DETAILED ACTION
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 .
The application has been examined. Claims 1 – 20 are pending in this office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1 – 20 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
The representative claim 1 (and other independent claims 10, 18) recites a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining a first set of tags and a first set of descriptions associated with a set of content items; causing a large language model (LLM) to generate a plurality of tags based on the first set of tags and the first set of descriptions, resulting in a second set of tags, wherein the second set of tags includes the first set of tags and the plurality of tags; and causing the LLM to apply one or more tags from the second set of tags to one or more content items based on information regarding the one or more content items.
The claims recite a mental process and a certain method of organizing human activity. Before computers it would have been obvious for a person to gather data to describe content items of various types and convert it into an easy understood/ explained/ regenerated data and based on different descriptions and labels for describing the data and producing it in accordance with the data and additional labels to describe the item, this process would also be considered a method of organizing human activity and mental processes. Additional, examples the courts have found recites an abstract idea includes filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016).
The claims additionally recite - Claim 1: a device, a processing system, a processor, memory. - Claim 10: a non-transitory machine readable medium, a processing system, a processor. - Claim 18: a method, processing system, a processor.
However, the limitations merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h). Furthermore, a storing information does not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h). It is well- understood, routine, and conventional to use a computer to gather, analysis, and present information to a plurality of users (also see court case A web browser’s back and forward button functionality, Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015). See MPEP 2106.05(d) as well as USPTO Memorandum: Revising 101 Eligibility Procedure in view of Berkheimer v. HP, Inc. (April 19, 2018). And the following court cases (See MPEP 2106.05(d). Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015); Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
Claims 2 – 9, 11 – 17 and 19 – 20 further narrow the abstract idea recited in the independent claims 1, 10 and 18 and are therefore directed towards the same abstract idea. The dependent claims are directed towards further narrowing the abstract idea. Claims 2 – 9, 11 – 17 and 19 – 20 do not recite any additional elements that have not already been analyzed above. Therefore, the claims do not direct the claims to recite a practical application.
Therefore, claims 1 – 20 are rejected under U.S.C. 101.
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 (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.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1 – 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Murudkar et al. (‘Murudkar’ herein after) (US 20250247588 A1) further in view of Sambit Padhi (‘Padhi’ herein after) (US 20250260883 A1).
With respect to claim 1, 10, 18,
Murudkar discloses a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining a first set of tags and a first set of descriptions associated with a set of content items (figure 1A, 1E, 4, paragraphs 14, 23 – 26 teaches content processing device may generate metadata for one or more logical entity sets, the content processing device may use a computer vision technique to generate metadata for one or more logical entity sets, such as, apply a computer vision algorithm to frames of a logical camera scene and generate a logical camera scene level set of tags based on objects, text, or audio recognized in the logical camera scene, Murudkar); causing a large language model (LLM) to generate a plurality of tags based on the first set of tags and the first set of descriptions, resulting in a second set of tags, wherein the second set of tags includes the first set of tags and the plurality of tags (paragraphs 23 – 26 teaches additionally the content processing device may use large language model (LLM) techniques to dynamically generate new metadata (e.g., which may include new tags not previously specified for any logical entities). For example, the content processing device may use computer vision to recognize one or more objects in a logical camera scene and may use an LLM to generate a description of the one or more objects, with the description being used as metadata, Murudkar); and causing the LLM to apply one or more tags from the second set of tags to one or more content items based on information regarding the one or more content items (paragraphs 23 – 26 and 47 – 53 teach additionally the content processing device may use large language model (LLM) techniques to dynamically generate new metadata (e.g., which may include new tags not previously specified for any logical entities) may use an LLM to generate a description of the one or more objects, with the description being used as metadata and processing the one or more logical entity sets to generate one or more metadata tags for the one or more logical entity sets, Murudkar).
Murudkar teaches using LLM to generate tags based on set data but does not specifically teach the interface for use of LLM.
However, Padhi teaches the use of an interface prompting the generation of summary by LLM in paragraphs 15, 24 and 32 – 36 stating the ML model uses the subtitle data as an input and generates the textual summary as an output and in response to selection of a user interface (UI) element (e.g., clicking a button) on a user interface, the streaming system may obtain subtitle data for a period of time and generate a prompt with a request to generate a summary using the subtitle data. The streaming system may transmit the prompt to the LLM. The streaming system may receive a prompt response that includes the summary generated by the LLM, and the streaming system provides a UI object with the summary for display on the user interface.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention because both references are directed to the study of summarization and tag generation using algorithms and models. Furthermore, Padhi improves on Murudkar’s device by initiating display of a media content item on a user interface displayed on a display device, the user interface including a user interface element; in response to a selection of the UI element, obtaining subtitle data for a portion of the media content item; generating a prompt request with a request to generate a textual summary by a machine-learning (ML) model using the subtitle data, receiving from the ML model, a prompt response that includes the textual summary and displaying a UI object with the textual summary on the user interface.
With respect to claim 2,
Murudkar as modified discloses the device of claim 1, wherein the set of content items comprise video content, audio content, text-based content, or a combination thereof (figure 4, paragraph 7, 14, where a multimedia content file may include multimedia content, such as video content, audio content, or virtual reality content, among other examples, Murudkar).
With respect to claim 3, 11,
Murudkar as modified discloses the device of claim 1, wherein the set of content items comprise videos, and wherein the operations further comprise deriving the first set of descriptions by: extracting audio data from the videos using one or more audio extraction algorithms and converting the audio data into text using one or more speech recognition algorithms (paragraph 23 teaches the content processing device may apply a computer vision algorithm to frames of a logical camera scene and generate a logical camera scene level set of tags based on objects, text, or audio recognized in the logical camera scene, Murudkar).
With respect to claim 4, 12,
Murudkar as modified discloses the device of claim 1, wherein the set of content items comprises a random sample set of content items included in a content database or a file system (paragraphs 29, 30, Murudkar).
With respect to claim 5, 13,
Murudkar as modified discloses the device of claim 1, wherein the information comprises one or more titles associated with the one or more content items, one or more descriptions or summaries associated with the one or more content items, or a combination thereof (paragraphs 24, 27 and 31 – 36, Padhi).
With respect to claim 6, 14,
Murudkar as modified discloses the device of claim 1, wherein one or more of the causing the LLM to generate the plurality of tags and the causing the LLM to apply the one or more tags are performed using one or more application programming interface (API) requests (paragraphs 23 – 26 and 47 – 53, Murudkar).
With respect to claim 7, 15,
Murudkar as modified discloses the device of claim 1, wherein one or more of the causing the LLM to generate the plurality of tags and the causing the LLM to apply the one or more tags are based on inputting of one or more prompts to the LLM (paragraphs 23 – 26 and 47 – 53, Murudkar).
With respect to claim 8, 16,
Murudkar as modified discloses the device of claim 1, wherein the LLM is pre-trained on a corpus of data and finetuned or instruction-tuned for responding to prompts (paragraphs 32 – 34, Padhi).
With respect to claim 9, 17,
Murudkar as modified discloses the device of claim 1, wherein the one or more content items are stored in a content database or a file system, and wherein the operations further comprise, for at least one content item of the one or more content items, storing, in the content database or the file system, at least one tag that is applied to the at least one content item as a result of the causing the LLM to apply the one or more tags (paragraphs 29, 30, and 47 – 53, Murudkar).
With respect to claim 19,
Murudkar as modified discloses the method of claim 18, wherein the second LLM is the first LLM (paragraph 23, Murudkar and paragraphs 13 – 14 and 32 – 35, Padhi).
With respect to claim 20,
Murudkar as modified discloses the method of claim 18, wherein the first LLM is different from the second LLM (paragraph 23, Murudkar and paragraphs 13 – 14 and 32 – 35, Padhi).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250265303 A1 teaches in response to the query, a plurality of search results corresponding to a plurality of internet resources and plurality of content items may be generated based upon the plurality of search results. The plurality of content items may be generated using a plurality of content extraction tools. A language model may be used to generate a plurality of summaries of the plurality of content items.
US 20200065589 A1 teaches automatically tagging one or more images or video clips using an audio stream. The audio stream may be processed using an automatic speech recognition algorithm, to extract possible keywords. The images and video clips may then be tagged with the possible keywords. In some embodiments, the images and video clips may be tagged automatically.
US 20250358492 A1 teaches using receiving content and a call requesting a generative model to generate a video summary of the content; constructing a prompt including the content and instructions to the model to identify semantic context of the content, to identify a text data item, an audio data item, and/or a video data item embedded in the content to generate a text transcript of the audio data item and/or the video data item, or a textual description of the video data item, to summarize the text data item, the text transcripts, and/or the textual description as a summary of the content based on the semantic context, and to generate the video summary based on the summary and a portion of the text data item, the audio data item, and/or the video data item; providing the first prompt to the generative model; providing the video summary to a client device for presentation.
US 20250322152 A1 teaches (LLM) configured to automatically label non-labeled textual data-items for the purpose of creating a training dataset for training a Machine Learning (ML) model. The ML model is thus trained on LLM-labeled textual data-items and the ML model can be deployed to classify new or incoming documents or messages or other textual data-items. Additionally, a Vision and Language Model (VLM) or a Large Multimodal Model (LMM) or a large multiple-modalities model (LMM) can process data from two or more modalities (visual data, textual data), and is configured to automatically label non-labeled images for the purpose of creating a training dataset for training a Deep Neural Network (DNN).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is (571)272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on (571) 272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 3/27/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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