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 effective filing date is July 31st, 2023.
Status of Claims
Claims 1-15 are currently pending and examined on the merits.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“data extraction module”, “data filtration module” and “final expression level calculator” in claim 1,
“string extraction module”, and “string filtering module” in claim 2,
“probability filtering module” in claim 5,
“grouping module” in claim 6,
“final expression level calculator” in claim 7, and
“Natural Language Processing (NLP) module” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7 and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 states “The system according to claim 1, wherein the final expression level calculator calculates the final expression level from the identified relevant information by comparing the probability values of the word strings.” The term “final expression level calculator” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. One of ordinary skill in the art of bioinformatics would not understand what “final expression level calculator” is. The specification defines the calculator as: “The final expression level is calculated by the final expression level calculator (150) by subtracting the probability of high and low expression levels. This score is then used to predict the curable action of the target protein in the disease.” (Spec, 00014)
For the purposes of speedy examination, the Examiner interprets the claim as an equation to calculate expression levels.
Claim 9 states “The system according to claim 1, wherein the system includes a Natural Language Processing (NLP) module, wherein the protein data extraction module uses the NLP module for parsing and identifying information related to the target proteins.” The term “Natural Language Processing (NLP) module” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. One of ordinary skill in the art of bioinformatics would not understand what “final expression level calculator” is. The specification states:
“In various embodiments, the system (100) may include a Natural Language Processing (NLP) module (160). For example, the protein data extraction module (110) may use the NLP module (160) for parsing and identifying information related to the target proteins. “ (Spec, para. 00032). For the purposes of speedy examination, the Examiner interprets the claim as any natural language processing method.
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.
Regarding claim 7, For the purposes of speedy examination, the Examiner interprets the claim as an equation to calculate expression levels.
Regarding claim 9, For the purposes of speedy examination, the Examiner interprets the claim as any natural language processing method.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 2-9 further limit claim 1.
The claim(s) do not fall within at least one of the four categories of patent eligible subject matter because they are drawn to “system” that has no physical or tangible elements, and can be completely met by software, transmitted or embodied in transitory signals. The “system” merely comprises various key information “module(s)”, calculations, keywords, and machine learning classifiers.
Non-limiting examples of claims that are not directed to any of the statutory categories include:
Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations;
Transitory forms of signal transmission (often referred to as "signals per se"), such as a propagating electrical or electromagnetic signal or carrier wave; and
Subject matter that the statute expressly prohibits from being patented, such as humans per se, which are excluded under The Leahy-Smith America Invents Act (AIA ), Public Law 112-29, sec. 33, 125 Stat. 284 (September 16, 2011).
As the courts' definitions of machines, manufactures and compositions of matter indicate, a product must have a physical or tangible form in order to fall within one of these statutory categories. Digitech, 758 F.3d at 1348, 111 USPQ2d at 1719. Thus, the Federal Circuit has held that a product claim to an intangible collection of information, even if created by human effort, does not fall within any statutory category. Digitech, 758 F.3d at 1350, 111 USPQ2d at 1720 (claimed "device profile" comprising two sets of data did not meet any of the categories because it was neither a process nor a tangible product). Similarly, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category. Another example of an intangible product that does not fall within a statutory category is a paradigm or business model for a marketing company. In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1039-40 (Fed. Cir. 2009). See MPEP 2106.03.
For the purpose of compact prosecution, these claims will be evaluated for subject matter eligibility, however the claims must be amended to fall within the four categories of patent eligible material.
Claim 1 states:
“A system for determining a curable action value of a target protein for aiding in drug development, the system comprising:
a protein data extraction module (which is data per se, i.e. has no physical form)
the protein data extraction module parsing and identifying information related to the target protein from a public database (which limits the module which is data per se)
…the public database…(data per se)
comprising data related to proteins ((which limits the database which is data per se)
…a protein data filtration module for filtering out irrelevant information from the identified information related to the target proteins; (which is data per se, i.e. has no physical form)
and a final expression level calculator, (which is data per se, i.e. has no physical form)
the final expression level calculator being configured for calculating the final expression level from the identified relevant information, the curable action of the target protein being based upon the final expression level. (which limits the module which is data per se)
Claim 2 further limits the “system” of claim 1 and states:
“a string extraction module, the string extraction module identifying strings of words in the public database related to the target protein” (data per se)
“…keywords…” (data per se)
“a string filtering module, the string filtering module filtering relevant word strings from the identified strings of words…” (data per se)
Claim 3 further limits the “system” of claim 1 states:
“…a…classifier” (data per se)
Claim 4 further limits the “system” of claim 3
Claim 5 states:
“the system comprises probability filtering module, the probability filtering module configured to identify high probability word strings from the conclusive word strings” (data per se)
Claim 6 states:
“a grouping module, the grouping module being configured to aggregate the identified high probability word strings into groups…” (data per se)
Claim 7 further limits the “system” of claim 1
Claim 8 further limits the “system” of claim 1
Claim 9 states: “…a NLP module…” (data per se)
As such, claim(s) 1-9 recite non-statutory subject matter (Step 2A, Prong 1: NO).
Claims 10-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (claims 10-15 are drawn to a method) (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 (listed numerically) recite the following limitations that equate to an abstract idea (reasonings in parentheses):
Claim 10 states:
“parsing and identifying information” and “filtering out irrelevant information” (which is a mental step, i.e. can be performed with pen and paper)
“calculating a final expression level of the identified relevant information” (which is a mathematical concept of a mathematical calculation)
Claim 11 states:
“identifying strings of words”, “searching for the target protein…”, “filtering relevant word strings…”, “checking the presence of at least one word from a set of keywords in the identified string of words using a string filtering module” (mental step)
Claim 12 states:
“classifying each of the relevant word strings into a [first, second, third]-level classification…” (mathematical calculation)
Claim 13 states:
“identifying high probability word strings” and “comparing an assigned probability value with a predefined threshold probability value” (mathematical calculation)
Claim 14 states:
“aggregating, using a grouping module, the identified high probability word strings into groups based upon the disease to which the target protein in the high probability word strings pertains” (mental step)
Claim 15 states:
“calculating the final expression level from the identified relevant information by comparing the probability values of the word strings” (mathematical calculation)
“calculating the final expression level from the identified relevant information by comparing the probability values of the word strings” (mathematical calculation)
The claims recite an abstract idea of gathering data from and analyzing biomedical text sources (See MPEP 2106.07(a)).
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 mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas.
While claims 12 and 15 recites performing some aspects of the analysis using a “classifier” and a “NLP module”, there are no additional limitations that indicate that this system requires anything other than carrying out the recited mental process or mathematical concept 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 if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 10-15 recite(s) an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1: YES).
Step 2A, Prong 2
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). This judicial exception is not integrated into a practical application because the
claims do not recite an additional element that reflects an improvement to technology or applies or uses
the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims
recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic
treatment.
There are no limitations that indicate that the claimed analysis engine or the formats of the
provided data require anything other than generic computing systems. As such, these limitations equate does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. As such, claims 10-15 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
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 do not recite additional elements
Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 10-15 is/are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 7-11 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hee-Jin Lee et al. (Nucleic Acids Research, 2014, Vol. 42, Web Server issue. W416–W421).
Claim references are italicized.
Regarding claims 1 and 10, Lee et al. teaches OncoSeach, a cancer gene search engine with literature evidence, including: (i) a Web-based user query interface and a display tool to present search results, (ii) a text-mining process to identify gene expression changes and cancer changes from text and to store them into a database and(iii) a search process for sentences that describe gene–cancer relations as specified by a given query (pg. W147) (re: clm. 1, 10, … A system for determining a curable action value of a target protein for aiding in drug development, the system comprising…)
Lee et al. further teaches obtaining inference rules for gene class types and sensitivity of gene class interference on tables 2 and 3 respectively, with table 3 including data related to proteins (re: clm. 1, … a protein data extraction module, the protein data extraction module parsing and identifying information related to the target protein from a public database, the public database comprising data related to proteins…).
Lee et al. further teaches on pg. W417 that “The retrieved sentences are ranked by the confidence scores produced by text-mining modules and provided via three different views” and continues to state the searching and ranking criteria on pg. W420. This reads on a filtration step (re: clm. 1, 10 … a protein data filtration module for filtering out irrelevant information from the identified information related to the target proteins…). Lee et al. additionally teaches inclusion of gene expression changes and gene expression levels in the classification analysis of oncogenes, tumor suppressor genes (which is a curable action), and/or biomarker in Table 2 and on pg. W420 (re: clm. 1, 10 … a final expression level calculator, the final expression level calculator being configured for calculating the final expression level from the identified relevant information, the curable action of the target protein being based upon the final expression level). Lee et al. teaches OncoSearch in anticipation of claims 1 and 10.
Regarding claims 2 and 11, Lee et al. teaches identifying protein gene names and keywords from UniprotKB, stating: on pg. 420:
“Table 3 shows the sensitivity of gene class inference when measured against the oncogenes and tumor suppressor genes registered in UniProtKB (we used the genes annotated with the keywords ‘proto-oncogene (KW-0656)’and‘ tumor suppressor (KW-0043)’) (3) and the Vogelstein cancer genes list (20). We consider the sensitivity rates adequate, given the fact that only 6.87% (18/262) of the oncogenes and 3.76% (16/426) of the tumor suppressor genes in UniProtKB are designated as such in the Vogelstein list.” This reads on extracting and filtering strings to identify targeted words (re: clm. 2, 11, … a string extraction module, the string extraction module identifying strings of words in the public database related to the target protein, the identification being done by searching for the target protein and a related disease within the public database; a set of keywords, the set of keywords including a set of words; and a string filtering module, the string filtering module filtering relevant word strings from the identified strings of words, the filtering being performed by checking the presence of at least one word from the set of keywords in the identified string of words.). Lee et al. teaches protein gene name acquisition in anticipation of claims 2 and 11.
Regarding claim 7, Lee et al. teaches expression level calculated from word string probability values, stating on pgs. W419-W420:
“After identifying gene expression change mentions and cancer names from the abstracts, we selected only the sentences that contain at least one cancer name and at least one mention of gene expression change. For each of such sentences, we identified the respective types of…” (Lee et al. continues to describe the types of sentences).
Lee et al. further discloses using said sentences mentioning gene expression changes with a classifier used to detect accuracy, stating on pgs. W419-W420:
“We trained the classifier using a corpus provided by Lee and colleagues, or CoMAGC, since we adopted the query concepts from their work. The classifier achieved accuracies of 79.78% on 10-fold cross validation on CoMAGC and 73.03% on 152 randomly chosen test sentences, where accuracy is defined as the proportion of correctly classified results among the classification results of all test data. Last, the type of GC is identified by applying deterministic inference rules on top of the GE type, CC type and the type of an additional concept, or ‘proposition type (PT)’. PT indicates whether the causality between the gene and the cancer is claimed in the sentence or not, and the type of PT can be either ‘causality’ or ‘observation’. We identified the PT type by using another MaxEnt classifier, which is also trained on CoMAGC. This second MaxEnt classifier achieved accuracies of 85.71% on 10-fold cross validation and 89.69% on random test sentences. The test sentences are provided in OncoSearch Web site.” The proportion of correctly classified results is a probability value comparison (re: clm. 7, … wherein the final expression level calculator calculates the final expression level from the identified relevant information by comparing the probability values of the word strings.). Lee et al. anticipates claim 7.
Regarding claim 8, Lee et al. teaches the use of UniprotKB which is a dynamic digital database (re: clm. 8, … the public data base is a dynamic digital database.). Lee et al. anticipates claim 8.
Regarding claim 9, Lee et al. states: “After locating the gene names and the cancer names, we tokenized, POS tagged and parsed the sentences in the abstracts by using the Charniak–Johnson parser…” which is a NLP module (re: clm. 9, … wherein the system includes a Natural Language Processing (NLP) module, wherein the protein data extraction module uses the NLP module for parsing and identifying information related to the target proteins.). Lee et al. anticipates claim 9.
Regarding claim 15, Lee et al. teaches expression level calculated from word string probability values, stating on pgs. W419-W420 (re: clm. 15, … wherein the step of calculating includes calculating the final expression level from the identified relevant information by comparing the probability values of the word strings.). Lee et al. further teaches use of a NLP module via use of the “Charniak–Johnson parser” (re: clm. 15, … wherein the steps of the method are performed using a Natural Language Processing (NLP) module.). Lee et al. anticipates claim 15.
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) 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. as applied to claims 1, 2, 7-11 and 15 above and in view of Hae-Jung Kim et al. (2018 Apr 9;20(4):262. Pg. 1-19).
Lee et al. is applied to claims 1, 2, 7-11 and 15 above.
Regarding claims 3 and 12, the instant claims disclose three classifiers used for word association (…conclusive word string…inconclusive word string…), association with gene expression (…high/low expression word string…), and impact of words on disease (…an inhibit effect, an activate effect and an associate effect, the third-level classification being done based upon the impact of the protein on the related disease …)
Lee et al. teaches: that both the type of cancer change (CC) and proposition type (whether the causality between the gene and the cancer is claimed in the sentence or not) are determined using the two MaxEnt classifiers.
Lee et al. further teaches that the relationship between words and expression levels are determined (scored) by Turku Event Extraction System (TEES) (“First, the type of [Gene Expression] is deterministically identified from the event types provided by TEES…” and “…Each sentence is scored with the weighted harmonic mean of the confidence scores provided by TEES, the CC classifier and the PT classifier, with weights 0.5, 0.3 and 0.2, respectively. Sentences that are likely to describe hypothesis or study purpose are penalized. When a sentence contain expressions such as ‘We investigated’, ‘To study’ and ‘Objective: ’, the score of the sentence is multiplied by 0.5…”) (pg. W420).
These elements are similar to the impact on the disease and conclusively of the association between word and disease, respectively (re: clm. 3, 12, … the first-level ML classifier classifying each of the relevant word strings into a first-level classification, the fist level classification being one of a conclusive word string and an inconclusive word string, the first-level classification being done based upon the nature of the relevant word string… re: clm. 3, 12, … the third-level ML classifier classifying each of the conclusive word strings into one of an inhibit effect, an activate effect and an associate effect, the third-level classification being done based upon the impact of the protein on the related disease…)
Lee et al. does not explicitly disclose that a third classifier is used in combination with TEES (re: clm. 3, 12, … classifying each of the conclusive word strings into one of a high expression word string and a low expression word string, the second-level classification being done based upon the expression level of the protein…) Additionally, Lee et al. does not explicitly disclose a ML classifier (re: clm. 3, 12, … ML classifier)
Kim et al. discloses MaxEnt classifiers as logistic regression models (pg. 2, re: clm. 3, 12, … ML classifier).
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to Lee et al. and Kim et al., the examiner concludes that the combination of the text-mining module TEES as disclosed by Lee et al. with an additional logistic regression classifier from Kim et al. as used specifically for text-gene-expression relationships represents the use of a known technique to improve similar methods.
The nature of the problem to be solved may lead inventors to look at references relating to possible solutions to that problem. Additional classifiers provides a technical solution which would have improved upon the text-mining and relationships derived from them as Lee et al. explicitly disclose that additional performance and enhancement can be obtained from the classifiers already applied in their study. Lee et al. states on pg. W420:
“Last, although the current version of OncoSearch can effectively search for cancer-related genes, we believe that the performance of the system can be improved further. First, we can enhance the overall precision of the system by improving the performance of the text-mining modules such as TEES and the CCS/PT classifiers. We plan to devise a post-processing method for TEES to filter out false positive results. We also plan to apply semi-supervised learning or transfer learning techniques to train CCS/PT classifiers…we can improve the overall sensitivity of the system by including other types of data. We plan to include full-texts of biomedical articles as well as abstracts, and to account for gene changes of other types such as mutation.”
Therefore, it would have been obvious to use the scoring of TEES (as shown in Lee et al.) with an additional classification method (such as Kim et al.’s explicit MaxEnt classifier) as applied to gene expression to further discern gene-expression-text relationships. Using the known technique of classification to provide enhanced data filtration would have been prima facie obvious to one of ordinary skill in the art at the time of filing, absent evidence to the contrary (re: clm. 3, 12, … the second-level ML classifier classifying each of the conclusive word strings into one of a high expression word string and a low expression word string…). Therefore, claims 3 and 12 of the instant claims would have been prima facie obvious to one of ordinary skill in the art at the time of filing, absent evidence to the contrary.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. in view of Kim et al. as applied to claims 1-3, 7-12 and 15 above in view of Björne et al. (BMC Bioinformatics 2015, 16(Suppl 16):S4. Pg. 1-20)
Lee et al. in view of Kim et al. is applied to claims 1-3, 7-12 and 15 above.
Lee et al. in view of Kim et al. teaches a cancer gene search engine with literature evidence but does not explicitly disclose a group of keywords (re: clm. 4, … wherein the set of keywords consists of at least one word chosen from a group consisting of mutat, express, polymorph, regulat, level, associat, inhibit, activat, high, low, less, up, and down…)
Björne et al. teaches The Turku Event Extraction System (TEES), a text mining program developed for the extraction of events, complex biomedical relationships, from scientific literature (abstract). Björne et al. teaches on pg. 17:
“The trigger feature with the highest weight is unsurprisingly the VB part-of-speech label, as after all, trigger words are generally verbs. Subtoken features are also important, such as the common "induction" trigger word and the two-letter duplet "xp" most likely correlating with various forms of the word "expression".”
As “expression” is a trigger word, this reads as a keyword (re: clm. 4, … wherein the set of keywords consists of at least one word chosen from a group consisting of mutat, express, polymorph, regulat, level, associat, inhibit, activat, high, low, less, up, and down…).
Applying the KSR standard of obviousness to Lee et al., Kim et al., and Björne et al., the Examiner concludes that the combination of a cancer gene search engine with literature evidence with the keyword “expression” is applying a known technique to a known method with no more than a predictable outcome of a cancer gene search engine with literature evidence using the word “expression” as a keyword. One of skill would have had a reasonable expectation of success at applying the keyword “expression” to the method of Lee et al. as Björne et al. provides all the necessary instructions or elements. Therefore, claim 4 of the instant claims would have been prima facie obvious to one of ordinary skill in the art at the time of filing, absent evidence to the contrary.
Claims 5, 6, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. in view of Kim et al. and Björne et al. as applied to claims 1-4, 7-12, and 15 above in view of Alexander Lachmann et al. (Nucleic Acids Research, 2019, Vol. 47, Web Server issue W571–W577).
Lee et al. in view of Kim et al. and Björne et al is applied to claims 1-4, 7-12, and 15 above.
Regarding claims 5 and 13, Lee et al. teaches identifying high probability word strings using a probability value on pgs. W419-420:
“We trained the classifier using a corpus provided by Lee and colleagues, or CoMAGC, since we adopted the query concepts from their work. The classifier achieved accuracies of 79.78% on 10-fold cross validation on CoMAGC and 73.03% on 152 randomly chosen test sentences, where accuracy is defined as the proportion of correctly classified results among the classification results of all test data…”
Lee et al. does not explicitly define user input (re: clm. 5, 13, …being input by a user of the system…)
Lachmann et al. teaches Geneshot, a search engine for ranking genes from arbitrary text queries, including a user interface in Fig. 1, pg. W547 such that a user can submit search terms and obtain a list of relevant genes sorted by a ranking of how relevant they are, and with a filter to select genes based upon a gene prediction matrix.
Applying the KSR standard to Lee et al., Kim et al., Björne et al. and Lachmann et al. the examiner concludes that the combination of search engine as disclosed by Lee et al. with the user input method of Lachmann et al., represents some teaching, suggestion or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
One of ordinary skill in the art of bioinformatics would be motivated to combine the teachings of Lee et al. with the user input method of Lachmann et al. because the combination would result in a stronger search engine allowing for more-precise user parameters while conducting a search.
One of ordinary skill in the art would have a reasonable expectation of success as the teachings of Lee et al. and Lachmann et al. and Bae et al. text search methods in the same field of invention. The combination would result in a search engine allowing users to search for gene-word associations with filtering by probability of word association. Therefore, claims 5 and 13 of the instant claims would have been prima facie obvious to one of ordinary skill in the art at the time of filing, absent evidence to the contrary.
Regarding claims 6 and 14, Lee et al. teaches expression level calculated from word string probability values, stating on pgs. W419-W420, and additionally teaches grouping, stating on pg. W418:
“The Summary view shows groups of sentences, where the sentences with the same gene,
the same cancer type and the same types of query concepts are grouped together.” (re: clm. 6, wherein the system comprises a grouping module, the grouping module being configured to aggregate the identified high probability word strings into groups based upon the disease to which the target protein in the high probability word strings pertains, the aggregated high probability word strings being the identified relevant information, clm. 14, … the method comprises the steps of aggregating, using a grouping module, the identified high probability word strings into groups based upon the disease to which the target protein in the high probability word strings pertains, the aggregated high probability word strings being the identified relevant information.) Lee et al. teaches grouping in address of the limitations of claims 6 and 14.
Conclusion
No claims are allowed.
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/J.T.S./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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