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
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08 July 2025 and 15 April 2026 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because the words “examples,” “example,’” “comprises,” and “comprising” should be avoided. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 an abstract idea without significantly more.
Regarding claim 1, a method enacted on a computing system, the method comprising: receiving a query comprising chemical structure information for a target chemical object; based on the query, inputting the chemical structure information into a trained neural network configured to form embeddings of chemical structures; receiving an embedding for the target chemical object from the trained neural network, the embedding comprising a vector representation of the target chemical object; based at least on a similarity score between the embedding for the target chemical object and each of one or more embeddings stored in a vector database, retrieving query results from the vector database, the query results comprising a set of embeddings and metadata for a corresponding set of chemical objects; and outputting the query results.
Step 1: Directed to an abstract idea. The claim recites mathematical concepts (vector embeddings, similarity scoring) and mental processes (comparing representations of chemical structures and retrieving matching ones) that can be performed in the human mind or with pen-and-paper, merely automated on a computer. Collecting data, generating a mathematical representation, comparing it, and outputting results is a classic abstract idea (similar to cases involving data processing, similarity search, or generic ML inference).
Step 2: No inventive concept. The additional elements are generic: a “computing system,” a “trained neural network,” and a “vector database.” These are conventional computer components performing their ordinary functions (receiving input, computing vectors, looking up similar vectors, outputting results). There is no recitation of a specific technical improvement to the neural network, the database, computer performance, or the chemical domain beyond applying the abstract idea.
Result: ineligible.
Regarding claim 2, further comprising, for each chemical object in the corresponding set of chemical objects of the query results, outputting structural information for the chemical object with the query results.
Step 1: Still directed to the same abstract idea (data retrieval + output).
Step 2: Merely adds conventional output of additional data (structural information). No technical improvement.
Result: ineligible.
Regarding claim 3, wherein retrieving the query results from the vector database comprises determining one or more of a distance, a dot product, or a cosine similarity for the embedding for the target chemical object and an embedding stored in the vector database.
Step 1: Explicitly claims mathematical concepts (standard similarity metrics).
Step 2: These are the conventional mathematical operations used for vector similarity; no inventive concept.
Result: ineligible.
Regarding claim 4, wherein inputting the chemical structure information into the trained neural network comprises inputting the chemical structure information into a graph neural network (GNN).
Step 1: Still abstract (mathematical/ML processing of graph-structured chemical data).
Step 2: Specifying a GNN is a field-of-use limitation or conventional choice for molecular graphs; it does not recite a particular improvement in how the GNN operates or integrates with the database; no inventive concept.
Result: ineligible.
Regarding claim 5, wherein receiving the query comprising the chemical structure information comprises receiving one or more of XYZ data for the target chemical object or a text string representation of the target chemical object.
Step 1: Abstract data-gathering step.
Step 2: Conventional input formats for chemical structures; insignificant extra-solution activity.
Result: ineligible.
Regarding claim 6, further comprising receiving labeled training data for a plurality of chemical objects, the labeled training data comprising a property for each chemical object of the plurality of chemical objects, and further training the neural network to predict the property.
Step 1: Directed to the abstract idea of training a model on labeled data (mathematical optimization / mental process of learning associations).
Step 2: Generic “further training” on labeled data is WURC activity for neural networks. No specific training algorithm, architecture detail, or technical improvement is claimed.
Result: ineligible.
Regarding claim 7, further comprising, after further training the neural network, updating the vector database by using the neural network to determine, for each chemical object of a plurality of chemical objects in the vector database, a predicted value for the property.
Step 1: Abstract idea (using the model to compute predicted values and store them).
Step 2: Conventional post-training inference + database update. No inventive concept.
Result: ineligible.
Regarding claim 8, wherein updating the vector database is performed prior to the receiving the query, receiving the query comprises receiving property information, and retrieving query results from the vector database is further based on the property information and the predicted values for the property for corresponding chemical objects of the query results.
Step 1: Still abstract idea (data processing / filtering).
Step 2: Adds conventional pre-computation and filtering by property values. No technical improvement to the computer or database.
Result: ineligible.
Regarding claim 9, wherein updating the vector database comprises forming a user version of the vector database.
Step 1: Abstract idea.
Step 2: Merely creates a personalized copy of the database—conventional.
Result: ineligible.
Regarding claim 10, wherein the chemical structure information for the target chemical object comprises one or more of structural information for a molecule or structural information for a solid state material.
Step 1: Field-of-use limitation (chemical domain).
Step 2: Does not transform the abstract idea; still generic application.
Result: ineligible.
Regarding claim 11, a method of forming a vector database using a neural network, the method comprising: inputting a set of training data into the neural network, the set of training data comprising structural information for each chemical object of a plurality of chemical objects; training the neural network; and using the trained neural network to form the vector database by generating embeddings of chemical structures, each embedding comprising a vector representation of a chemical structure, and storing the embeddings with metadata in the vector database.
Step 1: Directed to an abstract idea (mathematical generation of embeddings and storage of data).
Step 2: Generic neural-network training + embedding generation + database storage are Well-understood, routine and conventional. No specific technical improvement claimed.
Result: ineligible.
Regarding claim 12, wherein training the neural network comprises using unsupervised training, wherein the unsupervised training comprises masking one or more atoms in a molecule of a training data set and training the neural network to predict the location of the one or more atoms.
Step 1: Mathematical concept / abstract training objective (masked-atom prediction is a known self-supervised technique).
Step 2: Merely recites a specific training task. It is still a conventional ML training technique applied to molecules.
Result: ineligible.
Regarding claim 13, wherein training the neural network comprises using supervised training.
Step 1: Abstract idea.
Step 2: Purely generic. No specific technical improvement claimed.
Result: ineligible.
Regarding claim 14, wherein the supervised training comprises inputting, for each chemical object of the plurality of chemical objects, chemical property information comprising an energy, and wherein training the neural network comprises training the neural network to predict energy.
Step 1: Abstract idea (supervised regression on energy values).
Step 2: Conventional property-prediction task; no inventive concept beyond the field of use.
Result: ineligible.
Regarding claim 15, wherein training the neural network comprises training a graph neural network.
Step 1: Still abstract (mathematical/ML processing of graph-structured chemical data).
Step 2: Specifying a GNN is a field-of-use limitation or conventional choice for molecular graphs; it does not recite a particular improvement in how the GNN operates or integrates with the database; no inventive concept.
Result: ineligible.
Regarding claim 16, a computing system implementing a neural network, the computing system comprising: a logic subsystem; and a storage subsystem comprising a vector database comprising a plurality of embeddings of a corresponding plurality of chemical objects, each embedding comprising a vector representation of a chemical structure for a corresponding chemical object, and metadata comprising chemical object identifications corresponding to the plurality of embeddings, and instructions executable by the logic subsystem to receive a query comprising chemical structure information for a target chemical object, based on the query, input the chemical structure information into the neural network, the neural network configured to produce embeddings of chemical structures, receive, from the trained neural network, an embedding comprising a vector representation of the target chemical object, based on the vector representation of the target chemical object, retrieve query results from the vector database, the query results comprising a set of embeddings of chemical objects, and output the query results.
Step 1: Directed to the same abstract idea as claim 1 (data reception → embedding → similarity retrieval → output), now claimed as a system.
Step 2: The “logic subsystem,” “storage subsystem,” and “vector database” are generic computer components. Instructions that simply implement the abstract idea on conventional hardware do not supply an inventive concept.
Result: ineligible.
Regarding claim 17, wherein the neural network comprises a graph neural network (GNN).
Step 1: Still abstract idea (mathematical/ML processing of graph-structured chemical data).
Step 2: Specifying a GNN is a field-of-use limitation or conventional choice for molecular graphs; it does not recite a particular improvement in how the GNN operates or integrates with the database; no inventive concept.
Result: ineligible.
Regarding claim 18, wherein the instructions executable to receive the query comprise instructions executable to receive one or more of XYZ data for the target chemical object or a text string representation of the target chemical object.
Step 1: Abstract data-gathering step.
Step 2: Conventional input formats for chemical structures; insignificant extra-solution activity.
Result: ineligible.
Regarding claim 19, wherein the instructions are further executable to receive labeled training data for a set of chemical objects, the labeled training data comprising property information for a property for each chemical object of the set of chemical objects, and train the neural network to predict the property.
Step 1: Directed to the abstract idea of training a model on labeled data (mathematical optimization / mental process of learning associations).
Step 2: Generic “further training” on labeled data is WURC activity for neural networks. No specific training algorithm, architecture detail, or technical improvement is claimed.
Result: ineligible.
Regarding claim 20, wherein the instructions are further executable to, after training the neural network, update the vector database by using the neural network to, for each chemical object of the plurality of chemical objects in the vector database, determine a predicted property value for the chemical object, and store the predicted property value in the vector database.
Step 1: Abstract idea (using the model to compute predicted values and store them).
Step 2: Conventional post-training inference + database update. No inventive concept.
Result: ineligible.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Varshney et al (US 12,481,710) disclose a method that includes: generating, based on the request, one or more query tags, the one or more query tags including one or more preferred tags, at least one of the preferred tags specifying an item of interest; generating one or more subqueries based on the one or more query tags; generating, based on the one or more subqueries, a query vector in an embedding space of the vector database; identifying, in the vector database and using the query vector, a plurality of candidate items; and selecting a recommended item from the plurality of candidate items based on the request. Sarshogh et al (US 12,327,616) disclose a system and a method for a training dataset comprising the positive samples and negative samples, that include using the trained molecule embedding module to generate molecule embeddings and for building an end-to-end machine learning model configured to perform molecular embedding analysis and molecular property prediction, the model comprising the trained molecule embedding module and a property prediction module. Wu e al (US 2024/0346315) disclose a method that includes determining a plurality of neural network models each corresponding to one of a plurality of molecular representations, and, for each molecular representation in the plurality of molecular representations, determining, using the neural network model corresponding to the molecular representation, a molecular property prediction result and prediction confidence corresponding to unlabeled data in an unlabeled data set, obtaining at least a portion of the unlabeled data as reference unlabeled data, the reference unlabeled data having corresponding prediction confidence higher than a preset threshold, and determining, based on the reference unlabeled data and a molecular property prediction result corresponding to the reference unlabeled data, pseudo-labeled data of a neural network model corresponding to another molecular representation in the plurality of molecular representations.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AN H DO whose telephone number is (571)272-2143. The examiner can normally be reached on M-F 7:00am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ricardo Magallanes can be reached on 571-272-5960. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AN H DO/Primary Examiner, Art Unit 2853
