This storyline collects patents on scrubbing personal data before it leaves a device, training AI models without exposing raw data, and filtering sensitive information before it reaches cloud AI or ad auctions. Together they show Google and Samsung racing to prove AI features can run locally without sending data to company servers.
50 filings · tracking since Apr 2026 · latest Jul 2026 · updates automatically as new filings publish
US 2026/0197374 A1
Broadcast signals from cameras and microphones alert nearby phones to their presence, letting users send privacy requests directly to the recording device rather than relying on posted notices or company policies.
US 2026/0197172 A1
Filtering identity tokens at the device level before they reach apps prevents unnecessary exposure of claims like name and address that aren't required for authentication alone.
US 2026/0195639 A1
Training across distributed private data sources confirms the storyline's shift from device-level scrubbing to multi-party computation, where mathematical noise replaces data movement as the privacy mechanism.
US 2026/0197168 A1
Hardware encryption vaults that process data internally rather than exposing keys to software running on the same chip, preventing local code from intercepting sensitive material during encryption operations.
US 2026/0195481 A1
The storyline so far has focused on filtering before upload. Sony's approach fragments data across multiple processors so no single machine holds enough to breach privacy, even if compromised.
US 2026/0195583 A1
Training AI on devices without retaining raw data stays central to this storyline. Google's approach replaces actual user examples with synthetic proxies that preserve statistical patterns, pushing the technical boundary on what "no storage" actually means.
US 2026/0195607 A1
Training loop extensions confirm the storyline's focus: feedback signals can shape models without exposing inputs, only the neural state itself needs to leave the device.
US 2026/0196234 A1
Decomposing mixed audio into discrete sound objects on-device avoids transmitting raw recordings to cloud servers, letting users filter sensitive background sounds before any data leaves their phone.
US 2026/0197531 A1
On-device interest summaries let ad platforms match ads without collecting browsing history on their servers, shifting the profiling work entirely to the user's device where raw data stays put.
US 2026/0187526 A1
Organizing training data into discrete compartments during model creation lets companies selectively remove learned patterns tied to specific records without full retraining, solving the operational headache of honoring deletion requests at scale.
US 2026/0189546 A1
Within the data-filtering layer, this filing focuses on the detection problem: identifying credential leaks at the moment of composition, before content ever reaches the destination app or service.
US 2026/0187480 A1
Pooling radio-layer observations across nearby devices lets each phone improve signal handling from collective experience while keeping wireless data local, extending the privacy approach from cloud models down to physical-layer learning.
US 2026/0187278 A1
Running A/B tests on device features requires collecting behavioral data at scale. Google's patent describes how to conduct these experiments and aggregate results without storing individual user logs, keeping the raw data ephemeral.
US 2026/0186869 A1
Filtering noise from aggregated ad reports recovers measurement accuracy without reconstructing individual user paths, letting advertisers validate campaign performance while keeping the Privacy Sandbox's anonymity layer intact.
US 2026/0187279 A1
Device-side analytics means servers never need raw user data. Google's patent shows how to answer business questions through statistical noise rather than individual records, keeping sensitive behavior patterns off centralized servers.
US 2026/0188323 A1
On-device AI needs training data scrubbed before deployment. Google's method detects when speech models have memorized private voice fragments during training, stopping that leakage at the source rather than after models reach user devices.
US 2026/0187537 A1
Differential privacy's computational overhead limits hospital-scale deployments. Google's approach caps total compute requirements, making on-device training feasible for organizations that currently can't afford the privacy tax.
US 2026/0187283 A1
Generating synthetic factory data rather than anonymizing real records sidesteps the risk that stripped identifiers could be reverse-engineered to expose actual production methods.
US 2026/0187268 A1
Keeping the interest profile on-device and sending only aggregated signals to servers sidesteps the need to store individual browsing histories in the cloud, reducing what data actually leaves the phone.
US 2026/0189450 A1
Within the data-filtering layer, Samsung adds a method for pooling intelligence across isolated network segments without exposing raw data to a central trainer.
US 2026/0187679 A1
Devices deciding which ads to show users locally eliminates the server step entirely, keeping targeting data from ever uploading to Google's infrastructure.
US 2026/0187288 A1
On-device AI needs ads that work without exposing user profiles. Google's split-server auction keeps ad selection distributed so no machine sees the full picture of who's watching.
US 2026/0189363 A1
Where cloud processing needs raw data exposure, FHE removes that requirement by computing directly on encrypted values. Intel's chip design targets the speed bottleneck that currently makes this impractical at scale.
US 2026/0189624 A1
The storyline so far focuses on filtering before data leaves the device. This filing adds a ranking system that classifies events by importance, letting devices transmit only what matters most rather than everything logged.
US 2026/0186517 A1
Within the on-device AI privacy push, this filing adds a circuit-level defense: stopping attackers from inferring encryption keys by monitoring power consumption during data processing on the device itself.
US 2026/0187228 A1
Keeping ad selection logic split across isolated systems prevents any single server from matching specific users to the ads they see, a key technical requirement for on-device ad serving.
US 2026/0178910 A1
Running AI training on scrambled data means hospitals and banks could use real patient records and transactions without exposing the actual diagnoses or account details to anyone, even the engineers building the model.
US 2026/0178304 A1
The ad stack itself becomes a target: Google proposes running the entire bidding process in one sealed environment rather than scattering user data across vendor networks.
US 2026/0170349 A1
On-device AI needs to stay reliable even when attackers know the training data. Google's approach uses a teacher-student model to filter out poisoned inputs before they corrupt the local model.
US 2026/0172240 A1
Bundling multiple ad requests into one trip prevents any single server from assembling a complete user profile, solving the fragmentation problem that currently lets advertisers stitch together behavioral data across separate slot-filling calls.
US 2026/0170390 A1
Injecting calibrated noise during training lets models learn from real user data while meeting formal privacy guarantees, solving the core tradeoff between personalization accuracy and data protection.
US 2026/0170171 A1
Intercepting identity handoffs between websites and trackers, this system substitutes cryptographic tokens for real email addresses, preventing publishers from linking your activity across the ad ecosystem.
US 2026/0172245 A1
On-device AI needs servers to train models, but servers see everything. Google's split-data approach lets multiple servers collaborate on learning without any one holding the complete profile.
US 2026/0164075 A1
Devices holding ad-profiling data locally means ad networks stop building remote dossiers on individual users. Google's approach shifts the tracking infrastructure from centralized servers to local processing, eliminating the data collection step entirely.
US 2026/0163715 A1
The distributed auction approach extends the privacy-first logic beyond model inference to advertising infrastructure, keeping user profiles fragmented across parties so no entity assembles a complete bidding picture.
US 2026/0161677 A1
Knowing when personal data actually improves an answer cuts unnecessary data exposure. Google's patent describes AI that retrieves your information only for questions where context genuinely matters, reducing privacy risk from indiscriminate profiling.
US 2026/0163719 A1
The on-device push so far has focused on running the whole model locally. Samsung's filing shows another path: keeping queries encrypted so even when processing happens on remote servers, the company operating them stays blind to what users are asking.
US 2026/0162325 A1
Filtering sensitive objects out of images before the AI model even sees them keeps private data from entering the processing pipeline at all, sidestepping the need to trust the model itself with restraint.
US 2026/0161654 A1
Running personalized AI queries entirely on-device means the phone itself becomes the knowledge base, eliminating the server round-trip that currently exposes user behavior data.
US 2026/0162114 A1
Keeping restricted cards from being casually transferred between phones requires identity verification baked into the wallet app itself, not just at the bank's backend.
US 2026/0155994 A1
Running cryptographic verification on a secure chipset isolates signature-checking from the main processor, preventing quantum attacks from compromising authentication even if device software gets breached.
US 2026/0154616 A1
Monitoring local AI performance without exporting user data requires the phone to generate health metrics on-device, then send only aggregated summaries back to Google, solving the measurement problem that on-device deployment creates.
US 2026/0147921 A1
On-device models still need context to work well, but feeding them real data risks exposure. Samsung's solution: anonymize inputs before the AI processes them, then restore the original data after, keeping sensitive information away from the model entirely.
US 2026/0147904 A1
On-device data walls would let users group apps by trust level, preventing sensitive information from leaking between an approved work cluster and untrusted personal apps without requiring constant manual permission checks.
US 2026/0142845 A1
Distributing AI processing across multiple participants' devices during a call eliminates the need for a central server to handle meeting tasks like summarization and action-item extraction, keeping conversation data local throughout the interaction.
US 2026/0134058 A1
Training AI across millions of devices requires a way to share learning without exposing raw data. Google's approach sends summaries of what each phone learns to a central server, which combines them into a stronger model.
US 2026/0129262 A1
On-device recommendations mean streaming apps could learn viewing patterns without uploading watch history to the cloud, letting personalization happen where the data already lives.
US 2026/0128038 A1
As companies route queries between on-device and cloud AI systems, Google is solving the handoff problem: filtering sensitive details before voice data moves to untrusted external assistants.
US 2026/0119700 A1
Filtering sensitive data before cloud upload shifts the privacy burden from servers to the device itself, moving the filter upstream rather than asking cloud systems to handle it.
US 2026/0120709 A1
Keeping audio analysis on the device itself, Google's system lets smart speakers learn user-specific sound patterns through local feedback loops, sidestepping the need to transmit raw recordings for model training.
Most of these filings concentrate on moving AI processing onto the device itself, whether that means scrubbing personal details before cloud upload, filtering voice queries before they reach third-party assistants, or building recommendation engines that run entirely on a TV or phone. Samsung and Google both file variations on the same idea: keep raw data local and only send back results, models, or aggregated signals that reveal nothing about the individual user.
A recurring problem across the batch is proving that on-device AI actually works without letting the company see user data. Filings describe checking model performance without exposing what a phone's AI saw, training shared models across many devices without collecting raw inputs, and swapping real data for substitutes before a model reads messages or contacts. The same pattern shows up in ad targeting and ad auctions, where the goal is to keep browsing behavior on the device while still deciding what to show.
Watch for two things next: whether the filing pool spreads further beyond Google and Samsung to other device makers, and whether security groundwork like post-quantum authentication starts connecting to the privacy features already filed. Right now the storyline reads as an infrastructure build-out, patent by patent, rather than a single finished product, and each new filing narrows in on one more place where data used to leave the device and now might not.
These filings show Big Tech engineers working on ways to run AI tasks like recommendations, voice assistants, and ad targeting directly on a phone or TV instead of a server, so personal data never has to leave the device. The goal is doing useful AI work while keeping raw user data local.
Not yet, and patents are not proof of a shipped feature. What we see is a documented direction: both companies are filing designs for scrubbing, filtering, and training AI locally, which signals engineering priority even before any feature reaches a real device or app.
It fits because the storyline tracks device-level trust, not just data scrubbing. A post-quantum authentication system is about making sure a device can prove its own identity securely even after future computers break today's encryption, which supports the same on-device privacy goal from a different angle.
Right now the sample leans heavily toward Google, with Samsung also contributing filings on data substitution and future-proof security. The storyline is designed to track new filers as they appear, so the balance between companies can shift as more patents get added each week.
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