Patent Drafting Analysis of Tesla’s Autonomous Vehicle Training Data Collection System | US 2021/0271259 A1
Patent Drafting Analysis of Tesla's Autonomous Vehicle Neural Network Training Data System | US 2021/0271259 A1
A structural and strategic analysis of Tesla's trigger classifier patent covering claim architecture, drafting quality signals, critical gaps, and prosecution positioning for autonomous driving training data collection.
Spec Words
8,200
Across 6 sections
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Total Claims
20
3 independent · 17 dependent
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Figure Sheets
8
System architecture, flow diagrams, block diagrams
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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 84% of total estimated words (~5,850 of ~7,005 across all sections), reflecting a specification heavily focused on operational embodiments across autonomous driving use cases. The claim set contains 20 claims total — 3 independent (Claims 1, 19, 20) and 17 dependent — with a 5.67:1 dependent-to-independent ratio that provides layered fallback but concentrates heavily on the method claim (Claim 1). The 8 figure sheets span schematic vehicle diagrams, multiple process flow charts, and one hardware block diagram, providing adequate but not exhaustive coverage of the disclosed system components.
Section Word Distribution
↗ Click bars to exploreFigure Inventory — 8 Sheets
| Figure | Description | Role |
|---|---|---|
| FIG. 1A | Schematic diagram of vehicle 102 driving on a road with sensor volume 104, sensor 107, and tire 106 as an edge-case object to be identified.Search in Eureka ↗ | Key embodiment |
| FIG. 1B | Block diagram of training data generation system showing vehicle 102, deep learning system 700, classifiers 110A–110N, features 112, and training data generation system 120 connected via network.Search in Eureka ↗ | System architecture |
| FIG. 2 | Flow diagram illustrating the overall process: receive sensor data (201), pre-process (203), initiate deep learning analysis (205), identify training data (207), transmit (209), post-process (211), provide to vehicle control (213).Search in Eureka ↗ | Flow diagram |
| FIG. 3 | Flow diagram for creating a trigger classifier: prepare training data (301), apply deep learning analysis (303), train trigger classifier (305), determine trigger properties (307), deploy classifier and properties (309).Search in Eureka ↗ | Flow diagram |
| FIG. 4 | Detailed flow diagram for identifying training data using trigger classifier, including layer-by-layer neural network inference loop (401–415) with threshold score comparison and conditional sensor data transmission.Search in Eureka ↗ | Claim support |
| FIG. 5 | Flow diagram for deploying training data from trigger-classified sensor data: receive data meeting trigger conditions (501), convert to training data (503), prepare train/validation sets (505), train model (507), deploy model (509).Search in Eureka ↗ | Flow diagram |
| FIG. 6 | Flow diagram for dynamic classifier selection on a vehicle: execute classifiers (601), receive trigger to select new classifier (603), execute new classifier (605), determine classifier score (607), transmit sensor data (609).Search in Eureka ↗ | Flow diagram |
| FIG. 7 | Block diagram of deep learning system 700 showing sensors 701, image pre-processor 703, deep learning network 705, AI processor 707, vehicle control module 709, network interface 711, and trigger classifier module 713.Search in Eureka ↗ | System architecture |
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Claims
Claim Architecture Analysis
The patent presents 3 independent claims: Claim 1 (method), Claim 19 (computer program product/CRM), and Claim 20 (system/apparatus), providing a tripartite coverage structure across enforcement formats. The 17 dependent claims yield a 5.67:1 dependent-to-independent ratio, which is above average for software/AI patents in the G06N class, providing substantial fallback depth. Notably, all 17 dependent claims append to Claim 1 (method) only — Claims 19 and 20 receive no dependent claim elaboration, creating a significant enforcement asymmetry.
Core inventive concept: The patent solves the problem of inefficient and costly collection of edge-case training data for autonomous driving neural networks by deploying a lightweight "trigger classifier" that operates on intermediate layer outputs of an already-running neural network to score sensor data in real-time, selectively transmitting only high-scoring data via computer network for training data generation — as recited across Claims 1, 19, and 20.
Independent Claim Dissection
| Claim | Preamble | Transition | Key Body Elements |
|---|---|---|---|
| Claim 1 | A method | comprising | receiving sensor data; applying a neural network to the sensor data; applying a trigger classifier to an intermediate result of the neural network to determine a classifier score; determining whether to transmit via a computer network at least a portion of the sensor data based at least in part on the classifier scoreSearch prior art ↗ |
| Claim 19 | A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions | for | receiving sensor data; applying a neural network to the sensor data; applying a trigger classifier to an intermediate result of the neural network to determine a classifier score; determining whether to transmit via a computer network at least a portion of the sensor data based at least in part on the classifier scoreSearch prior art ↗ |
| Claim 20 | A system | comprising | a sensor on a vehicle; an artificial intelligence processor; a vehicle control module; an image signal processor configured to receive/process/provide image; a memory coupled with AI processor with instructions to receive processed image, perform inference using neural network, provide intermediate result to trigger classifier to determine classifier score, provide inference result to vehicle control module; a network interface configured to transmit at least a portion of captured image based at least in part on the classifier scoreSearch prior art ↗ |
Claim Dependency Tree
1 Method: receive sensor data → apply neural network → apply trigger classifier to intermediate result → determine classifier score → decide whether to transmit sensor dataSearch Claim 1 prior art ↗
2 Adds: intermediate result is output of an intermediate layer of the neural networkSearch in Eureka ↗
3 Further: intermediate result is output of a second to last layer of the neural networkSearch in Eureka ↗
4 Adds: neural network is a convolutional neural networkSearch in Eureka ↗
5 Adds: trigger classifier is trained using a training data set analyzed by a second neural network using a machine learning model based on the neural networkSearch in Eureka ↗
6 Further: trigger classifier is trained using an input vector that is an output of a layer of the second neural networkSearch in Eureka ↗
7 Further (from 6): the layer of the second neural network is dynamically selectedSearch in Eureka ↗
8 Further (from 6): trigger classifier is transmitted wirelessly to a vehicle applying the neural networkSearch in Eureka ↗
9 Adds: trigger classifier has been generated based on an identified improvement need for the neural networkSearch in Eureka ↗
10 Adds: trigger classifier identifies tunnel entrance, tunnel exit, fork in road, obstacle in road, road lane lines, or drivable spaceSearch in Eureka ↗
11 Adds: determining whether to transmit includes comparing classifier score with a threshold valueSearch in Eureka ↗
12 Adds: further comprising determining whether to apply the trigger classifier based on one or more required conditionsSearch in Eureka ↗
13 Further (from 12): required conditions based on driving time, minimum time since last retained data, autonomous driving disengagement event, vehicle type, steering angle threshold, or road typeSearch in Eureka ↗
14 Adds: trigger classifier specifies a particular layer of the neural network from which to receive the intermediate resultSearch in Eureka ↗
15 Adds: transmitting sensor data with metadata including classifier score, location, timestamp, road type, time since previous transmission, or vehicle typeSearch in Eureka ↗
16 Adds: transmitting sensor data with vehicle operating conditions including speed, acceleration, braking, or steering angleSearch in Eureka ↗
17 Adds: receiving via computer network the trigger classifier represented by a vector of weightsSearch in Eureka ↗
18 Further (from 17): trigger classifier is represented by the vector of weights and a biasSearch in Eureka ↗
19 CRM: non-transitory computer readable storage medium with instructions for the same steps as Claim 1 (receive sensor data, apply neural network, apply trigger classifier to intermediate result, determine classifier score, determine whether to transmit)Search Claim 19 prior art ↗
20 System: sensor on vehicle, AI processor, vehicle control module, image signal processor, memory with instructions to perform inference and provide intermediate result to trigger classifier for classifier score, network interface to transmit captured image based on classifier scoreSearch Claim 20 prior art ↗
| Metric | This Application | Software / AI Industry Norm |
|---|---|---|
| Total claims | 20 | 15 – 25 |
| Independent claim count | 3 | 2 – 4 |
| Dependent : Independent ratio | 5.67 : 1 | 4 – 7 : 1 |
| Method claims present? | Yes — Claim 1 | Common |
| System / apparatus claims? | Yes — Claim 20 | Common |
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Drafting Quality
Drafting Quality Signals
Claim 1's preamble — 'A method, comprising' — sets a maximally broad starting point, and the use of 'comprising' throughout is strategically sound for an open-ended software method claim. However, a significant structural weakness exists: all 17 dependent claims attach exclusively to Claim 1, leaving Claims 19 (CRM) and 20 (system) without any dependent fallback positions, which substantially weakens prosecution defensibility for those independent claims.
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Antecedent Basis
The antecedent basis is generally clean across the 20 claims. Claim 1 introduces "sensor data," "a neural network," and "a trigger classifier" correctly with indefinite articles before referencing them with "the" in subsequent limitations. Claim 20 properly introduces "a sensor," "an artificial intelligence processor," and other hardware components before referencing them with "the" in the memory instruction sub-elements. No instances of unsupported "the" references were identified across Claims 1–18.
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Spec–Claim Consistency
Key Claim 1 limitations map directly to specific specification sections. The "intermediate result of the neural network" limitation is supported by ¶[0033], ¶[0041], and FIG. 4 (steps 403–411). The "classifier score" and threshold determination map to ¶[0028], ¶[0068], and FIG. 4 (step 413). The "transmit via a computer network" limitation maps to ¶[0029], ¶[0058], and FIG. 4 (step 415). Claim 20's image signal processor element maps to FIG. 7 (component 703) and ¶[0095].
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Transition Word Usage
All three independent claims use "comprising" or its functional equivalent, which is the optimal open-ended transition for this technology space — it permits additional steps or elements beyond those claimed. Claim 1 uses "comprising" directly; Claim 19 uses "comprising computer instructions for" which preserves open-ended scope for the CRM claim type; Claim 20 uses "comprising" for its system elements. No restrictive "consisting of" language appears, which would have unnecessarily narrowed scope in this broad AI/software space.
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§112(f) Means-Plus-Function Risk
Claim 20 recites an "image signal processor configured to" perform multiple functions (receive, process, provide), which in the Federal Circuit's post-Williamson framework can trigger §112(f) analysis even without explicit "means for" language if the term does not connote sufficiently definite structure. While "image signal processor" likely qualifies as a structural term, the specification's description of image pre-processor 703 at ¶[0095] as optionally a GPU, CPU, or "specialized image processor" may render the structural boundary sufficiently indefinite to invite an examiner challenge under §112(b) for indefiniteness. A prudent filing would have provided clearer structural definition.
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§101 Eligibility Risk
Claims 1 and 19 face meaningful Alice/Mayo exposure because the core steps — applying a neural network, applying a classifier, scoring, and deciding to transmit — are abstract data processing steps without structural hardware anchoring in the independent claim body. The §101 defense relies primarily on the autonomous vehicle context in dependent claims (Claim 8: wireless transmission to vehicle; Claim 10: specific use cases) and Claim 20's hardware elements, but an examiner reading only Claims 1 or 19 would likely issue a §101 rejection requiring invocation of the hardware-integrated dependent claims for defense. A stronger filing would have incorporated a vehicle or sensor limitation directly into Claim 1.
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Dependent Claim Fallback Quality
The 17 dependent claims all attach exclusively to Claim 1, creating a critical structural gap: Claims 19 and 20 have zero dependent fallback positions. Among the method claim dependents, Claims 15–16 (metadata and vehicle operating conditions) and Claims 5–8 (trigger classifier training chain) add genuinely distinct technical limitations with independent prosecution value. However, Claims 9 ("generated based on an identified improvement need") and 10 (use case list) are primarily descriptive elaborations that add minimal claim scope differentiation and would provide limited fallback utility if Claim 1 were narrowed.
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Abstract Quality
An examiner reading only the abstract may fail to identify the specific novel contribution — the abstract describes the trigger classifier applying to an intermediate result of the neural network correctly, but the phrase "receiving sensor data" in the abstract without specifying that this occurs on a deployed vehicle in active operation obscures the inventive context. The abstract omits the key technical differentiator: that the trigger classifier leverages an already-running inference neural network's intermediate activations rather than processing data independently, which is the primary efficiency gain described in ¶[0023] and ¶[0031].
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Figure Support Quality
The 8 figures provide strong support for the primary claim limitations. FIG. 4 directly maps to the complete Claim 1 method flow (steps 401–415), and FIG. 7 maps to the Claim 20 system components including AI processor 707, sensor 701, deep learning network 705, vehicle control module 709, network interface 711, and trigger classifier module 713. FIG. 1B supports the classifier feature extraction concept. One gap: no figure directly illustrates the trigger classifier's internal representation as a weighted vector and bias (Claims 17–18), relying solely on text description in ¶[0067].
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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
3.8
Prosecution Defensibility
3
Spec–Claim Consistency
4.2
Dependent Claim Coverage
3
Claim Type Diversity
3.5
Figure Support Quality
4
Key observation: Spec–Claim Consistency scores highest (4.2/5.0) because FIG. 4 and FIG. 7 map precisely to every structural limitation across Claims 1 and 20, and paragraphs ¶[0033]–¶[0044] and ¶[0092]–¶[0102] provide dense, claim-specific written description support. Prosecution Defensibility and Dependent Claim Coverage score equally lowest (3.0/5.0) due to the critical structural gap of all 17 dependent claims attaching exclusively to Claim 1 — Claims 19 and 20 have no dependent fallback, meaning if either independent claim is narrowed during prosecution, no claim amendment pathway protects those claim formats. Practitioners reading this analysis should consider filing a continuation with dependent claims specifically tailored to Claims 19 and 20 to address this enforcement gap.
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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 dependents on Claims 19 and 20 Claim 1 lacks hardware tether for §101 Missing server-side training data generation claims
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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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