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 .
Drawings
The drawings are objected to because Fig. 2 element “118” should be “116” and element “120” should be “118;” Fig. 3 element “118” should be “116;” Fig. 4 element “118” should be “116;” . Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Fig. 6 element “616” and Fig. 7 elements “721” and “728.” Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 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. Using the subject matter eligibility test from page 74621 of the Federal Register Notice titled “2014 Interim Guidance on Patent Subject Matter Eligibility,” a two-step process is performed. Under step 1, the claims are analyzed to determine if the claim is directed to a process, machine, article of manufacture, or composition of matter. In this case, claims 1-7 are directed to a method, which is a process; claims 8-14 are directed to a machine-readable medium, which is a machine or an article of manufacture; and claims 15-20 are directed to a system, which is a machine or an article of manufacture. Step 2A (part 1 of the Mayo test), using the guidance from pages 50-57 of the Federal Register Vol. 84 No. 4 from Monday, January 7, 2019, requires applying a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception, determining if the claim is directed to a law of nature, a natural phenomenon, or an abstract idea. In this case, claim 1 recites identifying relationships as being present or absent, and adding relationships and types to an ontology, which are mental processes. In Prong Two, examiners evaluate whether the judicial exception is integrated into a practical application that imposes a meaningful limit on the judicial exception. In this case, additional elements of providing and receiving data are mere extrasolution activity, and do not integrate the abstract ideas into a practical application.
Step 2B (part 2 of the Mayo test) requires analyzing the claims to determine if they recite additional elements that amount to significantly more than the judicial exception. In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea itself.
Regarding claims 1, 8, and 15, identifying relationships as being present or absent, and adding relationships and types to an ontology are mental processes, which is an abstract idea. Additional elements of providing data and receiving data are mere extrasolution activity, and do not integrate the abstract ideas into a practical application or constitute significantly more.
Regarding claims 2-3, 5-7, 9-10, 12-14, 16-17, and 19-20, the limitations are further clarifications of the above abstract ideas.
Regarding claims 4, 11, and 18, hardening relationships appears to be the result of comparison of confidence levels to a threshold, which is a mental process or mathematical calculation, both of which are abstract ideas without integration into a practical application and without significantly more.
The limitations of the claims, taken alone, do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Applicable case law cited in the Federal Register includes, but is not limited to: Alice Corp., 134 S. Ct. at 2355-56, Digitech Image Tech., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014), Benson, 409 U.S. at 63.
See "Preliminary Examination Instructions in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.," dated June 25, 2014, and the Federal Register notice titled "2014 Interim Guidance on Patent Subject Matter Eligibility" (79 FR 74618).
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 (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 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zaitoun et al. (Zaitoun, A., Sagi, T., Wilk, S., & Peleg, M. (2023). Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?. arXiv preprint arXiv:2311.06858.), hereinafter referred to as Zaitoun, in view of Glauer et al. (Glauer, M., Memariani, A., Neuhaus, F., Mossakowski, T., & Hastings, J. (2024). Interpretable ontology extension in chemistry. Semantic Web, 15(4), 937-958.), hereinafter referred to as Glauer.
Regarding claim 1, Zaitoun teaches:
A method comprising:
providing, to a machine learning (ML) model, unstructured data including a vocabulary of words of an ontology (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine concepts from the unstructured data);
receiving, from the ML model, a first learned term relationship model indicating respective relationships, the respective relationships indicating which words of the vocabulary of words are related to each other (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine relations between concepts);
identifying a relationship of the respective relationships that is (i) present in the first ontology and (ii) absent from a second ontology (page 4 first paragraph, where the determined triples are compared to the gold standard ontology to determine overlap);
providing a prompt including data indicating the relationship to a large language model (LLM) (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine relations between concepts);
receiving a result from the LLM indicating a type of the relationship (page 3 "ChatGPT Prompts" first paragraph, where chatGPT determines relations between concepts); and
adding the relationship and the type to the second ontology (page 4 first paragraph, where the determined triples that are not found in the gold standard are evaluated by experts and used to extend the gold standard ontology).
Zaitoun does not teach the use of a ML model that is not an LLM.
Glauer teaches:
providing, to a machine learning (ML) model, unstructured data including a vocabulary of words of an ontology (pages 5-6 section 3, where machine learning is used for ontology extension);
receiving, from the ML model, a first learned term relationship model indicating respective relationships, the respective relationships indicating which words of the vocabulary of words are related to each other (page 11 last paragraph, where relationships are predicted);
Zaitoun teaches using machine learning to determine relationships from unstructured data (page 3 "ChatGPT Prompts" first paragraph). However, Zaitoun teaches using ChatGPT to perform the tasks, while para [0012] of Applicant's Specification excludes LLMs from the definition of ML models. Glauer teaches using machine learning models for relationship determination (pages 5-6 section 3). Glauer pages 4-5 "Machine learning and deep learning approaches" recognizes different ways of performing machine learning are within the level of ordinary skill in the art, and that the results of using any method would have been predictable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the general machine learning of Glauer for the LLM of Zaitoun where the result of the substitution would predictably result in relationship prediction.
Regarding claim 2, Zaitoun in view of Glauer teaches:
The method of claim 1, wherein the second ontology is generated based on input from a subject matter expert (Zaitoun pages 2-3 "Gold standard" both paragraphs, where a gold standard ontology is constructed by experts, and page 4 first paragraph, where the determined triples that are not found in the gold standard are evaluated by experts and used to extend the gold standard ontology).
Regarding claim 3, Zaitoun in view of Glauer teaches:
The method of claim 1, wherein the ML model includes word clustering, Word2Vec, Word Embedding, latent semantic analysis (LSA), a semantic analysis model, a term by document analysis, a document-term matrix (DTM) analysis, or an automatic document classification (ADC) technique (Glauer page 7, section 3.2, where word2vec or word embeddings are used).
Regarding claim 4, Zaitoun in view of Glauer teaches:
The method of claim 1, wherein the respective relationships are unhardened relationships and the method further comprises:
hardening the relationships received from the ML model resulting in hardened relationships (Glauer pages 10-11 paragraph spanning pages, where classifications are compared to a threshold); and
wherein identifying is based on hardened relationships (Glauer pages 10-11 paragraph spanning pages, where classifications are determined based on comparison to a threshold).
Regarding claim 5, Zaitoun in view of Glauer teaches:
The method of claim 1, wherein the prompt includes a description of each word in the relationship (Zaitoun page 3 "ChatGPT Prompts" first paragraph cites reference 15 for the prompts use, where the "relations-prompt-example" text includes context for the concept words).
Regarding claim 6, Zaitoun in view of Glauer teaches:
The method of claim 5, wherein the prompt further includes a description of words related to each of words in the relationship, use of the related words and the words in the relationship, definitions of the related words and the words in the relationship, or a combination thereof (Zaitoun page 3 "ChatGPT Prompts" first paragraph cites reference 15 for the prompts use, where the "relations-prompt-example" text includes context for the concept words).
Regarding claim 7, Zaitoun in view of Glauer teaches:
The method of claim 6, wherein the prompt further includes choices constraining the type of relationship that can be returned by the LLM (Zaitoun page 3 "ChatGPT Prompts" first paragraph cites reference 15 for the prompts use, where the "relations-prompt-example" text includes the possible relations).
Regarding claim 8, Zaitoun teaches:
A non-transitory machine-readable medium including instructions (page 2 second to last paragraph of section 1, where a neural network based AI system would necessarily require use of memory and instructions) that, when executed by a machine, cause the machine to perform operations for completing an ontology, the operations comprising:
providing, to a machine learning (ML) model, unstructured data including a vocabulary of words of an ontology (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine concepts from the unstructured data);
receiving, from the ML model, a first learned term relationship model indicating respective relationships, the respective relationships indicating which words of the vocabulary of words are related to each other (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine relations between concepts);
identifying a relationship of the respective relationships that is (i) present in the first ontology and (ii) absent from a second ontology (page 4 first paragraph, where the determined triples are compared to the gold standard ontology to determine overlap);
providing a prompt including data indicating the relationship to a large language model (LLM) (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine relations between concepts);
receiving a result from the LLM indicating a type of the relationship (page 3 "ChatGPT Prompts" first paragraph, where chatGPT determines relations between concepts); and
adding the relationship and the type to the second ontology (page 4 first paragraph, where the determined triples that are not found in the gold standard are evaluated by experts and used to extend the gold standard ontology).
Zaitoun does not teach the use of a ML model that is not an LLM.
Glauer teaches:
providing, to a machine learning (ML) model, unstructured data including a vocabulary of words of an ontology (pages 5-6 section 3, where machine learning is used for ontology extension);
receiving, from the ML model, a first learned term relationship model indicating respective relationships, the respective relationships indicating which words of the vocabulary of words are related to each other (page 11 last paragraph, where relationships are predicted);
Zaitoun teaches using machine learning to determine relationships from unstructured data (page 3 "ChatGPT Prompts" first paragraph). However, Zaitoun teaches using ChatGPT to perform the tasks, while para [0012] of Applicant's Specification excludes LLMs from the definition of ML models. Glauer teaches using machine learning models for relationship determination (pages 5-6 section 3). Glauer pages 4-5 "Machine learning and deep learning approaches" recognizes different ways of performing machine learning are within the level of ordinary skill in the art, and that the results of using any method would have been predictable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the general machine learning of Glauer for the LLM of Zaitoun where the result of the substitution would predictably result in relationship prediction.
Regarding claim 9, Zaitoun in view of Glauer teaches:
The non-transitory machine-readable medium of claim 8, wherein the second ontology is generated based on input from a subject matter expert (Zaitoun pages 2-3 "Gold standard" both paragraphs, where a gold standard ontology is constructed by experts, and page 4 first paragraph, where the determined triples that are not found in the gold standard are evaluated by experts and used to extend the gold standard ontology).
Regarding claim 10, Zaitoun in view of Glauer teaches:
The non-transitory machine-readable medium of claim 8, wherein the ML model includes word clustering, Word2Vec, Word Embedding, latent semantic analysis (LSA), a semantic analysis model, a term by document analysis, a document-term matrix (DTM) analysis, or an automatic document classification (ADC) technique (Glauer page 7, section 3.2, where word2vec or word embeddings are used).
Regarding claim 11, Zaitoun in view of Glauer teaches:
The non-transitory machine-readable medium of claim 8, wherein the respective relationships are unhardened relationships and the operations further comprise:
hardening the relationships received from the ML model resulting in hardened relationships (Glauer pages 10-11 paragraph spanning pages, where classifications are compared to a threshold); and
wherein identifying is based on hardened relationships (Glauer pages 10-11 paragraph spanning pages, where classifications are determined based on comparison to a threshold).
Regarding claim 12, Zaitoun in view of Glauer teaches:
The non-transitory machine-readable medium of claim 8, wherein the prompt includes a description of each word in the relationship (Zaitoun page 3 "ChatGPT Prompts" first paragraph cites reference 15 for the prompts use, where the "relations-prompt-example" text includes context for the concept words).
Regarding claim 13, Zaitoun in view of Glauer teaches:
The non-transitory machine-readable medium of claim 12, wherein the prompt further includes a description of words related to each of words in the relationship, use of the related words and the words in the relationship, definitions of the related words and the words in the relationship, or a combination thereof (Zaitoun page 3 "ChatGPT Prompts" first paragraph cites reference 15 for the prompts use, where the "relations-prompt-example" text includes context for the concept words).
Regarding claim 14, Zaitoun in view of Glauer teaches:
The non-transitory machine-readable medium of claim 13, wherein the prompt further includes choices constraining the type of relationship that can be returned by the LLM (Zaitoun page 3 "ChatGPT Prompts" first paragraph cites reference 15 for the prompts use, where the "relations-prompt-example" text includes the possible relations).
Regarding claim 15, Zaitoun teaches:
A system comprising:
processing circuitry (page 2 second to last paragraph of section 1, where a neural network based AI system would necessarily require use of processing circuitry);
a memory coupled to the processing circuitry (page 2 second to last paragraph of section 1, where a neural network based AI system would necessarily require use of memory), the memory including instructions that, when executed by the processing circuitry, cause the processing circuitry to perform operations for completing an ontology, the operations comprising:
providing, to a machine learning (ML) model, unstructured data including a vocabulary of words of an ontology (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine concepts from the unstructured data);
receiving, from the ML model, a first learned term relationship model indicating respective relationships, the respective relationships indicating which words of the vocabulary of words are related to each other (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine relations between concepts);
identifying a relationship of the respective relationships that is (i) present in the first ontology and (ii) absent from a second ontology (page 4 first paragraph, where the determined triples are compared to the gold standard ontology to determine overlap);
providing a prompt including data indicating the relationship to a large language model (LLM) (page 3 "ChatGPT Prompts" first paragraph, where chatGPT is prompted to determine relations between concepts);
receiving a result from the LLM indicating a type of the relationship (page 3 "ChatGPT Prompts" first paragraph, where chatGPT determines relations between concepts); and
adding the relationship and the type to the second ontology (page 4 first paragraph, where the determined triples that are not found in the gold standard are evaluated by experts and used to extend the gold standard ontology).
Zaitoun does not teach the use of a ML model that is not an LLM.
Glauer teaches:
providing, to a machine learning (ML) model, unstructured data including a vocabulary of words of an ontology (pages 5-6 section 3, where machine learning is used for ontology extension);
receiving, from the ML model, a first learned term relationship model indicating respective relationships, the respective relationships indicating which words of the vocabulary of words are related to each other (page 11 last paragraph, where relationships are predicted);
Zaitoun teaches using machine learning to determine relationships from unstructured data (page 3 "ChatGPT Prompts" first paragraph). However, Zaitoun teaches using ChatGPT to perform the tasks, while para [0012] of Applicant's Specification excludes LLMs from the definition of ML models. Glauer teaches using machine learning models for relationship determination (pages 5-6 section 3). Glauer pages 4-5 "Machine learning and deep learning approaches" recognizes different ways of performing machine learning are within the level of ordinary skill in the art, and that the results of using any method would have been predictable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the general machine learning of Glauer for the LLM of Zaitoun where the result of the substitution would predictably result in relationship prediction.
Regarding claim 16, Zaitoun in view of Glauer teaches:
The system of claim 15, wherein the second ontology is generated based on input from a subject matter expert (Zaitoun pages 2-3 "Gold standard" both paragraphs, where a gold standard ontology is constructed by experts, and page 4 first paragraph, where the determined triples that are not found in the gold standard are evaluated by experts and used to extend the gold standard ontology).
Regarding claim 17, Zaitoun in view of Glauer teaches:
The system of claim 15, wherein the ML model includes word clustering, Word2Vec, Word Embedding, latent semantic analysis (LSA), a semantic analysis model, a term by document analysis, a document-term matrix (DTM) analysis, or an automatic document classification (ADC) technique (Glauer page 7, section 3.2, where word2vec or word embeddings are used).
Regarding claim 18, Zaitoun in view of Glauer teaches:
The system of claim 15, wherein the respective relationships are unhardened relationships and the operations further comprise:
hardening the relationships received from the ML model resulting in hardened relationships (Glauer pages 10-11 paragraph spanning pages, where classifications are compared to a threshold); and
wherein identifying is based on hardened relationships (Glauer pages 10-11 paragraph spanning pages, where classifications are determined based on comparison to a threshold).
Regarding claim 19, Zaitoun in view of Glauer teaches:
The system of claim 15, wherein the prompt includes a description of each word in the relationship (Zaitoun page 3 "ChatGPT Prompts" first paragraph cites reference 15 for the prompts use, where the "relations-prompt-example" text includes context for the concept words).
Regarding claim 20, Zaitoun in view of Glauer teaches:
The system of claim 19, wherein the prompt further includes a description of words related to each of words in the relationship, use of the related words and the words in the relationship, definitions of the related words and the words in the relationship, or a combination thereof (Zaitoun page 3 "ChatGPT Prompts" first paragraph cites reference 15 for the prompts use, where the "relations-prompt-example" text includes context for the concept words).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2022/0215167 A1 para [0032] teaches ontology learning and learning models, while para [0048] teaches augmenting an existing ontology; Liu et al. (Liu, F., & Li, G. (2018, August). The extension of domain ontology based on text clustering. In 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (Vol. 1, pp. 301-304). IEEE.) section 3 teaches about ontology expansion based on text clustering and supervised learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN S BLANKENAGEL whose telephone number is (571)270-0685. The examiner can normally be reached 8:00am-5:30pm.
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/BRYAN S BLANKENAGEL/Primary Examiner, Art Unit 2658
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