US 11611785

US Patent 11611785, titled "Systems and methods for encoding and streaming video encoded using a plurality of maximum bitrate levels," was issued on March 21, 2023. The patent was filed on February 22, 2021, and its current assignee is Divx LLC. The inventor is Kourosh Soroushian.

Abstract:
The patent describes systems and methods for streaming and playing back video that has a variety of resolutions, frame rates, and/or sample aspect ratios. The key aspect is that these video streams are encoded at one of a limited number of "maximum bitrate levels." One embodiment includes a processor and storage containing an encoding application and data that relates combinations of resolution and frame rates to maximum bitrates. A plurality of resolutions and frame rates that share the same maximum bitrate form a "maximum bitrate level." The encoding application configures the processor to encode a video stream into multiple video streams with different resolutions and frame rates. The target maximum bitrate used for encoding is chosen based on the maximum bitrate levels associated with the resolution and frame rate combinations specified in the stored data.

Plain-language overview of independent claims:

• Claim 1 (Method): This claim outlines a method for encoding source video content into multiple alternative video streams and adaptively streaming them. The process involves:

  1. Identifying the source video content.
  2. Identifying a plurality of resolutions for encoding the video content.
  3. For each identified resolution, encoding a portion of the source video multiple times using that resolution and different target bitrates, and then evaluating the quality of each encoding.
  4. Selecting a plurality of resolution and target bitrate combinations for the alternative streams based on the evaluated quality.
  5. Uploading these encoded sections (for each selected combination) to servers within a content distribution system.
  6. Generating entries in a top-level index file that describes at least one of these alternative streams.
  7. Providing this top-level index from the content distribution system to one or more playback devices capable of adaptive streaming.
  8. Responding to requests from these playback devices, where the requests are for specific encoded sections.

• Claim 11 (Non-transitory Machine Readable Medium): This claim describes a non-transitory machine-readable medium containing processor instructions. When a processor executes these instructions, it performs a process identical to the method described in Claim 1. This means the claim covers the software or firmware that enables the encoding and adaptive streaming process.

CAFC 2026 Dockets:
As of April 26, 2026, a search of CAFC 2026 dockets does not specifically show any cases directly involving US patent 11611785. The provided search results for CAFC in 2026 discuss general patent litigation trends, such as issues with inventorship, patent eligibility under 35 U.S.C. § 101, and appeals concerning damages and IPRs. However, no specific mention of US11611785B2 was found in the provided CAFC dockets.

Known litigation involving US patent 11611785 includes the following cases:

1. PTAB Case:

   • Case Number: IPR2025-01223
   • Plaintiff(s) / Petitioner(s): Unified Patents, LLC (inferred as petitioner based on source)
   • Defendant(s) / Patent Owner(s): Divx LLC (inferred as patent owner/respondent)
   • Jurisdiction: Patent Trial and Appeal Board (PTAB)
   • Filing Date: The Google Patents page indicates "IPR2025-01223 filed," implying a filing in 2025, but a specific date is not provided in the snippet.
   • Outcome/Current Status: Procedural Termination

2. District Court Case 1:

   • Case Number: 3:24-cv-00818
   • Plaintiff(s): Not explicitly stated in the provided information, but Divx LLC is the current assignee and typically the plaintiff in such cases.
   • Defendant(s): Not explicitly stated in the provided information.
   • Jurisdiction: Virginia Eastern District Court
   • Filing Date: The case number indicates a 2024 filing year, but a specific date is not provided in the snippet.
   • Outcome/Current Status: No outcome or current status is specified beyond being filed.

3. District Court Case 2:

   • Case Number: 1:24-cv-02061
   • Plaintiff(s): Not explicitly stated in the provided information, but Divx LLC is the current assignee and typically the plaintiff in such cases.
   • Defendant(s): Not explicitly stated in the provided information.
   • Jurisdiction: Virginia Eastern District Court
   • Filing Date: The case number indicates a 2024 filing year, but a specific date is not provided in the snippet.
   • Outcome/Current Status: No outcome or current status is specified beyond being filed.

Proceedings overview

One AIA trial proceeding is on file for US Patent 11611785, which was terminated. This means the patent's claims have not been formally challenged on the merits at the PTAB, leaving them untested.

IPR2025-01223 — Amazon.com, Inc. et al. v. Kourosh Soroushian

• Type: Inter Partes Review
• Filed: 2025-06-30
• Status: Terminated. The proceeding ended prematurely without a decision on the merits.
• Judge panel: Information on the specific judge panel is not publicly available at the current stage of termination.
• Petition grounds: Details of the specific claims challenged and prior art grounds are typically included in the petition. However, due to the procedural termination, an institution decision detailing these was not issued.
• Institution decision: This IPR was not instituted. It was terminated procedurally before a decision on institution.
• Final Written Decision (if issued): No Final Written Decision was issued as the proceeding was terminated prior to institution.
• Settlement / termination: The proceeding was terminated on 2025-10-21, as indicated by the "last modified" date and "status: Terminated" in the provided data. Google Patents also notes "Procedural Termination". Procedural terminations often occur due to settlement between the parties or a joint request for adverse judgment before the Board reaches an institution decision. Specific settlement terms are typically confidential.
• Appeal: No appeal to the Federal Circuit occurred as there was no Final Written Decision to appeal.
• Defensive value: This proceeding offers no direct defensive value in terms of claims being invalidated or sustained. The patent claims remain untested on the merits by the PTAB. However, the fact that a large entity like Amazon initiated an IPR suggests potential validity concerns that may have led to a private settlement.

Strategic summary

All claims of US Patent 116111785 remain UNTESTED by the PTAB on their merits, as the sole IPR filed against it (IPR2025-01223) was terminated before institution. Consequently, no claims have been canceled or formally sustained by a PTAB Final Written Decision.

The estoppel landscape remains largely open. Since IPR2025-01223 was terminated before institution, the petitioner (Amazon.com, Inc. et al.) and its privies are not subject to the statutory estoppel provisions of 35 U.S.C. § 315(e)(2) regarding any grounds that were raised or could have been reasonably raised in the petition. This means that these grounds, and others, could potentially be asserted in future proceedings or litigation by the petitioner or other parties.

Regarding pattern signals, the patent has seen only one IPR, which was terminated. The petitioner was Amazon.com, Inc. et al., and the Google Patents entry indicates "Unified Patents PTAB Data" as the source for the petitioner, implying Unified Patents may have been involved in tracking or supporting the IPR. The "Procedural Termination" status suggests a resolution outside of a full PTAB trial, possibly a settlement. There is no evidence of the patent owner aggressively pursuing PTAB appeals, as no FWD was issued.

Recommended next steps

Given that IPR2025-01223 was terminated before a decision on the merits, all claims of US11611785 remain patentable as issued by the USPTO, subject to any challenges that may arise in district court litigation. For a defendant facing assertion of this patent today:

• Consider initiating a new IPR if robust prior art can be identified against the asserted claims. The prior termination of IPR2025-01223 does not create estoppel for future petitioners (unless they are in privity with Amazon.com, Inc. et al.).
• Thoroughly review the petition filed in IPR2025-01223 to understand the prior art and arguments Amazon intended to use. While not binding, it can provide valuable insights for a new challenge. Access to IPR2025-01223 documents can be sought via the USPTO PTAB E2E system.
• The patent has an anticipated expiration date of 2032-08-30.

Inventors

• Kourosh Soroushian (DivX LLC at time of filing)

Original assignee

The original assignee named on the issued patent is DivX LLC. DivX LLC's primary line of business has historically been focused on video compression technologies and digital media solutions, including codecs, software, and tools for playing back high-quality video. As of 2026, DivX LLC appears to be an active operating company, continuing to develop and license video technology.

Assignment timeline

• 2021-06-07 (executed) / recorded 2021-06-07 — Reel 058090/0858

  • Conveyance: Assignment
  • Assignor: Divx, LLC
  • Assignee: SONIC IP, INC.
  • Correspondent: David S. Level, ESQ., 2033 Gateway Place, Suite 500, San Jose, CA 95110.
  • Context: Internal reorg

• 2021-06-07 (executed) / recorded 2021-06-07 — Reel 058090/0859

  • Conveyance: Assignment
  • Assignor: SONIC IP, INC.
  • Assignee: DIVX CF HOLDINGS LLC
  • Correspondent: David S. Level, ESQ., 2033 Gateway Place, Suite 500, San Jose, CA 95110. This correspondent recurs in this chain.
  • Context: Internal reorg

• 2021-06-07 (executed) / recorded 2021-06-07 — Reel 058090/0860

  • Conveyance: Change of Name
  • Assignor: DIVX CF HOLDINGS LLC
  • Assignee: DIVX, LLC
  • Correspondent: David S. Level, ESQ., 2033 Gateway Place, Suite 500, San Jose, CA 95110. This correspondent recurs in this chain.
  • Context: Change of name

• 2021-06-07 (executed) / recorded 2021-06-07 — Reel 058090/0861

  • Conveyance: Assignment
  • Assignor: SOROUSHIAN, KOUROSH
  • Assignee: DIVX, LLC
  • Correspondent: David S. Level, ESQ., 2033 Gateway Place, Suite 500, San Jose, CA 95110. This correspondent recurs in this chain.
  • Context: Assignment from inventor to company

Timeline diagram

timeline
    title Ownership of US 11611785
    2011 : Priority date
    2021 : Filed by Divx LLC
         : Assigned to SONIC IP INC
         : Assigned to DIVX CF HOLDINGS LLC
         : Name changed to DIVX LLC
         : Assigned from inventor to DIVX LLC
    2023 : Issued
    2024 : District Court case filed 3:24-cv-00818
         : District Court case filed 1:24-cv-02061
    2025 : PTAB case IPR2025-01223 filed

NPE / troll-pattern signals

1. Shell-entity transfer — not present. While there are transfers between similarly named entities (Divx, SONIC IP, DIVX CF HOLDINGS), these appear to be internal reorganizations within the DivX corporate structure, as evidenced by the immediate re-assignment back to DivX, LLC and the consistent correspondent.

2. Known asserter in the chain — not present. None of the assignees (DivX LLC, SONIC IP INC, DIVX CF HOLDINGS LLC) are listed on common public NPE lists. Unified Patents is a petitioner in IPR2025-01223, but Unified Patents is an anti-NPE defensive aggregator, not an asserter.

3. Repeat correspondent across the chain — present. David S. Level, ESQ., with the firm and address 2033 Gateway Place, Suite 500, San Jose, CA 95110, is listed as the correspondent for all four recorded assignments on 2021-06-07 (Reel 058090/0858, 058090/0859, 058090/0860, and 058090/0861).

4. Cascading transfers — present. There are four consecutive assignments and a change of name recorded on the same date (2021-06-07) for this patent family (Reel 058090/0858, 058090/0859, 058090/0860, 058090/0861), indicating a rapid series of transfers. This, coupled with the same correspondent, suggests an internal restructuring or preparation for future transactions.

5. Pre-litigation transfer — unclear. The transfers occurred in June 2021. The district court cases were filed in 2024. While there is a gap, the series of transfers could be preparatory for later assertion. Without a precise filing date for the 2024 litigation, it's hard to definitively state if it falls within a 6-month window.

6. Bankruptcy fire-sale — not present. There is no indication of bankruptcy filings by DivX LLC or any entity in the assignment chain.

7. Privateering — unclear. There's no public information in the patent record or Google Patents that suggests DivX LLC is engaged in privateering activities with this patent.

8. Defensive aggregator (anti-NPE) — not present (as an assignee). While Unified Patents filed an IPR against this patent, they are a petitioner, not an assignee, indicating an attempt to challenge the patent rather than acquire it defensively.

Verdict

Operating-company assertion
The assignment chain predominantly reflects internal corporate reorganizations and transfers within DivX LLC, an operating company in the video technology space. The recurrent correspondent and cascading transfers are consistent with a company managing its IP portfolio, possibly in preparation for licensing or enforcement, rather than a transfer to a shell NPE. The subsequent district court litigation suggests an assertion by the operating company.

USPTO Assignment Center search: https://assignmentcenter.uspto.gov/

I'll research US patent 11611785 and its cited prior art references.

Let me dig into the specific prior art references cited in the IPR petition and the patent's citation lists.

Let me identify the "Ronca" reference and confirm details of the other key primary references.

Prior Art Analysis — US Patent 11,611,785 (US 11611785 B2)

Scope and method

I searched for the specific patent number 11611785 and confirmed the record matches the one already analyzed in the earlier sections (Soroushian, "Systems and methods for encoding and streaming video encoded using a plurality of maximum bitrate levels," App. No. 17/181,996, granted Mar 21, 2023, priority Aug 30, 2011, assignee Divx LLC). I did not substitute results for any similar-looking numbers.

Two important framing points before the reference-by-reference list:

1. No single reference on the face of US 11,611,785 appears to anticipate Claim 1 or Claim 11 under § 102. The independent claims require a specific combination of (a) per-resolution multi-bitrate encoding, (b) quality evaluation and selection, and (c) adaptive-streaming distribution with a top-level index. The art splits cleanly: video-encoding/quality-evaluation references cover (a)–(b), and adaptive-streaming/indexing references cover (c). The patent's citation list (519 items) is a combination landscape, i.e. § 103 terrain, not § 102 terrain.

2. The most on-point references are the four actually asserted in IPR2025-01223, and they were not located by the examiner. The petition states that Liao "was not disclosed to the Patent Office during prosecution of the 785 patent." That is significant: the examiner's citation list is largely DivX's own family and broad container/streaming art, whereas the petitioner's art (Ozer, Liao, Gu, Ronca) targets the encoding/quality-evaluation core of the issued claims.

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I. References asserted in IPR2025-01223 (most relevant)

Source: Petition for Inter Partes Review, Amazon.com, Inc. and Amazon Web Services, Inc. v. DivX, LLC, IPR2025-01223, filed June 30, 2025 (Exhibits 1005–1008).
https://www.docketalarm.com/cases/[PTAB](/ptab)/IPR2025-01223/Amazon.com_Inc/docs/06-30-2025-Petitioner/Petition_as_filed-2-Petition_for_Inter_Partes_Review_of_US_Patent_No_11,611,785.pdf

1. Ozer, "The 2012 Encoding and Transcoding Buyers' Guide" (EX1005)

Description: A buyer's guide that documents commercially released adaptive-streaming encoding presets. Table 7-3 lists resolution/bitrate pairs — 512×288 @ 300/450/650 kbps, 768×432 @ 1,140 kbps, 1280×720 @ 2,440/3,440 kbps — each with profile, key-frame interval, audio bitrate, and channels. Figure 7-4 lists "Picture Size[s]" in integer pixels: 256×144, 512×288, 768×432, 1280×720. All resolutions in the table share a 16×9 display aspect ratio while differing in pixel dimensions (i.e., different sample aspect ratios).

Claims potentially affected:

• Claim 1 / Claim 11 (in part) — discloses a plurality of resolutions encoded at multiple target bitrates for adaptive streaming.
• Claim 7 / Claim 17 ("both a width and a height of each resolution … have an integer number of pixels") — the petition maps this to Ozer's Fig. 7-4 (256×144 … 1280×720) and Table 7-3.
• Claim 8 / Claim 18 ("each resolution … has a same display aspect ratio and at least two resolutions … have different sample aspect ratios") — the petition maps this to Table 7-3 (all 16×9 display, differing resolutions/pixels).

§ 102 caveat (important): Ozer is dated 2012, which is after the Aug 30, 2011 provisional priority date. It is only prior art if the effective filing date for the challenged claims is the Aug 30, 2012 non-provisional filing date (Ser. No. 13/600,046) rather than the provisional. The petitioner's reliance on a 2012 reference implies exactly that priority position. If the § 102 date is Aug 30, 2011, Ozer drops out. This is the single biggest swimmable issue in the art.

2. Liao et al., U.S. Patent No. 9,832,540 B2 (EX1006)

Description: Liao teaches transcoding a source media program to constant video quality. It discloses encoding different versions at different resolutions ("typically 480p, 360p, 288p or 240p"), encoding a given program at multiple target bitrates (its Fig. 6 shows the TEST program encoded at 300, 400, 500, 600, 700, and 800 kbps), evaluating quality using SSIM, and selecting the lowest bitrate that achieves a minimum SSIM (e.g., 0.95) — reaching this by experimentation rather than by fixed presets.

Claims potentially affected:

• Claim 1 / Claim 11, limitations 1(a)–1(d): identifying source video; identifying a plurality of resolutions; "for each particular resolution … encoding at least a portion of the source video content multiple times using the particular resolution and multiple different target bitrates"; "evaluating quality for each of the multiple encodings"; and "selecting a plurality of resolution and target bitrate combinations … based on the evaluated quality."
• Claim 9 / Claim 19 ("target bitrate for each resolution is determined using a formula") — petition maps this to Liao's SSIM formula.
• Claim 10 ("target bitrate … determined using experimentation") — petition maps this to Liao's empirical SSIM sweeps.

§ 102 caveat: Liao does not disclose 1(e)–1(h) (uploading encodings to a content-distribution system, generating a top-level index, providing the index to playback devices, responding to requests). It therefore does not anticipate Claim 1 or 11 as a whole; it is a § 103 reference for the encoding/quality-evaluation limitations.

3. Gu et al., U.S. Patent No. 8,396,114 B2 (EX1007)

Description: A video encoding system that produces a set of multi-bit-rate (MBR) streams ranging from a top-quality stream to a bottom stream, where peak/average bitrates "decrease progressively" (proportional, uniform, logarithmic, or by 25% steps) and resolution is varied dynamically with scene complexity. It discloses selectively streaming segments to a client, with stream selection driven by client-side rate control based on network conditions.

Claims potentially affected:

• Claim 1 / Claim 11 (in part) — the plurality of alternative streams at different bitrates/resolutions for adaptive streaming.
• Claim 6 / Claim 16 ("identifying resolutions with target bitrates within a predetermined percentage of a bitrate associated with an alternative stream") — petition maps this to Gu's proportional 25% (1/4) bitrate decrement.

§ 102 caveat: Gu's stream set is fixed by a distribution rule, not by per-resolution quality evaluation. It does not anticipate Claim 1/11; it is a § 103 reference (and its 25% spacing maps to claims 6/16).

4. Ronca (EX1008) — adaptive-streaming encoder/indexing reference

Description (per petition quotes): Teaches an "encoding tool" used to "generate multiple video (and potentially audio) encodings of the same media presentation, each encoded at different bitrates," producing "a number of different alternate streams containing the same material encoded at a variety of data rates" (EX1008 ¶¶24, 38), with client devices adaptively selecting among the alternate streams based on available data rate, and describing a top-level index.

Claims potentially affected: Claim 1/11 limitations 1(e)–1(h) and the preamble; Claims 2, 3, 4, 5 (container file, container index, request handling, dynamic index generation).

§ 102 caveat and honesty note: The petition uses Ronca as the primary reference in Grounds 3 and 4 (Ronca in view of Liao; Ronca + Liao + Gu). I was unable to independently verify Ronca's exact publication number and dates within this session; I am reporting it as identified in the petition rather than asserting bibliographic details I could not confirm. Treat the publication number as unconfirmed.

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II. Most relevant references on the face of the '785 patent

The '785 front page and its continuation parents (US 10,645,429; US 10,931,982) list the same extensive reference set of 519 U.S. citations plus foreign patents and NPL. The subset most relevant to the issued claims is below.

5. Braness et al., U.S. Patent No. 8,914,534 B2

Description: The foundational adaptive-bitrate-streaming architecture cited by the '785 patent itself. Discloses storing a top-level index pointing to alternate streams; encoding alternative streams at different resolutions, sample-aspect-ratio combinations, and maximum bitrates; uploading container files and the top-level index to an HTTP server; and playback devices requesting portions and switching streams based on streaming conditions.

Claims potentially affected: Claim 1/11 limitations 1(e)–1(h); Claims 2–5 / 12–15. This is the strongest single reference for the distribution half of the claims, but it does not teach the per-resolution multi-bitrate quality-evaluation loop of 1(c)–1(d). Not anticipatory of Claim 1/11; it is the lead § 103 reference for the streaming side (and the previously generated Obviousness section already relied on it).

6. Butt et al., U.S. Patent No. 8,472,792 B2

Description: Multimedia container/index file structures for streaming distribution; foundational to the "top-level index"/"container file/index" limitations (Claims 1, 2, 3, 5 / 11, 12, 13, 15). Applicant's own art.

7. Soroushian et al., U.S. Patent No. 8,818,171 B2

Description: Encoding alternative streams targeted to display aspect ratios and connection data rates; directly relevant to Claim 1 preamble and Claim 8/18 (aspect-ratio relationships). Applicant's own art; also the subject of the cross-referenced application in the '785 specification.

8. Soroushian et al., U.S. Patent No. 9,467,708 B2

Description: Selection of streaming resolutions (integer width/height, common fractions of a primary resolution) for seamless switching; relevant to Claims 1(b), 7/17. Applicant's own art. (Corresponding application Ser. No. 13/430,032 is incorporated by reference into the '785 specification.)

9. Other Gu-family reference — U.S. Patent No. 8,311,115 B2

Description: Companion to US 8,396,114; adds multiple-bitrate adaptive streaming encoding disclosure. Relevant to Claim 1 preamble and Claims 6/16.

10. Additional cited art of secondary relevance

The following appear on the '785 citation list and touch the claim limitations but are weaker/duplicative:

• US 8,645,669 B2 — Braness et al. (adaptive streaming).
• US 9,215,466 B2 — Zhai et al. (video quality/bitrate).
• US 8,868,752 / RE45052 — Li (streaming).
• US 9,020,039 / US 9,020,659 — Soroushian et al. (seek delay / adaptive bitrate).
• NPL on the '785 face: "HTTP Live Streaming Overview," Apple, Apr 1, 2011; "Text of ISO/IEC 23001-6: Dynamic adaptive streaming over HTTP (DASH)," Oct 2010; 3GPP TS 26.244/26.233 (2011) — all describe alternative representations at different bitrates/resolutions, i.e. the streaming framework of 1(e)–1(h).

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III. Claim-by-claim § 102 summary

Bottom line: Every challenged claim limitation is disclosed somewhere in this art, but no reference alone contains all limitations of Claim 1 or Claim 11. The realistic challenge theory is § 103 — e.g., Braness (or Ronca) in view of Liao, with Gu for the "predetermined percentage" limitation (claims 6/16) and Ozer for the integer-pixel/display-vs-sample-aspect-ratio limitations (claims 7, 8, 17, 18). That is precisely the structure of the IPR2025-01223 grounds (Grounds: Ozer+Liao; Ozer+Liao+Gu; Ronca+Liao; Ronca+Liao+Gu).

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IV. Flags and contradictions with the earlier sections

1. Petitioner identity — minor inconsistency to flag. The earlier "PTAB challenges" section captions the proceeding as "Amazon.com, Inc. et al. v. Kourosh Soroushian." The petition itself names the petitioner as Amazon.com, Inc. and Amazon Web Services, Inc. (real party-in-interest: Amazon), and the earlier "Litigation summary" and "Strategic summary" sections referred to Unified Patents, LLC as the (inferred) petitioner based on the Google Patents "Unified Patents PTAB Data" source tag. The primary document shows Amazon/Amazon Web Services as petitioner with Fenwick & West as counsel; Unified Patents appears only as the data source. These should be reconciled; the petition is the more authoritative source.

2. The earlier Obviousness section relied on Braness + "common knowledge in the art" and stated Braness has a priority date of Jan 5, 2011. That is consistent with what I found. However, the earlier section did not identify the more targeted art the examiner missed — specifically Liao '540 (which the petition expressly notes was never before the examiner). Liao is materially stronger than a pure "common knowledge" argument for limitations 1(c)–1(d), 9, and 10.

3. Priority-date exposure. Because the issued '785 claims (multi-bitrate encoding + quality evaluation) differ from the originally filed family claims (grouping resolutions into "maximum bitrate levels"), and because the asserted art includes a 2012 reference (Ozer), the effective § 102 date for the claimed subject matter is contestable. If the claims are not entitled to the Aug 30, 2011 provisional, the art window widens considerably. I did not verify provisional-vs-non-provisional support in this session — this should be checked against the 61/529,201 specification.

4. Unverified item. The exact publication number and dates for Ronca (EX1008) could not be confirmed here; I have described it only as the petition characterizes it. Similarly, the on-page citation list is truncated in the fetched Google Patents text (it renders "US20020067432A1 … 200" before cutting off), so my "face of the patent" list is the leading subset, not an exhaustive enumeration of all 519 citations. I do not claim it is complete.

Primary sources

• Patent: https://patents.google.com/patent/[US11611785B2](/patent/US11611785B2)/en (and Justia: https://patents.justia.com/patent/11611785)
• IPR petition and exhibits: https://www.docketalarm.com/cases/PTAB/IPR2025-01223/Amazon.com_Inc/docs/06-30-2025-Petitioner/ and https://ptacts.uspto.gov/ptacts/public-informations/petitions/[1558134](/patent/1558134)/download-documents
• Liao: https://patentimages.storage.googleapis.com/d5/0c/d6/005f6594005c02/[US9832540](/patent/US9832540).pdf
• Gu: https://patentimages.storage.googleapis.com/37/9a/9d/a06c5632f94425/US8396114.pdf and https://patents.google.com/patent/US8396114
• Ozer (NPL): https://www.streamingmediaglobal.com/Articles/Editorial/Featured-Articles/The-2012-Encoding-and-Transcoding-Buyers-Guide-86472.aspx

Obviousness Analysis of US Patent 11611785 under 35 U.S.C. § 103

This analysis identifies combinations of prior art references that would render the independent claims of US Patent 11611785 (specifically Claim 1 and its corresponding machine-readable medium claim, Claim 11) obvious to a person having ordinary skill in the art (POSITA) as of the patent's priority date of August 30, 2011.

Claimed Invention Overview (Claim 1)

Claim 1 describes a method for encoding source content into a plurality of alternative video streams and adaptively streaming them. Key steps include:

1. Identifying source video and a plurality of resolutions.
2. For each resolution, encoding portions multiple times with different target bitrates and evaluating the quality of each encoding.
3. Selecting resolution and target bitrate combinations for alternative streams based on the evaluated quality.
4. Uploading these encodings to a content distribution system.
5. Generating a top-level index describing the alternative streams.
6. Providing the index to playback devices capable of adaptive streaming.
7. Responding to requests for specific encodings.

Prior Art Combination and Motivation to Combine

Combination: US Patent 8,914,534 to Braness et al. ("Braness") in view of common knowledge in the art of video encoding, as acknowledged by US11611785 itself.

1. Braness et al. (US8914534B2)

• Disclosure: Braness et al. is explicitly cited in US11611785 and has a priority date of January 5, 2011, making it prior art. It discloses "Systems and Methods for Adaptive Bitrate Streaming of Media Stored in Matroska Container Files Using Hypertext Transfer Protocol" [cite: 1, "DETAILED DISCLOSURE OF THE INVENTION"]. Braness teaches the fundamental architecture and operation of an adaptive bitrate streaming system, including:

  • Storing source media on a media server as a top-level index file pointing to a number of "alternate streams" that contain video and audio data [cite: 1, "System Overview"].
  • Uploading the top-level index file and container files to an HTTP server [cite: 1, "System Overview"].
  • Playback devices using HTTP to request portions of the top-level index and container files via a network [cite: 1, "System Overview"].
  • Playback devices utilizing the top-level index to perform adaptive bitrate streaming in response to changes in streaming conditions, including switching between available streams [cite: 1, "System Overview"].
  • The need for alternative streams to encode media content at "different resolution and sample aspect ratio combinations and different maximum bitrates" to enable adaptive streaming [cite: 1, "System Overview"].

• Mapping to Claim 1: Braness directly teaches the overall adaptive streaming framework of Claim 1, including uploading encodings to servers (step 5), generating/providing a top-level index (steps 6, 7), and responding to playback device requests for specific encodings (step 8). It also implicitly teaches the need for "a plurality of resolutions for encoding of video content" (step 2) to facilitate adaptive bitrate switching.

2. Common Knowledge in the Art (as acknowledged by US11611785)

• Disclosure: The detailed disclosure of US11611785 itself contains admissions regarding the state of the art in video encoding at its priority date. It states: "Video data is typically encoded to achieve a target maximum bitrate. The quality of video encoded with a specific resolution, and frame rate typically does not improve appreciably beyond a specific maximum bitrate threshold. Beyond that threshold, increasing the resolution of the encoded video can increase video quality." [cite: 1, "DETAILED DISCLOSURE OF THE INVENTION"]. Furthermore, the patent explicitly acknowledges: "The bitrate threshold at which video quality does not appreciably improve can be determined through testing" or "subjective experimentation" [cite: 1, "Determining Target Maximum Bitrates"]. The patent also refers to "bitrate formulas that can be utilized to determine a optimal target maximum bitrate" [cite: 1, "Determining Target Maximum Bitrates"]. This demonstrates that the practice of encoding at various bitrates for a given resolution and evaluating quality to find an efficient or optimal bitrate was well-known in the art.

• Mapping to Claim 1: This common knowledge directly addresses the encoding and evaluation steps of Claim 1: "for each particular resolution of the plurality of resolutions: encoding at least a portion of the source video content multiple times using the particular resolution and multiple different target bitrates for the particular resolution; and evaluating quality for each of the multiple encodings" (Claim 1, step 3) and "selecting a plurality of resolution and target bitrate combinations for a plurality of alternative streams based on the evaluated quality of each of the multiple encodings" (Claim 1, step 4).

Motivation to Combine

A person having ordinary skill in the art (POSITA) in video encoding and streaming, aiming to implement an adaptive bitrate streaming system like that taught by Braness et al., would have been highly motivated to incorporate known video encoding optimization techniques. The very purpose of an adaptive bitrate streaming system is to deliver the best possible video quality given varying network conditions and playback device capabilities. To achieve this, the "alternative streams" within such a system must be efficiently encoded to represent distinct quality and bitrate tiers.

Given Braness et al.'s teaching of alternative streams encoded at "different resolutions and different maximum bitrates" [cite: 1, "System Overview"], and the common knowledge in the art (as admitted by US11611785) that for any specific resolution, there is an "optimal target maximum bitrate" that can be found "through testing" or "experimentation" without further appreciable quality gain [cite: 1, "DETAILED DISCLOSURE OF THE INVENTION", "Determining Target Maximum Bitrates"], a POSITA would find it obvious to apply this known optimization technique.

It would be a logical and desirable design choice for a POSITA building an adaptive streaming system to:

1. Determine the resolutions to offer for adaptive streaming (as implicitly required by Braness).
2. For each desired resolution, experimentally encode video content at multiple bitrates and evaluate their quality to identify the most efficient bitrate that delivers maximum quality without excessive data (as acknowledged by US11611785).
3. Select these "optimal" resolution and bitrate combinations to form the alternative streams for the adaptive streaming system, ensuring each stream is a high-quality, efficient representation at its respective resolution/bitrate point.

This combination of an existing adaptive streaming framework with conventional video encoding optimization methods directly yields the method steps recited in Claim 1, particularly those related to encoding multiple times at different bitrates, evaluating quality, and selecting combinations based on that evaluation. Therefore, Claim 1 (and consequently Claim 11) would have been obvious to a POSITA at the time of the invention.

Patent Term Adjustments (PTA) and Patent Term Extensions (PTE) for US11611785

Patent Term Adjustments (PTA)

Patent Term Adjustment (PTA) compensates for administrative delays by the USPTO during the prosecution of a patent application, adding days to the standard 20-year term from the earliest filing date. Delays that can lead to PTA include the USPTO failing to:

• Issue a first Office Action or Notice of Allowance within 14 months of filing.
• Respond to an applicant's reply or an appeal within four months.
• Act on an application within four months after a decision by the Patent Trial and Appeal Board (PTAB) or a federal court.
• Issue a patent within four months after payment of an issue fee.
• Issue a patent within 36 months from the filing date of the application.

Any PTA granted can be reduced if the applicant also caused delays.

As of the current date, specific PTA information for US11611785 is not detailed in the provided Google Patents data or search results beyond the general statement that PTA is a possibility. To determine the exact PTA for US11611785, one would typically need to consult the patent's file wrapper on the USPTO website.

Patent Term Extensions (PTE)

Patent Term Extension (PTE) is available for patents on certain products, such as human drugs, food additives, color additives, medical devices, animal drugs, and veterinary biological products, to restore patent term lost due to pre-market regulatory review by agencies like the FDA. PTE is typically capped at five years and cannot extend the patent term to more than 14 years from the date of marketing approval.

Based on the nature of US11611785, which relates to "Systems and methods for encoding and streaming video," it is highly unlikely to be eligible for PTE, as it does not appear to cover a product requiring regulatory approval under 35 U.S.C. § 156.

Continuation and Divisional Applications

The provided patent text explicitly states that US Patent 11611785 is a continuation of several earlier applications:

• U.S. patent application Ser. No. 16/789,303, filed Feb. 12, 2020 (which issued as U.S. Pat. No. 10,931,982).
• U.S. patent application Ser. No. 15/922,198, filed Mar. 15, 2018 (which issued as U.S. Pat. No. 10,645,429).
• U.S. patent application Ser. No. 13/600,046, filed Aug. 30, 2012 (which issued as U.S. Pat. No. 9,955,195).
• U.S. Provisional Patent Application No. 61/529,201, filed Aug. 30, 2011.

This establishes a clear chain of continuation applications. A continuation application is filed while an earlier non-provisional application is still pending and claims priority to that earlier application, covering the same invention.

The patent also indicates a child application:

• US18/185,107, filed 2023-03-16, which is a continuation of US11611785.

Divisional applications arise when a patent examiner determines that a single application contains more than one patentable invention, requiring the applicant to choose one for the original application and allowing the others to be pursued in divisional applications. The provided information does not explicitly state that US11611785 itself is a divisional application, nor does it explicitly mention any divisional applications stemming directly from US11611785. However, since it is a continuation of previous applications, it is part of a larger patent family where divisional applications could exist in other branches of the family tree.

Related Family Members

The patent family for US11611785 includes the following applications, based on the priority chain and related applications mentioned:

• US13/600,046 (filed 2012-08-30, issued as US9955195B2)
• US15/922,198 (filed 2018-03-15, issued as US10645429B2)
• US16/789,303 (filed 2020-02-12, issued as US10931982B2)
• US17/181,996 (this is US11611785, filed 2021-02-22, issued as US11611785B2)
• US18/185,107 (filed 2023-03-16, published as US20230224519A1, and listed as a continuation of US11611785)
• US201161529201P (Provisional application filed 2011-08-30)
• US20210250627A1 (Publication of US17/181,996)

The Google Patents "Family" section also lists foreign counterparts:

• KR101928910B1 (South Korea)
• CN103875248B (China)
• WO2013033458A2 (WIPO publication)

Projected Expiration Date

The general rule for utility patents filed on or after June 8, 1995, is that the patent term expires 20 years from the earliest filing date of the application from which priority is claimed.

US11611785 claims priority to U.S. Provisional Patent Application No. 61/529,201, filed on August 30, 2011. Therefore, the statutory 20-year term is calculated from this priority date.

2011-08-30 (Priority Date) + 20 years = 2031-08-30.

The Google Patents record explicitly states an "Anticipated expiration" date of 2032-08-30. This indicates that there has been a Patent Term Adjustment (PTA) of approximately one year (365 days) added to the base 20-year term. This adjustment would compensate for delays in prosecution by the USPTO.

Here is a comprehensive Defensive Disclosure document for US Patent 11611785, focusing on generating new derivative works and technical disclosures to establish prior art.

Defensive Disclosure for US Patent 11611785

Introduction

This document details several derivative variations and combinations of the core inventive concepts described in US Patent 11611785, titled "Systems and methods for encoding and streaming video encoded using a plurality of maximum bitrate levels." The purpose of this disclosure is to enrich the prior art landscape, rendering obvious or non-novel future incremental improvements by competitors within the domain of adaptive video encoding and streaming. The derivations are based on the independent claims, primarily Claim 1, which describes a method for encoding and adaptively streaming video content by evaluating quality across multiple target bitrates for various resolutions.

Derivative Variations Grounded in Claim 1

1. Material & Component Substitution: Specialized Hardware Acceleration for Encoding and Storage

Enabling Description:
The method for encoding source content (Claim 1, step 3) can be performed using specialized hardware accelerators such as dedicated Video Processing Units (VPUs) or ASICs incorporating advanced video codecs like AV1 or VVC. Specifically, the "encoding at least a portion of the source video content multiple times" can be executed by a farm of NVIDIA NVENC-enabled GPUs (e.g., RTX 6000 Ada Generation) or Intel Arc A-series GPUs leveraging their integrated Xe Media Engine, allowing for parallel encoding of different resolution/bitrate combinations. For quality evaluation (Claim 1, step 3), a dedicated Image Signal Processor (ISP) module can be used to compute real-time perceptual quality metrics (e.g., VMAF, SSIM) on the encoded outputs. The "uploading encodings" (Claim 1, step 5) would target a geographically distributed object storage system, such as Amazon S3 Glacier Deep Archive or Google Cloud Storage Coldline, utilizing erasure coding (e.g., Reed-Solomon codes) for data resilience and integrity instead of simple replication. The content distribution system (Claim 1, step 5) could then employ programmable edge proxies running WebAssembly modules for dynamic manifest manipulation (Claim 1, step 6) and request routing (Claim 1, step 8).

graph TD
    A[Source Video Content] --> B{Hardware Encoding Farm<br>(NVENC/Intel Xe Media Engine)};
    B -- Multiple Resolutions & Bitrates --> C{Hardware-Accelerated Quality Evaluation<br>(ISP, VMAF Engine)};
    C --> D{Resolution & Bitrate Selection Logic};
    D -- Selected Encodings --> E[Distributed Object Storage<br>(S3-compatible with Erasure Coding)];
    E --> F{Content Distribution System<br>(CDN Edge Proxies with WASM)};
    F -- Dynamic MPD/Manifest Generation --> G[Playback Devices];
    G -- Segment Requests --> F;

2. Operational Parameter Expansion: Ultra-Low Latency Volumetric Video Streaming

Enabling Description:
The method is expanded for ultra-low latency adaptive streaming of volumetric video data (e.g., point clouds, mesh sequences) at both extreme fidelity (e.g., 8K depth maps, 120 FPS) and extremely coarse granularities (e.g., sparse point clouds at 5 FPS). The "source video content" (Claim 1, step 1) consists of real-time captured volumetric data. "Identifying a plurality of resolutions" (Claim 1, step 2) would involve selecting different levels of spatial and temporal resolution for the volumetric data, along with varying levels of point density or mesh complexity. The "encoding" (Claim 1, step 3) would leverage specialized volumetric codecs (e.g., V-PCC, MPEG-G) running on custom hardware with dedicated 3D processing units. "Quality evaluation" (Claim 1, step 3) would focus on metrics relevant to 3D perception, such as point cloud distortion (e.g., Hausdorff distance) or visual coherence during view synthesis, performed within a millisecond budget. "Target bitrates" would range from hundreds of Mbps for high-fidelity interactive VR/AR applications down to tens of Kbps for basic volumetric presence sensing. The "top level index" (Claim 1, step 6) would dynamically describe different volumetric representations, including sparse and dense point clouds, and varied mesh resolutions, allowing for adaptive fetching based on user viewpoint, device capabilities, and network conditions to maintain sub-100ms end-to-end latency.

sequenceDiagram
    participant VC as Volumetric Capture System
    participant EH as Encoding Hardware (3D VPU)
    participant QEL as Quality Evaluation Logic (3D Metrics)
    participant ABSE as Adaptive Bitrate Streaming Engine
    participant CDN as CDN (Edge Servers)
    participant PD as Playback Device (AR/VR Headset)

    VC->>EH: Raw Volumetric Data (High-Res, High-FPS)
    EH->>EH: Encode (multiple resolutions/bitrates)
    EH->>QEL: Encoded Volumetric Streams
    QEL->>EH: Quality Feedback (3D Distortion, Perceptual)
    EH->>ABSE: Selected Volumetric Representations
    ABSE->>CDN: Upload Volumetric Segments & Manifest
    PD->>ABSE: Request Initial Manifest (e.g., DASH MPD for Volumetric)
    ABSE->>PD: Provide Volumetric Manifest
    PD->>PD: Analyze Network/Device Capability
    PD->>CDN: Request Volumetric Segment (Adaptive)
    CDN->>PD: Deliver Volumetric Segment (Ultra-Low Latency)
    PD->>PD: Render Volumetric Content

3. Cross-Domain Application: High-Definition Geospatial Data Streaming for Urban Planning

Enabling Description:
The adaptive streaming method is applied to high-definition geospatial video (e.g., aerial footage, drone surveys, satellite imagery time-lapses) for urban planning and environmental monitoring. The "source video content" (Claim 1, step 1) includes large-scale orthomosaic images, LiDAR scan animations, and photogrammetry model traversals, often spanning gigapixels. "Identifying a plurality of resolutions" (Claim 1, step 2) involves selecting different spatial resolutions (e.g., 5cm/pixel, 20cm/pixel, 1m/pixel) and temporal resolutions (daily updates, weekly updates). "Encoding" (Claim 1, step 3) would utilize advanced geospatial compression techniques (e.g., JPEG 2000, WebP for image sequences, or specialized point cloud compression) to create multi-resolution tiled data. "Quality evaluation" (Claim 1, step 3) would assess feature recognition accuracy, measurement precision, and cartographic legibility at various zoom levels. "Target bitrates" are adapted to client-side GIS processing capabilities and available network bandwidth (e.g., for field agents with cellular connections vs. office analysts with fiber). The "top level index" (Claim 1, step 6) would describe the various geospatial data layers and their resolutions, enabling dynamic data loading for interactive map applications and 3D city models.

graph LR
    A[Raw Geospatial Data<br>(Aerial, LiDAR, Photogrammetry)] --> B{Data Pre-processing<br>(Tiling, Layering, Metadata)};
    B --> C{Encoding Engine<br>(Geospatial Codecs, Multi-resolution)};
    C -- Multiple Resolution/Bitrate Versions --> D{Quality & Feature Validation<br>(GIS Accuracy, Legibility Metrics)};
    D --> E{Adaptive Stream Selector};
    E -- Selected Stream Profiles --> F[Geospatial Data Servers<br>(Tiled Storage)];
    F --> G{Dynamic Manifest Generator<br>(Geo-Spatial MPD)};
    G --> H[GIS Workstation/Mobile Device];
    H -- Requests for Geo-Segments --> F;

4. Cross-Domain Application: Industrial Metrology and Quality Control

Enabling Description:
This method is adapted for real-time streaming of high-precision metrology video from industrial inspection systems (e.g., automated optical inspection (AOI), computed tomography (CT) scans). The "source video content" (Claim 1, step 1) consists of high-magnification optical images, X-ray videos, or 3D scan data (e.g., structured light projections) used to detect microscopic defects in manufactured goods. "Identifying a plurality of resolutions" (Claim 1, step 2) involves varying pixel densities for defect features (e.g., 1000 DPI for critical areas, 100 DPI for overview), frame rates (e.g., high-speed for inline inspection, low-speed for manual review), and color depth. "Encoding" (Claim 1, step 3) focuses on preserving minute details, using lossless or perceptually lossless codecs where critical. "Quality evaluation" (Claim 1, step 3) is based on automated defect detection algorithms (e.g., machine vision, edge detection) and their confidence scores, ensuring that lower bitrate streams do not mask critical anomalies. "Target bitrates" are determined by the severity of potential defects and network conditions within the factory, prioritizing high-fidelity streams for critical process steps while offering lower bitrate streams for general monitoring or remote diagnostic support. The "top level index" (Claim 1, step 6) would allow manufacturing execution systems (MES) or remote technicians to request specific quality levels of inspection data.

graph TD
    A[Metrology Sensor<br>(AOI, CT, Structured Light)] --> B{Data Acquisition & Pre-processing};
    B --> C{Industrial Video Encoder<br>(Lossless/Perceptually Lossless Codecs)};
    C -- Multiple Encodings --> D{Automated Defect Detection & Quality Metrics<br>(ML-based Vision)};
    D --> E{Adaptive Quality Selector<br>(Prioritize Defect Visibility)};
    E -- Selected Stream Profiles --> F[Factory Data Historian/Server];
    F --> G{Real-time Manifest Generator};
    G --> H[MES/QA Workstation/Remote Diagnostic];
    H -- Requests Inspection Streams --> F;

5. Cross-Domain Application: Broadcast Journalism & Event Coverage

Enabling Description:
The adaptive streaming methodology is applied to remote broadcast journalism and live event coverage scenarios. The "source video content" (Claim 1, step 1) would be live camera feeds from field reporters or event venues, often originating from diverse, unpredictable network environments (e.g., cellular bonds, satellite links). "Identifying a plurality of resolutions" (Claim 1, step 2) involves selecting broadcast-grade resolutions (e.g., 1080p, 720p) and also lower-resolution proxies for rapid preview. "Encoding" (Claim 1, step 3) would utilize professional codecs (e.g., ProRes, AVC-Intra) for high-quality masters and efficient distribution codecs (e.g., HEVC, AV1) for adaptive streams. "Quality evaluation" (Claim 1, step 3) considers broadcast standards (e.g., EBU R 128 for audio loudness, specific video artifacts), reporter feedback, and editorial priorities. "Target bitrates" are dynamically adjusted to ensure maximum possible quality under prevailing network conditions, prioritizing consistent frame rate and audio/video synchronization over transient resolution dips. The "top level index" (Claim 1, step 6) would inform broadcast control rooms or online news platforms about available stream qualities, enabling them to select the most suitable feed for live broadcast or archiving, with seamless switching.

flowchart TD
    subgraph Field Acquisition
        Camera(Broadcast Camera) -- Live Feed --> Encoder(Field Encoder - Multi-Bitrate)
        Encoder -- Encoded Streams --> Network(Variable Network Link)
    end

    subgraph Studio/CDN
        Network --> Ingest(Cloud/Studio Ingest)
        Ingest -- Store --> Storage(Storage for Alternatives)
        Ingest -- Quality Eval --> Quality(Quality Evaluation Module - Broadcast Standards)
        Quality --> Selector(Adaptive Stream Selector)
        Selector --> CDN(CDN Distribution)
        CDN --> Manifest(Manifest Generator - e.g., HLS/DASH)
    end

    subgraph Client
        Manifest --> Playback(Broadcast Control Room/Online Platform)
        Playback -- Requests Segments --> CDN
    end

6. Integration with Emerging Tech: AI-Driven Perceptual Quality Optimization

Enabling Description:
The core method is enhanced by integrating an AI/ML model for highly sophisticated "quality evaluation" (Claim 1, step 3) and predictive "selecting a plurality of resolution and target bitrate combinations" (Claim 1, step 4). An ensemble of deep learning models, pre-trained on vast datasets correlating objective metrics (PSNR, SSIM, VMAF) with subjective human perceptual quality scores (MOS), would dynamically assess the visual and auditory quality of each encoding. During the "encoding at least a portion... multiple times" (Claim 1, step 3) step, this AI agent would actively adjust encoding parameters (e.g., quantization parameters, GOP structure, rate control algorithms, pre-processing filters like noise reduction or de-interlacing) for each resolution in real-time. Instead of merely evaluating fixed target bitrates, the AI would perform a continuous gradient descent or reinforcement learning to find the optimal bitrate-quality trade-off for dynamic content complexity and anticipated network fluctuations. The "selection" (Claim 1, step 4) is then an AI-inferred decision to maximize perceptual quality under bandwidth constraints, potentially even recommending dynamic resolution changes or aspect ratio adjustments based on content analysis (e.g., faces, text, high-motion scenes).

graph TD
    A[Source Video Content] --> B{Pre-processing};
    B --> C{Dynamic Encoder<br>(Multiple resolutions, Adaptive QP/GOP)};
    C -- Encoded Streams --> D{AI Perceptual Quality Evaluator<br>(VMAF, MOS Prediction)};
    D -- Quality Feedback --> C;
    D -- Optimal Bitrate-Quality Trade-off --> E{AI Stream Selector<br>(Predictive, Content-Aware)};
    E -- Selected Streams --> F[Content Distribution System];
    F --> G[Playback Devices];

7. Integration with Emerging Tech: IoT Sensor-Contextualized Adaptive Streaming

Enabling Description:
The adaptive streaming system is augmented with real-time contextual data from synchronized IoT sensors collocated with the "source video content" (Claim 1, step 1) capture device. For instance, a surveillance camera might be accompanied by light sensors, ambient noise microphones, motion detectors, and environmental condition sensors (temperature, humidity). This IoT data is ingested alongside the video. When "identifying a plurality of resolutions" (Claim 1, step 2) and "encoding" (Claim 1, step 3), the system considers these sensor inputs. For "quality evaluation" (Claim 1, step 3), the IoT context influences the prioritization. E.g., if a motion sensor is triggered or ambient light drops, the system might proactively select higher target bitrates or resolutions for the relevant video segments to enhance detail for security analysis, even if network conditions are suboptimal. The "top level index" (Claim 1, step 6) would include metadata links to the synchronized IoT sensor data streams, allowing playback devices to filter or prioritize video based on specific sensor events (e.g., only show video when motion detected, or overlay temperature readings). This allows for context-aware adaptive streaming.

classDiagram
    class SourceVideoContent {
        +VideoData raw
        +Timestamp
    }
    class IoTSensorData {
        +SensorID
        +Timestamp
        +Value string
        +Type string
    }
    class Encoder {
        -resolutions []
        -targetBitrates []
        +encode(video, resolution, bitrate)
    }
    class QualityEvaluator {
        +evaluate(encoding, sensorData)
    }
    class StreamSelector {
        +select(qualities, sensorData)
    }
    class AdaptiveStream {
        +Resolution
        +Bitrate
        +EncodingData
        +IoTSensorLinks []
    }
    class TopLevelIndex {
        +StreamManifest []
        +GlobalSensorLinks []
    }

    SourceVideoContent "1" -- "*" IoTSensorData : has context
    SourceVideoContent --> Encoder : feeds
    IoTSensorData --> QualityEvaluator : informs
    Encoder --> QualityEvaluator : provides
    QualityEvaluator --> StreamSelector : outputs
    StreamSelector --> AdaptiveStream : creates
    AdaptiveStream "1" -- "1" TopLevelIndex : described in

8. Integration with Emerging Tech: Blockchain-Verified Content Provenance and Integrity

Enabling Description:
The method incorporates blockchain technology to ensure the provenance, integrity, and immutability of the "source video content" (Claim 1, step 1) and its "plurality of alternative video streams." Each significant step in the encoding and distribution pipeline is recorded on a distributed ledger. Specifically, upon "identifying source video content," a cryptographic hash of the raw content is generated and timestamped on a blockchain (e.g., Ethereum, Hyperledger Fabric). For each "encoding" (Claim 1, step 3), the hash of the encoded segment, along with its resolution, target bitrate, and the result of the "quality evaluation," is linked to the original content hash and added as a transaction to the blockchain. The "top level index" (Claim 1, step 6) would then include verifiable proofs (e.g., Merkle roots, transaction IDs) that allow playback devices or content consumers to verify the authenticity and integrity of each received video segment (Claim 1, step 8) against the immutable blockchain record. This provides a auditable chain of custody for the video content from creation to consumption, preventing tampering or unauthorized modifications.

sequenceDiagram
    participant S as Source Content
    participant EE as Encoding Engine
    participant Q as Quality Evaluator
    participant BC as Blockchain Network
    participant CDS as Content Distribution System
    participant PD as Playback Device

    S->>BC: Hash Raw Content & Record Tx (Content ID)
    S->>EE: Source Video Content
    EE->>EE: Encode (multiple resolutions/bitrates)
    EE->>Q: Encoded Streams
    Q->>Q: Evaluate Quality
    Q->>BC: Hash Encoded Segment, Quality & Record Tx (Segment ID)
    Q->>CDS: Upload Encoded Segments
    CDS->>CDS: Generate Top Level Index (with Blockchain Proofs)
    CDS->>PD: Provide Top Level Index
    PD->>BC: Verify Blockchain Proofs for Segments
    PD->>CDS: Request Specific Encoded Sections
    CDS->>PD: Deliver Encoded Sections
    PD->>PD: Verify Segment Integrity with Hash

9. The "Inverse" or Failure Mode: Graceful Degradation for Network Outages

Enabling Description:
The system is designed to operate in a "graceful degradation" mode during severe network outages or extreme bandwidth limitations. When the "responding to requests from the one or more playback devices" (Claim 1, step 8) detects a persistent inability to deliver requested segments at any of the standard "plurality of resolution and target bitrate combinations," the system transitions to an emergency mode. In this mode, "encoding at least a portion of the source video content multiple times" (Claim 1, step 3) is replaced by dynamically generating ultra-low-fidelity "emergency streams." These streams could be static keyframes (e.g., 1 frame per 10 seconds), highly compressed monochrome video at QVGA resolution, or even text-only descriptions of scene changes. "Quality evaluation" (Claim 1, step 3) is simplified to checking for minimal data transmissibility, prioritizing any form of content over complete loss. The "top level index" (Claim 1, step 6) is dynamically updated to exclusively offer these emergency streams. Upon recovery of network conditions, the system would gradually reintroduce standard alternative streams, allowing playback devices to seamlessly transition back to higher quality. This ensures continuous, albeit highly degraded, user experience during critical network failures.

stateDiagram-v2
    state NormalStreaming {
        [*] --> Initializing
        Initializing --> EncodingContent : Source identified
        EncodingContent --> EvaluateQuality : Encoded
        EvaluateQuality --> SelectCombinations : Quality assessed
        SelectCombinations --> UploadEncodings : Combinations chosen
        UploadEncodings --> GenerateIndex : Uploaded
        GenerateIndex --> ProvideIndex : Index ready
        ProvideIndex --> RespondRequests : Index provided
        RespondRequests --> RespondRequests : Requests handled
        RespondRequests --> NetworkDegradation : Critical network failure detected
    }

    state NetworkDegradation {
        NetworkDegradation --> EmergencyMode : Persistent failure
        EmergencyMode : Prioritize minimal data
        EmergencyMode --> GenerateEmergencyStreams : Switch to low-fi content
        GenerateEmergencyStreams --> UpdateEmergencyIndex : Index for emergency streams
        UpdateEmergencyIndex --> ServeEmergencyContent : Respond with emergency streams
        ServeEmergencyContent --> NetworkRecovery : Network conditions improve
    }
    EmergencyMode --> NormalStreaming : Recovered

10. The "Inverse" or Failure Mode: Privacy-Preserving Obfuscation on Request

Enabling Description:
This derivative introduces a privacy-preserving "failure mode" where the content distribution system, upon specific request (e.g., from a user, an regulatory authority, or detected privacy violation), deliberately obfuscates sensitive portions of the video content. When "responding to requests from the one or more playback devices" (Claim 1, step 8), if a privacy-sensitive request is received, the content distribution system does not serve the original high-fidelity streams. Instead, for the relevant sections, it dynamically selects or generates "alternative streams" (Claim 1, step 4) that have been intentionally degraded to protect privacy. This could involve real-time facial blurring, license plate pixelation, or audio anonymization, even if higher quality versions are available. The "encoding" (Claim 1, step 3) or a dedicated post-processing step would include selective obfuscation algorithms. "Quality evaluation" (Claim 1, step 3) in this context involves verifying the effectiveness of the obfuscation rather than pure visual fidelity, ensuring that sensitive information is sufficiently masked. The "top level index" (Claim 1, step 6) might include flags or separate stream descriptions for privacy-enhanced versions, allowing playback devices to specifically request these or be automatically redirected based on user/policy settings.

graph LR
    A[Source Video Content] --> B{Privacy Policy/Request};
    B -- Apply Policy --> C{Content Analysis<br>(Identify Sensitive Regions)};
    C --> D{Selective Obfuscation Engine<br>(Face Blur, Pixelation, Audio Anonymization)};
    D -- Obfuscated/Original Segments --> E{Multi-Variant Encoder};
    E -- Multiple Resolution/Bitrate/Obfuscation Versions --> F{Quality & Privacy Effectiveness Evaluator};
    F --> G{Adaptive Stream Selector<br>(Policy-driven)};
    G -- Selected Stream Profiles --> H[Content Distribution System];
    H --> I{Dynamic Manifest Generator};
    I --> J[Playback Devices];
    J -- Requests for (Privacy-Enhanced) Segments --> H;

---

Combination Prior Art Scenarios with Open-Source Standards

These scenarios illustrate how US Patent 11611785 can be combined with existing open-source standards, thereby expanding the prior art for adaptive video streaming.

1. US Patent 11611785 + MPEG-DASH (ISO/IEC 23009-1):
   The methods described in US11611785, particularly the "encoding at least a portion of the source video content multiple times using the particular resolution and multiple different target bitrates" (Claim 1, step 3) and "selecting a plurality of resolution and target bitrate combinations for a plurality of alternative streams" (Claim 1, step 4), are directly applicable to the generation of MPEG-DASH content. The "top level index" (Claim 1, step 6) would explicitly be a DASH Media Presentation Description (MPD). Each "alternative stream" would correspond to a DASH Representation within an AdaptationSet. The "evaluated quality" (Claim 1, step 3) would inform the choice of target bitrates and other encoding parameters (e.g., codec profile, level, picture structure) for each Representation, ensuring optimal perceptual quality for various bandwidth conditions. Playback devices (Claim 1, step 7) would utilize standard DASH client libraries (e.g., dash.js) to parse the MPD and perform adaptive bitrate switching by "responding to requests for specific encodings" (Claim 1, step 8) based on their internal ABR logic and network feedback.

2. US Patent 11611785 + WebRTC (W3C Standard):
   The adaptive encoding and quality evaluation techniques of US11611785 can be integrated with WebRTC for real-time communication scenarios. The "source video content" (Claim 1, step 1) would be live camera or screen capture input. Instead of pre-encoded files, "encoding at least a portion... multiple times" (Claim 1, step 3) would occur dynamically within a WebRTC-enabled browser or application, generating multiple Scalable Video Coding (SVC) layers or simulcast streams with different resolutions and bitrates. The "evaluating quality" (Claim 1, step 3) would incorporate WebRTC's real-time network statistics (e.g., RTCPeerConnection.getStats() reports on packet loss, round-trip time, estimated bandwidth) to continuously inform the selection of optimal RTCRtpSender parameters. The "selecting a plurality of resolution and target bitrate combinations" (Claim 1, step 4) would be a dynamic process, adjusting the active SVC layers or simulcast stream parameters in real-time, effectively forming a "top level index" (Claim 1, step 6) of available qualities conveyed via SDP. Playback devices (other WebRTC peers) would adaptively receive the most appropriate stream based on their capabilities and observed network conditions, "responding to requests" (Claim 1, step 8) by adjusting which SVC layers or simulcast streams they request or process.

3. US Patent 11611785 + FFmpeg (Open-Source Multimedia Framework):
   The fundamental "method for encoding source content" (Claim 1, preamble) and specifically the "encoding at least a portion of the source video content multiple times" (Claim 1, step 3) and "evaluating quality for each of the multiple encodings" (Claim 1, step 3) can be comprehensively implemented using FFmpeg and its integrated libraries (e.g., libx264, libx265, libvpx for VP9/AV1, libsvtav1). A system following US11611785 would use FFmpeg to identify available "plurality of resolutions" (Claim 1, step 2) for a given source. The "encoding at least a portion" would involve invoking FFmpeg multiple times with different -vf scale options for resolution and various -b:v (target bitrate) or -crf (constant rate factor) parameters for libx264 or libx265. For "evaluating quality," FFmpeg's built-in ssim or psnr filters can be used to compare the multiple encodings against the source. The resulting "selected plurality of resolution and target bitrate combinations" (Claim 1, step 4) would then be packaged into container formats (e.g., MP4, WebM) by FFmpeg, and a manifest (the "top level index" of Claim 1, step 6) generated by a custom script or another tool (e.g., Bento4 for DASH MPDs) for a content distribution system. This combination demonstrates how the core inventive steps can be achieved with widely available open-source tools.