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 .
Claim Status
Claims 1-13 are currently pending and are herein under examination.
Claims 1-13 are rejected.
Claims 1-3, 6, 8, 10 and 12-13 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 for claims 1-13. As such, the effective filing date for claims 1-13 is 07/27/2021.
Information Disclosure Statement
The IDS filed 07/26/2022 follows the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of the list of references cited from this IDS is included with this Office Action.
Abstract
The abstract of the disclosure is objected to because it recites the following implied phrases “The present disclosure” and “The disclosure” that should be deleted. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Drawings
The drawings filed 07/26/2022 are objected to because FIG. 8A is duplicated. Examiner suggests removing the duplicated FIG. 8A. See MPEP 608.02(e).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim objections
Claims 1-3, 6, 8, 10 and 12-13 are objected to because of the following informalities:
Claim 1 (pg. 1, line 8), claim 12 (pg. 8, line 14), and claim 13 (pg. 11, line 9) recite the phrase “the disease” which should be “the identified disease (DS)” to maintain consistent claim terminology.
Claim 1 (pg. 2, line 4), claim 12 (pg. 9, lines 8-9), and claim 13 (pg. 12, line 9) recite the phrase “the first bacterial association network” which should be “the first bacterial association network (NT1)” to maintain consistent claim terminology.
Claim 1 (pg. 2, line 17), claim 12 (pg. 9, line 18), and claim 13 (pg. 13, line 2) recite the word “abstracts” which should be “abstract”.
Claim 1 (pg. 3, line 1-2), claim 12 (pg. 10, lines 2-3), and claim 13 (pg. 13, lines 7-8) recite the phrase “the sentences … is obtained” which should be “the sentences … are obtained”.
Claim 1 (pg. 3, line 2), claim 12 (pg. 10, line 3), and claim 13 (pg. 13, line 8) recite the phrase “classifier and if” which should be “classifier if” to correct grammar of phrase.
Claim 1 (pg. 3, lines 9-10, 15 and 17), claim 12 (pg. 10, lines 8-9, 11, and 12), and claim 13 (pg. 13, lines 15-16, 18 and 21) recite the phrase “biomedical text” which should be “biomedical texts”.
Claim 1 (pg. 3, line 18), claim 12 (pg. 10, line 15), and claim 13 (pg. 14, line 3) recite the phrase “identified sentences” which should be “identified sentence”.
Claim 1 (pg. 3, lines 19), claim 12 (pg. 10, line 20), and claim 13 (pg. 14, line 4) recite the phrase “the ID” which should be “the unique ID”.
Claim 1 (pg. 4, line 3), claim 12 (pg. 10, line 19), and claim 13 (pg. 14, line 7) recite the phrase “to calculate corresponding count” which appears that it should be “to calculate corresponding counts”.
Claim 2, lines 11-12, recites the phrase “the first bacterial association network NT1 and the second bacterial association network NT2 … a refined bacterial association network NT3” which should be “the first bacterial association network (NT1) and the second bacterial association network (NT2) … a refined bacterial association network (NT3)” to maintain consistency.
Claim 6, line 2, recites the phrase “the biomedical corpus” which should be “the biomedical text corpus”.
Claim 8, line 2, recites the word “composed” which should be “comprised”.
Claim 8, line 3, recites the phrase “the biomedical corpus” which should be “the biomedical text corpus”.
Claim 10, line 2, recites “the disease” which should be “the identified disease (DS)” to maintain consistency.
Claim 13, pg. 11, line 8, contains a comma after “obtaining” which should be removed.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
35 USC 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 12 and 13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 12 (pg. 8, lines 5-11) and claim 13 (pg. 11, lines 1-6) recite a computer processor that extracts physical samples from patients. However, neither the specification nor the drawings contain disclosure for a processor capable of extracting physical samples from patients. As such, claims 12 and 13 are rejected for failing to comply with the written description requirement.
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 1-13 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 1 (pg. 1, lines 7-9), claim 12 (pg. 8, lines 5-8 and lines 13-14), claim 13 (pg. 11, lines 1-3 and lines 8-9) recite using a computer processor to obtain bacterial abundance data from extracted samples using an experimental technique, which renders the claims indefinite. It is unclear if the claims require a processor perform an experimental technique such as sequencing to obtain bacterial abundance data, or if the claims require a processor obtain bacterial abundance data by analyzing output of an experimental technique previously performed on the samples. In the latter case, the experimental technique and output of the experimental technique would constitute product by process limitations. See MPEP 2113 regarding product by process limitations. To overcome this rejection, clarify how the processor obtains bacterial abundance data. For examination purposes, obtaining bacterial abundance data will be interpreted as the processor analyzing output of an experimental technique previously performed on the extracted samples to obtain the bacterial abundance data.
Claim 1 (pg. 1, last line – pg. 2, line 1), claim 12 (pg. 9, lines 4-6), and claim 13 (pg. 11, lines 5-6) recite the phrase “’m’ … ‘e’” which renders the claims indefinite. It is unclear if the single quotations around the m and e confer some as of yet unknown limitation. To overcome this rejection, remove the single quotations.
Claim 1 (pg. 1, last line – pg. 2, line 2), claim 12 (pg. 9, lines 5-6), and claim 13 (pg. 12, lines 5-7) recite the phrases “nodes (N1, N2,… Nm) … edges (E1,E2, ….En) … edge weight (EW1, EW2, …, EWn)” which render the claims indefinite. It is unclear if these phrases recite non-limiting examples of the number of nodes, edges, and edge weights, or if these phrases limit the number of nodes, edges, and edge weights to be greater than 2. It’s also unclear whether Nm, En, and EWm mean that a number of nodes, edges, and edge weights can be an infinite number. To overcome this rejection, clarify whether these phrases are further limiting.
Claim 1 (pg. 2, line 13 and 16), claim 12 (pg. 9, line 16 and 18), and claim 13 (pg. 12, line 18 and pg. 13, line 1) recite the phrase “‘Cz’” which renders the claims indefinite. It is unclear if the single quotations confer some as of yet unknown limitation. To overcome this rejection, remove the single quotations.
Claim 1 (pg. 3, lines 2-3), claim 12 (pg. 10, line 4), claim 13 (pg. 13, lines 8-9) recite the phrase “the set of features” which renders the claims indefinite. It is unclear which set of features is being referenced because each abstract in the feature count matrix has a set of features.
Claim 1 (pg. 3, lines 19), claim 12 (pg. 10, lines 16-17), and claim 13 (pg. 13, line 4) recite the phrase “the bacterial association” which renders the claims indefinite. It is unclear which bacterial association is being referenced because each identified sentence has probable bacterial associations. To overcome this rejection, clarify which specific bacterial association is being referenced, whether a specific potential or probable association or all bacterial associations.
Claim 1 (pg. 4, lines 1-2), claim 12 (pg. 10, line 18), and claim 13 (pg. 14, lines 5-6) recite the phrase “the list of predicted sentences”, which lacks antecedent basis. To overcome this rejection, change the phrase to “the table of predicted sentences”.
Claim 1 (pg. 4, line 2), claim 12 (pg. 10, lines 18-19), and claim 13 (pg. 14, line 6) recite the phrase “the bacterial associations” which renders the claims indefinite. It is unclear if the phrase refers to specific bacterial associations from the probable bacterial associations, or if the phrase refers to all of the probable bacterial associations. To overcome this rejection, clarify which specific bacterial association is being referenced, whether a specific potential or probable association or all bacterial associations.
Claim 1 (pg. 4, lines 1-3), claim 12 (pg. 10, lines 18-19), and claim 13 (pg. 14, lines 5-7) recite the following phrase the renders the claims indefinite: “record the list of predicted sentences corresponding to the bacterial associations to calculate corresponding count along with their unique IDs”. It is unclear whether this phrase requires counting a number of sentences that have bacterial associations, or if it requires counting all bacterial associations in each sentence. To overcome this rejection, clarify how this phrase should be interpreted.
Claim 1 (pg. 4, line 5), claim 12 (pg. 11, lines 1-2), and claim 13 (pg. 14, line 9) recite the phrase “the estimated threshold annotation time” which renders the claims indefinite. It is unclear which estimated threshold annotation time is being referenced because each biomedical text has an estimated threshold annotation time.
Claim 1 (pg. 4, lines 10-11), claim 12 (pg. 11, lines 5-6), and claim 13 (pg. 14, line 14-15) recite the phrase “the output of the crowdsourcing annotation system” which lacks antecedent basis. To overcome this rejection, provide antecedent basis for the output.
Furthermore, claims 2-11 are also rejected because they include the limitations of claim 1, which is rejected, and because they do not resolve the issue of indefiniteness.
Claim 2, line 4, recites “the sentences” which renders the claim indefinite. It is unclear if the phrase refers to all sentences in the biomedical texts, sentences with potential bacterial associations, or sentences identified as having probable bacterial associations. To overcome this rejection, clarify which sentences are being referenced.
Claim 2, line 3, recites the phrase “the list of biomedical texts” which renders the claims indefinite. It is unclear if the list refers to the lists in claim 1 of “a first list biomedical texts” or “a set of output lists containing a plurality of biomedical texts”. To overcome this rejection, clarify which list is being referenced.
Claim 2, line 6, recites the phrase “the lists” which renders the claim indefinite. It is unclear if the phrase refers to all lists in the “set of outputs lists”, as recited in claim 1, pg. 2, lines 6-8, or if the phrase refers to “the first list of biomedical tests” in claim 1, pg. 2, lines 19-20. To overcome this rejection, clarify which list/lists is/are being referenced.
Claim 2, lines 7-8, recites the phrase “’o’ … ‘p’” which renders the claim indefinite. It is unclear if these single quotations around the o and p confer some as of yet unknown limitation. To overcome this rejection, remove the single quotations.
Claim 2, lines 8-9, recites the phrases “(N1, N2,…,No) … (E1, E2, …., Ep) … (EW1, EW2,…., EWp)” which render the claim indefinite. It is unclear if these phrases recite non-limiting examples of the number of nodes, edges, and edge weights, or if these phrases limit the number of nodes, edges, and edge weights to be greater than 2. It’s also unclear whether No, Ep, and EWp mean that a number of nodes, edges, and edge weights can be an infinite number. To overcome this rejection, clarify whether these phrases are further limiting or optional.
Claim 2, line 9, recites “the normalized edge weights” which lacks antecedent basis. Claim 2, line 6, recites “a normalized edge weight” but there is no prior recitation of at least two normalized edge weights. To overcome this rejection, provide antecedent basis for the phrase.
Claim 2, line 10, recites the phrase “the normalized edge weights as identified using a score 2” which renders the claim indefinite. It is unclear is if there are two scores or if the number two represents some further limitation that has not yet been specified by the claim. To overcome this rejection, clarify how the “score 2” should be interpreted.
Claim 2, line 14, recites the phrase “’q’ … ‘r’” which renders the claim indefinite. It is unclear if these single quotations around the q and r confer some as of yet unknown limitation. To overcome this rejection, remove the single quotations.
Claim 2, lines 8-9, recites the phrases “(N1, N2,…..,Nq) … (E1,E2,….., Er) … (EW1, EW2,.., EWr)” which render the claim indefinite. It is unclear if these phrases recite non-limiting examples of the number of nodes, edges, and edge weights, or if these phrases limit the number of nodes, edges, and edge weights to be greater than 2. It’s also unclear whether Nq, Er, and EWr mean that a number of nodes, edges, and edge weights can be an infinite number. To overcome this rejection, clarify whether these phrases are further limiting or optional.
Claim 3, line 2, recites “the second bacterial association network” which lacks antecedent basis. To overcome this rection, provide antecedent basis.
Claim 3, lines 2-3, recites the phrase “the normalized edge weights” which lacks antecedent basis. To overcome this rection, provide antecedent basis.
Claim 3, line 3, recites the phrase “the normalized edge weight” which renders the claim indefinite. It is unclear which normalized edge weight is being referenced because claim 3, lines 2-3, recites “the normalized edge weights”. To overcome this rejection, clarify which normalized edge weight is being referenced.
Claim 3, lines 8-9, recites the phrase “the bacterial association” which renders the claim indefinite. It is unclear if the phrase refers to claim 3, lines 6-7, or if the phrase refers to another bacterial association recited in claim 1. To overcome this rejection, clarify which association is being referenced.
Claim 3, line 4, recites “wherein the normalized edge weight” which renders the claim indefinite. It is unclear which normalized edge weight is being referenced from “the normalized edge weights”. To overcome this rejection, clarify which one is being referenced.
Claim 4, line 2, recites “the extracted sample” which renders the claim indefinite. It is unclear which sample is being referenced because there is a sample for each individual, as recited in claim 1, pg. 1, lines 4-5. To overcome this rejection, clarify which sample is being referenced.
Claim 5, line 3, recites “the environmental sample” which lacks antecedent basis. To overcome this rejection, provide antecedent basis.
Claim 6, lines 1-2, recites the phrase “the set of domain features” which renders the claim indefinite. It is unclear which set of domain features is being referenced because each abstract in the biomedical text corpus has a set of domain features, as recited in claim 1, pg. 2, lines 14-15. To overcome this rejection, clarify which set of domain features is being referenced.
Claim 7 recites “features 16 to 21” which renders the clam indefinite. It is unclear if the phrase refers to specific features that have yet to be specified, or if the phrase intends to further limit the sets of features to contain at least 21 features, or if the phrase confers some other unspecified limitation. To overcome this rejection, clarify what the specific features are.
Claim 7 recites the phrase “the set of features” which renders the claim indefinite. It is unclear which set of features is being referenced because each abstract in the feature count matrix has a set of features, as recited on pg. 2, lines 16-17.
Claim 9, line 3, recites “the bacterial association network for the diseased group” which renders the claim indefinite. It is unclear if the phrase refers to the first bacterial association network, the first refined association network, or the second refined association network recited in claim 1. To overcome this rejection, clarify which network is being referenced.
Claim 9, lines 4-5, recites the phrase “the bacterial association network for the healthy group of individuals” which lacks antecedent basis. Provide antecedent basis for the phrase.
Claim 10, lines 2-3, recites the phrase “the refined association network” which renders the claim indefinite. It is unclear if the phrase refers to the first or second refined association network recited in claim 1. To overcome this rejection, clarify which refined network is being referenced.
Claim 11, line 4, recites the relative term “diverse”, which renders the claim indefinite. The term “diverse” 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. Because diverse is a subjective term, it is unclear what would constitute diverse data.
Claim 12, pg. 8, line 6, recites the phrase “the one or more first hardware processors” which lacks antecedent basis. To overcome this rejection, change the phrase to “the one or more hardware processors”.
Claim 12, pg. 8, lines 7-8, recites the phrase “the one or more first memories” which lacks antecedent basis. To overcome this rejection, provide antecedent basis.
Claim 12, pg. 8, line 13, recites “the sample” which renders the claim indefinite. It is unclear which sample is being reference because each individual from the group has a sample, as recited on pg. 8, lines 10-12.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1:
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 phenomena (Step 2A, Prong 1). In the instant application, claims 1-11 recite a method, claim 12 recites a system, claim 13 recites a CRM. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claims 1 and 12-13 recite “identifying a disease with known bacterial basis (DS); obtaining … bacterial abundance data from the samples corresponding to the disease using an experimental technique, 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 (N1, N2,… Nm) with their relationship as ‘e’ number of edges (E1,E2,….,En) and edge weights (EW1, EW2,…, EWn) 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 is obtained using the first classifier and if a condition is satisfied in the set of features; 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; 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; and creating … a second refined association network utilizing the output of the crowdsourcing annotation system and the first 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 (N1, N2,…,No) and ‘p’ number of edges (E1, E2,…., Ep) with the normalized edge weights (EW1, EW2,…., EWp) as identified using a score 2; 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 (N1, N2,…..,Nq) and ‘r’ number of edges (E1,E2,…..,Er) with edge weight (EW1, EW2,.., EWr) 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 features 16 to 21 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 “identifying bacterial biomarkers and drivers of a disease by comparing the bacterial association network for the diseased group of individuals with the bacterial association network for the healthy group of individuals.”
Claim 10 recites “identifying therapeutic interventions for curing the disease by using the refined association network.”
Claim 11 recites “creating a knowledge graph of bacterial associations pertaining to healthy and disease state using multiple refined association networks obtained from diverse data available from experimental studies and publicly available biomedical literature.”
Limitations reciting a mental process.
Claims 1-13 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 claims 1 and 12-13, the BRI of identifying a disease includes making a mental determination. The BRI of obtaining bacterial abundance data using an experimental technique includes using a frequency of mapping of signature genetic elements, as recited in claim 5. The BRI of constructing a first bacterial association network using a statistical correlation including 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]. The BRI of formulating search queries includes writing down search strings. The BRI of collating unique IDs to create a list includes collecting and writing down data. The BRI of obtaining biomedical texts includes collecting and organizing data, wherein data is inherently abstract. The BRI of 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]. The BRI of 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 BRI of utilizing sentences to create a first refined association network includes calculating more edge weights for nodes using a t-distribution. The BRI of 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]. The BRI of 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]. The BRI of identifying probable bacterial associations includes mental determinations. The BRI of 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. The BRI of recording the first list of biomedical texts includes writing down the list on pen and paper. The BRI of creating a second refined network includes creating another network with nodes and calculating edge weights using a statistical measure such as a t-distribution.
Regarding claim 2, the BRI of identifying, counting, and finding includes performing mental determinations and performing calculations using pen and paper. The BRI of 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, the BRI of modifying weights includes performing calculations. Regarding claim 4, the BRI includes performing calculations using a total sum scaling. Regarding claim 5, the BRI 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. Regarding claims 9-10, the BRI includes analyzing data and making determinations. 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 such as with a t-distribution.
Limitations reciting a mathematical concept.
Claims 1-5 and 9-13 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 claims 1 and 12-13, the BRI of obtaining bacterial abundance data includes using a mapping frequency of species-specific DNA/RNA. The BRI of 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]. The BRI of 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]. The BRI of 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]. The BRI of utilizing sentences to create a first refined association network includes calculating/modifying edge weights. The BRI of applying a second classifier to the feature count matrix includes performing calculation using a logistic regression, as recited in specification para. [95-98]. The BRI of estimating a threshold annotation time includes a calculation as recited in specification para. [117]. The BRI of 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 1-13 recite an abstract idea (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). 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 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:”
Claims 1 and 12-13 recite “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”
Claims 2-10 recite “The processor implemented method of claim 1”
Claim 12 recites “A system for annotation and classification of biomedical text having bacterial associations, the system comprises: a user interface; one or more hardware processors; a memory in communication with the one or more hardware processors, wherein the one or more first hardware processors are configured to execute programmed instructions stored in the one or more first memories, to:”
Claim 13 recites “One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: … via the one or more hardware processors …”
Regarding the limitation in claims 1-11 of a computer implemented method and the one or more hardware processors, in claim 12 of a system comprising a user interface, one or more hardware processors, and memory, and in claim 13 of a machine-readable storage medium with 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 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)).
Regarding the limitations in claim 1 and 12-13 of extracting samples and sending data to a crowdsourcing annotation system. These limitations equate to insignificant, extra-solution activity of necessary data gathering. Extracting a sample 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.
As such, claims 1-13 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 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:”
Claims 1 and 12-13 recite “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”
Claims 2-10 recite “The processor implemented method of claim 1”
Claim 12 recites “A system for annotation and classification of biomedical text having bacterial associations, the system comprises: a user interface; one or more hardware processors; a memory in communication with the one or more hardware processors, wherein the one or more first hardware processors are configured to execute programmed instructions stored in the one or more first memories, to:”
Claim 13 recites “One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: … with one or more hardware processors …”
Regarding the limitation in claims 1-11 of a computer implemented method and the one or more hardware processors, in claim 12 of a system comprising a user interface, one or more hardware processors, and memory, and in claim 13 of a machine-readable storage medium with 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).
Regarding the limitations in claim 1 and 12-13 of sending data to a crowdsourcing annotation system, this limitation 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).
Regarding the limitations in claims 12-13 of storing instructions in memory, these limitations equate to storing information in memory, which the courts have established as a WURC function of a generic computer in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Regarding the limitations in claim 1 and 12-13 of extracting a sample, this limitation is WURC as taught by Aldars-Garcia et al. (“Aldars-Garcia”; Microorganisms 9, no. 05 (2021): 977; published online 30 April 2021). 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.
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they 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 1-13 are not patent eligible.
Conclusion
No claims are allowed.
Claims 1-13 are free from the prior art because the prior art does not fairly teach or suggest the following limitations in claims 1 and 12-13: “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 creating, via the one or more hardware processors, a second refined association network utilizing the output of the crowdsourcing annotation system and the first refined association network.”
The closest prior art includes Srivastava et al. (“Srivastava”; Frontiers in genetics 10 (2019): 849), Zhong et al. (“Zhong”; In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 723-726. IEEE, 2019), Wei et al. (“Wei”; In AMIA Annual Symposium Proceedings, vol. 2018, p. 1552. 2018), and De Clercq et al. (“De Clerq”; Natural Language Engineering 20, no. 3 (2014): 293-325).
Srivastava discloses a web-based GUI tool called EviMass (Evidence based mining of human Microbial Associations) used for querying microbe disease associations and inferring intermicrobe association patterns mined from biomedical literature (abstract) (pg. 2, col. 1, last para. – col. 1, para. 1) (Figure 1). Srivastava generates a biomedical corpus containing biomedical texts corresponding to unique IDs when reciting (a first list of biomedical texts) (pg. 6, col. 2, last para. – pg. 7, col. 1, para. 1).
Zhong extracts microbial interactions from biomedical literature using a Max-Bi-LSTM (title). Zhong uses a continuous Bag-of-Words (CBOW) model to create feature vectors (a set of domain features) generated from sentences in a microbial interactions corpus (MICorpus) to create an embedding matrix that is used for a Max-Bi-LSTM (first classifier) (pg. 723, col. 2, last para. – pg. 724, col. 1, para. 1) (pg. 724, col. 2, sec. B). The output of the Max-Bi-LSTM being microbial interactions based on the sentence embeddings (predicted sentences using the first classifier and calculated domain features).
De Clercq evaluates methodologies to asses readability, one being a crowdsourcing set-up for users who are not experts (abstract). A lightweight crowdsourcing tool (crowdsourcing annotation system) was used to invite users to provide pairwise comparisons and was determined to be a viable alternative to expert labeling (abstract).
Wei discloses factors associated with the cost of time for clinical text annotation (title). Wei “defined a set of factors that we hypothesized might affect annotation time, and fitted them into a linear regression model to predict annotation time” (estimated threshold annotation time) (abstract) (pg. 1555, para. 2).
However, these references do not teach or fairly suggest sending a list of biomedical texts, estimated annotation times for each text in the list, and predicted sentences to a crowdsourcing system to then create a second refined bacterial association network based on the output of the system and a first refined bacterial association network.
Notable, but not relied upon, prior art includes: Kuntal et al. (“Kuntal”; The ISME journal 13, no. 2 (2019): 442-454).
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685