CTNF 19/268,202 CTNF 86364 DETAILED ACTION Priority Applicant’s claim for the benefit to 17/710009 filed March 3, 2022 is acknowledged. 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 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of U.S. Patent No. 12380145 . Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of Pat#12380145 read on the instant claims. The table below shows a claim mapping for claim 1. Claims 8 and 15 of the instant claims relate to claims 8 and 15 of Pat#12380145 in a similar manner. The dependent claims of Pat#12380145 relate to the dependent claims of the instant application . Instant Application: 1. (Currently Amended) A method comprising: receiving a search query pertaining to a request for information; accessing a set of documents, comprising a document query and a document response, wherein the document query is embedded in an embedding space according to an embedding models; creating a search query embedding of the search query using the embedding model; using the search query embedding to perform a semantic search on the embedding space of the set of documents; and returning at least one returned document of the set of documents as a search result based on a proximity of the document query from the at least one returned document to the search query in the embedding space, wherein the returned document, along with metadata that is associated with the returned document and that includes an identifier for an individual that generated or approved the returned document, is displayed in a graphical user interface (GUI). Pat#12380145: 1. A computer-implemented method comprising, via at least one computing device comprising at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the computing device to perform the computer-implemented method via : receiving a natural language search query pertaining to a request for information; accessing a set of documents, each document of the set of documents comprising a document query and a document response, each document query embedded in an embedding space according to an embedding model, wherein the set of documents are clustered into a plurality of cells based on the document query ; creating a search query embedding of the search query using the embedding model; assigning the search query embedding to a cell of the plurality of cells ; using the search query embedding to perform a semantic search on the embedding space of documents clustered in the cell ; and returning at least one returned document of the set of documents as a search result based on a proximity of the document query from the at least one returned document to the search query in the embedding space, wherein the returned document along with metadata associated with the returned document includes an identifier for an individual that generated or approved the returned document is displayed in a graphical user interface (GUI) . 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. 07-34-01 Claims 1-20 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. With regard to claims 1, 8, and 15, claim 1 recites “wherein the returned document, along with metadata that is associated with the returned document and that includes an identifier for an individual that generated or approved the returned document, is displayed in a graphical user interface (GUI)”. This claim limitation lacks antecedent basis as it is unclear what “that” refers to. It is unclear what includes the identifier for said individual. One of ordinary skill in the art may reasonably read the claim to mean that the returned document includes the identifier, or that the metadata includes the identifier. Furthermore, the arrangement of the limitations makes it difficult to decipher what is being returned, what is displayed, and what is a description of the metadata/individual/returned document. For examination purposes this claim limitation has been read in light of Paragraph [0040] to mean -- wherein the returned document is displayed in a graphical user interface (GUI) along with metadata, wherein the metadata includes an identifier for an individual that generated or approved the returned document--. With regard to claim 8, the claim recites “An apparatus comprising:… a graphical user interface (GUI) associated with the apparatus”. This claim limitation appears to describe the claimed apparatus as being comprised of items associated with itself. The claim limitation appears to contain circular references, where the claimed device is defined based upon itself. For examination purposes the claimed apparatus has been interpreted as comprising the GUI. Claim Objections 07-29-01 Claims 2-4 are objected to because of the following informalities. Appropriate correction is required. With regard to claim 2, the claim recites “wherein the request for information is a request from a regulatory entity , and the set of documents comprise previous responses to regulatory queries.” This claim limitation appears to use language that recites the same limitation twice as if it where a new limitation. This introduces confusion in the claim language, as the distinction between ‘the request’ and ‘the request for information’ is brought into question. For examination purposes this claim limitation has been construed to mean --wherein the request for information is from a regulatory entity, and the set of documents comprise previous responses to regulatory queries--. With regard to claim 3, the clam recites “wherein a plurality of documents are returned as search results , the plurality of documents being ranked based on the respective proximities of their respective document queries to the search query in the embedding space”. It is unclear if this claim limitation is reciting a new search result, or attempting to refer back to the previously recited search results. Furthermore, this claim limitation appears to recite a claim element (e.g. plurality of documents) which appears to overlap in scope with the previously recited “at least one returned document” which was recited as the search result in the parent claim. The distinction between the at least one returned document and the plurality of documents is unclear. For examination purposes this claim limitation has been construed to mean -- wherein the at least one returned document comprises a plurality of returned documents, the plurality of documents being ranked based on the respective proximities of their respective document queries to the search query in the embedding space -- Claim Rejections - 35 USC § 103 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, 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. 07-21-aia AIA Claim s 1, 2, 5-9, 14-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Divakaran [2022/0138433] in view of Balsz [2018/0261333] . With regard to claim 1 Divakaran teaches A method comprising : receiving a search query (Divakaran ¶ 85 “a search question /content can be input to a content comprehension system of the present principles … to search for content… When the comet comprehension system receives a question directed to a search for content; ¶102 “a question vector representation is determined for the received question.”) pertaining to a request for information as to search for content ( ID ) ; accessing a set of documents as the answer pairs 1008 (Divakaran ¶ 81 “the embedding model embeds information/data related to the generated question answer pairs 1008 determined for each of the domain adapted stem questions and the related contents in the content domain.”) , comprising a document query as the adapted stem questions ( ID ) and a document response as the related content ( ID ) , wherein the document query as embedding the adapted stem question (Divakaran ¶ 80 “a content domain for which a respective domain adapted stem question was generated can be embedded in a common embedding space 1010 during training.”) is embedded in an embedding space as the common embedding space ( Id ) according to an embedding models (Divakaran ¶ 86 “the training and embedding of the present principles, for example, as described with respect to FIG. 10, and generate a model (depicted in FIG. 11) for each of the domain adapted question in each layer of the hierarchal taxonomy”) ; creating a search query embedding as question answer pair vector embedding representation (Divakaran ¶ 85 “the content comprehension system receives a question directed to a search for content… A better representation of the received question is determined. The determining question that the representation is projected into the trained, common/joint embedded space 1010 of FIG. 10, a distance function is implemented to determine a question answer pair vector representation embedded in the trained, common/joint embedding space closest to the projected determined question vector representation to determine content in, for example, the storage device 180 relevant to the received question.”) of the search query as the question ( Id ) using the embedding model as common/joint embedding space ( Id; ¶80) ; using the search query embedding as question answer pair vector embedding (Divakaran ¶ 85) to perform a semantic (Divakaran, ¶35 “semantic content retrieval”) search on the embedding space as common embedding (Divakaran ¶ 80) of of the set of documents clustered in the cell as the respective content domain, for example the pancake domain (Divakaran ¶ 80, ¶81) ; and returning at least one returned document of the set of documents as a search result as the identified content (Divakaran, ¶ 85; ¶104 “at 1408, a distance measured is determined between the determine question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received common question”; ¶103) based on a proximity as the closest in distance ( Id ) of the document query from the at least one returned document as the respective embedded question vector representations in the common embedding space ( ID; ¶ 81; ¶96; ¶97; ¶100) to the search query as the determine question vector representation ( ID; ¶102 ) in the embedding space as the common/joint embedding space ( ID ) , wherein the returned document as the identified content (Divakaran, ¶ 85; ¶104 “at 1408, a distance measured is determined between the determine question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received common question”; ¶103) , [[ along with metadata that is associated with the returned document and that includes an identifier for an individual that generated or approved the returned document ]], is displayed in a [[ graphical ]] user interface (GUI) as the user interface on the display (Divakaran, ¶106 “Data associated with a content comprehension and response system in accordance with the present principles can be presented to a user using an output device of the computing device 1500, such as a display, a printer or any other form of output device.”; ¶108 “In various embodiments, a user interface can be generated and displayed on display 1580.”) . Divakaran does not explicitly teach wherein the returned document along with metadata that is associated with the returned document and that includes an identifier for an individual that generated or approved the returned document , is displayed in a graphical user interface (GUI). Balsz teaches wherein the returned document along with metadata (Balsz, ¶45 “the system, in response to a user initiated search, will extract and organize data from the sources, and display the extracted data in a first reporting step. The first reporting step may include displaying an annotated source include metadata”) that is associated with the returned document and that includes an identifier for an individual that generated as the authors (¶47 “The metadata to be displayed can include …, intellectual property ownership data, researchers, authors, contact information of owners or licensees, phase of clinical testing or regulatory approval, …regulatory documentation…”) or approved as regulatory approval, or regulatory documentation ( Id ) the returned document (Balsz, ¶45) , is displayed (Balsz, ¶45) in a graphical user interface (GUI) (Balsz, ¶45 “The search result data may be shown to the user in a graphical user interface (GUI) or dashboard that is interactive with the user. The GUI or dashboard may display the major attributes of the sources and/or drugs that were found in the search”) . It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the search system taught by Divakaran as the underlying search system for the Drug Discovery system taught by Balsz as it yields the predictable results of providing a system for performing the underling query operations (Balsz, ¶6). With regard to claims 2 and 16, the proposed combination further teaches wherein the search query is a natural language search query (Divakaran figure 4 see questions in element 408, for example “What is a Pancake?”) ; and wherein the request for information is a request from a regulatory entity (Balsz, ¶99 “The DD system can be used by many types of users. The user can be any person or persons, and may be any entity or entities.”; ¶56 “The regulatory information repository 24 may include FDA (Food and Drug Administration) resources, EMA (European Medicines Agency) resources, and other governmental and non-governmental resources.”) , and the set of documents comprise previous responses (Divakaran, ¶90 “the content comprehension system 100 of FIG. 1 can compare content information 1102 of ingredients for making crepes previously received by the content comprehension system 100 of FIG. 1 and stored”) to regulatory queries (Balsz, ¶95 “a regulatory submission or report”; ¶121 “In some embodiments, the metadata may include regulatory submissions, or regulatory documentation”) . With regard to claims 5 and 13, the proposed combination further teaches training (Divakaran, ¶ 80 “Fig. 10 depicts a graphical representation of an embedding/training process in accordance with an embodiment of the present principles including machine learning processes of the present principles as applied with respect to the first layer (remember layer) 202 of the hierarchal tax money 200 of the embodiment of figure 2. In accordance with the present principles, generate question answer pairs are embedded in a common/joint embedding space 1010”; ¶ “the embedding module embeds information/data related to the generated question answer pairs 1008 determined for each of the domain adapted stem questions and the related content”) the embedding model (Divakaran ¶ 86 “the training and embedding of the present principles, for example, as described with respect to FIG. 10, and generate a model (depicted in FIG. 11) for each of the domain adapted question in each layer of the hierarchal taxonomy”) with a set of labeled data (Divakaran, ¶ 45, “in some embodiments, a human can generate the adapted stem questions by applying stem questions to relevant content domains”; ¶63) ; and training an indexing model as the machine learning model implemented by the processor (¶80; ¶110) configured to perform the semantic search (Divakaran, ¶35 “semantic content retrieval”) on the embedding space (Divakaran ¶ 85 “in the trained common/joint embedding space of the present principles, as the trained, common/joint embedded space 1010 of FIG. 10, a distance function is implemented to determine a question answer pair vector representation embedded in the trained, common/joint embedding space closest to the projected determine question vector representation to determine content in, for example, the storage device 180 relevant to the received question. In some embodiments of the present principles, the distance function can include at least one of the cosine function, a Euclidean function, and/or a Lagrangian point 1, and an L1, function.”) . With regard to claims 6 and 14, the proposed combination further teaches wherein the set of documents as the answer pairs 1008 (Divakaran ¶ 81 “the embedding model embeds information/data related to the generated question answer pairs 1008 determined for each of the domain adapted stem questions and the related contents in the content domain.”) is a first set of documents (Divakaran, ¶ 41 “The first content example 302 comprises a recipe for making pancakes from scratch”) and the semantic search is a first semantic search (Divakaran, ¶35 “semantic content retrieval”; ¶85) , the method further comprising: Swapping as changing a pancake recipe to a crepe recipe (Divakaran, ¶90 “the changed content domain”; ¶4 “The content understanding of current system consists of answering questions about different types of content, with no regard to the difficulty of the questions or any other relationship between the questions.”; ¶45 in some embodiments a human can generate the domain adapted stem questions by applying stem questions to relevant content domains of, for example, content stored in the storage device 180. In yet alternative embodiments, a machine-learning process can be implemented to determine domain adapted stem questions 408 in embodiments in which a user adds to or modifies the domain knowledge applied, for example, by changing a recipe from a pancake recipe to a crepe recipe and/or by adding to or modifying the stem questions”) the first set of documents as recipes for making pancakes (Divakaran, ¶41 “FIG. 3 depicts two examples of content that can be received and processed by a content comprehension and response system of the present principles … the first content example 302 comprises a recipe for making pancakes from scratch”) for a second set of documents different as the second example of content, e.g. Nina’s stories or crepe recipes (Divakaran, ¶ 42 “the second example 324 depicted in FIG. 3 comprises a story entitled Nina’s Family Moves to New Delhi”; ¶80 The content to be embedded can include recipes for making pancakes from scratch, stories about Nina traveling on a train, recipes for making crepes, stories about Nina traveling in a car, and any other content a user may think is relevant to include in the embedding space “”) from the first set of documents (Divakaran, ¶ 41 “The first content example 302 comprises a recipe for making pancakes from scratch”) ; and applying the indexing model as the machine learning model implemented by the processor (¶80; ¶110) to perform a second semantic search (Divakaran ¶ 85 “in the trained common/joint embedding space of the present principles, as the trained, common/joint embedded space 1010 of FIG. 10, a distance function is implemented to determine a question answer pair vector representation embedded in the trained, common/joint embedding space closest to the projected determine question vector representation to determine content in, for example, the storage device 180 relevant to the received question. In some embodiments of the present principles, the distance function can include at least one of the cosine function, a Euclidean function, and/or a Lagrangian point 1, and an L1, function.”) based on a new (Divakaran figure 4 see two distinct questions clearly directed to the two distinct content in element 408) search query as the user entering a new question (Divakaran ¶ 85 “a search question /content can be input to a content comprehension system of the present principles … to search for content… When the comet comprehension system receives a question directed to a search for content) . With regard to claims 7 and 20 the proposed combination further teaches wherein the proximity of the document query from the returned document to the search query in the embedding space is represented as a cosine similarity as cosine function (Divakaran ¶ 85 “in the trained common/joint embedding space of the present principles, as the trained, common/joint embedded space 1010 of FIG. 10, a distance function is implemented to determine a question answer pair vector representation embedded in the trained, common/joint embedding space closest to the projected determine question vector representation to determine content in, for example, the storage device 180 relevant to the received question. In some embodiments of the present principles, the distance function can include at least one of the cosine function, a Euclidean function, and/or a Lagrangian point 1, and an L1, function.”) or Euclidean distance as Euclidean function ( Id ) . With regard to claim 8 Divakaran teaches An apparatus comprising: a processing circuit as the processor (Divakaran, ¶12 “a system for content comprehension and response of a content collection includes a processor and a memory coupled to the processor, the memory having stored therein at least one of programs or instructions executable by the processor to configure the system to”) ; and memory as memory ( id ) storing instructions as programs or instructions ( Id ) that, when executed by the processing circuit ( Id ) , cause the apparatus to: receive a search query (Divakaran ¶ 85 “a search question /content can be input to a content comprehension system of the present principles … to search for content… When the comet comprehension system receives a question directed to a search for content; ¶102 “a question vector representation is determined for the received question.”) pertaining to a request for information as to search for content ( ID ) ; access a set of documents as the answer pairs 1008 (Divakaran ¶ 81 “the embedding model embeds information/data related to the generated question answer pairs 1008 determined for each of the domain adapted stem questions and the related contents in the content domain.”) , comprising a document query as the adapted stem questions ( ID ) and a document response as the related content ( ID ) , wherein the document query as embedding the adapted stem question (Divakaran ¶ 80 “a content domain for which a respective domain adapted stem question was generated can be embedded in a common embedding space 1010 during training.”) is embedded in an embedding space as the common embedding space ( Id ) according to an embedding model (Divakaran ¶ 86 “the training and embedding of the present principles, for example, as described with respect to FIG. 10, and generate a model (depicted in FIG. 11) for each of the domain adapted question in each layer of the hierarchal taxonomy”) ; create a search query embedding as question answer pair vector embedding representation (Divakaran ¶ 85 “the content comprehension system receives a question directed to a search for content… A better representation of the received question is determined. The determining question that the representation is projected into the trained, common/joint embedded space 1010 of FIG. 10, a distance function is implemented to determine a question answer pair vector representation embedded in the trained, common/joint embedding space closest to the projected determined question vector representation to determine content in, for example, the storage device 180 relevant to the received question.”) of the search query as the question ( Id ) using the embedding model as common/joint embedding space ( Id; ¶80) ; use the search query embedding as question answer pair vector embedding (Divakaran ¶ 85) to perform a semantic (Divakaran, ¶35 “semantic content retrieval”) search on the embedding space as common embedding (Divakaran ¶ 80) of of the set of documents clustered in the cell as the respective content domain, for example the pancake domain (Divakaran ¶ 80, ¶81) ; and return at least one returned document of the set of documents as a search result as the identified content (Divakaran, ¶ 85; ¶104 “at 1408, a distance measured is determined between the determine question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received common question”; ¶103) based on a proximity as the closest in distance ( Id ) of the document query from the at least one returned document as the respective embedded question vector representations in the common embedding space ( ID; ¶ 81; ¶96; ¶97; ¶100) to the search query as the determine question vector representation ( ID; ¶102 ) in the embedding space as the common/joint embedding space ( ID ) , wherein the returned document as the identified content (Divakaran, ¶ 85; ¶104 “at 1408, a distance measured is determined between the determine question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received common question”; ¶103) , [[ along with metadata that is associated with the returned document and that includes an identifier for an individual that generated or approved the returned document ]], is displayed in a [[ graphical ]] user interface (GUI) as the user interface on the display (Divakaran, ¶106 “Data associated with a content comprehension and response system in accordance with the present principles can be presented to a user using an output device of the computing device 1500, such as a display, a printer or any other form of output device.”; ¶108 “In various embodiments, a user interface can be generated and displayed on display 1580.”) associated with the apparatus ( Id ) . Divakaran does not explicitly teach wherein the returned document along with metadata that is associated with the returned document and that includes an identifier for an individual that generated or approved the returned document , is displayed in a graphical user interface (GUI). Balsz teaches wherein the returned document along with metadata (Balsz, ¶45 “the system, in response to a user initiated search, will extract and organize data from the sources, and display the extracted data in a first reporting step. The first reporting step may include displaying an annotated source include metadata”) that is associated with the returned document and that includes an identifier for an individual that generated as the authors (¶47 “The metadata to be displayed can include …, intellectual property ownership data, researchers, authors, contact information of owners or licensees, phase of clinical testing or regulatory approval, …regulatory documentation…”) or approved as regulatory approval, or regulatory documentation ( Id ) the returned document (Balsz, ¶45) , is displayed (Balsz, ¶45) in a graphical user interface (GUI) (Balsz, ¶45 “The search result data may be shown to the user in a graphical user interface (GUI) or dashboard that is interactive with the user. The GUI or dashboard may display the major attributes of the sources and/or drugs that were found in the search”) . It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the search system taught by Divakaran as the underlying search system for the Drug Discovery system taught by Balsz as it yields the predictable results of providing a system for performing the underling query operations (Balsz, ¶6). With regard to claim 9 the proposed combination further teaches wherein the request for information is from a regulatory entity (Balsz, ¶99 “The DD system can be used by many types of users. The user can be any person or persons, and may be any entity or entities.”; ¶56 “The regulatory information repository 24 may include FDA (Food and Drug Administration) resources, EMA (European Medicines Agency) resources, and other governmental and non-governmental resources.”) , and the set of documents comprises previous responses as the respective embedded question vector representations in the common embedding space ( ID; ¶ 81; ¶96; ¶97; ¶100) to at least one previous query as the determine question vector representation ( ID; ¶102 ) from the regulatory entity (Balsz, ¶99, ¶56) ; wherein the search query is also displayed on the GUI (Balsz, ¶45) ; and wherein the search query is a natural language search query (Divakaran figure 4 see questions in element 408, for example “What is a Pancake?”) . With regard to claim 15 Divakaran teaches A non-transitory computer-readable storage medium having executable instructions stored thereon, which when executed by a processing circuit (Divakaran, ¶12 “a system for content comprehension and response of a content collection includes a processor and a memory coupled to the processor, the memory having stored therein at least one of programs or instructions executable by the processor to configure the system to”) , cause the processing circuit to: receive a search query (Divakaran ¶ 85 “a search question /content can be input to a content comprehension system of the present principles … to search for content… When the comet comprehension system receives a question directed to a search for content; ¶102 “a question vector representation is determined for the received question.”) pertaining to a request for information as to search for content ( ID ) ; access a set of documents as the answer pairs 1008 (Divakaran ¶ 81 “the embedding model embeds information/data related to the generated question answer pairs 1008 determined for each of the domain adapted stem questions and the related contents in the content domain.”) , comprising a document query as the adapted stem questions ( ID ) and a document response as the related content ( ID ) , wherein the document query as embedding the adapted stem question (Divakaran ¶ 80 “a content domain for which a respective domain adapted stem question was generated can be embedded in a common embedding space 1010 during training.”) is embedded in an embedding space as the common embedding space ( Id ) according to an embedding model (Divakaran ¶ 86 “the training and embedding of the present principles, for example, as described with respect to FIG. 10, and generate a model (depicted in FIG. 11) for each of the domain adapted question in each layer of the hierarchal taxonomy”) ; create a search query embedding as question answer pair vector embedding representation (Divakaran ¶ 85 “the content comprehension system receives a question directed to a search for content… A better representation of the received question is determined. The determining question that the representation is projected into the trained, common/joint embedded space 1010 of FIG. 10, a distance function is implemented to determine a question answer pair vector representation embedded in the trained, common/joint embedding space closest to the projected determined question vector representation to determine content in, for example, the storage device 180 relevant to the received question.”) of the search query as the question ( Id ) using the embedding model as common/joint embedding space ( Id; ¶80) ; use the search query embedding as question answer pair vector embedding (Divakaran ¶ 85) to perform a semantic (Divakaran, ¶35 “semantic content retrieval”) search on the embedding space as common embedding (Divakaran ¶ 80) of of the set of documents clustered in the cell as the respective content domain, for example the pancake domain (Divakaran ¶ 80, ¶81) ; and return at least one returned document of the set of documents as a search result as the identified content (Divakaran, ¶ 85; ¶104 “at 1408, a distance measured is determined between the determine question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received common question”; ¶103) based on a proximity as the closest in distance ( Id ) of the document query from the at least one returned document as the respective embedded question vector representations in the common embedding space ( ID; ¶ 81; ¶96; ¶97; ¶100) to the search query as the determine question vector representation ( ID; ¶102 ) in the embedding space as the common/joint embedding space ( ID ) , wherein the returned document as the identified content (Divakaran, ¶ 85; ¶104 “at 1408, a distance measured is determined between the determine question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received common question”; ¶103) , [[ along with metadata that is associated with the returned document and that includes an identifier for an individual that generated or approved the returned document ]], is displayed in a [[ graphical ]] user interface (GUI) as the user interface on the display (Divakaran, ¶106 “Data associated with a content comprehension and response system in accordance with the present principles can be presented to a user using an output device of the computing device 1500, such as a display, a printer or any other form of output device.”; ¶108 “In various embodiments, a user interface can be generated and displayed on display 1580.”) associated with the apparatus ( Id ) . Divakaran does not explicitly teach wherein the returned document along with metadata that is associated with the returned document and that includes an identifier for an individual that generated or approved the returned document , is displayed in a graphical user interface (GUI). Balsz teaches wherein the returned document along with metadata (Balsz, ¶45 “the system, in response to a user initiated search, will extract and organize data from the sources, and display the extracted data in a first reporting step. The first reporting step may include displaying an annotated source include metadata”) that is associated with the returned document and that includes an identifier for an individual that generated as the authors (¶47 “The metadata to be displayed can include …, intellectual property ownership data, researchers, authors, contact information of owners or licensees, phase of clinical testing or regulatory approval, …regulatory documentation…”) or approved as regulatory approval, or regulatory documentation ( Id ) the returned document (Balsz, ¶45) , is displayed (Balsz, ¶45) in a graphical user interface (GUI) (Balsz, ¶45 “The search result data may be shown to the user in a graphical user interface (GUI) or dashboard that is interactive with the user. The GUI or dashboard may display the major attributes of the sources and/or drugs that were found in the search”) . It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the search system taught by Divakaran as the underlying search system for the Drug Discovery system taught by Balsz as it yields the predictable results of providing a system for performing the underling query operations (Balsz, ¶6). With regard to claim 19 the proposed combination further teaches train (Divakaran, ¶ 80 “Fig. 10 depicts a graphical representation of an embedding/training process in accordance with an embodiment of the present principles including machine learning processes of the present principles as applied with respect to the first layer (remember layer) 202 of the hierarchal tax money 200 of the embodiment of figure 2. In accordance with the present principles, generate question answer pairs are embedded in a common/joint embedding space 1010”; ¶ “the embedding module embeds information/data related to the generated question answer pairs 1008 determined for each of the domain adapted stem questions and the related content”) the embedding model (Divakaran ¶ 86 “the training and embedding of the present principles, for example, as described with respect to FIG. 10, and generate a model (depicted in FIG. 11) for each of the domain adapted question in each layer of the hierarchal taxonomy”) with a set of labeled data (Divakaran, ¶ 45, “in some embodiments, a human can generate the adapted stem questions by applying stem questions to relevant content domains”; ¶63) ; train an indexing model as the machine learning model implemented by the processor (¶80; ¶110) configured to perform the semantic search (Divakaran, ¶35 “semantic content retrieval”) on the embedding space (Divakaran ¶ 85 “in the trained common/joint embedding space of the present principles, as the trained, common/joint embedded space 1010 of FIG. 10, a distance function is implemented to determine a question answer pair vector representation embedded in the trained, common/joint embedding space closest to the projected determine question vector representation to determine content in, for example, the storage device 180 relevant to the received question. In some embodiments of the present principles, the distance function can include at least one of the cosine function, a Euclidean function, and/or a Lagrangian point 1, and an L1, function.”) ; and wherein the set of documents as the answer pairs 1008 (Divakaran ¶ 81 “the embedding model embeds information/data related to the generated question answer pairs 1008 determined for each of the domain adapted stem questions and the related contents in the content domain.”) is a first set of documents (Divakaran, ¶ 41 “The first content example 302 comprises a recipe for making pancakes from scratch”) and the semantic search is a first semantic search (Divakaran, ¶35 “semantic content retrieval”; ¶85) , and wherein the instructions further configure the computer to: swap as changing a pancake recipe to a crepe recipe (Divakaran, ¶90 “the changed content domain”; ¶4 “The content understanding of current system consists of answering questions about different types of content, with no regard to the difficulty of the questions or any other relationship between the questions.”; ¶45 in some embodiments a human can generate the domain adapted stem questions by applying stem questions to relevant content domains of, for example, content stored in the storage device 180. In yet alternative embodiments, a machine-learning process can be implemented to determine domain adapted stem questions 408 in embodiments in which a user adds to or modifies the domain knowledge applied, for example, by changing a recipe from a pancake recipe to a crepe recipe and/or by adding to or modifying the stem questions”) the first set of documents as recipes for making pancakes (Divakaran, ¶41 “FIG. 3 depicts two examples of content that can be received and processed by a content comprehension and response system of the present principles … the first content example 302 comprises a recipe for making pancakes from scratch”) for a second set of documents different as the second example of content, e.g. Nina’s stories or crepe recipes (Divakaran, ¶ 42 “the second example 324 depicted in FIG. 3 comprises a story entitled Nina’s Family Moves to New Delhi”; ¶80 The content to be embedded can include recipes for making pancakes from scratch, stories about Nina traveling on a train, recipes for making crepes, stories about Nina traveling in a car, and any other content a user may think is relevant to include in the embedding space “”) from the first set of documents (Divakaran, ¶ 41 “The first content example 302 comprises a recipe for making pancakes from scratch”) ; and apply the indexing model as the machine learning model implemented by the processor (¶80; ¶110) to perform a second semantic search (Divakaran ¶ 85 “in the trained common/joint embedding space of the present principles, as the trained, common/joint embedded space 1010 of FIG. 10, a distance function is implemented to determine a question answer pair vector representation embedded in the trained, common/joint embedding space closest to the projected determine question vector representation to determine content in, for example, the storage device 180 relevant to the received question. In some embodiments of the present principles, the distance function can include at least one of the cosine function, a Euclidean function, and/or a Lagrangian point 1, and an L1, function.”) based on a new (Divakaran figure 4 see two distinct questions clearly directed to the two distinct content in element 408) natural language (Divakaran figure 4 see questions in element 408, for example “What is a Pancake?”) search query as the user entering a new question (Divakaran ¶ 85 “a search question /content can be input to a content comprehension system of the present principles … to search for content… When the comet comprehension system receives a question directed to a search for content) . 07-21-aia AIA Claim s 3, 4, 10-12, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Divakaran in view of Blasz and Childovskii [2004/0068486] . With regard to claim 3 the proposed combination further teaches wherein a plurality of documents are returned as search results as the identified content (Divakaran, ¶ 85; ¶104 “at 1408, a distance measured is determined between the determine question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received common question”; ¶103) , the plurality of documents ( Id ) being [[ ranked ]] based on the respective proximities as the distance measure (Divakaran, ¶9 “determining a distance measure between the determined question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received question”; ¶85) of their respective document queries as the respective question vector ( Id ) to the search query as the determined question vector ( Id ) in the embedding space as the common embedding space ( Id ) , the method further comprising: submitting the document queries as the adapted stem questions (Divakaran, ¶81) from the plurality of documents as the identified content (Divakaran, ¶ 85; ¶104; ¶103) to [[ a re-ranking model configured to re-rank the document queries based on prior user feedback; and re-ranking the plurality of documents based on the re-ranking model ]]. Divakaran does not explicitly teach ranked… a re-ranking model configured to re-rank the document queries based on prior user feedback; and re-ranking the plurality of documents based on the re-ranking model. Chidlovskii teaches the plurality of documents being ranked as ranked (Chidlovskii, ¶68 “The following method for ranking a list of results requires that at least one document is selected by a user”) based on the respective proximities (Chidlovskii, ¶70 “For each unmarked document d, the similar representation v(d)={(t i , v i )} is extracted.”) of their respective document queries as the vector for the unmarked document d ( Id ) to the search query in the embedding space as the vector for the marked document in vector space, e.g. V i 2 (Childvoskii, ¶69 “Following the Vector-Space Model (VSM) the document view is transformed into a vector: v(d)=(t i ,w i , where the term t i is (attribute, keyword) and the term w i is the frequency of term t i in the document. Selected documents yield terms with positive weights, and unselected documents with negative weights. For all selected and unselected documents, their vectors are merged by summing up weights for equivalent attribute-keyword pairs so as to obtain a relevance term vector L=(t i ,w i , that is ordered by the decreasing values of weights.”; See formula in ¶70) submitting the document queries … to a re-ranking model (Chidlovskii, ¶66 “D.2.3 Re-Ranking Without Query Refinement”; ¶69) configured to re-rank (Chidlovskii, ¶67 “Thus, after such re-ranking, all selected documents will appear at the beginning of the re-ranked list of results, with all unselected documents appearing at the end of list.”) the document queries based on prior user feedback (Chidlovskii, ¶67 “the user relevance feedback is used to re-rank documents in the answer list, without re-querying the information sources ( at 226 and 218).”) ; and re-ranking the plurality of documents (Chidlovskii, ¶67) based on the re-ranking model (Chidlovskii, ¶66; ¶69) . It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have the ranked/re-ranked the search results taught by Divakaran using the ranking/re-ranking methods taught by Chidlovskii as it yields the particular results of enabling the system to provide a unified ranking when the information is obtained from distinct sources (Chidlovskii, ¶6) such as the distinct content domains (Divakaran, ¶71; ¶76) With regard to claims 4 and 12, the proposed combination further teaches receiving user feedback (Chidlovskii, ¶67 “the user relevance feedback is used to re-rank documents in the answer list, without re-querying the information sources ( at 226 and 218).”) for a selected search result as the document selected by the user (Chidlovskiik, ¶68 “The following method for ranking a list of results requires that at least one document is selected by a user.”) ; and retraining the re-ranking model based on the received user feedback (Chidlovskii, ¶67 “the user relevance feedback is used to re-rank documents in the answer list, without re-querying the information sources ( at 226 and 218).”) . With regard to claims 10 and 17, the proposed combination further teaches wherein the at least one returned document comprises a plurality of returned documents as the identified content (Divakaran, ¶ 85; ¶104 “at 1408, a distance measured is determined between the determine question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received common question”; ¶103) , each of the plurality of returned documents ( Id ) being [[ ranked ]] based on the proximity as the distance measure (Divakaran, ¶9 “determining a distance measure between the determined question vector representations projected into the common embedding space and respective embedded question vector representations in the common embedding space using a distance function to identify content related to the received question”; ¶85) ; and wherein the processing circuit (Divakaran, ¶12 “a system for content comprehension and response of a content collection includes a processor and a memory coupled to the processor, the memory having stored therein at least one of programs or instructions executable by the processor to configure the system to”) is configured to send a control signal (Divakaran, ¶112 “In some embodiments, I/O interface 1530 can perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 1520) into a format suitable for use by another component (e.g., processor 1510).”) to the GUI (Balsz, ¶45 “The search result data may be shown to the user in a graphical user interface (GUI) or dashboard that is interactive with the user. The GUI or dashboard may display the major attributes of the sources and/or drugs that were found in the search”) to display the plurality of returned documents (Balsz, ¶45 “the system, in response to a user initiated search, will extract and organize data from the sources, and display the extracted data in a first reporting step. The first reporting step may include displaying an annotated source include metadata”) in a [[ ranked order ]] based on the proximity as the distance measure (Divakaran, ¶9) . Divakaran does not explicitly teach ranked… ranked order . Chidlovskii teaches the plurality of returned documents being ranked as ranked (Chidlovskii, ¶68 “The following method for ranking a list of results requires that at least one document is selected by a user”) based on the proximity (Chidlovskii, ¶70 “For each unmarked document d, the similar representation v(d)={(t i , v i )} is extracted.”) ... in a ranked order as ranked (Chidlovskii, ¶68 “The following method for ranking a list of results requires that at least one document is selected by a user”) based on the proximity (Chidlovskii, ¶70 “For each unmarked document d, the similar representation v(d)={(t i , v i )} is extracted.”). It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have the ranked/re-ranked the search results taught by Divakaran using the ranking/re-ranking methods taught by Chidlovskii as it yields the particular results of enabling the system to provide a unified ranking when the information is obtained from distinct sources (Chidlovskii, ¶6) such as the distinct content domains (Divakaran, ¶71; ¶76) With regard to claim 11 the proposed combination further teaches wherein the instructions further cause the processing circuit to: submit the document query as the adapted stem questions (Divakaran, ¶81) from the plurality of returned documents as the identified content (Divakaran, ¶ 85; ¶104; ¶103) to a re-ranking model (Chidlovskii, ¶66 “D.2.3 Re-Ranking Without Query Refinement”; ¶69) configured to re-rank (Chidlovskii, ¶67 “Thus, after such re-ranking, all selected documents will appear at the beginning of the re-ranked list of results, with all unselected documents appearing at the end of list.”) the plurality of returned document based on prior user feedback (Chidlovskii, ¶67 “the user relevance feedback is used to re-rank documents in the answer list, without re-querying the information sources ( at 226 and 218).”) provided on the GUI (Balsz, ¶45 “The search result data may be shown to the user in a graphical user interface (GUI) or dashboard that is interactive with the user. The GUI or dashboard may display the major attributes of the sources and/or drugs that were found in the search”) ; and re-rank the plurality of documents (Chidlovskii, ¶67) based on the re-ranking model (Chidlovskii, ¶66; ¶69) . With regard to claim 18 the proposed combination further teaches submit the document query as the adapted stem questions (Divakaran, ¶81) from each of the plurality of returned documents as the identified content (Divakaran, ¶ 85; ¶104; ¶103) a re-ranking model (Chidlovskii, ¶66 “D.2.3 Re-Ranking Without Query Refinement”; ¶69) configured to re-rank (Chidlovskii, ¶67 “Thus, after such re-ranking, all selected documents will appear at the beginning of the re-ranked list of results, with all unselected documents appearing at the end of list.”) the plurality of returned documents based on user feedback (Chidlovskii, ¶67 “the user relevance feedback is used to re-rank documents in the answer list, without re-querying the information sources ( at 226 and 218).” provided on the GUI (Balsz, ¶45 “The search result data may be shown to the user in a graphical user interface (GUI) or dashboard that is interactive with the user. The GUI or dashboard may display the major attributes of the sources and/or drugs that were found in the search”) ; re-rank the plurality of returned documents (Chidlovskii, ¶67) based on the re-ranking model (Chidlovskii, ¶66; ¶69) ; receive user feedback (Chidlovskii, ¶67 “the user relevance feedback is used to re-rank documents in the answer list, without re-querying the information sources ( at 226 and 218).”) for a selected search result as the document selected by the user (Chidlovskiik, ¶68 “The following method for ranking a list of results requires that at least one document is selected by a user.”) ; and retrain the re-ranking model based on the received user feedback (Chidlovskii, ¶67 “the user relevance feedback is used to re-rank documents in the answer list, without re-querying the information sources ( at 226 and 218).”) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA WILLIS whose telephone number is (571)270-7691. The examiner can normally be reached Monday-Friday 8am-2pm. 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, Ajay Bhatia can be reached at 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AMANDA L WILLIS/ Primary Examiner, Art Unit 2156 Application/Control Number: 19/268,202 Page 2 Art Unit: 2156 Application/Control Number: 19/268,202 Page 3 Art Unit: 2156 Application/Control Number: 19/268,202 Page 4 Art Unit: 2156 Application/Control Number: 19/268,202 Page 5 Art Unit: 2156 Application/Control Number: 19/268,202 Page 6 Art Unit: 2156 Application/Control Number: 19/268,202 Page 7 Art Unit: 2156 Application/Control Number: 19/268,202 Page 8 Art Unit: 2156 Application/Control Number: 19/268,202 Page 9 Art Unit: 2156 Application/Control Number: 19/268,202 Page 10 Art Unit: 2156 Application/Control Number: 19/268,202 Page 11 Art Unit: 2156 Application/Control Number: 19/268,202 Page 12 Art Unit: 2156 Application/Control Number: 19/268,202 Page 13 Art Unit: 2156 Application/Control Number: 19/268,202 Page 14 Art Unit: 2156 Application/Control Number: 19/268,202 Page 15 Art Unit: 2156 Application/Control Number: 19/268,202 Page 16 Art Unit: 2156 Application/Control Number: 19/268,202 Page 17 Art Unit: 2156 Application/Control Number: 19/268,202 Page 18 Art Unit: 2156 Application/Control Number: 19/268,202 Page 19 Art Unit: 2156 Application/Control Number: 19/268,202 Page 20 Art Unit: 2156 Application/Control Number: 19/268,202 Page 21 Art Unit: 2156 Application/Control Number: 19/268,202 Page 22 Art Unit: 2156 Application/Control Number: 19/268,202 Page 23 Art Unit: 2156 Application/Control Number: 19/268,202 Page 24 Art Unit: 2156 Application/Control Number: 19/268,202 Page 25 Art Unit: 2156 Application/Control Number: 19/268,202 Page 26 Art Unit: 2156 Application/Control Number: 19/268,202 Page 27 Art Unit: 2156 Application/Control Number: 19/268,202 Page 28 Art Unit: 2156 Application/Control Number: 19/268,202 Page 29 Art Unit: 2156 Application/Control Number: 19/268,202 Page 30 Art Unit: 2156 Application/Control Number: 19/268,202 Page 31 Art Unit: 2156 Application/Control Number: 19/268,202 Page 32 Art Unit: 2156 Application/Control Number: 19/268,202 Page 33 Art Unit: 2156 Application/Control Number: 19/268,202 Page 34 Art Unit: 2156 Application/Control Number: 19/268,202 Page 35 Art Unit: 2156 Application/Control Number: 19/268,202 Page 36 Art Unit: 2156
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