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 .
DETAILED ACTION
This Office Action is in response to the application filed on 11/21/2025.
Election/Restrictions
Applicant’s election without traverse of (Group I, claims 1-27 and 33) in the reply filed on 11/21/2025 is acknowledged.
Claims 28-32 and 34-37 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected group II and III, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/21/2025.
Claims 1-27 and 33 are pending.
Drawings
The drawings filed on 10/13/2022 are accepted.
Claim Rejections - 35 USC § 101
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The Claim recites the language of “providing a machine learning model receiving one or more inputs taken from an image selected from one of a plurality of images generated from one or more groups of imaging assays, wherein the image is associated with other images of the one or more groups according to one or more antibodies present in a biological sample from which the image was generated; and
using the machine learning model, computing an output comprising one or more predicted phenotypes represented in one or more of the plurality of images of that group, the output comprising an antibody profile.”
Claim 1 recites the limitation of “providing a machine learning model receiving one or more inputs taken from an image selected from one of a plurality of images generated from one or more groups of imaging assays, wherein the image is associated with other images of the one or more groups according to one or more antibodies present in a biological sample from which the image was generated”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim element precludes the step from practically being performed in the mind. For example, “providing” in the context of this claim encompasses the user manually providing user information. Also Similarly, the limitation of using the machine learning model, computing an output comprising one or more predicted phenotypes represented in one or more of the plurality of images of that group, the output comprising an antibody profile, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “using the machine” in the context of this claim encompasses the user manually interact with the information. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human activity but for the recitation of generic computer components, then it falls within the “Human Activity” and “Gathering Information” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using one or more processors to perform the providing and using machine model steps. The processor and memory in those steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of gathering or collect information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor the providing and using machine model steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-27 and 33 is dependent on independent claim 1 and includes all the limitations of claim 1. Claims 2-27 and 33 recite “biological sample comprises a plurality of cells, cells types, machine learning process is weekly supervised, and the models is neural, training the machine learning model, groups a common design with other image, predict image, select from the set of antibody identifier, display of phenotype versus antibody class group, inputs pixel intensities, pixel intensities correspond to a plurality of fluorescent probes, represent probe on target phenotype, on target phenotypes is cell stimulation and expansion, cell senescence, apoptosis and cytotoxicity, stimulation of a cell signaling pathway, output a graphical representation of the antibody profile, predicted classification of phenotypes, wherein the phenotype, the on target phenotype comprises inhibition of an activated state of a tumor cell, inhibition of activation of a non-tumor cell fibroblast, inhibition of activated state of a cell in contact with a tumor cell, the cell in contact with the tumor cell is a fibroblast, inhibition of tumor cell contact induced fibroblast activation, off target phenotypes is selected from the group consisting of autophagy, cytotoxicity, auto fluorescence, and senescence induction, generating two or more images of each antibody, probe represents an on target phenotypes and off target phenotype. The claim languages provides only further biological sample, cell types, training learning of images, input, probe represents, on target phenotypes, output which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Accordingly, the claims 1-27 and 33 are not patent eligible.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 103
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 1-23, 25-27 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Gifford et al. (US PGPUB 2019/0065677, hereinafter Gifford), in view of Gill et al. (US PGPUB 2013/0085079, hereinafter Gill).
As per as claim 1, Gifford discloses:
(Original) A method for profiling antibodies, comprising:
providing a machine learning model receiving one or more inputs taken from an image selected from one of a plurality of images generated from one or more groups of imaging assays, wherein the image is associated with other images of the one or more groups according to one or more antibodies present in a biological sample from which the image was generated (Gifford, e.g., [005-0011], “... receiving affinity information associated with an antibody having the proposed amino acid sequence with the antigen and training the machine learning engine using the affinity information... machine learning engine regarding levels of the characteristic if the discrete attribute is selected for the position...” and further see figs. 8A-8C for select one of a plurality of images in the group of biological sample, and see [0142-0143], “...demonstrating that panning results are consistent across replicates and can separate antibody sequences by affinity CDR sequences have almost identical enrichment from Pre-Pan to Pan-1 across two technical replicates...”); and
using the machine learning model, computing an output comprising one or more predicted phenotypes represented in one or more of the plurality of images of that group, the output comprising an antibody profile (Gifford, e.g., [0144-0146], “... training set produced predictions for the held out testing set that correlated well with the observed affinity, with an R.sup.2 of 0.58 and Spearman correlation of 0.767...each point represents a sequence held out from training. The x-axis denotes the observed binding affinity and y-axis shows the prediction from a CNN trained to predict affinity to influenza hemagglutinin from amino acid sequence...” and see [0109-0110], “...antibody sequences to profile in subsequent assays... machine learning models can be trained to estimate the relative binding affinity of unseen antibody sequences for the target. Once such a model is generated, antibody sequences that are designed to improve binding to a target can be predicted and tested...”).
To make records clearer regarding to the language of “one or more antibodies present in a biological sample” (although as stated above, Gifford functional discloses the features of biological sample (Gifford, e.g., [0142-0143])).
However Gill, in an analogous art, discloses “one or more antibodies present in a biological sample” (Gill, e.g., [abstract], “... a biological sample from an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers provided in Table 2...” and see [0031], [0041-0046], “...biological sample can be whole blood, plasma, serum, urine or the like. In a preferred embodiment, the biological sample is serum, plasma or urine...”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Gill and Gifford to validated or shown to be a reliable indicator for the original intended use for which it was selected and include small molecules, peptides, proteins, and nucleic acids which affect the identification of biomarkers include over-fitting of the available data and bias in the data (Gill, e.g., [0007-0009]).
As per as claim 2, the combination of Gill and Gifford disclose:
(Original) The method according to claim 1 wherein said biological sample comprises a plurality of cells (Gill, e.g., [0019], [0027], “blood cells”).
As per as claim 3, the combination of Gill and Gifford disclose:
(Original) The method according to claim 2 wherein said plurality of cells comprises two or more different cell types (Gill, e.g., [0023], [0027], “... biological sample, measured in a single assay, rather than multiple samples for different analyte types (lipids, proteins, metabolites) or panels of analytes. The central benefit to a single sample test is simplicity at the point of use, since a test with multiple sample collections is more complex to administer and this forms a barrier to adoption...”).
As per as claim 4, the combination of Gill and Gifford disclose:
(Currently Amended) The method according to claim 1 wherein the machine learning process is weakly supervised, and the model is neural network selected
from the group consisting of a multiple instance learning (MIL) model, a deep neural network (DNN), a convolutional neural network (CNN), and a neural network comprising one or more convolutional layers and an MIL pooling layer (Gillford, e.g., figs. 9A, 10-12, associating with texts description, [0039-0044], “...using a CNN trained to predict affinity to influenza hemagglutinin from amino acid sequences...” and [0076-0077], “...model may have a convolutional neural network (CNN), which may have any suitable of convolution layers...”).
As per as claim 5, the combination of Gill and Gifford disclose:
(Original) The method according to claim 1 further comprising training the machine learning model using the plurality of images, to enable the model to predict a group from the one or more groups of which the image is a member (Gifford, e.g., [0064-0067], [0072], “... model generated by training the machine learning engine may have one or more parameters corresponding to different combinations of the characteristics... Training data in some such embodiments may include amino acid sequences and information identifying affinity and specificity for each of the amino acid sequences, which when used to train a machine learning engine generates a model having a parameter representing a weight between affinity and specificity used to predict a proposed amino acid sequence...”).
As per as claim 6, the combination of Gill and Gifford disclose:
(Original) The method according to claim 5, wherein the one or more groups come from a common experimental design with other images (Gifford, e.g., [0081], “...performing phage panning experiments...” and [0109-0110], “... antibody phage display experiments for a target, machine learning models can be trained to estimate the relative binding affinity of unseen antibody sequences for the target...”).
As per as claim 7, the combination of Gill and Gifford disclose:
(Original) The method according to Claim 5, wherein the training further comprises enabling the model to predict whether an image from a group was generated from one or a plurality of experimental conditions in the assays from which the images were generated (Gifford, e.g., [0081], “...performing phage panning experiments...” and [0109-0110], “... antibody phage display experiments for a target, machine learning models can be trained to estimate the relative binding affinity of unseen antibody sequences for the target...”).
As per as claim 8, the combination of Gill and Gifford disclose:
(Original) The method according to claim 7, wherein the experimental conditions are selected from the set of antibody identifier, concentration of antibody, cell type, combination of cell types, cell seeding density, probe set, presence, absence or concentration of ligand, presence, absence or concentration of small molecule inhibitors, depletion, knockout, overexpression or modulation of genes, or presence, absence or concentration of combinations of any other
molecules that may modulate the activity of an antibody (Gifford, e.g., [0109-0110], “...Data from additional experiments may be used to improve the model's ability to accurately predict outcomes. Such models may design previously unseen sequences with both highly uncertain and a range of predicted affinities. These designs can be tested using phage display, and the observed high-throughput affinity data can be used to improve the models to enable the prediction of high-affinity and highly-specific binders. The recent commercialization of array-based oligonucleotide synthesis allows for a million specified DNA sequences to be manufactured at modest cost...”).
As per as claim 9, the combination of Gill and Gifford disclose:
(Original) The method according to claim 7, further comprising providing a graphical display of phenotype versus antibody class grouped according to a distribution of learned weights in one or more hidden layers of the trained machine learning model (Gifford, e.g., [0072], [0105], [0107], “...training the machine learning engine that includes a parameter representing a weight between affinity with the antigen and affinity with the second antigen used to predict the proposed amino acid sequence...”).
As per as claim 10, the combination of Gill and Gifford disclose:
(Original) The method according to claim 1, wherein the inputs comprise pixel
Intensities (Gifford, e.g., [0011], “... using training data that relates the discrete attributes to a characteristic of series of the discrete attributes is provided....receiving an initial series of discrete attributes as an input into the model. Each of the discrete attributes is located at a position within the initial series and is one of a plurality of discrete attributes...”).
As per as claim 11, the combination of Gill and Gifford disclose:
(Original) The method according to claim 10, wherein said pixel intensities correspond to a plurality of fluorescent probes in said biological sample in said assay, each probe representing a phenotype of interest (Gifford, e.g., [0144]) and further see (Gill, e.g.,[0064], [0121], “... fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots...” and see [0132-0136], [0154], “...a fluorescent tag can be used to label a component of the biomarker/capture complex to enable the detection of the biomarker value...”).
As per as claim 12, the combination of Gill and Gifford disclose:
(Original) The method according to claim 11, wherein at least one probe represents an on-target phenotype and at least one probe represents an off-target phenotype (Gill, e.g., [0064], [0088], [0182], [0185-0187], “... targets for the pair of affinity probes may be two distinct determinates on one protein or one determinate on each of two different proteins, which may exist as homo- or hetero-multimeric complexes... prediction of risk of CV events, where the methods comprise detecting, in a biological sample from an individual, at least N biomarker values that each correspond to a biomarker selected from the group consisting of the biomarkers...”).
As per as claim 13, the combination of Gill and Gifford disclose:
(Original) The method according to claim 12, wherein said on-target phenotype is cell stimulation and/or expansion (Gill, e.g., [0015], “...growth factors are secreted to stimulate the replication of other cells in the pathology...” and see [0064], [0088], [0182], [0185-0187]).
As per as claim 14, the combination of Gill and Gifford disclose:
(Original) The method according to claim 12, wherein said on-target phenotype is cell senescence, apoptosis and/or cytotoxicity (Gill, e.g., [0015], “...growth factors are secreted to stimulate the replication of other cells in the pathology...” and see [0064], [0088], [0182], [0185-0187]).
As per as claim 15, the combination of Gill and Gifford disclose:
(Original) The method according to claim 12, wherein said on-target phenotype is
stimulation of a cell-signaling pathway (Gill, e.g., [0015], “...biochemical pathways for complex human biology. Many biochemical pathways culminate in or are started by secreted proteins that work locally within the pathology...growth factors are secreted to stimulate the replication of other cells in the pathology...” and see [0064], [0088], [0182], [0185-0187]).
As per as claim 16, the combination of Gill and Gifford disclose:
(Original) The method according to claim 12, wherein said on-target phenotype is
inhibition of a cell-signaling pathway (Gill, e.g., [0015], “...biochemical pathways for complex human biology. Many biochemical pathways culminate in or are started by secreted proteins that work locally within the pathology...growth factors are secreted to stimulate the replication of other cells in the pathology...” and see [0060], [0064], [0088], [0108], [0182], [0185-0187], “...molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affybodies, antibody mimics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the foregoing...”)).
As per as claim 17, the combination of Gill and Gifford disclose:
(Original) The method of claim 1 wherein the output comprises a graphical representation of the antibody profile (Gifford, e.g., [0223-0224], “... individual's biological sample is performed. The biomarker information can comprise biomarker values that each correspond to one of at least N biomarkers selected from a group consisting of the biomarkers provided in Table... output to a display or other indicating device so that it is viewable by a person...”).
As per as claim 18, the combination of Gill and Gifford disclose:
(Currently Amended) The method according to claim 1, wherein the output antibody profile comprises a predicted classification of phenotype, wherein
the phenotype is selected from the group consisting of on-target and off-target (Gifford, e.g., [0223-0224], “... prediction biomarker analysis... computer can be utilized to classify each of the biomarker values... event based upon a plurality of classifications. The indication can be output to a display or other indicating device so that it is viewable by a person...”) and see (Gill, e.g., [0012-0014], [0044-0047]).
As per as claim 19, the combination of Gill and Gifford disclose:
(Original) The method of claim 18, wherein the on-target phenotype comprises inhibition of an activated state of a tumor cell, a non-tumor cell, or a cell in contact with a tumor cell (Gill, e.g., [0108-0112], “...measured levels, or the like, of, for, or corresponding to the biomarker in the biological sample..."value" or "level" depends on the specific design and components of the particular analytical method employed to detect the biomarker...”).
As per as claim 20, the combination of Gill and Gifford disclose:
(Original) The method of claim 19, wherein the on-target phenotype comprises inhibition of activation of a non-tumor cell fibroblast by an exogenously applied activating ligand (Gill, e.g., [0113-0117], “...over-expressed or under-expressed can also be referred to as being "differentially expressed" or as having a "differential level" or "differential value" as compared to a "normal" expression level or value of the biomarker that indicates or is a sign of a normal process or an absence of a disease or other condition in an individual... different stages of the same disease or condition. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product...”).
As per as claim 21, the combination of Gill and Gifford disclose:
(Original) The method of claim 19, wherein the on-target phenotype comprises inhibition of an activated state of a cell in contact with a tumor cell (Gill, e.g., [0113-0117]).
As per as claim 22, the combination of Gill and Gifford disclose:
(Original) The method of claim 21, wherein the cell in contact with the tumor cell is a fibroblast (Gifford, e.g., [0223-0224]) and (Gill, e.g., [0023-0024], [0026], “...ardiovascular disease are inflammation, thrombosis, disease-associated angiogenesis, platelet activation, macrophage activation, liver acute response, extracellular matrix remodeling, and renal function...gender, menopausal status, and age, and according to status of coagulation and vascular function...”).
As per as claim 23, the combination of Gill and Gifford disclose:
(Original) The method of claim 22, wherein the on-target phenotype comprises inhibition of tumor cell contact-induced fibroblast activation (Gill, e.g., [0023-0027], “...disease (e.g., angiogenesis, platelet activation, macrophage activation, liver acute response, other lymphocyte inflammation, extracellular matrix remodeling, and renal function)...multiple protein based prognostic single sample test for CV disease...” and see fig. 11, table 1).
As per as claim 25, the combination of Gill and Gifford disclose:
(Original) The method according to claim 1, further comprising a preliminary step of individually contacting each of a panel of antibodies with a biological sample, wherein said sample comprises a probe set representative of a plurality of phenotypes of interest, and generating at least one image of each antibody/biological sample pairing in said panel (Gill, e.g., [0018], [0023], [0098-0101], “...biological sample, measured in a single assay, rather than multiple samples for different analyte types (lipids, proteins, metabolites) or panels of analytes. The central benefit to a single sample test is simplicity at the point of use, since a test with multiple sample collections is more complex to administer and this forms...”).
As per as claim 26, the combination of Gill and Gifford disclose:
(Original) The method according to claim 25, comprising generating two or more images of each antibody/biological sample pairing at sequential time points (Gil, e.g., [0018], [0023], [0098-0101], “...one or more biomarker values detected in their biological sample, as having an increased risk of having a CV Event within 5 years...CV event within the same time period. "Sensitivity" indicates the performance of the biomarker(s) with respect to correctly classifying individuals that have increased risk of a CV event...) (the examiner asserts cv event with the same time period = biological sample pairing at sequential time points) and further see [0185], “... test sample is contacted with a pair of affinity probes that may be a pair of antibodies or a pair of SOMAmers, with each member of the pair extended with an oligonucleotide. The targets for the pair of affinity probes may be two distinct determinates on one protein or one determinate on each of two different proteins, which may exist as homo- or hetero-multimeric complexes...”).
As per as claim 27, the combination of Gill and Gifford disclose:
(Original) The method according to claim 25, wherein at least one probe represents an on-target phenotype and at least one probe represents an off-target phenotype (Gil, e.g., [0049], [0063], [0088], (probes of biological sample) and further see [0185-0187]).
Claim 33 contain essentially the same subject matter as claim 1 and therefore are rejected under the same rationale.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Gifford et al. (US PGPUB 2019/0065677, hereinafter Gifford), in view of Gill et al. (US PGPUB 2013/0085079, hereinafter Gill) and further in view of Yoo et al. (US PGPUB 2020/0131266, hereinafter Yoo).
As per as claim 24, the combination of Gill and Gifford disclose:
(Currently Amended) The method of claim 18, wherein the off-target phenotype (Gill, e.g., [0023-0027]), but to further clarify the features of “selected from the group consisting of autophagy, cytotoxicity, auto-fluorescence, and senescence induction”.
However Yoo, in an analogous art, discloses autophagy (Yoo, e.g., [0715], “autophagy”, cytotoxicity [0519], [0521], [0656], “cytotoxicity”, auto-fluorescence [0088-0092], “fluorescence images”, and senescence induction [0715], “senescence”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Yoo, Gill and Gifford for treating cancer using anti-BTN1A1 antibodies or other molecules having an antigen binding fragment that immunospecifically bind to BTN1A1 and effectively treat diseases by modulating the immune system remain an urgent need, especially for anti-PD1 therapy or anti-PD-L1 therapy resistant or refractory cancers (Yoo, e.g., [0003-0004]).
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to receive as input microscopy images, extract features, and apply layers of processing units to compute one or more sets of cellular phenotype features, particularly antibodies, corresponding to cellular densities and/or fluorescence measured under different conditions which produces predictions for one or more reference antibody variables based on microscopy images within populations of cells.
a. Amimeur et al. (US PGPUB 2022/0230710, hereinafter Amimeur); “Generation Of Protein Sequences Using Machine Learning Techniques” discloses “using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component generates amino acid sequences of antibody heavy chains”.
Amimeur also teaches sequences of antibodies that bind to a specified antigen (figs. 5-9).
Amimeur further teaches cell and type of antibody [0036], fibronectin type III (FNIII) proteins, avimers, antibodies, VHH domains, kinases [0029], [0031], [0038].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN A PHAM whose telephone number is (571)270-3173. The examiner can normally be reached M-F 7:45 AM - 6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUAN A PHAM/Primary Examiner, Art Unit 2163