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 amendments and arguments filed 25 August 2025 are acknowledged and entered.
Withdrawn Rejections/Objections
The rejection of claims 1-10 under 35 U.S.C. §112(b) in the Office action mailed 23 April 2025 is withdrawn in view of the amendments filed 25 August 2025.
The rejection of claims 1, 4, and 7-8 under 35 U.S.C. §103 over Durrant in view of Sander in the Office action mailed 23 April 2025 is withdrawn in view of the amendments filed 25 August 2025.
The rejection of claim 2 under 35 U.S.C. §103 over Durrant in view of Sander and in further view of Laskowski in the Office action mailed 23 April 2025 is withdrawn in view of the amendments filed 25 August 2025.
The rejection of claims 3 and 10 under 35 U.S.C. §103 over Durrant in view of Sander and in further view of Castillo in the Office action mailed 23 April 2025 is withdrawn in view of the amendments filed 25 August 2025.
The rejection of claim 5 under 35 U.S.C. §103 over Durrant in view of Sander and in further view of Salentin in the Office action mailed 23 April 2025 is withdrawn in view of the amendments filed 25 August 2025.
The rejection of claim 6 under 35 U.S.C. §103 over Durrant in view of Sander and in further view of Sander in the Office action mailed 23 April 2025 is withdrawn in view of the amendments filed 25 August 2025.
The rejection of claim 9 under 35 U.S.C. §103 over Durrant in view of Sander and in further view of Morris in the Office action mailed 23 April 2025 is withdrawn in view of the amendments filed 25 August 2025.
Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claim Status
Claims 1-8 were amended by Applicant’s paper filed 25 August 2025 (hereinafter “Amendment”).
Claims 9-10 are cancelled.
Claims 1-8 are currently pending and under exam herein.
Claims 1-8 are rejected.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim 7 in this application uses the word “module” with functional language but without sufficient structure, material, or acts to entirely perform the recited function; thus, claim 7 is being interpreted under 35 U.S.C. 112(f). Under MPEP § 2181(II)(B), for a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b). The sufficiency of the algorithm is determined in view of what one of ordinary skill in the art would understand as sufficient to define the structure and make the boundaries of the claim understandable. The instant specification recites at paras. [0032]-[0033] that the “compound evaluation module includes: substructure alert, selectivity prediction, activity prediction, structural similarity, molecular weight, number of rotating bonds, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rings, molecular docking score, FEP prediction value, pharmacophore score, lipid-aqueous partition coefficient value, compound toxicity prediction evaluation module. The compound evaluation module in the evaluation tool box subsystem includes the compound evaluation module of various properties such as the conformational characteristics, physical properties, chemical properties, pharmacokinetic properties, and structural novelty of the compound.”
While the specification describes, in general terms, that the compound evaluation module outputs a score based on a property of interest of the compound, it nonetheless fails to disclose an algorithm or description as to how those scores are actually generated, rendering the claim indefinite (see 112(b) rejection below). See Advanced Ground Information Systems, Inc. v. Life360, Inc., 830 F.3d 1341, 1349, 119 USPQ2d 1526, 1530 (Fed. Cir. 2016). For purposes of the present examination, the “compound evaluation module” will be interpreted broadly to mean any computer component that outputs a value based on some evaluation of one of the compound properties listed in the specification at paras. [0032]-[0033].
Claim Rejections - 35 USC § 112
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.
Claim 5 is 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 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 5 has been amended to recite “existing compounds” instead of “compounds that have been reported in existing patent literature.” The amended recitation of “existing compounds” is broader than the prior recitation of “compounds that have been reported in existing patent literature” because all compounds that have been reported in existing patent literature would be existing compounds, but not all existing compounds have been reported in patent literature. The specification does not contain adequate support for “existing compounds.” Additionally, the broader recitation constitutes new matter, which is prohibited under 35 U.S.C. 132(a). Therefore, claim 5 is rejected under 35 U.S.C. 112(a) because new matter is being claimed without an adequate written description.
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 1-8 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.
Regarding claims 1, 4, and 6-7, the term “subdevice” renders the claims indefinite. Applicants do not explain the source of said limitation in the Amendment, there is no definition for “subdevice” provided in the specification, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This renders the claim unclear as to what components comprise the virtual drug screening device for crystal complexes.
Regarding claim 1, the term “qualified candidate compounds” in ln. 5 is a relative term which renders the claim indefinite. The term “qualified” 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. This renders the claim unclear as to which candidate compounds are recommended after going through the components of the virtual drug screening device. This rejection can be overcome by (1) amending claim 1 to include a definite standard for the recommended candidate compounds (although applicant is reminded that no new matter may be added to the application), or (2) removing the recitation of “qualified” from claim 1.
Additionally, claim 1 recites the limitation “AI model” without previously defining the acronym “AI” within the claims. The acronym “AI” is also not defined in the specification, which renders the metes and bounds of the claim unclear. This rejection can be overcome by (1) amending claim 1 to spell out the acronym upon the initial recitation within the claims (although applicant is reminded that no new matter may be added to the application), or (2) removing the recitation of “AI” from the claims. Moreover, claim 1 recites the limitation “the score” in the 7th clause of the claim. There is insufficient antecedent basis for this limitation in the claim. Claims 2-8 are similarly rejected due to their dependency upon claim 1.
Regarding claim 5, the limitation “the screening” renders the claim indefinite because it is unclear which screening the claimed filter conditions apply to when claim 1 recites “a virtual screening device,” “a virtual screening subdevice,” “screen the trained AI model,” and “for further screening.”
Regarding claim 7, the term “unqualified” in Step D (ln. 16) is a relative term which renders the claim indefinite. The term “unqualified” 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. This renders the claim unclear as to which compounds are deleted/eliminated. This rejection can be overcome by (1) amending claim 7 to include a definite standard for which compounds are deleted/eliminated (although applicant is reminded that no new matter may be added to the application), or (2) removing the recitation of “unqualified” from claim 7.
Additionally, claim limitation “compound evaluation module” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (see claim interpretation section above). However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Under MPEP § 2181(II)(B), for a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b). The sufficiency of the algorithm is determined in view of what one of ordinary skill in the art would understand as sufficient to define the structure and make the boundaries of the claim understandable. The instant specification recites at paras. [0032]-[0033] that the “compound evaluation module includes: substructure alert, selectivity prediction, activity prediction, structural similarity, molecular weight, number of rotating bonds, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rings, molecular docking score, FEP prediction value, pharmacophore score, lipid-aqueous partition coefficient value, compound toxicity prediction evaluation module. The compound evaluation module in the evaluation tool box subsystem includes the compound evaluation module of various properties such as the conformational characteristics, physical properties, chemical properties, pharmacokinetic properties, and structural novelty of the compound.”
While the specification describes, in general terms, that the compound evaluation module outputs a score based on a property of interest of the compound, it nonetheless fails to disclose an algorithm or description as to how those scores are actually generated. See Advanced Ground Information Systems, Inc. v. Life360, Inc., 830 F.3d 1341, 1349, 119 USPQ2d 1526, 1530 (Fed. Cir. 2016). Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Regarding claim 8, the term “certain values” renders the claim indefinite because “certain values” is not defined by the claim, the specification does not provide a definition, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This renders the optimization of the AI model parameters indefinite because it is unclear at what point the parameters are sufficiently optimized. Additionally, the term “higher score” in claim 8 is a relative term which renders the claim indefinite. The term “higher” 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. This renders the claim indefinite because it is unclear what the repetition of Step C will result in for the compounds generated by the AI model. Moreover, the phrase “after repeating the Step C for a number of time” renders the claim indefinite. It is unclear whether Step C is repeated more than once because “time” is recited in the singular. Even if “time” was recited in the plural, it is unclear how many times Step C should be repeated when there is no indication of a stopping point in the claim.
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.
Claims 3-4 are 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 3 depends on claim 1 and recites wherein the evaluation function is a weighted arithmetic mean, a weighted geometric mean, or a user-defined function. This fails to further limit the subject matter of claim 1 when claim 1 recites wherein the evaluation function is an arithmetic weighted average:
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or a geometric weighted average:
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. Claim 4 depends on claim 1 and recites wherein the AI model management subdevice includes the AI model, the AI model training, and the update of the AI model parameter; wherein the AI model is a neural network device for generating the compounds; wherein the AI model parameter is the parameter of the neural network device; and the AI model itself can generate the compounds randomly. This fails to further limit the subject matter of claim 1 when claim 1 recites wherein the Al model management subdevice is used for Al model, Al model training, and update of Al model parameter; wherein the Al model is a neural network device for generating compounds; the Al model parameter is a parameter of the neural network device; and the Al model itself can generate the compounds randomly. Applicant may cancel the claims, amend the claims to place the claims in proper dependent form, rewrite the claims in independent form, or present a sufficient showing that the dependent claims comply with the statutory requirements.
Response to Arguments
Applicant’s arguments, see p. 6, filed 25 August 2025, with respect to the rejections of claims 1-10 under 35 USC § 112(b) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection are made in view of the Amendment.
Claim Rejections - 35 USC § 101
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract ideas and natural phenomenon) without significantly more. Under MPEP § 2106, subject matter is patent eligible when the claimed invention is to one of the four statutory categories of invention [Step 1], and the claim is not directed to a judicial exception [Step 2A] unless the claim as a whole includes additional limitations amounting to significantly more than the exception [Step 2B].
Step 1
Claims 1-8 describe inventions that are to one of the statutory categories. In Step 1, a claim must fall within one of the four enumerated categories of statutory subject matter (process, machine, manufacture, or composition of matter); a claim falling outside these categories is ineligible without further analysis. See MPEP § 2106.03. Claims 1-6 are properly to one of the four statutory categories because the claimed invention is a virtual drug screening device, which falls into the manufacture category [Step 1: Yes]. Claims 7-8 are properly to one of the four statutory categories because the claimed invention is a method, which falls into the process category [Step 1: Yes].
Step 2A
Under Step 2A, a claim is directed to a judicial exception if, under the broadest reasonable interpretation, it recites an abstract idea, law of nature, or natural phenomena [Prong One] without the claim as a whole integrating the exception into a practical application [Prong Two]. Abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activity. Mathematical concepts encompass mathematical relationships, formulas, equations, and mathematical calculations. See MPEP § 2106.04(a)(2)(I). Mental processes involve concepts that can be performed in the human mind or by a human with the aid of pen and paper, such as observations, evaluations, judgments, or opinions. See MPEP § 2106.04(a)(2)(III). Certain methods of organizing human activity include fundamental economic principles, commercial or legal interactions, and managing personal behavior or relationships. See MPEP § 2106.04(a)(2)(II). Laws of nature and natural phenomena, include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature. See MPEP § 2106.04(b)-(c).
Prong One
A claim recites a judicial exception when it sets forth or describes a law of nature, natural phenomenon, or abstract idea. Claims 1-8 recite abstract ideas that fall into the groupings of mathematical concepts and mental processes.
Claim 1 recites the following limitations, which describe abstract ideas within the mathematical concepts and/or mental processes groupings:
starting from a known crystal complexes, a batch of qualified candidate compounds are recommended after sequentially going through the visualization subdevice, the evaluation tool box subdevice, the Al model management subdevice, the large-scale sampling subdevice, and the virtual screening subdevice;
wherein the visualization subdevice is used to view the binding position of a ligand of a protein in the crystal complex, analyze a binding mode of the ligand and the protein, and extract features that enhance the affinity of the drug to the protein;
wherein the evaluation tool box subdevice encapsulates a plurality of compound evaluation modules, and is used to design an evaluation function by selecting the plurality of compound evaluation modules and assigning weights;
wherein the Al model management subdevice is used for Al model, Al model training, and update of Al model parameter; the Al model parameter is a parameter of the neural network device;
wherein the large-scale sampling subdevice is used to sample and screen the trained Al model to obtain a compound library composed of the corresponding compounds;
wherein the virtual screening subdevice is used for further screening of the compounds in the compound library;
wherein the data log storage subdevice is used to establish and store a user's log information file; the log information file is used to record user operations and generate corresponding data;
wherein in the evaluation function, a weight is set for each of the score: w1, w2, w3, ......wn to form the evaluation function, and the evaluation function is an arithmetic weighted average:
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Claims 2-6 recite the following limitations, which narrow or describe abstract ideas within the mathematical concepts and/or mental processes groupings:
Claim 2 recites wherein the features that enhance the affinity of the drug to the protein is hydrogen bonding and/or hydrophobic interaction.
Claim 3 recites wherein the evaluation function is a weighted arithmetic mean, a weighted geometric mean, or a user-defined function.
Claim 4 recites wherein the AI model management subdevice includes the AI model, the AI model training, and the update of the AI model parameter; wherein the AI model parameter is the parameter of the neural network device.
Claim 5 recites wherein a filter condition of the screening includes a number of heavy atoms of the compound, a number of hydrogen bond donors, a number of hydrogen bond acceptors, scaffold structure, false positives, and existing compounds.
Claim 6 recites wherein the data log storage subdevice further includes a function of standardizing user permissions.
The limitation of starting from a known crystal complexes involves analyzing structures, generating/scoring/filtering molecules, and optimization to recommend compounds with target properties, which constitutes an abstract idea within the mathematical concepts and mental processes groupings. The limitation of the visualization subdevice and the limitation of the virtual screening subdevice are abstract ideas within the mental processes and mathematical concepts groupings because a person skilled in the art can mentally, or with the aid of pen and paper, screen compounds and identify binding positions/modes and extract relevant pharmacophore features. The limitation of the visualization subdevice is narrowed by the limitation of claim 2 because it specifies the features to be extracted from the compound. The limitation of the virtual screening subdevice is narrowed by the limitation of claim 5 because it specifies the filter conditions of the screening.
The limitation of the evaluation tool box subdevice and the limitation of the evaluation function involve explicit weighted formulas, selecting modules, and assigning weights to compute a score, which constitute abstract ideas within the mathematical concepts and mental processes grouping. These limitations are narrowed by the limitation of claim 3 because it specifies the evaluation function to be used. The limitation of the AI model management subdevice involves neural network training and parameter updates, which involve optimization of high-dimensional mathematical functions via backpropagation, constituting an abstract idea within the mathematical concepts grouping. The same is true for the identical limitation of claim 4. The limitation of the large-scale sampling subdevice involves sampling from a probability distribution of a generative model and filtering the generated compound list, which constitutes an abstract idea within the mathematical concepts and mental processes groupings. The limitation of the data log storage subdevice involves the abstract idea of data organization and record-keeping, which constitutes a mental process or organizing human activity. This limitation is narrowed by the limitation of claim 6 because it specifies how the data is to be stored in relation to the user.
Claim 7 recites the following limitations, which describe abstract ideas within the mathematical concepts and/or mental processes groupings:
Step A: define binding characteristics of the ligand in the crystal complex through an analysis of the visualization subdevice, wherein the user downloads a target of the crystal complex structure from a protein crystal structure database, visualizes a binding position of the ligand in the protein, analyzes the binding mode of the ligand and the protein, and extracts the features that enhance the affinity of the drug to the protein;
Step B: input the compounds into the evaluation tool box subdevice, and each of the plurality of compound evaluation module in the evaluation tool box subdevice will output a score, which is then integrated into a comprehensive score through the evaluation function;
Step D: the large-scale sampling subdevice accepts a sampling quantity parameter input by the user, samples the trained AI model, generates a specified number of compounds, deletes unqualified and repetitive compounds, and then the user inputs filter conditions to eliminate unqualified compounds, and the remaining compounds form a compound library;
Step E: the virtual screening subdevice further screens the compounds in the compound library;
Step F: the data log storage subdevice creates and stores the user's log information file when the user uses the subdevice to design drugs.
The limitation of Step A is an abstract idea within the mental processes grouping because a person skilled in the art can mentally, or with the aid of pen and paper, define binding characteristics by analyzing binding position/mode and extracting features. The limitation of Step B involves scoring and integration into a composite score, which constitutes an abstract idea within the mathematical concepts and mental processes groupings. The limitation of Step D involves sampling from a generative model and filtering the compounds, which constitutes an abstract idea within the mathematical concepts and mental processes groupings. The limitation of Step E involves molecular dynamics, ranking, and selection, which constitutes an abstract idea within the mathematical concepts and mental processes groupings. The limitation of Step F involves the abstract idea of data collection, organization, and record-keeping, which constitutes a mental process or organizing human activity. Finally, claim 8 recites that the AI model collects scores of the compounds output by the evaluation pipeline, the AI model parameters are automatically updated; after repeating the Step C for a number of time, the compounds generated by the AI model will get a higher score in the evaluation pipeline; after the AI model training is completed, the AI model parameters are also optimized to certain values. This involves optimization of high-dimensional mathematical functions via backpropagation, constituting an abstract idea within the mathematical concepts grouping.
Therefore, claims 1-8 recite abstract ideas – namely mathematical concepts and mental processes [Step 2A, Prong One: Yes].
Prong Two
Claims 1-8 as a whole do not integrate the recited judicial exception into a practical application. A claim that recites a judicial exception [Prong One] is deemed to be directed to a judicial exception [Step 2A] unless the claim as a whole contains additional elements that integrate the exception into a practical application [Prong Two]. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP §§ 2106.04(d) and 2106.05(e). A claim does not integrate a judicial exception into a practical application by reciting insignificant extra-solution activity, generally linking the exception to a particular technological environment or field of use, merely reciting to apply the exception, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP § 2106.04(d)(I). Insignificant extra-solution activities are nominal or tangential additions to a claim that are incidental to the primary process or product, including both pre-solution and post-solution activity (e.g. pre-solution data gathering for use in a process). If integrated into a practical application, the claim is eligible; otherwise, it is directed to the judicial exception, necessitating further analysis at Step 2B.
Claims 1, 4, and 7-8 recite the following limitations, which are additional elements:
Claim 1 recites wherein the Al model is a neural network device for generating compounds; and the Al model itself can generate the compounds randomly.
Claim 4 recites wherein the AI model management subdevice includes the AI model, the AI model training, and the update of the AI model parameter; wherein the AI model is a neural network device for generating the compounds; wherein the AI model parameter is the parameter of the neural network device; and the AI model itself can generate the compounds randomly.
Claim 7 recites Step C: combine the visualization subdevice with the evaluation tool box subdevice to form a complete evaluation pipeline, start the AI model through the AI model management subdevice and start the AI model training;
Claim 8 recites wherein in the Step C, the AI model outputs the compounds generated by the AI model to the evaluation pipeline through interaction.
The limitations of claims 1, 4, and 7 are generic recitations of a neural network used to apply the recited abstract ideas. The limitations recite no specific architecture details, novel training technique, hardware improvement, or application that improves computer functioning. They simply recite using a known type of AI for the abstract purpose of compound generation, which does not integrate the judicial exceptions into a practical application. See MPEP § 2106.05(f). The limitation of claim 8 is a necessary data outputting step specifying where generated compounds are sent for further analysis. This constitutes insignificant extra-solution activity that generally links the use of the judicial exceptions to a particular technological environment or field of use and does not integrate the exceptions into a practical application. See MPEP §§ 2106.05(g)-(h) Finally, claims 2-3 and 5-6 do not include any additional elements.
The claims as a whole merely recite insignificant extra-solution activities and abstract ideas implemented on generic components without meaningful limitations that tie it to a specific technological improvement. Therefore, claims 1-8 do not contain additional elements that integrate the recited abstract ideas into a practical application [Step 2A, Prong Two: No].
Step 2B
Claims 1-8 do not include additional elements, whether considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception itself. Under Step 2B, the claim is analyzed to determine whether there are any additional elements that, individually or in combination, constitute an “inventive concept" sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. See MPEP § 2106.05; and Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014).
Claims 1, 4, and 7-8 recite the following limitations, which are additional elements:
Claim 1 recites wherein the Al model is a neural network device for generating compounds; and the Al model itself can generate the compounds randomly.
Claim 4 recites wherein the AI model management subdevice includes the AI model, the AI model training, and the update of the AI model parameter; wherein the AI model is a neural network device for generating the compounds; wherein the AI model parameter is the parameter of the neural network device; and the AI model itself can generate the compounds randomly.
Claim 7 recites Step C: combine the visualization subdevice with the evaluation tool box subdevice to form a complete evaluation pipeline, start the AI model through the AI model management subdevice and start the AI model training;
Claim 8 recites wherein in the Step C, the AI model outputs the compounds generated by the AI model to the evaluation pipeline through interaction.
The limitations of claims 1, 4, and 7 simply recite using a conventional type of AI for the abstract purpose of compound generation, which does not add significantly more than the judicial exceptions themselves. See MPEP § 2106.05(f); and Jessica Vamathevan et al., Applications of machine learning in drug discovery and development, 18(6) Nat Rev Drug Discov. 463, 466 col.1 paras.1-2 (11 April 2019). The limitation of claim 8 constitutes conventional insignificant extra-solution activity that generally links the use of the judicial exceptions to a particular technological environment or field of use and does not add significantly more than the exceptions themselves. See MPEP §§ 2106.05(g)-(h); and Nikita Vemuri, Scoring Confidence in Neural Networks, in Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2020-132, 1 para.3 (2 June 2020).
Overall, claims 1-8 amount to no more than conventional insignificant extra-solution activities and implementing the abstract ideas using conventional neural networks in a routine way. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself because the claims recite additional elements that equate to insignificant extra-solution activity and mere instructions to apply the recited abstract ideas in a generic way. Therefore, claims 1-8 are rejected for failing to set forth patent eligible subject matter under 35 U.S.C. 101 because the claimed invention recites abstract ideas [Step 2A, Prong One: Yes] and the additional elements do not integrate the judicial exception into a practical application [Step 2A, Prong Two: No] and do not amount to claiming significantly more than the recited exception [Step 2B: No].
Response to Arguments
Applicant's arguments filed 25 August 2025 have been fully considered but they are not persuasive. Applicant merely incorporated the mathematical concept of original claim 10 into the independent claim. While original claim 10 was not directly addressed in the Office action mailed 23 April 2025, it explicitly recited weighted formulas, which constitutes an abstract idea within the mathematical concepts grouping. Therefore, incorporating the explicit weighted formulas into the independent claim merely introduces another judicial exception to the claim and does not overcome the rejection under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4 and 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Blaschke et al., REINVENT 2.0 – an AI Tool for De Novo Drug Design, ChemRxiv (8 April 2020) (hereinafter “Blaschke”) and Kaushik et al., Structure Based Virtual Screening Studies to Identify Novel Potential Compounds for GPR142 and Their Relative Dynamic Analysis for Study of Type 2 Diabetes, 6 Front. Chem. (13 February 2018) (hereinafter “Kaushik”). The italicized text within parenthesis corresponds to the instant claim limitations.
Regarding claim 1, Blaschke discloses an AI-based system, REINVENT, for de novo molecular generation and optimization using a scoring function. At 3 para.3; 4 para.1 (an evaluation tool box subdevice; an Al model management subdevice). Blaschke teaches starting from known compounds to generate compounds that meet user-defined criteria. At 2 para.2; 9 para.1 – 10 para.1 (starting from a known crystal complexes, a batch of qualified candidate compounds are recommended after sequentially going through … the evaluation tool box subdevice, the Al model management subdevice, the large-scale sampling subdevice). Blaschke discloses that REINVENT employs a composite scoring function consisting of different user-defined components where each component is responsible for a target property and is assigned a different weight reflecting its importance. At 4 para.1; 5 para.1 (wherein the evaluation tool box subdevice encapsulates a plurality of compound evaluation modules, and is used to design an evaluation function by selecting the plurality of compound evaluation modules and assigning weights). Blaschke discloses that the individual components of the scoring function can be either combined as a weighted sum or as a weighted product:
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Blaschke teaches that two Recurrent Neural Networks (RNNs) are used, with one RNN being trained via parameter update to generate compounds (the Agent). At 4 paras.1-2 (wherein the Al model management subdevice is used for Al model, Al model training, and update of Al model parameter; wherein the Al model is a neural network device for generating compounds; the Al model parameter is a parameter of the neural network device; and the Al model itself can generate the compounds randomly). Blaschke discloses that the system includes a diversity filter function where generated compounds are screened via a score threshold and collected into buckets for keeping track of all generated scaffolds and the compounds that share those scaffolds. At 8 para.1 (a large-scale sampling subdevice; wherein the large-scale sampling subdevice is used to sample and screen the trained Al model to obtain a compound library composed of the corresponding compounds). Blaschke teaches that REINVENT includes a comprehensive logging system to store generated compounds and provide information about the evolution of the Agent during the operation. At 8 para.1; 11 para.1 (a data log storage subdevice; wherein the data log storage subdevice is used to establish and store a user's log information file; the log information file is used to record user operations and generate corresponding data).
Blaschke fails to teach a visualization subdevice, wherein the visualization subdevice is used to view the binding position of a ligand of a protein in the crystal complex, analyze a binding mode of the ligand and the protein, and extract features that enhance the affinity of the drug to the protein; and a virtual screening subdevice, wherein the virtual screening subdevice is used for further screening of the compounds in the compound library.
However, Kaushik discloses a structure based virtual screening workflow using the Schrödinger Maestro suite for identifying potential GPR142 agonists by screening large existing compound libraries. At abstract; 2 col.2 para.2. Kaushik uses the Schrödinger software suite to predict binding sites and binding modes of a ligand of the protein. At 2 col.2 para.2; 5 col.2 para.2 (a visualization subdevice, wherein the visualization subdevice is used to view the binding position of a ligand of a protein in the crystal complex, analyze a binding mode of the ligand and the protein). Kaushik further uses the Schrödinger software suite to extract pharmacophore features such as hydrogen bond acceptors, hydrogen bond donors, and hydrophobic interaction. At 4 col.1 para.2 (extract features that enhance the affinity of the drug to the protein). Kaushik then discloses using the Schrödinger software suite to search and screen the best compounds obtained from the initial virtual screening. At 4 col.2 para.3 (a virtual screening subdevice, wherein the virtual screening subdevice is used for further screening of the compounds in the compound library).
A person having ordinary skill in the art would understand that the virtual screening workflow of Kaushik could be combined with the AI-based system of Blaschke and each element would merely perform the same function as it does separately. One of ordinary skill in the art would initially use the virtual screening workflow of Kaushik for visualization and feature extraction of a starting compound. One of ordinary skill would then use the AI-based system of Blaschke with the desired extracted features as target properties to generate a library of potential compounds. Finally, one of ordinary skill in the art would use the virtual screening workflow of Kaushik to screen the compounds in the generated library. One of ordinary skill in the art would recognize that this combination would predictably result in a comprehensive pipeline that allows one to view and analyze a starting compound, generate new compounds with similar desirable properties, and visualize the generated compounds for further screening. Combining prior art elements according to known methods to yield predicable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, A.
Regarding claim 2, Kaushik uses the Schrödinger software suite to extract pharmacophore features such as hydrogen bond acceptors, hydrogen bond donors, and hydrophobic interaction. At 4 col.1 para.2; see also Blaschke, at 5 para.2 (wherein the features that enhance the affinity of the drug to the protein is hydrogen bonding and/or hydrophobic interaction).
Regarding claim 3, Blaschke discloses that the individual components of the scoring function can be either combined as a weighted sum or as a weighted product. At 5 para.1; equations 3 & 4 (wherein the evaluation function is a weighted arithmetic mean, a weighted geometric mean, or a user-defined function).
Regarding claim 4, Blaschke teaches that two Recurrent Neural Networks (RNNs) are used, with one RNN being trained via parameter update to generate compounds (the Agent). At 4 paras.1-2 (wherein the AI model management subdevice includes the AI model, the AI model training, and the update of the AI model parameter; wherein the AI model is a neural network device for generating the compounds; wherein the AI model parameter is the parameter of the neural network device; and the AI model itself can generate the compounds randomly).
Regarding claim 6, Blaschke discloses that the logging system can display real-time metrics or remotely log data depending on user selection. At 11 para.1 (wherein the data log storage subdevice further includes a function of standardizing user permissions).
Regarding claim 7, Blaschke discloses that when employing REINVENT, users define components where each component is responsible for a target property and outputs a value assigned different weights in the composite score reflecting its importance. At 4 para.1; 5 para.1 (Step B: input the compounds into the evaluation tool box subdevice, and each of the plurality of compound evaluation module in the evaluation tool box subdevice will output a score, which is then integrated into a comprehensive score through the evaluation function). Blaschke teaches that the Agent is trained to generate compounds based on target properties and the compounds already generated by the Agent. At 4 paras.1 & 3 (start the AI model through the AI model management subdevice and start the AI model training). Blaschke discloses that during operation of the diversity filter function, users select a diversity strategy, generated compounds are screened via a score threshold and collected into buckets with a limited capacity, and the user-selected diversity strategy is employed to screen the compounds based on a user-specified threshold. At 8 paras.1-2 (Step D: the large-scale sampling subdevice accepts a sampling quantity parameter input by the user, samples the trained AI model, generates a specified number of compounds, deletes unqualified … compounds, and then the user inputs filter conditions to eliminate unqualified compounds, and the remaining compounds form a compound library). Blaschke discloses that during operation, all collected compounds from each run are stored and become available in file format. At 8 para.1; 11 para.1 (Step F: the data log storage subdevice creates and stores the user's log information file when the user uses the subdevice to design drugs).
Blaschke fails to teach Step A, combining to form a complete pipeline at Step C, deleting repetitive compounds at Step D, and Step E.
However, Kaushik discloses eliminating redundant conformers from the generated compound library before further analysis. At 4 col.1 para.1 (deletes … repetitive compounds). Kaushik uses the Schrödinger software suite to analyze the crystal structure of GPR142 by downloading structure-data files with target ligand structures, predicting binding sites of the ligands, and analyzing binding modes of a ligand of the protein. At 2 col.1 para.4 – col.2 para.3; 5 col.2 para.2 (Step A: define binding characteristics of the ligand in the crystal complex through an analysis of the visualization subdevice, wherein the user downloads a target of the crystal complex structure from a protein crystal structure database, visualizes a binding position of the ligand in the protein, analyzes the binding mode of the ligand and the protein). Kaushik further uses the Schrödinger software suite to extract pharmacophore features such as hydrogen bond acceptors, hydrogen bond donors, and hydrophobic interaction. At 4 col.1 para.2 (extracts the features that enhance the affinity of the drug to the protein). Kaushik then discloses using the Schrödinger software suite to search and screen the best compounds obtained from the initial virtual screening. At 4 col.2 para.3 (Step E: the virtual screening subdevice further screens the compounds in the compound library).
In combining the disclosures of Blaschke and Kaushik, a person having ordinary skill in the art would understand that the Schrödinger software suite visualization of Kaushik should be combined with the composite score function of Blaschke to form a complete evaluation pipeline. (Step C: combine the visualization subdevice with the evaluation tool box subdevice to form a complete evaluation pipeline). One of ordinary skill in the art would understand that each element would merely perform the same function as it does separately. One of ordinary skill in the art would use the virtual screening workflow of Kaushik for visualization and feature extraction of a starting compound, then use the AI-based system of Blaschke with the desired extracted features as target properties to generate a library of potential compounds before returning to the workflow of Kaushik to screen the compounds in the generated library. One of ordinary skill in the art would recognize that this combination would predictably result in a comprehensive pipeline that allows one to view and analyze a starting compound, generate new compounds with similar desirable properties, and visualize the generated compounds for further screening. Combining prior art elements according to known methods to yield predicable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, A.
Regarding claim 8, Blaschke teaches that the compounds generated by the Agent are screened by the diversity filter and the scoring function guides the Agent towards the optimal chemical space. At 7 para.3; 8 para.1 (the AI model outputs the compounds generated by the AI model to the evaluation pipeline through interaction, and collects scores of the compounds output by the evaluation pipeline, the AI model parameters are automatically updated). Blaschke discloses that the scoring function guidance will result in the generation of compounds that yield high multi-parameter optimization scores. At 7 para.3 (after repeating the Step C for a number of time, the compounds generated by the AI model will get a higher score in the evaluation pipeline). Blaschke teaches that the Agent is optimized based on the filtering mode selected by the user. At 7 para.1 (after the AI model training is completed, the AI model parameters are also optimized to certain values).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Blaschke and Kaushik as applied to claims 1-4 and 6-8 above, and further in view of Spiegel et al., AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization, 12 J Cheminform. 25 (17 April 2020) (hereinafter “Spiegel”).
Blaschke in view of Kaushik are applied to claims 1-4 and 6-8 above.
Regarding claim 5, Blaschke discloses filtering compounds by the number of hydrogen bond doners and acceptors, scaffold structure, and previously generated compounds. At 5 para.2; 8 para.1 (wherein a filter condition of the screening includes … a number of hydrogen bond donors, a number of hydrogen bond acceptors, scaffold structure, … and existing compounds). Blaschke teaches that the diversity filter penalizes redundant or low-quality generated compounds that could lead to false positives. At 8 para.1 (false positives).
Neither Blaschke nor Kaushik explicitly teach filtering the generated compounds based on a number of heavy atoms of the compound.
However, Spiegel discloses a de novo drug design framework where the generated compounds are filtered/sorted based on the number of heavy atoms in the compound. At 11 col.1 para.4. Spiegel notes that this filter/sorting informs users of ligand efficiency and penalizes larger molecules that are more complex to synthesize. Id.
A person having ordinary skill in the art would be motivated to combine the teachings of Spiegel with the teachings of Blaschke and Kaushik by using the number of heavy atoms in the compound as an additional filter in the combined method of Blaschke and Kaushik because filtering by the number of heavy atoms is informative of ligand efficiency. One of ordinary skill in the art would reasonably expect the combination to result in an improved method of de novo drug design because filtering by the number of heavy atoms can ensure ligand efficiency. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, G.
Response to Arguments
Applicant’s arguments, see pp.9-10, filed 25 August 2025, with respect to the rejection of claims 1-8 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Blaschke, Kaushik, and Spiegel.
Conclusion
No claims are allowed.
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/E.A.D./ Examiner, Art Unit 1686
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
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