DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/07/26 has been entered.
Response to Amendment
The amendments filed 01/07/26 have been entered.
Claims 1-20 remain pending within the application.
The amendments filed 01/07/26 are sufficient to overcome the 112(b) rejections previously set forth in the Non-Final Office Action mailed 10/09/2025. The rejections have been withdrawn.
The amendments, in combination with the remarks filed 01/07/26, are sufficient to overcome the 101 rejections previously set forth in the Non-Final Office Action mailed 10/09/2025. The rejections have been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recite the limitation "determining a confidence level of the inference result". It is unclear whether "the inference result" in claim 10 refers to " obtaining an inference result” recited in claim 8, or if it refers to "an inference result for the second set" recited in claims 9. There is insufficient antecedent basis for this limitation in claim 10. For examination purposes, the examiner is interpreting "the inference result" to refer to "an inference result for the second set".
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 6, 8, 13, 15, 17, 18, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Abidi et al. (“Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach”), hereafter Abidi, in view of Li et al. ("Machine learning and optimization for production rescheduling in Industry 4.0"), as disclosed in the prior art made of record and not relied upon in the office action mailed 06/02/2025, hereafter Li, in further view of Morariu et al. ("Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems"), hereafter Morariu.
Regarding claim 1, Abidi discloses:
A computing system comprising: at least one hardware processor; at least one memory coupled to the at least one hardware processor; and one or more computer-readable storage media comprising computer-executable instructions that, when executed, cause the computing system to perform operations comprising (Abidi, page 96100, left column, final paragraph, lines 1-3 “The developed optimal scheduling for the FMS using the optimized intelligent model was implemented using MATLAB 2018a, and the performance of the model was evaluated.”),
receiving a first plurality of inputs … (Abidi, Figure 2 teaches “FMS Scheduling data” as the first plurality of inputs),
associating at least a portion of the first plurality of inputs with a set of one or more processing resources (Abidi, Figure 1 teaches a set of one or more processing resources, and Figure 2 and page 96094, left column last line – right column, lines 1-2 “Here, the best scheduling rule of the FMS is predicted using input attributes” Teaches associating at least a portion of the plurality of inputs with a set of one or more processing resources in the flexible manufacturing system),
at least a first processing resource of the one or more processing resources comprising a physical machine or physical system component being controllable within a process-execution environment using a machine-implemented interface of the physical machine or the physical component configured to receive task assignments to produce an output or an intermediate input that transforms or integrates a physical realization of an input of the first plurality of inputs (Abidi, Figure 1, Table 2, Table 3, and page 96093, left column, penultimate paragraph, lines 1-4 “The system operations performed by the four machines are presented in Table 2. Three types of parts are processed. Some operations are performed on more than one machine, and some of them are performed only on one machine.” Teaches processing resources in the FMS comprising physical machines controllable within the process-execution environment using a machine-implemented interface of the physical machine that receives task assignments to produce an output or an intermediate input that transforms or integrates a physical realization of an input of the first plurality of inputs through the operations performed),
training a predictive model using the at least a portion of the plurality of inputs and their associated processing resources of the set of one or more processing resources (Abidi, Figure 2 and Table 4 teaches training the FMS predictive model using portions of the plurality of inputs and their associated processing resources),
obtaining a set of inference data… (Abidi, Table 4 and Figure 2 teaches obtaining sample data as a set of inference data),
analyzing the set of inference data using the predictive model (Abidi, Figure 2 teaches analyzing the set of inference data in Table 4 using the FMS predictive model),
obtaining an inference result …(Abidi, Figure 2, and page 96093, right column, paragraph above dataset description, lines 1-8 “The simulation model … involves three system attributes to manufacture the model, which define the dynamic operations of the FMS: the input or output buffer size of each machine, the part arrival rate, and the speed of the AGV. In the input buffers, the dispatching rules used for the machines are first come first served (FCFS), SPT, and earliest due date (EDD). These six attributes are considered as inputs, and the optimal scheduling rules are the outputs” teaches obtaining optimal scheduling rules as obtaining an inference result).
Abidi teaches receiving a first plurality of inputs …, but does not disclose:
the first plurality of inputs comprising structured inputs each associated with at least (i) an identifying label, (ii) a quantity value, and (iii) relationship information encoding relationships among inputs of the first plurality of inputs across multiple hierarchical levels;
Li discloses:
the first plurality of inputs comprising structured inputs each associated with at least (i) an identifying label, (ii) a quantity value, and (iii) relationship information encoding relationships among inputs of the first plurality of inputs across multiple hierarchical levels (Li, page 2450, right column, paragraphs 3 and 4 “
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” teaches considered sets where structured inputs are each associated with an identifying label, i.e. J, T, M, O, etc., a quantity value, i.e. 1, 2, etc., and relationship information encoding relationships among inputs of the first plurality of inputs across multiple hierarchical levels using the directed graph).
Abidi and Li are analogous art because they are from the same field of endeavor, enterprise/organization modelling and machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi to include the first plurality of inputs comprising structured inputs each associated with at least (i) an identifying label, (ii) a quantity value, and (iii) relationship information encoding relationships among inputs of the first plurality of inputs across multiple hierarchical levels, based on the teachings of Li. One of ordinary skill in the art would have been motivated to make this modification in order to modify the plant to improve its robustness, reduce the bottleneck, and so on. as suggested by Li (page 2453, right column, paragraph 2, last 2 lines).
While Abidi discloses obtaining a set of inference data…, they do not disclose:
the set of inference data comprising a second plurality of inputs, the second plurality of inputs comprising structured inputs each associated with at least (i) an identifying label, (ii) a quantity value, and (iii) relationship information encoding relationships among inputs of the second plurality of inputs across multiple hierarchical levels;
Li discloses:
the set of inference data comprising a second plurality of inputs, the second plurality of inputs comprising structured inputs each associated with at least (i) an identifying label, (ii) a quantity value, and (iii) relationship information encoding relationships among inputs of the second plurality of inputs across multiple hierarchical levels (Li, Fig. 1, Fig. 7, page 2450, right column, paragraphs 3 and 4, discloses training model outputs as inference data comprising a second plurality of structured inputs associated with the considered sets associated with identifying labels, quantity values, and relationship information encoding relationships among inputs of the second plurality of inputs across multiple hierarchical levels).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi to include the set of inference data comprising a second plurality of inputs, the second plurality of inputs comprising structured inputs each associated with at least (i) an identifying label, (ii) a quantity value, and (iii) relationship information encoding relationships among inputs of the second plurality of inputs across multiple hierarchical levels, based on the teachings of Li. One of ordinary skill in the art would have been motivated to make this modification in order to modify the plant to improve its robustness, reduce the bottleneck, and so on as suggested by Li (page 2453, right column, paragraph 2, last 2 lines).
While Abidi discloses obtaining an inference result … , they do not disclose:
processing resources associated with one or more inputs of the second plurality of inputs predicted to perform at least one operation on at least one input of the second plurality of inputs.
Li discloses:
processing resources associated with one or more inputs of the second plurality of inputs predicted to perform at least one operation on at least one input of the second plurality of inputs (Li, Fig. 1, Fig. 7, page 2450, right column, paragraphs 3 and 4 teaches processing resources associated with training model outputs as the second plurality of inputs predicted to perform at least one operation on at least one input of the second plurality of inputs for rescheduling).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi to include processing resources associated with one or more inputs of the second plurality of inputs predicted to perform at least one operation on at least one input of the second plurality of inputs, based on the teachings of Li. One of ordinary skill in the art would have been motivated to make this modification in order to modify the plant to improve its robustness, reduce the bottleneck, and so on as suggested by Li (page 2453, right column, paragraph 2, last 2 lines).
Li further discloses:
providing the inference result to at least one control environment configured to schedule and cause execution of the at least one operation by a processing resource identified in the inference result, the causing execution comprising initiating execution, within the process-execution environment, of the at least one operation by the identified processing resource, wherein initiating execution comprises either (1) automated performance of the at least one operation by the identified processing resource, or (2) generation of a system-directed work instruction for performance of the at least one operation using the identified processing resource (Li, Fig. 1 teaches providing the inference results of the classification model to a control environment configured to cause execution of the at least one operation by the first processing resource, i.e. machine, where execution occurs through automated performance or generation of a system-directed work instruction for performance of the at least one operation using the identified processing resource through rescheduled and continued production with the identified processing resource).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi to include providing the inference result to at least one control environment configured to schedule and cause execution of the at least one operation by a processing resource identified in the inference result, the causing execution comprising initiating execution, within the process-execution environment, of the at least one operation by the identified processing resource, wherein initiating execution comprises either (1) automated performance of the at least one operation by the identified processing resource, or (2) generation of a system-directed work instruction for performance of the at least one operation using the identified processing resource, based on the teachings of Li. One of ordinary skill in the art would have been motivated to make this modification in order to modify the plant to improve its robustness, reduce the bottleneck, and so on as suggested by Li (page 2453, right column, paragraph 2, last 2 lines).
While Abidi, in view of Li, discloses obtaining an inference result … processing resources associated with one or more inputs of the second plurality of inputs predicted to perform at least one operation on at least one input of the second plurality of inputs, they do not disclose an inference result directly identifying one or more processing resources of the set of one or more processing resources.
Morariu discloses:
an inference result directly identifying one or more processing resources of the set of one or more processing resources (Morariu, Fig.. 1 teaches an inference result directly identifying one or more processing resources of the set of one or more processing resources for resource allocation).
Abidi, Li, and Morariu are analogous art because they are from the same field of endeavor, enterprise/organization modelling and machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, to include an inference result directly identifying one or more processing resources of the set of one or more processing resources, based on the teachings of Morariu. One of ordinary skill in the art would have been motivated to make this modification in order to optimize resource allocation to jobs based on predictions, as suggested by Morariu (page 2, right column, first paragraph, last 2 lines).
Regarding claim 6, Abidi, in view of Li, in further view of Morariu, discloses the computing system of claim 1. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality),
the operations further comprising: for respective training data instances of the plurality of training data instances, defining a respective bit vector identifying whether an input of the plurality of the first plurality of inputs is present in the third plurality of inputs of a respective training data instance (Abidi, Fig. 4 teaches the respective extracted feature representations, i.e. WF1 to WFNF, as defining a respective bit vector identifying whether an input of the plurality of the first plurality of inputs is present in the third plurality of inputs of a respective training data instance).
Claim 8 is substantially similar to claim 1, but for the recitation of associating at least a portion of the first plurality of inputs with a set of one or more operations, each operation representing a manufacturing, processing, or computational operation previously performed on or using one or more inputs of the first plurality of inputs and … an inference result directly identifying at least one operations… .
Abidi discloses:
associating at least a portion of the first plurality of inputs with a set of one or more operations, each operation representing a manufacturing, processing, or computational operation previously performed on or using one or more inputs of the first plurality of inputs (Abidi, Table 3 teaches a set of one or more operations, and Figure 1, Table 2, and Figure 2 and page 96094, left column last line – right column, lines 1-2 “Here, the best scheduling rule of the FMS is predicted using input attributes” and page 96093, left column, penultimate paragraph, lines 1-4 “The system operations performed by the four machines are presented in Table 2. Three types of parts are processed. Some operations are performed on more than one machine, and some of them are performed only on one machine.” Teaches associating at least a portion of the plurality of inputs with a set of one or more operations in the flexible manufacturing system and processing resources in the FMS, each operation representing a manufacturing or processing operation previously performed on or using one or more inputs of the first plurality of inputs),
an inference result directly identifying at least one operation… (Abidi, Figure 2, and page 96093, right column, paragraph above dataset description, lines 1-8 “The simulation model … involves three system attributes to manufacture the model, which define the dynamic operations of the FMS: the input or output buffer size of each machine, the part arrival rate, and the speed of the AGV. In the input buffers, the dispatching rules used for the machines are first come first served (FCFS), SPT, and earliest due date (EDD). These six attributes are considered as inputs, and the optimal scheduling rules are the outputs” teaches optimal scheduling rules for the operation of the FMS as the inference result identifying at least one operation).
The remainder of Claim 8 is substantially similar to claim 1, and thus is rejected on the same basis as claim 1.
Regarding claim 13, Abidi, in view of Li, in further view of Morariu, discloses the method of claim 8. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality),
the method further comprising: for respective training data instances of the plurality of training data instances, defining a respective bit vector identifying whether an input of the first plurality of inputs is present in the third plurality of inputs of the respective training data instance (Abidi, Fig. 4 teaches the respective extracted feature representations, i.e. WF1 to WFNF, as defining a respective bit vector identifying whether an input of the plurality of the first plurality of inputs is present in the third plurality of inputs of a respective training data instance).
Claim 15 is substantially similar to claim 1, but for the recitation of associate at least a first standard value with the at least one operation, the at least a first standard value indicating an amount of a resource used in performing the at least one operation, and an inference result directly identifying at least a second standard value for the at least a second operation performable on one or more inputs of the second plurality of inputs.
Abidi discloses:
associate at least a first standard value with the at least one operation, the at least a first standard value indicating an amount of a resource used in performing the at least one operation (Abidi, Table 3 and Table 4 teaches associate at least a first standard value with the at least one operation, the at least a first standard value, the standard value being performance in machine utilization, indicating an amount of a resource used in performing the at least one operation),
an inference result directly identifying at least a second standard value for the at least a second operation performable on one or more inputs of the second plurality of inputs (Abidi, Table 4 and page 96093, right column, paragraph above dataset description, lines 1-8 “The simulation model … involves three system attributes to manufacture the model, which define the dynamic operations of the FMS: the input or output buffer size of each machine, the part arrival rate, and the speed of the AGV. In the input buffers, the dispatching rules used for the machines are first come first served (FCFS), SPT, and earliest due date (EDD). These six attributes are considered as inputs, and the optimal scheduling rules are the outputs” teaches optimal scheduling rules for the operation of the FMS as the inference result, the optimal scheduling rule identifying the best performance in machine utilization as identifying the second standard value in Table 4, for at least second operations performable on second inputs during optimal scheduling).
The remainder of Claim 15 is substantially similar to claim 1, and thus is rejected on the same basis as claim 1.
Regarding claim 17, Abidi, in view of Li, in further view of Morariu, discloses the one or more computer-readable storage media of claim 15. Abidi further discloses:
wherein the set of inference data is a first set of inference data (Abidi, Table 4 and Figure 2 teaches sample data as the first set of inference data in the FMS scheduling data),
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches a plurality of training instances defined from the sample data),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with the at least one operation and a standard value for the at least one operation (Abidi, Figure 2 and Table 4, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches extracted features as a third plurality of inputs selected from the first plurality and associated with at least one operation and a standard value for the at least one operation),
further comprising: computer-executable instructions that, when executed by the computing system, cause the computing system to…(Abidi, page 96100, left column, final paragraph, lines 1-3 “The developed optimal scheduling for the FMS using the optimized intelligent model was implemented using MATLAB 2018a, and the performance of the model was evaluated.”),
… determine a confidence level of the inference result (Abidi, equation 41 and Figure 3 teaches an accuracy of the inference results as a confidence level),
… determine that the confidence level fails to satisfy a threshold (Abidi, equation 42 and Figure 3, element “Maximize Accuracy” is determined to fail to satisfy a threshold via the arrow leading to “no”),
… based at least in part on the determining that the confidence level fails to satisfy a threshold, identify a training data instance of the plurality of training data instances, being an identified training data instance, whose respective third set of input values is most similar to the second plurality of inputs (Abidi, Figure 3 teaches updating the solution, i.e. reclassifying, based on the confidence level failing to satisfy a threshold, and Figure 2, element feature extraction, and page 96095, right column, paragraph 2, lines 6-9 “For solving the problems related to the optimal discrimination projection matrix, the within-class and between-class scatter matrix is forecasted.” And paragraph 4, lines 1-2 “supervised dimensionality reduction process.” teaches using supervised feature extraction methods, i.e., using the fourth inputs, to find most similar training data instances to the fourth input),
… provide the standard value of the identified training data instance as an inference result for the set of inference data (Abidi, Figure 2, and Table 4 teaches providing the performance in machine utilization standard value of the identified training data instance as an inference result for the second set of inference data).
Regarding claim 18, Abidi, in view of Li, in further view of Morariu, discloses the one or more computer-readable storage media of claim 15. Abidi further discloses:
wherein the set of inference data is a first set of inference data (Abidi, Table 4 and Figure 2 teaches sample data as the first set of inference data in the FMS scheduling data),
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches a plurality of training instances defined from the sample data),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with the at least one operation and a standard value for the at least one operation (Abidi, Figure 2 and Table 4, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches extracted features as a third plurality of inputs selected from the first plurality and associated with at least one operation and a standard value for the at least one operation),
further comprising: computer-executable instructions that, when executed by the computing system, cause the computing system to…(Abidi, page 96100, left column, final paragraph, lines 1-3 “The developed optimal scheduling for the FMS using the optimized intelligent model was implemented using MATLAB 2018a, and the performance of the model was evaluated.”),
…for respective training data instances of the plurality of training data instances, define a respective bit vector identifying whether an input of the first plurality of inputs is present in the third plurality of inputs of the respective training data instance (Abidi, Fig. 4 teaches the respective extracted feature representations, i.e. WF1 to WFNF, as defining a respective bit vector identifying whether an input of the plurality of the first plurality of inputs is present in the third plurality of inputs of a respective training data instance).
Regarding claim 19, Abidi, in view of Li, in further view of Morariu, discloses the one or more computer-readable storage media of claim 15. Abidi further discloses:
wherein the computer-executable instructions that cause the computing system to train a predictive model using the first plurality of inputs comprises computer-executable instructions that, when executed by the computing system, cause the computing system to (Abidi, page 96100, left column, final paragraph, lines 1-3 “The developed optimal scheduling for the FMS using the optimized intelligent model was implemented using MATLAB 2018a, and the performance of the model was evaluated.” And Figure 2 teaches training a predictive model using the first plurality of inputs),
train the predictive model with a respective set of one or more characteristics of inputs of the first plurality of inputs (Abidi, Figure 2 and Table 4 teaches training the FMS predictive model with sample data of a respective set of one or more characteristics of inputs of the first plurality of inputs).
Regarding claim 20, Abidi, in view of Li, in further view of Morariu, discloses the one or more computer-readable storage media of claim 15. Abidi further discloses:
wherein the set of inference data is a first set of inference data (Abidi, Table 4 and Figure 2 teaches sample data as the first set of inference data in the FMS scheduling data),
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches a plurality of training instances defined from the sample data),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with the at least one operation and a standard value for the at least one operation (Abidi, Figure 2 and Table 4, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches extracted features as a third plurality of inputs selected from the first plurality and associated with at least one operation and a standard value for the at least one operation),
further comprising: computer-executable instructions that, when executed by the computing system, cause the computing system to…(Abidi, page 96100, left column, final paragraph, lines 1-3 “The developed optimal scheduling for the FMS using the optimized intelligent model was implemented using MATLAB 2018a, and the performance of the model was evaluated.”),
…associate a respective processing resource identifier with the respective at least one operation of a respective training data instance of the plurality of training data instances (Abidi, Table 4 and Figure 2 teaches associating FMS scheduling data as a respective processing resource identifier, with a best schedule rule as an operation of a respective training data instance of the plurality of training data instances),
wherein a respective processing resource identifier identifies a processing center on which the at least one operation was performed for respective training data instances of the plurality of training data instances (Examiner’s Note: Processing center is interpreted to be machines in the same location, as per specification ¶[065]) (Abidi, Tables 2-4 teaches a respective processing resource identifier identifying a processing center, i.e. machines in Table 2, on which the at least one operation was performed for respective training data instances of the plurality of training data instances),
wherein the computer-executable instructions that train the predictive model comprise computer-executable instructions that, when executed by the computing system, cause the computing system to train the predictive model using the respective processing resource identifiers (Abidi, page 96100, left column, final paragraph, lines 1-3 “The developed optimal scheduling for the FMS using the optimized intelligent model was implemented using MATLAB 2018a, and the performance of the model was evaluated.” And Figure 2 teaches training the predictive model using the respective processing resource identifiers in the FMS scheduling data).
Claims 2, 3, 9, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Abidi et al. (“Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach”), hereafter Abidi, in view of Li et al. ("Machine learning and optimization for production rescheduling in Industry 4.0"), as disclosed in the prior art made of record and not relied upon in the office action mailed 06/02/2025, hereafter Li, in further view of Morariu et al. ("Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems"), hereafter Morariu, in further view of Pourmohammad et al. (Pub. No.: US 2019/0096217 A1), hereafter Pour.
Regarding claim 2, Abidi, in view of Li, in further view of Morariu, discloses the computing system of claim 1. Abidi further discloses:
wherein the set of inference data is a first set of inference data (Abidi, Table 4 and Figure 2 teaches sample data as the first set of inference data in the FMS scheduling data),
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches a plurality of training instances defined from the sample data),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with a respective set of one or more processing resources (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches extracted features as a third plurality of inputs selected from the first plurality and associated with a set of processing resources),
the operations further comprising: obtaining a … set of inference data, the … set of inference data comprising a fourth plurality of inputs (Abidi, Table 4 teaches best scheduling rule as the obtained second set of inference data comprising fourth plurality of inputs),
identifying a training data instance of the plurality of training data instances, being an identified training data instance, whose respective third set of input values is most similar to the fourth plurality of inputs (Abidi, Figure 2, element feature extraction, and page 96095, right column, paragraph 2, lines 6-9 “For solving the problems related to the optimal discrimination projection matrix, the within-class and between-class scatter matrix is forecasted.” And paragraph 4, lines 1-2 “supervised dimensionality reduction process.” teaches using supervised feature extraction methods, i.e., using the fourth inputs, to find most similar training data instances to the fourth input),
providing at least a portion of processing resources of the set of one or more processing resources of the identified training data instance as an inference result for the second set of inference data (Abidi, Figure 3 teaches providing a portion of the identified training instance after minimizing correlations and maximizing accuracy as the inference result, i.e., predicted output).
Abidi, in view of Li, teaches obtaining a … set of inference data, the … set of inference data comprising a fourth plurality of inputs, but does not explicitly teach the obtained inference data to be a second set.
Pour teaches:
a second set of inference data (Pour, Fig. 2 teaches a second data source as a second set of inference data).
Abidi, in view of Li, in further view of Morariu, does not teach:
determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis.
Pour teaches:
determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis (Examiner’s Note: determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided is interpreted as inference data without an assigned class label value, according to specification paragraph [0109-0110]) (Pour, ¶[0012], lines 9-17 “In some embodiments, the instructions cause the one or more processors to generate the suggested subset of predetermined labels by performing a similarity analysis between the description of the historical threat event and the labels of the set of predetermined labels and including one or more predetermined labels from the set of predetermined labels having a highest similarity with the description of the historical threat event in the suggested subset.” Teaches inference data without an assigned label, which specifies that an inference is to be provided for the second set of inference data using a similarity analysis).
Abidi, Li, Morariu, and Pour are analogous art because they are from the same field of endeavor, enterprise/organization modelling and machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include a second set of inference data and determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis based on the teachings of Pour. One of ordinary skill in the art would have been motivated to make this modification in order to be able to accurately identify a subcategory as suggested by Pour (Pour, ¶[0261], lines 4-5).
Regarding claim 3, Abidi, in view of Li, in further view of Morariu, in further view of Pour, discloses the computing system of claim 2. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with a respective set of one or more processing resources (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality and associated with a set of processing resources),
the operations further comprising: determining a confidence level of the inference result for the … set of inference data (Abidi, equation 41 and Figure 3 teaches an accuracy of the inference results as a confidence level),
determining that the confidence level fails to satisfy a threshold (Abidi, equation 42 and Figure 3, element “Maximize Accuracy” is determined to fail to satisfy a threshold via the arrow leading to “no”),
based at least in part on the determining that the confidence level fails to satisfy a threshold, identifying a training data instance of the plurality of training data instances, being an identified training data instance, whose respective third set of input values is most similar to the fourth plurality of inputs (Abidi, Figure 3 teaches updating the solution, i.e. reclassifying, based on the confidence level failing to satisfy a threshold, and Figure 2, element feature extraction, and page 96095, right column, paragraph 2, lines 6-9 “For solving the problems related to the optimal discrimination projection matrix, the within-class and between-class scatter matrix is forecasted.” And paragraph 4, lines 1-2 “supervised dimensionality reduction process.” teaches using supervised feature extraction methods, i.e., using the fourth inputs, to find most similar training data instances to the fourth input),
providing at least a portion of processing resources of the set of one or more processing resources of the identified training data instance as a replacement inference result for the … set of inference data (Abidi, Figure 3 teaches providing the predicted output as providing at least a portion of processing resources of the set of one or more processing resources of the identified training data instance as a replacement inference result for the set of inference data).
Abidi discloses the operations further comprising: determining a confidence level of the inference result for the … set of inference data, but does not disclose but does not explicitly teach the obtained inference data to be a second set.
Pour teaches:
a second set of inference data (Pour, Fig. 2 teaches a second data source as a second set of inference data).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include a second set of inference data based on the teachings of Pour. One of ordinary skill in the art would have been motivated to make this modification in order to be able to accurately identify a subcategory as suggested by Pour (Pour, ¶[0261], lines 4-5),
Abidi teaches providing at least a portion of processing resources of the set of one or more processing resources of the identified training data instance as a replacement inference result for the … set of inference data, but does not teach providing an identified data instance as an inference result for the second set of inference data.
Pour discloses:
providing … identified … data instance as an inference result for the second set of inference data (Pour, Fig. 10 and Fig. 13 teaches selecting a category for standard threat in Fog. 10 element 1020 as an inference result for the second inference set).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include providing … identified … data instance as an inference result for the second set of inference data based on the teachings of Pour. One of ordinary skill in the art would have been motivated to make this modification in order to be able to accurately identify a subcategory as suggested by Pour (Pour, ¶[0261], lines 4-5).
Regarding claim 9, Abidi, in view of Li, in further view of Morariu, discloses the method of claim 8. Abidi further discloses:
wherein the set of inference data is a first set of inference data (Abidi, Table 4 and Figure 2 teaches sample data as the first set of inference data in the FMS scheduling data),
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with a respective set of one or more operations (Abidi, Figure 2 and Table 3, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches extracted features as a third plurality of inputs selected from the first plurality and associated with a set operations),
the operations further comprising: obtaining a … set of inference data, the … set of inference data comprising a fourth plurality of inputs (Abidi, Table 4 teaches best scheduling rule as the obtained second set of inference data comprising fourth plurality of inputs),
identifying a training data instance of the plurality of training data instances, being an identified training data instance, whose respective third set of input values is most similar to the fourth plurality of inputs (Abidi, Figure 2, element feature extraction, and page 96095, right column, paragraph 2, lines 6-9 “For solving the problems related to the optimal discrimination projection matrix, the within-class and between-class scatter matrix is forecasted.” And paragraph 4, lines 1-2 “supervised dimensionality reduction process.” teaches using supervised feature extraction methods, i.e., using the fourth inputs, to find most similar training data instances to the fourth input),
providing at least a portion of operations of the set of one or more operations of the identified training data instance as an inference result for the set of inference data (Abidi, Figure 3 teaches providing a portion of the identified training instance after minimizing correlations and maximizing accuracy as the inference result, i.e., predicted output).
Abidi, in view of Li, teaches obtaining a … set of inference data, the … set of inference data comprising a fourth plurality of inputs, but does not explicitly teach the obtained inference data to be a second set.
Pour teaches:
a second set of inference data (Pour, Fig. 2 teaches a second data source as a second set of inference data).
Abidi, in view of Li, does not teach:
determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis.
Pour teaches:
determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis (Examiner’s Note: determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided is interpreted as inference data without an assigned class label value, according to specification paragraph [0109-0110]) (Pour, ¶[0012], lines 9-17 “In some embodiments, the instructions cause the one or more processors to generate the suggested subset of predetermined labels by performing a similarity analysis between the description of the historical threat event and the labels of the set of predetermined labels and including one or more predetermined labels from the set of predetermined labels having a highest similarity with the description of the historical threat event in the suggested subset.” Teaches inference data without an assigned label, which specifies that an inference is to be provided for the second set of inference data using a similarity analysis).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include a second set of inference data and determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis based on the teachings of Pour. One of ordinary skill in the art would have been motivated to make this modification in order to be able to accurately identify a subcategory as suggested by Pour (Pour, ¶[0261], lines 4-5).
Regarding claim 10, Abidi, in view of Li, in further view of Morariu, in further view of Pour, discloses the method of claim 9. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with a respective set of one or more operations (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality and associated with a set of operations),
the method further comprising: determining a confidence level of the inference result (Abidi, equation 41 and Figure 3 teaches an accuracy of the inference results as a confidence level),
determining that the confidence level fails to satisfy a threshold (Abidi, equation 42 and Figure 3, element “Maximize Accuracy” is determined to fail to satisfy a threshold via the arrow leading to “no”),
based at least in part of the determining that the confidence level fails to satisfy a threshold, identifying a training data instance of the plurality of training data instances, being an identified training data instance, whose respective third set of input values is most similar to the fourth plurality of inputs (Abidi, Figure 3 teaches updating the solution, i.e. reclassifying, based on the confidence level failing to satisfy a threshold, and Figure 2, element feature extraction, and page 96095, right column, paragraph 2, lines 6-9 “For solving the problems related to the optimal discrimination projection matrix, the within-class and between-class scatter matrix is forecasted.” And paragraph 4, lines 1-2 “supervised dimensionality reduction process.” teaches using supervised feature extraction methods, i.e., using the fourth inputs, to find most similar training data instances to the fourth input),
providing at least a portion of operations of the set of one or more operations of the identified training data instance as an inference result for the set of inference data (Abidi, Figure 3 teaches providing the predicted output as providing at least a portion of operations of the set of one or more operations of the identified training data instance as an inference result for the set of inference data).
Regarding claim 16, Abidi, in view of Li, in further view of Morariu, discloses the one or more computer-readable storage media of claim 15. Abidi further discloses:
wherein the set of inference data is a first set of inference data (Abidi, Table 4 and Figure 2 teaches sample data as the first set of inference data in the FMS scheduling data),
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches a plurality of training instances defined from the sample data),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with the at least one operation and a standard value for the at least one operation (Abidi, Figure 2 and Table 4, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches extracted features as a third plurality of inputs selected from the first plurality and associated with at least one operation and a standard value for the at least one operation),
further comprising: computer-executable instructions that, when executed by the computing system, cause the computing system to…(Abidi, page 96100, left column, final paragraph, lines 1-3 “The developed optimal scheduling for the FMS using the optimized intelligent model was implemented using MATLAB 2018a, and the performance of the model was evaluated.”),
…obtain a … set of inference data, the second set of inference data comprising a fourth plurality of inputs (Abidi, Table 4 teaches best scheduling rule as the obtained second set of inference data comprising fourth plurality of inputs),
…identify a training data instance of the plurality of training data instances, being an identified training data instance, whose respective third set of input values is most similar to the fourth plurality of inputs (Abidi, Figure 2, element feature extraction, and page 96095, right column, paragraph 2, lines 6-9 “For solving the problems related to the optimal discrimination projection matrix, the within-class and between-class scatter matrix is forecasted.” And paragraph 4, lines 1-2 “supervised dimensionality reduction process.” teaches using supervised feature extraction methods, i.e., using the fourth inputs, to find most similar training data instances to the fourth input),
… provide the standard value of the identified training data instance as an inference result for the second set of inference data (Abidi, Figure 2, and Table 4 teaches providing the performance in machine utilization standard value of the identified training data instance as an inference result for the second set of inference data).
Abidi, in view of Li, teaches obtaining a … set of inference data, the … set of inference data comprising a fourth plurality of inputs, but does not explicitly teach the obtained inference data to be a second set.
Pour teaches:
a second set of inference data (Pour, Fig. 2 teaches a second data source as a second set of inference data).
Abidi, in view of Li, does not teach:
…determine that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis.
Pour teaches:
…determine that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis (Examiner’s Note: determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided is interpreted as inference data without an assigned class label value, according to specification paragraph [0109-0110]) (Pour, ¶[0012], lines 9-17 “In some embodiments, the instructions cause the one or more processors to generate the suggested subset of predetermined labels by performing a similarity analysis between the description of the historical threat event and the labels of the set of predetermined labels and including one or more predetermined labels from the set of predetermined labels having a highest similarity with the description of the historical threat event in the suggested subset.” Teaches inference data without an assigned label, which specifies that an inference is to be provided for the second set of inference data using a similarity analysis).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include a second set of inference data and determining that the second set of inference data is associated with a flag having a value specifying that an inference is to be provided for the second set of inference data using a similarity analysis based on the teachings of Pour. One of ordinary skill in the art would have been motivated to make this modification in order to be able to accurately identify a subcategory as suggested by Pour (Pour, ¶[0261], lines 4-5).
Claims 4, 5, 7, 11, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Abidi et al. (“Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach”), hereafter Abidi, in view of Li et al. ("Machine learning and optimization for production rescheduling in Industry 4.0"), as disclosed in the prior art made of record and not relied upon in the office action mailed 06/02/2025, hereafter Li, in further view of Morariu et al. ("Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems"), hereafter Morariu, in further view of Dias et al. (Pub. No.: US 2020/0057675 A1), hereafter Dias.
Regarding claim 4, Abidi, in view of Li, in further view of Morariu, discloses the computing system of claim 1. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with a respective set of one or more processing resources (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality and associated with a set of processing resources).
Abidi discloses respective training data instances of the plurality of training data instances having a set of processing resources that comprises a plurality of processing resources (Abidi, Table 4 and Figure 1 teaches sample data that are the respective plurality of training data instances having a set of processing resources that comprises a plurality of processing resources), and executing operations on inputs of the respective third plurality of inputs (Abidi, Table 2 and Figure 2 teaches executing operations on the inputs of the features of the input), but does not teach:
receiving respective sequence information.
Dias teaches:
receiving respective sequence information (Dia, Fig. 1, Fig. 7, and ¶[0010], lines 11-13 “collecting monitoring data that represents the state of said set of resources,” teaches receiving state information as respective sequence information).
Abidi discloses processing resources of the plurality of processing resources are used in executing operations on inputs of the respective third plurality of inputs (Abidi, Table 2 and Figure 2), but does not teach a sequence information that identifies processing resources.
Dias discloses:
the respective sequence information identifying a sequence in … processing resources (Dias, Fig. 7 and ¶[0069] teaches the sequence information to identify a sequence in the processing resources, i.e. states in the workflow).
Abidi discloses training the predictive model (Abidi, Figure 2 teaches training the FMS scheduling model), but does not disclose:
training … predictive model with the respective sequence information.
Dias discloses:
training … predictive model with the respective sequence information (Dias, Fig. 7 and ¶[0069] teaches training a DNN predictive model with the respective sequence information).
Abidi, Li, Morariu and Dias are analogous art because they are from the same field of endeavor, enterprise/organization modelling and machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include receiving respective sequence information, the respective sequence information identifying a sequence in … processing resources, and training … predictive model with the respective sequence information, based on the teachings of Dias. One of ordinary skill in the art would have been motivated to make this modification in order to train prediction models and improve workflow execution (Dias, ¶[0022], last 2 lines).
Regarding claim 5, Abidi, in view of Li, in further view of Morariu, discloses the computing system of claim 1. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with a respective set of one or more processing resources (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality and associated with a set of processing resources),
Abidi discloses respective training data instances of the plurality of training data instances having a set of processing resources that comprises a plurality of processing resources (Abidi, Table 4 and Figure 1 teaches sample data that are the respective plurality of training data instances having a set of processing resources that comprises a plurality of processing resources), and executing operations on inputs of the respective third plurality of inputs (Abidi, Table 2 and Figure 2 teaches executing operations on the inputs of the features of the input), but does not teach:
receiving respective sequence information.
Dias teaches:
receiving respective sequence information (Dia, Fig. 1, Fig. 7, and ¶[0010], lines 11-13 “collecting monitoring data that represents the state of said set of resources,” teaches receiving state information as respective sequence information).
Abidi discloses processing resources of the plurality of processing resources are used in executing operations on inputs of the respective third plurality of inputs (Abidi, Table 2 and Figure 2), but does not teach a sequence information that identifies processing resources.
Dias discloses:
the respective sequence information identifying a sequence in … processing resources (Dias, Fig. 7 and ¶[0069] teaches the sequence information to identify a sequence in the processing resources, i.e. states in the workflow).
Abidi discloses training the predictive model (Abidi, Figure 2 teaches an FMS scheduling model), but does not disclose:
training a sequence prediction transition probability model using the respective sequence information.
Dias discloses:
training a sequence prediction transition probability model using the respective sequence information (Examiner’s Note: a sequence prediction transition probability model is interpreted as a prediction model that uses state transitions to generate predictions, as disclosed by the specification Fig. 8A and ¶[0128]) (Dias, Fig. 7 and ¶[0069-0072] teaches the known state machine that is optimized by predictions from a DNN model as a sequence prediction transition probability model using the respective sequence information).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include receiving respective sequence information, the respective sequence information identifying a sequence in … processing resources, and training a sequence prediction transition probability model using the respective sequence information, based on the teachings of Dias. One of ordinary skill in the art would have been motivated to make this modification in order to train prediction models and improve workflow execution (Dias, ¶[0022], last 2 lines).
Regarding claim 7, Abidi, in view of Li, in further view of Morariu, discloses the computing system of claim 1. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality).
While Abidi teaches respective training data instances of the plurality of training data instances having a set of one or more processing resources comprising a plurality of processing resources (Abidi, Table 4, Figure 1), receiving information identifying an identification of … inputs of the third plurality of inputs to processing resources of the plurality of processing resources (Abidi, Figure 2 teaches receiving information identifying the extracted features as the weighted features for the classifier), and training a predictive model (Abidi, Figure 2), Abidi does not disclose:
receiving information identifying an identification of an allocation of inputs,
training … predictive model with the information identifying an allocation of inputs.
Dias discloses:
receiving information identifying an identification of an allocation of inputs (Dias, Fig. 5, Fig. 7 and ¶[0010], lines 16-17 “an allocation of resources of the set of resources to each task of the sets of tasks” teaches receiving information identifying an identification of an allocation of inputs),
training … predictive model with the information identifying an allocation of inputs (Dias, Fig. 5, Fig. 7, ¶[0023], lines 8-9 “the relationships among workflows, their resource allocation and their obtained performance” teaches training a predictive model DNN with the information identifying an allocation of inputs).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include receiving information identifying an identification of an allocation of inputs, and training … predictive model with the information identifying an allocation of inputs, based on the teachings of Dias. One of ordinary skill in the art would have been motivated to make this modification in order to train prediction models and improve workflow execution (Dias, ¶[0022], last 2 lines).
Regarding claim 11, Abidi, in view of Li, in further view of Morariu, discloses the method of claim 8. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with a respective set of one or more operations (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality and associated with a set of operations).
Abidi discloses respective training data instances of the plurality of training data instances having a set of operations resources that comprises a plurality of operations (Abidi, Figure 1, Tables 2-4 teaches sample data that are the respective plurality of training data instances having a set of operations resources that comprises a plurality of operations), and executing operations on inputs of the respective third plurality of inputs (Abidi, Tables 2-4 and Figure 2 teaches executing operations on the inputs of the features of the input), but does not teach:
receiving respective sequence information.
Dias teaches:
receiving respective sequence information (Dia, Fig. 1, Fig. 7, and ¶[0010], lines 11-13 “collecting monitoring data that represents the state of said set of resources,” teaches receiving state information as respective sequence information).
Abidi discloses operations of the plurality of operations are used in executing the operations of the respective set of one or more operations on inputs of the respective third plurality of inputs (Abidi, Tables 2-4 and Figure 2), but does not teach the respective sequence information identifying a sequence in which operations of the plurality of operations are used.
Dias discloses:
the respective sequence information identifying a sequence in which operations of the plurality of operations are used (Dias, Fig. 7 and ¶[0069] teaches the sequence information to identify a sequence in the operations used in execution, i.e. states in the workflow).
Abidi discloses training the predictive model (Abidi, Figure 2 teaches training the FMS scheduling model), but does not disclose:
training … predictive model with the respective sequence information.
Dias discloses:
training … predictive model with the respective sequence information (Dias, Fig. 7 and ¶[0069] teaches training a DNN predictive model with the respective sequence information).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include receiving respective sequence information, the respective sequence information identifying a sequence in which operations of the plurality of operations are used, and training … predictive model with the respective sequence information, based on the teachings of Dias. One of ordinary skill in the art would have been motivated to make this modification in order to train prediction models and improve workflow execution (Dias, ¶[0022], last 2 lines).
Regarding claim 12, Abidi, in view of Li, in further view of Morariu, discloses the method of claim 8. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs and being associated with a respective set of one or more operations (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality and associated with a set of operations).
Abidi discloses respective training data instances of the plurality of training data instances having a set of operations that comprises a plurality of operations (Abidi, Figure 1, Tables 2-4 teaches sample data that are the respective plurality of training data instances having a set of operations that comprises a plurality of operations), and executing operations on inputs of the respective third plurality of inputs (Abidi, Tables 2-4 and Figure 2 teaches executing operations on the inputs of the features of the input), but does not teach:
receiving respective sequence information.
Dias teaches:
receiving respective sequence information (Dia, Fig. 1, Fig. 7, and ¶[0010], lines 11-13 “collecting monitoring data that represents the state of said set of resources,” teaches receiving state information as respective sequence information).
Abidi discloses operations of the plurality of operations are used in executing the operations of the respective set of one or more operations on inputs of the respective third plurality of inputs (Abidi, Tables 2-4 and Figure 2), but does not teach the respective sequence information identifying a sequence in which operations of the plurality of operations are used.
Dias discloses:
the respective sequence information identifying a sequence in which operations of the plurality of operations are used (Dias, Fig. 7 and ¶[0069] teaches the sequence information to identify a sequence in the operations used in execution, i.e. states in the workflow).
Abidi discloses training the predictive model (Abidi, Figure 2 teaches an FMS scheduling model), but does not disclose:
training a sequence prediction transition probability model using the respective sequence information.
Dias discloses:
training a sequence prediction transition probability model using the respective sequence information (Examiner’s Note: a sequence prediction transition probability model is interpreted as a prediction model that uses state transitions to generate predictions, as disclosed by the specification Fig. 8A and ¶[0128]) (Dias, Fig. 7 and ¶[0069-0072] teaches the known state machine that is optimized by predictions from a DNN model as a sequence prediction transition probability model using the respective sequence information).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include receiving respective sequence information, the respective sequence information identifying a sequence in which operations of the plurality of operations are used, and training a sequence prediction transition probability model using the respective sequence information, based on the teachings of Dias. One of ordinary skill in the art would have been motivated to make this modification in order to train prediction models and improve workflow execution (Dias, ¶[0022], last 2 lines).
Regarding claim 14, Abidi, in view of Li, in further view of Morariu, discloses the method of claim 8. Abidi further discloses:
wherein the first plurality of inputs define a plurality of training data instances (Abidi, Table 4 and Figure 2 teaches sample data as a plurality of training data instances),
a given training data instance of the plurality of training data instances comprising a respective third plurality of inputs selected from the first plurality of inputs (Abidi, Figure 2, and page 96094, right column, lines 2-6 “these input attributes are subjected to feature extraction, where t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and linear square regression (LSR) features are extracted, among others” teaches a third plurality of inputs selected from the first plurality).
While Abidi teaches respective training data instances of the plurality of training data instances having a set of one or more operations comprising a plurality of operation (Abidi, Tables 2- 4, Figure 1), receiving information identifying an identification of … inputs of the third plurality of inputs to operations of the plurality of operations (Abidi, Figure 2 teaches receiving information identifying the extracted features as the weighted features for the classifier), and training a predictive model (Abidi, Figure 2), Abidi does not disclose:
receiving information identifying an identification of an allocation of inputs,
training … predictive model with the information identifying an allocation of inputs.
Dias discloses:
receiving information identifying an identification of an allocation of inputs (Dias, Fig. 5, Fig. 7 and ¶[0010], lines 16-17 “an allocation of resources of the set of resources to each task of the sets of tasks” teaches receiving information identifying an identification of an allocation of inputs),
training … predictive model with the information identifying an allocation of inputs (Dias, Fig. 5, Fig. 7, ¶[0023], lines 8-9 “the relationships among workflows, their resource allocation and their obtained performance” teaches training a predictive model DNN with the information identifying an allocation of inputs).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Abidi, in view of Li, in further view of Morariu, to include receiving information identifying an identification of an allocation of inputs, and training … predictive model with the information identifying an allocation of inputs, based on the teachings of Dias. One of ordinary skill in the art would have been motivated to make this modification in order to train prediction models and improve workflow execution (Dias, ¶[0022], last 2 lines).
Response to Arguments
Applicant's arguments filed 01/07/26 have been fully considered with regards to the 35 U.S.C. 101 rejection, and they are found persuasive. The rejection has been withdrawn.
Applicant's arguments filed 01/07/26 have been fully considered with regards to the 35 U.S.C. 102/103 rejection, but they are not persuasive.
The applicant asserts on page 15 of the remarks that Abidi does not directly identify a processing resource. Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The applicant asserts on page 16 of the remarks “Nothing in Li is understood to teach or suggest modifying Abidi's predictive model such that its inference result directly identifies a processing resource based on structured, hierarchical input representations”. The examiner respectfully disagrees, as Li teaches considered sets where structured inputs are each associated with an identifying label, i.e. J, T, M, O, etc., a quantity value, i.e. 1, 2, etc., and relationship information encoding relationships among inputs of the first plurality of inputs across multiple hierarchical levels using a directed graph (see 103 rejection above for further details).
Claims dependent on independent claims do not overcome the deficiencies of the rejected independent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUMAIRA ZAHIN MAUNI whose telephone number is (703)756-5654. The examiner can normally be reached Monday - Friday, 9 am - 5 pm (ET).
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/H.Z.M./Examiner, Art Unit 2141
/JEREMY L STANLEY/Examiner, Art Unit 2127
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