CTNF 18/928,305 CTNF 84589 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Double Patenting 08-33 AIA 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. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-34 AIA Claim s 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of U.S. Patent No. 12164873 . Although the claims at issue are not identical, they are not patentably distinct from each other because apart from the recited hop count proximity measure and cross attention based scoring recited in the independent claims of the instant application the claim resolve substantially similar subject matter and the recited additions cannot be considered novel such as by an average skilled practitioner in possession of a general understanding of the domain encompassed by the art listed in the IDS filed 10/28/24, or in possession of Chen, and/or Lukovnikov as provided by Examiner and detailed infra as a transformer model generally is considered a well-known example of a cross attention measure and hops are considered merely a discrete measure of distance . Claim Objections 07-29-01 AIA Claim 17 objected to because of the following informalities: the claim recites a candidate answer set comprising a group of other entities “located a predetermined proximity to the node within the knowledge graph,” this appears to lack a preposition which defines the set, such as within, outside, etc. Examiner will consider the entities resolved by being “within a predetermined proximity.” Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Independent claims 1 12, 17 recite “a group of other entities,” in a manner that lacks a clear referent. Examiner must presume that the recitation intends to resolve the topic entity. Subsequent dependent claims 6, 8, 9, 13, 14, 18, 19 do not remedy, in fact claims 9, 14, 19 introduce further ambiguity by introducing “whether that other entity is a person, place, or thing in a manner which renders the set in the singular. Appropriate correction is required. Claim 5 recites computing a dot product of the question against a plurality of vectors but only resolves a singular “score” value; this is considered indefinite as it is not clear if a singular score is computed per candidate such as discussed in the specification at ¶ 28: “the system 100 may calculate a respective similarity score between the question and each corresponding candidate answer set and select a final answer or answers according to the scores,“ or if the recited “score,” is somehow generated as an aggregate thereof. Appropriate correction is required. Claim 7, 16 recites “fine tuning of BERT encoding, does not clearly resolve either the “encoding by the processing system,” nor the “BERT encoder” of the parent claim; further the relationship between the recited the manner in which fine tuning of BERT encoding “according to a self multi-head attention encoder based on a convolutional neural network (CNN) encoder,” leaves the manner in which it is “according to,” and or “based on,” as an exercise upon the reader. Appropriate correction is required. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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 of this title, 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. 07-21-aia AIA Claims 1, 5-20 rejec ted under 35 U.S.C. 103 as being unpatentable over Lei: 2 0220207343 hereinafter Lei (made of record in the IDS filed 10/28/24 further in view of Chen: “Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases,” (copy provided by Examiner, copyright 5/2019 and hereinafter Chen). Regar ding claim 1 Lei teaches: A method, comprising: receiving, by a processing system including a processor, a text snippet (Lei: ¶ 7, 43-52, 99; Fig 3, 4, 12; Claims 1, 12: system receives a text snippet; system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations,); identifying, by the processing system and without human intervention, a topic entity of the natural language question (Lei: ¶ 4, 32, 43-52, 57-65; Fig 3, 4, 6: system determines entity mentions within snippet generates a query graph therefrom such as for entity disambiguation over a plurality of topical domain associated graphs); locating, by the processing system and within a knowledge graph, a focal node corresponding to the topic entity (Lei: Abstract; ¶ 8-13, 43-52, 58-65, 67-76, 91, 92; Fig 3, 4, 6, 9: system determines an unknown term, node, etc. within a query graph, constructs representation thereof and matches same with nodes in a knowledge graph); gathering, by the processing system and within the knowledge graph, a set of responses comprising groups of other entities within a predetermined proximity to the focal node, wherein the predetermined proximity comprises two or more hops from the focal node; (Lei: ¶ 30-37, 43-52, 58-72; Fig 1, 6, 8: system determines candidate node vectors matching the query graph, generating a matching score of each; said matching score representative of and interpreted as a proximity value(s); the system operates in a hop-wise manner to aggregate over neighbors and meta-path hop-wise relationships in a semantic ontology of a particular knowledge graph wherein the hop-wise relationships comprise two or more hops such as ); generating, by the processing system and with respect to the knowledge graph, contextual information for the group of other entities proximal to the focal node (Lei: ¶ 35-37, 43-52, 58-67, 86-92; Fig 3, 4: system generates graphs representations for candidate responses matching a query, unknown term, etc.; said graphs comprising related textual information in the form of nodes corresponding to entities, etc. and edges corresponding to relationships among the nodes; said matching score representative of and interpreted as a proximity value(s) such as over a meta-path and bear context in the form of node types and interaction relationships thereof); encoding, by the processing system, the natural language query, and the contextual information of the group of other entities of the candidate response set to obtain an encoded vectorial representation of the natural language question and a plurality of encoded vectorial representations of the candidate answer set, (Lei: Abstract; ¶ 8-13, 43-52, 58-65, 71-94; Fig 3, 4, 6, 9: system generates graphs representing an snippet and matches thereto, each of said graphs comprising vectorial representations thereof), wherein the encoding uses pre-trained language model embeddings obtained via a pre-trained bidirectional encoder representations from transformer (BERT) encoder (Lei: ¶ 73; Fig 8 system generates embeddings for graphs, vectors thereof using BERT operates graph neural network based thereon to determine similar terms to a particular unknown node, work, etc. such as in pre-trained language domains); and selecting, by the processing system, one of the other entities of the candidate response set to obtain a selected one of the candidate response set as a response to the natural language query (Lei: Abstract; ¶ 8-13, 43-52, 58-65, 71-94; Fig 3, 4, 6, 9, 10: system determines a matching term in the set of candidates for an unknown term based on a similarity in excess of a threshold), wherein the selecting is based on scoring of the encoded vectorial representation of the natural language question under an influence of the contextual information, and wherein the scoring involves evaluations of cross-attention between the natural language question and the respective answer type of each of the other entities of the candidate answer set. Lei does not explicitly teach the system ,method, etc. operative for natural language question answering to thereby receive a user question and provide a final answer wherein the taught scoring involves evaluations of cross-attention between the natural language question and each aspect of a plurality of aspects of each member of the candidate answer set to arrive at said final answer. wherein the contextual information comprises a respective answer type of each of the other entities of the candidate answer set; nor a system wherein the scoring involves evaluations of cross-attention between the natural language question and the respective answer type of each of the other entities of the candidate answer set. In a related field of endeavor Chen teaches a system and method for question answering over knowledge bases using an attention network(s) (Chen: Title) comprising: receiving, by a processing system including a processor, a natural language question (Chen: Abstract: system automatically determines relevant answers for a natural language question); identifying, by the processing system and without human intervention, a topic entity of the natural language question (Chen: § 3.2, 3.5, 3.6, 4.2, 4.3: system determines an answer such as an entity node and determines a “top result returned by a separately trained topic entity predictor,” to resolve appropriate answers as a predicted answer set); thereby gathering, by the processing system and within the knowledge graph, a candidate answer set comprising a group of other entities within a predetermined hop radius to the topic entity, wherein the predetermined radius comprises two or more hops from the determined entity (Chen: § 3.2, 3.4, 3.5, 4.2: system determines entities hop-wise proximal to the topic entity by determining “all the entities connected to it within h hops as candidate answers,” such as by extracting a “2-hop sub graph (i.e., h = 2) to collect candidate answers,” proximal to the topic entity); generating, by the processing system and from the knowledge graph, contextual information for the group of other entities of the candidate answer set, wherein the contextual information comprises a respective answer type of each of the other entities of the candidate answer set (Chen: § 3.2, 3.5: system operates by “encoding the answer type, path and context,” that is the disclosed system, method, etc. reifies knowledge base context in the form of an answer entity type, a contextual path with respect to a topic entity; and an answer entity context); and selecting, by the processing system, one of the other entities of the candidate answer set to obtain a selected one of the candidate answer set as an answer to the natural language question, wherein the selecting is based on scoring of the encoded vectorial representation of the natural language question under an influence of the contextual information, and wherein the scoring involves evaluations of cross-attention between the natural language question and the respective answer type of each of the other entities of the candidate answer set (Chen: § 2, 3.3, 3.5, 4.4; Table 2: system operates a two layered bidirectional attention network comprising interactions between the question over type/path/context to a priority of answers wherein an importance module determines question word relations over the answer type/path/context to score word embeddings with respect to candidate embeddings thereby providing cross attention “by modeling the bidirectional interactions between questions and a KB,”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize a knowledge graph matching pipeline such as that taught or suggested by Lei within the knowledge base question answering framework of Chen for at least the purpose of extracting topic entities from an input query and thereby determining a candidate answer set over a local hop-bounded contextual neighborhood such as by scoring each candidate answer over type, path, and context based on bi-directional cross-attention between the question, topic thereof, etc. and the candidates to thereby calculate a dot product to provide final answer with a highest similarity score as taught or suggested by Chen (Chen: § 3.5, 3.6); one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 5 Lei in view of Chen teaches or suggests: The method of claim 1, wherein the scoring further comprises: computing a dot product of the encoded vectorial representation of the natural language question and the plurality of encoded vectorial representations of the candidate answer set to obtain a computed score value (Chen: § 3.5, 3.6: system calculates dot products to determine a final answer with a highest similarity score). The claim is considered obvious over Lei as modified by Chen as addressed in the base claim as it would have been obvious to apply the further teaching of Lei and/or Chen to the modified device of Lei and Chen; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 6 Lei in view of Chen teaches or suggests: The method of claim 1, wherein the selecting of the one of the other entities of the candidate answer set further comprises: comparing a computed score value associated with the one of the other entities of the candidate answer set to a score threshold value; and selecting the one of the other entities of the candidate answer set responsive to the computed score value exceeding the score threshold value (Lei: ¶ 46, 52, etc.: system returns top candidates within a determined threshold); (Chen: § 3.5, 3.6, 4.2: system returns candidate answer scores over a predicted answer set within an answer threshold, set margin, theta , etc.). The claim is considered obvious over Lei as modified by Chen as addressed in the base claim as it would have been obvious to apply the further teaching of Lei and/or Chen to the modified device of Lei and Chen; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 7 Lei in view of Chen teaches or suggests: The method of claim 1, wherein the encoding further comprises fine-tuning of BERT encoding according to a self multi-head attention encoder based on a convolutional neural network (CNN) encoder. Examiner takes official notice that fine tuning of BERT based on a CNN such as operative of a multi-head attention encoder was well known in the art before the effective filing date of the instant invention and would have comprised an obvious inclusion such as for at least the purpose of optimizing an Question Answering system based on convolution and self-attention, such as by employ of multi-head attention; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 8 Lei in view of Chen teaches or suggests: The method of claim 1, wherein the contextual information further comprises a respective answer path and a respective answer context of each of the other entities of the candidate answer set (Chen: 3.2, 3.5, 3.6, 4.2: system determines an answer path and context for each candidate and with respect to the questions; determines a best candidate based on calculations baser thereon). The claim is considered obvious over Lei as modified by Chen as addressed in the base claim as it would have been obvious to apply the further teaching of Lei and/or Chen to the modified device of Lei and Chen; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 9 Lei in view of Chen teaches or suggests: The method of claim 1, wherein the respective answer type of each of the other entities of the candidate answer set comprises an indication of whether that other entity is a person, a place, or a thing (Chen: § 3.2, 3.5: such as based on determining relations with respect to interrogatives; e.g. a "where" interrogative returns responses relevant to location, etc.). The claim is considered obvious over Lei as modified by Chen as addressed in the base claim as it would have been obvious to apply the further teaching of Lei and/or Chen to the modified device of Lei and Chen; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 10 Lei in view of Chen teaches or suggests: The method of claim 1, wherein the knowledge graph comprises a domain-specific knowledge graph, and wherein the domain-specific knowledge graph comprises entities associated with a predetermined domain and relationships among the entities (Lei: ¶ 3-5, 30-37, etc.: system operates over domain specific knowledge graphs such as comprising entities among a medical domain, etc.). The claim is considered obvious over Lei as modified by Chen as addressed in the base claim as it would have been obvious to apply the further teaching of Lei and/or Chen to the modified device of Lei and Chen; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 11 Lei in view of Chen teaches or suggests: The method of claim 10, wherein the domain-specific knowledge graph comprises a curated knowledge graph (Lei: ¶ 3-5: such as over literature curated, edited, etc. with respect to the specific domain), the method further comprising: determining, by the processing system, a missing entity not included within the domain-specific knowledge graph (Lei: ¶ 9, 44-46, 86-90, 94: system operates by adding, operating with respect to matching, etc. an unknown term, missing entity, etc.); searching, by the processing system, a knowledge base other than the domain-specific knowledge graph to obtain missing entity information according to the missing entity (Lei: ¶ 2-5: such as by searching for or otherwise determining supplemental information upon a domain by human or computational editorial processes) ; and incorporating, by the processing system, a new entity comprising the missing entity information into the domain-specific knowledge graph to obtain an updated, curated domain-specific knowledge graph (Lei: ¶ 88: such as to include the unknown term by adding with respect to a determined synonym thereof). Thus Lei in view of Chen is considered to at least strongly suggest if not explicitly teach the recited subject matter, further Examiner takes official notice that searching over additional domains and supplementing and/or editing a knowledge base, graph, etc. based thereon was well-known in the art before the effective filing date of the instant application and would have comprised an obvious inclusion for at least the purpose of better addressing the presence of emerging concepts by updating based on more recent literature when encountering a gap in the knowledge base; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claims 12, 17—the claims are considered to recite substantially similar subject matter to that of claim 1 supra and are similarly rejected. IN the case of the recited 3 or more hops recited claim 17 as an alternative to the two hops of claims 1, 12 this is considered obvious as Lei in view of Chen teaches operating the system using an adjustable threshold by which a neighborhood to a topic is determined said threshold comprising all entities within h hops and doing so with respect to three hops is considered no more than routine optimization of such an algorithm and as such one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claims 13, 18—the claims are considered to recite substantially similar subject matter to that of claim 8 supra and are similarly rejected. Regarding claims 14, 19—the claims are considered to recite substantially similar subject matter to that of claim 9 supra and are similarly rejected. Regarding claims 15, 20—the claims are considered to recite substantially similar subject matter to that of claim 9 supra and are similarly rejected. Regarding claims 16—the claim is considered to recite substantially similar subject matter to that of claim 7 supra and is similarly rejected . 07-21-aia AIA Claim s 2-4 rejected under 35 U.S.C. 103 as being unpatentable over Lei: 20220207343 hereinafter Lei further in view of Chen: “Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases,” (copy provided by Examiner, copyright 2019 and hereinafter Chen) as applied to claims 1, 5-20 supra and further in view of Lukovnikov: “Pretrained Transformers for Simple Question Answering over Knowledge Graphs,” (copy provided by Examiner, copyright 1/2020 and hereinafter Luk) . Regarding claim 2 Lei in view of Chen teaches or suggests: The method of claim 1, further comprising: a BERT encoder. But does not explicitly discuss that training a BERT encoder comprises a pretraining phase and a fine-tuning phase. In a related field of endeavor Luk teaches a system and method for knowledge graph question answering (KGQA); wherein a pretrained transformer network, such as BERT which is pretrained to word embeddings (Luk: Abstract, § 1), is subsequently fine-tuned to a question answering task (Luk: § 1); by performance of entity span detection and relation prediction, candidate generation matching or similar to the detected entity, and ranking of entity and relation pairs to generate a reified query and retrieve and answer thereto (Luk: ¶ 2, 2.2-2.4). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to fine tune the Lei in view of Chen BERT based system in a manner similar to that taught or suggested by Luk and for at least the purpose of improving performance of a pretrained model based on fine tuning such as that disclosed; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 3 Lei in view of Chen in view of Luk teaches or suggests: The method of claim 2, wherein pre-training phase further comprises: pre-training, by the processing system, the BERT encoder according to a masked language modeling process adapted to predict missing tokens from their placeholders in a given sequence (Luk: § 1, 2.1: BERT is a transformer network pre trained on masked language modelling to predict masked portions of a sequence). The claim is considered obvious over Lei as modified by Chen and Luk as addressed in the base claim as it would have been obvious to apply the further teaching of Lei, Chen, and/or Luk to the modified device of Lei, Chen, and Luk; one of ordinary skill in the art would have expected only predictable results therefrom. Regarding claim 4 Lei in view of Chen in view of Luk teaches or suggests: The method of claim 2, wherein the fine-tuning phase further comprises: training the BERT encoder according to a next sentence prediction outcome task (Luk: § 2.1: BERT pretrained and fine tuned to predict likelihood of a next sentence in a text). The claim is considered obvious over Lei as modified by Chen and Luk as addressed in the base claim as it would have been obvious to apply the further teaching of Lei, Chen, and/or Luk to the modified device of Lei, Chen, and Luk; one of ordinary skill in the art would have expected only predictable results therefrom. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL C MCCORD whose telephone number is (571)270-3701. The examiner can normally be reached 730-630 M-F. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /PAUL C MCCORD/Primary Examiner, Art Unit 2692 Application/Control Number: 18/928,305 Page 2 Art Unit: 2692 Application/Control Number: 18/928,305 Page 3 Art Unit: 2692 Application/Control Number: 18/928,305 Page 4 Art Unit: 2692 Application/Control Number: 18/928,305 Page 5 Art Unit: 2692 Application/Control Number: 18/928,305 Page 6 Art Unit: 2692 Application/Control Number: 18/928,305 Page 7 Art Unit: 2692 Application/Control Number: 18/928,305 Page 8 Art Unit: 2692 Application/Control Number: 18/928,305 Page 9 Art Unit: 2692 Application/Control Number: 18/928,305 Page 10 Art Unit: 2692 Application/Control Number: 18/928,305 Page 12 Art Unit: 2692 Application/Control Number: 18/928,305 Page 13 Art Unit: 2692 Application/Control Number: 18/928,305 Page 14 Art Unit: 2692 Application/Control Number: 18/928,305 Page 15 Art Unit: 2692 Application/Control Number: 18/928,305 Page 16 Art Unit: 2692 Application/Control Number: 18/928,305 Page 17 Art Unit: 2692
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