DETAILED ACTION
Applicant’s response filed 05/19/2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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 .
Claim Status
Claims 12-13 are cancelled by Applicant.
Claims 1-11 are currently pending and are herein under examination.
Claims 1-11 are rejected.
Claims 1-2 and 4 are objected.
Priority
The instant application claims foreign priority to Indian Application No. IN202121033646 filed 07/27/2021. The claim to foreign priority is acknowledged. As such, the effective filing date for claims 1-11 is 07/27/2021.
Drawings
The objection to the drawings is withdrawn in view of the drawings filed 05/19/2026. The drawings filed 07/26/2022 are accepted.
Abstract
The objection to the abstract is withdrawn in view of the amended abstract filed 05/19/2026.
Withdrawn Rejections
35 USC 112(a)
The rejection of claims 12-13 under 35 USC 112(a) is withdrawn because of claim cancellation.
35 USC 112(b)
The rejection of claims 1-13 under 35 USC 112(b) is withdrawn in view of claim amendment.
35 USC 101
The rejection of claims 12-13 under 35 USC 101 is withdrawn because of claim cancellation.
Claim Objections
The objection to claims 1-3, 6, 8, 10 and 12-13 are withdrawn in view of claim amendment.
Claims 1-2 and 4 are objected to because of the following informalities:
Claim 1, pg. 5, line 3, recites a comma after ID which should be removed.
Claim 2, line 6, should recite “assigning” not “assign”.
Claim 4, line 1, should have comma after “claim 1”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
35 USC 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is newly recited as necessitated by claim amendment.
Claims dependent from a rejected claim are also rejected, unless otherwise noted.
Claim 1, pg. 4, line 11, recites “wherein the set of domain features”. It is unclear which set of domain features are being references because a set of domain features is calculated for each abstract in claim 1, pg. 4, lines 1-2. Clarify which set of features is being referenced.
Claim 1, pg. 4, line 12, recites “the process of computation” which lacks antecedent basis. Provide antecedent basis.
Claim 1, pg. 4, lines 20-21, recites “wherein the estimated threshold annotation time”. It is unclear which threshold annotation time is being referenced because each biomedical text has a threshold annotation time as recited in claim 1, pg. 4, lines 19-20.
Claim 1, pg. 4, lines 20-21, recites “wherein the estimated threshold annotation time … used to detect spam annotation in a crowdsourcing setup”. It is unclear if this recites an intended use or an active step of detecting spam annotations. For examination, this recites an intended use. Clarify whether this recites an active step or an intended use.
Claim 1, pg. 5, line 2, recites “the recorded list of predicted sentences” which lacks antecedent basis. It appears that the phrase should recite “the recorded table of predicted sentences”.
Claim 7 recites “the plurality of context aware features” which lacks antecedent basis. Provide antecedent basis or make claim 7 depend on claim 6 where “a plurality of context aware features” first appears.
Claim 9 recites “wherein a set of sentences … are optionally highlighted in a displayed user interface”. It is unclear if this recites an intended use or an active step because the wherein clause does not further limit a previously recited active step of highlighting. Clarify whether this recites an intended use or active step. For examination, this is interpreted as an active step that is optional.
Claim 9 recites “a set of sentences in the table of predicted sentences for each text chunk and associated information including entity information”. It is unclear if “each text chunk and associated information” refers to previously unrecited text chunks and associated information, or if it refers to the unique IDs and biomedical texts used to create the table of predicted sentences in claim 1. Clarify what the recitation refers to. For examination, the recitation refers to the biomedical texts and unique IDs associated with the table.
Claim 10, line 3, recites “wherein the set of domain features”. It is unclear which set of domain features are being referenced because a set of domain features is calculated for each abstract in claim 1, pg. 4, lines 1-2. Clarify which set of features is being referenced.
Claim 10 recites “wherein the set of domain features are referred from three domain dictionaries”. It is unclear if this recites an intended use, or if it recites an active step because the wherein clause does not further limit the active step in claim 1 of “calculating … a set of domain features”. Clarify whether this recites an intended use or active step.
Claim 11, lines 4-5, recites “the knowledge graphs” which lacks antecedent basis. Claim 11 recites “a knowledge graph” but not more than one knowledge graph. Clarify antecedent basis.
Claim 11, lines 4-5, recites “wherein the knowledge graphs is used to design”. It is unclear if this recites an intended use or an active step of designing. Clarify how whether this recites an intended use or an active step.
Claim 11, line 7, recites “bacterial associations”. It is unclear if this refers to the bacterial associations of the knowledge graph in claim 11, sentences with potential bacterial associations in claim 1, sentences with probable bacterial associations in claim 1, or bacterial associations associated with the crowdsourcing annotation system in claim 1. Clarify which bacterial associations are being referenced.
35 USC 112(d)
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 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
This rejection is newly recited as necessitated by claim amendment.
Claim 9 fails to further limit claim 1 because it recites an optional limitation not required by the claim. 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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 1:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, claims 1-11 recite a method. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “identifying a disease with known bacterial basis (DS); obtaining … bacterial abundance data from the samples corresponding to the disease, wherein the bacterial abundance data is used to construct a bacterial taxonomic abundance matrix consisting of abundance information of individual bacterial taxon across the group of patients; constructing … a first bacterial association network (NT1) using a statistical correlation to find relationships between the bacteria present in the bacterial taxonomic abundance matrix, wherein the first bacterial association network (NT1) comprises m number of bacteria as nodes with their relationship as e number of edges and edge weights as an association strength; formulating … a plurality of search queries for each node in the first bacterial association network, wherein each of the plurality of search queries is searched in a biomedical search engine to obtain output tuples as a set of output lists containing a plurality of biomedical texts, wherein each text is identified by an ID; collating … unique IDs from the set of output lists to form a list of unique IDs; obtaining … the biomedical text corresponding to each unique ID of the list of unique IDs to generate a biomedical text corpus Cz; calculating … a set of domain features for each abstract present in the biomedical text corpus Cz to generate a feature count matrix with one set of features for each abstracts; applying … a first classifier to the feature count matrix to obtain a first list of biomedical texts corresponding to each unique ID, wherein the first list of biomedical texts further comprising sentences with potential bacterial associations, wherein the sentences having potential bacterial associations are obtained using the first classifier if a condition is satisfied in the set of features for each abstract, wherein the first classifier consists of an abstract classifier trained on an abstract corpus and a sentence classifier trained on a sentence corpus, and the first classifier is trained to detect both abstracts and the sentences having the potential bacterial associations, wherein the set of domain features being less in number thereby make the process of computation less complex both in space and time complexity; utilizing … sentences having potential bacterial associations to create a first refined association network; applying … a second classifier to the feature count matrix corresponding to the first list of biomedical text to obtain a readability for each text in the first list of biomedical text; estimating … a threshold annotation time required to annotate each biomedical text based on its readability, wherein the estimated threshold annotation time provides an estimate of annotation quality and used to detect spam annotations in a crowdsourcing setup; identifying … sentences in the first list of biomedical text with probable bacterial associations; creating … a table of predicted sentences using the first classifier and calculated domain features for each identified sentences in the first list of biomedical text that contain the bacterial association along with the ID; recording … the list of predicted sentences corresponding to the bacterial associations to calculate corresponding count along with their unique IDs; creating … a second refined association network utilizing the output of the crowdsourcing annotation system and the first refined association network; identifying bacterial biomarkers and driver microbes of the identified disease (DS) by comparing the second refined association network for the diseased group of individuals with a refined bacterial association network for a healthy group of individuals, wherein the bacterial biomarkers and driver microbes of the identified disease (DS) is identified by comparing changes in local graph properties of nodes in the second refined association network and the refined bacterial association network; identifying and designing therapeutic interventions in the form of probiotic cocktails for curing the identified disease (DS) by using the second refined association network;”
Claim 2 recites “identifying sentences with bacterial entities, interactions entities and mechanism entities for the list of biomedical texts, wherein bacterial entities mentioned in the sentences are connected by an edge; counting a total occurrence of the edge across the biomedical texts in the lists and assign a normalized edge weight; generating a second bacterial association network (NT2) with o number of nodes and p number of edges with the normalized edge weights as identified using a score 2, wherein the score 2 is a score of experimental evidence of a bacterial association edge as seen in a biomedical literature; and finding one or more common edges present in the first bacterial association network NT1 and the second bacterial association network NT2 to calculate a refined bacterial association network NT3 with intersection edges having ‘q’ number of nodes and r number of edges with edge weight as a function of the edge weights of the association networks NT1 and NT2.”
Claim 3 recites “refining the second bacterial association network by modifying the normalized edge weights, wherein the normalized edge weight is a function of a first score, a second score and a third score, wherein, the first score is a correlation value of abundance count calculated between two bacteria forming a bacterial association edge from a microbiome experiment, the second score is a score of experimental evidence of the bacterial association as seen in biomedical literature, and the third score is a score obtained from manual curation of experimental evidence.”
Claim 4 recites “normalizing the extracted sample to remove various sampling and experimental biases using one of a total sum scaling or a percentage normalization.”
Claim 5 recites “wherein the bacterial abundance data is obtained using a frequency of mapping of signature genetic elements in the environmental sample.”
Claim 6 recites “wherein the set of domain features is calculated from the biomedical corpus further comprising of a plurality of compositional and a plurality of context aware features, wherein the plurality of compositional features comprises total and unique entity counts, sentence specific entity counts and entity presence in combination with parts of speeches, and the plurality of context aware features comprises a count of one or more entity patterns in a given order in one or more sentences with or without in combination to the parts of speeches, a sum of word distance between bacterial entities and a size of largest clusters of consecutive occurring bacterial entities.”
Claim 7 recites “wherein the condition is a positive nonzero value for the plurality of context aware features in the set of features.”
Claim 8 recites “wherein the feature count matrix is a two dimensional matrix composed of abundance of each feature across each unique ID of the biomedical corpus.”
Claim 9 recites “wherein a set of sentences in the table of predicted sentences for each text chunk and associated information including an entity information are optionally highlighted in a displayed user interface to lower annotator load.”
Claim 10 recites “wherein the set of domain features are referred from three domain dictionaries, wherein the three domain dictionaries include a first dictionary, a second dictionary, and a third dictionary, wherein the first dictionary is a dictionary of terms indicating bacterial entities, the second dictionary is a dictionary of terms indicating bacterial mechanisms of association, and the third dictionary is a dictionary of terms indicating bacterial associations grouped into three categories, based on their importance.”
Claim 11 recites “creating a knowledge graph of bacterial associations pertaining to healthy and disease state using multiple refined association networks obtained from data available from experimental studies and publicly available biomedical literature, wherein the knowledge graphs is used to design personalized recommendation systems pertaining to diet, drug, probiotics and prebiotics, and used for discovering and designing novel drug candidates utilizing information of the bacterial associations and metabolites secreted by them using dictionaries, the first classifier and the second classifier.”
Limitations reciting a mental process.
Claims 1-11 contain limitations recited at such a high level of generality that they equate to a mental process because they 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)), which the courts have identified as concepts that can be practically performed in the human mind or by pen and paper. The paragraphs below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
Regarding claim 1, identifying a disease includes making a mental determination. Obtaining bacterial abundance data from the samples includes using a frequency of mapping of signature genetic elements, as recited in claim 5. Constructing a first bacterial association network using a statistical correlation includes using pen and paper to create a network of nodes, wherein edge weights are calculated using a t-distribution, as recited in specification para. [68]. Formulating search queries includes writing down search strings. Collating unique IDs to create a list includes collecting and writing down data. Obtaining biomedical texts includes collecting and organizing data, wherein data is inherently abstract. Calculating a set of domain features to generate a count matrix includes counting a total number of sentences in a biomedical text, as recited in specification para. [59]. Applying a first classifier to the feature count matrix to obtain a first list of biomedical texts includes performing operations of a logistic regression on a matrix, which a human could practically do using pen and paper. The limitations regarding the first classifier being a trained abstract/sentence classifier equate to a product by process which defines how the classifier was previously trained. Utilizing sentences to create a first refined association network includes calculating more edge weights for nodes using a t-distribution. Applying a second classifier to the feature count matrix to obtain a readability for each text includes applying a logistic regression to a count matrix, which can be practically performed using pen and paper, as recited in specification para. [95]. Estimating a threshold annotation time includes making a determination based on the analysed readability of each text also includes a calculation as recited in specification para. [117]. Identifying probable bacterial associations includes mental determinations. Creating a table of predicted sequences using the first classifier and calculated domain features includes creating an annotated list using output from the first classifier and domain features. Recording the first list of biomedical texts includes writing down the list on pen and paper. Creating a second refined network includes creating another network with nodes and calculating edge weights using a statistical measure such as a t-distribution. Comparing refined networks includes comparing changes in local graph properties, which require mental analysis and determinations. Identifying and designing therapeutic interventions includes mental determinations as well as writing on pen and appear a combination of probiotics that could be used to treat the identified disease.
Regarding claim 2, identifying, counting, and finding includes performing mental determinations and performing calculations using pen and paper. Generating a second bacterial association network includes performing calculations using a t-distribution and creating a network of nodes on pen and paper.
Regarding claim 3, modifying weights includes performing calculations. Regarding claim 4, performing calculations includes using a total sum scaling. Claim 5 includes mapping and calculating frequencies of DNA/RNA markers. Claim 6 is included in the abstract idea in claim 1 of calculating domain features because it further limits what type of features are calculated. Claims 7-8 are included in the judicial exception in claim 1 of applying a first classifier and generating a feature count matrix, respectively. Claim 9 is an optional limitation and is thus not required. Claim 10 further limits the judicial exception in claim 1 of calculating domain features. Claim 11 includes using scores and edge weights of a bacterial association network to generate a knowledge graph, which necessitates organizing data and performing calculations such as with a t-distribution. The limitations in claim 11 of is used to design and is used for discovering recite intended uses and are thus not required.
Limitations reciting a mathematical concept.
Claims 1-5 and 9-11 recite limitations that equate to mathematical concepts because they are similar to the concepts of 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)), which the courts have identified as mathematical concepts. The paragraphs below discuss the limitations in these claims that recite a mathematical concept under their BRI.
Regarding claim 1, obtaining bacterial abundance data includes using a mapping frequency of species-specific DNA/RNA. Constructing a first bacterial association network using a statistical correlation includes performing calculations such as a t-distribution to construct a network with nodes, edges, and weights, as recited in specification para. [68]. Calculating a set of domain features to generate a count matrix includes counting a total number of sentences in a biomedical text, as recited in specification para. [59]. Applying a first classifier to the feature count matrix includes performing operations of a logistic regression on a matrix, as recited in specification para. [88-89]. Utilizing sentences to create a first refined association network includes calculating/modifying edge weights. Applying a second classifier to the feature count matrix includes performing calculation using a logistic regression, as recited in specification para. [95-98]. Estimating a threshold annotation time includes a calculation as recited in specification para. [117]. Creating a second refined network includes calculating/modifying edge weights.
Regarding claim 2, the BRI of counting a total occurrence includes addition. The BRI of generating a second bacterial association network and calculating a refined bacterial association network include calculating/modifying edge weights. Regarding claim 3, the BRI of modifying edge weights includes adjusting numerical parameters and calculations. Regarding claim 4, the BRI includes performing calculations using a total sum scaling. Regarding claim 5, the BRI includes calculating frequencies of DNA/RNA markers. Regarding claim 11, the BRI includes using scores and edge weights of a bacterial association network to generate a knowledge graph, which necessitates organizing data and performing calculations.
As such, claims 1-11 recite an abstract idea (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claim 1 recites “A processor implemented method for annotation and classification of biomedical text having bacterial associations, the method comprising: extracting a sample having a microbiological content from each individual in a group of patients suffering from the identified disease (DS); via one or more hardware processors; via the one or more hardware processors; sending, via the one or more hardware processors, the first list of biomedical texts, the estimated threshold annotation time and the recorded list of predicted sentences corresponding to each unique ID, to a crowdsourcing annotation system for improved prediction of bacterial associations; and treating the identified disease (DS) by targeting the identified bacterial biomarkers and driver microbes using the designed therapeutic interventions for curing the identified disease (DS).”
Claims 2-10 recite “The processor implemented method of claim 1”.
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B:
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). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)) and to insignificant extra-solution activity (MPEP § 2106.05(g)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claims 1-11 recite a computer implemented method and one or more processors. There are no limitations that require anything other than a generic computer. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983. These limitations also equate to invoking a computer as a tool to perform an existing process (i.e., to receive, store, or transmit data), which does not integrate a judicial exception into an abstract idea (MPEP 2106.05(f)(2)).
Claim 1 recites extracting samples and sending data to a crowdsourcing annotation system. These limitations equate to insignificant, extra-solution activity of necessary data gathering. Extracting samples collects data to perform the abstract idea of constructing a bacterial association network. The output of the annotation system is used to perform the abstract idea of creating a second refined association network.
Claim 1 recites treating the identified disease (DS) by targeting the identified bacterial biomarkers and driver microbes using the designed therapeutic interventions for curing the identified disease. This equates to a recitation of the words “apply it” (MPEP 2106.05(f)) because the treatment and disease are so generically recited that it encompasses all applications of the judicial exception (i.e. it seeks to treat any disease with a known bacterial basis using any probiotic cocktail) (MPEP 2106.04(d)(2)(a)). Moreover, the treatment step in claim 1 also has a nominal relationship to the recited judicial exception (MPEP 2106.04(d)(2)(b)). For example, bacterial sepsis is a condition with known bacterial basis and an unspecified probiotic cocktail may not treat the bacterial sepsis. In this case, the treatment step does not apply or use the judicial exception in a meaningful way, and thus does not integrate into a practical application.
As such, claims 1-11 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). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claims 1-11 recite a computer implemented method and one or more hardware processors. There are no limitations that require anything other than a generic computer. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 1 recites sending data to a crowdsourcing annotation system which equates to receiving/transmitting data over a network, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Claim 1 recites extracting samples which is WURC as taught by Aldars-Garcia et al. (“Aldars-Garcia”; Microorganisms 9, no. 05 (2021): 977; published online 30 April 2021; previously cited on PTO 892 mailed 02/23/2026). Aldars-Garcia reviews the gut microbiome and its clinical implication in inflammatory bowel disease (IBD) (abstract). Studies were collected form PubMed related to gut microbiome and IBD (pg. 2, sec. 2.1)-. Articles were kept if microbiome samples were collected and compared against IBD patients and controls (pg. 2, sec. 2.2). Table 2 lists the selected studies that perform next generation sequencing on the microbiome samples. These studies use bioinformatics methods for structural and functional analysis (pg. 29, sec. 3.1.4), indicating that these limitations are WURC in combination with a generic computer.
Claim 1 recites treating a disease which equates to instructions to “apply” the abstract idea, which cannot provide an inventive concept under Step 2B (MPEP 2106.05(f)). See above in section Step 2A, Prong 2 for further discussion.
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to mere instructions to apply the judicial exception, and equate to WURC functions/components of a generic computer in combination with WURC limitations of extracting microbiome samples of subjects with disease as taught above by Aldars-Garcia. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-11 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 05/19/2026 have been fully considered but they are not persuasive.
Applicant argues claims cannot be construed as merely mathematical concepts because they provide higher classification accuracy and train a classifier on abstracts and sentences (pg. 23, last para. – pg. 24, para. 1). Applicant’s argument is not persuasive because:
Claim 1 recites at least the mathematical concept of “calculating … a set of domain features for each abstract”. Moreover, claim 1 does not require an active step of training the first classifier. Rather, the first classifier was previously trained as indicated by “wherein the first classifier consists of an abstract classifier trained … and a sentence classifier trained”. These limitations equate to a product by process that defines how the first classifier was previously trained. See MPEP 2113.I regarding product by process limitations.
Applicant compares SME Example 39 to the instant claims and argues that certain limitations may be based on math but do not recite math, and using domain features is not abstract (pg. 24, para. 2-3). Applicant’s argument is not persuasive because:
Constructing NT1 using statistical correlations recites a mathematical calculation and relationship because it requires calculations such as Pearson correlation coefficient and organizing information through mathematical correlations. Estimating a threshold annotation time equates to a mathematical concept because it includes estimating a range of values as described in specification para. [117]. Creating a table of predicted sentences has been identified as a mental process and not a mathematical concept. Creating a refined network recites a mental process. Applying the first classifier to the feature count to obtain a first list of biomedical texts comprising sentences with potential bacterial associations recites a mathematical concept and mental process. The limitation of “for improved prediction of bacterial associations” recites an intended use and is thus not required by the claim.
SME Example 39 is not analogous to the instant claims. Unlike Example 39, the instant claims do not recite an active step of training a neural network on images. The instant claims also recite math while Example 39 does not.
Applicant argues that because the claims are performed by a computer processor they cannot be performed in the human mind, and the claims provide a technical solution of annotation and classification of biomedical text having bacterial associations, which cannot be performed in the mind (pg. 24, last para.). Applicant’s argument is not persuasive because:
MPEP 2106.04(a)(2).III.C recites that a mental process performed on a computer still recites a mental process. Applicant’s remarks regarding a technical solution are not persuasive because it is unclear which claim limitations are being referenced that confer the alleged solution. It is also noted that a technical problem has not been stated.
Applicant argues that claims 1 recites a particular treatment (pg. 25, para. 1-4 – pg. 29, para. 2). Applicant’s argument is not persuasive because:
The identified disease has a known bacterial basis. The probiotic cocktails target bacterial biomarkers and driver microbes associated with the identified disease. However, the treatment lacks particularity because it encompasses all applications of the judicial exception (i.e. it seeks to treat any disease with a known bacterial basis using any probiotic cocktail) (MPEP 2106.04(d)(2)(a)), and thus equates to a recitation of the words “apply it” (MPEP 2106.05(f)). This is because both the disease with a known bacterial basis and probiotic cocktail are recited generically.
Moreover, the treatment step in claim 1 also has a nominal or insignificant relationship to the recited judicial exception (MPEP 2106.04(d)(2)(b)). For example, bacterial sepsis is a condition with known bacterial basis and an unspecified probiotic cocktail may not treat the bacterial sepsis. In this case, the treatment step does not apply or use the judicial exception in a meaningful way, and thus does not integrate into a practical application.
Applicant appears to argue for improvement in computer functionality and better classification accuracy of a trained classifier (pg. 29, para. 3 – pg. 30, para. 2). Applicant’s argument is not persuasive because:
The limitation of “wherein the set of domain features being less in number” is an intended use because it does not positively recite that the domain features are less in number. Even if claim 1 did recite such a limitation, the alleged improvement appears to be a result of the set of domain features having a count of 29 as recited in specification para. [91]. Thus, the claims would not reflect the disclosed improvement.
Even if claim 1 recited the set of domain features being 29 or less, claim 1 would not improve computer functionality. The computer itself has not been altered in any way, i.e., by changing processor functionality or by changing the way in which the computer stores or accesses memory. Nothing about the physical components of the computer nor the way the computer operates is changed. Applicant has not provided any evidence or technical explanation for how the claimed invention improves the way in which the asserted abstract idea improves the way in which the computer processes or stores data. Rather, the computer is presented with less data (i.e. fewer domain feature), wherein less data being processed inherently requires fewer computing resources.
Furthermore, the trained first classifier having better accuracy, precision, recall, and F1 score is indicative of an improved abstract idea. This is because the limitations Applicant references that improve classification are a result of the judicial exception. MPEP 2106.05(a)(II) recites “an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Applicant argues improvement of computing technology conferred by estimating threshold annotation time required to annotate each biomedical text based on its readability (pg. 30, last para. – pg. 31, para. 1). Applicant’s argument is not persuasive because:
Estimating threshold annotation time has been identified as reciting a mental process and mathematical concept. As described in specification para. [117], a text classified as “Easy to read” has a range of 57 to 166 seconds, which is used to estimate a threshold annotation time range required to annotate an abstract based on its readability as predicted by the classifier. A human is capable of estimating a threshold based on time ranges outputted by a classifier. Thus, this alleged improvement is a result of the abstract idea itself. MPEP 2106.05(a) recites “the judicial exception alone cannot provide the improvement.”
It is noted that “used to detect spam annotation in a crowdsourcing setup” is an intended use and is thus not required by the claim.
Applicant argues the following claim 1 limitations provide significantly more: identifying bacterial biomarkers and driver microbes of the identified disease, identifying and designing therapeutic interventions in the form of probiotic cocktails, treating the identified disease by using the probiotic cocktails, using domain features which are less in number, and training the classifier (pg. 31, last para. – pg. 33, para. 1). Applicant’s argument is not persuasive because:
Only additional elements are evaluated under Step 2B. Of the above referenced limitations, only treating the identified disease recites an additional element. However, the treating step equates to mere instructions to apply the exception in a generic way, which does not provide significantly more under Step 2B (MPEP 2106.05(f)). Also, claim 1 does not require an active step of training the classifier.
Conclusion
No claims are allowed.
Claim 1-11 are free from the prior art foe the same reasons recited in the conclusion of the previous Office action.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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