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 .
Remarks
This office action fully acknowledges Applicant’s remarks and amendments submitted 16 February 2026.
Claims 1-2, 4, 7-15, and 18-21 are pending.
Claims 3, 5-6, and 16-17 are cancelled.
Claim 15 is withdrawn.
Claims 20 and 21 are newly added.
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:
one or more models (prong I: the generic placeholder)...for...obtaining measurement results...generating bioprocessing parameters...and controlling the bioprocess (prong II: the functional language and transition word)...as 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.
Prong III: ***No corresponding algorithm(s) for achieving the functions discussed above is found within Applicant’s disclosure – see the 35 USC 112 section below.***
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.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 4, 7-14, and 18-21 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 is reliant on particular software for accomplishing the functionality of “obtaining measurement results...generating bioprocessing parameters…and controlling the bioprocess...” through the training of the trained models. However, the written description fails to disclose the algorithm(s) for performing the claimed specific computer functions of “obtaining measurement results...generating bioprocessing parameters...and controlling the bioprocess...” as in Claim 1.
See MPEP 2181(II)(B): For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b). -- See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367, 88 USPQ2d 1751, 1757 (Fed. Cir. 2008).
See also In re Aoyama, 656 F.3d 1293, 1297, 99 USPQ2d 1936, 1939 (Fed. Cir. 2011) (“[W]hen the disclosed structure is a computer programmed to carry out an algorithm, ‘the disclosed structure is not the general-purpose computer, but rather that special purpose computer programmed to perform the disclosed algorithm.’”) (quoting WMS Gaming, Inc. v. Int’l Game Tech., 184 F.3d 1339, 1349, 51 USPQ2d 1385, 1391 (Fed. Cir. 1999)). In cases involving a special purpose computer-implemented means-plus-function limitation, the Federal Circuit has consistently required that the structure be more than simply a general-purpose computer or microprocessor and that the specification must disclose an algorithm for performing the claimed function.
See, e.g., Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239. For a computer-implemented means-plus-function claim limitation invoking 35 U.S.C. 112(f) the Federal Circuit has stated that “a microprocessor can serve as structure for a computer implemented function only where the claimed function is ‘coextensive’ with a microprocessor itself.”
See EON Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 622, 114 USPQ2d 1711, 1714 (Fed. Cir. 2015), citing In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316, 97 USPQ2d 1737, 1747 (Fed. Cir. 2011). “‘It is only in the rare circumstances where any general-purpose computer without any special programming can perform the function that an algorithm need not be disclosed.’” EON Corp., 785 F.3d at 621, 114 USPQ2 at 1714, quoting Ergo Licensing, LLC v. CareFusion 303, Inc., 673 F.3d 1361, 1365, 102 USPQ2d 1122, 1125 (Fed. Cir. 2012). “‘[S]pecial programming’ includes any functionality that is not ‘coextensive’ with a microprocessor or general-purpose computer.”
EON Corp., 785 F.3d at 623, 114 USPQ2d at 1715 (citations omitted). “Examples of such coextensive functions are ‘receiving’ data, ‘storing’ data, and ‘processing’ data—the only three functions on which the Katz court vacated the district court’s decision and remanded for the district court to determine whether disclosure of a microprocessor was sufficient.” 785 F.3d at 622, 114 USPQ2d at 1714. Thus, “[a] microprocessor or general-purpose computer lends sufficient structure only to basic functions of a microprocessor. All other computer implemented functions require disclosure of an algorithm.” Id., 114 USPQ2d at 1714.
To claim a means for performing a specific computer-implemented function and then to disclose only a general-purpose computer as the structure designed to perform that function amounts to pure functional claiming. Aristocrat, 521 F.3d 1328 at 1333, 86 USPQ2d at 1239. In this instance, the structure corresponding to a 35 U.S.C. 112(f) claim limitation for a computer-implemented function must include the algorithm needed to transform the general purpose computer or microprocessor disclosed in the specification. Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239; Finisar Corp. v. DirecTV Group, Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008); WMS Gaming, Inc. v. Int’l Game Tech., 184 F.3d 1339, 1349, 51 USPQ2d 1385, 1391 (Fed. Cir. 1999); Rain Computing, Inc. v. Samsung Electronics America Co., 989 F.3d 1002, 1007-8, 2021 USPQ2d 284 (Fed. Cir. 2021).
The corresponding structure is not simply a general-purpose computer by itself but the special purpose computer as programmed to perform the disclosed algorithm. Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239. Thus, the specification must sufficiently disclose an algorithm to transform a general-purpose microprocessor to the special purpose computer. See Aristocrat, 521 F.3d at 1338, 86 USPQ2d at 1241. (“Aristocrat was not required to produce a listing of source code or a highly detailed description of the algorithm to be used to achieve the claimed functions in order to satisfy 35 U.S.C. §112 ¶ 6. It was required, however, to at least disclose the algorithm that transforms the general-purpose microprocessor to a ‘special purpose computer programmed to perform the disclosed algorithm.’” (quoting WMS Gaming, 184 F.3d at 1349, 51 USPQ2d at 1391.))
An algorithm is defined, for example, as “a finite sequence of steps for solving a logical or mathematical problem or performing a task.” Microsoft Computer Dictionary, Microsoft Press, 5th edition, 2002. Applicant may express the algorithm in any understandable terms including as a mathematical formula, in prose, in a flow char t, or “in any other manner that provides sufficient structure.”
Finisar, 523 F.3d at 1340, 86 USPQ2d at 1623; see also Intel Corp. v. VIA Techs., Inc., 319 F.3d 1357, 1366, 65 USPQ2d 1934, 1941 (Fed. Cir. 2003); In re Dossel, 115 F.3d 942, 946-47, 42 USPQ2d 1881, 1885 (Fed. Cir.1997); Typhoon Touch Inc. v. Dell Inc., 659 F.3d 1376, 1385, 100 USPQ2d 1690, 1697 (Fed. Cir. 2011); In re Aoyama, 656 F.3d at 1306, 99 USPQ2d at 1945.
However, no specific algorithm(s) is/are defined and the functions therein Claim 1 go beyond a general-purpose computer and are not coextensive with the computer as defined in the MPEP passages cited above.
Therefore, the claim(s) is/are indefinite and rejected under 35 USC 112b/2nd. Applicant may provide a citation to the literature which introduces such software Applicant was in possession of coincident with the above discussion. Discussion with respect to the software/algorithms as in Claim 1 is not found within the disclosure.
Claims 1-2, 4, 7-14, and 18-21 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.
Claims 1-2, 4, 7-14, and 18-21 require “using” particular data elements for training the models and controlling the bioprocess. MPEP 2173.05(q) states: “It is appropriate to reject a claim that recites a use but fails to recite steps under 35 U.S.C. 101 and 35 U.S.C. 112(b) if the facts support both rejections.” Therein, Applicant’s instant claims fail to specify what constitutes the use of the measurement results and first/second bioprocessing parameters as in Claim 1, the training data set of Claim 4, the N parts of Claim 5, the bioprocessing system characteristics of Claim 7, and the smaller scale bioreactors of Claim 12. Thus, the claims are indefinite.
This is similarly seen with “cell survival time” wherein the claims indefinitely provide for relation/correlation to “cell survival time” and the “bioprocess,” which is presented as a categorical descriptor without its constituent, actively provided steps and one(s) that pertain to “cell survival time.” Further to this, the optimization of the “cell survival time” is indefinitely defined as it is without criterion/ia thereto and wherein “cell survival time” itself is indefinitely defined by way of Applicant’s disclosure. Examiner notes that there is generic discussion to this in paragraph [0080 of Applicant’s pregrant publication US 2022/0259532, but the disclosure does not provide to clearly defined such “cell survival time” nor its optimization.
Claim 1 now recites “averaging each part to generate N average values” wherein it is unclear as to how each part is averaged as a single part not having multiple values to be averaged. Does Applicant intend that all of the N parts are averaged together?
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 4, 7-14, and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention individually and as an ordered combination is directed to an abstract idea of utilizing spectral data to control a bioprocess without significantly more.
Step 2A, Prong 1: The claims individually and as an ordered combination recite the abstract idea of obtaining measurements via spectroscopy, converting said measurements into bioprocessing parameters wherein the recited steps may be accomplished through mental steps.
Generating bioprocessing parameters using the measurement results, one or more bioprocessing target parameters and one or more trained models are steps that may be achieved mentally by a technician using their personal experience in assessing the spectral data against a desired result to provide parameters thereto. Additionally, the recited one or more trained models are highly generalized and without particular algorithm(s) that may be constituted by mental judgments and qualitative assessments by the technician. See also MPEP 2106 III, B,C.
Step 2A, Prong 2: This judicial exemption is not integrated into a practical application because although the claim recites “obtaining measurement results by performing spectroscopy…” and “training one or more models…a first model…and a second model…” that amounts to mere data gathering that is insignificant extra solution activity (see also MPEP 2106.05(g)).
Further, with respect to Step 2B, the claim further recites “controlling the bioprocess using the generated bioprocessing parameters…” this step is highly generalized (and as in the controller of cl. 14), as well as with the bioprocessing parameters themselves, and does not amount to significantly more than the abstract idea as it is not a particular application, is recited at a high level of generality, and is ubiquitous across process control and not unique to a bioprocess. See also MPEP 2106.05(f), MPEP 2106.05(d).
Further, “a bioprocess comprised in a bioreactor”, does not provided a recitation to a particular machine as in MPEP 2106.05(B)(I). The “bioreactor” is highly generalized and absent any particular, constituent structural architecture, functionalities, and particular connection and usage with the abstract idea at-hand.
In addition, it is noted that a bioreactor itself is not a particular machine, and the bioreactor is not positively provided by the claims as it is merely inferentially related to the data gathering as a site at which spectroscopy took place, and such site is not a positive element the of the method.
Therein, examples of applying a judicial exception with a particular machine include Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86,40 USPQ 199 (1939) in which a mathematical formula was employed to use standing wave phenomena in an antenna system, and the claim recited the particular type of antenna and included details as to the shape of the antenna and the conductors, particularly the length and angle at which they were arranged.
This is likewise seen in Efbel Process Co. v. Minn & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) in which gravity (a law of nature or natural phenomenon) was applied by a Fourdrinier machine arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web.
At present, the “bioprocess comprised in a bioreactor” amounts to a general listing of known architecture in the art at a high level of generality wherein such claimed architecture is generally applicable to bioprocessing applications and does not have any particular, distinct or special correlation to the abstract idea at-hand.
Additionally, regarding dependent Claims 2-13, the recitations therein further fail to integrate the abstract idea into a provide for a practical application and do not amount to significantly more. While the claims do provide for specific characteristics regarding the obtaining measurements by performing spectroscopy (identified as NIR and analyzing different parts of the spectrum as in Claims 4-5), generating of bioprocessing parameters (listed in Claims 2-3 and 6), and control over the bioprocess (identified as cell cultivation in claim 8 and described in Claims 7 and 9-11), these recitations amount to no more than intended usages and fields of application that do not amount to significantly more than the abstract idea.
Further, regarding the Claim 1 amendment requiring training one or more models, this is seen as insignificant extra-solution activity of mere data gathering and processing; the recitation to the “training one or more models for generating…” is generalized and absent any particular, active step(s) carried out for the “training, as well, and relatedly the “one or more models” are also generalized, without any particular function(s)/equation(s) found with respect to the abstract idea at-hand. The recitation remains drawn to insignificant extra-solution activity that speaks to data gathering and utilizing known mathematical, statistical analysis approaches with data.
Step 2B: The claims do not provide to recite significantly more than the judicial exception itself and do not provide an “inventive concept”.
Additionally, regarding independent Claims 14 and 18, all of the above constitutions with Step 2A, Prongs 1 and 2, and Step 2B likewise apply.
Regarding independent Claim 14, and as in Step 2A Prong 2, the recited controller as processing circuitry and a memory does not provide a recitation to a particular machine as in MPEP 2106.05(B)(I). This amounts to a mere computer processor/memory arrangement that is highly generalized and absent any particular, constituent structural architecture and functionalities as it relates to actively affecting element(s) of a bioreactor in carrying out an actual, automated bioprocessing methodology.
The controller in this Claim 14 is thus recited on a highly general level (the control circuitry is defined merely as generic processing circuitry and a memory) such that it amounts to no more than mere instructions to apply the abstract idea implemented using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 18, and as in Step 2A Prong 2, the recited non-transitory computer readable storage medium does not provide a recitation to a particular machine as in MPEP 2106.05(B)(I). This amounts to a mere implementation of the abstract idea as a computer program. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, and akin to the above, with regard to Claims 14 and 18, the additional elements amount to well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d) (i.e. items ii. Performing repetitive calculations, exemplified in Flook and Bancorp Services v. Sun Life; iv. Storing and retrieving information in memory, exemplified in Versata Dev. Group, Inc. v. SAP Am., Inc., and OIP Techs. See also Smartgene, Inc. v Advanced Biological Labs (2014).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 7-11, 14, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Webster et al. (US 2019/0137338 A1), hereinafter “Webster”, in view of West (US 2015/0010990 A1), hereinafter “West”, Franco et al. (Vanina G. Franco, et al., “Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection”, Talanta, Volume 68, Issue 3, 2006, Pages 1005-1012, ISSN 0039-9140.), hereinafter “Franco”, and Wojsznis et al. (US 2005/0149209 A1), hereinafter “Wojsznis”.
Regarding Claim 1, Webster teaches a computer implemented method performed by a controller configured to control a bioprocess, the controller comprised in a bioreactor; the method comprising:
obtaining measurement results by performing spectroscopy of a bioprocessing fluid comprised in the bioreactor ([0007]: “The use of Raman spectroscopy in accordance with the present disclosure allows for the periodic or continuous monitoring of one or more parameters in a bioprocess”),
training one or more models for generating and/or predicting bioprocessing parameters, wherein the training comprises: obtaining a measurement result of a spectrum ([0095]: “The predictive model can be created using a design of experiments approach that contains concentrations of desired parameters and associated Raman spectra that covers as much of the process as possible…Once the calibration and predictive models from Raman spectra are developed, they can be used for process monitoring and feedback control within the bioreactor.” – See also [0011]: “…the controller can include a predictive model that extrapolates a future concentration of the parameter based on the determined concentration of the parameter and can selectively increase or decrease at least one parameter influencing substance in order to maintain the parameter within preset limits [matching reference data] based on the calculated future concentration.”),
normalizing the spectrum using Standard Normal Variate (SNV) techniques ([0089]: “standard normal variate can be applied to the Raman spectra in order to remove scattering effects”),
wherein the neural network is adapted by iterating using reference data such that bioprocessing target parameter outputs produced by the neural network substantially matches the reference data are trained to match corresponding values of bioprocessing parameters of the reference data ([0011]: “In one embodiment, for instance, the concentration of the parameter is determined by comparing the light intensity data to reference data contained within the controller. In one embodiment, the controller can include a predictive model that extrapolates a future concentration of the parameter based on the determined concentration of the parameter and can selectively increase or decrease at least one parameter influencing substance in order to maintain the parameter within preset limits based on the calculated future concentration.”),
the one or more models comprising:
a first model trained for a first set of bioprocessing target parameters that comprise target values of a selection of any of product concentration and viable cell density ([0010]: “The parameter measured according to the process can comprise... total cell concentration, product concentration, or mixtures thereof.”); and
a second model trained for a second set of bioprocessing target parameters that comprise cell survival time ([0101]: “...viability can be measured by dividing the viable cell count with the total cell count, which are two parameters that can both be measured using the Raman spectra.”);
generating first bioprocessing parameters using the measurement results ([0009]: “An intensity of the scattered light is measured using Raman spectroscopy. A concentration of at least one parameter in the cell culture is determined based upon the measured intensity of light…for instance, the concentration is determined by a controller.”),
the first set of bioprocessing target parameters ([0009]: “preset limits”),
and the first model of the one or more trained models ([0007]: “…the control system can include a predictive model that extrapolates parameter concentrations in the future for maintaining the bioprocess environment within carefully controlled limits.”),
controlling the bioprocess using the generated first bioprocessing parameters from the first model ([0009]: “Based on the determined concentration of the parameter, the controller can then selectively increase or decrease flow of a parameter influencing substance to the bioreactor in order to maintain the parameter within preset limits.”),
using one or more sensors of the bioprocessing system to collect measurements of the bioprocess ([0099]: “the controller can... receive information from various sensors and probes.”),
as in Claim 1.
Further regarding Claim 1, Webster does not specifically teach the method discussed above comprising detecting, based on the sensor measurements, a malfunction in the bioprocessing system, and, responsive to the detecting of the malfunction: generating second bioprocessing parameters using the measurement results, the second set of bioprocessing target parameters, and further controlling the bioprocess using the generated second bioprocessing parameter to optimize the cell survival time, as in Claim 1.
However, West teaches a respective integrated bioreactor monitor and control system comprising parameters monitored by sensors communicating with the control system, wherein in response to a parameter being not in a predetermined range (the alarm information), the controller performs actions to attempt to restore the parameter within range ([0078]: “If the first data signal is not within the predetermined range of values, in step 920 the control system 221 can determine an action for the sensor system 180 to perform. For example, the control system 221 can determine that the first data signal indicates an error condition exists in the sensor system 180, and that the sensor system 180 should perform an action (e.g...provide an additional data signal).”) so as to control the bioprocess conditions for promoting sustaining cell viability ([0005, 0058, 0079]).
Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to modify the method of Webster comprising detecting alarm information of the bioprocessing system, and, responsive to the alarm information: generating second bioprocessing parameters using the measurement results, the second set of bioprocessing target parameters, and second model of the one or more models, and further controlling the bioprocess using the generated second bioprocessing parameter to optimize the cell survival time, such as suggested by West, so as to maximize cell viability and reduce costs associated with unchecked cell death.
Further regarding Claim 1, Webster does not specifically teach the method discussed above further comprising splitting the spectrum into a number N parts, averaging each part to generate N average values, determining, from the N average values, one or more feature inputs to a neural network, and providing the one or more feature inputs to the neural network, as in Claim 1.
However, Franco teaches a respective method for monitoring and controlling a kombucha bioprocess comprising splitting the spectrum into a number N parts (Section 2.6: “Select the sensor ranges included in the histogram a certain percentage of times above a threshold, for example, 70%.”), averaging each part to generate N average values (Section 2.6: “Repeat steps (1) through (3), reinitializing the GA with the sensors obtained in step (2) and setting the maximum number of factors as obtained in step (3). Continue until the sensors selected in step (2) stabilize.”), determining, from the N average values, one or more feature inputs to a neural network, and providing the one or more feature inputs to the neural network (Section 2.6: “Use the wavelengths selected in step (2) for PLS/ANNs model building and prediction.”). Therein, Franco describes the benefits of this approach as enhancing the predictive ability over the bioprocess (Section 1).
Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to modify the method of Webster further comprising splitting the spectrum into a number N parts, averaging each part to generate N average values, determining, from the N average values, one or more feature inputs to a neural network, and providing the one or more feature inputs to the neural network, such as suggested by Franco, so as to enhance the predictive ability over the bioprocess.
Further regarding Claim 1, Webster does not specifically teach the method discussed above comprising, in response to detecting the malfunction, selecting the second model instead of the first model for controlling the bioprocess, as in Claim 1.
However, Wojsznis teaches a respective process controller for a reactor system wherein the system switches between separate and distinct trained models in response to pre-defined switching rules ([0059]: “As will be understood from U.S. Pat. No. 6,577,908, a set of models for the SISO model being adapted is established including a plurality of model subsets which may be automatically selected by any desired pre-defined switching rule. Each of the individual models may include a plurality of parameters, each parameter having a respective value that is selected from a set of predetermined initialization values corresponding to the parameter.”) so as to ensure accurate modeling of the process under different conditions and thereby minimize losses ([0003-0010]).
Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to modify the method of Webster further comprising, in response to detecting the malfunction, selecting the second model instead of the first model for controlling the bioprocess, such as suggested by Wojsznis, so as to ensure accurate modeling of the process under different conditions and thereby minimize losses.
Regarding Claim 2, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster teaches the method discussed above wherein:
the generated first bioprocessing parameters comprise bioprocessing variables and/or bioprocessing system control parameters indicative of a selection of any of glucose concentration, lactose concentration, ammonia concentration, glutamine concentration, glutamate concentration, product concentration and viable cell density of the bioprocessing fluid ([0087]: “Examples of nutrients that can be monitored include, for instance, glucose, glutamine and/or glutamate.”);
one or more target flow of one or more additive gases ([0099]: “The controller may also use a carbon dioxide gas supply to decrease pH.”);
one or more target flow of one or more additive fluids and controller parameters ([0018]: “The controller can be further configured to control the nutrient media feed based upon the determined parameter concentrations.”),
as in Claim 2.
Regarding Claim 7, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster teaches the method discussed above wherein the bioprocess is further controlled using bioprocessing system characteristics ([0099]: “For instance, the controller may control and/or monitor…the agitation conditions…” – See also [0083] of Applicant’s instant pre-grant publication US 2022/0259532 A1, which defines agitation conditions as a bioprocessing system characteristic.), as in Claim 7.\
Regarding Claim 8, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster teaches the method discussed above wherein the bioprocess comprises cell cultivation ([0007]: “…the processing system of the present disclosure is directed to propagating mammalian cell cultures.”), as in Claim 8.
Regarding Claim 9, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster teaches the method discussed above wherein:
the first set of bioprocessing target parameters are indicative of a desired product concentration and/or a desired viable cell density ([0010]: “The parameter measured according to the process can comprise, for instance…viable cell concentration…product concentration, or mixtures thereof.” – [0009]: “Based on the determined concentration of the parameter, the controller can then selectively increase or decrease flow of a parameter influencing substance to the bioreactor in order to maintain the parameter within preset limits.”),
and the generated bioprocessing parameters comprises bioprocessing system control parameters to obtain the desired product concentration and/or viable cell density when controlling the bioprocess ([0099]: “In addition to monitoring one or more parameters through Raman spectroscopy, the controller can control various other process conditions… For instance, the controller may control and/or monitor the pH, the oxygen tension, dissolved carbon dioxide, the temperature, the agitation conditions, the alkali condition, the pressure, foam levels, and the like.),
as in Claim 9.
Regarding Claim 10, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster teaches the method discussed above wherein controlling the bioprocess comprises controlling a flow of one or more additive fluids ([0018]: “The controller can be further configured to control the nutrient media feed based upon the determined parameter concentrations.”), as in Claim 10.
Regarding Claim 11, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster teaches the method discussed above wherein controlling the bioprocess comprises controlling a flow of one or more additive gases ([0099]: “The controller may also use a carbon dioxide gas supply to decrease pH.”), as in Claim 11.
Regarding Claim 14, Webster teaches a controller, the controller comprising:
processing circuitry; and a memory, said memory containing instructions executable by said processor ([0091]: “The controller 60 may comprise one or more programmable devices or microprocessors…The controller can be configured to increase or decrease the flow of materials and substances into the bioreactor 10 based upon the concentration of one or more parameters. For example, the controller 60 can analyze signals received from the Raman spectrometer 54 and generate output signals capable of controlling one or more input and/or output devices.” – Thus, the controller must inherently possess a memory containing instructions for carrying out the method discussed in para. [0091].),
whereby said controller is operative to perform the method steps according to Claim 1 ([0010]: “The controller can be configured to receive all of the concentration data and control one or more parameter influencing substances.”), as in Claim 14.
Regarding Claim 18, Webster teaches a non-transitory computer-readable storage medium, comprising computer- executable instructions for causing a controller, when the computer-executable instructions are executed on processing circuitry comprised in the controller, to perform any of the method steps according claim 1 ([0091]: “The controller 60 may comprise one or more programmable devices or microprocessors.” – [0010]: “The controller can be configured to receive all of the concentration data and control one or more parameter influencing substances.” – See also paras. [0018-0020] and [0091-0094].), as in Claim 18.
Regarding Claim 19, Webster teaches the method discussed above further comprising expanding a reference data set for bioreactors having different volumes (The limitation “expanding a reference data set” is defined by paras. [0036-0037] of Applicant’s instant pre-grant publication as “increasing or decreasing the concentration or concentration of various components in the bioprocessing fluid” and “systematically varying the unwanted bioprocessing variables in a systematic manner”. – Webster teaches this in para. [0095]: “Further improvements can be obtained by spiking in parameters at varying concentrations and measuring the resulting spectra. In addition, further improvements in predictive models can be obtained by forcibly breaking the correlations that may be present.” Further, as Webster teaches bioreactors of varying size ([0065]), it is implied that said expanding of the reference data set can be done for bioreactors having different volumes.), as in Claim 19.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Webster in view of West, Franco, and Wojsznis, as applied to Claims 1-2, 7-11, 14, and 18-19 above, and in further view of Berry et al. (US 2017/0130186 A1), referred to hereinafter as “Berry”.
Regarding Claim 4, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster/West does not specifically teach the method discussed above wherein the one or more trained models are generated by training machine learning models for each of the bioprocessing target parameters using a training data set, wherein the training data set comprises measurement results obtained by performing NIR spectroscopy of the bioprocessing fluid associated with corresponding values of bioprocessing parameters, as in Claim 4.
However, Berry teaches a respective trained model for a bioreactor wherein the trained model is generated by training machine learning models for each of the bioprocessing target parameters using a training data set ([0062]: “…prediction models that are to be used herein for evaluating culture components are developed using a training data set based on one or more informative subsets or an entire Raman spectra…”),
wherein the training data set comprises measurement results obtained by performing NIR spectroscopy of the bioprocessing fluid associated with corresponding values of bioprocessing parameters ([0004]: “…models developed based on Raman spectral data obtained from bioreactor cultures of one or more different scales.” – Raman spectroscopy is a near infrared technique.).
Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious modify the method taught by Webster/West with using an experimentally-obtained NIR data set for training a computer model, such as suggested by Berry, so as to provide sufficient Raman interpretation training to the model, as is similarly contemplated by Webster, which utilizes Raman spectroscopy for process monitoring.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Webster in view of West, Franco, and Wojsznis, as applied to Claims 1-2, 7-11, 14, and 18-19 above, and in further view of Ebil et al. (Ebil, Regine; et al., “Cell and Tissue Reaction Engineering”, 2009), referred to hereinafter as “Ebil”.
Regarding Claim 12, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster does not specifically teach the method discussed above wherein the one or more trained models are trained on data obtained using smaller scale bioreactors and applied on larger scale bioreactors, as in Claim 12.
However, it is well known in the art of biological manufacturing for bioreactors to be optimized at a small scale (thereby reducing the materials and costs of development) before scaling the bioreactor to a larger production scale, said scaling being performed in increments due to the complexity of the process, which involves many variables that can significantly impact cell growth and product yield; as taught through Ebil (pages 101 and 173).
Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to modify the method taught by Webster with initial optimization of the trained model preformed on a smaller scale reactor before scaling up, such as suggested by Ebil, so as to prevent significant drift of variables that impact the bioprocess at hand, thereby maximizing output and yield.
Examiner additionally notes that the “data obtained using smaller scale bioreactors” is not a positive element of the claim, and the recitation to “applied on larger scale bioreactors” is a mere intended use. By this, the claim merely recites one or more trained models.
Regarding Claim 13, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster does not specifically teach the method discussed above wherein the larger scale bioreactors have a volume 2 to 12 times the volume of the smaller scale bioreactors, as in Claim 13.
However, Ebil teaches preforming the scaling up of a small-scale bioreactor to no more than 10 times the volume of the small-scale bioreactor so as to avoid unreasonable scale values for the stirrer speed and other variables. Thus, Ebil discloses the range 1<scale-up<10 and the instant claim discloses the range 2<scale-up<12, wherein the prior art range of Ebil and the instant claimed range significantly overlap.
Thus, a prima facie case of obviousness exists in view of In re Woodruff, 919 F.2d 1575, 16 USPQ2d 1934 (Fed. Cir. 1990), absent contrary evidence of criticality or non-obviousness of the claimed range. By this, one of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to select the overlapping portion of the range so as to achieve the most efficient scaling operation to provide the best product yield.
Examiner additionally notes, akin to the above in Claim 16, the “larger scale bioreactors” and “smaller scale bioreactors” are not positive elements of the claims. Thus, the claim overall holds no patentable weight.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Webster in view of West, Franco, and Wojsznis, as applied to Claims 1-2, 7-11, 14, and 18-19 above, and in further view of Yang (US 2003/0234218 A1), hereinafter “Yang”.
Regarding Claim 20, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster does not specifically teach the method discussed above wherein the neural network is a multi-layer perceptron comprising at least one hidden layer, as in Claim 20.
However, Yang teaches a respective chemical reactor/process controller wherein the neural network is a multi-layer perceptron comprising at least one hidden layer ([0043]: “In this invention, a MLP (Multi-Layer Perception) model of back-propagation is used. Layers of the MLP model comprise input layer 503 receiving input from the environment, output layer 505 transmitting any output to the environment and a hidden layer or layers 504 between the input layer 503 and the output layer 505.”). Therein, the system of Yang enjoys the benefits of MLP neural networks including enhanced versatility and non-linear modeling.
Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to modify the method of Webster wherein the neural network is a multi-layer perceptron comprising at least one hidden layer, such as suggested by Yang, so as to provide the benefits of MLP neural networks known in the art including enhanced versatility and non-linear modeling.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Webster in view of West, Franco, and Wojsznis, as applied to Claims 1-2, 7-11, 14, and 18-19 above, and in further view of MabTec (MabTec® System Installation, Operation & Maintenance Instructions, Parker Laboratories, 2015.), hereinafter “MabTec”.
Regarding Claim 21, the prior art meets the limitations of Claim 1 as discussed above. Further, Webster does not specifically teach the method discussed above wherein the detected malfunction indicates a hose is disconnected, as in Claim 21.
However, MabTec teaches a feed/perfusion control system that uses scale feedback and pump control to maintain bioreactor weight/volume. Therein, said control system detects alarm information when feed control cannot be maintained due to leaks in or disconnection of the feed hose(s) (Page 37). Therein, this arrangement allows the system to stop operation to prevent pumping out of feed liquid, causing contamination and wasting resources.
Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to modify the method of Webster wherein the detected malfunction indicates a hose is disconnected, such as suggested by MabTec, so as to prevent pumping out of feed liquid, causing contamination and wasting resources.
Response to Arguments
35 USC 112
Applicant argues on the alleged grounds that the Claim 1 amendments reciting what the training comprises (obtaining, normalizing, splitting, averaging, determining, and providing) satisfies the requirement for algorithm disclosure set forth by the previous office action under 35 USC 112.
Applicant’s arguments are not persuasive because the amended steps outlined above remain as generic, high-level desired processing results, without disclosing the actual computational procedure by which the controller performs the claimed function.
Here, the recited operations—obtaining a spectral measurement, normalizing using SNV, splitting into N parts, averaging each part, determining feature inputs, and providing the inputs to a neural network identify what results are desired, but not how the controller is programmed to achieve them. For example, the disclosure does not explain the specific SNV calculation performed by the controller, how the spectrum is segmented into N parts, how N is selected, how the average values are computed or mapped into feature inputs, or what stopping/convergence criterion is used to determine that the outputs “substantially match” the reference data.
Thus, although the language resembles a sequence of algorithmic processing steps, they are essentially functional and result-oriented. The same can be said of Applicant’s “generating bioprocessing parameters” and “controlling the bioprocess” argued by Applicant as having algorithmic disclosure on specification page 8 and pages 11-12 respectively. The recitations to the generating parameters merely recite inputting results into the model to generate the parameters without reciting the particular steps taken by the controller to achieve such parameters. Ant the recitations to controlling the bioprocess are further drawn to mere generic signal response without discussing the particular topping/convergence criterion of the neural network. Regarding “obtaining measurement results”, while the specification discusses the Raman or NIR probe, the disclosure remains failing to specify the specific steps undertaken by the controller to obtain such measurement results, such as if the controller continuously monitors the spectra.
In view of the above, Examiner respectfully maintains the rejection of Claims 1-14 and 18-21 under 35 USC 112b for failing to disclose the particular algorithm for performing the computer-implemented 35 USC 112f limitations discussed above.
Applicant further argues on the alleged grounds that “using” would be readily understood by one skilled in the art as the elements are seen as inputs to the generation process.
Applicant’s arguments are not persuasive because what steps constitute the “using” remain unclear. While it is clear, as Applicant says, that the data elements are inputs in the generation process, how these elements are actually used in the process remains undefined.
Thus, Examiner respectfully maintains the rejection of Claims 1, 4-5, 7, and 12 for reciting “using” without specifying what constitutes the use.
Applicant argues on the alleged grounds that the “bioprocess” is merely broad and not indefinite, the specification indicating that the bioprocess may comprise cell cultivation. Examiner agrees – MPEP 2173.04 cautions that breadth is not indefiniteness where the scope is otherwise reasonably clear. As such, the rejection over indefinite understanding of the “bioprocess” under 35 USC 112b is withdrawn.
Applicant argues on the alleged grounds that “cell survival time” and optimization thereof is definite and understood as maximizing the amount of time a cell culture remains viable after a malfunction. However, Applicant has not provided objective boundaries to cell survival time defining how the survival time is measured such as when it begins, when it ends, what viability threshold is used, or what constitutes the survival time being “optimized”. Applicant’s arguments mostly explain the intended concept, not definite claim scope. As such, the “cell survival time” remains susceptible to numerous reasonable interpretations. Thus, Examiner respectfully maintains the 35 USC 112b indefiniteness rejection over the cell survival time and optimization thereof.
See also the newly added rejection of Claim 1 under 35 USC 112b on page 11 necessitated by Applicant’s amendments changing from the previous Claim 5 recitation of using the N parts to calculate average values, to the amended Claim 1 recitation of averaging the N parts themselves to reach the averaged values.
35 USC 101
Step 2A, Prong 1
Applicant argues on the alleged grounds that the recited spectral processing steps could not be performed by the human mind, and that a generic computer is not capable of controlling the bioprocess but rather requires the particular machine of the bioreactor.
Applicant’s arguments are not persuasive because the recited data processing steps can be performed by hand on pen and paper. While doing so may be a long and complex process, this does not except the idea from being performed without a computer. The limitations do not appear to specifically address any details regarding the neural network and therefore the BRI remains showing mere use of math and presence of an abstract data handling idea. Further, the bioreactor and neural network are recited at a high level of generality wherein the control may be achieved by a general-purpose computer – see further the Step 2A, Prong 2 section below regarding integration of the bioreactor and neural network not constituting a practical application.
Step 2A, Prong 2
Applicant argues that the claims are integrated into a practical application because they use trained neural network models to generate bioprocessing parameters and control a bioprocess in a bioreactor, including switching from a first model optimized for product concentration/viable cell density to a second model optimized for cell survival time in response to a malfunction. However, the mere recitation of a bioreactor, spectroscopic measurements, a controller, and trained neural-network models does not, by itself, integrate the judicial exception into a practical application.
The claims are directed to collecting measurement data, processing the data using mathematical/statistical techniques and trained models, detecting a condition, selecting between models, and generating output parameters. These operations are part of the abstract idea itself, namely mathematical analysis/modeling and the use of data to make a control decision. Merely limiting that analysis to the technological environment of “bioprocess control” or a “bioreactor” does not constitute integration into a practical application. MPEP § 2106.05(h) explains that generally linking the use of a judicial exception to a particular technological environment or field of use is insufficient.
Applicant’s argument that the claims recite a “specific technical improvement to bioprocess control” is not commensurate with the actual claim language. The claims do not recite a specific improvement to the structure or operation of a bioreactor, spectrometer, sensor, actuator, fluid path, culture vessel, or other bioprocessing hardware. Nor do the claims recite a specific improvement to neural-network technology itself, such as a new loss function, new weight-updating technique, or improved computer operation. Instead, the claims use conventional data-gathering and computer-modeling components as tools to apply the abstract idea of analyzing spectral data and selecting/generating target parameters.
The recited “spectroscopic measurements” also do not supply the required practical application. Obtaining data from a physical environment is, at most, data gathering that is ancillary to the abstract analysis. Likewise, using the generated parameters for “control” is recited at a high level of generality, without specifying a particular physical control action, actuator operation, feed-rate adjustment, temperature adjustment, pH adjustment, dissolved oxygen adjustment, agitation adjustment, or other concrete manipulation of the bioreactor that effects a particular technological improvement. MPEP § 2106.05(g) recognizes that insignificant extra-solution activity, including data gathering or outputting results, does not integrate an exception into a practical application.
Applicant further argues that switching from a first trained model to a second trained model in response to a malfunction is a practical control strategy. However, this limitation still merely reflects the abstract concept of selecting one algorithm/model instead of another based on detected data. The claims do not recite how the malfunction is detected in a non-abstract technological way, how the second model is technically configured differently from the first model beyond its intended optimization target, or how switching models improves the functioning of the controller, computer, neural network, or bioreactor itself. A result-oriented statement that the second model is “optimized for cell survival time” does not provide a concrete technological implementation.
The presence of a “specific machine” also is not dispositive. Although the claims may mention a bioreactor and controller, the claimed advance is not in a particular machine configuration or transformation of matter, but in using mathematical modeling/neural-network processing to generate or select parameter outputs. The Federal Circuit has repeatedly treated claims directed to collecting information, analyzing it, and generating/displaying results as abstract, even when applied in technical environments. See, e.g., Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1353-56 (Fed. Cir. 2016). The Federal Circuit has also held that claims involving mathematical calculations, statistical modeling, and use of algorithms on a computer are not rendered eligible merely because the calculations are useful in a particular scientific context. See In re Board of Trustees of Leland Stanford Junior Univ., 991 F.3d 1245, 1250-53 (Fed. Cir. 2021).
Accordingly, the additional elements do not amount to significantly more than the abstract idea. The claims do not improve the functioning of a computer, improve neural-network technology, improve spectroscopic hardware, or recite a particular physical bioreactor control mechanism. Rather, the claims use generic computer/modeling components and conventional measurement data in the field of bioprocessing to carry out the abstract idea. The alleged “practical application” is therefore merely a field-of-use limitation and desired result, not a meaningful integration of the judicial exception.
Step 2B
Applicant argues on the alleged grounds that the specific combination of training models, using a first model, detecting a malfunction, and using a second model in response to the malfunction amounts to significantly more than the abstract idea.
Applicant’s arguments are not persuasive because, as discussed in the Step 2A, Prong 2 section above, these elements are recited at a high level of generality which do not meaningfully integrate the abstract idea with significantly more. Applicant must claim the particular operation of the controller and bioreactor, such as specific adjustment of feed sources, or a particular sensor or series of steps for detecting the malfunction to amount to significantly more.
In view of the above, Examiner respectfully maintains the rejection of the instant claims under 35 USC 101 for reciting an abstract idea not integrated into a particular application and not amounting to significantly more.
35 USC 103
Applicant’s arguments are on the alleged grounds that Webster and West do not teach the amended Claim 1 recitations reciting that the training of the one or more models includes: obtaining a measurement result of a spectrum; normalizing the spectrum using Standard Normal Variate (SNV) techniques; splitting the spectrum into a number N parts; averaging each part to generate N average values; determining, from the N average values, one or more feature inputs to a neural network; and providing the one or more feature inputs to the neural network. Applicant further argues that Berry does not cure the alleged deficiencies of Webster and West, Barry not teaching the specific training process of the amended Claim 1.
Applicant’s arguments are not persuasive because Webster utilizes SNV techniques to normalize spectra used for analysis of the bioprocess: [0089]: “standard normal variate can be applied to the Raman spectra in order to remove scattering effects”, as well as the neural network being adapted by iterating using reference data such that bioprocessing target parameter outputs produced by the neural network substantially matches the reference data are trained to match corresponding values of bioprocessing parameters of the reference data ([0011]: “controller can include a predictive model that extrapolates a future concentration of the parameter based on the determined concentration of the parameter and can selectively increase or decrease at least one parameter influencing substance in order to maintain the parameter within preset limits based on the calculated future concentration.” as in the amended Claim 1.
Further, Applicant’s amendments more specifically reciting the averaging of each of the N parts to generate N average values instead of the N parts merely being “used” to calculate N average values necessitated the additional citing of the prior art of Franco herein. Franco teaches selecting N parts of the spectrum to split into: Section 2.6: “Select the sensor ranges included in the histogram a certain percentage of times above a threshold, for example, 70%.”, and repeating the splitting and measuring steps until an average value stabilizes: Section 2.6: “Repeat steps (1) through (3), reinitializing the GA with the sensors obtained in step (2) and setting the maximum number of factors as obtained in step (3). Continue until the sensors selected in step (2) stabilize.” Therein, Franco teaches the benefit of this approach as offering enhanced predictive ability to the neural network.
Further, the prior art of Wojsznis was additionally cited herein as necessitated by Applicant’s Claim 1 amendment specifically requiring the use of the second trained model instead of the first trained model in response to a malfunction being detected. Wojsznis teaches model switching as a result of predictive errors/malfunctions commensurately as claimed – see the discussion above in the body of the action.
Thus, Examiner sets forth the rejection of Claims 1-2, 7-11, 14, and 18-19 as unpatentable under 35 USC 103 over the prior art of Webster in view of West, Franco, and Wojsznis as discussed above in the body of the action.
New Claims 20 and 21
New Claim 20 is rejected herein as unpatentable under 35 USC 103 over Webster in view of West, Franco, and Wojsznis, as applied to Claims 1-2, 7-11, 14, and 18-19, and in further view of Yang.
New Claim 21 is rejected herein as unpatentable under 35 USC 103 over Webster in view of West, Franco, and Wojsznis, as applied to Claims 1-2, 7-11, 14, and 18-19, and in further view of MabTec.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN KASS whose telephone number is (703)756-5501. The examiner can normally be reached Monday - Friday from 9:00 A.M. to 5:00 P.M. EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Capozzi, can be reached at telephone number (571)270-3638. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
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 https://www.uspto.gov/patents/uspto-automated-interview-request-air-form.
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 visit 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 need assistance from a USPTO Customer Service Representative, call (800) 786-9199 (IN USA OR CANADA) or (571) 272-1000.
/B.J.K./Examiner, Art Unit 1798
/NEIL N TURK/Primary Examiner, Art Unit 1798
Prosecution Insights