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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1, 4-8, 11-15, and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 5, 6, 8, 9, 11, 13, 14, 16, 17, and 19 of U.S. Patent No. 12,242,808. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 are broader in scope, and therefore anticipated by, claims 1-20 of U.S. Patent No. 12,242,808.
For example, each limitation of claim 1 is expressly taught by claim 9 of U.S. Patent No. 12,242,808. Claim 9 of U.S. Patent No. 12,242,808 therefore anticipates claim 1.
The remaining claims are anticipated as follows:
Claim 4 is anticipated by claim 11 of U.S. Patent No. 12,242,808.
Claim 5 is anticipated by claim 13 of U.S. Patent No. 12,242,808.
Claim 6 is anticipated by claim 14 of U.S. Patent No. 12,242,808.
Claim 7 is anticipated by claim 16 of U.S. Patent No. 12,242,808.
Claim 8 is anticipated by claim 1 of U.S. Patent No. 12,242,808.
Claim 11 is anticipated by claim 3 of U.S. Patent No. 12,242,808.
Claim 12 is anticipated by claim 5 of U.S. Patent No. 12,242,808.
Claim 13 is anticipated by claim 6 of U.S. Patent No. 12,242,808.
Claim 14 is anticipated by claim 8 of U.S. Patent No. 12,242,808.
Claim 15 is anticipated by claim 17 of U.S. Patent No. 12,242,808.
Claim 18 is anticipated by claim 19 of U.S. Patent No. 12,242,808.
Claim 19 is directed to a non-transitory computer-readable medium including the same subject matter as claims 5 and 13 of U.S. Patent No. 12,242,808. Although claims 5 and 13 of U.S. Patent No. 12,242,808 are directed to a method and a system, respectively, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the subject matter of either claim into a non-transitory computer-readable medium, because as is widely recognized in the art, computer-readable media allow a computer-implemented method to be distributed and performed by any generic computing system.
Claim 20 is directed to a non-transitory computer-readable medium including the same subject matter as claims 6 and 14 of U.S. Patent No. 12,242,808. Although claims 6 and 14 of U.S. Patent No. 12,242,808 are directed to a method and a system, respectively, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the subject matter of either claim into a non-transitory computer-readable medium, because as is widely recognized in the art, computer-readable media allow a computer-implemented method to be distributed and performed by any generic computing system.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 3, 10, and 17 each recite “converting to vectors words and phrases of the text that are determined to be verbs”. However, the only discussion of converting text to vectors in the specification states that “each word of the text may be converted to a vector representation” (paragraph [0033] of Applicant’s specification). While converting “each word” of the text to vectors would include converting verbs to vectors, nothing in the specification as filed suggests making a determination that words and phrases are verbs before converting the text to vectors.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 5-9, 12-16, and 19-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Eavy et al. (U.S. Patent Application Pub. No. 2023/0281467, hereinafter “Eavy”).
In regard to claim 1, Eavy discloses a system (processing machine, paragraph [0137]) comprising:
a memory that stores instructions (memory, paragraph [0137]); and
one or more processors configured by the instructions to perform operations (processors, paragraph [0137]) comprising:
identifying a set of entities from text, at least one entity of the set of entities being an object recognized by a keyword that indicates a type of the object followed by a number that indicates a particular object of the type (see Fig. 3, nodes in a knowledge graph are identified from text, paragraph [0061]; the nodes include attribute nodes identifying a type of object followed by a number, such as “Internal ID ID_001” or “Version 3.0”, paragraphs [0066-0068]);
identifying, from the text, a set of relations between pairs of entities of the set of entities (relationships between nodes are identified, paragraph [0069]);
creating a knowledge graph based on the set of entities and the set of relations (knowledge graph 150, paragraph [0066]);
causing presentation of a user interface on a display device (the knowledge graph is displayed, paragraph [0095]);
receiving, via the user interface, a topic (a natural language query sentence comprising a subject is received, paragraph [0111]);
searching the knowledge graph for nodes that match the topic (nodes and edges that match the subject are retrieved as an answer, paragraph [0111]); and
causing display of at least one of the nodes that match the topic, a first node connected to the at least one of the nodes, and a second node connected to the at least one of the nodes (the nodes and edges are passed back for consumption by the user, paragraph [0111]).
In regard to claim 2, Eavy discloses the identifying of the set of entities from the text comprises determining a part of speech for words or phrases of the text (identification of a subject, predicate, and/or object of a sentence, paragraph [0082]).
In regard to claim 5, Eavy discloses identifying of the set of entities from the text comprises applying object recognition to the text (natural language processing identifies objects, paragraph [0070]).
In regard to claim 6, Eavy discloses comparing the knowledge graph and a second knowledge graph to determine a degree of similarity between the text and a second text (a query is transformed into a query graph to determine a matching pattern in the knowledge graph, paragraph [0118]).
In regard to claim 7, Eavy discloses using a trained machine learning model, generating an approximation of the text from the knowledge graph (the natural language engine trained on the language used to build the graph is used to present a natural language description of a set of retrieved nodes and edges from the knowledge graph, paragraphs [0114-0116]).
In regard to claim 8, Eavy discloses a method comprising:
identifying a set of entities from text, at least one entity of the set of entities being an object recognized by a keyword that indicates a type of the object followed by a number that indicates a particular object of the type (see Fig. 3, nodes in a knowledge graph are identified from text, paragraph [0061]; the nodes include attribute nodes identifying a type of object followed by a number, such as “Internal ID ID_001” or “Version 3.0”, paragraphs [0066-0068]);
identifying, from the text, a set of relations between pairs of entities of the set of entities (relationships between nodes are identified, paragraph [0069]);
creating a knowledge graph based on the set of entities and the set of relations (knowledge graph 150, paragraph [0066]);
causing presentation of a user interface on a display device (the knowledge graph is displayed, paragraph [0095]);
receiving, via the user interface, a topic (a natural language query sentence comprising a subject is received, paragraph [0111]);
searching the knowledge graph for nodes that match the topic (nodes and edges that match the subject are retrieved as an answer, paragraph [0111]); and
causing display of at least one of the nodes that match the topic, a first node connected to the at least one of the nodes, and a second node connected to the at least one of the nodes (the nodes and edges are passed back for consumption by the user, paragraph [0111]).
In regard to claim 9, Eavy discloses the identifying of the set of entities from the text comprises determining a part of speech for words or phrases of the text (identification of a subject, predicate, and/or object of a sentence, paragraph [0082]).
In regard to claim 12, Eavy discloses identifying of the set of entities from the text comprises applying object recognition to the text (natural language processing identifies objects, paragraph [0070]).
In regard to claim 13, Eavy discloses comparing the knowledge graph and a second knowledge graph to determine a degree of similarity between the text and a second text (a query is transformed into a query graph to determine a matching pattern in the knowledge graph, paragraph [0118]).
In regard to claim 14, Eavy discloses using a trained machine learning model, generating an approximation of the text from the knowledge graph (the natural language engine trained on the language used to build the graph is used to present a natural language description of a set of retrieved nodes and edges from the knowledge graph, paragraphs [0114-0116]).
In regard to claim 15, Eavy discloses a non-transitory computer-readable medium (paragraph [0137]) that stores instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
identifying a set of entities from text, at least one entity of the set of entities being an object recognized by a keyword that indicates a type of the object followed by a number that indicates a particular object of the type (see Fig. 3, nodes in a knowledge graph are identified from text, paragraph [0061]; the nodes include attribute nodes identifying a type of object followed by a number, such as “Internal ID ID_001” or “Version 3.0”, paragraphs [0066-0068]);
identifying, from the text, a set of relations between pairs of entities of the set of entities (relationships between nodes are identified, paragraph [0069]);
creating a knowledge graph based on the set of entities and the set of relations (knowledge graph 150, paragraph [0066]);
causing presentation of a user interface on a display device (the knowledge graph is displayed, paragraph [0095]);
receiving, via the user interface, a topic (a natural language query sentence comprising a subject is received, paragraph [0111]);
searching the knowledge graph for nodes that match the topic (nodes and edges that match the subject are retrieved as an answer, paragraph [0111]); and
causing display of at least one of the nodes that match the topic, a first node connected to the at least one of the nodes, and a second node connected to the at least one of the nodes (the nodes and edges are passed back for consumption by the user, paragraph [0111]).
In regard to claim 16, Eavy discloses the identifying of the set of entities from the text comprises determining a part of speech for words or phrases of the text (identification of a subject, predicate, and/or object of a sentence, paragraph [0082]).
In regard to claim 19, Eavy discloses identifying of the set of entities from the text comprises applying object recognition to the text (natural language processing identifies objects, paragraph [0070]).
In regard to claim 20, Eavy discloses comparing the knowledge graph and a second knowledge graph to determine a degree of similarity between the text and a second text (a query is transformed into a query graph to determine a matching pattern in the knowledge graph, paragraph [0118]).
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.
Claim(s) 3, 4, 10, 11, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eavy, in view of Hoang et al. (U.S. Patent Application Pub. No. 2023/0297855, hereinafter “Hoang”).
In regard to claims 3, 10, and 17, Eavy does not disclose converting to vectors words and phrases of the text that are determined to be verbs (embedding vectors are determined for entities and their relationships as identified by verbs in the text, paragraphs [0020] and [0026-0028]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to convert to vectors words and phrases of the text that are determined to be verbs, because it would allow further analysis of the knowledge graph using clustering of the vector representations, as taught by Hoang (paragraphs [0021-0022]).
In regard to claims 4, 11, and 18, Eavy discloses entities are identified using natural language processing, but does not expressly disclose applying named entity recognition (NER) to the text.
Hoang discloses a method for identifying a set of entities from a text, wherein the identifying of the set of entities from the text comprises applying named entity recognition (NER) to the text (a named entity detector is used to identify entities, paragraphs [0019-0020]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply NER to the text, because NER improves the extraction of entities from text, as taught by Hoang (paragraphs [0015-0017]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN LOUIS ALBERTALLI whose telephone number is (571)272-7616. The examiner can normally be reached M-F 8AM-3PM, 4PM-5PM.
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BLA 9/29/26
/BRIAN L ALBERTALLI/Primary Examiner, Art Unit 2656
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