# US 2026/0087516 A1 · Systems and methods for capturing and processing screen-recorded user-specific recommended output, digital advertisements, and ai-generated mixed media > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0087516 A1 - Document ID: US20260087516A1 - Status: Published application - Kind: A1 - Application number: 19/264,714 - Filed: 2025-07-09 - Published: 2026-03-26 - Earliest claimed: 2024-07-09 - Inventors: MANH NGUYEN; BRICE GOWER; AKASH CHOUDHARY; ETHAN CABRAL; MENGSHU NIE; ANNE-MARIE MULUMBA; WASALA Rankothge Waruna Gayan KULAWANSHA; JOSEPH ALBI; SAACHI BAGDE; HO SHING KWONG; Kareem Rahaman; Akash Sidhu; Amir Ali Vahid Kassiri - Web page: https://patentgrep.com/patent/US20260087516A1 ## CPC - G06Q 30/0245: {Surveys} - G06F 21/6254: {by anonymising data, e.g. decorrelating personal data from the owner's identification} - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system and method for analyzing screen-recorded personalized digital content using on-device computer vision and generative AI. A user captures content from a recommender system interface, such as screen activity or browser-rendered content, optionally with concurrent voice commentary. On-device processing generates intermediate representations using CLIP-style embeddings, OCR text, and transcribed audio tokens, flagging advertisements, changes in content, diversity in content, and similarities in content. A compact generative AI model produces metadata summaries, which users may annotate with tags or comments. A composite metadata package is transmitted to a cloud system, while raw media is deleted. The invention enables privacy-preserving, bandwidth-efficient insight into recommender-based media and user feedback. ## Claims ### Claim 1 A computer-implemented method for analyzing and displaying personalized digital content, digital advertisements, online shopping behaviour, and AI generated mixed media related to a third-party recommender system, the method comprising: (a) receiving, on a computing device, content presented via a visual user interface of a third-party recommender system and associated personalized media content, including digital advertisements and generative media, wherein the content comprises at least one of: (i) a screen-recorded video depicting the rendered interface; or (ii) document object model (DOM) elements extracted from a web browser; (b) optionally recording, during step (a), audio commentary provided by the user during the screen recording session; (c) executing, under orchestration-server control, a computer-vision pipeline to extract an intermediate representation comprising: (i) image-text embeddings; (ii) OCR-derived structured text regions; and (iii) transcribed speech, aligned to video segments; (d) generating, on the computing device, metadata describing the personalized content, the metadata including at least: (i) a promotion-indicator flag set to TRUE when optical-character recognition detects any keyword selected from the group consisting of “Sponsored,” “AD,” “Promoted,” and “Shop” within a text region that overlaps the visual boundary of the media tile; (ii) a diversity-indicator flag set to TRUE when an asset identifier of a current segment is absent from a rolling cache of asset identifiers extracted from a predetermined number of immediately preceding segments; (iii) a similarity-indicator flag that is TRUE when, within a rolling window of N segments, a majority exhibit a cosine similarity ≥T1 in a unified image-text embedding space; (iv) a context-switch flag that is TRUE when a newly ingested segment diverges from a previous centroid by more than T2 and at least M subsequent segments align more closely with a new centroid; and (v) a recommender-system-output satisfaction indicator comprising, a self-reported representation score, a change-propensity score; and a comfort score, each supplied on a Likert scale; (e) receiving user-supplied tags and Likert-scale scores; (f) receiving, from the user via an interactive interface, one or more metadata tags associated with the content of the screen-recorded video or its AI-generated summary; (g) merging metadata, tags, summaries, and scores into a composite metadata package; (h) associating the user-supplied tags with the corresponding metadata generated in step (d), and integrating transcribed user commentary into the metadata structure; (i) transmitting the metadata of step (f), and optionally the depersonalized video, to a cloud-based storage or analytics service; (j) redacting PII, generating a verification indicator, and transmitting the package to a cloud-based service while deleting raw media; (k) visualizing the metadata in a dashboard that groups segments by consumer-profile type, orders such groups by change, and renders a representative “For-You Page” preview responsive to a user selection of the group name; wherein steps (b)-(h) are executed without transmitting raw video or unprocessed audio commentary, thereby preserving privacy and minimizing bandwidth usage. ### Claim 2 (depends on 1) The method of claim 1, wherein advertisements include generative AI-based media or dynamically rendered formats. ### Claim 3 (depends on 1) The method of claim 1, wherein advertisement regions are detected and segmented from screen-recorded or DOM-captured content using visual and text features. ### Claim 4 (depends on 1) The method of claim 1, wherein personally identifiable visual and textual elements are automatically redacted using a computer vision model prior to step (g), and a verification indicator is generated. ### Claim 5 (depends on 1) The method of claim 1, wherein the speech tokens are processed to infer emotional tone. ### Claim 6 (depends on 1) The method of claim 1, wherein the orchestration server directs the computing device to initiate, pause, or resume any of steps (b)-(f). ### Claim 7 (depends on 1) The method of claim 1, wherein the computing device operates in a DOM-only mode, without capturing screen video. ### Claim 8 (depends on 1) The method of claim 1, wherein steps (a)-(g) are executed within a third-party digital-survey platform rendered in a web browser. ### Claim 9 (depends on 1, 11) A computing device comprising: a processor and memory storing instructions that, when executed, cause the computing device to: (i) receive a screen-recorded video or DOM elements of a third-party personalized recommender system interface; (ii) optionally record audio commentary from a user; (iii) extract an intermediate representation from the video comprising: (a) joint visual-semantic embeddings; (b) OCR-derived structured text data with bounding regions; (c) transcribed audio tokens aligned to frame timestamps, including transcription of the user's recorded audio commentary; (iv) apply an on-device generative AI model to generate metadata describing the screen-recorded content, the metadata including: (a) the promotion-indicator flag, and (b) the diversity-indicator score, and (c) the similarity indicator score, and (d) the context switch flag, and (f) the recommender-system-output satisfaction score each defined as in claim 1(d)(i)-(v); (v) receive one or more user-generated metadata tags via an interactive interface; (vi) associate the user-generated tags, transcribed user commentary, and likert scores with the metadata generated in step (iv); (vii) merge the tags, flags, scores, and summaries into a complete metapackage, and, optionally, a depersonalized version of the screen-recorded video to a remote service; The system of claim 11, further comprising a privacy-dashboard module that displays (i) active research campaigns utilising a participant's data, (ii) campaign budget and duration, and (iii) a usage counter indicating how many campaigns currently reference the participant's metadata wherein the computing device deletes the original user commentary audio recording after transcription and association. ### Claim 10 (depends on 9) The system of claim 9, wherein the computing device transmits a verification indicator confirming redaction of personally identifiable information. ### Claim 11 (depends on 9) The system of claim 9, wherein the processor uses quantized neural networks optimized for on-device inference using less than 2 GB RAM. ### Claim 12 (depends on 9) The system of claim 9, wherein the metadata includes topic summaries and optional relevance scores for each video segment. ### Claim 13 (depends on 9) The system of claim 9, wherein the generative AI model supports multimodal alignment across video, text, and audio inputs. ### Claim 14 (depends on 9) The system of claim 9, further comprising a visual interface configured to render: (a) an interactive pivot table of aggregated metadata; and (b) a three-dimensional orb network clustered by similarity. ### Claim 15 (depends on 9) The system of claim 9, wherein the pivot table and orb network are synchronised such that selecting a value in one view updates the other. ### Claim 16 (depends on 9) The system of claim 9, wherein each orb represents either an individual user or a consumer profile type, with clustering determined by shared metadata or similarity embeddings. ### Claim 17 (depends on 9) The system of claim 9, further comprising a consumer profile card view, wherein each card displays: (a) a profile name; (b) a growth trend indicator; and (c) a scrollable preview of representative content for that profile. ### Claim 18 (depends on 1) A non-transitory computer-readable medium storing instructions that, when executed by a processor of a computing device, cause the processor to: (a) receive a screen-recorded video showing personalized digital content rendered by a third-party recommender system; (b) optionally record user-provided voice commentary during the screen-recording session; (c) process the video and any recorded audio to extract: (i) image-text embeddings; (ii) OCR-derived text regions; (iii) speech transcripts, including voice commentary, aligned to video segments; (d) apply an on-device AI model to generate textual metadata describing or classifying the recorded content, the metadata including: (i) the promotion-indicator flag, and (ii) the diversity-indicator score, each defined as in claim 1(d)(i)-(ii); (e) receive user-supplied tags associated with the metadata from an application interface; (f) combine the user tags and transcribed commentary with the generated metadata to form a composite metadata package; (g) output the composite metadata for transmission to a network service or for local storage; wherein the processor deletes any stored audio file recorded in step (b) after step (f) is complete. ### Claim 19 (depends on 18) The medium of claim 18, wherein the instructions further cause the processor to: (a) perform product, company, publisher, or advertiser name extraction, sentiment analysis, or inferred user interest detection with metadata generation; (b) include language confidence scores in the OCR-derived text, with support for multiple languages; (c) segment the speech transcripts into speaker turns or utterances; and (d) store user-supplied tags in association with the AI-generated metadata and link the tags to specific segments of the screen-recorded video. ### Claim 20 (depends on 18) The medium of claim 18, wherein the instructions further cause the processor to: (a) transmit the composite metadata of step (f), and optionally the depersonalized video, to a cloud-based storage or analytics service; (b) redact personally identifiable information (PII), generate a verification indicator, and transmit the package to a cloud-based service while deleting raw media; (c) visualize data in a dashboard that (i) groups segments by consumer-profile type, (ii) orders such groups by change, and (iii) renders a representative “For-You Page” or “Explore Page” preview responsive to a user selection of the group name; wherein the instructions of claim 18(a) through 18(g) are executed without transmitting raw video or unprocessed audio commentary to the cloud, thereby preserving privacy and minimizing bandwidth usage; and wherein the instructions optionally cause the processor to provide monetary compensation or rewards to the user based on participation, usage, or contribution of metadata.