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 .
Status of Claims
Claims 1-25 are pending and examined on the merits.
Priority
The instant application filed on 6/2/2022 is a 371 national stage entry of PCT/GB2020/053061 having an international filing date of 11/27/2020, and claims the benefit of priority to provisional U.S. Application No. 62/942,958 filed on 12/3/2019. Thus, the effective filing date of the claims is 12/3/2019.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 6/2/2022 has been entered and considered. A signed copy of the corresponding 1449 form has been included with this Office action.
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.
Claim 9 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.
Claim 9 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 9 recites "determining the extents of alignment from one or more data sources", which does not further limit claim 1 because claim 1 already has a step of determining the extent of alignment of the biological target to each selected class which involves one or more data sources (the selected class(es) in this case).
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
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1, 24, and 25: “for each of a plurality of biological targets, determining an extent of alignment of the biological target to each selected class” provides an evaluation (determining the extent of a sequence alignment to a reference) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“prioritising the biological targets based on the extents of alignment” provides for classifying or organizing information (prioritizing targets based on data involves sorting or structuring data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 10: “aggregating the extents of alignment from classifications based on respective data sources” provides for organizing information (aggregating data involves structuring data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 13: “identifying biological targets that match the user input by applying a minimum required extent of alignment for each selected class” provides a comparison (identifying targets by matching and applying a threshold involves comparing) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 14: “determining confidence metrics for the extents of alignment” provides a mathematical calculation (calculating a confidence metric as described in the instant spec para.0031) that is considered a mathematical concept, which is an abstract idea.
“ranking the biological targets that match the user input based on the confidence metrics” provides for classifying or organizing information (ranking targets based a metric involves sorting or structuring data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 16: “prioritising the biological targets comprises ranking the biological targets based on their extents of alignment to the selected classes” provides for classifying or organizing information (ranking targets based on data involves sorting or structuring data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea. For clarity of record, ranking (specific ordering with unique labels, e.g. numbering) is more limiting than prioritizing (applying a non-unique label, e.g. for binning targets into groups).
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 24 and 25 recite performing some aspects of the analysis on “A computer-readable medium storing code that, when executed by a computer, causes the computer to perform the method of claim 1” (claim 24) and “A system for prioritising biological targets” (claim 25), there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-25 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claims 1, 24, and 25: “receiving a selection of classes of one or more categories” provides insignificant extra-solution activities (receiving a selection of data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“outputting a representation of one or more prioritised biological targets” provides insignificant extra-solution activities (outputting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 6: “receiving a user input comprising the selection of classes of the one or more categories” provides insignificant extra-solution activities (receiving input from a user is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 18: “outputting a representation of the biological targets that match the user input” provides insignificant extra-solution activities (outputting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 19: “outputting a representation of the ranking” provides insignificant extra-solution activities (outputting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 20: “outputting a representation of the confidence metrics” provides insignificant extra-solution activities (outputting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 21: “providing a graphical user interface as an input and/or output tool” provides insignificant extra-solution activities (displaying data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 22: “providing a user input tool to enable to a user to generate a manual tagging command to override at least part of the output, the manual tagging command specifying whether or not one of the biological targets falls within one of the classes” provides insignificant extra-solution activities (editing data with an annotation tool is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 23: “training a classifier based on the manual tagging command and/or using the override command to augment a set of training data” provides insignificant extra-solution activities (training a classifier on annotated data is a pre- and post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 24: “A computer-readable medium storing code that, when executed by a computer, causes the computer to perform the method of claim 1” (as described in the instant spec para.0049-64) provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Claim 25: “A system for prioritising biological targets” (as described in the instant spec para.0049-64) provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
The steps for receiving (inputting), outputting, editing, and displaying data, and training classifiers on annotated data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering and manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-25 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “A computer-readable medium storing code that, when executed by a computer, causes the computer to perform the method of claim 1” (claim 24) and “A system for prioritising biological targets” (claim 25) requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for receiving (inputting), outputting, editing, and displaying data, and training classifiers on annotated data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-25 are not patent eligible.
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.
Claims 1-25 rejected under 35 U.S.C. 103 as being unpatentable over Iyengar et al. (US-20080261820) in view of Bixby et al. (US-20170147743).
Regarding independent claims 1, 24, and 25, Iyengar teaches a computer-implemented method of prioritising biological targets (Abstract "the present invention relates to a computer-aided method for the in-silico analysis of signaling and other cellular interaction pathways to rank drug targets, identify biomarkers, predict side effects, and classify/diagnose patients").
Iyengar also teaches receiving a selection of classes of one or more categories (Para.0009 "Within the enzymatic category, reactions should be further specified as phosphorylation, dephosphorylation, hydrolysis, etc" suggests a category with multiple classes, and para.0014 "selecting from the interaction data set a list of nodes shown to be altered in a cell upon treatment with said known drug as an algorithmic starting point", and figure 1 shows clusters of nodes organized into different categories (e.g. transcription machinery, translation machinery, secretion apparatus, etc)).
Iyengar also teaches prioritising the biological targets based on the extents of alignment (Para.0092 "In the fourth embodiment, modeling protocols and software that can rank components within the cell as targets for drugs that regulate complex cellular processes is developed").
Iyengar also teaches outputting a representation of one or more prioritised biological targets (Para.0067 "In order to identify the potential drug target or targets, the connectivity data (the nodes and connections representing the functional network or subnetwork) can be output in a visual or textual manner and manually inspected for the existence of nodes (representing proteins) not normally known to be modulated by the drug being evaluated").
Iyengar does not explicitly teach determining an extent of alignment of the biological target to each selected class.
However, Bixby teaches determining an extent of alignment of the biological target to each selected class (Para.0055 "FIG. 10 illustrates an example implementation of the process 112. The testing set of proteins are received at 1002, and, at 1004, a pairwise sequence alignment analysis is performed on the set using amino acid sequence data received from the database 108. The pairwise sequence alignment analysis quantifies the sequence similarity between each pair of proteins. Typically, such an analysis is used as a measure of the evolutionary distance (relatedness) between protein pairs. The more similar the sequences are, the shorter the evolutionary distance is between the two proteins (i.e., the more related they are). In the present techniques, however, the system uses the sequence similarity as a proxy measure for biochemical similarity, i.e., the likelihood that the proteins would bind the same molecules with similar potency").
Therefore, while Bixby aligns sequences to selected other sequences, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the node selection of Iyengar as taught by Bixby in order to identify subsets of proteins (or other biological targets) that will produce a particular biologic activity (para.0070 "the system included (selecting, identifying, and/or prioritizing) a Maximum Information Set (MAXIS) of Kinases Using Maximum Relevance and Support Vector Machines (MR-SVM) that is used identify the subset of protein kinases to be engaged for in order to produce a sufficient amount of biologic activity. In an example that biologic activity was neurite outgrowth"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with using machine learning to identify and prioritize biological targets for pharmaceuticals.
Regarding claim 2, Iyengar in view of Bixby teach the method of Claim 1 on which this claim depends/these claims depend. Iyengar also teaches the classes of the categories represent values or value ranges of the categories (Para.0073 "The 50 top-scoring kinases were trimmed using a support vector machine (SVM) learning algorithm. Inhibition profiles were discretized to convert the continuous (0-100%) inhibition range to a discrete integer range (0-10%=1, 10-20%=2, . . . , 90-100%=10). The SVM was trained to classify compounds as hits or non-hits based on their inhibition profiles against the 50 most relevant kinases" and and para.0085 teaches testing various dose ranges as well, suggesting user input dosage categories).
Regarding claims 3-5, Iyengar in view of Bixby teach the method of Claim 1 on which this claim depends/these claims depend. Iyengar also teaches: the selected classes of one of the categories are not mutually adjacent; the selection of classes comprises at least two classes of the same category; and the categories represent properties of the biological targets (Figure 1 shows clusters of nodes organized into different categories which represent properties of the biological targets (e.g. transcription machinery, translation machinery, secretion apparatus, etc), from which a user may select categories that are not mutually adjacent or one or more classes from the same category).
Regarding claim 6, Iyengar in view of Bixby teach the method of Claim 1 on which this claim depends/these claims depend. Iyengar also teaches receiving a user input comprising the selection of classes of the one or more categories (para.0014 "selecting from the interaction data set a list of nodes shown to be altered in a cell upon treatment with said known drug as an algorithmic starting point").
Regarding claim 7, Iyengar in view of Bixby teach the method of Claim 1 on which this claim depends/these claims depend. Bixby also teaches the extent of alignment between a biological target and a selected class comprises a likelihood of the biological target falling within the selected class (Para.0064 "Screened compounds were classified based on their effects on neurite total length, expressed as percentage of control (% NTL), which served as the biological activity referenced in FIG. 1, for this example", as a percent represents the likelihood of classification).
Regarding claim 8, Iyengar in view of Bixby teach the method of Claim 7 on which this claim depends/these claims depend. Bixby also teaches the likelihood corresponds to a distribution normalised across all classes of the same category (Para.0080 "The computer system standardizes and aggregates the data into an integrated database 504, as shown. That aggregated data is collectively used to compute pharmacological linkage strengths, 506, for all pairs of kinases. The pharmacological linkage strength values are incorporated into a target/anti-target deconvolution algorithm, MRMR.sub.kin 508").
Regarding claim 10, Iyengar in view of Bixby teach the method of Claim 1 on which this claim depends/these claims depend. Bixby also teaches aggregating the extents of alignment from classifications based on respective data sources (Claim 1 "ranking the pharmacologically linked protein groups based on an aggregated biological activity score").
Regarding claim 11, Iyengar in view of Bixby teach the method of Claim 1 on which this claim depends/these claims depend. Bixby also teaches determining the extents of alignment using a trained machine learning classifier (Para.0013 "The present techniques are able to integrate the two predominant drug discovery technologies, target-based and phenotypic screening, combining their respective strengths through the use of information theory and machine learning").
Regarding claim 12, Iyengar in view of Bixby teach the method of Claim 6 on which this claim depends/these claims depend. Iyengar also teaches the biological targets comprise genes, nucleic acid sequences, proteins, amino acid sequences, protein complexes, and/or biological pathways (Para.0053 "the entities would be two interacting proteins for example" and para.0054 "Potential sources of information regarding interaction data include the scientific literature and high content experimentation such as expression profiling or microarray").
Regarding claims 13-14, Iyengar in view of Bixby teach the method of Claim 12 on which this claim depends/these claims depend. Bixby also teaches: prioritising the biological targets comprises identifying biological targets that match the user input by applying a minimum required extent of alignment for each selected class; and determining confidence metrics for the extents of alignment and optionally ranking the biological targets that match the user input based on the confidence metrics (Abstract "The system separates compounds into subsets based on biological threshold data and groups proteins through pharmacological similarity").
Regarding claim 15, Iyengar in view of Bixby teach the method of Claim 14 on which this claim depends. Bixby also teaches determining the confidence metrics using a machine learning technique (Para.0071 "Any number of machine learning algorithm-based processes may be used []. Other example machine learning algorithms include [], similarity and metric learning algorithm").
Regarding claim 16, Iyengar in view of Bixby teach the method of Claim 1 on which this claim depends. Iyengar also teaches prioritising the biological targets comprises ranking the biological targets based on their extents of alignment to the selected classes (Abstract "the present invention relates to a computer-aided method for the in-silico analysis of signaling and other cellular interaction pathways to rank drug targets, identify biomarkers, predict side effects, and classify/diagnose patients").
Regarding claim 17, Iyengar in view of Bixby teach the method of Claim 6 on which this claim depends. Bixby also teaches the user input comprises an indication of relative importance of the categories and prioritising the biological targets comprises using the indication of relative importance (Para.0046 "A user may input such population or demographic data, and the identification system may automatically assess those data for relevant population and demographic conditions and use those conditions to request biologic activity data on those compounds that have been determined to correspond to those conditions").
Regarding claims 18-20, Iyengar in view of Bixby teach the method of Claims 13, 16, and 14 on which this claim depends/these claims depend, respectively. Bixby also teaches: outputting a representation of the biological targets that match the user input; outputting a representation of the ranking; and outputting a representation of the confidence metrics (Para.0108 "Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information").
Regarding claims 21-23, Iyengar in view of Bixby teach the method of Claims 1 and 10 on which this claim depends/these claims depend, respectively. Iyengar also teaches: providing a graphical user interface as an input and/or output tool; providing a user input tool to enable to a user to generate a manual tagging command to override at least part of the output, the manual tagging command specifying whether or not one of the biological targets falls within one of the classes; and training a classifier based on the manual tagging command and/or using the override command to augment a set of training data (Para.0089 "The user interface of the software allows the user to reject or accept interactions, link protein names to database identifiable numbers and store ontology on the same screen. The software has a learning algorithm that drives an internal process that recognizes previous entries to validate new components and interactions").
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US-20190057182 teaches identifying a biological target in the context of a drug for a patient.
Conclusion
No claims are allowed.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Robert A. Player whose telephone number is 571-272-6350. The examiner can normally be reached Mon-Fri, 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D. Riggs can be reached at 571-270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686