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 .
Priority
The present application was filed on 11/21/2022. Acknowledgment is made of the present application as a proper National Stage (371) entry of PCT/GB2021/051244, filed on 05/21/2021, which claims benefit of the foreign Application United Kingdom 2007690.7, filed on 05/22/2020.
Claim status
Claims 19-27 are canceled. Claims 1, 10, and 16 are amended. Claim 28 is new. Claims 1-18 and 28 are examined herein.
Update Objections/Rejections
Claim interpretation under 35 U.S.C. 112(f) is maintained.
The rejection of claims 5-7 under 35 U.S.C. 112(d) is withdrawn because claims 5-7 recite the function of the structures recited in claim 1, so they further limit the subject matter of the claim.
The rejection of claims 12-18 under 35 U.S.C. 112(d) is maintained.
The rejection of claims 1-18 under 35 U.S.C. 103 is updated in view of the amendment of the claims and the Applicant’s arguments.
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.
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 limitations are: “a measurement module configured to…”, “a storage location configured to…”, “an analysis module configured to…”, “the distribution channel is adapted to generate a distribution of biomolecules” in claim 1; “a controller configured to…”, “an output module… configured to…” in claim 2. These limitations are repeated in claims 5-7.
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.
The specification fails to provide details on how the measurement module is configured. The limitation is interpreted as a fluorescence detector according to one of the embodiment (page 15, par.3).
The specification fails to provide details on how the analysis module is configured. The limitation is interpreted as a processor and software according to one of the embodiment (page 3, par.7-8).
The specification fails to provide details on how the storage location is configured. The limitation is interpreted as a memory according to one of the embodiment (page 3, par.6).
The specification fails to provide details on how the controller is configured. The limitation is interpreted as a processor according to one of the embodiment (page 5, par.6).
The specification fails to provide details on how the output module is configured. The limitation is interpreted as a display according to one of the embodiment (page 5, par.6; page 6 par.1).
The limitation “the distribution channel is adapted to generate a distribution of biomolecules” in claim 1 is interpreted as the distribution channel may be of sufficient length/width to enable lateral distribution by diffusion, or the distribution channel has an electric field spanning it to enable distribution of components by electrophoresis or can be adapted by providing a heat source which is configured to enable thermophoresis to distribute biomolecules according to page 7 of the specification.
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 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.
Claims 12-18 are 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.
Claims 12-18 do not recite any additional structure of the claimed device. It is noted that the sample, e.g., biomolecule, is not a part of a device, thus the limitation regarding the sample is considered to be an intended use of the device. Therefore, the claims fail to further limit the subject matter of the claim upon which it depends.
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 § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 8-9, 11-13, 15 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yates (US20160266138) as evidence by Ryan et al. (Simulation-based fully Bayesian experimental design for mixed effects models, Computational Statistics and Data Analysis 92 (2015) 26–39) in view of Brown et al. (US5616504) and Rehman et al. (A Bayesian approach for estimating protein–protein interactions by integrating structural and non-structural biological data, Mol. BioSyst., 2017, 13, 2592—2602, PTO-892 dated 07/23/2025).
Regarding claim 1, Yates discloses a flow apparatus for analyzing a component using fluidic techniques (see Abstract, and par.9). The apparatus comprises:
a sample inlet channel configured to introduce a sample fluid, including the biomolecule, to the apparatus (see par.32: disclosing a first flow or a first fluid flow, par.85: teaching that the component is a biomolecule, par.92: teaching that the component is provided in the first fluid flow);
an auxiliary inlet channel configured to introduce an auxiliary fluid to the apparatus (see par.32: disclosing a second flow, pars.85-86: teaching that the fluid flow may be a buffer and the second fluid flow does not contain the component);
a distribution channel in fluid communication with the sample inlet channel and the auxiliary inlet channel; wherein the distribution channel is adapted to generate a distribution of biomolecules (see par.32: teaching that a separation channel for first and second flows in contact, the separation channel is adapted to permit lateral movement of components between contacting first and second flows). See paragraph 262 and figure 13 for the illustration of the apparatus.
Yates also teaches an analytical device that is suitable for use with the flow apparatus:
a measurement module configured to detect a signature profile of the biomolecule to obtain a measured dataset of the detected biomolecule (see par.75: teaching that the fluidic device, particularly the analysis channel, is adapted for use with a detector for the components; see pars.162, 413 and 415: teaching that the detector can be spectroscopy, a fluorescence spectrometer, or a spectrophotometer; see more in paragraphs 218-219, 225-228: teaching that the detection zone may comprise an analytical device for analyzing component, a plurality of analytical devices may be provided to determine different physical and chemical characteristics of the component, the analytical devices may be arranged sequentially or in parallel; see par.13: teaching that the apparatus of the invention allows analyzing transient protein-protein interactions, aggregation and dissociation events which provides an opportunity to non-disruptively quantify relative binding kinetics; see at least pars.12 and 330: the fluorescence intensity can be quantitatively used to determine protein concentration);
wherein the measured dataset includes one or more of the following: auto fluorescence background measurements (see par.443: teaching that for each set of measurements and imaging settings, at least one dye background image was taken to account for the minimal fluorescence of the unreacted dye and a flatfield background image was also acquired),
flow profile (see pars.366 and 384: measuring diffusion ratios based on flow rate),
concentration (see par.12: teaching that the fluorescence intensity can be quantitatively used to determine protein concentration),
stoichiometry of interactions (see par.413: teaching that a variety of fluorogens, stoichiometries, and denaturing conditions were surveyed using a fluorescence spectrometer),
the hydrodynamic radius (see pars.352-353: teaching that it is additionally possible to use diffusion-based separation and detection methods to obtain absolute hydrodynamic radius).
Yates also teaches that the device can detect two characteristics of the biomolecule simultaneously (see par.225: a plurality of analytical devices may be provided to determine different physical and chemical characteristics of the component and the analytical devices may be arranged sequentially or in parallel, thus it means that at least two characteristics of the biomolecule can be detected and analyzed simultaneously).
Yates teaches that it is possible to obtain absolute hydrodynamic radii by adapting a known numerical simulation algorithm as part of diffusion spectrometry (see par.353).
While Yates does not clearly teach the use of Bayesian analysis in characterizing the biomolecule, the numerical simulation algorithm taught by Yates may encompass Bayesian analysis as evidenced by Ryan (Ryan at least in Abstract, page 28 section 1.6, and page 29: teaches that Bayesian analysis involves the use of numerical simulation-based optimal design methods to solve the maximization and integration problem in nonlinear mixed effects models. Bayesian analysis can be used for collecting informative data to obtain precise parameter estimates, but the number of samples per subject is limited.). Accordingly, Yates is accommodating to using the Bayesian algorithm for analyzing the result because Bayesian is a numerical simulation algorithm.
Yates does not teach the apparatus comprising a storage location, an analysis module, wherein the analysis module uses a correlation value to determine at least two characteristics of the biomolecule simultaneously.
Brown teaches a computer system used for performing statistical analysis of an affinity assay (see col.6 lines 50-60). The computer system connects to detectors which provide an output indicative of the measured response (see col.8 lines 1-2, and 40-42). The computer comprises a storage and an analysis module using Bayesian analysis (see Abstract, Figs.4-5 col.8 lines 24-35 and 45-55).
Rehman teaches that Bayesian can be used to estimate protein-protein interactions (see Abstract). Rehman teaches that Bayesian analysis is configured to use the correlation value to determine at least two characteristics of the biomolecule simultaneously (see page 2595 col.1 par(s).1 and 4: teaching that Bayesian classification can combine the previous information with localization on the sub-cellular level, domain co-occurrence and post transition modification data to infer protein-protein interactions; Bayesian classifiers integrate structural and nonstructural types of information (i.e., two characteristics of the biomolecule) into computational models to infer interactions of putative proteins).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the computer system of Brown and the analytical device of Yates to provide the apparatus for characterizing a biomolecule as claimed. The combination provides an accurate identification of potential protein–protein interactions which can be used as a powerful tool to understand biological processes (Rehman page 2601 col.1 par.2).
One having an ordinary skill in the art would have had a reasonable expectation of success in Yates and Brown because Yang is generic for the analytical devices which can determine different physical and chemical characteristics of the biomolecule component (Yang par.225) and Brown is specific for the computer system comprising Bayesian analysis for performing statistical analysis from the assay data (Brown Figs.6-7, col.6 lines 50-67). Moreover, Yang suggests adapting a known numerical simulation algorithm to analyze the characteristics of a biomolecule. It is well-known in the art that a known numerical simulation algorithm can be Bayesian analysis, as evidence by Ryan on page 29, paragraph 1. Thus, the combination would be successful in detecting and analyzing the target biomolecule.
Regarding claim 2, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates does not teach wherein the apparatus further comprises a controller configured to receive the stored dataset from the storage location and to receive the measured dataset from the analysis module to further tune one or more parameters associated with the measured dataset; and an output module associated with the controller configured to provide a notification to an operator indicating a further cycle of measurements such that further measurements obtained provide a pre-determined level of confidence in the determined characteristics of the biomolecule.
Brown teaches the computer system comprising a controller (see col.15 lines 20-35), an output module (see col.8 lines 24-43) and an operator (see col.8 lines 24-43: e.g., central processing unit) to do the claimed functions (see at least Fig.8, col.8 lines 24-4 and col.15 lines 20-35).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the computer system of Brown and the analytical device of Yates to provide the apparatus for characterizing a biomolecule as claimed. The combination provides an accurate identification of potential protein–protein interactions which can be used as a powerful tool to understand biological processes (Rehman page 2601 col.1 par.2).
One having an ordinary skill in the art would have had a reasonable expectation of success in combining Yates and Brown because Yang is generic for the analytical devices which can determine different physical and chemical characteristics of the component (Yang par.225) and Brown is specific for the computer system comprising Bayesian analysis for performing statistical analysis from the assay data (Brown Figs.6-7, col.6 lines 50-67). Moreover, Yang suggests adapting a known numerical simulation algorithm to analyze the characteristics of a biomolecule. It is well-known in the art that a known numerical simulation algorithm can be Bayesian analysis, as evidence by Ryan on page 29, paragraph 1. Thus, the combination would be successful in detecting and analyzing the target biomolecule.
Regarding claim 3, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches that the distribution channel is adapted to generate a lateral distribution of biomolecule (see Yates par.32 and 261: teaching that the separation channel for first and second flows in contact wherein the separation channel is adapted to permit lateral movement of components between contacting first and second flows; fig.13: the separation channel 1 is in fluid connection with the first and second flows and the flow separator).
Regarding claim 4, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches the apparatus further comprises at least two outlet channels to divide the fluid in the distribution channel into two or more flows (see Yates Fig.13: the separation channel 1 is in fluid connection with the flow separator 7 that divert the fluid flows to detection channel 8 or collection channel 11).
Regarding claim 8, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches that the separation channel having two inlets (first and second flows), thereby encompasses the T-sensor channel as discloses in the instant specification page 9 (T-sensor is used to describe a distribution channel with exactly two inlets).
Regarding claim 9, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches the biomolecule is labelled with a fluorophore (see par.9: the analysis may include the step of labelling the component for ease of detection, see par.12: quantitative labelling procedures, such as the fluorescent labelling procedures described herein, allow the concentration of a component to be directly determined from the recorded analytical signal).
Regarding claim 11, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches that electrodes 16 and 17 are provided either side of the separation channel 1 to provide an electric field across the separation channel (see par.279, and Fig.14). Even though the electrodes do not locate at the upstream and downstream of the channel, the claimed limitation is not sufficient by itself to patentably distinguish over the otherwise electrode system taught by Yates because it is only based on the rearrangement of parts, e.g., the position of the electrodes (see MPEP 2144.04(VI)(C)). Therefore, the particular placement of the parts is held to be an obvious matter of design choice.
Regarding claims 12-13, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches that the biomolecule is a polypeptide, a polynucleotide or a polysaccharide (see par.2: component is a mixture of polypeptides; par.29: component is or comprises a polypeptide, a polynucleotide or a polysaccharide, a protein). While Yates does not clearly teach that the biomolecule is an antibody, the teaching of Yates encompasses antibody because antibody is also a protein. Brown also supports that the analyte of the assay can be an antigen or antibody in an immunoassay (col.1 lines 25-27).
Regarding claim 15, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches wherein the biomolecule is a multi-biomolecule mixture (Yates par.30).
Regarding claim 28, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Claim 28 recites “the measured dataset includes post translational modification”. According to the disclosure of the instant specification, the post translational modification is methylation and/or the hydrodynamic radius (see instant specification page 5 lines 20-21).
Yates teaches that the methods of the invention also allow the hydrodynamic radius of a component to be determined (see par.15), thereby the measured dataset includes post translational modification.
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yates as evidence by Ryan et al. in view of Brown et al., Rehman et al., as applied to claim 1 above, further in view of Feng (Bayesian Estimation of the Active Concentration and Affinity Constants Using Surface Plasmon Resonance Technology, Plos One 10(6): e0130812, 2015).
Regarding claims 5-6, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches that the apparatus of the invention may be used to analyze transient protein-protein interactions and the behavior of non-obligatory protein complexes formed by proteins and multiple possible binders. Accordingly, the present invention provides an alternative strategy to analyze aggregation and dissociation events compared to traditional disruptive detection schemes. The analysis of association and dissociation events provides an opportunity to non-disruptively quantify relative binding kinetics. See paragraph 13. This teaching means that the apparatus of Yates can be used for analyzing the affinity and avidity of biomolecule.
Yates teaches that the fluidic device, particularly the analysis channel, is adapted for use with a detector for the components (see par.75), wherein the detector may be fluorescence spectroscopy (see par.180). Yates teaches that the fluorescence intensity can be quantitatively used to determine protein concentration (see at least par.12 and par.330).
Rehman supports that Bayesian can be used to characterize at least two characteristics of a biomolecule (see page 2595 col.1)
Feng supports that the Bayesian method can determine both active concentration and affinity constants (abstract) and the Bayesian method produces more accurate estimates (Feng page 14 pars.1-2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the computer system of Brown and the analytical device of Yates to provide the apparatus for characterizing a biomolecule as claimed. One having an ordinary skill in the art would have been motivated to use the Bayesian method for detecting the concentration and the affinity (or the avidity) of each biomolecule because the Bayesian method leads to more accurate estimation of kinetic rate constants than the other methods (Feng page 14 par.2).
One having an ordinary skill in the art would have had a reasonable expectation of success in combining Yates and Brown for the reason discussed in claim 1 above.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yates as evidence by Ryan et al. in view of Brown et al., Rehman et al., as applied to claim 1 above, further in view of Ben-Johny et al. (Detecting stoichiometry of macromolecular complexes in live cells using FRET, Nature Communications, 2016, PTO-892 dated 07/23/2025).
Regarding claim 7, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. The device of Yates can be used for protein-protein interactions (see Yates par.13) wherein the interaction is examined by fluorescence resonance energy transfer (FRET) (see Yates par.3).
Yates does not teach that the analysis module is configured to detect and determine the stoichiometry of a molecule.
Ben-Johny teaches that FRET is a powerful phenomenon for characterizing close-range interactions whereby a donor fluorophore transfers energy to a closely juxtaposed acceptor. Recognizing that FRET measured from the acceptor’s perspective reports a related but distinct quantity versus the donor, Ben-Johny uses the ratio-metric comparison of the two to obtain the stoichiometry of a complex. See abstract. The teaching from Ben-Johny means that FRET can be used to detect the stoichiometry of a molecule.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the method of Yates and Brown in characterizing stoichiometry of a molecule, because the device can be used for analyzing protein-protein interactions via FRET methodology as taught by Yates, wherein the FRET can be used to detect the stoichiometry of a molecule as taught by Ben-Johny.
One having an ordinary skill in the art would have been motivated to apply the method of Yates and Brown to detect the stoichiometry of a molecule because it would facilitate the study of macromolecular quaternary organization and elucidate mechanisms underlying normal and pathological molecular functions (Ben-Johny page 2 col.1 par.1).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yates as evidence by Ryan et al. in view of Brown et al., Rehman et al., as applied to claim 1 above, further in view of Shcherbakova et al. (Red Fluorescent Proteins: Advanced Imaging Applications and Future Design, Angew. Chem. Int. Ed. 2012, 51, 10724 – 10738, PTO-892 dated 07/23/2025).
Regarding claim 10, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates teaches the biomolecule is labelled with a fluorophore (see par.9: the analysis may include the step of labelling the component for ease of detection, see par.12: quantitative labelling procedures, such as the fluorescent labelling procedures described herein, allow the concentration of a component to be directly determined from the recorded analytical signal).
Yates does not teach that the fluorophore is in the far-red spectral region.
Shcherbakova reviews the advantages of far-red fluorescent protein (FP) and the emerging imaging approaches of far-red FP in biosensor (see Abstract page 10725, see page 10726 col.2 par.3: it would be good to use far-red FP acceptors in FRET). The advantages of the far-red FP include: low cytotoxicity so that it can be applied to live imaging (see page 10730 col.1 par.1); high photostability (see page 10730 col.1 par.2); deep tissue imaging to obtain the highest light transmission and the lowest autofluorescence (see page 10727 col.2 Fig.1 caption).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the fluorescent label in the device taught by Yates and Brown for the far-red spectral region to render obvious the claimed invention because Shcherbakova teaches that biosensor using far-red fluorescence provides high photostability, low autofluorescence and low cytotoxicity (see page 10727 col.2 Fig.1 caption, see page 10730 col.1 par.1-2).
A skilled artisan would have had a reasonable expectation of success in using far-red fluorescence taught by Shcherbakova in the modified device of Yates and Brown because Yates is generic to the fluorophore used in the analysis. Therefore, the substitution would have yielded a predictable result in detecting a target biomolecule.
Furthermore, Shcherbakova also teaches that to choose an optimal fluorescent probe, all the red shifted protein’s properties should be considered in their relation to the chosen microscopy technique, the biological object, and the experimental set-up (see page 10736 col.1 par.5). Therefore, one would know that it is a design choice of an optimal fluorescent probe and the corresponding detector relative to the target analyte.
Claim(s) 14, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yates as evidence by Ryan et al. in view of Brown et al., Rehman et al., as applied to claims 1 and 15 above, further in view of Gu et al. (Defining the structural basis for human alloantibody binding to human leukocyte antigen allele HLA-A*11:01, Nat Commun. 2019 Feb 21;10:893).
Regarding claims 14, 16 and 18, Yates, Ryan, Brown and Rehman teach the apparatus in claim 1. Yates does not teach wherein the antibody is an alloantibody.
For claim 16, Yates does not clearly teach wherein the multi-biomolecule mixture comprises an antibody and an antigen.
For claim 18, Yates does not teach wherein the sample fluid is a human serum comprising the biomolecule.
Gu teaches that anti-human leukocyte antigen (HLA) antibodies are important mediators of alloresponses, but structural insights on antibody:HLA interaction are still lacking. The predicted binding motifs for alloantibodies determined using these methods are termed Eplets which its weakness is that it does not define the true epitope of an alloantibody on HLA. The lack of structural data on the fine-specificity and related function(s) of human monoclonal anti-HLA alloantibodies potentially complicates the development of prognostic assays and associated clinical countermeasures in solid-organ transplantation. (See Introduction)
Gu teaches the need to analyze the immunological, biochemical and biophysical properties of an HLA-specific, human monoclonal antibody in addressing a significant gap that currently exists in our understanding of the fundamental biology of human alloantibodies (see Introduction). The interaction between human alloantibody and HLA allele in human serum was analyzed (see at least page 2 col.2 pars.2-3, Fig.5).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the method of Yates and Brown in characterizing alloantibody in human serum samples. One having an ordinary skill in the art would have been motivated to analyze the interaction between alloantibody and HLA antigen because it improves our understanding of the fundamental biology of human alloantibodies for the development of prognostic assays and associated clinical countermeasures in solid-organ transplantation as taught by Gu.
One having an ordinary skill in the art would have had a reasonable expectation of success in characterizing the interaction of alloantibody and its antigen in human serum samples by using the method of Yates, Brown and Rehman because the method can combine structural and non-structural biological data to precisely predict protein interactions and define the binding sites of an antibody on its antigen (Rehman page 2601 Conclusion).
Regarding claim 17, Yates, Ryan, Brown, Rehman and Gu teach the apparatus in claim 16. Yates teaches the biomolecule is labelled with a fluorophore (see par.9: the analysis may include the step of labelling the component for ease of detection, see par.12: quantitative labelling procedures, such as the fluorescent labelling procedures described herein, allow the concentration of a component to be directly determined from the recorded analytical signal). Thus, if the biomolecule is an antigen, the antigen is labelled as claimed.
Response to Arguments
Applicant argued that “The Patent Office is respectfully reminded that functional features must be evaluated and considered just like any other feature of the claim. See MPEP 2173.05(g) ("A functional limitation must be evaluated and considered, just like any other limitation of the claim, for what it fairly conveys to a person of ordinary skill in the pertinent art in the context in which it is used"). Therefore, a feature recited in a dependent claim does not need to be structural to further limit the claim from which it depends, and so claims 5-7 and 12-18 do further limit claim 1.” Therefore, the rejections of claims 5-7 and 12-18 under 35 USC 112(d) for failing to recite any additional structure of the device of claim 1 and do not further limit the subject matter of claim 1 should be withdrawn.
Examiner agrees to withdraw the rejection of claims 5-7 under 35 USC 112(d) because claims 5-7 further limit the function of the structures recited in claim.
However, Examiner still maintains the rejections of claims 12-18 under 35 USC 112(d) because the sample, e.g., a biomolecule, is not a part of a device. Thus, the limitation regarding the sample is considered to be an intended use of the device, thereby failing to recite any additional structure of the device of claim 1.
Applicant’s arguments, see Remarks, filed10/23/2025, with respect to the rejection(s) of claim(s) 1-18 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Yates, Ryan, Brown and Rehman.
Conclusion
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/CHAU N.B. TRAN/ Examiner, Art Unit 1677
/BAO-THUY L NGUYEN/Supervisory Patent Examiner, Art Unit 1677 March 31, 2026
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