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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed toward an abstract idea without significantly more.
Claims 1-4 are directed toward a method, claims 5-12 are directed toward a method, and claims 13-20 are directed toward a system to implement the method of claims 5-12.
Claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) and does not include additional elements that amount significantly more than the judicial exception.
Step 1
Claims 1 is directed toward a “method”, and thus falls within a statutory category under the most recent guidelines of 35 U.S.C. 101.
Step 2A, Prong 1
Claim 1 recites the steps of “… detecting, from a genealogical-record text, a first reference instance corresponding to an entity in the genealogical-record text and a second reference instance corresponding to the entity in the genealogical-record text;” “determining, using a model-selection router, an entity-matching large language model corresponding to the first reference instance and the second reference instance from among a plurality of candidate entity-matching large language models comprising at least an entity-entity large language model;” and “generating a coreference match for the entity indicated by the first reference.” These limitations collectively recite the collection and evaluation of information, including language evaluation and processing using machine learning models. As characterized by the USPTO guidance and case law, such activities fall within the abstract-idea groupings of mental processes (e.g. observations, evaluations, and judgments that could be performed in the human mind or with pen and paper) and organizing /transmitting information. Reference can be made to latest patent eligibility guidelines. Accordingly, claim 1 recites an abstract idea.
Step 2A, Prong 2
The claim is implemented on a “computer.” The use of a generic computer components performing their well-understood, routine, and conventional functions of storing and executing instructions, receiving requests, and sending content.
The claim does not recite any specific improvement to computer functionality (e.g., a particular translation algorithm, model architecture, data structure, memory organization, caching mechanism, latency-reduction technique, or network protocol that improves the operation of the computer or network). Nor does it effect a transformation of a physical article or use the abstract idea in any other manner that imposes a meaningful limit on the claim’s scope. Therefore, the claim does not integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B
Beyond the abstract idea, the additional elements are the generic “computer,” “device”(s) performing their conventional functions. Implementing the abstract idea on generic computer components does not amount to significantly more. Alice, 573 U.S. at 223–24).
The ordered combination of limitations mirrors the abstract idea itself performed using routine computer operations. There is no recited unconventional hardware, no technical improvement to the functioning of the computer itself, and no nonconventional arrangement of known components etc.
Accordingly, claim 1 does not include an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.
Therefore , claim 1 is directed to an abstract idea and does not recite additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. Claim 1 is therefore rejected under 35 U.S.C. § 101. Dependent claims 2-4 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 9-20 are directed toward a system and non-transitory medium with instructions to implement the method of claims 1-10, and are similar in scope and content, and therefore are rejected under similar rationale.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
As per claim 1, Reese et al., teach a computer-implemented method comprising:
detecting, from a genealogical-record text, a first reference instance corresponding to an entity in the genealogical-record text and a second reference instance corresponding to the entity in the genealogical-record text (0063-0064, 0094-0095, 0011, 0016);
determining, using a model-selection router, an entity-matching large language model corresponding to the first reference instance and the second reference instance from among a plurality of candidate entity-matching large language models comprising at least an entity-entity large language model (0011, 0016, 0150) and
generating a coreference match for the entity indicated by the first reference. instance and the second reference instance utilizing the entity-matching large language model (0088-0089, 0094-0095).
As per claim 2, Reese et al., teach the computer-implemented method of claim 1, further comprising encoding, using a multilingual encoder, the first reference instance to generate a first vector representation and the second reference instance to generate a second vector representation (0193, 0195-0196).
As per claim 3, Reese et al., teach the computer-implemented method of claim 2, further comprising providing, using the model-selection router comprising multilingual heuristics, the first vector representation and the second vector representation to the entity-matching large language model for analysis (0193, 0195-0196).
As per claim 4, Reese et al., teach the computer-implemented method of claim 1, wherein determining the entity- matching large language model comprises: determining that the first reference instance and the second reference instance each correspond to a reference type from a plurality of reference types comprising a pronoun reference type and an entity reference type; and selecting, from among the plurality of candidate entity-matching large language models, the entity-entity large language model, a pronoun-pronoun large language model, or a pronoun- entity large language model based on the reference type for each of the first reference instance and the second reference instance (0052, 0127-0128, 0158-0160).
As per claim 5, Reese et al., teach the computer-implemented method of claim 1, wherein determining the entity- matching large language model comprises: determining that at least one of the first reference instance or the second reference instance is not a pronoun reference or an entity reference; and selecting, from among the plurality of candidate entity-matching large language models, an alternative-reference large language model for generating the coreference match for the entity (0063, 0094-0096, 0126).
As per claim 6, Reese et al., teach the computer-implemented method of claim 1, wherein determining the entity- matching large language model comprises: determining that at least one of the first reference instance or the second reference instance is an unknown reference or a text-inconsistency reference; and selecting, from among the plurality of candidate entity-matching large language models, an alternative-reference large language model for generating the coreference match for the entity (0063, 0094-0096, 0126).
As per claim 7, Reese et al., teach the computer-implemented method of claim 6, wherein generating the coreference match for the entity comprises associating, in response to determining that at least one of the first reference instance or the second reference instance is an unknown reference or a text-inconsistency reference, the first reference instance with the second reference instance based on analyzing the genealogical-record text utilizing the alternative-reference large language model (0193, 0195-0196).
As per claim 8, Reese et al., teach the computer-implemented method of claim 1, wherein generating the coreference match comprises: utilizing the entity-matching large language model to generate a first confidence score indicating a likelihood that the first reference instance and the second reference instance refer to the entity; generating, utilizing a non-entity-matching large language model, a second confidence score indicating a second likelihood as to whether the first reference instance and the second reference instance refer to the entity; and combining the first confidence score and the second confidence score (0108, 0116, 0140, 0142, 0148, 0150, Figs, 5A – 5E).
Claims 9-15 are directed toward system claims similar in scope and content of method claims 1-8, and are rejected under similar rationale.
Claims 16-20 are directed toward a non-transitory computer readable medium similar in scope and content of method claims 1-8 and are rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
The following is applicable prior art to Applicant claimed invention.
Yan et al., (US 2025/0094718 A1) teach where a text from a predetermined domain is taken and converted to canonical named entities based on an established taxonomy. Using a shared machine learning encoder model, contextual embeddings for these named entities are produced. These embeddings are then fed into a domain-specific scoring model related to the text's domain. This model ranks the embeddings based on relevance. The derived relevance scores, along with the entities, are sent to another system for further tasks. For instance, a recommendation system might use these scores to suggest relevant named entities.
Lauber (US 2022/0261545 A1) teaches a system and method for determining an embedding for a document (e.g. representing the document in vector space) by determining for the document a preliminary document embedding; determining for the document a document topic embedding based on a set of nearest topics to the preliminary document embedding; determining for each phrase in the document a topic relevancy score based on the document topic embedding and the embedding associated with the phrase; using a ranking algorithm to determine a saliency score for each phrase in the document, each saliency score based on the topic relevancy score for the phrase, and an inverse frequency score for the phrase; and calculating an embedding for the document using the saliency scores and embedding, for the phrases in the document.
Zhong et al., (US 2021/0303638 A1) teach a system for processing user-generated input. During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.
Beller et al., (US 2020/0265114 A1) teach a query system identifies a collection of discovered entity bins each comprising unstructured documents with mentions of a name element from a name query and each identified with a particular named entity identifiable from the name element. The query system identifies, from a knowledge base of structured documents, based on identifier components with the name element, candidate records identifying the respective identifier components with the name element, the one or more identifier components identified among the discovery entity bins. For each respective selection of candidate records associated with each bin, the query system applies one or more alignment threshold rules to rank the likelihood that each candidate record within each respective selection matches one or more characteristics of the respective discovery entity bin. The query system aligns, with each of the discovery entity bins, a highest ranked record from among each respective selection of candidate records, where the respective aligned highest ranked record identifies a distinct named entity from among the named entities.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY B CHAWAN whose telephone number is (571)272-7601. The examiner can normally be reached 7-5 Monday thru Thursday.
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/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658
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