Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendments and remarks filed on January 21, 2026.
Claims 1, 3-8, 10-12, 15, and 17-19 are currently amended.
Claims 1-20 are currently pending and have been examined.
Applicant’s remarks and arguments are addressed below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
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.
Claims 1, 2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Hughes (US 2024/0185039 A1) in view of Hensley et al. (US 2025/0251932 A1, hereinafter “Hensley”).
Claim 1. Hughes teaches: A system for processing and analyzing digital content, comprising:
at least one processor (see, e.g., Figure 8 feature 102, Figure 9 feature 810, and ¶s 25, 28, 231-232, 297, 302, 306, 307, and 309); and
at least one memory storing instructions that, when executed by the at least one processor, cause the system to (see, e.g., ¶s 25, 232, and 306-310):
receive, via a plurality of ingest pipelines, a plurality of digital objects of different types that include a plurality of characteristics, wherein the plurality of characteristics includes a text characteristic, an image characteristic, a series of image frames characteristic, an audio characteristic, and an embedded metadata characteristic (see, e.g., ¶s 58-59 teaching receiving a plurality of different data elements that are extracted from the documents, noting that the plurality of characteristics is further addressed below);
decompose at least one complex digital object from the plurality of digital objects into a plurality of first-level sub-objects (see, e.g., ¶s 58-61 teaching extracting the data elements and applying processing that recognizes, classifies, and/or labels the data elements; see further ¶ 133 teaching substantially the same);
route the plurality of first-level sub-objects to respective ingest pipelines of the plurality of ingest pipelines based on characteristics of the first-level sub-objects (this limitation is further addressed below);
decompose at least one first-level sub-object into a plurality of second-level sub-objects, wherein decomposing the at least one first-level sub-object comprises at least one of: (i) extracting text from an image sub-object using optical character recognition, or (ii) generating a textual description of visual content of the image sub-object (see, e.g., Tables 1, 2, and 3 teaching various exemplary sets of first and second level sub-objects, i.e., “category” or “action” “sub-types;” see further ¶s 58 and 134 teaching utilizing optical character recognition to extract the data elements);
generate indexed data based on the plurality of digital objects, the plurality of first-level sub-objects, and the plurality of second level sub-objects, wherein the indexed data includes references that track relationships between the second level sub-objects, the first level sub-objects, and the at least one complex digital object (see, e.g., Tables 1, 2, and 3 teaching various exemplary sets of tracked and indexed data based on the relationships; see further ¶s 274-281 and 292 teaching another exemplary set of generated indexed data based on various objects that have relationships for material defects in defective brake cases);
generate vector embeddings based on the indexed data (see, e.g., ¶ 68 teaching using graph data structures and vectors to codify and represent relationships in mathematical terms; see further ¶s 84, 110, and 274-276); and
process the indexed data using the vector embeddings to perform semantic similarity searches and generate analysis results (see, e.g., ¶s 274-276 and 280 teaching using the semantic vector space models to enable recognition of different semantic representations of equivalent facts).
As noted above, Hughes fails to expressly teach a plurality of characteristics, wherein the plurality of characteristics includes a text characteristic, an image characteristic, a series of image frames characteristic, an audio characteristic, and an embedded metadata characteristic or a step where the invention route[s] the plurality of first-level sub-objects to respective ingest pipelines of the plurality of ingest pipelines based on characteristics of the first-level sub-objects. Nevertheless, such a feature is taught in the analogous prior art. For example, Hensley teaches the plurality of characteristics includes a text characteristic, an image characteristic, a series of image frames characteristic, an audio characteristic, and an embedded metadata characteristic (see Hensley see, e.g., ¶ 83 teaching receiving compressed files; see ¶ 33 teaching PDF documents; see ¶ 62 teaching that the sources can include unstructured assets like audio and images and in combination). Hensley further teaches a step where the invention route[s] the plurality of first-level sub-objects to respective ingest pipelines of the plurality of ingest pipelines based on characteristics of the first-level sub-objects (see, e.g., Figure 2 features 56 and 58, where the parts are classified according to content type and then processing templates are selected based on the content types; see further ¶s 101 and 151-154 classifying the various types of files and processing different types in different templates). Hensley is analogous to the instant application and Hughes because it relates to artificial intelligence-mediated search and retrieval (see Hensley ¶ 2), including in contexts such as litigation (see Hensley ¶s 3 and 33).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of addressing a variety of characteristics and ingesting each one into processing by different templates (as disclosed by Hensley) to the known method and system of using a large language model for complex litigation (as disclosed by Hughes). One of ordinary skill in the art would have been motivated to apply the known technique of addressing a variety of characteristics and ingesting each one into processing by different templates because it would address the issue of LLMs providing poor output from a large corpus if the LLMs tasks are kept narrow in scope and focused on discrete results (see Hensley ¶s 21-23).
Furthermore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of addressing a variety of characteristics and ingesting each one into processing by different templates (as disclosed by Hensley) to the known method and system of using a large language model for complex litigation (as disclosed by Hughes), because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of addressing a variety of characteristics and ingesting each one into processing by different templates to the known method and system of using a large language model for complex litigation, because predictably a system that seeks to use a large corpus of information to train an LLM for litigation-related tasks can be modified to tailor the ingestion of different kinds of ingested data that was designed for the same purpose). See also MPEP § 2143(I)(D).
Regarding Claims 8 and 15, these claims recite the same limitations as Claim 1 above but recite different statutory categories (a method and a non-transitory computer-readable medium, respectively). Thus, the rejection of Claim 1 above utilizing the combination of Hughes and Hensley to render obvious the limitations is incorporated herein. Because Hughes teaches a method (see, e.g., the Abstract) as well as the use of a non-transitory computer readable medium (see ¶s 25, 26, 31, 306, and 309), the combination of Hughes and Hensley renders obvious Claims 8 and 15 with this additional note. Similarly co-extensive claims will be addressed below for the sake of brevity.
Claims 2, 9, and 16. The combination of Hughes and Hensley teaches the limitations of Claims 1, 8, and 15. Hensley further teaches: The system of claim 1, wherein the plurality of digital objects comprises at least two of: text documents, portable document format (PDF) files, images, audio files, video files, and compressed files (see, e.g., ¶ 83 teaching receiving compressed files; see ¶ 33 teaching PDF documents; see ¶ 62 teaching that the sources can include unstructured assets like audio and images and in combination). The rationale for modifying Hughes in view of Hensley is provided in the rejection of Claim 1 above.
Claims 4, 11, and 18. The combination of Hughes and Hensley teaches the limitations of Claims 1, 8, and 15. Hughes further teaches: The system of claim 1, wherein the references that track relationships are stored alongside the first-level sub-objects and the second-level sub-objects to maintain provenance information and enable reconstruction of the at least one complex digital object (see, e.g., at least Tables 1 and 2 and ¶s 81-84 teaching a set of tracked relationships stored alongside the different level sub-objects to maintain the provenance information; see also Table 3 and ¶s 102-105 teaching substantially the same including using tagging as a means of tracking the relationships).
Claims 5, 12, and 19. The combination of Hughes and Hensley teaches the limitations of Claims 1, 8, and 15. Hughes further teaches: The system of claim 1, wherein the instructions, when executed by the at least one processor, further cause the system to:
receive a search query (see, e.g., at least ¶ 238 teaching querying the knowledge base 106 for correlations between new sets of factual circumstances arising between legal entities and past sets of factual circumstances that gave rise to causes of action in the past; see further ¶s 225, 266, and 280 teaching substantially the same);
identify semantically similar content to the search query using the vector embeddings (see, e.g., at least ¶ 238 teaching querying the knowledge base 106 for correlations between new sets of factual circumstances arising between legal entities and past sets of factual circumstances that gave rise to causes of action in the past; see further ¶s 225, 266, and 280 teaching substantially the same); and
return search results based on the identified semantically similar content (see, e.g., at least ¶ 238 teaching querying the knowledge base 106 for correlations between new sets of factual circumstances arising between legal entities and past sets of factual circumstances that gave rise to causes of action in the past; see further ¶s 225, 266, and 280 teaching substantially the same).
Claims 6 and 13. The combination of Hughes and Hensley teaches the limitations of Claims 1, 8, and 15. Hughes further teaches: The system of claim 1, wherein the instructions, when executed by the at least one processor, further cause the system to:
parse conversations from the plurality of digital objects (see, e.g., Figures 3A-3G and ¶s 104-108 teaching parsing the natural language from the plurality of digital objects); and
store the parsed conversations in a conversation database (see, e.g., ¶s 64, 65, and 68 teaching that the parsed and classified data are placed in a centralized or federated database system).
As noted in the rejection of Claim 1 above, Hensley teaches that the ingested data can include “conversations” such as “deposition or court transcripts” (see Hensley ¶ 33; see also ¶ 3). Thus, in the combination of Hughes and Hensley, the parsed data that are stored would include conversations. The rationale for modifying Hughes in view of Hensley is provided in the rejection of Claim 1 above.
Claims 7, 14, and 20. The combination of Hughes and Hensley teaches the limitations of Claims 6, 13, and 15. Hughes further teaches: The system of claim 6, wherein the instructions, when executed by the at least one processor, further cause the system to:
receive a conversation search query (see, e.g., at least ¶ 238 teaching querying the knowledge base 106 for correlations between new sets of factual circumstances arising between legal entities and past sets of factual circumstances that gave rise to causes of action in the past; see further ¶s 225, 266, and 280 teaching substantially the same);
search the conversation database based on the conversation search query (see, e.g., at least ¶ 238 teaching querying the knowledge base 106 for correlations between new sets of factual circumstances arising between legal entities and past sets of factual circumstances that gave rise to causes of action in the past; see further ¶s 225, 266, and 280 teaching substantially the same); and
return conversation search results based on the search of the conversation database (see, e.g., at least ¶ 238 teaching querying the knowledge base 106 for correlations between new sets of factual circumstances arising between legal entities and past sets of factual circumstances that gave rise to causes of action in the past; see further ¶s 225, 266, and 280 teaching substantially the same).
As noted in the rejection of Claim 1 above, Hensley teaches that the ingested data can include “conversations” such as “deposition or court transcripts” (see Hensley ¶ 33; see also ¶ 3). Thus, in the combination of Hughes and Hensley, the parsed data that are stored would include conversations. The rationale for modifying Hughes in view of Hensley is provided in the rejection of Claim 1 above.
Claims 3, 10, and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Hughes (US 2024/0185039 A1) in view of Hensley et al. (US 2025/0251932 A1, hereinafter “Hensley”) and further in view of Vidyashankar Keresanthe et al. (US 2024/0212812 A1, hereinafter “Vidyashankar”).
Claims 3, 10, and 17. The combination of Hughes and Hensley teaches the limitations of Claims 1, 8, and 15. That combination fails to teach, however, analogous reference Vidyashankar teaches: The system of claim 1, wherein the instructions, when executed by the at least one processor, further cause the system to decompose a video object into:
a plurality of image frames extracted at intervals or key points of interest, and audio track, and embedded metadata (see, e.g., ¶s 52, 56, 60, 90, 91, 113, 114, 129-131, 145, 148, and 158 teaching analyzing a plurality of image frames extracted at intervals/key points of interest from an input video feed; regarding the audio track, see, e.g., ¶s 26, 51, 58, 68, 81, 84, 98, and 121; regarding embedded metadata, see, e.g., ¶s 114 teaching embedding metadata into the frame of the video; see further ¶ 118),
wherein the plurality of image frames are routed to an image analysis pipeline and the audio track is routed to an audio transcription pipeline (see, e.g., ¶s 139-140 teaching annotation model 754 annotating the image frames and ¶ 142 teaching routing the audio track to an audio transcription pipeline, i.e., speech processing model 756 that generates text representing the audio via speech-to-text; see also ¶ 159 teaching processing and annotating images and audio separately), and
wherein decomposing the audio track comprises: transcribing spoken content from the audio track into a text sub-object using speech recognition (see ¶ 142 teaching routing the audio track to an audio transcription pipeline, i.e., speech processing model 756 that generates text representing the audio via speech-to-text; see also ¶ 159); and
generating metadata identifying at least one of: speaker changes, detected audio events, or language identification (see, e.g., ¶ 142 teaching detecting an audio event of an utterance and identifying the language, i.e., processing the speech to generate text representing the audio).
Vidyashankar is analogous to the instant application, Hughes, and Hensley because it relates to using artificial intelligence, specifically LLMs, to process disparate types of data, in the aid of work related to potential litigation (see Vidyashankar ¶ 98 regarding using machine learning models like LLMs to draft a medical report and ¶ 100 teaching that the report could be used for various different purposes including in an investigation during the course of a medical malpractice lawsuit).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of including ingesting and annotating image and audio data (as disclosed by Vidyashankar) to the known method and system of using a large language model for ingestion of data for analysis for complex litigation (as disclosed by Hughes and Hensley). One of ordinary skill in the art would have been motivated to apply the known technique of including ingesting and annotating image and audio data because it could feed an LLM to aid in the generation of an annotated report that could be used for determining root causes of adverse events or the support of an investigation during the course of a medical malpractice lawsuit (see Vidyashankar ¶s 98-100).
Furthermore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of including ingesting and annotating image and audio data (as disclosed by Vidyashankar) to the known method and system of using a large language model for ingestion of data for analysis for complex litigation (as disclosed by Hughes and Hensley), because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of including ingesting and annotating image and audio data to the known method and system of using a large language model for ingestion of data for analysis for complex litigation, because predictably a system that seeks to use a large corpus of information to train an LLM for litigation-related tasks can be modified to tailor the ingestion of different kinds of ingested and enriched data that was designed for the same purpose). See also MPEP § 2143(I)(D).
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant specifically addresses the following:
Claim Rejections - 35 U.S.C. § 101:
Claims 1-20 were rejected under § 101 as being directed toward the judicial exception of an abstract idea without any integration into a practical application or significantly more. Applicant’s amendments have rendered the rejection moot. While Examiner asserts that the claims are still directed toward legal interactions in step 2A prong 1, the claims as amended integrate the abstract idea into a practical application, because they apply the judicial exception in a meaningful way beyond a general link to computer technology. Instead, the claims as now amended recite the ingestions of various different types of data objects and the generation of vector embeddings. The claim as now drafted (as a whole, in its ordered combination) is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.04(d)(I); see also MPEP § 2106.05(e).
Claim Rejections - Prior Art:
Regarding the application of the prior art to the claims, Applicant’s arguments are moot in light of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure: Madisetti et al., US 2024/0345993 A1; Saldana, US 2025/0086727 A1; Sencan et al., US 2024/0241896 A1.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAN P MINCARELLI whose telephone number is (571)270-5909. The examiner can normally be reached on Monday through Friday, 8:00 AM to 4:30 PM Eastern Time.
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/JAN P MINCARELLI/Primary Examiner, Art Unit 3626
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