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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bertrand et al (WO 2013/093217).
Bertrand, in Figs. 1 and 5, discloses the same system as specified in claims 1-20 of the present invention, comprising a memory 3 configured to store a computer-executable instruction; and one or more processors 3 coupled to the memory, wherein the one or more processors are configured to receive operational data associated with a desalination plant I to produce a filtered liquid stream from a first liquid stream (e.g. p. 1, l. 10-19), wherein the operational data comprises a first set of parameters (e.g. p. 9, l. 20 – p. 10, l. 10 and 28-33) associated with the first liquid stream; determine a second set of parameters (e.g. p. 9, l. 20 – p. 10, l. 26) based on pre-processing of the first set of parameters; determine a third set of parameters (e.g. p. 10, l. 35-38) based on the first set of parameters and the second set of parameters, wherein the third set of parameters is a subset of the first set of parameters and the second set of parameters; provide, as an input, the third set of parameters to a Neural Network (NN) model, wherein the NN model is trained on a training dataset (e.g. p. 12, l. 6-9); estimate a Silt Density Index (SDI) data of the first liquid stream based on an output (e.g. p. 13, l. 1-2) of the NN model; and modify a coagulant dosing rate (e.g. p. 13, l. 9-10) in the first liquid stream based on the estimated SDI data.
With respect to claim 2, Bertrand also discloses a set of historical parameters (e.g. NN model measuring operational parameters) associated with one or more liquid streams (e.g. p. 1, l. 10-19) and a corresponding historical SDI data associated with each of the one or more liquid streams, and wherein the one or more processors 3 are further configured to train the NN model based on the set of historical parameters and the corresponding historical SDI data associated with each of the one or more liquid streams (e.g. desalination).
With respect to claims 3 and 13, Bertrand discloses re-training the NN model based on the third set of parameters (e.g. p. 10, l. 35-38), and the estimated SDI data of the first liquid stream (e.g. p. 9, l. 20 - p. 10, l. 10); and store the re-trained NN model in the memory 3.
With respect to claims 4 and 14, Bertrand discloses a first turbidity value (e.g. p. 9, l. 25) of the first liquid stream, a first Potential of Hydrogen (pH) value of the first liquid stream, a first SDI value of the first liquid stream, a first coagulant dosing rate of the first liquid stream, and a first flocculants dosing rate of the first liquid stream.
With respect to claim 5, Bertrand discloses a set of historical values (i.e. NN training data) associated with the one or more liquid streams, and wherein the set of historical values comprises at least of a historical turbidity value of the one or more liquid streams (e.g. p. 9, l. 25), a historical Potential of Hydrogen (pH) value of the one or more liquid streams, a historical SDI value of the one or more liquid streams, a historical coagulant dosing rate of the one or more liquid streams, and a historical flocculants dosing rate of the one or more liquid streams.
With respect to claims 6 and 15, Bertrand discloses at least: a first turbidity value of the first liquid stream (e.g. p. 9, l. 25), a first pH value of the first liquid stream, a first SDI value of the first liquid stream, a first coagulation dosing rate of the first liquid stream, a first flocculants dosing rate of the first liquid stream, a historical turbidity value of the one or more liquid streams, a historical pH value of the one or more liquid streams, a historical SDI value of the one or more liquid streams, a historical coagulation dosing rate of the one or more liquid streams, a historical flocculants dosing rate of the one or more liquid streams, an optimal turbidity value of the first liquid stream, an optimal pH value of the first liquid stream, an optimal SDI value of the first liquid stream, an optimal coagulation dosing rate of the first liquid stream, and an optimal flocculants dosing rate of the first liquid stream.
With respect to claim 7, Bertrand discloses a historical turbidity value of one or more liquid streams (e.g. p. 9, l. 25), a first turbidity value of the first liquid stream, a first pH of the first liquid stream, a historical pH value of the one or more liquid streams, a historical SDI value of the one or more liquid streams, a historical coagulant dosing rate
of the one or more liquid streams, and an optimal coagulant dosing rate of the first liquid stream.
With respect to claims 8 and 16, Bertrand discloses a sequential execution of a data cleaning operation, a data transformation operation, and a data validation operation on the first set of parameters (e.g. Fig. 1 NN training model).
With respect to claims 9 and 17, Bertrand discloses determining the third set of parameters based on an application of a correlation-based operation (e.g. p. 15, l. 3-24) on the first set of parameters and the second set of parameters.
With respect to claim 10 and 18, Bertrand discloses determining a relationship (e.g. p. 15, l. 3-24) between each parameter of the first set of parameters and a corresponding parameter of the second set of parameters based on the application of the correlation-based operation.
With respect to claims 11 and 19, Bertrand discloses determining a coefficient value associated with the determined relationship between each parameter of the first set of parameters and the corresponding parameter of the second set of parameters, wherein the determined coefficient value lies within a range of -1, and 1; determine the third set of parameters based on the determined coefficient value; and estimate, based on the output of the NN model, the SDI data of the first liquid stream based on the determined third set of parameters (e.g. salient solution up to ± 100%).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOUNG LEE whose telephone number is (571)272-7334. The examiner can normally be reached M - F, 11 - 7.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jay Patel can be reached at 571-272-2988. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Y LEE/Primary Examiner, Art Unit 2485
