Patent Drafting Analysis of Tesla’s Labeling Training Data Using High-Volume Navigation Data | WO 2024/073033 A1
Patent Drafting Analysis of Tesla's Labeling Training Data Using High-Volume Navigation Data | WO 2024/073033 A1
A structural and strategic analysis of Tesla's PCT application covering claim architecture, drafting quality signals, critical prosecution gaps, and §101 eligibility positioning for this autonomous vehicle AI training data labeling technology.
Spec Words
9,800
Across 6 sections
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Total Claims
20
3 independent · 17 dependent
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Figure Sheets
9
System architecture, sensors, method flow, 3D models, camera feeds
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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 roughly 62% of total words (approximately 6,100 of ~9,800 words), reflecting deep embodiment coverage across §§ [0039]–[0146] spanning system components, sensor descriptions, multi-trip reconstruction protocols, and auto-labeling workflows. The claim set comprises 20 claims total — 3 independent claims (method, CRM, system) with 17 dependents, yielding a 5.67:1 dependent-to-independent ratio that is reasonable for AI/software art. Figure coverage across 9 sheets (FIGs. 1A–7) addresses system architecture, sensor layouts, process flow, raw camera data, 3D environment models, and multi-condition camera feeds, providing good visual anchoring for the core method steps.
Section Word Distribution
↗ Click bars to exploreFigure Inventory — 9 Sheets
| Figure | Description | Role |
|---|---|---|
| FIG. 1A | Illustrates the AI-enabled data analysis system 100 showing analytics server 110a, system database 110b, AI models 110c, egos 140a-c with ego computing devices 141a-c, administrator computing device 120, network 130, and server 160.Search in Eureka ↗ | System architecture |
| FIG. 1B | Block diagram of sensors integrated within ego 140, including orientation sensor 170b, speed sensor 170d, gyroscope/accelerometer 170f, GNSS 170h, cameras 170m, radar 170n, and autonomous driving system 170o.Search in Eureka ↗ | Key embodiment |
| FIG. 1C | Top-down schematic of vehicle ego 140 showing placement of eight exterior cameras (front cameras 170m-1 to 170m-3, rearward side cameras 170m-4, B-pillar cameras 170m-5, rear camera 170m-6) and autonomous driving system 170o.Search in Eureka ↗ | Key embodiment |
| FIG. 2 | Flow diagram of method 200 executed in the AI-enabled data analysis system, showing steps 210–260 for retrieving navigation/image data, generating a 3D environment model, identifying ML labels, receiving second ego data, auto-generating labels, and optionally training an AI model.Search in Eureka ↗ | Flow diagram |
| FIG. 3 | Grid of nine camera feed images 302–318 (collectively camera feed 301) received from a single ego navigating within an environment, depicting lane lines, trees, buildings, and traffic lights from eight different camera angles plus trajectory data 310.Search in Eureka ↗ | Claim support |
| FIG. 4 | Side-by-side comparison of camera feed images 402–410 from a single ego navigating a street (left panel) and the corresponding 3D model 412 showing environment 416, ego location 414, and reconstructed sidewalk 418 and road features.Search in Eureka ↗ | Claim support |
| FIG. 5 | Aggregated 3D model 500 generated from multiple egos 502–510 navigating the same environment, showing how individual per-ego models are combined to form a comprehensive environment model with road surfaces, lane markings, and structures.Search in Eureka ↗ | Key embodiment |
| FIG. 6 | Illustrates auto-labeling of a new ego's camera feed 600 (images 602–610) using the previously generated model 612, showing identification of feature 616 (crosswalk 618) within model region 614 for automatic label transfer.Search in Eureka ↗ | Claim support |
| FIG. 7 | Grid showing the same environment captured under four different weather/lighting conditions — dark (700), foggy (702), occluded (704), and raining (706) — demonstrating the robustness of cross-condition auto-labeling by the method 200.Search in Eureka ↗ | Claim support |
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Claims
Claim Architecture Analysis
The claim set contains 3 independent claims — Claim 1 (method), Claim 9 (computer-readable medium/CRM), and Claim 17 (system) — covering the full tripartite enforcement spectrum. The 17 dependent claims distributed across the three independent claims yield a 5.67:1 dependent-to-independent ratio, which is in line with the software/AI norm of 4–8:1. The parallel structure of Claims 1, 9, and 17 — each reciting identical core limitations in method, CRM, and apparatus form — ensures broad enforcement coverage but also exposes a significant functional symmetry risk if Claims 1 or 9 are subject to §101 challenge, as Claim 17's apparatus form provides the most hardware-grounded fallback.
Core inventive concept: The claims address the inefficiency and error-proneness of manual training data labeling for autonomous navigation AI models by using navigation and image data from a set of known egos to 'generat[e] a three-dimensional (3D) model of the environment' (Claim 1) and then 'automatically generat[ing] a machine learning label for the at least one feature depicted within the second image data' from a new, second ego not in the original set — thus transferring validated labels from the 3D model to novel, unlabeled camera feeds without human intervention.
Independent Claim Dissection
| Claim | Preamble | Transition | Key Body Elements |
|---|---|---|---|
| Claim 1 | A method | comprising | retrieving navigation data and image data from a set of egos navigating within an environment comprising at least one feature; generating a 3D model of the environment using navigation/image data of at least a subset of the set of egos; identifying a machine learning label associated with the at least one feature within the image data; receiving second navigation data and second image data from a second ego not included within the set of egos; automatically generating a machine learning label for the at least one feature depicted within the second image dataSearch prior art ↗ |
| Claim 9 | A computer-readable medium comprising a set of instructions that when executed, cause a processor to | comprising | retrieve navigation data and image data from a set of egos navigating within an environment comprising at least one feature; generate a 3D model of the environment using navigation/image data of at least a subset of the egos; identify a machine learning label associated with the at least one feature within the image data; receive second navigation data and second image data from a second ego not included within the set; automatically generate a machine learning label for the at least one feature depicted within the second image dataSearch prior art ↗ |
| Claim 17 | A system | comprising | a set of egos; and a processor in communication with the set of egos configured to: retrieve navigation data and image data from a set of egos navigating within an environment comprising at least one feature; generate a 3D model of the environment using navigation/image data of at least a subset of the egos; identify a machine learning label associated with the at least one feature; receive second navigation data and second image data from a second ego not included in the set; automatically generate a machine learning label for the at least one feature depicted within the second image dataSearch prior art ↗ |
Claim Dependency Tree
1 Method: retrieve ego navigation+image data, generate 3D environment model, identify ML label, receive second ego data, auto-generate ML labelSearch Claim 1 prior art ↗
2 Adds: filtering navigation/image data into a subset based on trajectory of each egoSearch in Eureka ↗
3 Adds: localizing the second ego using second navigation data or second image data in accordance with the 3D modelSearch in Eureka ↗
4 Adds: the at least one feature is at least one of a drivable surface, traffic sign, traffic light, lane lines, or 3D structureSearch in Eureka ↗
5 Adds: transmitting the machine learning label and the second image data to an artificial intelligence modelSearch in Eureka ↗
6 Further: the artificial intelligence model is an occupancy detection modelSearch in Eureka ↗
7 Adds: executing an AI model to predict the machine learning label associated with the at least one feature within the image dataSearch in Eureka ↗
8 Further: receiving from a human reviewer a validation regarding the machine learning label predicted by the AI modelSearch in Eureka ↗
9 CRM: instructions cause processor to retrieve ego navigation+image data, generate 3D model, identify ML label, receive second ego data, auto-generate ML labelSearch Claim 9 prior art ↗
10 Adds: filter navigation/image data into a subset per ego trajectorySearch in Eureka ↗
11 Adds: localize the second ego using second navigation/image data per the 3D modelSearch in Eureka ↗
12 Adds: the at least one feature is at least one of a drivable surface, traffic sign, traffic light, lane lines, or 3D structureSearch in Eureka ↗
13 Adds: transmit the machine learning label and second image data to an AI modelSearch in Eureka ↗
14 Further: the AI model is an occupancy detection modelSearch in Eureka ↗
15 Adds: execute an AI model to predict the ML label associated with the at least one featureSearch in Eureka ↗
16 Further: receive from a human reviewer a validation regarding the predicted ML labelSearch in Eureka ↗
17 System: set of egos + processor configured to retrieve ego navigation+image data, generate 3D model, identify ML label, receive second ego data, auto-generate ML labelSearch Claim 17 prior art ↗
18 Adds: processor further configured to filter navigation/image data into a subset per ego trajectorySearch in Eureka ↗
19 Adds: processor further configured to localize second ego using second navigation/image data per the 3D modelSearch in Eureka ↗
20 Adds: the at least one feature is at least one of a drivable surface, traffic sign, traffic light, lane lines, or 3D structureSearch in Eureka ↗
| 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 – 8 : 1 |
| Method claims present? | Yes — Claim 1 | Always |
| System / apparatus claims? | Yes — Claim 17 | Common |
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Drafting Quality
Drafting Quality Signals
The application demonstrates solid spec-claim consistency, with FIG. 2 mapping directly to all five operative steps of Claim 1 (steps 210–250) and ¶¶[0101]–[0136] providing granular description of each limitation. The primary weakness is significant §101 eligibility exposure in Claims 1 and 9, where the method and CRM claims lack any hardware-tethering language beyond a generic 'processor,' increasing Alice challenge risk compared to the system Claim 17 which recites a 'set of egos' as physical hardware.
✅
Antecedent Basis
Antecedent basis is well-managed across all 20 claims. In Claim 1, 'the set of egos' at step 220 properly refers back to 'a set of egos' introduced in the preamble retrieval step. 'The second ego' at step 240 follows clean introduction of 'a second ego' at step 240 itself. 'The at least one feature' is consistently anchored to its first introduction in 'an environment comprising at least one feature.' No dangling 'the' references were identified across Claims 2–20.
✅
Spec–Claim Consistency
Every operative limitation of independent Claim 1 maps to a specific figure and paragraph. The retrieval step (Claim 1, step 210) is supported by FIG. 2 step 210 and ¶[0101]; the 3D model generation (step 220) by FIG. 4 (model 412) and ¶¶[0111]–[0125]; the label identification (step 230) by FIG. 6 (feature 616) and ¶[0126]; the second ego data receipt (step 240) by ¶[0129]; and the auto-label generation (step 250) by ¶[0131]. FIG. 5 specifically supports the 'at least a subset of the set of egos' generating the 3D model limitation.
✅
Transition Word Usage
All three independent claims use 'comprising,' which is strategically appropriate for this open-ended AI/software technology where additional method steps or system components should not be excludable. Claims 2–8 use 'further comprising' consistently, correctly indicating additive dependent steps. No 'consisting' or 'consisting essentially of' language appears, which is correct — use of those transitions would unnecessarily narrow the claim scope in an AI training data labeling context where numerous additional processing steps may coexist.
✅
§112(f) Means-Plus-Function Risk
No 'means for' or 'step for' language appears in any of the 20 claims. Functional recitations such as 'configured to' in Claims 17–20 are paired with structural hardware ('a processor in communication with the set of egos'), which avoids §112(f) invocation under post-Williamson standards. The CRM claims (9–16) use infinitive verb form ('retrieve,' 'generate,' 'identify') consistent with standard software claim drafting that avoids means-plus-function interpretation. No §112(f) exposure is identified.
⚠️
§101 Eligibility Risk
Claims 1 and 9 carry moderate Alice/Mayo exposure because their method and CRM forms could be characterized as abstract ideas — collecting data (navigation + image data), generating a model (abstract mathematical step), and transferring labels — performed by a generic 'processor.' The hardware tie-in in Claim 1 is limited to 'by a processor' without structural specificity, and the CRM claim merely recites a 'computer-readable medium.' Claim 17's system form provides the strongest §101 defense by reciting a physical 'set of egos' as claimed hardware. Prosecution strategy should prioritize showing that the 3D model generation via multi-ego VIO data constitutes an improvement to computer-implemented autonomous navigation technology rather than an abstract labeling method.
⚠️
Dependent Claim Fallback Quality
The dependent claims provide adequate but structurally repetitive fallback positions. Claims 2, 10, and 18 add identical filtering-by-trajectory limitations; Claims 3, 11, and 19 add identical localization limitations; Claims 4, 12, and 20 add identical feature-type enumerations — these parallel mirror claims add no new subject matter beyond format conversion. Claims 6 and 14 (occupancy detection model) and Claims 8 and 16 (human reviewer validation) add genuinely distinct technical limitations. Notably absent are dependent claims covering the specific VIO/IMU localization mechanism, pairwise matching protocol, pose-graph optimization, bundle adjustment, or coarse alignment procedures described extensively in ¶¶[0114]–[0123], which would have provided valuable prosecution fallback positions.
⚠️
Abstract Quality
The abstract accurately recites the five operative method steps from Claim 1 but omits the novel contribution — specifically, the mechanism of using a pre-built 3D environment model from a first set of egos to automatically transfer labels to image data from a second ego not in the original set. An examiner reading only the abstract would understand 'a method comprises retrieving navigation data... generating a 3D model... automatically generating a machine learning label' but would not distinguish this from generic crowdsourced map-labeling approaches already in the prior art. The abstract should have emphasized the 'second ego not included within the set' distinction as the key inventive step.
✅
Figure Support Quality
The 9-sheet figure set provides strong visual support for the core claim limitations. FIG. 2 maps to all five operative Claim 1 steps; FIG. 4 directly supports the '3D model comprising a virtual representation of the at least one feature' (showing model 412 with identified sidewalk 418); FIG. 6 directly supports the 'second ego' auto-labeling step showing label transfer to camera feed 600 (feature 616 identified as crosswalk 618); FIG. 7 supports the robustness of the labeling under varied conditions. The detailed sensor diagram (FIG. 1B) supports the 'navigation data' breadth definition in ¶[0102]. No significant uncovered claim limitations were identified.
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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
4.5
Dependent Claim Coverage
2.5
Claim Type Diversity
4.5
Figure Support Quality
4.5
Key observation: Spec–Claim Consistency, Claim Type Diversity, and Figure Support Quality all score 4.5/5.0 — Tesla's filing team ensured every Claim 1 limitation maps to a numbered figure and specific paragraphs (e.g., FIG. 4 model 412 for the 3D model limitation, FIG. 6 for label transfer to the second ego), and the tripartite method/CRM/system structure provides excellent enforcement breadth. The weakest dimension is Dependent Claim Coverage at 2.5/5.0 — the 17 dependent claims are largely mirror images of each other across the three independent claims, and critically omit the technically rich VIO localization, pairwise matching, and bundle adjustment protocols described in ¶¶[0114]–[0123] that would have created the strongest prosecution fallback positions if the independent claims are narrowed during national phase examination. Practitioners should consider filing continuation claims specifically directed to the multi-trip reconstruction protocols before national phase deadlines.
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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.
VIO/reconstruction protocol not claimed Method claim §101 hardware gap Cross-condition weather labeling 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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