DETAILED ACTION
Applicant's response, filed 05 January 2026, has been fully considered. The following
rejections and/or objections are either reiterated or newly applied. They constitute the complete set
presently being applied to the instant application.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in within the application. Accordingly, the effective filing date of claims 1-11 is 11/9/2019.
Claim Status
Claims 1-11 are pending.
Claims 12 and 13 are cancelled.
Claims 1-11 are rejected.
Specification
Response to Amendment
In view of applicant’s amendments to the specification previous objections to the specification are withdrawn.
Drawings
Response to Amendment
In view of applicant’s amendments to the drawings previous objections to the drawings are withdrawn.
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: “detection means for detecting”, “means for producing detection curves”, and “data processing means for classifying” in claim 11.
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.
Claim limitations “detection means for detecting”, “means for producing detection curves”, “data processing means for classifying”, and “program code means stored on a computer readable medium for performing the method” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph as there is no structure recited within the claims. However, the specification provides in the detailed description a BIACORE instrument possessing the requisite structure necessary for detecting and producing detection curves. Additionally, the specification provides in the detailed description an ANN or expert system which possess the requisite structure necessary for data processing and classification, as well as a computer readable medium for performing the method.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 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.
The term “good enough quality” in claim 1 is a relative term which renders the claim indefinite. The term “good enough quality” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The use of the term renders the determination of which curves to use the kinetic analysis on indefinite. Claims 2-11 depend from claim 1 and do not resolve the indefiniteness issue and as such are rejected for the same reason.
Regarding claim 10, the phrase "the artificial neural network(s) " renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Specifically, there are two possible ANNs the phrase could refer to, the ANN for the mathematical model in claim 1 or the ANN for the second mathematical model in claim 9 and it is unclear to which neural network of the two that it is referencing.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 7 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Specifically, it is no longer further limiting because it recites the same limitation that was added into claim 1 in the new amendment. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 101 have been reviewed, updated, and provided below.
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method, system, computer program, and computer product for classifying monitoring results from an analytical sensor system. The judicial exception is not integrated into a practical application because while claims 1-11 attempt to integrate the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [See MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a method (Claims 1-10), and a system (Claim 11).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [See MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 1: Fitting a mathematical model to the set of detection curves, and calculating a set of features comprising those listed in the group provided are verbal articulations of mathematical processes and therefore abstract ideas, specifically mathematical concepts. Classifying detection curves into a quality classification group via an ANN, and determining which curves are of good enough quality are merely a process of comparing/contrasting and selecting data that can be done with a pen and paper or in the human mind and is therefore an abstract idea, specifically a mental process.
Claim 2: The mathematical model being one of those specified in the group provided is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 4: Determining which detection curves to use in a kinetic analysis is a process of selecting data that can be done with a pen and paper or in the human mind and is therefore an abstract idea, specifically a mental process.
Claim 5: Determining a second mathematical model to be used for the kinetic analysis is a process of selecting data that can be done with a pen and paper or in the human mind and is therefore an abstract idea, specifically a mental process.
Claim 6: The second mathematical model being one of those specified in the group provided is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 8: The artificial neural network being trained using detection curves, and classifying each curve into a quality classification group are processes of refining calculations, comparing/contrasting and selecting data that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically a mental processes.
Claim 10: The artificial neural network being trained using detection curves including what model is fit to the curve, is a process of refining calculations, comparing/contrasting and selecting data that can be done with a pen and paper or in the human mind and is therefore an abstract ideas, specifically a mental process.
Claim 11: Performing the method of claim 1 encompasses the same judicial exceptions as claim 1, specifically mental processes and mathematical concepts.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [See MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 1: Acquiring a set of detection curves is an insignificant extra solution activity, specifically mere data gathering (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. Using an artificial neural network or expert system to classify detection curves is an additional element that is equivalent to “perform this on a generic computer” because the claim merely generically describes an artificial neural network and does not provide any specific structural limitations (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984) [See MPEP § 2106.05(a)(I)].
Claim 3: The molecular interactions being monitored at a sensing surface is an insignificant extra solution activity, specifically merely selecting a particular data source (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claims 1 and 7: Using an artificial neural network or expert system to classify detection curves is an additional element that is equivalent to “perform this on a generic computer” because the claim merely generically describes an artificial neural network and does not provide any specific structural limitations (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984) [See MPEP § 2106.05(a)(I)].
Claim 9: Selection of the mathematical model being performed by the artificial neural network or expert system is an additional element that is equivalent to “perform this on a generic computer” (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984) [See MPEP § 2106.05(a)(I)].
Claim 11: The analytical system, sensor device, detection means, means for producing detection curves, and data processing means are mere data gathering components that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [See MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to
significantly more than the judicial exception because the claims recite additional
elements that are generic, conventional or nonspecific. These additional elements
include:
The additional elements of a computer, an analytical system (Conventional: Specification Page 10, Line 24-30, and Figure 1 – “The analytical system comprises a BIACORE TM instrument…”), sensor device (Conventional: Specification Page 10, Line 24-30, and Figure 1 – “The analytical system comprises a BIACORE TM instrument…”), detection means (Conventional: Specification Page 10, Line 24-30, and Figure 1 – “The analytical system comprises a BIACORE TM instrument…”), means for producing detection curves (Conventional: Specification Page 10, Line 24-30, and Figure 1 – “The analytical system comprises a BIACORE TM instrument…”), data processing means, a computer program product, a computer readable medium, and computer program code are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See § MPEP 2106.05(d)(II)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of acquiring a set of detection curves, is an insignificant extra solution activity, specifically mere data gathering that is conventional as it is inherent to the system being used. Specifically, Biacore™ systems (Conventional: Specification Page 10, Line 24-30, and Figure 1 – “The analytical system comprises a BIACORE TM instrument…”) produce a real-time detection curve called a sensorgram (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of the molecular interactions being monitored at a sensing surface is an insignificant extra solution activity, specifically merely selecting a particular set of data that is conventional as it is also inherent to the system being used. Specifically, Biacore™ systems (Conventional: Specification Page 10, Line 24-30, and Figure 1 – “The analytical system comprises a BIACORE TM instrument…”) require a sensing surface to function (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-11, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 1/5/2026 have been fully considered but they are not persuasive.
Applicant asserts on page 10 of the Remarks filed 1/5/2025 that the claims integrate the judicial exception into a practical application by providing an improvement to the field of biosensing and monitoring processes. Specifically, applicant asserts an improvement to the assessment of biosensing through a simpler and faster method. However, examiner reminds applicant that according to MPEP 2106.05(a) - It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. Here the improvement is directed to the assessment, not the additional elements such as the data storage, or computer structure. Therefore, the claims are not directed to an improvement to technology.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 103 have been reviewed, updated, and provided below.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Jason-Moller et al. (Current protocols in protein science (2006) 19-13; previously cited), Luo et al. (Journal of Protein Chemistry (1999) 709-719; previously cited), Andersson et al. (WO 03081245 A1; previously cited), Yang-chun et al. (Frontiers in Laboratory Medicine (2017) 125-128; previously cited), Zhang et al. (IEEE Transactions on Systems, Man, and Cybernetics (Applications and Reviews) (2002) 451-462; newly cited), and Gaudar et al. (Journal of Microbiological Methods (2004) 317-326; newly cited).
Claim 1 is directed to a method of classifying monitoring results from an analytical sensor system via the fitting of a model to detection curves, calculating features, and from those features classifying the detection curves.
Andersson et al. teaches in the abstract “The invention also relates to an analytical system including means for classifying the response curves with regard to quality”, to which a response curve is a detection curve, reading on a method for classifying monitoring results from an analytical sensor system arranged to monitor molecular interactions, wherein detection curves representing progress of the molecular interactions with time are produced, the method comprising steps of, and acquiring a set of detection curves, wherein a set of detection curves comprises one or more detection curves representing molecular interactions at respective molecular concentrations. Andersson et al. teaches on page 7, line 4 “The first step is to select the sensorgram features (curve parameters) used to determine the quality of the sensorgrams”, reading on based on the calculated set of features, classifying each detection curve into a quality classification group indicative of the quality of the detection curve. Furthermore, it would have been obvious if classifying the detection curve into a quality classification that there would be a determination of which curves are good enough quality, thereby reading on based on the classification, determining which detection curves are of good enough quality to use in the kinetic analysis of the monitored molecular interactions.
Gaudar et al. teaches in the abstract “We present a simple method for estimating kinetic parameters from progress curve analysis of biologically catalyzed reactions that reduce to forms analogous to the Michaelis–Menten equation…The algebraic nature of this solution, coupled with its relatively high accuracy, makes it an attractive candidate for kinetic parameter estimation from progress curve data”, reading on classifying monitoring results from an analytical sensor system arranged to monitor molecular interactions so as to provide a kinetic analysis of the monitored molecular interactions.
Jason-Moller et al. teaches on page 8, column 1, paragraph 1 “Optimal assay conditions to minimize mass transport limitations to measure rate constants are a combination of high flow rates and low surface binding capacity”, and “Affinity values can be derived either from interactions that have reached equilibrium or from the ratio of the dissociation and association rate constants in cases where the system does not reach steady state during the time frame of the experiment”.
Yang-chun et al. teaches on page 1, column 1, paragraph 1 “T-test, which is also called the Student’s t-test, is often used as a statistical method to assess whether the mean value of the data from an independent sample which follows a normal distribution is consistent with or depart significantly from the mean value of a null hypothesis, or whether the difference between the means of two independent samples which follow a normal distribution are statistically significant…T-test is suitable for the small samples (such as n < 30), in which the statistics follow a normal distribution…all the results of internal quality control (IQC) should follow a normal distribution”, a t-statistic is merely a calculated value divided by its standard error, therefore in view of the teachings of Jason-Moller et al. focusing on kinetic constants and binding capacity this reads on, calculating a set of features from the set of detection curves and fitted mathematical model, the calculated set of features comprising three or more of: association rate constant(s), ka, divided with a standard error of ka, dissociation rate constant(s), kd, divided with a standard error of kd, maximum binding capacity, Rmax, divided with a standard error of Rmax, mass transport limitation value, tc, divided with a standard error of tc, late binding response, B, divided with Rmax, and average mean square error, MSE, between the detection curve and the fitted mathematical model divided with a squared late binding response, B2.
Luo et al. teaches on page 710, column 2, paragraph 4 “Then multiple association and dissociation sensorgrams were fitted using global fitting by the models described above”, reading on fitting a mathematical model to the set of detection curves.
Zhang et al. teaches in abstract “Classification is one of the most active research and application areas of neural networks”, and on page 458, column 1, paragraph 3 “This paper has presented a focused review of several important issues and recent developments of neural networks for classification problems…The research efforts during the last decade have made significant progresses in both theoretical development and practical applications. Neural networks have been demonstrated to be a competitive alternative to traditional classifiers for many practical classification problems”, reading on wherein classifying of each detection curve into a respective quality classification group is performed by means of at least one artificial neural network or at least one expert system.
It would have been obvious at the time of first filling to modify the teachings of Andersson et al. for a system and method of producing detection curves and classifying said curves in terms of their quality, with the teachings of Jason-Moller et al. and Yang-chun et al. for the use of binding capacity and kinetic constants as test statistics, and the teachings of Luo et al. for applying a mathematical model using said statistics as Andersson et al. teaches “The first step is to select the sensorgram features (curve parameters) used to determine the quality of the sensorgrams”, Luo et al. is teaching the best way of representing the sensorgrams in terms of models with Jason-Moller et al. teaching that the system is designed for “…quantitative measurements for affinity, kinetics, and concentration determination” of which the cited features were most important, and Yang-chun et al. is teaching how the use of test statistics (t-tests) are the best representation for quality control. Furthermore, it would have been obvious to combine those with the teachings from Guadar et al. for the application of detection curves to kinetic reactions as the abstract teaches “The algebraic nature of this solution, coupled with its relatively high accuracy, makes it an attractive candidate for kinetic parameter estimation from progress curve data”, and it would have been obvious to combine the previous teachings with the teachings of Zhang et al. for the use of neural networks in classification as the latter teaches on page 458, column 1, paragraph 4 “Neural networks have been demonstrated to be a competitive alternative to traditional classifiers for many practical classification problems”. One would have had a reasonable expectation of success given that both Jason-Moller et al. and Andersson et al. are using the same platforms, the information Yang-chun et al. is presenting is more review material on what are best practices, Luo et al. is merely trying to figure out the best models to represent the quantitative measurements Jason-Moller et al., Andersson et al. are obtaining through sensorgram data, Gaudar et al. is applying them to kinetic analysis, and Zhang et al. is merely presenting alternative methods for overall model structure. Therefore, it would have been obvious at the time of first filling to have modified the teachings of each and to be successful.
Claim 2 is directed to the method of claim 1 but further specifies that the model be selected from one of those within the specified group.
Luo et al. teaches on page 711, column 1, in Table 1 the use of an Inhomogeneous analyte model, which is another way of stating heterogenous analyte binding model therefore reading on, wherein the mathematical model is selected from a 1:1 binding model, a heterogenous ligand binding model, a heterogenous analyte binding model, and a bivalent analyte binding model.
Claim 3 is directed to the method of claim 1 but further specifies that molecular interactions be monitored at a sensing surface.
Jason-Moller et al. teaches on page 1, column 1 under System Features “An exchangeable sensor chip upon which one of the interacting biomolecules is immobilized or captured”, reading on wherein the molecular interactions are monitored at a sensing surface.
Claim 4 is directed to the method of claim 1 but further specifies using the classification to determine which detection curves to use in further analyses.
Andersson et al. teaches on page 6, line 30 “This algorithm is designed to remove curves with a quality different from most of a large set of sensorgrams, so-called "outliers"”, reading on based on the classification, determining which detection curves to use in a kinetic analysis of the monitored molecular interactions.
Claim 5 is directed to the method of claim 4 and thus claim 1, but further specifies determining a second mathematical model to be used in the kinetic analysis.
Luo et al. teaches in the abstract “Various models were applied to describe these observations: mass transport-controlled processes, inhomogeneous immobilized ligands, or inhomogeneous soluble analytes”, and on page 710, column 2, paragraph 4 “Then multiple association and dissociation sensorgrams were fitted using global fitting by the models described above”, reading on further comprising determining a second mathematical model to be used in the kinetic analysis.
Claim 6 is directed to the method of claim 5 and thus claim 1, but further specifies that the model be selected from one of those within the specified group.
Luo et al. teaches on page 711, column 1, in Table 1 the use of an Inhomogeneous analyte model, which is another way of stating heterogenous analyte binding model therefore reading on, wherein the mathematical model is selected from a 1:1 binding model, a heterogenous ligand binding model, a heterogenous analyte binding model, and a bivalent analyte binding model.
Claim 7 is directed to the method of claim 1 but further specifies that the method be performed using an artificial neural network or expert system.
Andersson et al. teaches on page 9, line 20 “Alternative classification methods include the use of a cluster algorithm, e.g. a KNN cluster algorithm, which classifies the sensorgrams in groups having a similar quality; a neural network or an expert system”, reading on wherein classifying a detection curve into a quality classification group is performed by means of (an) artificial neural network(s) or (an) expert system(s).
Claim 8 is directed to the method of claim 7 but further specifies that the neural network or expert system be trained using the selected detection curves.
Andersson et al. teaches on page 9, line 20 “Alternative classification methods include the use of a cluster algorithm, e.g. a KNN cluster algorithm, which classifies the sensorgrams in groups having a similar quality; a neural network or an expert system”, training is an inherent part of neural networks and if you are classifying detection curves, training on said curves would be inherent, therefore this reads on wherein the artificial neural network(s) is trained using a plurality of sets of detection curves representing progress of different molecular interactions with time , the artificial neural network(s) being provided with, for each set of detection curves. Andersson et al. teaches in the abstract “The invention also relates to an analytical system including means for classifying the response curves with regard to quality”, reading on a classification of each detection curve into a quality classification group.
Jason-Moller et al. teaches on page 8, column 1, paragraph 1 “Optimal assay conditions to minimize mass transport limitations to measure rate constants are a combination of high flow rates and low surface binding capacity”, and “Affinity values can be derived either from interactions that have reached equilibrium or from the ratio of the dissociation and association rate constants in cases where the system does not reach steady state during the time frame of the experiment”.
Yang-chun et al. teaches on page 1, column 1, paragraph 1 “T-test, which is also called the Student’s t-test, is often used as a statistical method to assess whether the mean value of the data from an independent sample which follows a normal distribution is consistent with or depart significantly from the mean value of a null hypothesis, or whether the difference between the means of two independent samples which follow a normal distribution are statistically significant…T-test is suitable for the small samples (such as n < 30), in which the statistics follow a normal distribution…all the results of internal quality control (IQC) should follow a normal distribution”, a t-statistic is merely a calculated value divided by its standard error, therefore in view of the teachings of Jason-Moller et al. focusing on kinetic constants and binding capacity this reads on, a set of features calculated from the set of detection curves and a mathematical model fitted to the set of detection curves , the calculated set of features comprising three or more of: - association rate constant, ka, divided with a standard error of ka, - dissociation rate constant, kd, divided with a standard error of kd, - maximum binding capacity, Rmax, divided with a standard error of Rmax, - mass transport limitation value, tc, divided with a standard error of tc, - late binding response, B, divided with Rmax, and - average mean square error, MSE, between the detection curve and the fitted mathematical model divided with a squared late binding respone, B2.
Claim 9 is directed to the method of claim 5 and thus 1 but further specifies that the method be performed using an artificial neural network or expert system.
Andersson et al. teaches on page 9, line 20 “Alternative classification methods include the use of a cluster algorithm, e.g. a KNN cluster algorithm, which classifies the sensorgrams in groups having a similar quality; a neural network or an expert system”, reading on wherein classifying a detection curve into a quality classification group is performed by means of (an) artificial neural network(s) or (an) expert system(s).
Claim 10 is directed to the method of claim 9 and thus claim 1, but further specifies that the neural networks be trained using the selected detection curves.
Andersson et al. teaches on page 9, line 20 “Alternative classification methods include the use of a cluster algorithm, e.g. a KNN cluster algorithm, which classifies the sensorgrams in groups having a similar quality; a neural network or an expert system”, training is an inherent part of neural networks and if you are classifying detection curves, training on said curves would be inherent, therefore this reads on wherein the artificial neural network(s) is trained using a plurality of sets of detection curves representing progress with time of molecular interactions, the artificial neural network(s) being provided with a classification of the detection curves as to what mathematical model is fit to the detection curves.
Claim 11 is directed to the method of claim 1 but further specifies that an analytical system be used to perform the method.
Jason-Moller et al. teaches on page 1, column 1, under System Features “An optical detector system”, “An exchangeable sensor chip”, “a microfluidic and liquid handling system”, furthermore on pages 2-3, available instrument platforms are discussed with “instrument control software”, reading on analytical system for detecting molecular binding interactions and classifying monitoring results, comprising: a) a sensor device comprising at least one sensing surface , detection means for detecting molecular interactions at the at least one sensing surface , and means for producing detection curves representing the progress of the interactions with time, and b) data processing means for classifying each detection curve into a quality classification group, wherein the data processing means perform steps b) to d) according to claim 1.
Response to Arguments
Applicant's arguments filed 1/5/2026 have been fully considered but they are not persuasive.
Applicant asserts on page 11 of the Remarks filed 1/5/2026 that the amended claims presented are not taught by the combination of references previously presented. Examiner agrees and has added an additional reference to rectify any deficiencies.
Applicant asserts on page 12 of the Remarks filed 1/5/2026 that there is no reason nor motivation to combine the cited references as none of the methods could benefit from improvement in any way. However, examiner reminds applicant that an explicit reference to any missing piece within the art is not needed rather according to MPEP 2141 Subsection (III) The key to supporting any rejection under 35 U.S.C. 103 is the clear articulation of the reason(s) why the claimed invention would have been obvious. The Supreme Court in KSR noted that the analysis supporting a rejection under 35 U.S.C. 103 should be made explicit…Examples of rationales that may support a conclusion of obviousness include: (A) Combining prior art elements according to known methods to yield predictable results. Here all of the elements of the claims have been provided within the prior art with a rationale for combination of the art under KSR.
Applicant asserts on page 12 of the Remarks filed 1/5/2026 that the previously cited references do not disclose or suggest the suitability for detection curves for the kinetic analysis. Examiner agrees and has added an additional reference to rectify any deficiencies.
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.
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/K.N.A./Examiner, Art Unit 1687
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685