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 .
Response to Arguments
Applicant’s arguments regarding the instant amendment have been considered but are unpersuasive. Regarding the 35 USC 101 rejection, Applicant’s amendment is directed to updating operation of the industrial process by modifying the one or more process parameters that impact the batch of products generated in the industrial process based on a result of the comparison. A parameter update as a responsive action is not the same as controlling a batch process responsive to modifying one or more process parameters. The Examiner suggests controlling operation of the batch process by modifying one or more process parameters that impact the batch of products. Applicant’s arguments, page 8, reflect a similar analysis but the claim limitation needs further clarification in terms of control opposed to updating.
Applicant’s arguments regarding the applied prior art have been considered but not persuasive. The Examiner submits Zhao et al. teaches:
in response to determining that the key performance indicators are outside bounds based on the pre-determined key performance indicators, updating operation of the industrial process by modifying the one or more process parameters that impact the batch of products generated in the industrial process based on a result of the comparison (0100 e.g. “In contrast, embodiments use both “in-spec” and “out-of-spec” batch data. Thus, embodiments are able to learn and predict end-product quality of a currently running batch. Based on comparisons and analytic data analyses on “out-of-spec” batch data, embodiments are able to further help plant operators to find out what are possible root-causes. For instance, if a batch process is predicted to fail, an embodiment can identify similar “out-of-spec” historical batches and from comparison between the current batch and historical batch to identify causes of the failure. Further, if a batch is predicted to fail, an embodiment can identify similar historical batches that yielded in-spec products and identify differences between the current batch, which is predicted to fail, and the historical batches that are similar, but ultimately produced desirable results. These differences can then be used to modify the current batch operation. For example, if it is determined that a previous similar batch, which was successful, operated at a temperature of 100° C. and the current batch, which is predicted to fail, is operating at 90° C., the current operating temperature of the current batch can be increased in an attempt to fix the ongoing process,” see also 0104 e.g. “n the other hand, embodiments are able to predict the outcome of a running batch, i.e. what is the probability the current batch will produce “in-spec” products and what is the probability the current batch will produce “out-of-spec” products. For batch monitoring, a prediction of an “in-spec” batch with high probability will gain great confidence for batch operators. Such a prediction indicates that currently the process is operating normally and the current batch is going to be a successful batch. When embodiments predict the current batch run is going to produce “out-of-spec” products with high probability, embodiments can perform a detailed follow-up analysis to assist batch operators with finding possible “root-causes” and provide recommended actionable advice. In such a way, embodiments enable batch operators to take actions early and correct subsequent batch operations in a timely manner to reduce the rate of “out-of-spec” products.”)
The Examiner agrees with Applicant Zhao does not expressly teach the model limitations described below. The Examiner submits Brill teaches:
wherein the batch data model is a graph based model that describes relationships between the data describing the batch, a phase, and one or more process parameters for the batch (Brill, figure 2 e.g. see the relationships between the data describing the batch, a phase, and one or more process parameters as the quality, performance (e.g. data describing the batch), a phase (processes to form structural elements), and one or more process parameters (e.g. inputs to operate). The claim language generally describes the model components, but the claim language does not appear to implement the model as part of a responsive action described in the instant amendment. The particular response is not tied to a particular model component, and it is suggested to further define the inter-relationship between the model and responsive actions. The description of model components but without a meaningful use of the components is a data modification of a model or including additional data. It is also noted the model of Brill provides a batch prediction based on process parameters, phase, and batch (Figure 2 e.g. see performance based on batch process and inputs, Figure 2)
In response to Applicant’s arguments the combination of Zhao and Brill does not teach or suggest the model limitations, the Examiner submits, as described above, the limitations further describe model components. The model of Zhao, further adapted to include representative hierarchical relationships of Brill, merely provides definition of model relationships showing components of a batch process. The inclusion of an additional representation as part of a batch model is an expanded form of a model, however, the claim limitations do not apply any component of the amended model for control. The batch model of Brill, upon execution, provides functional relationships between batch process , functions, parameters, and structures (e.g. see performance derived from elements, processes, tools, and inputs, figure 2). The result of applying the model of Brill at least shows performance parameters based on the inter-relationships (e.g. batch, phase, parameters, Figure 2). The claim broadly describes phase and parameters but without distinguishing between the phase, parameters, and batch, as per Applicant’s specification, in view over Brill.
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. The claim(s) recite(s) a mental process directed to contextualizing, by the processing circuit, the data describing the batch of products generated in the industrial process; generating, by the processing circuit, a batch data model based on the contextualized data; and comparing, by the processing circuit, the key performance indicators to pre-determined key performance indicators (claims 1, 10, 19). Dependent claims 6 and 15 further describe mental processes including generating, by the processing circuit, a batch performance index based on a combination of the key performance indicators. Dependent claims 7 and 16 further describe mental processes including determining, by the processing circuit, a difference between the key performance indicators and the pre-determined key performance indicators; and comparing, by the processing circuit, the difference to a threshold;
This judicial exception is not integrated into a practical application because the inclusion of a batch, products, processing circuit, data sources, data model, data types (performance indicators), nodes, and edges are generally recited so as to generically link the abstract idea to the field of process control (claims 1-20 respectively) . Moreover, the batch model of claim 1, as amended, generally recites general components but with a practical application of the model.
The following combination of limitations represent insignificant extra solution activity comprising receiving, by a processing circuit, data describing a batch of products generated in the industrial process from one or more data sources, (see tangential data gathering MPEP 2106.05(g)); performing an automated action based on a result of the comparison; and sending, by the processing circuit, an informed and prioritized notification to plant personnel regarding the comparison, see generally applying control actions as the equivalent of “apply it,” see MPEP 2106.05(g); in response to the difference being above the threshold, performing, by the processing circuit, a root cause analysis process to determine a root cause of the difference, see MPEP 2106.05(g); and taking a corrective action to address the root cause of the difference; performing the automated action comprises sending, by the processing circuit, an informed and prioritized notification to plant personnel regarding the comparison; MPEP 2106.05(g); and wherein performing the automated action adjusting comprises the industrial process based on the result of the comparison, see respective claims 1, 4, 7-9, 10, and 16-19. The batch data model providing a graph having nodes and edges is generally described so as to generally link the abstract idea to the field of process control, see claims 3, 12, and 20, see MPEP 2106.05(h). In particular, the instant amendment provides insignificant extra solution via updating parameters in response to a deviation, claim 1
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the inclusion of a process circuit and medium represent instructions to apply the abstract idea. The insignificant extra solution activity of receiving batch data and extensible models as well as performing automated actions and sending informed and prioritized notifications is well-known, conventional, and routine, see general corrective actions, 14/578898, 8489530, infra applied prior art, see also MPEP 2106.05(d)
Examiner note: the corrective actions, if clarified to represent an specified, controlled adjustment to the batch process would appear to represent a practical application because the claimed corrective actions are generically recited.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 3-4, 2-11, 12-13, 14-19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (PG/PUB 20240004355) in view over Brill (PG/PUB 20030220709)
Claim 1.
Zhao teaches a method for monitoring and controlling production of batches of products in an industrial process (ABSTRACT, Figure 5A), but does not expressly teach the model limitations described below. Brill teaches the model limitations described below, the method comprising:
receiving, by a processing circuit, data describing a batch of products generated in the industrial process from one or more data sources (ABSTRACT, Figure 5A-555 e.g. see pre-processing received batch data from multiple sources including sensors)
contextualizing, by the processing circuit, the data describing the batch of products generated in the industrial process (Figure 5A-555, 556, 0007 e.g. see signature generation)
generating, by the processing circuit, a batch data model based on the contextualized data (0012, 0016-0018, 0023-25, 0037-39, 0048-53 e.g. see generating batch model for prediction for batch control based on the signatures), wherein the batch data model is a graph based model that describes relationships between the data describing the batch, a phase, and one or more process parameters for the batch (Brill, figure 2 e.g. see the relationships between the data describing the batch, a phase, and one or more process parameters as the quality, performance (e.g. data describing the batch), a phase (processes to form structural elements), and one or more process parameters (e.g. inputs to operate). The claim language generally describes the model component, but the claim language does not appear to implement the model as part of a responsive action described in the instant amendment. The particular response is not tied to a particular model component, and it is suggested to further define the inter-relationship between the model and responsive actions.
executing, by the processing circuit, the batch data model to determine key performance indicators for the batch of products (0014 , 0016-0018, 0023-25, 0037-39, 0048-52 e.g. see generating batch model for prediction for batch control, the “conforming to operational standards” reading on performance indicators)
comparing, by the processing circuit, the key performance indicators to pre-determined key performance indicators (0014, 0016 e.g. see comparison of batch model prediction to performance indicators comprising standards)
in response to determining that the key performance indicators are outside bounds based on the pre-determined key performance indicators, updating operation of the industrial process by modifying the one or more process parameters that impact the batch of products generated in the industrial process based on a result of the comparison (0100 e.g. “In contrast, embodiments use both “in-spec” and “out-of-spec” batch data. Thus, embodiments are able to learn and predict end-product quality of a currently running batch. Based on comparisons and analytic data analyses on “out-of-spec” batch data, embodiments are able to further help plant operators to find out what are possible root-causes. For instance, if a batch process is predicted to fail, an embodiment can identify similar “out-of-spec” historical batches and from comparison between the current batch and historical batch to identify causes of the failure. Further, if a batch is predicted to fail, an embodiment can identify similar historical batches that yielded in-spec products and identify differences between the current batch, which is predicted to fail, and the historical batches that are similar, but ultimately produced desirable results. These differences can then be used to modify the current batch operation. For example, if it is determined that a previous similar batch, which was successful, operated at a temperature of 100° C. and the current batch, which is predicted to fail, is operating at 90° C., the current operating temperature of the current batch can be increased in an attempt to fix the ongoing process,” see also 0104 e.g. “n the other hand, embodiments are able to predict the outcome of a running batch, i.e. what is the probability the current batch will produce “in-spec” products and what is the probability the current batch will produce “out-of-spec” products. For batch monitoring, a prediction of an “in-spec” batch with high probability will gain great confidence for batch operators. Such a prediction indicates that currently the process is operating normally and the current batch is going to be a successful batch. When embodiments predict the current batch run is going to produce “out-of-spec” products with high probability, embodiments can perform a detailed follow-up analysis to assist batch operators with finding possible “root-causes” and provide recommended actionable advice. In such a way, embodiments enable batch operators to take actions early and correct subsequent batch operations in a timely manner to reduce the rate of “out-of-spec” products.”)
see also (0100 e.g. “In contrast, embodiments use both “in-spec” and “out-of-spec” batch data. Thus, embodiments are able to learn and predict end-product quality of a currently running batch. Based on comparisons and analytic data analyses on “out-of-spec” batch data, embodiments are able to further help plant operators to find out what are possible root-causes. For instance, if a batch process is predicted to fail, an embodiment can identify similar “out-of-spec” historical batches and from comparison between the current batch and historical batch to identify causes of the failure. Further, if a batch is predicted to fail, an embodiment can identify similar historical batches that yielded in-spec products and identify differences between the current batch, which is predicted to fail, and the historical batches that are similar, but ultimately produced desirable results. These differences can then be used to modify the current batch operation. For example, if it is determined that a previous similar batch, which was successful, operated at a temperature of 100° C. and the current batch, which is predicted to fail, is operating at 90° C., the current operating temperature of the current batch can be increased in an attempt to fix the ongoing process,” see also 0104 e.g. “n the other hand, embodiments are able to predict the outcome of a running batch, i.e. what is the probability the current batch will produce “in-spec” products and what is the probability the current batch will produce “out-of-spec” products. For batch monitoring, a prediction of an “in-spec” batch with high probability will gain great confidence for batch operators. Such a prediction indicates that currently the process is operating normally and the current batch is going to be a successful batch. When embodiments predict the current batch run is going to produce “out-of-spec” products with high probability, embodiments can perform a detailed follow-up analysis to assist batch operators with finding possible “root-causes” and provide recommended actionable advice. In such a way, embodiments enable batch operators to take actions early and correct subsequent batch operations in a timely manner to reduce the rate of “out-of-spec” products.”)
sending, by the processing circuit, an informed and prioritized notification to plant personnel regarding the comparison (0016, 0027, 0069, 0095, 0099-0100, 0104 e.g. see providing to batch operators at least alerts and recommendations as reading on informed and prioritized notification to plant personnel)
One of ordinary skill in the art before the effective filing date of claimed invention applying the teachings of Brill, namely graphically defining batch model relationships, to the teachings of Zhao, namely providing a predictive batch model, would achieve an expected and predictable result via integrating the hierarchical representations as part of a batch model, which adapted, performs both predictive functions while conveying functional relationships between batch processes. One of ordinary skill in the art would be motivated to combine model functions for a dual purpose (e.. absent the claim limitations further defining the purpose of the model components). Brill is in the same field of endeavor and reasonably pertinent to a problem of representing batch relationships.
Claim 2.
The method of claim 1, wherein the data describing the batch of products includes critical mass attributes, critical to quality attributes, and critical process parameters (e.g. see standard data as critical quality, supra claim 1, 0012, 0016-0018, 0023-25, 0037-39, 0048-53 e.g. see generating batch model for prediction for batch control)
Claim 3. The method of claim 1 but does not teach the node and edge limitations described below. Brill et al. teaches the node and edge limitations described below
wherein the batch data model is a graph data structure comprising:
a plurality of nodes representing entities associated with a batch (ABSTRACT, Figure 2 e.g. see elements as nodes)
a plurality of edges describing relationships between the plurality of nodes (ABSTRACT, Figure 2 e.g. see flow diagram as relationships)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Brill, namely generating a global model representation comprising data structures and including an in part an extensible model, to the teachings of Zhao, namely providing batch model, would achieve an expected and predictable result of representing the batch model of Zhao in a graph data structure format. Brill is in the same field of endeavor and pertinent to a problem of model representation as described, ABSTRACT, summary of invention.
Claim 4. The method of claim 1 but does not teach the extensible data model. Brill teaches the extensible data model described below, wherein the method further comprises:
receiving, by the processing circuit, an extensible data model describing an organizational structure of an enterprise associated with a batch (Brill, see receiving extensible model as creation of learning tree model, ABSTRACT, Figure 2)
extending, by the processing circuit, the extensible data model to include the batch data model (ABSTRACT, 0027 e.g. see creating a global model as reading on extending a model, and see the batch model of Zhao)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Brill, namely generating a global model representation comprising data structures and providing a function of creating or expanding the model, to the teachings of Zhao, namely providing batch model, would achieve an expected and predictable result of expanding the global model to include the batch model of Zhao for representing additional relationships for global control. Brill is in the same field of endeavor and pertinent to a problem of model representation as described, ABSTRACT, summary of invention.
Claim 5. The method of claim 1, wherein the key performance indicators comprise at least one of a cycle time value, a quality value, a raw materials value, an operator performance value, and a yield value ((e.g. see standard data as quality value, supra claim 1, 0012, 0016-0018, 0023-25, 0037-39, 0048-53 e.g. see generating batch model for prediction for batch control)
Claim 6. The method of claim 5, further comprising generating, by the processing circuit, a batch performance index based on a combination of the key performance indicators (0010 e.g. see generating one or more KPIs)
Claim 7. The method of claim 1, further comprising:
determining, by the processing circuit, a difference between the key performance indicators and the pre-determined key performance indicators (0016, 0069-0070, 0078, 0091-92, 0095)
comparing, by the processing circuit, the difference to a threshold (0016, 0069-0070, 0078, 0091-92, 0095)
in response to the difference being above the threshold, performing, by the processing circuit, a root cause analysis process to determine a root cause of the difference (0016, 0069-0070, 0078, 0091-92, 0095)
taking a corrective action to address the root cause of the difference (0016, 0069-0070, 0078, 0091-92, 0095)
Claim 8. The method of claim 1, wherein performing the automated action comprises sending, by the processing circuit, an informed and prioritized notification to plant personnel regarding the comparison ((0016, 0027, 0069, 0095, 0099-0100, 0104 e.g. see providing to batch operators at least alerts and recommendations as reading on informed and prioritized notification to plant personnel)
Claim 9. The method of claim 1 modifying the one or more process parameters comprises adjusting a critical process parameter, a critical to quality attribute, or a critical mass attribute (0016, 0069-0070, 0078, 0091-92, 0099, supra claim1
Claim 10. A non-transitory computer readable medium having computer-executable instructions embodied therein that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising,
receiving data describing a batch of products generated in an industrial process from one or more data sources; supra claim 1
contextualizing the data describing the batch of products generated in the industrial process;
generating a batch data model based on the contextualized data, data, wherein the batch data model is a graph based model that describes relationships between the data describing the batch, a phase, and one or more process parameters for the batch; supra claim 1
executing the batch data model to determine key performance indicators for the batch of products; supra claim 1
comparing the key performance indicators to pre-determined key performance indicators;
in response to determining that the key performance indicators are outside bounds based on the pre-determined key performance indicators, updating operation of the industrial process by modifying the one or more process parameters that impact the batch of products generated in the industrial process based on a result of the comparison; andsending an informed and prioritized notification to plant personnel regarding the comparison.; supra claim 1
sending an informed and prioritized notification to plant personnel regarding the comparison. supra claim 1
Claim 11. The non-transitory computer readable medium of claim 10, wherein the data describing the batch of products includes critical mass attributes, critical to quality attributes, and critical process parameters, supra claim 2
Claim 12. The non-transitory computer readable medium of claim 10, wherein the batch data model is a graph data structure comprising:
a plurality of nodes representing entities associated with a batch; supra claim 4
a plurality of edges describing relationships between the plurality of nodes, supra claim 4
Claim 12 is rejected under the same rationale and combination of prior art set forth in claim 4
Claim 13. The non-transitory computer readable medium of claim 10, the operations further comprising:
receiving an extensible data model describing an organizational structure of an enterprise associated with a batch; supra claim 4
extending the extensible data model to include the batch data model, supra claim 4
Claim 13 is rejected under the same rationale and combination of prior art set forth in claim 4
Claim 14. The non-transitory computer readable medium of claim 10, wherein the key performance indicators comprise at least one of a cycle time value, a quality value, a raw materials value, an operator performance value, and a yield value, supra claim 5
Claim 15. The non-transitory computer readable medium of claim 14, further comprising generating a batch performance index based on a combination of the key performance indicators, supra claim 6
Claim 16. The non-transitory computer readable medium of claim 10, the operations further comprising:
determining a difference between the key performance indicators and the pre-determined key performance indicators, supra claim 7
comparing the difference to a threshold; supra claim 7
in response to the difference being above the threshold, performing a root cause analysis process to determine a root cause of the difference; supra claim 7
taking a corrective action to address the root cause of the difference. supra claim 7
Claim 17. The non-transitory computer readable medium of claim 10, wherein performing the automated action comprises sending an informed and prioritized notification to plant personnel regarding the comparison, supra claim 9
Claim18. The non-transitory computer readable medium of claim 10, wherein modifying the one or more process parameters comprises adjusting a critical process parameter, a critical to quality attribute, or a critical mass attribute n, supra claim 18
Claim 19. A system for monitoring and controlling production of batches of products in an industrial process, the system comprising one or more memory devices configured to store instructions, that, when executed by one or more processors, cause the one or more processors to:
receiving data describing a batch of products generated in the industrial process from one or more data sources; supra claim 1
contextualizing the data describing the batch of products generated in the industrial process;
generating a batch data model based on the contextualized data, data, wherein the batch data model is a graph based model that describes relationships between the data describing the batch, a phase, and one or more process parameters for the batch; supra claim 1
executing the batch data model to determine key performance indicators for the batch of products; supra claim 1
comparing the key performance indicators to pre-determined key performance indicators;
in response to determining that the key performance indicators are outside bounds based on the pre-determined key performance indicators, updating operation of the industrial process by modifying the one or more process parameters that impact the batch of products generated in the industrial process based on a result of the comparison; and sending an informed and prioritized notification to plant personnel regarding the comparison.; supra claim 1
sending an informed and prioritized notification to plant personnel regarding the comparison. supra claim 1
Claim 20. The system of claim 19, wherein the batch data model is a graph data structure comprising:
a plurality of nodes representing entities associated with a batch; supra claim 3
a plurality of edges describing relationships between the plurality of nodes, supra claim 3
Claim 20 is rejected under the same rationale and combination of prior art set forth in claim 3
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Claim 1 relevancy
20090149981 20090143873 20220068440 (e.g. see root cause analysis as it relates to a batch process “The present disclosure generally relates to the field of model-based quality prediction of a chemical compound-and/or of a formulation thereof as the outcome of a production process comprising more than one sub-process. It further relates to a solution for root cause analysis of variations of one or more quality attributes of said product or formulation thereof.)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DARRIN D DUNN/Patent Examiner, Art Unit 2117
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