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 .
DETAILED ACTION
This is the first action on the merits. Claims 1-20 are currently pending.
Priority
This application claims priority from Provisional Application Nos. 63302418 dated 01/24/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/24/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to for the following informality: “A computing system for evaluating candidate molecules for use in candidate compound discovery, the computer system comprising:..” should read “A computing system for evaluating candidate molecules for use in candidate compound discovery, the computing system comprising:…”
Claims 2-13 are objected to for the following informality: “The system of claim …” should read “The computing system of claim …”
Claims 10 and 18 are objected to for the following informality: “… define/ing a virtual network configured to facilitate communication between at least one database…” should read “… define/ing a virtual network configured to facilitate communication between the at least one database…”
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 14 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a system, a method and a non-transitory computer-readable medium (CRM) for predictive candidate compound discovery, which are within a statutory category.
Step 2A1
Regarding claims 1, 14 and 20, the limitation of (claim 1 being representative) receive a type of standardized data, wherein the standardized data is for compounds, metals, molecules, atoms, sub-atomic particles or any combination thereof, and wherein the standardized data types comprise numerical data sets, images, graphs, text, or any combination thereof; receive a user query, wherein the user query comprises a request for a desired attribute for the candidate compound; process each of the standardized data types to recommend at least one of a plurality of the candidate molecules to be bonded with the candidate compound based on the user query; generate an interactive environment comprising graphical representation of the candidate molecule and candidate compound based on the user query; based on a received a signal from a user, alter a configuration of the candidate compound to allow the user to interact with the candidate compound and the plurality of candidate molecules, wherein based on a received additional signal from the user, further alter the candidate compound with another of the plurality of molecules; and generate clinical characteristics of the candidate compound based on the further alteration by the user as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. That is other than reciting (in claim 1) a computing system, a computer system, a non-transitory computer-readable memory and a processor, (in claim 14) a processor, or (in claim 20) a non-transitory computer-readable medium and a computing device, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the recited computer components, the claims encompasses receiving a type of standardized data, receiving a user query, processing each of the standardized data types to recommend at least one of a plurality of the candidate molecules to be bonded with the candidate compound, generating an interactive environment, altering a configuration of the candidate compound and generating clinical characteristics of the candidate compound in the manner described in the identified abstract idea(s), supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claims further recites “one or more trained machine learning models.” When given its broadest reasonable interpretation in light of the disclosure, the one or more trained machine learning models, by processing each of the standardized data types to recommend at least one of a plurality of the candidate molecules to be bonded with the candidate compound, represents the creation of mathematical interrelationships between data (see Specification at para. [0013]-[0016], [0076], [0086], [0092]-[0093], [0098], [00112]). As such, one or more trained machine learning models represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of a computing system, a computer system, a non-transitory computer-readable memory and a processor. Claim 14 recites the additional element of a processor. Claim 20 recites the additional elements of a non-transitory computer-readable medium and a computing device. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computers or components thereof. See Specification at [0013], [0061], [0063], [0064], [0069]) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1, 14 and 20 further recite the additional elements of at least a database. This additional element represents a location to which data is received from. Each of receiving steps are recited at a high level of generality (i.e. a general means to receive data from) and amount to extra solution activity. MPEP 2106.04(d)(1) indicates that extra solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Claims 1, 14 and 20 further recite the additional elements of using virtual reality (VR), augmented reality (AR) or both. This additional element merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Claims 1, 14 and 20 further recite the additional elements of one or more trained machine learning models. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Alternatively or in addition, the implementation of the trained machine learning model to process each of the standardized data types to recommend at least one of a plurality of the candidate molecules to be bonded with the candidate compound merely confines the use of the abstract idea (i.e., the one or more trained machine learning models) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing system, a computer system, a non-transitory computer-readable memory, a processor, a non-transitory computer-readable medium and a computing device to perform the noted steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas do not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
As discussed with respect to integration of the abstract idea into a practical application, the additional element of at least a database from which data is received was considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Also as discussed with respect to integration of the abstract idea into a practical application, the additional element of using virtual reality (VR), augmented reality (AR) or both was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Furthermore, the prior art of record indicates that using virtual reality (VR) or augmented reality (AR) to interact with the candidate compound and the plurality of candidate molecules is well-understood, routine, conventional activity (see Cai (WO 2005/024756) at abstract and pages 3 and 4 and Feinberg (US 2019/0272468) at [0043], [0072], [0075], [0077]). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional element of one or more trained machine learning models to process each of the standardized data types to recommend at least one of a plurality of the candidate molecules to be bonded with the candidate compound was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the one or more trained machine learning models) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-13 and 15-19 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 and 15 further merely describe(s) selecting one of the one or more machine learning models based on the type of standardized data received. Claim(s) 2 and 15 also include the additional elements of “a neural net model”, “a convolutional neural network model” and “a natural language processing with neural nets model” which are interpreted the same as the one or more machine learning models and do not provide practical application nor significantly more. Claim(s) 3 and 16 further merely describe(s) pooling/combining the trained data sets and performing a Pareto analysis. Claim(s) 4 further merely describe(s) processing the trained data sets. Claim(s) 4 also include the additional element of “a recurrent neural network model” which is interpreted the same as the one or more machine learning models and does not provide practical application nor significantly more. Claim(s) 5 further merely describe(s) the standardized data. Claim(s) 6 further merely describe(s) to load normalize the standardized data received. Claim(s) 7 further merely describe(s) transforming the standardized data and tag, index and assign a value to each of the data set. Claim(s) 8 and 17 further merely describe(s) generate, using a neural net, a first user interface; load a menu with recommended molecules; and order the recommended molecules in the menu. Claim(s) 8 and 17 also include the additional elements of “a neural net” and “a first user interface” which equate to saying “apply it” and they do not provide practical application nor significantly more. Claim(s) 9 further merely describe(s) loading a second layer menu; order the recommended metals. Claim(s) 9 also include the additional element of “a first user interface” which equates to saying “apply it” and does not provide practical application nor significantly more. Claim(s) 10 and 18 further merely describe(s) defining a virtual network configured to facilitate communication between at least a database and a plurality of servers. Claim(s) 10 and 18 also include the additional element of “a virtual network” and “a plurality of servers” which are interpreted as generic computer components and they do not provide practical application nor significantly more. Claim(s) 13 further merely describe(s). Claim(s) 11 further merely describe(s) generating a caching layer. Claim(s) 12 and 19 further merely describe(s) receiving a request at a load balancer, and evaluating listener rules; selecting a target; executing a cloud deep learning model. Claim(s) 12 and 19 also include the additional element of “a cloud deep learning model” which is interpreted the same as the one or more machine learning models and does not provide practical application nor significantly more. Claim(s) 13 further merely describe(s) creating on-demand instance and a plurality of stacks for a streaming application and associate a fleet.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wei (US 2021/0027862) and in further view of Cai (WO 2005/024756).
REGARDING CLAIM 1
Wei discloses a computing system for evaluating candidate molecules for use in candidate compound discovery, the computer system comprising: a non-transitory computer-readable memory; and a processor configured to execute instructions stored on the non-transitory computer-readable memory which, when executed, cause the processor to ([0011]-[0016]): receive a type of standardized data from at least a database, wherein the standardized data is for compounds, metals, molecules, atoms, sub-atomic particles or any combination thereof, and wherein the standardized data types comprise numerical data sets, images, graphs, text, or any combination thereof ([0062] teaches machine learning systems may characterize molecules (e.g., biomolecules) in order to identify/predict one or more characteristics of those molecules (e.g., partition coefficient, aqueous solubility, toxicity, protein binding affinity, drug virtual screening, protein folding stability changes upon mutation, protein flexibility (B factors), solvation free energy, plasma protein binding affinity, and protein-protein binding affinity, among others) using, for example, algebraic topology (e.g., persistent homology or ESPH) and graph theory based approaches. [0094] teaches that once a defined set of feature data corresponding to the protein-ligand complex has been compiled, a machine learning algorithm may receive the set of feature data as an input, process the feature data, and output an estimate of the binding affinity of the protein-ligand complex and teaches training using a database containing thousand entries of protein-ligand binding affinity data and corresponding protein-ligand complex atomic structure data (interpreted by examiner as receive a type of standardized data from at least a database, wherein the standardized data is for compounds, metals, molecules, atoms, sub-atomic particles or any combination thereof, and wherein the standardized data types comprise numerical data sets, images, graphs, text, or any combination thereof)); receive a user query, wherein the user query comprises a request for a desired attribute for the candidate compound ([0287] teaches user input to the computer system. For example, the target clinical application may correspond to a lead drug candidate to be discovered from among a class of candidate compounds and tested. [0288] teaches a set of one or more characteristics may be defined (e.g., via user input) and received by the computer system(s). Thus, the set of characteristics may be referred to herein as a set of “desired” characteristics (interpreted by examiner as receive a user query, wherein the user query comprises a request for a desired attribute for the candidate compound)); process each of the standardized data types with one or more trained machine learning models, wherein the one or more machine learning models are configured to recommend at least one of a plurality of the candidate molecules to be bonded with the candidate compound based on the user query ([0101] teaches the trained machine learning model outputs a predicted binding affinity value for the protein-ligand and identifying potential drug candidates for applications with defined binding affinity requirements. [0287] teaches for example, the target clinical application may correspond to a lead drug candidate to be discovered from among a class of candidate compounds and tested and [0288] teaches the set of characteristics may include characteristics that would be exhibited by a drug candidate that would be applicable for the target clinical application. [0290] teaches the set of compounds may be identified automatically based on the defined class of compounds, the set of desired characteristics, and the target application. [0292] teaches data processed by a set of trained machine learning algorithms/models. [0298] teaches a class of compounds containing, for example, 100 different compounds may be screened to identify a subset of compounds containing only 5 different compounds, which may beneficially speed up the process of identifying applicable drug candidates (interpreted by examiner as process each of the standardized data types with one or more trained machine learning models to recommend at least one of a plurality of the candidate molecules to be bonded with the candidate compound based on the user query)); generate an interactive environment comprising graphical representation of the candidate molecule and candidate compound based on the user query ([0061] teaches low-dimensional differential geometry representation of high-dimensional molecular structures can then be paired with machine learning algorithms to predict drug-discovery related molecular properties of interest, such as the free energies of solvation, protein-ligand binding affinities, and drug toxicity (interpreted by examiner as generate an interactive environment comprising graphical representation of the candidate molecule and candidate compound based on the user query)); and generate clinical characteristics of the candidate compound based on the further alteration by the user ([0011] teaches predicted characteristic values for each compound of the set of compounds and [0288] teaches a set of one or more characteristics may be defined (e.g., via user input) and received by the computer system(s). The set of characteristics may include characteristics that would be exhibited by a drug candidate that would be applicable for the target clinical application (interpreted by examiner as generate clinical characteristics of the candidate compound based on the further alteration by the user)).
Wei does not explicitly disclose, however Cai discloses:
based on a received a signal from a user, alter a configuration of the candidate compound to allow the user, using virtual reality (VR), augmented reality (AR) or both, to interact with the candidate compound and the plurality of candidate molecules, wherein based on a received additional signal from the user, further alter the candidate compound with another of the plurality of molecules (Cai at Fig. 2 teaches virtual reality molecular images. [page 3, para. 6] teaches interacting virtually with the modelled molecule structure to allow an interactive user to interact with the structure in a VR environment. [page 3, para. 16] teaches the protein structure at both molecular and atom levels may be modified by the virtual user or viewer. [page 4, para. 1] teaches in an alternative aspect, the invention consists in a method of providing virtual interaction with a molecule comprising modeling a molecule in three dimensions, displaying the molecule in a stereo VR environment, and interacting with the molecule by virtual movement, modification, or replacement of protein structures and molecule atoms. [page 5, para. 8] teaches the unit is comprised of typical VR interactive devices and basic functions required, however, are "picking up" graphic entities of the bio-molecular structures and "moving" or "modifying" the entities selected. The glove should also be able to work in a 3D stereo VR environment with enough functional keys (programmable) to support "deleting" and "inserting" bio-molecular entities selected (interpreted by examiner as alter a configuration of the candidate compound to allow the user, using virtual reality (VR), augmented reality (AR) or both, to interact with the candidate compound and the plurality of candidate molecules, wherein based on a received additional signal from the user, further alter the candidate compound with another of the plurality of molecules));
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods for drug design and discovery of Wei to incorporate virtual reality interactions as taught by Cai, with the motivation of offering a new channel for active participation and interactive communication during the process of bio- molecular education and research (Cai at [page 3, para. 2]).
REGARDING CLAIM 2
Wei and Cai disclose the limitation of claim 1.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 1, wherein the instructions, when executed, further cause the processor to: based on the type of the standardized data received, select one of the one or more machine learning models to output a trained data set, wherein: if the standardized data is the numerical data set, execute a neural net model; if the standardized data set is an image, execute a convolutional neural network model; if the standardized data set is a graph, execute a multilayer perceptron model; and If the standardized data is a text, execute a natural language processing with neural nets model (Wei at [0011] teaches process the sets of feature data with one or more trained machine learning models to produce predicted characteristic values for each compound of the set of compounds for each of the set of desired characteristics, the one or more trained machine learning models being selected based on at least the set of desired characteristics. [0016] teaches the one or more trained machine learning models may be selected from a database of trained machine learning models. The one or more trained machine learning models may include at least one trained machine learning model corresponding to a machine learning algorithm selected from the group including: a gradient boosted regression trees algorithm, a deep neural network, and a convolutional neural network and [0234] teaches the trained machine learning network may be trained to predict protein flexibility of protein dynamical systems based on the feature data (interpreted by examiner as based on the type of the standardized data received, select one of the one or more machine learning models to output a trained data set wherein: if the standardized data is the numerical data set, execute a neural net model; if the standardized data set is an image, execute a convolutional neural network model; if the standardized data set is a graph, execute a multilayer perceptron model; and If the standardized data is a text, execute a natural language processing with neural nets model)).
REGARDING CLAIM 3
Wei and Cai disclose the limitation of claim 2.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 2, wherein the instructions, when executed, further cause the processor to: pool the trained data sets and combine the sets; and perform a Pareto analysis to output a predicted most accurate of the data sets (Wei at [0169] teaches a geometric graph model that offers accurate and reliable protein flexibility analysis and B-factor prediction. [0244] teaches a differential geometry based geometric data analysis (DG-GDA), as will be described, may provide an accurate, efficient and robust representation of molecular and biomolecular structures and their interactions. [0326] teaches pooling units can perform variety of pooling functions, including max pooling, average pooling, and L2-norm pooling (interpreted by examiner as pool the trained data sets and combine the sets; and perform a Pareto analysis to output a predicted most accurate of the data sets)).
REGARDING CLAIM 4
Wei and Cai disclose the limitation of claim 3.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 3, wherein the instructions, when executed, further cause the processor to: further process the trained data sets using a recurrent neural network model to form a loop for convergence (Wei at [0094] teaches recurrent neural network could be trained and used (interpreted by examiner as process the trained data sets using a recurrent neural network model to form a loop for convergence)).
REGARDING CLAIM 5
Wei and Cai disclose the limitation of claim 1.
Wei Cai does not explicitly disclose, however Wei Cai further discloses:
The system of claim 1, wherein the standardized data comprises bonding angles, electron, proton and neutron configurations, melting point, toxicity, physical characteristic, chemical characteristic, atomical characteristic, biological dimensions, or any combination thereof ([0014] teaches characteristics may include one or more toxicity endpoints and [0105] teaches chemical and biological properties).
REGARDING CLAIM 6
Wei and Cai disclose the limitation of claim 1.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 1, wherein the instructions, when executed, further cause the processor to load normalize the standardized data received (Wei at [0296] teaches predicted values/scores for each of these characteristics for a given compound of the set of compounds may be normalized and averaged (interpreted by examiner as cause the processor to load normalize the standardized data received)).
REGARDING CLAIM 7
Wei and Cai disclose the limitation of claim 1.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 1, wherein the instructions, when executed, further cause the processor to: transform the standardized data into numerical data sets; and tag, index and assign a value to each of the data set based on the user query (Wei at [0296] teaches predicted values/scores for each of these characteristics for a given compound of the set of compounds may be normalized and averaged (e.g., using a weighted average in some embodiments to differentiate between desired characteristics having different levels of importance) to calculate an aggregate score, and the given compound may be assigned an aggregate ranking based on how its aggregate score compares to the aggregate scores of the other compounds of the set of compounds (interpreted by examiner as transform the standardized data into numerical data sets; and tag, index and assign a value to each of the data set based on the user query)).
REGARDING CLAIM 8
Wei and Cai disclose the limitation of claim 3.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 3, wherein the instructions, when executed, further cause the processor to: generate, using a neural net, a first user interface; subsequent the user input, load a menu on the user interface with recommended molecules based on the user input using the neural net; and order the recommended molecules in the menu based on a predictive success parameter using the trained data sets (Wei at [0011] teaches display an ordered list of the subset of the set of compounds via an electronic display (interpreted by examiner as the first user interface) and [0016] teaches The one or more trained machine learning models may include at least one trained machine learning model corresponding to a machine learning algorithm selected from the group including: a gradient boosted regression trees algorithm, a deep neural network, and a convolutional neural network. [0298] teaches only the compounds having aggregate scores and/or characteristic predicted values/scores (e.g., depending on the ordered list being considered) above a threshold of 90% (e.g., 90% of the maximum aggregate score for an aggregate ordered list, or 90% of the maximum characteristic predicted score/value for a characteristic ordered list) and/or only the compounds having the top five scores out of the set of compounds may be included in the subset and displayed. In this way, a class of compounds containing, for example, 100 different compounds may be screened to identify a subset of compounds containing only 5 different compounds, which may beneficially speed up the process of identifying applicable drug candidates (interpreted by examiner as subsequent the user input, load a menu on the user interface with recommended molecules based on the user input using the neural net; and order the recommended molecules in the menu based on a predictive success parameter using the trained data sets)).
REGARDING CLAIM 9
Wei and Cai disclose the limitation of claim 8.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 8, wherein the instructions, when executed, further cause the processor to: load a second layer menu on the first user interface; order the recommended metals on the second layer menu based the predictive success parameter using the trained data sets (Wei [0011] teaches display an ordered list of the subset of the set of compounds via an electronic display (interpreted by examiner as the first user interface) and [0012] teaches the ordered list is ordered according to the assigned rankings. [0298] teaches only the compounds having aggregate scores and/or characteristic predicted values/scores (e.g., depending on the ordered list being considered) above a threshold of 90% (e.g., 90% of the maximum aggregate score for an aggregate ordered list, or 90% of the maximum characteristic predicted score/value for a characteristic ordered list) and/or only the compounds having the top five scores out of the set of compounds may be included in the subset and displayed. In this way, a class of compounds containing, for example, 100 different compounds may be screened to identify a subset of compounds containing only 5 different compounds, which may beneficially speed up the process of identifying applicable drug candidates (interpreted by examiner as load a second layer menu on the first user interface; order the recommended metals on the second layer menu based the predictive success parameter using the trained data sets)).
REGARDING CLAIM 10
Wei and Cai disclose the limitation of claim 9.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 9, wherein the instructions, when executed, further cause the processor to: define a virtual network configured to facilitate communication between at least a database and a plurality of servers having the processor and the memory in communication therewith (Wei at [0340] teaches a cloud-based server cluster. In FIG. 22, operations of a computing device (e.g., computing device 2100) may be distributed between server devices 2202, data storage 2204, and routers 2206, all of which may be connected by local cluster network 2208. The server cluster 2200 may, for example, be configured to execute (e.g., via computer processors of the server devices 2202 thereof) [0343] teaches [0343] Routers 2206 may include networking equipment configured to provide internal and external communications for server cluster 2200. For example, routers 2206 may include one or more packet-switching and/or routing devices (including switches and/or gateways) configured to provide (i) network communications between server devices 2202 and data storage 2204 via cluster network 2208, and/or (ii) network communications between the server cluster 2200 and other devices via communication link 2210 to network 2212 (interpreted by examiner as define a virtual network configured to facilitate communication between at least a database and a plurality of servers having the processor and the memory in communication therewith)).
REGARDING CLAIM 11
Wei and Cai disclose the limitation of claim 1.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 1, wherein the instructions, when executed, further cause the processor to: when generating the interactive environment, further generate a caching layer to construct a spider graph, radar chart, or both (Wei at [0291] teaches the set of compounds (or the specifically-identified individual compound) may be pre-processed to generate sets of feature data. For each compound of the set of compounds, calculating barcodes (e.g., TF/ESTF/ASTF/interactive ESPH/EH/electrostatic PH barcodes) or other fingerprints for each compound, calculating BBR or Wasserstein/bottleneck distance for each compound, calculating/identifying auxiliary features for each compound, generating multiscale weighted colored graphs for each compound using graph theory based approaches (interpreted by examiner as generate a caching layer to construct a spider graph, radar chart, or both)).
REGARDING CLAIM 12
Wei and Cai disclose the limitation of claim 1.
Wei does not explicitly disclose, however Cai further discloses:
The system of claim 1, wherein the instructions, when executed, further cause the processor to: receive a request at a load balancer, and evaluate listener rules in a priority order to determine which of the listener rules to apply; select a target from a target group for listener rule to route requests to different target groups based on the content of application traffic; execute a cloud deep learning model to run a three-dimensional rendering script to produce the interactive environment comprising the candidate molecule in atomic resolution (Cai at [abstract] teaches a system for studying molecules provides a virtual three-dimensional map of a molecule and allows a virtual user to move through the molecule. A user may also manipulate portions of the molecule using a VR sensor glove after riding closer to the targeted site of a particular ligand, a peptide atom, an amino acid, etc. A user may actively cross section the molecule to understand the multiple features and their relation of the molecule. [page 3, para. 6] teaches interacting virtually with the modelled molecule structure to allow an interactive user to interact with the structure in a VR environment. [page 5, para. 8] teaches "picking up" graphic entities of the bio-molecular structures and "moving" or "modifying" the entities selected. The glove should also be able to work in a 3D stereo VR environment with enough functional keys (programmable) to support "deleting" and "inserting" bio-molecular entities selected and [page 6, para. 5] teaches LOD (level of detail) is preferably also adopted here to improve the efficiency of graphical visualization and interaction (interpreted by examiner as )).
REGARDING CLAIM 13
Wei and Cai disclose the limitation of claim 12.
Cai does not explicitly disclose, however Wei further discloses:
The system of claim 12, wherein the instructions, when executed, further cause the processor to: create on-demand instance and a plurality of stacks for a streaming application and associate a fleet comprising a plurality of streaming instances, wherein the stack comprises an associated fleet to produce the candidate molecule and corresponding sub-atomic particles in a sub-atomic visualization (Wei at [0242] teaches ∏-∏ stacking and [0244] teaches The low-dimensional differential geometry representation of high-dimensional molecular structures is paired with machine learning algorithms to predict drug-discovery related molecular properties of interest, such as the free energies of solvation, protein-ligand binding affinities, and drug toxicity (interpreted by examiner as create on-demand instance and a plurality of stacks for a streaming application and associate a fleet comprising a plurality of streaming instances, wherein the stack comprises an associated fleet to produce the candidate molecule and corresponding sub-atomic particles in a sub-atomic visualization)).
REGARDING CLAIMS 14-20
Claims 14-20 are analogous to Claims 1-13 thus Claims 14-20 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 1-13.
Conclusion
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Feinberg (US 2019/0272468) teaches systems and methods for spatial graph convolutions with applications to drug discovery.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683