Patent Drafting Analysis of Tesla’s Neural Network Hardware Adaptation System | US 11,610,117 B2
Patent Drafting Analysis of Tesla's Neural Network Hardware Adaptation System | US 11,610,117 B2
A structural and strategic analysis of Tesla's SMT-solver-driven neural network model adaptation patent, examining claim architecture, drafting quality, critical gaps, and prosecution positioning across all 19 claims.
Spec Words
5,200
Across 6 sections
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Total Claims
19
3 independent · 16 dependent
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Figure Sheets
2
System architecture, method flowchart
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Published by PatSnap Insights Team · · 12 min read Verified by PatSnap Eureka Data
Overview
Structural Overview
The detailed description dominates at approximately 56% of total words (~2,900 of ~5,200), providing reasonable — though not deep — technical grounding for the SMT-solver-based adaptation mechanism. The claim architecture comprises 19 claims total: 3 independent claims (method, system, and CRM) each covering the same core invention, supported by 16 dependent claims at a ratio of 5.33:1. Figure coverage is minimal with only 2 drawing sheets — one system block diagram (FIG. 1) and one method flowchart (FIG. 2) — leaving several specific structural components described in the specification without dedicated visual support.
Section Word Distribution
↗ Click bars to exploreFigure Inventory — 2 Sheets
| Figure | Description | Role |
|---|---|---|
| FIG. 1 | Schematic block diagram of the model configuration system 100, showing hardware platform 102, neural network model 105, model optimization platform 110, traversal module 120, constraints module 130, SMT solver 140, datastore 150, configurations module 160, and performance module 170.Search in Eureka ↗ | System architecture |
| FIG. 2 | Flowchart of the model configuration method, illustrating steps S210 (traversing neural network to identify decision points), S220 (identifying configuration constraints), S230 (determining candidate configurations via SMT solver), S240 (receiving candidate configurations), S250 (determining satisfiability), and S260 (determining a configuration satisfying target performance metrics).Search in Eureka ↗ | Flow diagram |
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Claims
Claim Architecture Analysis
The patent presents 3 independent claims: Claim 1 (method), Claim 11 (system/apparatus), and Claim 17 (non-transitory computer storage media/CRM), each directed to the same core adaptation process. The 16 dependent claims yield a 5.33:1 dependent-to-independent ratio, which sits at the lower edge of the norm for AI/software platform patents in the G06N class where ratios of 6–10:1 are typical. The tripartite method/system/CRM structure provides meaningful enforcement coverage across implementation types, though the dependent claims largely mirror each other across the three independent claims rather than exploring genuinely distinct technical variations.
Core inventive concept: The claims solve the problem of manually navigating a combinatorially complex neural network configuration space for a target hardware platform by automatically casting the configuration problem as a satisfiability problem — Claims 1, 11, and 17 each require "generating a candidate configuration for the neural network via execution of a satisfiability solver based on the constraints," where the solver assigns values to "a plurality of decision points" while satisfying both a processing-resource constraint and a performance-metric constraint, with the determined constraints iteratively updated to include the candidate configuration "as a negation" to generate additional distinct configurations.
Independent Claim Dissection
| Claim | Preamble | Transition | Key Body Elements |
|---|---|---|---|
| Claim 1 | A method implemented by a system of one or more processors | comprising | obtaining neural network model information comprising plurality of decision points with first decision points associated with neural network layout; accessing platform information for hardware platform; determining constraints based on platform information with first constraint tied to processing resource and second constraint tied to performance metric; generating candidate configuration via satisfiability solver assigning values to decision points; updating constraints to include candidate configuration as negation to generate further candidatesSearch prior art ↗ |
| Claim 11 | A system comprising one or more processors and non-transitory computer storage media storing instructions | comprising | obtaining neural network model information comprising plurality of decision points with first decision points associated with neural network layout; accessing platform information for hardware platform; determining constraints based on platform information with first constraint tied to processing resource and second constraint tied to performance metric; generating candidate configuration via satisfiability solver assigning values to decision points; updating constraints to include candidate configuration as negation to generate further candidatesSearch prior art ↗ |
| Claim 17 | Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors to perform operations | comprising | obtaining neural network model information comprising plurality of decision points with first decision points associated with neural network layout; accessing platform information for hardware platform; determining constraints based on platform information with first constraint tied to processing resource and second constraint tied to performance metric; generating candidate configuration via satisfiability solver assigning values to decision points; updating constraints to include candidate configuration as negation to generate further candidatesSearch prior art ↗ |
Claim Dependency Tree
1 Method: obtain NN decision points, access platform info, determine dual constraints, generate candidate config via satisfiability solver, negate and iterateSearch Claim 1 prior art ↗
2 Adds: first decision point associated with tensor size; candidate configuration selects tensor size value to fit in platform memorySearch in Eureka ↗
3 Adds: other decision points associated with numerical precision, algorithm selection, data padding, accelerator use, or strideSearch in Eureka ↗
4 Adds: neural network model information associated with directed graph; traversing directed graph to identify decision points for each node and edgeSearch in Eureka ↗
5 Adds: performance metric comprises one or more of evaluation time, power consumption, or memory consumptionSearch in Eureka ↗
6 Adds: third constraint associated with adapting neural network to software platform relating to operating system on hardware platformSearch in Eureka ↗
7 Adds: selecting output candidate configuration based on analyzing candidate configuration and other candidates; input data provided, selection based on performance metricsSearch in Eureka ↗
8 Adds: successively generating plurality of candidate configurations with different decision point values; halting based on threshold count, unsatisfiability signal, or performance metric thresholdSearch in Eureka ↗
9 Adds: generating interactive user interface presenting dashboard with candidate configuration; responds to user input to update constraints triggering satisfiability solver for updated configurationSearch in Eureka ↗
10 Adds: satisfiability solver is a satisfiability modulo theories (SMT) solverSearch in Eureka ↗
11 System: processors + non-transitory storage; obtain NN decision points, access platform info, determine dual constraints, generate candidate config via satisfiability solver, negate and iterateSearch Claim 11 prior art ↗
12 Adds: first decision point associated with tensor size; candidate configuration selects tensor size value to fit in platform memorySearch in Eureka ↗
13 Adds: neural network model information associated with directed graph; traversing directed graph to identify decision points for each node and edgeSearch in Eureka ↗
14 Adds: performance metric comprises one or more of evaluation time, power consumption, or memory consumptionSearch in Eureka ↗
15 Adds: selecting output candidate configuration based on analyzing candidates with input data; selection based on performance metrics across configurationsSearch in Eureka ↗
16 Adds: generating interactive user interface — dashboard presenting candidate configuration; responds to user input to update constraints triggering satisfiability solverSearch in Eureka ↗
17 CRM: non-transitory storage media; obtain NN decision points, access platform info, determine dual constraints, generate candidate config via satisfiability solver, negate and iterateSearch Claim 17 prior art ↗
18 Adds: first decision point associated with tensor size; candidate configuration selects tensor size value configured to fit in platform memorySearch in Eureka ↗
19 Adds: generating interactive user interface — dashboard presenting candidate configuration; responds to user input to update constraints triggering satisfiability solverSearch in Eureka ↗
| Metric | This Application | Software / AI Platform Norm |
|---|---|---|
| Total claims | 19 | 15 – 25 |
| Independent claim count | 3 | 2 – 4 |
| Dependent : Independent ratio | 5.33 : 1 | 5 – 8 : 1 |
| Method claims present? | Yes — Claim 1 | Always |
| System / apparatus claims? | Yes — Claim 11 | Common |
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Drafting Quality
Drafting Quality Signals
The claim set demonstrates strong structural discipline in its tripartite independent claim architecture and the iterative negation-and-regenerate limitation, which provides a concrete technical differentiator that survived examination. However, the near-identical mirroring of dependent claims across Claims 1, 11, and 17 — for example, Claims 2/12/18 all cover tensor size memory fitting, and Claims 9/16/19 all cover the interactive UI — substantially reduces the fallback diversity that the dependent claim count implies.
✅
Antecedent Basis
The antecedent basis is clean across all 19 claims. "The plurality of decision points" in the generating step of Claim 1 properly refers back to "a plurality of decision points" introduced in the obtaining step. Similarly, "the constraints" in the generating step refers back to "constraints associated with adapting the neural network model information" in the determining step. "The candidate configuration" used in the negation limitation refers back to "a candidate configuration" generated in the prior step. No orphaned "the" references were identified across Claims 1–19.
✅
Spec–Claim Consistency
The independent claim limitations map well to the specification. The traversal module 120 and FIG. 2 step S210 directly support the "obtaining neural network model information comprising a plurality of decision points" limitation. The constraints module 130 description supports the dual-constraint requirement of Claim 1. The SMT solver 140 description provides explicit written description for the satisfiability solver limitation. The negation-and-reiterate limitation — "wherein the determined constraints are updated to include the candidate configuration as a negation" — is supported by the detailed description's discussion of adding negated valid configurations to the constraint set.
✅
Transition Word Usage
All three independent claims correctly use "comprising" as the transition word, preserving open-ended scope and permitting accused systems to include additional elements beyond the recited steps without defeating infringement. The method claim (Claim 1) uses "the method comprising" followed by individual method steps, which is appropriate for the G06N art unit. No instances of "consisting of" or "consisting essentially of" were used, which would have inappropriately narrowed the claims. The use of "comprising" in Claims 1, 11, and 17 is strategically sound given the software-defined nature of the invention where implementations will inevitably include unlisted components.
⚠️
§112(f) Means-Plus-Function Risk
Claim 11 recites "a system comprising one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations comprising" — this functional claiming pattern applied to a processor-plus-instructions structure may invite §112(f) scrutiny if an examiner or court reads the operations as "means for" performing the listed functions. While no "means for" language appears explicitly, the structural disclosure for the system claim is thin — FIG. 1 names modules (120, 130, 140, 160, 170) but does not provide circuit-level or code-level structure to support each functional operation. A more robust filing would have included a dedicated apparatus claim with named structural modules as limitations.
⚠️
§101 Eligibility Risk
Claims 1, 11, and 17 carry meaningful Alice Step 2A exposure because the core concept — using a constraint satisfaction solver to select a configuration — is arguably an abstract mathematical process (satisfiability solving is a well-known algorithmic technique). The hardware tie-in in Claim 1 is functional rather than structural: the method is merely "implemented by a system of one or more processors," which courts have found insufficient under Step 2A, Prong 2. The strongest §101 defense lies in Claims 2/12/18 (tensor size memory fitting) and the negation-update limitation, which together ground the abstract idea in concrete computer platform operation. However, the independent claims themselves leave the §101 defense weaker than necessary; a specific hardware-tied limitation in Claim 1 would have been more defensible.
⚠️
Dependent Claim Fallback Quality
The 16 dependent claims are heavily mirrored across the three independent claims, providing far less fallback diversity than the raw count implies. Claims 2, 12, and 18 all add the identical tensor-size-to-memory limitation; Claims 5 and 14 both add the identical performance metric enumeration; Claims 9, 16, and 19 all add the identical interactive UI limitation. Only Claim 3 (decision point types: numerical precision, algorithm selection, stride), Claim 6 (software platform/OS third constraint), Claim 8 (successive generation with halt conditions), and Claim 10 (SMT solver specificity) add genuinely novel fallback positions not captured in the system or CRM claims — these four claims are the most strategically valuable dependent claims in the set.
⚠️
Abstract Quality
The abstract describes the invention accurately at a functional level but omits the single most important technical differentiator: the iterative negation mechanism by which the satisfiability solver is re-run with the prior candidate configuration added as a negated constraint to generate successive distinct configurations. The abstract states only that "a candidate configuration for the neural network is generated via execution of a satisfiability solver based on the constraints, with the candidate configuration assigns values to the plurality of decision points" — an examiner reading only the abstract may characterize the invention as generically applying constraint satisfaction to NN configuration, missing the iterative enumeration novelty that distinguishes this patent from prior art SMT-solver applications.
⚠️
Figure Support Quality
With only 2 drawing sheets, figure support is minimal relative to the specification's disclosure depth. FIG. 1 supports the system-level claim limitations by depicting modules 120, 130, 140, 150, 160, and 170, and FIG. 2 supports the method steps of Claims 1/11/17 through steps S210–S260. However, several claim limitations lack dedicated figure support: the iterative negation-and-regeneration process (the key inventive step) has no dedicated figure showing the constraint update loop; the directed acyclic graph representation of the neural network described in Claims 4 and 13 has no figure; and the interactive GUI described in Claims 9, 16, and 19 has no figure. A stronger filing would have included at least a data-flow diagram showing the negation loop and a sample GUI screenshot.
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Scorecard
Strategic Intent Scorecard
Multi-dimensional assessment of this application's patent strategy quality, based on claim structure, specification depth, and prosecution positioning.
Claim Breadth
3.5
Prosecution Defensibility
3
Spec–Claim Consistency
3.5
Dependent Claim Coverage
2.5
Claim Type Diversity
4.5
Figure Support Quality
2.5
Key observation: Claim Type Diversity scores highest (4.5/5) because the tripartite method/system/CRM structure of Claims 1, 11, and 17 comprehensively covers all standard enforcement vectors for an AI platform patent — a sophisticated filing choice that prevents design-around through claim-type substitution. Dependent Claim Coverage and Figure Support Quality tie for lowest (2.5/5): the dependent claims score poorly because 12 of the 16 simply mirror the same limitations across three independent claims rather than adding genuinely distinct technical fallback positions, and figure support is critically thin with only 2 sheets that leave the iterative negation mechanism, directed graph representation, and interactive UI without visual disclosure. Practitioners examining continuation opportunities should prioritize adding a dedicated figure for the constraint-negation loop and drafting continuation claims directed to the autonomous vehicle application domain explicitly mentioned in the specification but absent from the claims.
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Critical Gaps
3 Critical Gaps in This Claim Set
A senior-attorney lens on the three highest-priority structural weaknesses — what each exposes in prosecution and litigation, and what a stronger filing would have done differently.
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3 Critical Gaps in This Claim Set
See the full attorney-level analysis of what this application leaves unprotected — and how to draft it more defensively for your own filings.
No structural module apparatus claim Autonomous vehicle domain not claimed Parallel solver instance execution unclaimed
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Disclaimer: This analysis is generated by PatSnap Eureka AI based on publicly available patent data from the USPTO. It does not constitute legal advice and should not be relied upon as such. Patent data may be subject to change as prosecution progresses. Scores and assessments reflect automated analysis and may not capture all relevant legal or technical nuances. Always consult a qualified patent attorney for formal legal opinions on patentability, freedom to operate, or infringement.
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