CTNF 18/908,334 CTNF 81104 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. 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 2. This communication is responsive to the RCE filed on 04/09/2026. 3. Claims 1-20 are currently pending in this Office action. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 5. 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 6. Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2024/0394965 (hereinafter Doggett) in view of U.S. 2026/0134308 (hereinafter Nagarajan et al.). Regarding claims 1, 15 and 18, Doggett discloses an apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured (fig. 1): to obtain a query, the query comprising search text and a context, the context identifying one or more documents to be searched using the search text ([0032-0033 and 0036]; “… More specifically, management engine 122 includes a query creation module 204 that generates a set of queries 202(1)-202(N) (each of which is referred to individually herein as query 202) from message 220 ”); to generate a plurality of document chunks by parsing the one or more documents, each of the plurality of document chunks comprising a portion of content of one of the one or more documents ([0043-0044]; “ After knowledge source 222 is divided into chunks 304, memories 310 are generated from individual chunks 304. In particular, one or more memories 310 can be extracted from a current chunk 308 that is combined with a summary 306 of additional chunks 304 from knowledge source 222 …”); to determine one or more chunk boosting factors for at least a subset of the plurality of document chunks ([0044-0045 and 0050]; figs. 2 and 4 as shown below; “… Each time the memory is accessed, the stability S can be updated by multiplying with a boost factor b ∈ R + . The boost factor thus determines how quickly the virtual character is capable of learning and/or how quickly memories can be strengthened through repetition …”); PNG media_image1.png 1260 1780 media_image1.png Greyscale PNG media_image2.png 1002 1831 media_image2.png Greyscale to select a subset of the plurality of document chunks based at least in part on determining a similarity between content of the plurality of document chunks and the search text, the determined similarity being based at least in part on the determined one or more chunk boosting factors for the subset of the plurality of document chunks ([0051-0055]; “ In some embodiments, matching module 206 matches embeddings 212 of queries 202 to embeddings 210 of memories 214 extracted from knowledge sources 222. For example, matching module 206 could use a vector database to perform a k-nearest-neighbors search for embeddings 210 of memories 214 that are closet to and/or within a threshold distance of embeddings 212 of queries 202 …”); to generate, based at least in part on the query, a prompt for input to a machine learning system, the prompt comprising the selected subset of the plurality of document chunks ([0057-0059]; fig. 3B as shown below; “ Management engine 122 generates one or more prompts 240 that include the returned memories 230, a representation of chat history 224 , one or more instructions 226, and/or a character description 242 …”); PNG media_image3.png 1109 738 media_image3.png Greyscale PNG media_image4.png 1789 1291 media_image4.png Greyscale to apply the prompt to the machine learning system to generate an output; and to provide an answer to the query based at least in part on the output of the machine learning system ([0085-0089]; fig. 5 as shown above; “ In step 512, management engine 122 generates, via execution of the machine learning model, a response by the virtual character to the message …”). Doggett does not explicitly disclose the features of wherein to determine one or more chunk boosting factors for at least a subset of the plurality of document chunks, the one or more chunk boosting factors being determined based at least in part on document formatting-based textual element boosting factors of textual elements within the subset of the plurality of document chunks, each of the document formatting-based textual element boosting factors characterizing whether a given textual element utilizes a given one of two or more different types of document formatting. However, Nagarajan discloses that “ The named entity recognition (NER) system 125 may extract entities and relationships from the text content of all documents in the corpus of documents 105 and generate the knowledge graph 130 ….” ([0049]) and “… In some embodiments, the entity weight may be determined based on a term frequency and a co-occurrence frequency. For example, the entity weight may be based on an entity frequency in the text indicating how often the entity appears in the specific section of the document. As another example, the entity weight may be determined based on a length of the text indicating an overall size of the text segment, adjusted for the entity's occurrence …” ([0061]). Nagarajan additionally discloses that “ …In some embodiments, extracted lines 210 may include tuples of different line segments identified by a line number. Groups of such sentences from extracted lines 210 may be concatenated to form chunks 215. As indicated, a chunk is a group of lines or sentences so that the total number of words in the chunk does not exceed a chunk threshold (e.g., 400 words). The chunk threshold is significant as the quality of results in a vector similarity match may be significantly impaired as the size of a chunk increases ” ([0067]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Nagarajan in the system of Doggeett in view of the desire to enhance the user query process by utilizing the document entity weights resulting in improving the efficiency of searching for relevant documents. In addition, Doggett discloses a computer program product comprising a non-transitory processor-readable storage medium ([0107]). Regarding claim 2, Doggett in view of Nagarajan discloses the apparatus wherein two or more different types of document formatting comprise text with a designated heading style, at least a designated font size, text emphasis and text color (Doggett: [0048 and 0050]; fig. 4) and (Nagarajan: [0073 and 0170]). Therefore, the limitations of claim 2 are rejected in the analysis of claim 1, and the claim is rejected on that basis. Regarding claim 3, Doggett in view of Nagarajan discloses the apparatus wherein the two or more different types of document formatting comprise text that is part of a numbered list and text that is part of a bulleted list (Doggett: [0045 and 0050]; fig. 4) and (Nagarajan: [0077 and 0130]; fig. 5A). Therefore, the limitations of claim 3 are rejected in the analysis of claim 1, and the claim is rejected on that basis. Regarding claim 4, Doggett in view of Nagarajan discloses the apparatus wherein the one or more chunk boosting factors are further based at least in part on whether the textual elements within the subset of the plurality of document chunks contain one or more designated keywords (Doggett: [0003 and 0033]) and (Nagarajan: [0077-0078 and 0130]; fig. 5A). Therefore, the limitations of claim 4 are rejected in the analysis of claim 1, and the claim is rejected on that basis. . Regarding claims 5, 16 and 19, Doggett in view of Nagarajan discloses the apparatus wherein a given document chunk in the subset of document chunks comprises two or more textual elements each associated with at least one of the document formatting-based textual element boosting factors, the one or more chunk boosting factors for the given document chunk being based at least in part on a combination of the document formatting-based textual element boosting factors of the two or more textual elements in the given document chunk (Doggett: [0045, 0054, 0080 and 0088]) and (Nagaranjan: [0061 and 0139]). Therefore, the limitations of claims 5, 16 and 19 are rejected in the analysis of claims 1, 15 or 18, and the claims are rejected on that basis. Regarding claim 6, Doggett in view of Nagarajan discloses the apparatus wherein a given one of the two or more textual elements in the given document chunk is associated with two or more of the document formatting-based textual element boosting factors (Doggett: [0045, 0054 and 0088]) and (Nagaranjan: [0059 and 0061]). Therefore, the limitations of claim 6 are rejected in the analysis of claim 1, and the claim is rejected on that basis. Regarding claim 7, Doggett in view of Nagarajan discloses the apparatus wherein the one or more chunk boosting factors are based at least in part on frequencies of use of the two or more different types of document formatting in the textual elements in the one or more documents (Doggett: [0050 and 0060]). Regarding claim 8, Doggett in view of Nagarajan discloses the apparatus wherein the one or more chunk boosting factors are document-specific for a given one of the one or more documents (Doggett: [0050, 0053 and 0060]). Regarding claim 9, Doggett in view of Nagarajan discloses the apparatus wherein the one or more chunk boosting factors are document-specific for a given one of the one or more documents responsive to determining that frequencies of different types of document formatting in textual elements in the given document exhibit at least a threshold difference from frequencies of use of the two or more different types of document formatting in textual elements in one or more other ones of the one or more documents (Doggett: [0041, 0052 and 0056]) and (Nagaranjan: [0059, 0061 and 0139]). Therefore, the limitations of claim 9 are rejected in the analysis of claim 1, and the claim is rejected on that basis. Regarding claims 10, 17 and 20, Doggett discloses the apparatus wherein the one or more chunk boosting factors utilized for a given one of the one or more documents are based at least in part on at least one of an entity that produced the given document and a document type of the given document (Doggett: [0033, 0038, 0041 and 0050]). Regarding claim 11, Doggett in view of Nagarajan discloses the apparatus wherein the one or more chunk boosting factors are further determined based at least in part on named entity recognition in the textual elements within the subset of the plurality of document chunks (Doggett: [0033, 0037. 0043 and 0050]). Regarding claim 12, Doggett in view of Nagarajan discloses the apparatus wherein the machine learning system comprises a large language model (Doggett: [0004-0005]). Regarding claim 13, Doggett in view of Nagarajan discloses the apparatus wherein the query is directed to performing configuration of an information technology asset, and wherein the one or more documents comprise one or more technical guides for the information technology asset (Doggett: [0038 and 0091]). Regarding claim 14, while Doggett in view of Nagarajan discloses the feature of utilizing a corpus of documents and a topic-based hierarchy of a technical manual including sections of installation, operation and troubleshooting ([0084-0085]), the references do not explicitly disclose wherein the query is directed to performing at least one of troubleshooting and remediation of one or more issues encountered on an information technology asset, and wherein the one or more documents comprise one or more support tickets associated with the one or more issues encountered on the information technology asset. However, the specific program configuration utilized for processing query and data would have been obvious to ordinary skill in the art in view of meeting designer’s programming requirements and achieving the particular desired performance. Response to Arguments 7. Applicant’s arguments have been considered but are deemed to be moot in view of new grounds of rejection presented in this Office action. Doggett in view of Nagarajan discloses the applicant’s claimed invention as explained in the rejection above. Conclusion 8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONICA M PYO whose telephone number is (571)272-8192. The examiner can normally be reached Monday-Friday 8am-4pm. 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, APU MOFIZ can be reached at 571-272-4080. 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. /MONICA M PYO/ Primary Examiner, Art Unit 2161 Application/Control Number: 18/908,334 Page 2 Art Unit: 2161 Application/Control Number: 18/908,334 Page 3 Art Unit: 2161 Application/Control Number: 18/908,334 Page 4 Art Unit: 2161 Application/Control Number: 18/908,334 Page 5 Art Unit: 2161 Application/Control Number: 18/908,334 Page 6 Art Unit: 2161 Application/Control Number: 18/908,334 Page 7 Art Unit: 2161 Application/Control Number: 18/908,334 Page 8 Art Unit: 2161 Application/Control Number: 18/908,334 Page 9 Art Unit: 2161 Application/Control Number: 18/908,334 Page 10 Art Unit: 2161 Application/Control Number: 18/908,334 Page 11 Art Unit: 2161
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