DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-2, 6-13 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) elements which under their broadest reasonable interpretation are directed to mental processes. This judicial exception is not integrated into a practical application as explained below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as explained below.
Regarding Claim 1, the claim recites a method, comprising:
using an artificial intelligence/machine learning (AI/ML) model to map content between an interface of a first entity for interaction with other entities and an interface of a second entity for interaction with other entities, and/or classify content of the interface of the first entity and/or of the interface of the second entity; and
obtaining, from the AI/ML model, a first output indicative of a result of the mapping of the content, and/or of a result of the classification of the content.
Claim Interpretation: Under the broadest reasonable interpretation, the terms of the claim are
presumed to have their plain meaning consistent with the specification as it would be interpreted by
one of ordinary skill in the art. See MPEP 2111.
Claim element a as written covers any method for using an AI model to map content between entities
Claim element b as written covers any method for obtaining a result from the AI model
Additional elements recited is AI/ML model.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory
category. See MPEP 2106.03. The claim is directed to a method, which falls within one of the statutory
categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a
judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception
when the judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest reasonable interpretation of limitations a and b is that those limitations fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including
observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2),subsection III.
Claim element a is directed to a mental step since a human can map content between interfaces.
Claim element b is directed to a mental step since a human obtain an output regarding a mapping through reading the output through a piece of paper or through hearing it from another human.
Hence, these steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making determinations and identifications, which are mental tasks humans routinely do,' ” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). Therefore, these limitations are considered together as an abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole
integrates the recited judicial exception into a practical application of the exception or whether the
claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether
there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating
those additional elements individually and in combination to determine whether the claim as a whole
integrates the exception into a practical application. See MPEP 2106.04(d). The additional element recited is AI/ML model,. The additional element provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e. the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Further, obtaining a result is an insignificant extra-solutional activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amount to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
At Step 2A the additional element was found to represent no more than mere instructions to apply the judicial exception on a computer suing generic computer components. Mere instructions to “apply” the abstract ideas, cannot provide an inventive concept. See MPEP 2106.05(f). Further, obtaining a result was found to be an insignificant extra-solutional activity. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the re- evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). Obtaining a result is a well understood, routine and conventional activity in the field at the time of the effective filing date of the claimed invention. This claim element remains an insignificant extra solutional activity even upon reconsideration and does not amount to significantly more. The analysis under Step 2A Prong Two is carried through to Step 2B. Even when considered in combination, these additional limitations merely confine the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) (Step 2B: NO).
As such Claim 1 is not patent eligible.
Regarding Claim 2, a human can map content between engineering libraries.
Regarding Claim 6, the human mind can first classify content before mapping said content.
Regarding Claim 7, arguments are analogous to Claim 6 with the addition that humans can map concepts.
Regarding Claim 8, the human mind can perform 1 to 1 matching between different concepts
Regarding Claim 9, a human can perform named entity recognition
Regarding Claim 10, a knowledge-based component is a generic computer component recited at a high level of generality.
Regarding Claim 11, a human can decide if a result meets an uncertainty threshold and decide whether or not to accept the result.
Regarding Claim 12, arguments analogous to that of Claim 7 are applicable.
Additionally, Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the computer readable medium claimed is directed to signals per se.
Applicant is advised to amend the claim to limit the computer readable medium to a non-transitory computer readable medium as supported by the specification.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 6-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Meduri (ALFA:active learning for graph neural network-based semantic schema alignment).
Regarding Claim 1, Meduri teaches a method, comprising:
using an artificial intelligence/machine learning (AI/ML) model(We then introduce a GNN
based supervised model for schema alignment where the schemas are represented as ontologies.[3 Preliminaries and system overview], MLP Classifier[Figure 4]) to map content(schema elements[4.1 Ontology-aware sample selection]) between an interface of a first entity(semantic schemas (ontologies) OL[3.5 ALFA system overview], Human anatomy[5.1.1 Datasets]) for interaction with other entities and an interface of a second entity(semantic schemas (ontologies) OR[3.5 ALFA system overview], Mouse anatomy [5.1.1 Datasets]) for interaction with other entities(Semantic schema alignment aims to match elements across a pair of schemas based on their semantic representation[Abstract]), and/or classify content of the interface of the first entity and/or of the interface of the second entity; and
obtaining, from the AI/ML model, a first output indicative of a result of the mapping(the MLP
takes the embeddings for the pair of concepts as input and predicts the matching probability for the pair at the(sigmoid) layer. The pair is deemed as a match if the matching probability is above 0.5.[3.3 GNN-based semantic schema alignment]) of the content(Finally the set of candidate pairs is returned[4.2 Ontology aware label propagation]), and/or of a result of the classification of the content.
Regarding Claim 2, Meduri as in Claim 1 further teaches the first entity is at least one of a first engineering library, a first standard, a first schema(semantic schemas (ontologies) OL[3.5 ALFA system overview], Human anatomy[5.1.1 Datasets]), and a first file format, and wherein the second entity is at least one of a second engineering library, a second standard, a second schema(semantic schemas (ontologies) OR[3.5 ALFA system overview], Mouse anatomy [5.1.1 Datasets]), and a second file format.
Regarding Claim 6, Meduri as in Claim 1 teaches the obtaining of the first output(the MLP
takes the embeddings for the pair of concepts as input and predicts the matching probability for the pair at the(sigmoid) layer. The pair is deemed as a match if the matching probability is above 0.5.[3.3 GNN-based semantic schema alignment]) is based on a one-step-approach or on a two-step-approach, the first output being a result of a mapping according to the one-step-approach or of a mapping according to the two-step- approach,
the one-step-approach comprising using the AI/ML model to map the content(a GNN-based supervised model for schema alignment[3. Preliminaries and Overview]) between the interface of the first entity(semantic schemas (ontologies) OL[3.5 ALFA system overview], Human anatomy[5.1.1 Datasets]) and the interface of the second entity(semantic schemas (ontologies) OR[3.5 ALFA system overview], Mouse anatomy [5.1.1 Datasets]) given inputs(schemas inherently have an input) and outputs(node (i.e., a schema element)[3.3 GNN-based semantic schema alignment] , Interpreted as the content) provided via the respective interfaces, the inputs and outputs related to the content;
the two-step-approach comprising using the AI/ML model to classify the content of the interface of the first entity and/or of the interface of the second entity given inputs and outputs provided via at least one of the respective interfaces, the inputs and outputs related to the content; and
using the AI/ML model to map the classified inputs and the classified outputs to the interface of the first entity and/or the interface of the second entity.
Regarding Claim 7, Meduri as in Claim 1 teaches the mapping and/or classifying given the inputs and the outputs comprises:
inputting a first entity input and/or a first entity output(node (i.e., a schema element)[3.3 GNN-based semantic schema alignment], ) related to the content of the interface of the first entity (semantic schemas (ontologies) OL[3.5 ALFA system overview], Human anatomy[5.1.1 Datasets]) into the AI/ML model(RGCN+MLP-based alignment model[4.1 Ontology-aware sample selection]), wherein the first entity input and/or the first entity output is related to a first content(node (i.e., a schema element)[3.3 GNN-based semantic schema alignment], ) element of the first entity,
the first content element being at least one of a first name, a first class, a first concept(schema element (concept) pairs[3.5 ALFA system overview]), a first relation and a first parameter, and the first entity representing a source entity;
based on the first entity input and/or the first entity output, receiving a model output from the AI/ML model, wherein the model output(the MLP takes the embeddings for the pair of concepts as input and predicts the matching probability for the pair at the(sigmoid) layer. The pair is deemed as a match if the matching probability is above 0.5.[3.3 GNN-based semantic schema alignment]) is related to a second content element(node (i.e., a schema element)[3.3 GNN-based semantic schema alignment], where this schema is (semantic schemas (ontologies) OR[3.5 ALFA system overview], Mouse anatomy [5.1.1 Datasets]) ) different from the first content element, the second content element being at least one of a second name, a second class, a second concept(schema element (concept) pairs[3.5 ALFA system overview]), a second relation and a second parameter, and/or wherein the model output is based on a training of the AI/ML model; and
determining, for the model output, a match(the MLP takes the embeddings for the pair of concepts as input and predicts the matching probability for the pair at the(sigmoid) layer. The pair is deemed as a match if the matching probability is above 0.5.[3.3 GNN-based semantic schema alignment]) to a second entity input and/or a second entity output(node (i.e., a schema element)[3.3 GNN-based semantic schema alignment]) related to the content of the interface of the second entity( (semantic schemas (ontologies) OR[3.5 ALFA system overview], Mouse anatomy [5.1.1 Datasets]) ) in relation to at least one of a name, a class, a concept(schema element (concept) pairs[3.5 ALFA system overview]), a relation and a parameter, wherein the second entity represents a target entity,
wherein the model output is associated with the first output.
Regarding Claim 8, Meduri as in Claim 1 teaches the determining of the matching comprises at least one of:
determining a 1-to-1-matching between the first entity input and/or the first entity output and the second entity input and/or the second entity output (the MLP takes the embeddings for the pair of concepts as input and predicts the matching probability for the pair at the(sigmoid) layer. The pair is deemed as a match if the matching probability is above 0.5.[3.3 GNN-based semantic schema alignment]);
determining a 1-to-many-matching between the first entity input and/or the first entity output and the second entity input and/or the second entity output;
determining a many-to-i-matching between the first entity input and/or the first entity output and the second entity input and/or the second entity output; and
determining a many-to-many-matching between the first entity input and/or the first entity output and the second entity input and/or the second entity output.
Regarding Claim 9, Meduri as in Claim 1 teaches using the AI/ML model comprises making use of one of: Joint embeddings and nearest neighbor-search, Named Entity Recognition, NER, and Graph-Neural Networks(GNN
based supervised model for schema alignment[3 Preliminaries and system overview]), Graph-NNs.
Regarding Claim 10, Meduri as in Claim 1 teaches using a knowledge- based component associated(RGCN/GNN [Figure 4]) with the AI/ML model(RGCN+MLP Classifier [Figure 4]), which uses underlying knowledge representation systems to exploit(Alfa exploits the schema element properties as well as the relationships between schema elements[Abstract]) graph-contained information via the interface of the first entity and/or the interface of the second entity(Figure 4 discloses first and second entity embeddings input into the GNN); and further considering the exploited graph- contained information for the mapping(the MLP
takes the embeddings for the pair of concepts as input and predicts the matching probability for the pair at the(sigmoid) layer. The pair is deemed as a match if the matching probability is above 0.5.[3.3 GNN-based semantic schema alignment]) and/or the classification related to the interface of the first entity and/or the interface of the second entity.
Regarding Claim 11, Meduri as in Claim 1 teaches the result indicated by the first output comprises an uncertainty value equal to or above a predetermined uncertainty threshold, providing the result to a user for confirmation (If the sigmoid function emits a probability close to 0.5, this indicates that the MLP is unsure about the label of this pair of tuples, which should be assigned to an oracle for labeling[2.2 Active Learning]), and feeding a decision received by the user about whether or not to accept the result(The pair receives a high ambiguity score of 0.75 (i.e., 1.0—model.PredProb) and is sent to the human oracle who labels it as a match[4.1 Ontology-aware sample selection]) back to a knowledge-base associated with the AI/ML model(Training Pairs [Figure 4]) and/or to the AI/ML model.
Regarding Claim 12, Meduri as in Claim 1 teaches the content is related to at least one of concepts(pair of concepts[3.3 GNN-based semantic schema alignment]), naming conventions, classes, relations, parameters, units, method signatures and method functionalities.
Regarding Claim 13, while Meduri does not explicitly teach the use of a computer readable medium to perform the method of Claim 1, the use of a computer readable medium is embodied in the art. The remaining analysis is analogous to that of Claim 1.
Claim Rejections - 35 USC § 103
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.
Claim(s) 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Meduri(ALFA:active learning for graph neural network-based semantic schema alignment) in view of Pang(US PGPub 20220293272).
Regarding Claim 3, Meduri teaches using pre-trained embeddings(RGCN takes the initial USE embeddings of each concept[3.3 GNN-based semantic schema alignment]) to map content between an interface of a third entity(semantic schemas (ontologies) OL[3.5 ALFA system overview], FMA [5.1.1 Datasets]) for interaction with other entities and an interface of a fourth entity(semantic schemas (ontologies) OL[3.5 ALFA system overview], NCI [5.1.1 Datasets]) for interaction with other entities, and/or classify content of the interface of the third entity and/or of the interface of the fourth entity;
obtaining, from the pre-trained embeddings(RGCN takes the initial USE embeddings of each concept[3.3 GNN-based semantic schema alignment]), a second output indicative of a result of the mapping(the MLP takes the embeddings for the pair of concepts as input and predicts the matching probability for the pair at the(sigmoid) layer. The pair is deemed as a match if the matching probability is above 0.5.[3.3 GNN-based semantic schema alignment]) of the content(Finally the set of candidate pairs is returned[4.2 Ontology aware label propagation]), and/or of a result of the classification of the content;
training and operating the AI/ML model(Binary Cross Entropy Loss, update [Figure 4]) based on a user feedback(Oracle[Figure 4]) received in relation to the second output, and/or based on leveraged labeled data(Label pairs, Labeled training pairs [Figure 4]) related to the second output; extending the pre-trained embeddings by the AI/ML model(compact node embeddings produced by the RGCN model in each AL iteration[4.1 Ontology-aware sample selection] Interpretation: after output embeddings are updated/extended with each iteration).
Meduri does not teach operating the AI/ML model in shadow mode nor does Meduri teach if the AI/ML model in shadow mode achieves a predetermined maturity,
However, Pang teaches operating the AI/ML model in shadow mode(the model is deployed in shadow mode[0012]) and if the AI/ML model in shadow mode achieves a predetermined maturity(shadow deployed branch passes QA evaluation[0112]),
It would have been obvious to a person having ordinary skill in the art at the time of the effective filing date of the claimed invention to combine the method of Meduri with the shadow mode usage of Pang because it would allow one to test machine learning systems without risk(Pang 0112)
Regarding Claim 4, arguments analogous to that of Claim 3 are applicable.
Regarding Claim 5, Meduri as in Claim 3, teaches the third entity is at least one of a third engineering Library, a third standard, a third schema(semantic schemas (ontologies) OL[3.5 ALFA system overview], FMA [5.1.1 Datasets]), and a third file format, and wherein the fourth entity is at least one of a fourth engineering library, a fourth standard, a fourth schema(semantic schemas (ontologies) OL[3.5 ALFA system overview], NCI [5.1.1 Datasets]), and a fourth file format.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kotecha(US PGPub 20230244644) teaches a schema mapping system that utilizes a machine learning model with NER.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARJUN R SWAMY whose telephone number is (571)272-9763. The examiner can normally be reached Mon, Tue, Thur, Fri 8-5.
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, Hai Phan can be reached at (571) 272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ARJUN SWAMY/Examiner, Art Unit 2654 /Richa Sonifrank/Primary Examiner, Art Unit 2654
Prosecution Insights