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Plans

Plan-first. Every step traceable.

Cyrus reads your prompt, picks the right tools, and shows you the plan before it runs. Every action is logged, every citation sourced.

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PLANTOOLS · SEARCHCITED OUTPUT
Every step logged, every output anchored
PTAB research · skilled-artisan recognized-benefit doctrine
5 PTAB Final Written Decisions where a skilled artisan would recognize a benefit of a proposed prior art modification even though the modification may impede a function of the primary reference, and the claim is found unpatentable as obvious. 2025–2026, preferably 2026.
Plan3 of 5 done
Searching IPR (PTAB) for skilled-artisan recognized-benefit doctrine
Filtered to FWDs in 2025–2026 with claims found unpatentable as obvious · 247 candidates
Verifying 247 IPR candidates against query
Narrowed to 18 FWDs where Patent Owner argued the modification would impede a function
Reading IPR documents for Board's benefit-recognition framing
Verified passages anchored to FWD page numbers · 5 cases survive
Getting PDFs for the 5 IPR cases and highlighting cited passages
Yellow-highlight every cited passage · 3 of 5 PDFs annotated
Compile report with anchored citations + Highlighted-FWDs folder
Found · 5 FWDs (2025–2026)
Thermaltake v. Chen — IPR2024-01230 · ’336 patent · claims 1–5 unpatentable2026-02-17
Biofrontera v. Sun Pharma — IPR2024-01312 · ’028 patent · all challenged claims unpatentable2026-02-23
Imperative Care v. INARI Medical — IPR2024-01157 · ’011 patent · all challenged claims unpatentable2026-01-16
Solaris Oilfield v. Masaba — IPR2024-01179 · ’689 patent · claims 1–25 cancelled2026-01-26
FOX Factory v. SRAM — IPR2024-00492 · ’207 patent · claims 1–3, 7, 8, 10–12 unpatentable2025-08-04
Firm voice

Drafting that reads like you wrote it.

Cyrus learns your firm's signature phrasings, claim structure, and citation conventions. Press Tab to accept — the next phrase appears before you finish typing.

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US 11,887,367 — Claims.docx
Specification
Claims
Listing of Claims
U.S. Patent No. 11,887,367 B1 · Using Machine Learning to Train and Use a Model to Perform Automatic Interface Actions Based on Video and Input Datasets · Baker et al., OpenAI Opco LLC · Reissue draft under 37 C.F.R. § 1.173

1.(Original)

A method for training a machine learning model to perform automated actions, comprising:receiving unlabeled digital video data;generating pseudo-labels for the unlabeled digital video data, the generating comprising: receiving labeled digital video data; training a first machine learning model including an inverse dynamics model (IDM) using the labeled digital video data; and generating at least one pseudo-label for the unlabeled digital video data, wherein the at least one pseudo-label is based on a prediction, generated by the IDM, of one or more actions that mimic at least one timestep of the unlabeled digital video data, and the prediction of the one or more actions is generated based on a non-causal combination of past information and future information within the unlabeled digital video data, the past and future information being relative to one or more reference frames within the unlabeled digital video data;adding the at least one pseudo-label to the unlabeled digital video data to form pseudo-labeled digital video data; andfurther training the first machine learning model or a second machine learning model using the pseudo-labeled digital video data to generate at least one additional pseudo-label for the unlabeled digital video.

2.(Currently amended)

The method of claim 1, wherein the IDM or machine learning model is trained to generate one or more predicted actions to be performed via a graphical user-interface overlay rendered atop a live application windowTab ↹without invoking an operating-system input event on the host machine.wherein the overlay is generated by a second neural network distinct from the IDM.and refreshed at a rate exceeding sixty frames per second.

Formats

Speaks the file formats your firm files in.

Drop an office action PDF, invention disclosure, or PTAB petition. Cyrus reads it, decomposes the claims, and ships back Word, PowerPoint, and PDF — formatted for filing.

See supported formats →
Cyrus · intake
Reading office actionextracting
PDF
OA-18-342-109.pdf
14 pages · USPTO non-final · received 2026-05-18
82%
Detected §103 rejection over Vaswani in view of Lewis
Decomposed claim 1 into 14 elements
Cross-referencing claim mapping table
Ready to file · 3 outputs

Response package

App 18/342,109 · drafted in firm voice from matter 2024-PA-0084

DOCX
response-§103.docx
7 pages · Graham + KSR analysis · firm signature block
PPTX
claim-chart-1.pptx
14 elements × 2 references · element-by-element grid
PDF
IDS-PTO-SB-08.pdf
PTO/SB/08 · 14 references · ready for EFS-Web
Cyrus · intakeextracting
PDF
OA-18-342-109.pdf
14 pages · non-final OA
82%
  • Detected §103 over Vaswani i/v Lewis
  • Decomposed claim 1 into 14 elements
  • Cross-referencing claim mapping table
Ready to file · 3 outputs

Response package

App 18/342,109 · firm voice from matter 2024-PA-0084

DOCX
response-§103.docx
7 pages · Graham + KSR
PPTX
claim-chart-1.pptx
14 elements × 2 refs
PDF
IDS-PTO-SB-08.pdf
PTO/SB/08 · EFS-Web ready

One workspace. Seven products.

Each grounded in a different source — the MPEP, patents and publications, every PTAB case, every CAFC order and opinion — and all wired into Cyrus.

Cyrus · agentic IP partner
Plan-first agent for patent prosecution
Citation-grounded. Plan-driven. Agentic from day one — searches, drafts, charts, and opinions, all from one workspace.
Final OA on 18/342,109 just dropped — §103 rejection over Lee + Tanaka + Chen, claims 1–5. I have seven days. Read all three refs, chart each combination, find which arguments survived in my prior §103 responses, and draft an amendment + response in my voice.
●5 tools · file wrapper · cited refs parser · argument archive · claim chart · amendment gen · 9.4s
Response strategy — 18/342,109 (matter 2024-PROS-0142):
1. §103 Lee + Tanaka — Tanaka’s batched training does not teach claim 1’s incremental schedule; argue hindsight per1 Plantronics v Aliph (CAFC 2014).
2. §103 Lee + Chen — Chen teaches away from sub-batch normalization (col. 7:22–48); KSR teaching-away applies2.
3. Argument precedent — three of your prior wins (3 16/834,221, 17/123,456, 17/889,012) paired the same hindsight + teaching-away framing successfully.
4. Amendment — narrow claim 1 to incremental schedule with sub-batch normalization — clearly outside Lee + Chen’s disclosure. Cost: ~6 attorney hours.
Proposed next step · Draft amendment + response in firm voice
1Re-validate Tanaka batched-training read vs spec ¶¶ 41–53
2Pull Plantronics + KSR teaching-away quotes for arg block
3Cite 3 prior wins (16/834,221, 17/123,456, 17/889,012)
4Draft amendment + arguments in firm voice · 2024-PROS-0142
OutputsDOCXOA-response-18-342-109.docxPPTXclaim-chart-Lee-Tanaka-Chen.pptxPDFargument-precedent-cite-list.pdf
Vault · privileged matter store
Your matters, your privilege boundary
Centralized intake, audit-logged access, privilege-by-default.
2025-PA-0142
Arclight Robotics Inc.
18/901,553 · 14 files
Privileged
2024-PA-0084
Halogen Optics LLC
17/998,221 · 27 files
Privileged
2024-PA-0079
Helix Biosystems Corp.
17/882,406 · 41 files
Privileged
2025-LT-0031
Sentinel Imaging Inc.
N.D. Cal. 25-cv-00041 · 89 files
Privileged
2025-PA-0166
Northwind Energy LLC
18/944,012 · 9 files
Privileged
Patents · US grants and publications
Prior art search that reads like a partner did it
Every US patent. Every publication. Every cited paper.
US10452978B2Claim 1
Google LLC · Vaswani
0.94
US11138392B2Abstract
Google · Macherey
0.89
US10789529B2Claim 4
Microsoft · Zhang
0.83
US11232358B1Spec ¶42
Amazon · Kolesnikov
0.78
US11042796B2Claim 2
Meta · Lewis
0.71
US10963493B1Claim 1
Salesforce · Socher
0.62
MPEP · canonical patent doctrine
The MPEP at your fingertips — verified, current
Patent procedure, citation-grounded.
When is a claim limitation interpreted as means-plus-function under § 112(f), and how do I avoid invoking it inadvertently?

Apply the three-prong test from MPEP § 2181.I (post-Williamson):

  1. Nonce term. The claim uses "means" or a non-structural placeholder — "module," "mechanism," "element," "unit," "device for."
  2. Functional language. The term is modified by functional language, typically introduced by "for" or "configured to."
  3. Insufficient structure. The claim itself does not recite sufficient structure or material to perform the claimed function.

If all three are met, § 112(f) is invoked and the claim is construed to cover only the corresponding structure disclosed in the specification plus equivalents (MPEP § 2181.II). To avoid invocation, use structural terms — "processor," "circuitry," "memory storing instructions to" — rather than functional placeholders.

Williamson v. Citrix Online, LLC, 792 F.3d 1339 (Fed. Cir. 2015) (en banc) CAFC overruled the strong presumption that absence of "means" avoids § 112(f).

PTAB · post-grant agent
Win institution before you file
PTAB-grade petition and response work.
#194%All UnpatentableComp. Arch.
IPR2024-00345
Acme Networks v. Quantum Labs
#287%All SurviveNetworking
IPR2024-00891
Sentinel Imaging v. Helix Bio
#381%Mixed FWDBiotech
IPR2023-01124
Northwind Energy v. Arclight Robotics
CAFC · Federal Circuit precedent
Find the case that decides yours
18k+ Federal Circuit opinions, indexed.
Phillips v. AWH Corp.
415 F.3d 1303 (Fed. Cir. 2005)
Claim construction · cited ~4,200 times
Markman v. Westview
52 F.3d 967 (Fed. Cir. 1995)
Construction is a question of law · ~8,400 cites
Vitronics Corp. v. Conceptronic
90 F.3d 1576 (Fed. Cir. 1996)
Intrinsic-evidence hierarchy · ~3,100 cites
Innova/Pure Water v. Safari
381 F.3d 1111 (Fed. Cir. 2004)
POSITA-at-filing rule · ~1,800 cites
Texas Digital v. Telegenix
308 F.3d 1193 (Fed. Cir. 2002)
Dictionaries as fallback (limited by Phillips)
Notes · semantic search over your archive
Find what you've written before
Every claim, plan, and brief you've drafted, one query away.
antecedent basis fix for claim term first introduced in a dep claim3 hits
§112(b) antecedent-basis fixplan
… reframe “the said modulator” as “the modulator of claim 1” when the term is first introduced in a dependent claim to cure antecedent-basis indefiniteness …
edited 9 days agoMPEP § 2173Williamson v. Citrix
Response to OA — claim 14 indefinitenessbrief
… Applicant respectfully traverses the rejection. The term “control signal” finds antecedent basis in dependent claim 14, which depends from independent claim 1 reciting a signal generator …
edited 6 wk agoMatter 2024-PA-0079
Claim 1 — adaptive controller (rev. 3)claim
… 14. The controller of claim 1, further comprising a feedback path coupled to the modulator, the feedback path configured to adjust a control signal based on …
edited 4 mo agoApp 17/998,341
PTAB

Every PTAB case, searchable.

Every Final Written Decision, citation-grounded. Filter by tech area, outcome, statutory grounds, and APJ panel — every holding traceable to the exact passage in the FWD PDF.

18k+PTAB cases
1.8M+documents
Dailyupdated
Open the PTAB module →
app.mpepai.com/ipr
IPR · PTAB search
skilled artisan recognized benefit + modification impedes function⌘ K
FiltersOutcome · All Unpatentable, Mixed FWD ×Grounds · § 103 ×FWD date · 2025–2026 ×
5 cases · sorted by relevance Sort: Relevance ↓
#194%
IPR2024-01230
U.S. Pat. 10,690,336
Thermaltake Technology v. Chen, Chien-Hao
Filed Sep 2024 · FWD Feb 17, 2026
§ 103 · Echazarreta i/v Lai/Hasegawa
Claims 1–5 unpatentable as obvious
All UnpatentableSemiconductorsIPR52 docs
#291%
IPR2024-01312
U.S. Pat. 11,697,028
Biofrontera Inc. v. Sun Pharma Indus.
Filed Oct 2024 · FWD Feb 23, 2026
§ 103 · Lundahl i/v Larsen
All challenged claims unpatentable
All UnpatentableBiotechIPR47 docs
#388%
IPR2024-01157
U.S. Pat. 11,697,011
Imperative Care v. INARI Medical
Filed Jul 2024 · FWD Jan 16, 2026
§ 103 · Schaffer + secondary refs
All challenged claims unpatentable
All UnpatentableMedical DeviceIPR39 docs
#485%
IPR2024-01179
U.S. Pat. 11,780,689
Solaris Oilfield v. Masaba
Filed Aug 2024 · FWD Jan 26, 2026
§ 103 · cites Corephotonics tradeoff doctrine
Claims 1–25 cancelled
All UnpatentableMech. Eng.IPR36 docs
#582%
IPR2024-00492
U.S. Pat. 7,147,207
FOX Factory v. SRAM
Filed Feb 2024 · FWD Aug 4, 2025
§ 103 · Yi i/v JP’279
Claims 1–3, 7, 8, 10–12 unpatentable
Mixed FWDMech. Eng.IPR44 docs
Prior Art

Every US patent, and publication.

Search grants and applications by claims, abstract, detailed description, background, CPC. Ask in natural language, filter by date and technology area, and every response is grounded in the exact passage of the cited document.

Open the prior art module →
app.mpepai.com/prior-art-search
Prior art search
US patents + applications + scientific papers
Claim 1 · decomposition
Top K3 Auto-judge running
Element
REF 1
REF 2
REF 3
[1a]an encoder neural network configured to receive an input sequence comprising a plurality of tokens
Query: encoder neural network receive input sequence of tokens
Strong coverageAuto-judge: complete 3 / 3
TeachesPDF
US10452978B2
an encoder neural network configured to receive the input sequence and to process the input sequence to generate a respective encoded representation
TeachesPDF
US11138392B2
wherein the encoder neural network comprises a stack of self-attention layers processing the token sequence
PartialPDF
US10726784B2
the encoder receives a sequence of input embeddings, each embedding corresponding to a token in the input
[1b]a self-attention sub-layer applying multi-head attention over the encoded representations
Query: multi-head self-attention over encoded representations
Strong coverageAuto-judge: running 1 / 3
TeachesPDF
US10452978B2
applying a self-attention mechanism over the input sequence to compute, for each position, a weighted combination of values derived from the entire sequence
US10719587B2
[Claim 5]
the attention layer comprises multiple parallel attention heads, each computing scaled dot-product attention over the input...
0.87
US20210073644A1
[Spec ¶ 0042]
in some embodiments, a plurality of attention heads operate in parallel on the encoded sequence...
0.76
[1c]a learned weighting gate conditioned on the input sequence and applied prior to the softmax normalization
Query: learned weighting gate input-conditioned before softmax
Moderate coverageAuto-judge: 1 / 3 validated
PartialPDF
US10789529B2
a learned weighting gate adjusts retrieval scores conditioned on the input query context, applied before softmax normalization
US11042796B2
[Claim 7]
a re-ranking component configured to re-score the retrieved passages using a cross-encoder before generation
0.71
US20210073644A1
[Spec ¶ 0042]
the gating function comprises a learned linear projection of the input context, followed by a sigmoid non-linearity...
0.78
[1d]a decoder neural network applying cross-attention over the encoder outputs to autoregressively generate an output sequence
Query: decoder cross-attention autoregressive output sequence
Strong coverageAuto-judge: running 2 / 3
TeachesPDF
US11138392B2
a decoder neural network applying multi-head attention with a learned per-head temperature parameter over the encoder outputs
PartialPDF
US10452978B2
the decoder autoregressively generates each output position conditioned on previously generated outputs and the encoder representations
US10726784B2
[Spec ¶ 0089]
the decoder layer attends to encoder outputs via a cross-attention sub-layer interleaved with masked self-attention...
0.79
[1e]wherein the system generates the output sequence based on the weighted combinations from the self-attention sub-layer
Query: generate output sequence from weighted attention combinations
Moderate coverageAuto-judge: 1 / 3 validated
US20220198250A1
[Spec ¶ 0089]
the disclosed graph-attention retrieval engine traverses a citation network to score candidate prior-art documents...
0.66
PartialPDF
US10452978B2
producing an output sequence based on the weighted combinations from the self-attention layer applied to the encoded representations
US11042796B2
[Claim 1]
generating a response using a retrieval-augmented generator conditioned on retrieved passages from a corpus
0.69
ValidatedJudgingRejectedPreview
End to end

Six jobs Cyrus does end-to-end.

Each grounded in a different combination of the platform's tools. Each verified, sourced, and signable.

  1. Every US patent and publication, foreign equivalents, and peer-reviewed scientific literature — ranked by §102 anticipation and §103 obviousness relevance.

  2. Reads invention disclosures, drafts the spec, generates dependent claim trees — every limitation traceable.

  3. §101 / 102 / 103 / 112 rejections with cited responses and claim amendments.

  4. Decompose claim 1 into its elements, run top-K retrieval per element, then judge each candidate teaches / lacks / partial — with shimmer while judging and reject-flash when an element fails.

  5. Maps the landscape around your product, identifies risk, surfaces design-arounds.

  6. Watches every prosecution + PTAB proceeding. Pulls + renames docs automatically. Push + email reminders the moment something lands.

app.mpepai.com/search
dynamic clause weighting attention transformer
248 candidates · §102 anticipation 3 · §103 combinable 12 · claims, abstracts, spec ¶¶ + drawings
US 10,452,978 · Google
Attention-based sequence transduction neural networks
US 11,138,392 · Google
Machine learning models for translation prediction
US 10,789,529 · MS
Neural info retrieval with dynamic re-weighting
US 11,232,358 · Amazon
Graph-attention retrieval over citation networks
US 11,042,796 · Meta
Multi-stage reranking using cross-encoder scores
US 10,963,493 · Salesforce
Adaptive passage scoring with session-level prior
EP 3,887,221 A1 · Siemens
Query-conditioned weighting of encoder hidden states
WO 2019/143,210 · DeepMind
Sparse attention with learned routing gates
NPL · NeurIPS 2021
Sparse is enough in scaling transformers
CPC G06N 3/08priority < 2019-06-12clause → segment, spangate → gating, mask
Ground truth

Built for the way IP firms actually work.

What changes when every answer must be signable — privileged, audit-logged, and anchored to the record

Search the data that wins your case.

Every canonical corpus pre-indexed with frontier embedding models — exact-passage semantic match across MPEP, PTAB, CAFC, the full US patent corpus, and peer-reviewed papers. Competitors fall back to web search; we mathematically reach the closest passage every time.

Explore the corpora →
Search every embedded corpus at once
Corpus 5 active
MPEP Every chapter✓
PTAB Final Written Decisions✓
CAFC Federal Circuit opinions✓
Patents US grants + applications✓
Scientific papers Peer-reviewed✓

Every corpus, embedded

MPEP, PTAB, CAFC, the full US patent corpus, and peer-reviewed papers — pre-indexed. The closest passage, every time.

Privileged. Audit-logged. By default.

Attorney-client and work-product privilege baked in. Every access logged, every download attributed. Compliance-ready for state-bar audits and SOC 2 (in progress).

Security and privilege →
Who accessed the Acme matter file in the last 30 days?
Found14 access events
By3 attorneys, 1 paralegal
Privilegeboundary intact

Privileged by default

Every search, source, and citation logged to the matter. Nothing leaves the building.

Citation-grounded by construction

Every output Cyrus produces — every paragraph in an opinion letter, every cell in a claim chart, every line in a §103 response — carries an inline N badge linking to the source. Nothing ungrounded ships.

How citations work →

Under MPEP §21431 the examiner must articulate a reason with rational underpinning. Therasense2 holds disclosed-but-not-enabled is not anticipatory.

1MPEP § 2143 · Chapter 2100pp. 12

“The examiner must articulate a reason with rational underpinning to support the legal conclusion of obviousness.”

Cited by construction

Most legal AI asserts. Cyrus cites — every paragraph anchored to the record.

Roadmap

ShippedJun 12, 2026
Custom agent skills
ShippedJun 8, 2026
Every PTAB filing, full-text searchable
ShippedMay 17, 2026
Per-stage activity trace + findings UI
See the full roadmap →

Cyrussearches prior art exhaustively, drafts patent applications, responds to Office Actions, judges every claim element, runs FTO clearances, tracks your whole docket, cites MPEP, CAFC, and PTAB — and learns your way.

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