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 .
Applicant's election with traverse of Invention I (a DNA product), that is the product of species A,(a DNA synthesis process), using evaluation by species B-vii (using a biological product language) in the reply filed on 1/20/2026 is acknowledged. The traversal is on the ground(s) that there is no burden of search to search all IX separate and distinct physical product categories, made by a multiplicity of distinct processes, and evaluated by a multiplicity of distinct methods. This is not found persuasive because Each invention, and listed species, is separately classified, has obtained a differing status in the prior art, they set forth differing physical products having differing physical structures and differing biological and chemical properties, with no common core structure. Each synthesis process utilizes differing reagents, and methods steps to obtain each distinct product. Each evaluation process utilizes differing information, in differing analyses, to obtain differing information. The claims cover nearly all of molecular biology. The search burden was properly established.
The requirement is still deemed proper and is therefore made FINAL.
Claims 5-13 and 16-24 stand withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected species, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 1/20/2026.
Claims 1-4, 14-15 are under examination to the extent they read on the elected invention and species. Claim 1 is generic. Claims 2-3 represent the elected DNA product and the DNA synthesis process. Claim 4 is generic. Claims 14-15 represent the species of evaluation.
This application is a continuation of PCT/US2025/0318891, filed 6/2/2025, which claims priority to two US provisional applications. The effective filing date for the elected claims is that of the second provisional application, 63/803,471, filed 5/9/2025. The earlier filed provisional application (63/655575) fails to provide specific support for the elected inventions. The’575 provisional is directed to the creation of a simulation of a cell, using flux balance equations, a variety of differential equations, stoichiometry, enzyme kinetics, biochemical reactions, metabolic calculations, metabolite databases, bipartite graphs, etc. The examiner has reviewed all papers related to the PCT filing.
This application has published as US PG-Pub: US 2026/0023899 A1.
The TrackOne petition was granted under separate cover.
The preliminary amendment filed 9/29/2025 was entered.
The replacement drawings, filed 9/29/2025, have been entered and are suitable for examination.
Two separate IDS statements have been entered and considered.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) 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.
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 1-4, 14-15 is/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.
Applicant is directed to MPEP 2106 and the Federal Register notice (FR89, no 137 (7/17/2024) p 58128-58138) for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES, the claims are drawn to statutory categories: Processes.
With respect to step (2A) (1): YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
1. A method of generating a biologic product of a biologic synthesis process, comprising:
(EIA: preamble, setting forth a method, and the goal of the method.)
selecting a first biologic parent having a first feature;
(EIA: mental process of observation of a first feature, and selecting the parent having the observed feature. The feature is without limitation. The parent is without limitation beyond the presence of an observed feature. (MPEP 2106.04(a)(2) section III))
selecting a second biologic parent having a second feature; and
(EIA: mental process of observation of a second feature, and selecting the parent having the observed feature. The feature is without limitation. The parent is without limitation beyond the presence of an observed feature. (MPEP 2106.04(a)(2) section III))
selecting the biologic product based on an evaluation of a set of combinations of the first biologic parent and the second biologic parent.
(EIA: mental process of generating combinations of possible products having features, evaluating the features on any basis, by any means, and judging/ selecting the desired combination that produces the desired product. The features are without limitation, beyond a combination. (MPEP 2106.04(a)(2) section III))
2. The method of claim 1, wherein the biologic product includes at least one of an enzyme protein, a non-enzyme protein, a DNA sequence, an RNA sequence, a plasmid, a metabolite, a biologic strain, a bioreactor process, or a downstream purification process.
(EIA: data gathering: a description of the product to be made. The product is not specifically synthesized, but merely selected.)
3. The method of claim 1, wherein the biologic synthesis process includes at least one of a DNA synthesis process, an RNA synthesis process, a protein synthesis process, a metabolite synthesis process, a metabolic process, at least one pathway of a metabolic system, a plate growth process, or a fermentation process.
(EIA: data gathering, describing “biologic synthesis processes” from the preamble that could be used to make the desired product. Claim 1 has no synthesis steps.)
4. The method of claim 1, wherein at least one of the first feature or the second feature includes at least one of a product expression feature, a product activation feature, a product reaction feature, an enzyme cleaning feature, a product stability feature, a product biocompatibility feature, a process rate feature, a process catalyzation rate feature, a process efficiency feature, a process cost feature, or a process yield feature.
(Mental process modification, setting forth types of features to be observed (MPEP 2106.04(a)(2) section III))
14. The method of claim 1, wherein the evaluation of the set of combinations of the first biologic parent and the second biologic parent includes:
generating a representation of a portion of a respective combination according to a biologic product language, and
evaluating the representation of the portion of the respective combination according to the biologic product language.
(Mental Process of creating a representation, such as a figure or drawing, and mental process of evaluation of the representation. (MPEP 2106.04(a)(2) section III))
15. The method of claim 14, wherein the biologic product language includes a protein language, and evaluating the representation includes evaluating the representation of the portion of the respective combination in the protein language according to a protein language model.
(Mental Process modification, identifying a type of language model used in the evaluation of the representation. (MPEP 2106.04(a)(2) section III))
With respect to step 2A (2): NO. The claims were examined further to determine whether they integrated any JE into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone, or in combination to determine if the JE is integrated into a practical application (MPEP 2106.05(a-c, e, f and h)).
Claim(s) 2, 3 recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantec Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Dependent claim(s) 4, 14, 15 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO. The claims recite a JE, do not integrate that JE into a practical application, and thus are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). The additional elements were considered individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to claim(s) 2, 3: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
Bruckner (2008) obtains parent biologic molecules each with differing features.
Brunelli (2005) obtains parent elements each with differing features.
deHaas (2021) obtains parent biologic elements with differing features.
Liu (2024) obtains parent biologic molecules each with differing features.
Pedersen (2009) obtains parent biologic molecules each with differing features.
Scheben (2018) obtains parent biologic molecules each with differing features.
Wang (2025) obtains parent biologic molecules each with differing features.
Yang G (2025) obtains parent biologic molecules each with differing features.
Yang L (2025) obtains parent biologic molecules each with differing features.
Mabey (2025) obtains parent biologic molecules each with differing features.
Brixi (2025) obtains parent biologic molecules each with differing features.
Wang (2024) obtains parent biologic molecules each with differing features.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art (as in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook).
Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
Dependent claim(s) 4, 14, 15 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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 1-4, 14-15 are rejected on the basis that it contains an improper Markush grouping of alternatives. See In re Harnisch, 631 F.2d 716, 721-22 (CCPA 1980) and Ex parte Hozumi, 3 USPQ2d 1059, 1060 (Bd. Pat. App. & Int. 1984). A Markush grouping is proper if the alternatives defined by the Markush group (i.e., alternatives from which a selection is to be made in the context of a combination or process, or alternative chemical compounds as a whole) share a “single structural similarity” and a common use. A Markush grouping meets these requirements in two situations. First, a Markush grouping is proper if the alternatives are all members of the same recognized physical or chemical class or the same art-recognized class, and are disclosed in the specification or known in the art to be functionally equivalent and have a common use. Second, where a Markush grouping describes alternative chemical compounds, whether by words or chemical formulas, and the alternatives do not belong to a recognized class as set forth above, the members of the Markush grouping may be considered to share a “single structural similarity” and common use where the alternatives share both a substantial structural feature and a common use that flows from the substantial structural feature. See MPEP § 2117.
The Markush grouping of “biologic products” is improper because the alternatives defined by the Markush grouping do not share both a single structural similarity and a common use for the following reasons: The products of claim 2 represent the desired biologic products as the result of the method of claim 1. Claim 1 is completely unlimited as to the nature of the product. The listed products of claim 2 do not share any single structural similarity or common use. The listed products are not specific chemical structures, they are generic encompassing ANY type of product. The listed products are not alternative chemical compounds, that share any single structural similarity and common use. The listed products are not functionally equivalent.
Each encompassed protein (within the generic types of “enzyme” or “non-enzyme”) has a differing primary structure, differing secondary structure, and differing biological and biochemical characteristics, not shared by any other product.
Each encompassed DNA sequence product has a differing primary structure, differing secondary structure, and differing biochemical/ biological / functional characteristics not shared by any other product.
Each encompassed RNA sequence product has a differing primary and secondary structures, and differing biochemical/ biological/ functional characteristics not shared by any other product.
Each encompassed plasmid has a differing primary structure, and differing biochemical/ biological/ functional characteristics and features not shared by any other product.
Each encompassed metabolite has a differing chemical structure and differing set of biologic or chemical or functional characteristics not shared by any other product.
Each encompassed “biologic strain” has differing physical and chemical structures, differing underlying genomic sequences, as well as differing biochemical/ biological/ functional features or characteristics not shared by any other product.
The listed “bioreactor processes” and “downstream purification process” are not products, and do not belong in the Markush group.
The list of types of products do not list any specific product having a specific structure, or substructure or specific characteristic that could be shared.
None of the types of products are functional equivalents. No specific function is claimed or identified.
Each identified type of product has a multitude of differing uses, and no specific common use has been claimed.
The Markush grouping of “biologic parent(s)” is improper because the alternatives defined by the Markush grouping do not share both a single structural similarity and a common use for the following reasons: The biologic parents are set forth in the specification as encompassing “a parent DNA or RNA sequence” [1487], “a parent protein” [1487], “a parent cell line” [1487], “a parent strain of a microbe” [1487], “Other examples of the first (or second) biologic parent include an enzyme protein, a non-enzyme protein, a DNA sequence, an RNA sequence, a plasmid, a metabolite, a biologic strain… a metabolite…” [multiple places], “The biologic parent may include… a metabolic precursor, a cell or cell line, a strain of a biological species, or the like. The biologic parent may be or may include a material that is similar to the biologic product, and that is to be modified to generate the biologic product” [1510]. Each of these possible biologic parents encompassed by the claim has separate and distinct chemical structures, biological sequences, biochemical properties, biological functions, biological features, and a plethora of differing uses. The biologic parents encompassed by the claim share no common structure or substructure. The biologic parents encompassed by the claim share no common specific use that depends on a shared or common structure. Claim 1 is completely unlimited as to the nature of the biologic parent. The possible parents encompassed by the claim are not functional equivalents. The biologic parents encompassed by the claim do not share any single structural similarity or common use. The biologic parents encompassed by the claim are not specific chemical structures, they are generic. The biologic parents encompassed by the claim are not alternative chemical compounds, that share any single structural similarity and common use.
To overcome this rejection, Applicant may set forth each alternative (or grouping of patentably indistinct alternatives) within an improper Markush grouping in a series of independent or dependent claims and/or present convincing arguments that the group members recited in the alternative within a single claim in fact share a single structural similarity as well as a common use.
Claims 1-4, 14-15 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.
The metes and bounds of claim 1 are entirely unclear. The claim fails to particularly point out and distinctly claim the particular biologic product to be selected, as well as how the actual selections, combinations and evaluations are performed. The biologic product is selected by the selection of two “parent” elements, each of which has an unnamed feature, and then evaluation of combinations of the parents. It is entirely unclear how “biologic parent” is to be interpreted in this context, as the plain meaning of the term is an individual who has procreated with another individual and had progeny. However, as shown in claim 2 (and discussed at length above in the improper Markush rejection), the desired product is not limited to individual progeny having a feature that represents a combination of parent features, nor was progeny the elected product.
The specification does not provide a specific definition of a “biologic parent” (MPEP 2173).
"The presumption that a term is given its ordinary and customary meaning may be rebutted by the applicant by clearly setting forth a different definition of the term in the specification. In re Morris, 127 F.3d 1048, 1054, 44 USPQ2d 1023, 1028 (Fed. Cir. 1997) (the USPTO looks to the ordinary use of the claim terms taking into account definitions or other "enlightenment" contained in the written description)."
"The only exceptions to giving the words in a claim their ordinary and customary meaning in the art are (1) when the applicant acts as their own lexicographer; and (2) when the applicant disavows or disclaims the full scope of a claim term in the specification.
To act as their own lexicographer, the applicant must clearly set forth a special definition of a claim term in the specification that differs from the plain and ordinary meaning it would otherwise possess. CCS Fitness, Inc. v. Brunswick Corp., 288 F.3d 1359, 1366, 62 USPQ2d 1658, 1662 (Fed. Cir. 2002)."
"See In re Paulsen, 30 F.3d 1475, 1480, 31 USPQ2d 1671, 1674 (Fed. Cir. 1994) (holding that an inventor may define specific terms used to describe invention, but must do so "with reasonable clarity, deliberateness, and precision" and, if done, must "‘set out his uncommon definition in some manner within the patent disclosure’ so as to give one of ordinary skill in the art notice of the change" in meaning) (quoting Intellicall, Inc. v. Phonometrics, Inc., 952 F.2d 1384, 1387-88, 21 USPQ2d 1383, 1386 (Fed. Cir. 1992))."
"However, it is important to note that any special meaning assigned to a term "must be sufficiently clear in the specification that any departure from common usage would be so understood by a person of experience in the field of the invention." Multiform Desiccants Inc. v. Medzam Ltd., 133 F.3d 1473, 1477, 45 USPQ2d 1429, 1432 (Fed. Cir. 1998)."
The specification attempts to define a biologic parent as: “protein parents” [1320], “a parent DNA or RNA sequence” [1487], “a parent protein” [1487], “a parent cell line” [1487], “a parent strain of a microbe” [1487], “Other examples of the first (or second) biologic parent include an enzyme protein, a non-enzyme protein, a DNA sequence, an RNA sequence, a plasmid, a metabolite, a biologic strain… a metabolite…” [multiple places], “The biologic parent may include, for example, a DNA sequence, an RNA sequence, a protein such as an enzyme, a metabolic precursor, a cell or cell line, a strain of a biological species, or the like. The biologic parent may be or may include a material from which the biologic product is synthesized, such as a metabolic precursor that is included in a metabolic pathway to produce the biologic product, or a DNA or RNA sequence that can be transcribed and/or translated to synthesize the biologic product. The biologic parent may be or may include a material that is similar to the biologic product, and that is to be modified to generate the biologic product (e.g., a DNA sequence that is to be edited to produce an edited DNA sequence as the biologic product).” [1510].
However, these are open-ended possibilities of what a biologic parent might be, but they are not a special definition set forth “with reasonable clarity, deliberateness and precision” such that one of skill would understand the particular special definition, and its departure from common usage.
While breadth of the claim is not the same as indefiniteness, it is entirely unclear how to identify “biologic parents” which would lead to the selection of a DNA sequence product, made by a DNA synthesis process, “according to” a biologic language, as elected.
The claim is not limited to selecting a first/ second “parent DNA sequence”. The claim is not limited to evaluating specific combinations of DNA sequences and features from each parent. The claim does not direct the synthesis of a DNA sequence product.
The nature of the evaluation is not set forth in claims 1-4 such that the point of the selection could be carried out. The aspect of the combination to be evaluated in claims 1-4 is not identified. It is entirely unclear how to select a specific DNA sequence product, when the point, and aspect of the evaluation is completely generic. One of skill would not know how to select one combination over another, without a guiding principle, underlying scientific goal, or particular desired function.
One of skill would not be able to determine the metes and bounds of the desired “biologic product” or the metes and bounds of the “biologic parents” as it is entirely unclear how to determine what a parent of the desired product could be, what features are to be observed or selected or how to evaluate any product. Even within the election of a DNA sequence product and the disclosures quoted above, the concept of a “biologic parent of a DNA sequence product” is indefinite: DNA sequences exist a) as sequence data elements (strings of letters representing a DNA sequence), b) as isolated DNA, c) as and within plasmids, d) can be transcribed to RNA, e) exist within living individuals (prokaryotic, eukaryotic, single celled, multi-cellular, plants, mammals, microbes, vertebrates, invertebrates, et al.) f) can encode proteins, g) exist in chromosomal structures and extrachromosomal structures (mitochondria), h) have binding sites for proteins, have promoter sites, have enhancer sites, i) DNA sequence products can be synthesized using nucleotide precursors and metabolites, j) RNA could be considered “similar” to DNA and k) exist in cell lines (immortalized cells in culture).
If the desired biologic product is a DNA sequence product, what is the “biologic parent”? Is it a) sequence data, 2) a parental individual who has procreated with another individual, 3) a cell line, 4) metabolites used in the synthesis of DNA, 5) a plasmid, 6) a cell, 7) an organism, 8) an organelle, 9) a chromosome? These do not appear to be functional equivalents nor do they all have a common use in generating biologic DNA sequence products. It is entirely unclear how to select a “biologic parent having a first/ second feature” when there is no particular desired feature identified. It is entirely unclear how to create the desired combinations of the (undefined) parents, in order to evaluate and judge those (undefined) combinations. It is entirely unclear how the combination is to be judged, in particular, that would lead to its selection.
While claims are read in light of the specification, limitations from the specification cannot be read into the claims. MPEP 2111.
"Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment." Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004).”
The metes and bounds of claim 2 are unclear, as the claim allegedly describes products, but lists processes. It is entirely unclear how a product, a DNA sequence product, could be a process, which is a series of steps.
The metes and bounds of claim 4 are entirely unclear with respect to what the listed features encompass, how they are identified in a biologic parent, and how they are combined to provide a set of candidate biologic products. The features are not clearly identified, nor are they all clearly related to the elected embodiments. For example, a DNA sequence product, does not necessarily have “a product expression feature” as not all DNA are “expressed”. Not all DNA sequence products have gene features that may have a “product expression feature”.
It is entirely unclear how to identify a “product activation feature” for a biologic parent to generate a DNA sequence product. DNA is not generally determined to be “activated”. It is unclear how to generate DNA sequence products with “activation features.”
It is unclear how to identify a “product reaction feature” in a biologic parent to generate a DNA sequence product. It is entirely unclear what a “product reaction” is intended to encompass in this situation, and it is unclear how to identify product reactions in a “biologic parent.” It is unclear how to generate DNA sequence products with a “reaction feature.”
It is entirely unclear what a “enzyme cleaning feature” is intended to encompass, with respect to the “biologic parent” and the desired DNA sequence product. The elected product is not an enzyme, nor is the biologic parent. It is entirely unclear how to generate DNA sequence products with an “enzyme cleaning feature.”
It is entirely unclear how to determine “a product biocompatibility feature” for the biologic parent, as what it must be compatible with is not identified. It is unclear how to identify a biocompatibility feature for the desired product: the DNA sequence product. It is unclear how to generate DNA sequence products that have a “biocompatibility feature.”
It is entirely unclear how to identify “a process rate feature” in a biologic parent, as no process having a rate is specified. It is further unclear how to determine a process rate feature in the elected product: a DNA sequence. It is unclear how to generate a DNA sequence product that has a “process rate feature.” A DNA sequence product does not have a process, which might be scored for a rate.
It is entirely unclear how to identify “a process catalyzation rate feature” in a biologic parent, as no process of catalyzation is specified. It is further unclear how to determine a process catalyzation rate feature in the elected product: a DNA sequence. It is unclear how to generate DNA sequence products that have a catalyzation rate feature. A DNA sequence product does not have clearly have a catalytic function, which might be scored for a rate of completion.
It is entirely unclear how to identify “a process efficiency feature” in a biologic parent, as no process of determining any sort of efficiency is specified. It is further unclear how to determine a process efficiency feature in the elected product: a DNA sequence. It is unclear how to generate DNA sequence products that have an “efficiency feature.” A DNA sequence product does not have clearly have a function related to efficiency, which might be scored for a rate of completion.
It is entirely unclear how to identify “a process cost feature” in a biologic parent, as no costs of any element of the biologic parent of is specified. It is further unclear how to determine a process cost feature in the elected product: a DNA sequence. A DNA sequence product does not have clearly have a cost process, which might be scored for a total cost. No specific steps of synthesizing the product are required, which might provide such related information are present.
It is entirely unclear how to identify “a process yield feature” in a biologic parent, as no process of analysis of yield of a product is specified. It is further unclear how to determine a process yield feature in the elected product: a DNA sequence. A DNA sequence product does not have clearly have an innate yield characteristic, which might be scored.
Further in claim 4, it is entirely unclear how to generate combinations of biologic parents having any of the listed features, in a way that would lead to the desired and elected product, a DNA sequence product, retaining some aspect of the parent features. It is further unclear how to evaluate the generated products having combinations of features, to select any specific DNA sequence product that has some combination of features.
The metes and bounds of claim 14 are unclear, with respect to how the evaluation is carried out, and what portion of a combination is to be evaluated. Claim 14 sets forth that “a representation of a portion of a respective combination” is generated. It is unclear how much of a combination is required to meet the “portion” limitation, with respect to the elected product: a DNA sequence product: one nucleotide, 10 nucleotides, 1000 nucleotides? It is unclear how much of any sequence combination is required for the evaluation to proceed. It is further unclear how each representation is to be “generated” “according to a biological product language.” The specification does not clearly set forth a DNA product language, or a DNA product language model. It is unclear if this is intended to be a representation such as a drawing, tokenization of specific elements of the combination, a textual description of the DNA sequence product, tabular data, or some other representation. No particular biological product language is provided such that a representation generated “according to” that language would have any inherent characteristics, which could then be evaluated. The claim fails to particularly point out and distinctly claim how any representation is to be evaluated, with any specificity. The aspect of the representation to be judged is not identified. No steps of ranking, or comparing scores are set forth. No particular conclusions are provided as to the nature of the representation.
The metes and bounds of claim 15 are unclear, with respect to the use of a protein language, or a protein language model, and the elected biologic product: a DNA sequence product. It is entirely unclear what the protein language encompasses. It is entirely unclear how a protein language, or protein language model is to be applied to various representations of DNA sequence products. DNA and proteins use differing codes: DNA uses nucleotide coding, while proteins utilize amino acid coding. No particular protein language is provided such that a representation generated “according to” that language would have any inherent characteristics, which could then be evaluated. The claim fails to particularly point out and distinctly claim how any representation is to be evaluated, with any specificity. The aspect of the representation to be judged is not identified. No steps of ranking, or comparing scores are set forth. No particular conclusions are provided as to the nature of the representation.
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 3 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. Claim 1, in the preamble set forth “A method of generating a biologic product of a biologic synthesis process” where the synthesis process is a description of an aspect of the product. Claim 3 lists types of synthesis processes. Claim 1 does not require any steps of biologic synthesis of any biologic product. A biologic product is “selected” from a set of products representing combinations of features. This is not equivalent to “a DNA synthesis process” as elected, or to any process recited within claim 3. This claim does not further limit the method of claim 1. 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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) s 1-4 are is/are rejected under 35 U.S.C. 102a1 as being anticipated by Bruckner (2009).
Bruckner, A. et al. (2009) Yeast two-hybrid, a powerful tool for systems biology. Int. J. Mol. Sci, vol 10, p2763-2788.
Claim 1 is directed to method of generating a biologic product, wherein step 1) selects a parent with a feature, step 2) selects a second parent with a second feature, and 3) selecting a product based on evaluations of combinations of the parents.
Bruckner is directed to the yeast two-hybrid system, wherein one yeast biologic parent, having a first feature, and a second yeast biologic parent having a second feature, are combined, to generate biologic products having combinations of features. Yeast are biological cells, which comprise DNA, which is generated using DNA synthesis processes. The products of the combination are evaluated using an assay readout, such as fluorescence. As such claim 1 is anticipated. With respect to the elected elements of DNA sequence products and a DNA synthesis process (claims 2-3) the DNA sequence product can be cloned and sequenced from the yeast, to generate a DNA sequence product. (p2765). See also section 2.1, p2766. With respect to claim 4, a product expression feature is one of the features of the parents, or the product.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Brunelli (2005).
Brunelli, S. A. (2005) Development and evolution of hidden regulators: selective breeding for an infantile phenotype. Dev. Psychobiology vol 47:243-252.
Brunelli is directed to selective breeding of rats, to obtain particular phenotypes which result from gene expression. Parent rats having one feature (high ultrasonic vocal responses), and parent rats having a second feature (low ultrasonic vocal responses), were selected. Progeny from up to 20 generations of breeding, cross breeding, and inbreeding, having varying phenotypes representing the combinations of features are produced and evaluated. A variety of phenotypes were analyzed, including the UVR, thermoregulation, weight, grooming behaviors and locomotion. As such, claim 1 is anticipated. With respect to DNA, and DNA synthesis, rats have DNA genomes, which generate DNA sequence products using DNA synthesis processes, meeting claims 2-3. With respect to claim 4, “expression features” is a feature of the parents and the progeny.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Pedersen (2009).
Pedersen, L. D. et al. (2009) ADAM: a computer program to simulate selective breeding schemes for animals. Livestock Science, vol 121, p343-344.
Pedersen is directed to a computer program for selecting biologic parents, having a feature, and generating sets of possible progeny, then evaluating the progeny for the desired combination of features. Each biologic parent is selected for a given feature. Then, the program simulates descendent generations having combinations of the features, based on certain transmission models. Selection can be based on single or multiple traits, using truncation, random combinations, or optimum contribution selection. “The results that can be obtained include the true and estimated breeding values for each time step, overall and trait wise genetic gain, generation interval and accuracy of the
prediction. Inbreeding based on pedigree information and, optionally, inbreeding based on DNA information including the length of fixed regions on the genome is also calculated. Results are presented as summaries for all animals in each time step, and for each individual animal. The amount of output is optional as all data is stored in memory and can be printed if needed.” P344. As such, claim 1 is anticipated when “DNA sequence product” is given the broadest reasonable interpretation of being DNA sequence data product- a data representation of DNA sequences. Features can include genes, proteins, genetic markers, et al.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 102a1 as being anticipated by de Haas (2021).
de Haas (2021) Selective breeding as a mitigation tool for methane emissions from dairy cattle. Animal, vol 15, e100294, 10 pages.
de Haas is directed to selective breeding in cattle. Cattle parent biologics, having varying features, are selected and bred to produce sets of progeny, each having assortments of the features of the parents. Features analyzed by de Haas include milk yield, longevity, health, fertility, conformation, feed efficiency and methane production. The parents and progeny are genotyped to determine DNA sequence elements, and phenotyped, to evaluate each trait. Cattle genomes are DNA, and breeding generates various recombined DNA genomes in the progeny using DNA synthesis processes. The listed traits meet at least one of the features of claim 4.
Claim(s) 1-4, 14 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Pickar-Oliver (2019).
Picker-Oliver, A. et al. (2019) The next generation of CRISPR-Cas technologies and applications. Nature Reviews, vol 20, p490-507.
Picker-Oliver is directed to the discussion of the genome editing systems known as CRISPR-Cas systems. Biologic parents having differing traits are selected, and the genome of each parent is subjected to the particular cleavage system, for the purpose of gene editing or epigenic modulation. CRISPR-Cas can be applied for the purpose of “perturbation of the transcriptome and non-coding
genome, single-base editing, genome-wide pooled screens, chromatin reorganization and the therapeutic potential…” (p490) and the edited parents can then be further combined to generate progeny having combinations of features. Gene sequences can be deleted or inserted (p493). Chromosomal regions can be translocated. Single base pair editing is disclosed. (p493). The edited genome is a DNA sequence product, and generation of progeny uses DNA synthesis processes. The features can be screened in a multiplicity of ways, including screening for activity, efficiency, expression, et al. (p497-499). The CRISPR-Cas system can be used to regulate gene expression by engineering Cas9 as a DNA recognition complex. (p498). The CRISPR-Cas system uses a “biologic language” as it uses sequence matching to achieve targeted editing, and affects internal DNA synthesis processes. (Table 1).
Claim(s) 1-4, 14 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Scheben, A. et al. (2018).
Scheben, A. et al. (2018) Towards a more predictable plant breeding pipeline with CRISPR/ Cas-induced allelic series to optimize quantitative and qualitative traits. Curr. Op. Plant Biol. Vol 48:218-225.
Scheben is directed to the use of CRISPR-Cas systems to select parent biological crop plants having various features, then produce progeny each having a differing combination of features of each parent, which can be evaluated, to select the desired feature combination. “Genome editing can generate targeted allelic series of trait-related genes and regulatory elements, creating a series of variable phenotypes for breeding within a single generation. Disrupting genic and regulatory regions is particularly effective for engineering quantitative traits. Although qualitative traits can be more difficult to engineer using disruption, precise base editing may allow an efficient path to rationally improve qualitative traits if protein function can be accurately modelled.” (abstract). Desired features include crop yield, fruit size, grain size, grain number, oil levels, gene gain/ loss of function, flowering time, root length, fertility, pathogen resistance et al. One example meeting the limitations of the claims relates to oil type/ lipid profile and levels: “to identify the optimal allele for a target trait, a trial-and-error approach based on genome editing can be used. By generating an allelic series of individuals with mutations at different sites, optimal alleles can be identified using a comparison of the gene expression, gene products and phenotypes associated with each allele. Using two gRNAs to induce mutations in three genes encoding delta-12-desaturase (FAD2) in the hexaploid oilseed crop Camelina sativa, a set of diverse genetic combinations with single, double and triple mutants was produced in three generations [36]. The lines in the set varied strongly in their lipid profiles, with oleic acid levels of 10–62% in the oil.” (p219). See Fig 1b, and Fig 2. Scheben notes that use of computer automation and machine learning can enhance these processes, using biological “language” features such as identifying regulatory elements in DNA sequences. “Predicting genome editing targets in silico based on all available knowledge is a task well suited to automated machine learning approaches [54,55]. For instance, in an approach readily applicable to plants, chromatin signatures in the human genome were used to predict 600 regulatory elements (200 promotors and 400 enhancers) within a 30-Mb region.” P222. Automation is also applied to the analysis of the resulting phenotypes of the progeny, using sensor technology, imaging and machine learning. Sensor phenotyping is particularly suited to analysis of traits/ features such as biomass, yield, water use, and photosynthetic efficiency. (p222). Plant genomes are DNA genomes. Crossbreeding generates sets of DNA sequence products (progeny plants) with various combinations of desired phenotypes, which can be analyzed and the particular desired progeny selected.
Claim(s) 1-4, 14-15 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Yang, G. et al. (1/2025).
Yang, G. et al. (1/2025) Integrating remote sensing data assimilation, deep learning and large language model for interactive wheat breeding yield prediction. ArXiv: 04487, 30 pages.
Yang is directed to the use of large language models to select wheat plants having desired features, generate predicted progeny having combinations of the features, and evaluate those predicted progeny to select the desired combination. Yan is interested in the trait of yield prediction in wheat. Wheat is a plant with a DNA genome, comprising DNA sequence products. The wheat parent plants are assessed for features such as leaf area index, and yield. These characteristics are input into the LLM, to develop an interactive yield prediction for predicted progeny. “When combined with RAG technology, LLMs can meet real-time data update demands by integrating information retrieval and text generation to deliver more accurate and timely predictions and analyses [38], [39]. This combination of technologies not only enables diverse and personalized interactions but also
effectively integrates information from remote sensing data, phenotyping data, and breeding
experiment results. It provides breeders with accurate and up-to-date decision support on crop
germplasm characteristics, yield performance, cultivation techniques, and suitable planting areas
[40].” (p3). Yang meets the rejected claims as they disclose and discuss the goals of the study:
“1. Using the newly designed data assimilation algorithm (WW-4VES) to assimilate the leaf area index (LAI) into the WOFOST model to improve the efficiency and accuracy of data assimilation; 2. Using the partial output results after assimilation and the results of remote sensing inversion to drive the time series temporal fusion transformer (TFT) model to predict wheat yield, and explore its yield prediction performance in different regions and natural weed competition environments; 3. Based on the above yield prediction method and using LLM and RAG technology, build a friendly and continuously updated yield prediction interactive Web tool to integrate multi-source data to support breeding decisions.”
Yang selects a variety of wheat parent types: wheat germplasms, and high generation wheat lines. (section 2.1. and table 3) “The natural growing environment was maintained in this study in order to breed wheat varieties that have greater competitive advantages over weeds.” P4. Traits of LAI, CH and wheat grain yield were collected, as well as related meteorological data and soil data. (Fig 2, and Table 1). All collected data is applied to their models as set forth in Section 3. The LLMs of Yang appear to meet the BRI of biologic product language, and protein product languages, as the wheat yield contains protein levels. Fig 4 represents a typical LLM process for predicting estimated yield of a specific plant.
Claim(s) 1-4, 14-15 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Yang, L. et al. (2025).
Yang, L. et al. (2025) Artificial intelligence-driven plant bio-genomics research: a new era. Tropical Plants, 4: e015, 13 pages.
Yang discusses the use of differing types of large language models in the analysis and generation and analysis of biological, agricultural and crop science data. Yang discusses Statistical language models (SLMs), Neural language models (NLM), pre-trained language models (PLM), and large language models (LLM) at pages 2-3. The Graphical abstract illustrates how the models are trained, and used to generate relevant predictions. Genetic language models are discussed beginning at page 3, and in Fig 2. “Genetic language is subject to biochemical processes; for instance, the transcription process from DNA to mRNA is precisely regulated by enzymes such as RNA polymerase, and the translation process from mRNA to proteins depends on the synergistic action of ribosomes and tRNA. These processes are subject to strict biochemical regulation to ensure the accurate transmission of genetic information[59];… In the enigma of life, the genome sequence serves as the
language. It forms the foundation of genetic information that underpins life. Composed of the base sequence of DNA or RNA (A, T, G, C, U), it directs the gene expression and protein synthesis in organisms, thereby constructing a unique informational coding system[60]..” (p4).
“Biological entities, ranging from unicellular bacteria to intricate mammals, function as sophisticated systems for information retention[64]. These organisms encode hereditary traits within the nucleotide sequences of their genetic material, ensuring the perpetuation of biological traits and the propagation of their species. With each generation, the reproduction of offspring entails the
conveyance of genetic information. Beyond genetic communication, organisms also engage in the transmission of knowledge through behaviors, social engagements, and cultural legacies[65]. The conveyance and accumulation of such information facilitates the adaptation to fluctuating environments and the evolution of more intricate and effective survival tactics. From a broad evolutionary perspective, DNA sequences, and human languages exhibit striking similarities in their evolutionary processes[54,66]. This observation offers a novel approach to analyzing DNA sequences by employing NLP technologies and methodologies (Fig. 3).” P5.
Yang discusses DNABERT, PDLLM, Enformer, GPN, IEnhancer-BERT, DNAGPT, GenSLMs and other biological language models (See Table 1), and their use to analyze and predict various features or traits in genetic data. (p6-8). These genomic LLMs are trained largely on DNA data from humans, and animals, as well as plants, viruses and prokaryotes. P8. Yang details the application of LLM to tropical plant breeding, to identify genes or mutations that facilitate adaptation to a tropical environment.
“Taking tropical plants such as macadamia[110] and passion fruits[111] as examples, relevant open-source datasets are often lacking. In such cases, the cross-species transfer learning capabilities of LLMs become particularly crucial. By leveraging genomic data from related or phylogenetically close species and employing transfer learning strategies with genomic LLMs[93], it is possible to predict and analyze the genomic structure and function of tropical plants… . Furthermore, in-depth research on the genomes of tropical plants can aid in the discovery of new germplasm resources, opening new avenues for crop improvement and biotechnological breeding” P8.
A key aspect of using LLM in genome analysis is pointed out at page 9, “sequence generation.” This takes parent DNA sequences, and combines, mutates, or otherwise create artificial sequences “that mimic authentic biological sequences.” DNAGPT has been used to generate synthetic human genomes which can then be analyzed for the desired features or traits. The analysis of the features or traits can be carried out based on gene function, expressed protein structure, chromatin structure, sequence variation, and evolution of sequences over time. (p9-10). One example was in the prediction of variations in SARS-CoV-2 genomes.
“GenSLMs[97] was successfully trained by pre-training on over 110 million prokaryotic gene
sequences and fine-tuning on 1.5 million SARS-CoV-2 genomes, thereby enabling the identification of salient variations. Concurrently, to enhance the model's interpretability, GenSLMs incorporates an integrated visualization tool designed to graphically represent genomic relationships and model attention mechanisms.” P9
As such, the claims are anticipated.
Claim(s) 1-4, 14-15 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Liu et al (2025).
Liu et al. (Feb 3, 2025) PDLLMs: a group of tailored DNA large language models for analyzing plant genomes. Molecular Plant, vol 18, p175-178, and some supplemental material.
Liu is directed to the use of large language models to analyze and improve plant genomes, and for genomic prediction tasks. 14 different plant genomes were obtained. 5 plant specific foundation DNA language models were developed. (“Plant DNAMamba, Plant DNABERT, Plant DNAGPT, Plant DNAGemma, and Plant nucleotide transformer based on byte pair encoding (BPE), k-mer, and single-base tokenizers (Figure 1A).” p1). 10 plant datasets were generated for differing biological applications, such as feature prediction. (“including predictions of core promoters, sequence conservation, histone modification, open chromatin, long non-coding RNAs (lncRNAs), and promoter strength in multiple plant species (Figure 1B). Foundation models were further fine-tuned on these datasets, and 198 specific models were built for downstream genomic prediction tasks. Among these tasks, the prediction of core promoters and sequence conservation are binary classification tasks
with fixed sequence lengths, while the classification of epigenetic tasks such as histone modification, open chromatin, and lncRNAs is trained with variable sequence length. For the open chromatin
task, we predict for three classes including not, full, and partial open chromatin regions based on the proportion of open chromatin regions in a sequence (Supplemental Figure 1).” (p1).) See Fig 1.
Lin concludes: “LLMs have versatile applications when applied to biology research,
Including protein structure prediction, identification of regulatory elements, gene expression prediction, and others (Consens et al., 2023). Here, we demonstrated that our fine-tuned models are
able to perform diverse prediction jobs.” p3. As such, the claims are anticipated.
Claim(s) 1-4, 14-15 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Wang et al. (March 2025).
Wang, T. et al. (March 4, 2025) A Feature Engineering Method for Whole-Genome DNA
Sequence with Nucleotide Resolution. Int. J. Mol. Sci. Vol 26: 2281, 21 pages.
Wang is directed to: “Feature engineering for whole-genome DNA sequences plays a critical role in predicting plant phenotypic traits… This study proposes FE-WDNA, a whole-genome DNA sequence feature engineering method, using HyenaDNA to fine-tune it on whole-genome data from 1000 soybean samples. We thus provide deep insights into the contextual and long-range dependencies among nucleotide sites to derive comprehensive genome-wide feature vectors. We further evaluated the application of FE-WDNA in agronomic trait prediction, examining factors such as the context window length of the DNA input, feature vector dimensions, and trait prediction
methods, achieving significant improvements compared to the existing SNP-based approaches. FE-WDNA provides a mode of high-quality DNA sequence feature engineering at nucleotide resolution, which can be transformed to other plants and directly applied to various computational breeding tasks.” (abstract).
Wang meets the rejected claims, as described in the introduction:
“In this study, we propose a novel whole-genome DNA sequence feature construction model, FE-WDNA, which leverages the LLM framework of HyenaDNA that was fine-tuned on plant genomes. First, we introduce FE-WDNA, followed by a performance comparison with several existing methods in genomic selection. We also analyze the factors that affect FE-WDNA, including the DNA sequence input style, the fusion mode of feature vectors from different chromosomes, and so on. Our contributions are summarized as follows: (1) By analyzing the whole-genome DNA sequence at nucleotide resolution, FE-WDNA optimizes the feature construction process for genomic prediction in plant breeding. (2) To the best of our knowledge, this study represents the first application of LLMs to feature construction for plant whole genome analysis and the first to utilize complete nucleotide sequences for trait prediction. (3) We implement FE-WDNA in a soybean phenotypic trait prediction task, achieving significant improvements compared to current state-of-the-art (SOTA) methods.” P3.
Traits analyzed and predicted include quantitative features of plant height, oil content, protein content, flowering time, maturity time, yield and hundred-seed-weight, as well as qualitative features of flower color, stem termination, pod color, and pubescence density. Both SNP based DNA sequence data, and whole genome sequence data was utilized in training, and analyzed. (section 2). Various methods of trait prediction for a sequence were analyzed in section 2.2.4. Wang specifically notes that these models are useful in selecting parent plants for large scale breeding programs to enhance genetic gains for crop traits. P11. Section 4 sets forth the particular methods, frameworks, training, and analysis of FEWDNA. See Fig 10. Fig 13 illustrates the process predictions for soybean DNA data. Table 2 illustrates traits predicted. As such, the claims are anticipated.
Claim(s) 1 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Gross, w. (2025).
Gross, W. T. systems and methods for enhancing the performance of a large language model using a genetic algorithm. US 2025/0045598 A1, published 2/6/2025, having priority to at least 7/10/2024.
Gross identifies parental individuals which are potential solutions, having certain features such as fitness or quality. “[0027] … The fittest individuals are selected as parents for the next generation, and their genetic information is combined through crossover and mutation operations to create new offspring with potentially better fitness. [0028] The process of selection, crossover, and mutation is repeated for multiple generations, creating a new population each time. The goal is that through this iterative process, the population gradually evolves and improves until a satisfactory solution is found or a stopping criteria is met. [0029] The fitness function is an important component of a genetic algorithm as it determines the quality of the solutions generated. The fitness function is a measure of how well the candidate solution performs a given task. In the case of large language models, the fitness function may be the perplexity or the accuracy of the model on a validation set. Perplexity is a measure of how well a language model predicts a given sequence of words. In the context of large language models, perplexity is commonly used as an evaluation metric to measure the quality of the model's output.”
Gross discloses the architecture of the LLM, encoders, decoders and transformers, and the genetic algorithm at [0039-0050] and fig 2. As such, claim 1 is anticipated.
Claim(s) 1-4, 14-15 is/are rejected under 35 U.S.C. 102a2 as being anticipated by Mabey et al. (2025).
Mabey, B. J. et al. Utilizing language machine learning models for autonomous executions of computerized tech-bio exploration tools. US 2025/0225161 A1, published 7/10/2025, having priority at least to 12/23/2024.
Mabey is directed to generating biologic based LLM, for the prediction and analysis of selected features or traits. [0024]… “Moreover, in some instances, the AI tech-bio system can also utilize the language machine learning model-based autonomous agents to automatically execute various combinations of tasks within the computerized bio-activity discovery pipeline to identify and/or generate various bio-activity data and/or insights within a tech-bio exploration system ecosystem (e.g., compound identification, gene and compound relationship identification, protein of interest identification, compound program recommendations, perturbation image generation, perturbation heatmap generation, mechanism-of-action data).”
The AI tech bio system of Mabey uses the LLM to execute combinations of tasks, such as identifying actionable information. [0039]. Different LLM can be assigned to differing parts of the tasks. [0041]. “Then, the AI tech-bio system can enable various language machine learning models to identify, generate, retrieve, and transmit bio-activity data from the one or more tech-bio exploration tools to other language machine learning models to accomplish various tasks in a bio-activity discovery pipeline.” The system of Mabey can generate information, or direct the use of specific tools to generate the desired information. [0042] “For example, the communicating autonomous language machine learning models can generate bio-activity data, such as, but not limited to, compounds, genes, or proteins of interest, hypotheses, assay initiations, and/or program recommendations.”
Figure 1 of Mabey illustrates the system.
“[0053] As shown in FIG. 1, the server(s) 102 can include the tech-bio exploration system 104. In some embodiments, the tech-bio exploration system 104 can determine, store, generate, and/or display tech-bio information including maps of biology, biology (or chemistry) experiments from various sources, and/or machine learning tech-bio predictions. For instance, the tech-bio exploration system 104 can analyze data signals corresponding to various treatments or interventions (e.g., compounds or biologics) and the corresponding relationships in genetics, protenomics, phenomics (i.e., cellular phenotypes), invivomics (e.g., expressions or results within a living animal), and/or transcriptomics…”
[0054] For instance, the tech-bio exploration system 104 can generate and access experimental results corresponding to gene sequences, protein shapes/folding, protein/compound interactions, phenotypes resulting from various interventions or perturbations (e.g., gene knockout sequences or compound treatments), and/or in vivo experimentation on various treatments in living animals. By analyzing these signals (e.g., utilizing various machine learning models), the tech-bio exploration system 104 can generate or determine a variety of predictions and inter-relationships for improving treatments/interventions.
[0055] To illustrate, the tech-bio exploration system 104 can generate maps of biology indicating biological inter-relationships or similarities between these various input signals to discover potential new treatments. For example, the tech-bio exploration system 104 can utilize machine learning and/or maps of biology to identify a similarity between a first gene associated with disease treatment and a second gene previously unassociated with the disease based on a similarity in resulting phenotypes from gene knockout experiments. The tech-bio exploration system 104 can then identify new treatments based on the gene similarity (e.g., by targeting compounds that impact the second gene). Similarly, the tech-bio exploration system 104 can analyze signals from a variety of sources (e.g., protein interactions, or invivo experiments) to predict efficacious treatments based on various levels of biological data.”
[0072] “… The tech-bio exploration system 104 can also interact with a variety of other testing device(s) such as devices for determining, generating, or extracting gene sequences or protein information. The experiment design (or experiment components of the experiment design) and/or tech-bio exploration tools can include, but is not limited to, configurations, inputs, outputs, models, and/or settings for the testing device(s).”
DNA sequence products are disclosed, as are DNA synthesis processes. Feature selection is driven by the query at hand, but can include gene expression or gene knockout, protein expression, protein folding, phenome analysis et al. The LLM of Mabey appears to meet the limitations of the language models of claims 14 and 15.
Claim(s) 1-4, 14-15 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Brixi, G. et al. (2024).
Brixi, G. et al. (UBIQUITX) Sequence based framework to design peptide-guided degraders. WO 2024/102733 A2, published 16 May 2024, having priority to at least 7 Nov, 2023.)
Brixi obtains parent protein sequences each having a different feature. The sequences of each protein are analyzed, and sets of possible new polypeptide sequences are generated which are predicted to have the desired features. “A Method of generating binding peptide sequences to a target sequence, the method comprising: Receiving, using a processor configured by code executing therein, a data object corresponding to a protein target; Searching, using the data object, a protein interaction database for at least one partner protein to the target protein; Identifying at least one partner protein to the target protein; Providing the at least one partner protein to a computational model configured to output a predicted protein sequence predicted to interact with the target sequence; and Identifying at least one subsequence within the predicted protein sequence that meets a predetermined interaction threshold.” (abstract.)
Brixi obtains parent protein sequences from known databases, wherein the proteins have known features, binding sites, inhibitors, activators, interactions with small molecules, et al. Pre-trained protein language models are obtained [0007]. The model can be structure-aware, or structure-agnostic. Peptide sequences can be generated based on the analysis of the parent protein sequences, the desired or observed features, and the language model configuration. For example, polypeptide sequences predicted to bind one a target sequence can be generated. [0008]. Brixi specifically directs synthesis of the generated polypeptide sequences, for example, at [0009]. Brixi specifically denotes large language models and protein language models at [0067]. Brixi discloses both polypeptide and polynucleotide sequence processes, and synthesis processes. As such, the claims are anticipated.
Claim(s) 1-4, 14-15 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Wang, W. (2024).
Wang, W. et al. (The Regents of the University of California) Intelligent design and engineering of proteins. WO 2024/123574 A2, published 13 June 2024, with priority to at least 29 Nov 2023.
Wang is directed to generating intelligently designed protein sequences, using large language modeling. “A pipeline for designing and engineering a protein is composed of directed evolution, sequencing, and machine learning analysis. This pipeline can explore the directed evolution sequences that are not present in libraries and can be used in facilitating the discovery of therapeutic proteins and imagine probes, and/or enhancing efficiency of CRISPR and enzyme activities.” (abstract). Wang is particularly directed to antibody sequence generation, with predictable features related to binding of target.
“In certain embodiments, the present disclosure provides a set of twenty-one (21 ) scFv sequences (a fusion of the heavy and light chain variable regions) that tightly binds to PD-L1 . These scFvs are all modified variants of the parent Atezolizumab mAb and are discovered through the machine learning-assisted pipeline disclosed herein for discovering antibodies that are tight binders to targets of interest. The measured binding affinity for the top candidate from this set of the twenty- one (21 ) is about 17-fold tighter than Atezolizumab, which means it has one of the tightest affinities of any current anti-PD-L1 mAbs. These scFvs appear to have a slower dissociation rate (off-rate) than Durvalumab and Avelumab, as well.” (introduction). Example 1 sets forth identification of parent protein sequences, and the generation of progeny sequences predicted to have better binding affinity to the target. LLM can be used to analyze embedded sequence information from multiple sequence alignments. Random convolutions or combinations of sequences is performed to generate the set of progeny sequences, which are then analyzed for a variety of relevant parameters such as thermostability, molecular weight, binding affinity, etc. See Table 1 and its description in the text. As such the claims are anticipated.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim s 1-4, 14-15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of copending Application No. 19/415171 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both select biologic parents each having differing features, determining products combining the features, and selecting the product based on an evaluation of the features.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim s 1-4, 14-15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of copending Application No. 19/432513 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both select biologic parents each having differing features, determining products combining the features, and selecting the product based on an evaluation of the features.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim s 1-4, 14-15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of copending Application No. 19/415089 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both select biologic parents each having differing features, determining products combining the features, and selecting the product based on an evaluation of the features.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim s 1-4, 14-15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of copending Application No. 19/435504 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both select biologic parents each having differing features, determining products combining the features, and selecting the product based on an evaluation of the features.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim s 1-4, 14-15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of copending Application No. 19/340736 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both select biologic parents each having differing features, determining products combining the features, and selecting the product based on an evaluation of the features.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim s 1-4, 14-15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of copending Application No. 19/430043 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both methods or systems select biologic parents each having differing features, determining products combining the features, and selecting the product based on an evaluation of the features.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
Applicant is strongly requested to enlarge the font used in the claims in any subsequent filing.
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686