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 .
Title
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Examiner believes that the title of the invention is imprecise. A descriptive title indicative of the invention will help in proper indexing, classifying, searching, etc. See MPEP 606.01. However, the title of the invention should be limited to 500 characters. Examiner suggests including the aspect(s) of the claims which Applicant believes to be novel or nonobvious over the prior art.
Drawings
The drawings are objected to because of the following informalities.
The view numbers must be larger than the numbers used for reference characters. See 37 C.F.R. 1.84(u)(2).
Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a first regressor unit is implemented to receive…and to output and a second regressor unit is implemented to receive…and to output in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Specification page 21 states that, “It is understood that the regressors may be implemented as software services, hardware units or distributed computer networks.”
Claim Objections
Claim 11 in line 4 should recite: providing, for a first and at least one second product (j=1...p), a plurality of np training data sets by adding a comma after “providing.”
Claim 11 in line 7 should begin a new line after the semicolon starting with training…
Claim 11 in line 11 should remove the number (10).
Claim 12 in line 4 should remove the number (9).
Claim 14 in line 4 should begin a new line after the semicolon and and/or starting with generating…
Claim 15 is objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim should refer to other claims in the alternative only. See MPEP § 608.01(n). Accordingly, the claim has not been further treated on the merits.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1-15 are 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 1:
Line 8: the regressor lacks clear antecedent basis
Line 13: said educt quality parameters (xi) lacks clear antecedent basis since the claim previously introduced educt quality parameters (x1, x2)
Claim 2:
Line 5: the second ANN lacks clear antecedent basis
Claim 3
Line 1: the first ANN lacks clear antecedent basis
Line 2: the trained second ANN lacks clear antecedent basis
Claim 4
Line 3: (Xi=1…4) is unclear, but appears it should recite (Xi=1,…,4) indicating a range
Line 4: (Ri=1…4) is unclear, but appears it should recite (Ri=1,…,4) indicating a range
Claim 5
Line 2: the ANNs lacks clear antecedent basis
Claim 9
Line 6: a regressor was previously introduced in claim 1. It is unclear if this is the same element, a different element, or related elements.
Line 8: the regressor unit lacks clear antecedent basis since claim 1 introduced at least two regressor units
Claim 10
Line 4: the regressor unit lacks clear antecedent basis since claim 1 introduced at least two regressor units
Claim 11
Line 2: the regressor units lacks clear antecedent basis since claim 1 introduced at least two regressor units. Elements should maintain the same terminology throughout the claims. Accordingly, if referring to the same elements, this should recite the at least two regressor units.
Line 4: (j=1…p) is unclear, but appears it should recite (j=1,…,p) indicating a range
Line 11: the regressor unit lacks clear antecedent basis
Lines 15 and 17: the residual lacks clear antecedent basis
Clarity and consistency of the claims are important to clearly convey the metes and bounds of their scope. Applicant is encouraged to review the claims for additional clarity issues.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-7, 9-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tian et al. (hereinafter Tian), Optimal control of a fed-batch bioreactor based upon an augmented recurrent neural network model.
Regarding Claim 1, Tian discloses a computer-implemented regressor for simulating, monitoring and/or controlling a plant [“Batch reactors are routinely used for manufacturing high value added chemicals such as specialty chemicals, pharmaceuticals, and bio-chemicals.” §1 ¶1], wherein the plant is implemented:
to receive one or more educts having associated educt quality parameters (x1, x2) [“The state variables in the above equations are the amount of secreted protein on a unit culture volume basis (PM), the total protein amount on a unit culture volume basis (PT), the culture cell density (X), the culture glucose concentration (S), and the culture volume (V). In the above equations, q is the feed flow rate which is used as the control variable, m is the glucose concentration of the feed stream, Y is the yield of glucose per cell mass and Φ, fp, and ϻx are the protein secretion rate, the protein expression rate and the specific growth rate of the host cell, respectively.” §2 ¶1; Tables 1-3],
to process said educt(s) wherein the process has associated process parameters (y) [“objective of the optimal control policy is to maximise the total secreted protein in the reactor at the end of the batch with the control policy satisfying the constraint” §2¶2], and
to output a product having associated product quality parameters (Q1, Q2) [“In optimal batch process control, the prime interest is the end-of-batch product quality which, given known initial reactor conditions, is determined by a trajectory of control actions applied during the batch.” §1 ¶5];
and the regressor comprises: at least two regressor units, each regressor unit comprising: an input for receiving input data, and an output for outputting output data [“an augmented recurrent neural network is developed. It contains two recurrent networks in series” §1 ¶6],
wherein:
a first regressor unit is implemented to receive said educt quality parameters (x1) and to output at least one educt impact parameter (R1) [“The first network predicts an important process state variable” §1 ¶6]; and
a second regressor unit is implemented to receive said educt impact parameter (R1) and said process parameters (yj) and to output at least one product quality parameter (Qj) [“while the second network predicts the product quality variable” §1 ¶6]; and
the first and the second regressor unit are based on machine-learning principles [“two recurrent networks in series” §1 ¶6].
Regarding Claim 2, Tian discloses the regressor of claim 1. Tian further discloses wherein the first and second regressor units are artificial neural networks ("ANN"), each including an input layer having input nodes for receiving input data, and an output layer having output nodes for outputting output data [“an augmented recurrent neural network is developed. It contains two recurrent networks in series” §1 ¶6],
wherein the second ANN is trained based on training data sets including process parameters and product target variables corresponding to a product quality parameter associated to a respective product [“The network has one hidden layer and was trained using the Levenberg–Marquart optimisation algorithm which was also applied to other networks in this paper.” §3.2 ¶1; “For each model structure shown in Table 1, a number of networks with different hidden neurons were trained and the one with the smallest error on the validation data was selected.” §3.2 ¶2; Table 1; Equations 1-8].
Regarding Claim 3, Tian discloses the regressor of claim 2. Tian further disclose wherein the first ANN is trained based on training data sets including the educt quality parameters and a residual of the trained second ANN as target variable for the educt impact parameter, both corresponding to the respective product [“The network has one hidden layer and was trained using the Levenberg–Marquart optimisation algorithm which was also applied to other networks in this paper.” §3.2 ¶1; “For each model structure shown in Table 1, a number of networks with different hidden neurons were trained and the one with the smallest error on the validation data was selected.” §3.2 ¶2; Table 1; Equations 1-8; “the training objective in the feed forward network is to minimise the one-step-ahead prediction errors while the training objective in the recurrent network is to minimise the multi-step-ahead prediction errors” §3.2 ¶1].
Regarding Claim 5, Tian discloses the regressor of claim 1. Tian further discloses wherein at least one of the ANNs is a feed forward ANN, a Baysian neural network and/or at least one of the ANNs further comprises hidden nodes [The network has one hidden layer and was trained using the Levenberg–Marquart optimisation algorithm which was also applied to other networks in this paper.” §3.2 ¶1].
Regarding Claim 6, Tian discloses the regressor of claim 1. Tian further discloses wherein said educt quality parameter comprises at least one of a viscosity value, a hydroxyl value, a concentration value, and a color parameter [“The state variables in the above equations are the amount of secreted protein on a unit culture volume basis (PM), the total protein amount on a unit culture volume basis (PT), the culture cell density (X), the culture glucose concentration (S), and the culture volume (V). In the above equations, q is the feed flow rate which is used as the control variable, m is the glucose concentration of the feed stream, Y is the yield of glucose per cell mass and Φ, fp, and ϻx are the protein secretion rate, the protein expression rate and the specific growth rate of the host cell, respectively.” §2 ¶1; Tables 1-3].
Regarding Claim 7, Tian discloses the regressor of claim 1. Tian further discloses wherein said process parameters (yj) comprise at least one of a measured observable, a temperature value, a maximum temperature value, a time span, a reaction time, a storage time of a catalyst, a number of free isocyanate (NCO) groups, and characteristics of a time series [“objective of the optimal control policy is to maximise the total secreted protein in the reactor at the end of the batch with the control policy satisfying the constraint” §2¶2].
Regarding Claim 9, Tian discloses a control device for controlling a plant, wherein the plant is implemented:
to receive one or more educts having associated educt quality parameters (xi) [“The state variables in the above equations are the amount of secreted protein on a unit culture volume basis (PM), the total protein amount on a unit culture volume basis (PT), the culture cell density (X), the culture glucose concentration (S), and the culture volume (V). In the above equations, q is the feed flow rate which is used as the control variable, m is the glucose concentration of the feed stream, Y is the yield of glucose per cell mass and Φ, fp, and ϻx are the protein secretion rate, the protein expression rate and the specific growth rate of the host cell, respectively.” §2 ¶1; Tables 1-3],
to process said educt(s) wherein the process has associated process parameters (yj) [“objective of the optimal control policy is to maximise the total secreted protein in the reactor at the end of the batch with the control policy satisfying the constraint” §2¶2], and to output a product having associated product quality parameters (Qj) [“In optimal batch process control, the prime interest is the end-of-batch product quality which, given known initial reactor conditions, is determined by a trajectory of control actions applied during the batch.” §1 ¶5];
wherein the control device comprises a regressor of claim 1 [see rejection of claim 1 above], wherein the control device is implemented to adapt the process as a function of the product quality parameter (Qj) output from the regressor unit in response to adapted process parameters [“The objective of the optimal control policy is to maximise the total secreted protein in the reactor at the end of the batch” §2 ¶2].
Regarding Claim 10, Tian discloses the control device of claim 9. Tian further discloses wherein the control device comprises a computer processing device implemented to perform operations implementing the regressor [“To achieve accurate long range predictions, an augmented recurrent neural network is developed. It contains two recurrent networks in series and utilises available process knowledge.” §1 ¶5; Examiner Note: A person having ordinary skill in the art understands that neurocomputing is implemented using a computing system.] and to carry out an optimization algorithm for adapting process parameters such that the product quality parameters (Qj) output from the regressor unit correspond to a predetermined product quality [“The objective of the optimal control policy is to maximise the total secreted protein in the reactor at the end of the batch” §2 ¶2].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tian in view of Rasti et al. (hereinafter Rasti), Breast Cancer Diagnosis in DCE-MRI using Mixture Ensemble of Convolutional Neural Networks.
Regarding Claim 4, Tian discloses the regressor of claim 1.
However, Tian fails to explicitly disclose comprising:
a plurality of first ANNs, wherein each ANN of the first ANNs corresponds to an educt and is implemented to receive corresponding educt quality parameters (Xi=1...4) and to output at least one corresponding educt impact parameter (Ri=1...4); and
a plurality of second ANNs, wherein each ANN of the second ANNs corresponds to a process for producing a product and is implemented to receive corresponding process parameters (Y=1...3) and a combination of educt impact parameters (R=1...4) from the first ANNs, and to output a corresponding product quality parameter (Q =1...3).
Rasti discloses comprising:
a plurality of first ANNs, wherein each ANN of the first ANNs corresponds to an educt and is implemented to receive corresponding educt quality parameters (Xi=1...4) and to output at least one corresponding educt impact parameter (Ri=1...4) [“a mixture ensemble of convolutional neural networks (or ME-CNN)” §3.4 ¶1; “Each expert could be specialized in one region of the high-dimensional input space” §3.4 ¶1; Fig. 4]; and
a plurality of second ANNs, wherein each ANN of the second ANNs corresponds to a process for producing a product and is implemented to receive corresponding process parameters (Y=1...3) and a combination of educt impact parameters (R=1...4) from the first ANNs, and to output a corresponding product quality parameter (Q =1...3) [“a mixture ensemble of convolutional neural networks (or ME-CNN)” §3.4 ¶1; “Each expert could be specialized in one region of the high-dimensional input space” §3.4 ¶1; “let O1, O2, ..., OL be the outputs of L convolutional experts” §3.4 ¶2; Fig. 4; Examiner Note: Using the O1…OL outputs of the first layer of experts as input to a second layer of experts].
It would have been obvious to one having ordinary skill in the art, having the teachings of Tian and Rasti before him before the effective filing date of the claimed invention, to modify the regressor utilizing the educts, processes, and products elements of Tian to incorporate mixture of neural network experts of Rasti.
Given the advantage of accurate predictions, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tian in view of Abdulaziz et al. (hereinafter Abdulaziz), U.S. Patent Application Publication 2024/0060773.
Regarding Claim 8, Tian discloses the regressor of claim 1.
However, Tian fails to explicitly disclose wherein said product quality parameters (Qj) comprise at least one of a viscosity value, a hardness value, a roughness value, drug interaction, a pH value, and a solubility.
Abdulaziz discloses wherein said product quality parameters (Qj) comprise at least one of a viscosity value, a hardness value, a roughness value, drug interaction, a pH value, and a solubility [“If the surface roughness is less than the threshold value, the product may pass the quality control test. If the surface roughness is greater than the threshold, the product may fail the quality control test.” §38].
It would have been obvious to one having ordinary skill in the art, having the teachings of Tian and Abdulaziz before him before the effective filing date of the claimed invention, to modify Tian to incorporate the roughness property of a product of Abdulaziz.
Given the advantage of having a desired product quality factored into the outcome in order to achieve the desired result, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim(s) 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tian in view of Ammar et al. (hereinafter Ammar), ANN for hybrid modelling of batch and fed-batch chemical reactors.
Regarding Claim 11, Tian discloses a method for training the regressor of claim 1. Tian further discloses wherein the regressor units are machine learning units, the method comprising:
providing for a first and at least one second product (j=1...p) [“in multi-product manufacturing” §1 ¶1], a plurality of np training data sets [“From the generated data, 30 batches were used as training data, five batches were used as validation data, and one batch was used as unseen testing data to further test the model.” §3 ¶1], each training data set including educt quality parameters (x) [“The state variables in the above equations are the amount of secreted protein on a unit culture volume basis (PM), the total protein amount on a unit culture volume basis (PT), the culture cell density (X), the culture glucose concentration (S), and the culture volume (V). In the above equations, q is the feed flow rate which is used as the control variable, m is the glucose concentration of the feed stream, Y is the yield of glucose per cell mass and Φ, fp, and ϻx are the protein secretion rate, the protein expression rate and the specific growth rate of the host cell, respectively.” §2 ¶1; Tables 1-3], process parameters (y) [“objective of the optimal control policy is to maximise the total secreted protein in the reactor at the end of the batch with the control policy satisfying the constraint” §2¶2] and a product target variable (Q) corresponding to a product quality parameter associated to the product (j) [“In optimal batch process control, the prime interest is the end-of-batch product quality which, given known initial reactor conditions, is determined by a trajectory of control actions applied during the batch.” §1 ¶5];
training the second regressor unit and/or a third regressor unit based on training data subsets including the process parameters (y) and the product target variables (Q) [“From the generated data, 30 batches were used as training data, five batches were used as validation data, and one batch was used as unseen testing data to further test the model.” §3 ¶1], corresponding to a first product (j=1), thereby obtaining a first residual (R) for each training data subset [“the training objective in the feed forward network is to minimise the one-step-ahead prediction errors while the training objective in the recurrent network is to minimise the multi-step-ahead prediction errors” §3.2 ¶1];
training the regressor unit (10) based on training data subsets including the process parameters (y) and the product target variables (Q) [“From the generated data, 30 batches were used as training data, five batches were used as validation data, and one batch was used as unseen testing data to further test the model.” §3 ¶1], corresponding to at least one further product (j≠1), thereby obtaining a second residual (R) for each training data subset [“the training objective in the feed forward network is to minimise the one-step-ahead prediction errors while the training objective in the recurrent network is to minimise the multi-step-ahead prediction errors” §3.2 ¶1]; and
training the first regressor unit based on training data subsets including the educt quality parameters (x) [“From the generated data, 30 batches were used as training data, five batches were used as validation data, and one batch was used as unseen testing data to further test the model.” §3 ¶1] and the residual (R) as target variable for the educt impact parameter [“the training objective in the feed forward network is to minimise the one-step-ahead prediction errors while the training objective in the recurrent network is to minimise the multi-step-ahead prediction errors” §3.2 ¶1], both corresponding to the first product (j=1), and on further training data subsets including the educt quality parameters (x) [“From the generated data, 30 batches were used as training data, five batches were used as validation data, and one batch was used as unseen testing data to further test the model.” §3 ¶1] and the residual (R) as target variable for the educt impact parameter [“the training objective in the feed forward network is to minimise the one-step-ahead prediction errors while the training objective in the recurrent network is to minimise the multi-step-ahead prediction errors” §3.2 ¶1], both corresponding to the at least one further product (j≠1).
However, Tian fails to explicitly disclose corresponding to a first product (j=1),
corresponding to at least one further product (j≠1),
both corresponding to the first product (j=1),
both corresponding to the at least one further product (j≠1).
Ammar discloses corresponding to a first product (j=1),
corresponding to at least one further product (j≠1),
both corresponding to the first product (j=1),
both corresponding to the at least one further product (j≠1) [“different types of approach using the concentrations of reagents and products” §4.1 ¶1; “determine the four outputs that are the concentrations of acetic acid, methanol, methyl acetate and water” §4.1 ¶2; Fig. 2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Tian and Ammar before him before the effective filing date of the claimed invention, to modify the regressor of Tian to incorporate the use for various products of Ammar.
Given the advantage of increased utility across different products, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 12, Tian and Ammar disclose the method of claim 11. Tian further discloses for each product (j), training the second regressor unit based on training data subsets including the process parameters (y), the educt impact parameter output from the trained first ANN (9) in response to the educt quality parameters (x) associated to the educt used for producing the respective product, and the product target variable (Q) corresponding to the product quality parameter associated to the respective product [“The network has one hidden layer and was trained using the Levenberg–Marquart optimisation algorithm which was also applied to other networks in this paper.” §3.2 ¶1; “For each model structure shown in Table 1, a number of networks with different hidden neurons were trained and the one with the smallest error on the validation data was selected.” §3.2 ¶2; Table 1; Equations 1-8; “the training objective in the feed forward network is to minimise the one-step-ahead prediction errors while the training objective in the recurrent network is to minimise the multi-step-ahead prediction errors” §3.2 ¶1].
However, Tian fails to explicitly disclose for each product (j).
Ammar discloses for each product (j) [“different types of approach using the concentrations of reagents and products” §4.1 ¶1; “determine the four outputs that are the concentrations of acetic acid, methanol, methyl acetate and water” §4.1 ¶2; Fig. 2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Tian and Ammar before him before the effective filing date of the claimed invention, to modify the combination to incorporate the use for various products of Ammar.
Given the advantage of increased utility across different products, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 13, Tian and Ammar disclose the method of claim 12. Tian further discloses wherein the step of training the first regressor unit and the step of training the second regressor unit are repeatedly carried out [“The network has one hidden layer and was trained using the Levenberg–Marquart optimisation algorithm which was also applied to other networks in this paper.” §3.2 ¶1; “For each model structure shown in Table 1, a number of networks with different hidden neurons were trained and the one with the smallest error on the validation data was selected.” §3.2 ¶2; Table 1; Equations 1-8; “the training objective in the feed forward network is to minimise the one-step-ahead prediction errors while the training objective in the recurrent network is to minimise the multi-step-ahead prediction errors” §3.2 ¶1].
Regarding Claim 14, Tian and Ammar disclose the method of claim 11. Tian further discloses generating said training data sets by operating the plant and measuring process parameters and product quality parameters; and/or generating said training data sets deploying a whitebox-numerical model for simulating a plant process based on educt quality parameters and generating process parameters, and product quality parameters [“data from 36 batch runs were generated from the process simulation (treated as real process runs in this study). The data contain simulated “measurements” of PM; S; q, and V. Since it is generally difficult to on-line measure bio-material concentration, it is assumed in this study that measurements of q and V are obtained on-line while the measurements of PM and S are obtained off-line. This reflects the current industrial practice where many bio-product quality variables are still measured off-line through laboratory analysis. The data were generated by adding random perturbations to the monitored nominal control policies (q). These nominal control policies can be those used by different process operators. In this study, we take the control policy reported in [12] as the baseline policy and add on random variations to reflect the different skill levels of different process operators. Normally distributed random noises with zero means are added to all the “measurements” to simulate the effects of measurement noises. The standard deviations of the noises for q, PM, S, and V are 0:02 L=h, 0:01; 0:02 g=L, and 0:05 L, respectively. The sampling time is 6 min and the final time is fixed at 15 h as in [12,22]. From the generated data, 30 batches were used as training data, five batches were used as validation data, and one batch was used as unseen testing data to further test the model.” §3 ¶1].
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Conclusion
Any prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c). Additionally when amending, in their remarks Applicant should particularly cite to the supporting paragraphs in the original disclosure for the amendments.
The following references were found during the examination of this patent application and were found to be relevant to patentability. Applicant is advised to review these references prior to responding to this Office action.
Mujtaba et al. (NEURAL NETWORK BASED MODELLING AND CONTROL IN BATCH REACTOR) disclose control strategies are developed and implemented in batch reactors using NN techniques.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T. Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
Prosecution Insights