Overview
- 8 Publications
- 3 Patents
New Innovations
No information available
Advancements
1. Optimizing Sensor Data for Autonomous Driving
What could Tesla be doing?
Tesla is enhancing the processing of sensor data for autonomous driving by refining how data is fed into deep learning networks.
What’s new?
The new patent introduces a method to analyze more complete versions of sensor data by customizing input layers, ensuring accurate feature detection and improved efficiency through compression and downsampling.
Related Patents
JP2024119895A2
2. Enhancing Machine Learning Training Data Generation
What could Tesla be doing?
Tesla is improving the generation of training data for machine learning models, focusing on challenging use cases.
What’s new ?
The new patent introduces a method where a trigger classifier analyzes intermediate outputs from an existing model to identify relevant training data. This allows for selective data capture, enhancing model performance in specific use cases that are difficult to analyze.
Related Patents
JP2024123217A
3. Efficient Model Deployment in Autonomous Vehicles
What could Tesla be doing?
Optimizing autonomous vehicle models for diverse hardware without re-training.
What’s new ?
Intermediate model representations enable platform-independent deployment, enhancing efficiency and performance adaptability.
Related Patents
US12079723B2
4. Efficient Neural Networks for Low-Bit IoT Devices
What could Tesla be doing?
Tesla is developing neural network architectures optimized for low-power, low-cost IoT devices with limited processing and storage capabilities.
What’s new ?
The new patent introduces the StarNet architecture with star-shuffle blocks, which include 1×1 and 3×3 convolutions, ReLU activations, and shuffling. It emphasizes quantization of weights and activations to optimize layer configurations for reduced-bit arithmetic, enhancing accuracy on resource-constrained devices.
Related Patents
US20240296330A15
5. Enhancing Autonomous Driving with Time Series Data
What could Tesla be doing?
Tesla is improving machine learning for autonomous driving by using time series sensor data to enhance model accuracy.
What’s new ?
The new patent focuses on labeling entire time series for model training, improving accuracy by leveraging sequential context and capturing occluded features, unlike the family member’s emphasis on 3D representations.
Related Patents
US20240304003A1
Focus Shifts in Tesla’s Artificial Intelligence(AI) Activities
Key Areas of Innovation
| Innovation Area | Q2 | Q3 | Δ |
|---|---|---|---|
| Autonomous Driving Sensor Data for Accurate ML Prediction | 17 | 5 | -70.59% |
| Optimized Microprocessor Architectures for AI Workloads | 5 | 2 | -60.00% |
Areas of Consistent Focus
1. Generating Training Data for Machine Learning Models
What is Apple doing?
Efficiently generating high-quality training data for machine learning models, especially in challenging use cases, using methods like trigger classifiers and video data extraction to minimize the need for large labeled datasets.
Portfolio Growth Rate: -52.94%
- Q2 – 34
- Q3 – 16
Tesla’s Artificial Intelligence(AI) Patent Activities During Quarter Q3
Overview
Trend of Publication
| Month | Count | |
|---|---|---|
| July | 4 | ████████░░░░░░░░░ |
| August | 4 | ████████░░░░░░░░░ |
| September | 8 | ██████████████████ |
Top Publishing Jurisdictions
| Jurisdictions | Count | |
|---|---|---|
| US | 5 | ██████████████████ |
| JP | 5 | ██████████████████ |
| CN | 3 | ████████████░░░░░░ |
| EP | 2 | ███████░░░░░░░░░░░ |
Other Key Innovations in this Quarter
1. Privacy-Preserving Vehicle Maintenance
What could be Tesla doing?
Tesla might be developing methods to request vehicle maintenance without accessing sensitive vehicle data or owner information. This involves using kana logs to generate maintenance instructions that are decoded by the vehicle’s onboard computer.
What does this mean?
This innovation could significantly enhance privacy for vehicle owners by ensuring that maintenance requests do not compromise personal data. It allows service providers to perform necessary maintenance while protecting the owner’s privacy.
Related patents: CN118355398A
2. Efficient Training Data Generation with Trigger Classifiers
What could be Tesla doing?
Tesla could be using trigger classifiers to identify and generate high-quality training data for machine learning models, especially in scenarios where labeled data is scarce. This involves scanning input data to find relevant examples and using them to improve model performance.
What does this mean?
This approach allows for the rapid generation of specialized training datasets, enhancing the model’s ability to handle challenging cases without the need for extensive manual labeling.
Related patents: CN112771548B, CN118447348A, JP2024123217A, KR102662474B1
3. Customized Collision Risk Prediction
What could be Tesla doing?
Tesla might be developing AI models that predict collision risk for individual drivers using historical driving data. These models provide personalized risk assessments and can adjust insurance premiums based on driving behavior.
What does this mean?
This innovation allows for more accurate and personalized risk assessments, potentially leading to safer driving practices and tailored insurance offerings.
Related patents: CN118119540A, EP4387877A1, JP2024534789A
4. Low-Bit Neural Network Architectures for IoT Devices
What could be Tesla doing?
Tesla could be optimizing neural network architectures for low-bit processing devices like IoT chips. This involves using specialized layers and quantization techniques to reduce computational load and prevent overflow.
What does this mean?
This innovation enables the deployment of efficient neural networks on resource-constrained devices, expanding the potential for AI applications in low-power environments.
Related patents: US11983630B2, US20240296330A1
5. Privacy-Preserving Vehicle Data Reporting
What could be Tesla doing?
Tesla might be implementing methods for anonymizing vehicle data to enable secure sharing without revealing identifying information. This involves using temporary, randomized identifiers for data reporting and analysis.
What does this mean?
This approach enhances data privacy and security, allowing for the safe sharing of vehicle data for analysis and prognostics without compromising owner privacy.
Related patents: WO2023069635A9
Where is Tesla focused on in this Quarter?
Based on the provided patent information, here is a tabular representation of Tesla’s focus areas, descriptions, potential technologies, and related patents:
| Focus Area | Description | Potential Technologies | Related Patents |
|---|---|---|---|
| Generating Training Data for Machine Learning Models | Methods for efficiently generating training data for machine learning models, especially for complex use cases. | Trigger classifiers, rapid data generation techniques, data augmentation | CN118447348A, US12079723B2 and more |
Quick Insights
New Patent Family
| Publication Number | What is this about? |
|---|---|
| CN118355398A | A privacy-preserving method for vehicle maintenance requests that allows service providers to request vehicle maintenance without directly accessing vehicle data or owner information. The method involves using kana logs, which are sequences of phonetic symbols representing Japanese words, to generate maintenance instructions. The kana logs are associated with vehicle models and issues, and service providers can request maintenance by providing the corresponding kana log sequences. The vehicle’s onboard computer receives the kana logs and decodes them into specific maintenance tasks, without revealing vehicle-specific information like VIN or owner details. This enables privacy-preserved maintenance requests that protect vehicle data and ownership. |
| WO2023069635A9 | Privacy-preserving vehicle data reporting and prognostics using anonymized vehicle identifiers to enable secure sharing of vehicle data without revealing identifying information like VINs. The method involves assigning temporary, randomized identifiers to vehicles for data reporting, analysis, and prognostics. These temporary IDs are regenerated periodically to prevent linking of historical data. By decoupling vehicle data from identifying information, it enables secure sharing of vehicle data without revealing sensitive vehicle details like ownership or history. |
| JP2024534789A | Using AI to analyze vehicle sensor data and predict crash risk for each driver. The AI model is trained on past driver and driving session data to predict a score indicative of crash likelihood for a new driving session. The model considers driver behavior, vehicle conditions, and environmental factors. The score is presented to the driver along with reasoning. This personalized risk analysis helps drivers improve driving and reduce crashes. |
and 1 more.
Highly Cited Families
| Publication Number | What is this about? | Cites | Cited By |
|---|---|---|---|
| US12079723B2 | Method for generating and applying machine-learned models in autonomous vehicles that allows efficient deployment on various hardware platforms without re-training. The method involves generating intermediate representations of the models that are independent of the target hardware configuration. These representations are executed through virtual machines on the target platforms. By iteratively generating and testing models with reduced complexity, the pipeline determines if a model can be applied on a platform with desired performance. This allows selecting the best model for each platform without re-training. | 37 | Neuralmagic, Deepscale and 3 more |
| US20240304003A1 | A technique for generating highly accurate machine learning results for autonomous driving applications by using time series sensor data to train models. Instead of manually labeling training data, it captures sensor data like images and vehicle parameters over time as the vehicle drives. This allows accurately labeling features like lane lines through the whole time series rather than just individual images. The labeled time series is then used to train the model. This improves accuracy by leveraging the sequential context and capturing occluded or distant features that become clearer as the vehicle moves. | 35 | Hewlett Packard, Deepscale and 3 more |
| CN118447348A | Generating training data for machine learning models by using a trigger classifier to identify potential training examples. The trigger classifier scans input data to find instances matching a specific use case, like tunnel exits or obstacles. These identified examples are then retained and used as new training data for the main model. This allows quickly generating tailored training sets for challenging cases without manually labeling large amounts of data. The trigger classifier scores input data based on likelihood of matching the use case, and retains examples above a threshold. | 28 | Zenseact, Dr and 3 more |
| JP2024123217A | Generating training data for machine learning models, particularly for use cases that are difficult to analyze correctly. The technique involves using an existing machine learning model with a trigger classifier to identify relevant training data for improving the model. The trigger classifier is trained offline based on an initial dataset representative of the target use case. When the existing model is applied to new data, the trigger classifier is used to analyze intermediate outputs from the model. If the trigger classifier identifies a relevant use case, the intermediate outputs are returned for further processing to create new training data. This allows selectively capturing additional training examples for the target use case that the existing model struggles with. | 28 | Zenseact, Dr and 3 more |
| JP7512452B2 | Rapidly generating training data for machine learning models, particularly for use cases that are difficult to analyze correctly. The method involves using a trigger classifier trained offline to identify relevant training data based on intermediate outputs of existing machine learning models. This allows collecting targeted data for improving performance in specific use cases. The trigger classifier is trained on an initial training set using outputs from the penultimate layer of the existing model. It is then deployed alongside the existing model to determine a classifier score from the intermediate output. This score is used to identify additional training data for the specific use case. | 28 | Zenseact, Dr and 3 more |
New Addition to Big Families
| Publication Number | What is this about? | Family Size |
|---|---|---|
| US20240304003A1 | A technique for generating highly accurate machine learning results for autonomous driving applications by using time series sensor data to train models. Instead of manually labeling training data, it captures sensor data like images and vehicle parameters over time as the vehicle drives. This allows accurately labeling features like lane lines through the whole time series rather than just individual images. The labeled time series is then used to train the model. This improves accuracy by leveraging the sequential context and capturing occluded or distant features that become clearer as the vehicle moves. | 20 |
| EP3811293B1 | Customized data pipeline for autonomous driving using deep learning that extracts and provides sensor data as separate components to a deep learning network for autonomous driving. The pipeline extracts different data components from captured sensor data based on signal information like features vs global illumination. These components retain targeted relevant data and are provided to the network at different layers for more efficient processing. The components are preprocessed to enhance the signal information. This allows the network to accurately detect features using the extracted components at appropriate layers. The complete sensor data can be analyzed since the components can fully utilize resolution. | 19 |
| JP2024119895A | A customized data pipeline for autonomous driving systems that extracts sensor data into separate components to provide them to a deep learning network at appropriate layers. The components retain specific signal information like edges, features, and global illumination. This allows accurate feature detection using the extracted components instead of raw sensor data, as it provides the most useful information at the right layers. The components can also be compressed/downsampled to improve efficiency. By customizing the input to the network this way, it analyzes more complete versions of the captured sensor data. | 19 |
| CN118447348A | Generating training data for machine learning models by using a trigger classifier to identify potential training examples. The trigger classifier scans input data to find instances matching a specific use case, like tunnel exits or obstacles. These identified examples are then retained and used as new training data for the main model. This allows quickly generating tailored training sets for challenging cases without manually labeling large amounts of data. The trigger classifier scores input data based on likelihood of matching the use case, and retains examples above a threshold. | 16 |
| JP2024123217A | Generating training data for machine learning models, particularly for use cases that are difficult to analyze correctly. The technique involves using an existing machine learning model with a trigger classifier to identify relevant training data for improving the model. The trigger classifier is trained offline based on an initial dataset representative of the target use case. When the existing model is applied to new data, the trigger classifier is used to analyze intermediate outputs from the model. If the trigger classifier identifies a relevant use case, the intermediate outputs are returned for further processing to create new training data. This allows selectively capturing additional training examples for the target use case that the existing model struggles with. | 16 |
Issued Patents in this Quarter
| Publication Number | What is this about? |
|---|---|
| EP3811293B1 | Customized data pipeline for autonomous driving using deep learning that extracts and provides sensor data as separate components to a deep learning network for autonomous driving. The pipeline extracts different data components from captured sensor data based on signal information like features vs global illumination. These components retain targeted relevant data and are provided to the network at different layers for more efficient processing. The components are preprocessed to enhance the signal information. This allows the network to accurately detect features using the extracted components at appropriate layers. The complete sensor data can be analyzed since the components can fully utilize resolution. |
| US12079723B2 | Method for generating and applying machine-learned models in autonomous vehicles that allows efficient deployment on various hardware platforms without re-training. The method involves generating intermediate representations of the models that are independent of the target hardware configuration. These representations are executed through virtual machines on the target platforms. By iteratively generating and testing models with reduced complexity, the pipeline determines if a model can be applied on a platform with desired performance. This allows selecting the best model for each platform without re-training. |
| CN111758107B | Hardware-based pooling technique for improving efficiency of CNNs, especially in computer vision applications, by leveraging existing matrix processors instead of using separate vector engines for pooling operations. The technique involves reformatting the output of the convolution layer to improve the pooling algorithm’s convenience and efficiency. This allows leveraging the matrix processor’s efficient arithmetic logic unit utilization for the pooling operation rather than using separate vector engines. |
and 1 more.
