the maximum expected value over the entire operating horizon) may be computed through an application of dynamic programming over the time dimension, computing the value function at time t on the basis of the values at t+1 by selecting the value maximizing action in each state as follows).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for BRYCH et al to optimize the set of inputs and the set of outputs uses dynamic programming or genetic algorithm optimization, as in de Nijs et al, for the purpose of improve recommendations that satisfy preferences.
As to claim 12, de Nijs et al teaches wherein optimizing the set of inputs and the set of outputs solves an optimization problem having a number of dimensions associated with the set of inputs and/or the set of outputs, and the method further comprises selecting to use one of the dynamic programming or genetic algorithm optimization based on the number of dimensions (paragraph [0061]…The optimal policy π* (i.e., the policy which obtains the maximum expected value over the entire operating horizon) may be computed through an application of dynamic programming over the time dimension, computing the value function at time t on the basis of the values at t+1 by selecting the value maximizing action in each state as follows).
It would have been obvious to optimize the set of inputs and the set of outputs solves an optimization problem having a number of dimensions associated with the set of inputs and/or the set of outputs, and the method further comprises selecting to use one of the dynamic programming or genetic algorithm optimization based on the number of dimensions, for the same reason as above.
Claim 20 has similar limitations as claim 12. Therefore, the claim is rejected for the same reasons as above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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