CTNF 18/134,006 CTNF 86689 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This Office Action is in response to claims filed on 04/12/2023 Claims 1-13 are pending. Claims 1-13 were amended. Information Disclosure Statement The information disclosure statement (IDS) submitted on 08/10/2023 is being considered by the examiner. Drawings 06-22 AIA The drawings are objected to because in . 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. Specification 07-29-04 The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. The specification on par 50, 59 and 87 recite hyperlinks. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1 and 9-10 and 12-13 recites the limitation "in case the property profile”. It is unclear from the claim language whether “this condition would be met”. It’s unclear what happens if the condition is not met. Correction is required. For compact prosecution, Examiner is interpreting the claim as follows “The condition being met, thus outputting the discrete molecular representation”. Dependent claims do not resolve the indefinite issue in the independent claim, and thus are also rejected under 112(b) by virtue of their dependence on the rejected independent claim. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: 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. 07-30-05 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 limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 07-30-07 This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: Linear transformation unit in claims 1, 12 and 13. Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof. If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Rafael Gomez-Bombarelli, NPL “Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules”, Published: January 2, 2018, (hereafter Gomez), in views of Samuel C. Hoffman, NPL, “Optimizing molecules using efficient queries from property evaluations”, Published: January 2022 (hereafter Hoffman) . Regarding claim 1 . Gomez teaches a computer-implemented method, the method comprising: providing a trained machine learning model, the trained machine learning model comprising an encoder, a decoder, and a linear transformation unit (Page 268, col 1, par 2, based on learned proxy models) (Page 269, Col 1, Par 2, Machine learning) (Page 269, Col 2, Par 2, network to learn a representation) , wherein the encoder is configured and trained to convert a discrete molecular representation of a chemical compound into a vector in continuous latent space (Page 269, Fig 1, Encoder) , wherein the decoder is configured and trained to convert a vector in the continuous latent space into a discrete molecular representation of a chemical compound (Page 269, Fig 1, Decoder) , wherein the linear transformation unit is configured and trained to map a vector in the continuous latent space to a property vector representing a property profile (Page 269, Fig 1, Property f(z)) ; r eceiving a target property vector representing a target property profile (Page 269, Fig 1, perceptron network estimates the value of target properties associated with each molecule) ; mapping the target property vector to the continuous latent space via the linear transformation unit, thereby determining a subset in the continuous latent space (Page 269, Fig 1b, Most Probable Decoding, a projection of the property) ; receiving a molecular representation of a lead compound (Page 269, Fig 1, SMILES input) ; converting the molecular representation of the lead compound to a vector representing the lead compound (LC) in the continuous latent space via the encoder (Page 269, Fig 1, Property prediction) ; projecting the vector representing the lead compound in the continuous latent space onto the subset, thereby generating a first vector representing a first test compound in the continuous latent space (Page 269, Fig 1b, projection of the property f(z), Latent space) ; inputting the first vector representing the first test compound in the continuous latent space into the decoder, thereby generating a discrete molecular representation of the first test compound (Page 269, Fig 1, SMILES output) ; inputting the discrete molecular representation into the encoder, thereby generating a second vector representing the first test compound in the continuous latent space (Page 269, Fig 1, SMILES input) ; inputting the second vector representing the first test compound in the continuous latent space into the linear transformation unit, thereby generating a property vector representing a property profile of the first test compound (Page 269, Fig 1b, projection of the property f(z), Latent space) ; comparing the property profile of the first test compound with the target property profile (Page 273, col 2, par 1, trained to predict target properties for molecules given the latent space) (Page 273, col 2, Par 2, find molecule that maximized our objective, compared results against molecules found) ; in case the property profile of the first test compound has a pre-defined similarity to the target property profile (Page 273, Fig 4, QED, SAS, Percentile score) (Page 273, Col 2, Par 3, Quantitative Estimation of Drug-likeness) : outputting the discrete molecular representation of the first test compound and/or another representation of the first test compound (Page 269, Fig 1, SMILES output) (Page 273, fig 4, Finish iteration, for output) . Gomez does not teach inputting the discrete molecular representation of the first test compound into the encoder. Hoffman teaches inputting the discrete molecular representation of the first test compound into the encoder (Page 22, Fig 1, feedback Loss landscape) . It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Gomez to incorporate the teachings of Hoffman to feedback the output in the next iteration because results show high consistency with external validations, suggesting an effective means to facilitate material optimization problems with design constraints (Hoffman, abstract) Regarding claim 2 . Gomez and Hoffman teach the method according to claim 1, wherein the target property profile comprises one or more target values of one of more of the following properties: biological activity, selectivity, toxicity, solubility, chemical stability (Hoffman, Page 23, Col 1, Par 2, optimizing, selective toxicity) (Hoffman, Page 25, Col 1, Par 6, MO goals, binding or selectivity attributes) . Regarding claim 3 . Gomez and Hoffman teach the method according to claim 1, wherein the encoder and the decoder are parts of a variational autoencoder (Gomez, Page 270, Col 1, Par 1, variational autoencoder (VAE)) . Regarding claim 4 . Gomez and Hoffman teach the method according to claim 1 , wherein the molecular representation of the lead compound and the molecular representation of the first test compound are SMILES codes, preferably canonical SMILES codes (Gomez, Page 269, Fig 1, SMILES, input and output) (Gomez, Page 274, Col2, Par 3, canonicalized SMILES) . Regarding claim 5 . Gomez and Hoffman teach the method according to claim 1 , wherein the trained machine learning model was trained on training data (Gomez, Page 274, Col 1, Par 2, GP model trained with 1000 molecules) , the training data comprising, for each chemical compound of a plurality of chemical compounds (Gomez, Page 273, Col 2, Par 2, molecules used for training the gaussian process) , a molecular representation of the chemical compound and at least one property representing a property profile of the chemical compound (Gomez, Page 273, Fig 4, Start, Intermediate path, QED, SAS, percentile) . Regarding claim 6 . Gomez and Hoffman teach the method according claim 5, wherein training of the machine learning model comprises: for each chemical compound of the plurality of chemical compounds (Gomez, Page 273, Col 2, Par 1, few minutes to train on a dataset of a few thousand molecules) : inputting the molecular representation of the chemical compound into the encoder (Gomez, Page 270, Fig 2c, ibuprofen) ; receiving from the decoder an output molecular representation (Gomez, Page 270, Fig 2d, End Propafenone) ; quantifying the differences between the inputted molecular representation and the output molecular representation using a first loss term (Gomez, Page 270, Fig 2b, Distance) ; receiving from the linear transformation unit a predicted property profile (Gomez, Page 272, Table 1, QED, SAS, LogP) ; quantifying the differences between the property profile and the predicted property profile using a second loss term (Gomez, Page 273, Table 2, prediction error) ; computing a loss using a loss function, the loss function comprising the first loss term and the second loss term (Gomez, Page 273, Fig 4, objective function, QED, SAS, objective) ; modifying parameters of the machine learning model based on the computed loss (Gomez, Page 273, Col 2, Par 4, objective score 10%, optimized to higher percentile score) . Regarding claim 7 . Gomez and Hoffman teach the method according to claim 1 , wherein the property profile of the first test compound and the target property profile are represented by feature vectors (Gomez, Page 270, Col 2, Par 2, correlated with the target properties, MLP was used to predict the property from the latent vector of the encoded molecule) . Regarding claim 8 . Gomez and Hoffman teach the method according claim 7, wherein comparing the property profile of the first test compound with the target property profile, comprises: computing a similarity value, the similarity value quantifying the similarity between the feature vector representing the property profile of the first test compound and the feature vector representing the target property profile (Gomez, Page 273, Fig 4, QED, SAS, Percentile score) (Page 273, Col 2, Par 3, Quantitative Estimation of Drug-likeness) ; comparing the similarity value with a pre-defined threshold (Gomez, Page 274, col 1, par 1, final molecule ending up in the region of high objective value, optimization with GP results in higher percentile score) . Regarding claim 9 . Gomez and Hoffman teach the method according to claim 1 , further comprising: modifying the target property profile (Hoffman, Page 25, Col 2, Par 2, show a similarity map to emphasize the changes) (Hoffman, Page 22, Fig 1, Zt+1, updated embedding) ; mapping the modified target property vector to the continuous latent space via the linear transformation unit, thereby determining a modified subset in the continuous latent space (Hoffman, Page 22, Fig 1, Zt+1, updated embedding becomes embedded vector, and sampled points for the iteration) ; projecting the vector representing the lead compound in the continuous latent space onto the modified subset, thereby receiving a second vector representing a second test compound in the continuous latent space (Gomez, Page 269, Fig 1b, projection of the property f(z), Latent space) ; generating a discrete molecular representation of the second test compound using the decoder (Gomez, Page 269, Fig 1, SMILES output) ; inputting the discrete molecular representation of the second test compound into the encoder and determining a property profile for the second test compound via the linear transformation unit (Hoffman, Page 22, Fig 1, Zt+1, updated embedding, feedback) ; comparing the property profile of the second test compound with the target property profile (Gomez, Page 273, col 2, par 1, trained to predict target properties for molecules given the latent space) (Gomez, Page 273, col 2, Par 2, find molecule that maximized our objective, compared results against molecules found) ; in case the property profile of the second test compound has a pre-defined similarity to the target property profile (Gomez, Page 273, Fig 4, QED, SAS, Percentile score) (Gomez, Page 273, Col 2, Par 3, Quantitative Estimation of Drug-likeness) : outputting the discrete molecular representation of the second test compound and/or another representation of the second test compound (Page 269, Fig 1, SMILES output) (Page 273, fig 4, Finish iteration, for output) . Regarding claim 10 . Gomez and Hoffman teach the method according to claim 1 , further comprising: moving a pre-defined distance in a pre-defined direction from a point representing the first test compound in the continuous latent, thereby defining a second vector representing a second test compound in the continuous latent space (Hoffman, Page 22, Fig 1, Zt+1, updated embedding, loss landscape, feedback) ; generating a discrete molecular representation of the second test compound using the decoder (Gomez, Page 269, Fig 1, Decoder, SMILES output) ; inputting the discrete molecular representation of the second test compound into the encoder and determining a property profile for the second test compound via the linear transformation unit (Hoffman, Page 22, Fig 1, Zt+1, updated embedding, feedback) ; comparing the property profile of the second test compound with the target property profile (Gomez, Page 273, col 2, par 1, trained to predict target properties for molecules given the latent space) (Gomez, Page 273, col 2, Par 2, find molecule that maximized our objective, compared results against molecules found); in case the property profile of the second test compound has a pre-defined similarity to the target property profile (Gomez, Page 273, Fig 4, QED, SAS, Percentile score) (Gomez, Page 273, Col 2, Par 3, Quantitative Estimation of Drug-likeness) : outputting the discrete molecular representation of the second test compound and/or another representation of the second test compound (Page 269, Fig 1, SMILES output) (Page 273, fig 4, Finish iteration, for output) . Regarding claim 11 . Gomez and Hoffman teach the method according to claim 1 , further comprising: initiating synthesis and/or characterization of the first test compound (Gomez, Page 273, Col 2, Par 3, finding the most drug-like molecule that is also easy to synthesize, thus having the characteristics that would make it easier to synthesize) . Regarding claim 12 . Gomez teaches a computer system comprising: a processing unit (Page 269, Col 1, Par 1, machine readable, machine learning, implementations, thus a processor is required to read and learn) ; and a memory storing an application program configured to perform (Page 269, Col 1, Par 1, machine readable, machine learning, implementations, thus a processor communicating with memory to read) , when executed by the processing unit, an operation, the operation comprising: providing a trained machine learning model, the trained machine learning model comprising an encoder, a decoder, and a linear transformation unit (Page 268, col 1, par 2, based on learned proxy models) (Page 269, Col 1, Par 2, Machine learning) (Page 269, Col 2, Par 2, network to learn a representation) ; wherein the encoder is configured and trained to convert a discrete molecular representation of a chemical compound into a vector in continuous latent space (Page 269, Fig 1, Encoder) , wherein the decoder is configured and trained to convert a vector in the continuous latent space into a discrete molecular representation of a chemical compound (Page 269, Fig 1, Decoder) , wherein the linear transformation unit is configured and trained to map a vector in the continuous latent space to a property vector representing a property profile (Page 269, Fig 1, Property f(z)) ; receiving a target property vector representing a target property profile (Page 269, Fig 1, perceptron network estimates the value of target properties associated with each molecule) ; mapping the target property vector to the continuous latent space via the linear transformation unit, thereby determining a subset in the continuous latent space (Page 269, Fig 1b, Most Probable Decoding, a projection of the property) ; receiving a molecular representation of a lead compound (Page 269, Fig 1, SMILES input) ; converting the molecular representation of the lead compound to a vector representing the lead compound in the continuous latent space via the encoder (Page 269, Fig 1, Property prediction) ; projecting the vector representing the lead compound in the continuous latent space onto the subset, thereby generating a first vector representing a first test compound in the continuous latent space (Page 269, Fig 1b, projection of the property f(z), Latent space) ; inputting the first vector representing the first test compound in the continuous latent space into the decoder, thereby generating a discrete molecular representation of the first test compound (Page 269, Fig 1, SMILES output) ; inputting the discrete molecular representation into the encoder, thereby generating a second vector representing the first test compound in the continuous latent space (Page 269, Fig 1, SMILES input) ; inputting the second vector representing the first test compound in the continuous latent space into the linear transformation unit, thereby generating a property vector representing a property profile of the first test compound (Page 269, Fig 1b, projection of the property f(z), Latent space) ; comparing the property profile of the first test compound with the target property profile (Page 273, col 2, par 1, trained to predict target properties for molecules given the latent space) (Page 273, col 2, Par 2, find molecule that maximized our objective, compared results against molecules found) ; in case the property profile of the first test compound has a pre-defined similarity to the target property profile (Page 273, Fig 4, QED, SAS, Percentile score) (Page 273, Col 2, Par 3, Quantitative Estimation of Drug-likeness) : outputting the discrete molecular representation of the first test compound and/or another representation of the first test compound (Page 269, Fig 1, SMILES output) (Page 273, fig 4, Finish iteration, for output) . Gomez does not teach inputting the discrete molecular representation of the first test compound into the encoder. Hoffman teaches inputting the discrete molecular representation of the first test compound into the encoder (Page 22, Fig 1, feedback Loss landscape) . It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Gomez to incorporate the teachings of Hoffman to feedback the output in the next iteration because results show high consistency with external validations, suggesting an effective means to facilitate material optimization problems with design constraints (Hoffman, abstract) Regarding claim 13 . Gomez teaches a non-transitory computer readable medium having stored thereon software instructions that, when executed by a processing unit (20) of a computer system (1), cause the computer system (1) to execute the following steps (Page 269, Col 1, Par 1, machine readable, machine learning, implementations, thus a processor is required to read and learn) : providing a trained machine learning model, the trained machine learning model comprising an encoder, a decoder, and a linear transformation unit (LTU, LTU*) (Page 268, col 1, par 2, based on learned proxy models) (Page 269, Col 1, Par 2, Machine learning) (Page 269, Col 2, Par 2, network to learn a representation) , wherein the encoder is configured and trained to convert a discrete molecular representation of a chemical compound into a vector in continuous latent space (Page 269, Fig 1, Encoder) , wherein the decoder is configured and trained to convert a vector in the continuous latent space into a discrete molecular representation of a chemical compound (Page 269, Fig 1, Decoder) , wherein the linear transformation unit is configured and trained to map a vector in the continuous latent space to a property vector representing a property profile (Page 269, Fig 1, Property f(z)) ; receiving a target property vector representing a target property profile (Page 269, Fig 1, perceptron network estimates the value of target properties associated with each molecule) ; mapping the target property vector to the continuous latent space via the linear transformation unit, thereby determining a subset in the continuous latent space (Page 269, Fig 1b, Most Probable Decoding, a projection of the property) ; receiving a molecular representation of a lead compound (Page 269, Fig 1, SMILES input) ; converting the molecular representation of the lead compound to a vector representing the lead compound in the continuous latent space via the encoder (Page 269, Fig 1, Property prediction) ; projecting the vector representing the lead compound in the continuous latent space onto the subset, thereby generating a first vector representing a first test compound in the continuous latent space (Page 269, Fig 1b, projection of the property f(z), Latent space) ; inputting the first vector representing the first test compound in the continuous latent space into the decoder, thereby generating a discrete molecular representation of the first test compound (Page 269, Fig 1, SMILES output) ; inputting the discrete molecular representation into the encoder, thereby generating a second vector representing the first test compound in the continuous latent space (Page 269, Fig 1, SMILES input) ; inputting the second vector representing the first test compound in the continuous latent space into the linear transformation unit, thereby generating a property vector representing a property profile of the first test compound (Page 269, Fig 1b, projection of the property f(z), Latent space) ; comparing the property profile of the first test compound with the target property profile (Page 273, col 2, par 1, trained to predict target properties for molecules given the latent space) (Page 273, col 2, Par 2, find molecule that maximized our objective, compared results against molecules found) ; in case the property profile of the first test compound has a pre-defined similarity to the target property profile (Page 273, Fig 4, QED, SAS, Percentile score) (Page 273, Col 2, Par 3, Quantitative Estimation of Drug-likeness) : outputting the discrete molecular representation of the first test compound and/or another representation of the first test compound (Page 269, Fig 1, SMILES output) (Page 273, fig 4, Finish iteration, for output) . Gomez does not teach inputting the discrete molecular representation of the first test compound into the encoder. Hoffman teaches inputting the discrete molecular representation of the first test compound into the encoder (Page 22, Fig 1, feedback Loss landscape) . It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Gomez to incorporate the teachings of Hoffman to feedback the output in the next iteration because results show high consistency with external validations, suggesting an effective means to facilitate material optimization problems with design constraints (Hoffman, abstract) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGEL JAVIER CALLE whose telephone number is (571)272-0463. The examiner can normally be reached Monday - Friday 7:30 a.m. - 5 p.m.. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen can be reached at (571)-272-3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.C./Examiner, Art Unit 2189 /REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189 Application/Control Number: 18/134,006 Page 2 Art Unit: 2189 Application/Control Number: 18/134,006 Page 3 Art Unit: 2189 Application/Control Number: 18/134,006 Page 4 Art Unit: 2189 Application/Control Number: 18/134,006 Page 5 Art Unit: 2189 Application/Control Number: 18/134,006 Page 6 Art Unit: 2189 Application/Control Number: 18/134,006 Page 7 Art Unit: 2189 Application/Control Number: 18/134,006 Page 8 Art Unit: 2189 Application/Control Number: 18/134,006 Page 9 Art Unit: 2189 Application/Control Number: 18/134,006 Page 10 Art Unit: 2189 Application/Control Number: 18/134,006 Page 11 Art Unit: 2189 Application/Control Number: 18/134,006 Page 12 Art Unit: 2189 Application/Control Number: 18/134,006 Page 13 Art Unit: 2189 Application/Control Number: 18/134,006 Page 14 Art Unit: 2189 Application/Control Number: 18/134,006 Page 15 Art Unit: 2189 Application/Control Number: 18/134,006 Page 16 Art Unit: 2189 Application/Control Number: 18/134,006 Page 17 Art Unit: 2189 Application/Control Number: 18/134,006 Page 18 Art Unit: 2189 Application/Control Number: 18/134,006 Page 19 Art Unit: 2189 Application/Control Number: 18/134,006 Page 20 Art Unit: 2189
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