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 .
Drawings
The drawings are objected to because Fig. 4 step 424 to step 412 should be marked no instead of yes. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim 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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
Subject Matter Eligibility Analysis Step 1:
Claims 1-18 recite method claims. Claims 19-20 are machine/system/product claims. Therefore, claims 1-20 are directed to one of the four statutory categories of patentable subject matter.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites the step:
“evaluating, ..., a similarity of a given one of the generated molecules with a candidate molecule;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually evaluate the similarity of the molecules)
“aggregating, ..., the evaluated similarity across multiple constraints to generate a single score;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually combine the similarity on paper and determine a single score)
“quantifying, ..., an ability of a given generative model to mimic the input molecules based on an aggregation of scores for multiple properties;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually look at the scores and make a determination on the model’s ability to mimic the input molecules)
“ranking, ..., the generative models by their ability to mimic the input molecules and evaluation properties based on the aggregated scores;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually write out on paper their rankings for the models with respect to the abilities and scores)
“quantifying, ..., one or more rates in the generated molecules based on the aggregated single score;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine the rate with consideration for the score)
“selecting, ..., one of the generative models based on the ranking;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can choose to select a model)
Thus, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 recites the additional elements:
“extracting, ... ,domain-specific properties from a plurality of datasets related to input molecules and generated molecules;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“using at least one hardware processor” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“and generating, ..., a new molecule using the selected generative model.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner note: High level recitation of generative model)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“extracting, ... ,domain-specific properties from a plurality of datasets related to input molecules and generated molecules;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“using at least one hardware processor” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“and generating, ..., a new molecule using the selected generative model.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner note: High level recitation of generative model)
Therefore, claim 1 is subject-matter ineligible.
Regarding claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 recites the additional elements:
“wherein the candidate molecule is one of the input molecules or one of the generated molecules that is generated from a different generative model of the generative models than the given generative model.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“wherein the candidate molecule is one of the input molecules or one of the generated molecules that is generated from a different generative model of the generative models than the given generative model.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 2 is subject-matter ineligible.
Regarding claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“wherein the evaluating of the similarity is based on a property of the corresponding molecule, a structural feature of the corresponding molecule, or both.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually evaluate the similarity with consideration for the properties and/or structural features of the molecule)
Thus, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 does not further recite any additional elements. Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 3 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 3 is subject-matter ineligible.
Regarding claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“wherein the quantifying of the ability uses one of a normalized odds ratio and a distribution-similarity metric.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 does not further recite any additional elements. Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 4 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 4 is subject-matter ineligible.
Regarding claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 recites the additional elements:
“wherein the properties are chemical properties, biological properties or both.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“wherein the properties are chemical properties, biological properties or both.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 5 is subject-matter ineligible.
Regarding claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 recites the additional elements:
“wherein the multiple constraints are one or more of chemical properties and structural details.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“wherein the multiple constraints are one or more of chemical properties and structural details.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 6 is subject-matter ineligible.
Regarding claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 recites the additional elements:
“wherein each dataset of the plurality of datasets comprises different modalities of the input molecules and the generated molecules.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“wherein each dataset of the plurality of datasets comprises different modalities of the input molecules and the generated molecules.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 7 is subject-matter ineligible.
Regarding claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 recites the additional elements:
“wherein the extracting of the domain-specific properties is performed using one or more pretrained modality-specific preprocessing machine learning models.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“wherein the extracting of the domain-specific properties is performed using one or more pretrained modality-specific preprocessing machine learning models.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 8 is subject-matter ineligible.
Regarding claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites:
“The method of claim 8,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 8.)
“wherein the one or more modality-specific preprocessing machine learning models includes a quantization of continuous features.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 does not further recite any additional elements. Therefore, claim 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 9 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 9 is subject-matter ineligible.
Regarding claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“further comprising computing a normalized odds ratio, wherein the normalized odds ratio quantifies the similarity of a generated molecule with one of the input molecules.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 does not further recite any additional elements. Therefore, claim 10 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 10 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 10 is subject-matter ineligible.
Regarding claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 11 recites:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“further comprising customizing a normalized distribution-based evaluation technique for non-binary features, wherein the evaluating the similarity is based on the normalized distribution-based evaluation technique.” (Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);)
Thus, claim 11 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 11 does not further recite any additional elements. Therefore, claim 11 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 11 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 11 is subject-matter ineligible.
Regarding claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 12 recites:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“wherein the ranking of the generative models is based on the corresponding aggregated scores and a list of desired molecule features.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually rank the models with consideration for the scores and a list)
Thus, claim 12 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 12 does not further recite any additional elements. Therefore, claim 12 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 12 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 12 is subject-matter ineligible.
Regarding claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 13 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“further comprising incorporating input from a domain expert” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually incorporate the input from the domain expert)
“by exploiting generated insights on the generative models and the input molecules, the generated molecules or both.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually exploit the insights based on their own evaluation)
Thus, claim 13 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 13 recites the additional elements:
“to improve a generation capability” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 13 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 13 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“further comprising incorporating input from a domain expert to improve a generation capability by exploiting generated insights on the generative models and the input molecules, the generated molecules or both.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner note – application of domain expert input)
Therefore, claim 13 is subject-matter ineligible.
Regarding claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 14 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 14 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 14 recites the additional elements:
“wherein the aggregating the similarity is performed across different features using informed-weighting of feature-based metrics.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 14 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 14 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“wherein the aggregating the similarity is performed across different features using informed-weighting of feature-based metrics.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 14 is subject-matter ineligible.
Regarding claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 recites the additional elements:
“further comprising displaying evaluation results to an end-user,” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“transmitting the evaluation results to another system or both.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 15 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“further comprising displaying evaluation results to an end-user,” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of presenting offers and gathering statistics, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“transmitting the evaluation results to another system or both.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g) in the form of receiving or transmitting data over a network, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 15 is subject-matter ineligible.
Regarding claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 16 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“further comprising: generating a detected divergent subset of molecules using scanning over the extracted domain-specific properties;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually write down a subset of molecules with consideration for the extracted domain-specific properties)
“determining model improvement features based on the domain-expert input for model improvement;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user decide improvement features manually with consideration for input from the domain-expert)
Thus, claim 16 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 16 recites the additional elements:
“displaying the generated divergent subset of molecules on a user device;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“receiving domain-expert input for model improvement;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“and triggering a model retraining process, inferencing process or both.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 16 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 16 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“displaying the generated divergent subset of molecules on a user device;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of presenting offers and gathering statistics, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“receiving domain-expert input for model improvement;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“and triggering a model retraining process, inferencing process or both.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
Therefore, claim 16 is subject-matter ineligible.
Regarding claim 17:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 17 recites the step:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 17 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 17 recites the additional elements:
“further comprising synthesizing a physical molecule from a design corresponding to the new molecule.” (This element does not integrate the abstract idea into a practical application because it amounts to linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Examiner notes high level recitation of synthesizing a physical molecule.)
Therefore, claim 17 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 17 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“further comprising synthesizing a physical molecule from a design corresponding to the new molecule.” (This element does not integrate the abstract idea into a practical application because it amounts to linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Examiner notes high level recitation of synthesizing a physical molecule.)
Therefore, claim 17 is subject-matter ineligible.
Regarding claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 18 recites:
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“wherein the rates are one or more of validity, uniqueness, and novelty.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine rates specifically concerning the validity, uniqueness and novelty)
Thus, claim 18 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 18 does not further recite any additional elements. Therefore, claim 18 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 18 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 18 is subject-matter ineligible.
Regarding claim 19:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 19 recites the step:
“evaluating a similarity of a given one of the generated molecules with a candidate molecule;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually evaluate the similarity of the molecules)
“aggregating the evaluated similarity across multiple constraints to generate a single score;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually combine the similarity on paper and determine a single score)
“quantifying an ability of a given generative model to mimic the input molecules based on an aggregation of scores for multiple properties;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually look at the scores and make a determination on the model’s ability to mimic the input molecules)
“ranking the generative models by their ability to mimic the input molecules and evaluation properties based on the aggregated scores;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually write out on paper their rankings for the models with respect to the abilities and scores)
“quantifying one or more rates in the generated molecules based on the aggregated single score;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine the rate with consideration for the score)
“selecting one of the generative models based on the ranking;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can choose to select a model)
Thus, claim 19 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 19 recites the additional elements:
“one or more tangible computer-readable storage media and program instructions stored on at least one of the one or more tangible computer-readable storage media,” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“the program instructions executable by a processor, the program instructions comprising:” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“extracting domain-specific properties from a plurality of datasets related to input molecules and generated molecules;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“and generating a new molecule using the selected generative model.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner note: High level recitation of generative model)
Therefore, claim 19 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 19 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“one or more tangible computer-readable storage media and program instructions stored on at least one of the one or more tangible computer-readable storage media,” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“the program instructions executable by a processor, the program instructions comprising:” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“extracting domain-specific properties from a plurality of datasets related to input molecules and generated molecules;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“and generating a new molecule using the selected generative model.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner note: High level recitation of generative model)
Therefore, claim 19 is subject-matter ineligible.
Regarding claim 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 20 recites the step:
“evaluating a similarity of a given one of the generated molecules with a candidate molecule;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually evaluate the similarity of the molecules)
“aggregating the evaluated similarity across multiple constraints to generate a single score;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually combine the similarity on paper and determine a single score)
“quantifying an ability of a given generative model to mimic the input molecules based on an aggregation of scores for multiple properties;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually look at the scores and make a determination on the model’s ability to mimic the input molecules)
“ranking the generative models by their ability to mimic the input molecules and evaluation properties based on the aggregated scores;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually write out on paper their rankings for the models with respect to the abilities and scores)
“quantifying one or more rates in the generated molecules based on the aggregated single score;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine the rate with consideration for the score)
“selecting one of the generative models based on the ranking;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can choose to select a model)
Thus, claim 20 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 20 recites the additional elements:
“a memory; and at least one processor, coupled to said memory, and operative to perform operations comprising:” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“extracting domain-specific properties from a plurality of datasets related to input molecules and generated molecules;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“and generating a new molecule using the selected generative model.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner note: High level recitation of generative model)
Therefore, claim 20 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 20 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“a memory; and at least one processor, coupled to said memory, and operative to perform operations comprising:” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“extracting domain-specific properties from a plurality of datasets related to input molecules and generated molecules;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“and generating a new molecule using the selected generative model.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner note: High level recitation of generative model)
Therefore, claim 20 is subject-matter ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5-6, 12, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai).
Regarding claim 1:
Lee teaches:
“quantifying, using the at least one hardware processor, an ability of a given generative model to mimic the input molecules based on an aggregation of scores for multiple properties;” (Lee ¶284 The method 1200 includes additional operations included in block 1108 of FIG. 11. At block 1202, the processing device generates, via the scientist module 143, a score for a parameter of the creator module 151 that generated the candidate drug compound. The parameter may include a validity of the candidate drug compound, uniqueness of the candidate drug compound, novelty of the candidate drug compound, similarity of the candidate drug compound to another candidate drug compound, or some combination thereof. ¶131 Although just one creator module 151 is depicted, there may any suitable number of creator modules 151. Each of the creator modules 151 may include one or more generative machine learning models trained to generate new candidate drug compounds.)
“ranking, ..., the generative models by their ability to mimic the input molecules and evaluation properties based on the aggregated scores;”(Lee ¶285 At block 1204, the processing device may rank a set of creator modules 151 based on the score, where the set of creator modules comprises the creator module. For example, other creator modules in the set of creator modules may be scored based on the candidate drug compounds they generated. The set of creator modules may be ranked for each respective category from highest scoring to lowest scoring or vice versa.)
“using the at least one hardware processor” (Lee ¶283 Method 1200 includes operations performed by processors of a computing device (e.g., any component of FIG. 1, such as server 128 executing the artificial intelligence engine 140). In some embodiments, one or more operations of the method 1200 are implemented in computer instructions that are stored on a memory device and executed by a processing device. The method 1200 may be performed in the same or a similar manner as described above in regard to method 400. The operations of the method 1200 may be performed in some combination with any of the operations of any of the methods described herein.)
“quantifying, using the at least one hardware processor, one or more rates in the generated molecules based on the aggregated single score;” (Lee ¶284 The parameter may include a validity of the candidate drug compound, uniqueness of the candidate drug compound, novelty of the candidate drug compound, similarity of the candidate drug compound to another candidate drug compound, or some combination thereof.) (Examiner notes applicant defines rates in specification in ¶16 of the specifications in the brief summary as “quantification of validity, uniqueness, and novelty rates in generated molecules based on a principally aggregated similarity or divergence score;”)
“selecting, using the at least one hardware processor, one of the generative models based on the ranking;” (Lee ¶288 At block 1210, the processing device may select, based on the parameters, a subset of the set of creator modules 151 to use to generate subsequent candidate drug compounds having desired parameter scores. For example, it may be desired to generate drug candidate compounds that result in a high uniqueness score. The creator module(s) 151 associated with high uniqueness scores may be selected in the subset of creator modules 151.)
“and generating, using the at least one hardware processor, a new molecule using the selected generative model.” (Lee ¶288 At block 1210, the processing device may select, based on the parameters, a subset of the set of creator modules 151 to use to generate subsequent candidate drug compounds having desired parameter scores.)
Lee does not distinctly disclose:
“extracting, using at least one hardware processor, domain-specific properties from a plurality of datasets related to input molecules and generated molecules;”
“evaluating, using the at least one hardware processor, a similarity of a given one of the generated molecules with a candidate molecule;”
“aggregating using the at least one hardware processor, the evaluated similarity across multiple constraints to generate a single score;”
However, Safiulin teaches:
“extracting, ...domain-specific properties from a plurality of datasets related to input molecules and generated molecules;” (Safiulin ¶11 The database of scored molecules can be input into the autoencoder-based generative model. Each scored molecule can have an objective function value that is calculated from an objective function. The scored molecules can be selected from the database to have relatively larger objective function values compared to other scored molecules in the database. The selected scored molecules can be processed through an encoder of the autoencoder-based generative model to obtain latent points in a latent space. A latent point in the latent space can be selected, and neighbor latent points can be sampled that are within a distance from the selected latent point. The sampled neighbor latent points can be processed with a decoder to generate at least one generated molecule. A report having the at least one generated molecule can be provided. In some aspects, the scored molecules have at least one property. In some aspects, the method can include comparing the generated molecules with the selected scored molecules and selecting molecules from the generated molecules that are closest to the selected scored molecules. The selected molecules can be provided as candidates for having the at least one property. ¶90 The protocols described herein can be applied to any dataset of object/property pairs (x, y). When a computing process operates with the models described herein, the computer can extract common information from the object and the condition/property and rank generated objects by their relevance to a given condition and/or rank generated conditions by their relevance to a given object.) (Examiner notes the reference lists an initial database and a report for the generated molecules which can be considered a dataset. The objects can then have their information extracted.)
“using at least one hardware processor,” (Safiulin ¶25 In some embodiments, a computer system can include: one or more processors; and one or more non-transitory computer readable media storing instructions that in response to being executed by the one or more processors, cause the computer system to perform operations, the operations comprising the method of one of the embodiments.)
“evaluating, using the at least one hardware processor, a similarity of a given one of the generated molecules with a candidate molecule;”(Safiulin ¶15 In some aspects, the newly generated molecules are selected by: determine a property for a target molecule; obtain a potential set of molecules; determine a similarity metric for the molecules in the potential set; and select molecules in the potential set with the similarity metric that is closest to the target molecule having the property.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee with the data extraction of Safiulin in order to facilitate the generation of molecules with desired properties (Safiulin ¶90, lines 1-3).
Lee as modified does not distinctly disclose:
“aggregating, using the at least one hardware processor, the evaluated similarity across multiple constraints to generate a single score;”
However, Bajpai teaches:
“aggregating, …, the evaluated similarity across multiple constraints to generate a single score;” (Bajpai Col. 4 lines 31-62 Members (e.g., multiple pharmaceutical targets) of the cluster to which the candidate pharmaceutical molecule is assigned may be scored based on comparisons between the pharmaceutical targets and the candidate pharmaceutical molecule, and a subset of the pharmaceutical targets may be selected based on the scores. In some implementations, for each pharmaceutical target that is a member of the cluster, comparisons between molecular fingerprints of the pharmaceutical target and molecular fingerprints of the candidate pharmaceutical molecule may be performed to generate multiple similarity scores, such as a Tanimoto coefficient, a cosine similarity, a largest common string (LCS) similarity, a Library for the Enumeration of Modular Natural Structures (LEMONS)-based similarity, a retrobiosynthesis and alignment (GRAPE) similarity, and the like, and a composite score for the pharmaceutical target is generated by averaging the multiple similarity scores.)
“using the at least one hardware processor” (Bajpai Col. 10 lines 57-63 Software configured to facilitate operations and functionality of the computing device 102 may be stored in the memory 106 as instructions 108 that, when executed by the one or more processors 104, cause the one or more processors 104 to perform the operations described herein with respect to the computing device 102, as described in more detail below.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the aggregating of scores of Bajpai in order to predict which generated molecules may be the most successful as determined by a determined metric such as commercial success. (Bajpai Col. 4 line 65 – Col. 5 line 3).
Regarding claim 2, Lee as modified teaches all the limitations of claim 1.
Lee as modified further teaches:
“wherein the candidate molecule is one of the input molecules or one of the generated molecules that is generated from a different generative model of the generative models than the given generative model.” (Lee ¶94 In some embodiments, a machine learning model may be trained to receive, as input, a sequence generated by another machine learning model.)
Regarding claim 3, Lee as modified teaches all the limitations of claim 1.
Lee as modified further teaches:
“wherein the evaluating of the similarity is based on a property of the corresponding molecule, a structural feature of the corresponding molecule, or both.” (Bajpai Col. 4 lines 31-62 Members (e.g., multiple pharmaceutical targets) of the cluster to which the candidate pharmaceutical molecule is assigned may be scored based on comparisons between the pharmaceutical targets and the candidate pharmaceutical molecule, and a subset of the pharmaceutical targets may be selected based on the scores. In some implementations, for each pharmaceutical target that is a member of the cluster, comparisons between molecular fingerprints of the pharmaceutical target and molecular fingerprints of the candidate pharmaceutical molecule may be performed to generate multiple similarity scores, such as a Tanimoto coefficient, a cosine similarity, a largest common string (LCS) similarity, a Library for the Enumeration of Modular Natural Structures (LEMONS)-based similarity, a retrobiosynthesis and alignment (GRAPE) similarity, and the like, and a composite score for the pharmaceutical target is generated by averaging the multiple similarity scores.) (Examiner notes the use of structural similarity metrics.)
Regarding claim 5, Lee as modified teaches all the limitations of claim 1.
Lee as modified further teaches:
“wherein the properties are chemical properties, biological properties or both.” (Safiulin ¶64 In some embodiments, molecules can be characterized by descriptors-based similarity function. The molecules are characterized by a descriptor vector that reflects the chemical properties of the molecule.)
Regarding claim 6, Lee as modified teaches all the limitations of claim 1.
Lee as modified further teaches:
“wherein the multiple constraints are one or more of chemical properties and structural details.” (Bajpai Col. 4 lines 31-62 Members (e.g., multiple pharmaceutical targets) of the cluster to which the candidate pharmaceutical molecule is assigned may be scored based on comparisons between the pharmaceutical targets and the candidate pharmaceutical molecule, and a subset of the pharmaceutical targets may be selected based on the scores. In some implementations, for each pharmaceutical target that is a member of the cluster, comparisons between molecular fingerprints of the pharmaceutical target and molecular fingerprints of the candidate pharmaceutical molecule may be performed to generate multiple similarity scores, such as a Tanimoto coefficient, a cosine similarity, a largest common string (LCS) similarity, a Library for the Enumeration of Modular Natural Structures (LEMONS)-based similarity, a retrobiosynthesis and alignment (GRAPE) similarity, and the like, and a composite score for the pharmaceutical target is generated by averaging the multiple similarity scores.)
Regarding claim 12, Lee as modified teaches all the limitations of claim 1.
Lee as modified further teaches:
“wherein the ranking of the generative models is based on the corresponding aggregated scores and a list of desired molecule features.” (Lee ¶284 The method 1200 includes additional operations included in block 1108 of FIG. 11. At block 1202, the processing device generates, via the scientist module 143, a score for a parameter of the creator module 151 that generated the candidate drug compound. The parameter may include a validity of the candidate drug compound, uniqueness of the candidate drug compound, novelty of the candidate drug compound, similarity of the candidate drug compound to another candidate drug compound, or some combination thereof. Lee ¶285 At block 1204, the processing device may rank a set of creator modules 151 based on the score, where the set of creator modules comprises the creator module. For example, other creator modules in the set of creator modules may be scored based on the candidate drug compounds they generated. The set of creator modules may be ranked for each respective) (Examiner notes the creator module are the generative models. Examiner also notes the drug compounds are the generated molecules)
Regarding claim 17, Lee as modified teaches all the limitations of claim 1.
Lee as modified further teaches:
“further comprising synthesizing a physical molecule from a design corresponding to the new molecule.” (Lee ¶341 The AFSP 2400 may use the hardware components 2401 to perform an automated flow process to synthesize candidate drug compounds (e.g., sequences representing proteins (peptides, peptidomimetics, etc.)) generated by the artificial intelligence engine 140.)
Regarding claim 18, Lee as modified teaches all the limitations of claim 1.
Lee as modified further teaches:
“wherein the rates are one or more of validity, uniqueness, and novelty.” (Lee ¶284 The parameter may include a validity of the candidate drug compound, uniqueness of the candidate drug compound, novelty of the candidate drug compound, similarity of the candidate drug compound to another candidate drug compound, or some combination thereof.)
Regarding claim 19, Lee as modified teaches a “computer program product, comprising: one or more tangible computer-readable storage media and program instructions stored on at least one of the one or more tangible computer-readable storage media, the program instructions executable by a processor,”(Lee ¶283 Method 1200 includes operations performed by processors of a computing device (e.g., any component of FIG. 1, such as server 128 executing the artificial intelligence engine 140). In some embodiments, one or more operations of the method 1200 are implemented in computer instructions that are stored on a memory device and executed by a processing device. The method 1200 may be performed in the same or a similar manner as described above in regard to method 400. The operations of the method 1200 may be performed in some combination with any of the operations of any of the methods described herein.) to perform the method of claim 1 (see rejection for claim 1) and is therefore rejected under the same analysis.
Regarding claim 20, Lee as modified teaches “a system comprising: a memory; and at least one processor, coupled to said memory, and operative to perform operations comprising” (Lee ¶283 Method 1200 includes operations performed by processors of a computing device (e.g., any component of FIG. 1, such as server 128 executing the artificial intelligence engine 140). In some embodiments, one or more operations of the method 1200 are implemented in computer instructions that are stored on a memory device and executed by a processing device. The method 1200 may be performed in the same or a similar manner as described above in regard to method 400. The operations of the method 1200 may be performed in some combination with any of the operations of any of the methods described herein.) to perform the method of claim 1 (see rejection for claim 1) and is therefore rejected under the same analysis.
Claim(s) 4 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai), as applied to claim 1, further in view of Girmaw et al. (https://arxiv.org/pdf/2203.04386.) (hereafter referred to as Girmaw).
Regarding claim 4, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“wherein the quantifying of the ability uses one of a normalized odds ratio and a distribution-similarity metric.”
However, Girmaw teaches:
“wherein the quantifying of the ability uses one of a normalized odds ratio and a distribution-similarity metric.” (Girmaw Page 3 ¶1 In this paper, we propose a sparsity-based automatic feature selection framework (SAFS) which employs normalized odds ratio as an objective measure to evaluate the association between a feature value and the target outcome.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the normalized odds ratio of Girmaw in order to encode any deviations in similarity based on features of molecules (Girmaw Page 3 ¶1, lines 4-5).
Regarding claim 10, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“further comprising computing a normalized odds ratio, wherein the normalized odds ratio quantifies the similarity of a generated molecule with one of the input molecules.”
However, Girmaw teaches
“further comprising computing a normalized odds ratio, wherein the normalized odds ratio quantifies the similarity of a generated molecule with one of the input molecules.” (Girmaw Page 3 ¶1 In this paper, we propose a sparsity-based automatic feature selection framework (SAFS) which employs normalized odds ratio as an objective measure to evaluate the association between a feature value and the target outcome.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the normalized odds ratio of Girmaw in order to encode any deviations in similarity based on features of molecules (Girmaw Page 3 ¶1, lines 4-5).
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai), as applied to claim 1, further in view of Guo et al. (US 2023/0067528 A1) (hereafter referred to as Guo).
Regarding claim 7, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“wherein each dataset of the plurality of datasets comprises different modalities of the input molecules and the generated molecules.”
However, Guo teaches:
“wherein each dataset of the plurality of datasets comprises different modalities of the input molecules and the generated molecules.” (Guo ¶50 The computing system 110 is configured to build a machine learning model 145 that is trainable to perform in-domain embedding analysis. One or more computer-readable instructions that are executable by the one or more processors to configure the computing system to obtain a first encoder of the machine learning model trained to receive as input one or more entities represented in a first modality and encode the one or more entities in the first modality such that the first encoder is configured to output a first set of embeddings. The computing system 110 is also configured to obtain a second encoder of the machine learning model trained to receive as input one or more entities represented in the second modality and encode the one or more entities in the second modality such that the second encoder is configured to output a second set of embeddings,)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the data comprising different modalities of Guo in order to project differing data in a shared contrastive space (Guo ¶50, last 6 lines).
Regarding claim 8, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“wherein the extracting of the domain-specific properties is performed using one or more pretrained modality-specific preprocessing machine learning models.”
However, Guo teaches:
“wherein the extracting of the domain-specific properties is performed using one or more pretrained modality-specific preprocessing machine learning models.” (Guo ¶53 An additional storage unit for storing machine learning (ML) Engine(s) 150 is presently shown in FIG. 1 as storing a plurality of machine learning models and/or engines. For example, computing system 110 comprises one or more of the following: a data retrieval engine 151, a training engine 152, a tokenizing engine 153, an encoding engine 154, a projection engine 155, a contrasting engine 156, and an implementation engine 157, which are individually and/or collectively configured to implement the different functionality described herein. Guo ¶54 For example, the data retrieval engine 151 is configured to locate and access data sources, databases, and/or storage devices comprising one or more data types from which the data retrieval engine 151 can extract sets or subsets of data to be used as training data or entity data (e.g., new text data/document data/molecular data). The data retrieval engine 151 receives data (e.g., molecular data 143 and/or drug data 144) from the databases and/or hardware storage devices, wherein the data retrieval engine 151 is configured to reformat or otherwise augment the received data to be used as training data. Additionally, or alternatively, the data retrieval engine 151 is in communication with one or more remote systems (e.g., third-party system(s) 120) comprising third-party datasets and/or data sources.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with machine learning models of Guo in order to use the data as training or entity data for other models. (Guo ¶54, lines 1-6).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai), as applied to claim 1, further in view of Guo et al. (US 2023/0067528 A1), as applied to claim 8, further in view of Guo et al. (CN 115983362 A) (hereafter referred to as Guo).
Regarding claim 9, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“wherein the one or more modality-specific preprocessing machine learning models includes a quantization of continuous features.”
However, Guo teaches:
“wherein the one or more modality-specific preprocessing machine learning models includes a quantization of continuous features.” (Guo Page 18, ¶1 In the quantization method based on the self-adaptive quantization step size, when the click rate prediction model is trained offline, the continuous features are normalized and then are automatically discretized.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the quantization and normalizing step of Guo in order to deal with data that is high-dimensional and sparse (Guo Page 17, last paragraph).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai), as applied to claim 1, further in view of Gankin et al. (WO 2022/032044 A2) (hereafter referred to as Gankin).
Regarding claim 11, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“further comprising customizing a normalized distribution-based evaluation technique for non-binary features, wherein the evaluating the similarity is based on the normalized distribution-based evaluation technique.”
However, Gankin teaches:
“further comprising customizing a normalized distribution-based evaluation technique for non-binary features, wherein the evaluating the similarity is based on the normalized distribution-based evaluation technique.” (Gankin Page 17, last 4 lines - Page 19, line 2, The Euclidean distance can be viewed as an extension of the Tanimoto similarity measure for non-binary fingerprints... Since the fingerprint coordinates (c) (c) x and x are normalized (i.e. have values between 0 and 1 for each coordinate ri) the resulting q,n r,n overlap is maximized with the value equal to 1 when the fingerprints of both conformers are q,r identical and can take the smallest value equal to 0 when all the fingerprint coordinates have a difference equal to 1 i.e. as different as possible at the normalized scale.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the normalized distribution-based evaluation of Gankin in order to quickly compute similarity compared to other methods (Gankin Page 17).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai), as applied to claim 1, further in view of Zevoronkov et al. (CN 114667498 A) (hereafter referred to as Zevoronkov).
Regarding claim 13, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“further comprising incorporating input from a domain expert to improve a generation capability by exploiting generated insights on the generative models and the input molecules, the generated molecules or both.”
However, Zevoronkov teaches:
“further comprising incorporating input from a domain expert to improve a generation capability by exploiting generated insights on the generative models and the input molecules, the generated molecules or both.” (Zevoronkov Page 16, ¶6, According to human expert reviews, almost all compounds tested are considered novel and attractive for further biological testing. Some compounds have been classified as having a unique structure. On the other hand, characteristics identified by experts that require more work in further drug development, including metabolic instability, relatively poor overall accessibility, and potential need for solubility modulation, have been properly attributed to certain segments. These are features that can be added to more complex screening protocols or integrated into a second round of refinement. Thus, the protocol for generating the compound may include the step of a human expert in chemistry to analyze the structure of the compound to facilitate the selection of a lead compound, or a compound for which biological activity is to be synthesized and validated.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the human expert of Zevoronkov in order to overcome problems of compounds that are adverse or do not meet certain criteria (Zevoronkov Page 17, ¶5).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai), as applied to claim 1, further in view of Terreux et al. (US 2023/0154571 A1) (hereafter referred to as Terreux).
Regarding claim 14, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“wherein the aggregating the similarity is performed across different features using informed-weighting of feature-based metrics.”
However, Terreux teaches:
“wherein the aggregating the similarity is performed across different features using informed-weighting of feature-based metrics.”(Terreux ¶59-61 In a particular embodiment, in the evaluation step, the overall similarity measure evaluated for said molecule is the ratio between: the weighted sum of the N local similarity metrics calculated for the N descriptor features for that molecule, and twice the sum of the weights applied to the local similarity metrics in said weighted sum less said weighted sum.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the metrics of Terreux in order to take into account several states of expression of the same features in molecules being compared (Terreux ¶62).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai), as applied to claim 1, further in view of Sarshogh et al. (US 2023/0230662 A1) (hereafter referred to as Sarshogh).
Regarding claim 15, Lee as modified teaches all the limitations of claim 1.
Lee as modified does not distinctly disclose:
“further comprising displaying evaluation results to an end-user, transmitting the evaluation results to another system or both.”
However, Sarshogh teaches:
“further comprising displaying evaluation results to an end-user, transmitting the evaluation results to another system or both.” (Sarshogh ¶75 FIG. 6 illustrates an example embodiment of a cluster map for a single modality generated by a machine learning model, for example as depicted in FIG. 5. FIG. 6 displays clustering results based on molecule embeddings generated based on molecular scaffold similarity.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the displaying of results of Sarshogh in order to visually represent the molecules (Sarshogh ¶75).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0359041 A1) (hereafter referred to as Lee), in view of Safiulin et al. (US 2023/0253076 A1) (hereafter referred to as Safiulin), further in view of Bajpai et al. (US 11,127,488 B1) (hereafter referred to as Bajpai), as applied to claim 1, further in view of Sarshogh et al. (US 2023/0230662 A1) (hereafter referred to as Sarshogh), further in view of Zevoronkon et al. (CN 114667498 A) (hereafter referred to as Zevoronkov).
Regarding claim 16, Lee as modified teaches all the limitations of claim 1.
Lee as modified further teaches:
“further comprising: generating a detected divergent subset of molecules using scanning over the extracted domain-specific properties;” (Safiulin ¶54 An example protocol of FIG. 4 is as follows: (1) Select acceptable molecules (AF(x)=1); (2) Calculate Morgan fingerprints for selected molecules; (3) Apply the clustering method on the calculated fingerprints; (4) Select in every cluster N molecules with the highest values of objective function; and (5) From the selected molecules, randomly choose one molecule in every cluster. This results in a molecule from each cluster being selected. The selected molecules are a subset of different (in terms of chemistry) molecules with the best objective function values. Therefore, these selected molecules likely have the one or more properties.)
“and triggering a model retraining process, inferencing process or both.” (Lee ¶96 Quality control may be performed on the synthesized sequence. The quality control may include performing structural screening or functional screening on the synthesized sequence. The structural and functional data generated during the quality control may be transmitted to the artificial intelligence engine to be associated with the synthesized sequence that was tested. The artificial intelligence engine may retrain one or more machine learning models, such that sequences having desired structural and functional data are subsequently selected.)
Lee as modified does not distinctly disclose:
“displaying the generated divergent subset of molecules on a user device;”
“receiving domain-expert input for model improvement;”
“determining model improvement features based on the domain-expert input for model improvement;”
However, Sarshogh teaches:
“displaying the generated divergent subset of molecules on a user device;” (Sarshogh ¶75 FIG. 6 illustrates an example embodiment of a cluster map for a single modality generated by a machine learning model, for example as depicted in FIG. 5. FIG. 6 displays clustering results based on molecule embeddings generated based on molecular scaffold similarity.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the displaying of results of Sarshogh in order to visually represent the molecules (Sarshogh ¶75).
Lee as modified does not distinctly disclose:
“receiving domain-expert input for model improvement;”
“determining model improvement features based on the domain-expert input for model improvement;”
However, Zevoronkov teaches:
“receiving domain-expert input for model improvement;” (Zevoronkov Page, 16 ¶5 To overcome some of the problems of compounds that are adverse or do not meet certain criteria, the protocol may include obtaining a list of expert opinions about the selected structure provided by the professional medicinal chemistry team.)
“determining model improvement features based on the domain-expert input for model improvement;” (Zevoronkov Page 16, ¶6, According to human expert reviews, almost all compounds tested are considered novel and attractive for further biological testing. Some compounds have been classified as having a unique structure. On the other hand, characteristics identified by experts that require more work in further drug development, including metabolic instability, relatively poor overall accessibility, and potential need for solubility modulation, have been properly attributed to certain segments. These are features that can be added to more complex screening protocols or integrated into a second round of refinement. Thus, the protocol for generating the compound may include the step of a human expert in chemistry to analyze the structure of the compound to facilitate the selection of a lead compound, or a compound for which biological activity is to be synthesized and validated.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method of ranking models and generating molecules of Lee as modified with the human expert of Zevoronkov in order to overcome problems of compounds that are adverse or do not meet certain criteria (Zevoronkov Page 17, ¶5).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tal et al. (US 11,710,049 B2) also discloses a method of determining similarity of molecules based on features.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter T Annis whose telephone number is (571)270-1059. The examiner can normally be reached M-F, 7:30am to 5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER THOMAS ANNIS/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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