Patent Drafting Analysis of Tesla’s AI Vision-Based Occupancy Determination | US 2024/0185445 A1
Patent Drafting Analysis of Tesla's AI Vision-Based Occupancy Determination | US 2024/0185445 A1
A structural and strategic analysis of Tesla's camera-only AI occupancy prediction patent, examining claim architecture, drafting quality, §101 eligibility risk, prosecution positioning, and critical gaps across 20 claims.
Spec Words
6,200
Across 6 sections
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Total Claims
20
3 independent · 17 dependent
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Figure Sheets
6
System architecture, sensors, AI pipeline, occupancy maps
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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 61% of total words (~3,800 of ~6,200), reflecting a thorough treatment of the AI pipeline architecture, training methodology, and voxel occupancy mechanics, but the background section is notably sparse at under 300 words — a potential prosecution risk if prior art context becomes contested. The claim set comprises 20 claims across 3 independent claims (Claims 1, 10, and 16), with 17 dependent claims yielding a 5.67:1 dependent-to-independent ratio typical for software/AI patent filings. Figure coverage spans 6 sheets illustrating the system network, ego sensor layout, vehicle camera placement, the full AI model pipeline (FIG. 2), and two occupancy map output visualisations (FIGS. 3A-B).
Section Word Distribution
↗ Click bars to exploreFigure Inventory — 6 Sheets
| Figure | Description | Role |
|---|---|---|
| FIG. 1A | Illustrates the AI-enabled visual data analysis system 100, showing the analytics server 110a, system database 110b, AI model 110c, egos 140a-c, ego computing devices 141a-c, administrator computing device 120, network 130, and server 160 interconnected via data streams 172 and 174.Search in Eureka ↗ | System architecture |
| FIG. 1B | Block diagram of sensors integrated within the egos 140, including user interface 170a, controller 170c, communication module 170e, steering sensor 170g, GNSS 170h, cameras 170m, autonomous driving system 170o, and vehicle computing device 171.Search in Eureka ↗ | System architecture |
| FIG. 1C | Top-down diagram of a vehicle ego 140 showing placement of front cameras 170m-1 through 170m-3, rearward-looking side cameras 170m-4, side cameras 170m-5, rear camera 170m-6, and autonomous driving/steering system 170o.Search in Eureka ↗ | Claim support |
| FIG. 2 | Flow diagram of the full AI model pipeline 200, illustrating stages from image input 210, image featurizers 220, spatial attention module 230, temporal alignment 240, spatial frame alignment, deconvolutions 250, volume outputs 260 (occupancy, occupancy flow, spatial information, 3D semantic data), and queryable outputs 270.Search in Eureka ↗ | Flow diagram |
| FIG. 3A | Grid of multi-camera feeds 300 from eight different ego cameras showing camera feeds 310a-c (front-facing), 320a-b (right-side-facing), 330a-b (left-side-facing), and 340 (rear-facing), with overlapping regions 380a-c highlighted.Search in Eureka ↗ | Claim support |
| FIG. 3B | 3D occupancy map simulation 350 showing simulated ego 360 surrounded by voxel-represented masses 370a-c corresponding to real-world objects (vehicles, buildings) identified from camera feeds, with distinct graphical indicators per occupancy status.Search in Eureka ↗ | Claim support |
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Claims
Claim Architecture Analysis
The application contains 3 independent claims covering a method (Claim 1), an apparatus/ego object (Claim 10), and a training method (Claim 16), providing tripartite enforcement coverage across operational method, hardware system, and training process formats. The 17 dependent claims yield a 5.67:1 ratio, which is on the higher end of the software/AI industry norm of approximately 4–8:1, reflecting meaningful layering around the core autonomous navigation use case. Notably, the claim set omits a dedicated computer-readable medium (CRM) claim, leaving a significant design-around opportunity for cloud-only inference implementations that do not involve an 'ego object' as claimed in Claim 10.
Core inventive concept: The claims address the problem of real-time spatial occupancy determination for autonomous navigation without reliance on radar or LiDAR, solving it through an AI model that ingests camera image data from an ego object and predicts 'an occupancy attribute of a plurality of voxels' (Claim 1), generating a queryable dataset (Claim 5) usable by autonomous driving protocols — a pure vision-based 3D occupancy prediction pipeline. The training method of Claim 16 further secures the data-correlation training approach, whereby the AI model is taught to correlate image attributes to voxel occupancy using location-keyed data points from a first dataset as ground truth.
Independent Claim Dissection
| Claim | Preamble | Transition | Key Body Elements |
|---|---|---|---|
| Claim 1 | A method | comprising | inputting camera image data of space around an ego object into an AI model; predicting occupancy attribute of a plurality of voxels by executing the AI model; generating a dataset based on the plurality of voxels and their corresponding occupancy attributeSearch prior art ↗ |
| Claim 10 | An ego object | comprising | a camera; a first processor; a second processor; a non-transitory computer-readable medium containing an AI model executable by first processor configured to input image data, predict occupancy attribute of plurality of voxels, generate dataset; second processor configured to autonomously navigate the ego object using the datasetSearch prior art ↗ |
| Claim 16 | A method | comprising | training an AI model using a training dataset from a camera of an ego object having a first set of data points corresponding to location and image attribute of at least one voxel; AI model correlates first set with second set using each data point's location; when trained, AI model configured to receive camera feed from a second ego and predict third set of data points indicating occupancy of at least one voxelSearch prior art ↗ |
Claim Dependency Tree
1 Method: inputting camera image data into AI model, predicting voxel occupancy attributes, generating datasetSearch Claim 1 prior art ↗
2 Adds: generating an output representing ego environment illustrating voxels and occupancy attribute with graphical indicatorSearch in Eureka ↗
3 Further: graphical indicator corresponds to a detected object associated with the voxel portionSearch in Eureka ↗
4 Further: displaying the output on a screen associated with the ego objectSearch in Eureka ↗
5 Adds: dataset is a queryable dataset configured to transmit occupancy attribute to an autonomous driving protocolSearch in Eureka ↗
6 Adds: AI model is trained using a sensor attribute of the plurality of voxelsSearch in Eureka ↗
7 Adds: ego object is an autonomous vehicle executing a driving protocol based on the datasetSearch in Eureka ↗
8 Adds: featurizing the image data prior to executing the AI modelSearch in Eureka ↗
9 Adds: image data comprises plurality of camera feeds from plurality of cameras; further comprising temporally aligning the plurality of camera feedsSearch in Eureka ↗
10 Ego object apparatus: camera, first processor, second processor, non-transitory CRM with AI model; first processor inputs/predicts/generates; second processor autonomously navigatesSearch Claim 10 prior art ↗
11 Adds: first processor further configured to generate output representing ego environment with graphical indicator of occupancy attributeSearch in Eureka ↗
12 Further: graphical indicator corresponds to a detected object associated with the voxel portionSearch in Eureka ↗
13 Further: first processor further configured to display the output on a screen associated with ego objectSearch in Eureka ↗
14 Adds: AI model is trained using a sensor attribute of the plurality of voxelsSearch in Eureka ↗
15 Adds: ego object is an autonomous vehicle executing a driving protocol based on the datasetSearch in Eureka ↗
16 Training method: training AI model using dataset from camera of ego object with location/image-attribute data points; AI model correlates first set with second set; when trained, predicts occupancy of voxels for second ego from camera feed aloneSearch Claim 16 prior art ↗
17 Adds: AI model further configured to generate output representing ego environment illustrating at least one voxel and corresponding occupancy attributeSearch in Eureka ↗
18 Adds: training dataset further comprises second set of data points where each corresponds to location and sensor attribute of at least one voxelSearch in Eureka ↗
19 Further: graphical indicator corresponds to a detected object associated with at least a portion of the at least one voxelSearch in Eureka ↗
20 Further: AI model uses three-dimensional multiview reconstruction protocol to generate the outputSearch 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 — Claims 1, 16 | Common |
| System / apparatus claims? | Yes — Claim 10 | Common |
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Drafting Quality
Drafting Quality Signals
The claims demonstrate clear structural strengths in antecedent basis management and broad functional preambles in Claims 1 and 10, but face meaningful §101 eligibility risk given the AI/software-centric claim language that lacks explicit hardware tie-ins beyond the generic 'processor' recitation in Claim 1. The dependent claim structure adds genuine fallback positions — notably Claims 5 (queryable dataset), 8 (featurizing step), and 9 (temporal alignment of multi-camera feeds) — but several parallel dependent claims across Claims 1 and 10 create redundancy rather than distinct protections.
✅
Antecedent Basis
The claim language maintains clean antecedent basis throughout: 'a plurality of voxels' is introduced in the predicting step of Claim 1 and properly referenced as 'the plurality of voxels' in the generating step. Similarly, Claim 10 introduces 'a camera,' 'a first processor,' 'a second processor,' and 'a non-transitory computer-readable medium' before referencing them with definite articles. No orphaned 'the' references were identified across Claims 1–20, which reduces prosecution amendment risk.
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Spec–Claim Consistency
The three independent claims are well-supported by specific specification passages and figures. The 'inputting camera image data' limitation of Claim 1 maps to ¶[0084] and FIG. 2 step 210; the 'predicting occupancy attribute of a plurality of voxels' maps to ¶[0085] and FIG. 2 step 260; and 'generating a dataset' maps to ¶[0086] and FIG. 2 step 270. The training method of Claim 16 is supported by ¶¶[0074]–[0079] with the correlation mechanism explicitly described. The apparatus claim of Claim 10 maps to FIG. 1A (analytics server/ego architecture) and ¶[0066]–[0067].
✅
Transition Word Usage
All three independent claims use 'comprising,' the broadest open-ended transition, which is strategically appropriate for AI software-hardware systems where future implementations may include additional components not explicitly recited. Using 'consisting of' would have unnecessarily foreclosed AI pipeline variations. The open 'comprising' in Claim 10 permits third-party systems incorporating the ego object apparatus with additional processors or sensors to still fall within the claim scope — a deliberate and correct choice for this technology space.
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§112(f) Means-Plus-Function Risk
Claim 10 recites 'the first processor is configured to' and 'the second processor is configured to' perform specific functions — while not 'means for' language, functional claiming via 'configured to' in a software/AI context can attract §112(f) treatment if a court determines the claim element is essentially a nonce word. The spec does provide structural support via ¶[0051] describing the controller and ¶[0066] describing ego computing devices, but the AI model itself is described functionally without structural definition of the neural network architecture, which may be challenged under §112(a) written description if the claim scope is read broadly beyond the specific transformer/RegNet embodiments of FIG. 2.
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§101 Eligibility Risk
Claim 1 faces meaningful Alice exposure: its three method steps — inputting image data, predicting occupancy via an AI model, and generating a dataset — are arguably directed to an abstract idea of information analysis and data generation without a concrete hardware tie-in beyond 'a processor using a camera.' Under Alice Step 2A prong 1, an examiner may characterize this as processing data to generate a prediction, a classic abstract idea category. The strongest §101 defense lies in Claim 10's apparatus recitation of specific hardware (first processor, second processor, non-transitory CRM) and in Claims 7 and 15's limitation to an autonomous vehicle executing a driving protocol — a concrete, real-world application that should survive Step 2B. However, Claim 1 standing alone lacks this hardware anchor, creating prosecution rejection risk.
✅
Dependent Claim Fallback Quality
The dependent claims add several genuinely distinct fallback positions: Claim 5 adds the 'queryable dataset' limitation that ties the invention to autonomous driving protocol integration; Claim 8 adds the image featurization step, capturing the RegNet/BiFPN processing described in ¶[0095]–[0096]; Claim 9 adds temporal alignment of multiple camera feeds, directly corresponding to FIG. 2's temporal alignment module 240. However, Claims 3/12 and 4/13 and 7/15 and 6/14 mirror each other across independent Claims 1 and 10 respectively, representing parallel structural redundancy rather than distinct technical fallback — a missed opportunity to add differentiated limitations such as specific voxel sizing, the deconvolution step, or the 3D semantic output.
⚠️
Abstract Quality
An examiner reading only the abstract may accurately identify the core claimed steps — inputting camera data, predicting occupancy attributes of voxels, and generating a dataset — but the abstract omits the technically distinctive elements that differentiate this invention from prior art: the camera-only (radar-free) nature of the system, the spatial attention transformer architecture, the temporal alignment of multi-camera feeds, and the queryable output format for autonomous navigation. The abstract's statement 'using artificial intelligence modeling techniques' is unhelpfully generic and does not convey the novel AI architecture or the ego-specific training loop that forms the heart of the inventive concept.
✅
Figure Support Quality
Figure support for the claimed limitations is strong: FIG. 1A supports the system architecture of Claim 10; FIG. 1C directly supports the multi-camera claim limitation of Claim 9; FIG. 2 supports all method steps of Claim 1 (steps 210–270), the featurization step of Claim 8, and the temporal alignment of Claim 9; FIGS. 3A-B support the 'output representing an environment' limitations of Claims 2 and 11 with occupancy map visualizations. One limitation lacking dedicated figure support is the 'sensor attribute' training limitation of Claims 6 and 14, which is described textually in ¶[0022] and ¶[0070] but not illustrated diagrammatically.
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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
3.2
Spec–Claim Consistency
4.2
Dependent Claim Coverage
3.5
Claim Type Diversity
3.5
Figure Support Quality
4
Key observation: Spec–Claim Consistency scores highest (4.2/5.0) because FIG. 2's detailed AI pipeline diagram and the detailed description paragraphs ¶[0084]–[0108] provide direct, traceable support for virtually every limitation in all three independent claims, giving examiners and litigants little room to challenge written description adequacy. Prosecution Defensibility scores lowest (3.2/5.0) because Claim 1's method steps lack an explicit hardware anchor sufficient to survive a §101 Alice rejection on their own, and the absence of a CRM independent claim means cloud-based inference implementations of the voxel occupancy AI are not directly captured. Practitioners drafting continuations should prioritize adding a CRM claim and amending Claim 1 to recite specific hardware elements that are currently only present in Claim 10.
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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.
Missing CRM independent claim Claim 1 §101 hardware anchor absent Temporal alignment and 3D reconstruction 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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