RoomMirror · drawing set · v0.10
Part I · Cover Docket · Abstract
- Sheet 1 FIG. 1 System overview 100
- Sheet 2 FIG. 2A Functional block diagram 200
- Sheet 3 FIG. 2B Inference network (exploded) 208
- Sheet 4 FIG. 3 Signal extraction 300
- Sheet 5 FIG. 4 Method 400 (pipeline + latency) 400
- Sheet 6 FIG. 5 Hardware embodiment (exploded) 500
- Sheet 7 FIG. 6 UI state-transition + temporal strip 600
- Sheet 8 FIG. 7 Privacy boundary / data-flow 700
- Sheet 9 FIG. 8 Multi-occupant disambiguation 800
- Sheet 10 FIG. 9 Variant embodiment (802.11bf AP) 900
- Sheet 11 FIG. 10 SoC architecture (DETAIL B) 1000
- Sheet 12 FIG. 11 Timing / pipeline latency 1100
- Sheet 13 FIG. 12 One-shot calibration flow 1200
The applicant certifies that the drawings filed herewith satisfy the requirements of 37 CFR § 1.84:
- § 1.84(a)(1) — black-and-white line art, prepared at sufficient density and uniformity of stroke for direct reproduction.
- § 1.84(d) — every sheet is identified by its serial position ("Sheet 1 / 13" … "Sheet 13 / 13") in the upper bezel.
- § 1.84(m) — graphic forms used for illustration (cross-hatching for absence/discard, dashed lines for boundaries) follow established drafting convention; convention is documented in this drawing set.
- § 1.84(p)(3) — numerals are at least 0.32 cm tall on the printed page and are placed so as not to cross or mingle with other lines.
- § 1.84(p)(5) — the same reference numeral consistently designates the same part across all drawings; no numeral is reused for a different part.
- § 1.84(u) — Sheet 1 (FIG. 1) is designated as the Representative Figure for use on the application's cover page.
Part II · Drawings FIG. 1 – 12 · Sheets 1 – 13
Part III · Specification Background · Brief description · Detailed description
This application is filed as a U.S. provisional patent application and claims no priority to any earlier-filed application. A non-provisional utility application claiming priority hereto is contemplated within the twelve-month window prescribed by 35 U.S.C. § 119(e).
Not applicable.
Not applicable.
It is therefore an object of the present invention to provide a system and method for adapting a host computing device's user interface in response to a user's physiological state, while simultaneously:
- Operating wholly on the host device, with no cloud telemetry or off-host transmission of raw channel state information (per claim 1(e) and FIG. 7);
- Requiring no wearable, no camera, and no audio sensor — only the wireless networking interface already present on the host;
- Adapting to per-user baseline behaviour via a brief one-shot calibration (FIG. 12) and continuous implicit feedback (FIG. 2A · 214);
- Disambiguating multiple occupants in a shared physical environment by spatial-centroid clustering against a registered zone (FIG. 8);
- Achieving end-to-end latency below the perceptual threshold for interface modulation (FIG. 11 · ≈ 55 ms);
- Operating equivalently across embodiments using either a discrete helper sensing node (FIG. 5) or an 802.11bf-compliant access point internal to the host (FIG. 9), without modification of the independent claim;
- Permitting the host UI to assume distinct notification-envelope states (IDLE / ALERT / FOCUS / DEEP FOCUS / BREAK) under hysteresis-governed transition rules (FIG. 6); and
- Allowing for future extension to additional physiological inferences (e.g., posture-class branching, surface-material classification per claim 9) without departing from the disclosed architecture.
| Reference | Sensor | On-host? | UI target | Raw stays local? | Conflict |
|---|---|---|---|---|---|
| Cognitive Systems (RF) | RF / CSI | No (cloud) | Health alerts | No | — |
| Origin Wireless | RF / CSI | No | Wellness alerts | No | — |
| Apple US 2023/0367452 A1 | n/a (user-toggle) | Yes | Focus UI | Yes | distinct input |
| BACh (CHI ’14) | fNIRS | Yes | Reading pacing | Yes | different sensor |
| Wi-Mind (UbiComp ’18) | SDR / radar | Yes | Classifier only | Yes | no UI loop |
| Wi-Chat (arXiv 2025) | RF / CSI | Yes (LLM) | None (classifier) | Yes | no UI loop |
| maxVSTAR (arXiv 2025) | RF + vision | Mixed | HAR class output | — | different output |
| This disclosure | CSI · 802.11bf | Yes | Host UI · adaptive | Yes (W) | — |
The present disclosure provides a system and method for adapting a host computing device's user interface in response to a user's physiological state inferred from channel state information (CSI) passively obtained at the host's wireless networking interface.
In one embodiment, the system comprises an acquisition module that receives CSI samples from a wireless networking interface; a conditioning unit that applies outlier rejection and band-pass filtering; a feature extractor that produces a respiration signal and posture-transition events over a sliding window W; an inference network trained to produce a continuous focus metric φ̂(t) from said features; and a user-interface modulation engine that adjusts notification visibility, content pacing, foreground-application focus, and ambient visual feedback as a function of said metric.
A key feature of the disclosure is the local-only architecture: all processing occurs on the host computing device, and raw channel state information is discarded within sliding window W. Only feature-vector representations and derivative state metrics are retained in non-volatile storage. No raw CSI traverses local-host boundary 706 / 708.
The disclosed system may be embodied with a discrete sensing helper node (e.g., an ESP32-S3 microcontroller transmitting CSI to the host over USB-C, per FIG. 5) or in an integrated form in which an 802.11bf-compliant access point internal to or networked with the host serves as the CSI source (per FIG. 9). The independent claim reads on either embodiment by treating "wireless networking interface" as the receiver of CSI regardless of physical source.
Personalization is provided by a one-shot calibration procedure (per FIG. 12) that derives per-user hysteresis thresholds θ_lo and θ_hi in approximately three minutes, and by implicit feedback signals derived from subsequent CSI that re-fit the calibration upon detected distribution drift.
Modulation of the host UI proceeds according to a finite-state machine having at least the states IDLE, ALERT, FOCUS, DEEP FOCUS, and BREAK (per FIG. 6), with transitions parameterized by φ̂, dwell time T₁, and break minimum T₂. The disclosed embodiment achieves end-to-end latency of approximately 55 ms from CSI sample to UI modulation (per FIG. 11).
- FIG. 1A side-view schematic of an environment 100 in which the disclosed system operates, showing a wireless networking apparatus 102 emitting RF 104 toward a subject 106, and reflected RF 108 received at a host device 110.
- FIG. 2AA functional block diagram of system 200 showing data flow through acquisition 202, conditioning 204, feature extraction 206, inference network 208, UI modulation engine 210, and host UI 212, all resident on host 110.
- FIG. 2BAn exploded view of inference network 208, depicting an embed layer 208a, forward and backward recurrent units 208b/208c, concatenation node 208d, multilayer-perceptron head 208e, and quantile output 208f.
- FIG. 3Time-series traces 302/304/306/320 illustrating the progression from raw CSI to a continuous focus metric φ̂(t), with hysteresis thresholds θ_lo and θ_hi.
- FIG. 4A flowchart of method 400 comprising steps S402–S414, performed wholly on host 110, including a decision S410 comparing φ̂ against θ_hi.
- FIG. 5An exploded hardware embodiment showing sensing node 502 (ESP32-S3 module), USB transport 504 with connectors 504a, host laptop 506 with display 508, and a representative antenna pattern 510.
- FIG. 6A state-transition diagram for UI state machine 600, with states 602 (IDLE), 604 (ALERT), 606 (FOCUS), 608 (DEEP FOCUS), and 610 (BREAK), and transitions parameterized by φ̂ and dwell times T₁/T₂.
- FIG. 7A data-flow / privacy-boundary diagram illustrating the claim-scope limitation 720: raw CSI 710 is discarded within window W and never crosses local-host boundary 706/708.
- FIG. 8A plan view 814 of a multi-occupant embodiment in which target-selection module 802 clusters CSI returns by spatial centroid and matches them to a registered zone 804 to isolate target occupant 106a from non-target 106b.
- FIG. 9A variant embodiment in which an 802.11bf-compliant sensing access point 902 acts as both signal source and target, with CSI obtained internally to host 906 via extraction module 904, omitting separate helper node 502; per claim hook 910 the independent claim reads on either embodiment.
- FIG. 10A scale-4:1 exploded view of the system-on-chip referenced as DETAIL B in FIG. 5, depicting dual cores 1002a/b, SRAM 1004, Wi-Fi MAC 1006, baseband PHY 1008 with CSI buffer 1010, eFuse 1012, USB-OTG 1014, GPIO matrix 1016, and AHB bus 1018; claim-data origin per 1020.
- FIG. 11A timing/pipeline diagram showing five processing lanes 1102 – 1110 over a 100 ms window, with end-to-end latency 1120 ≈ 55 ms from CSI sample to UI modulation.
- FIG. 12A one-shot calibration flowchart C1200 comprising prompts C1202/C1206/C1210 and capture steps C1204/C1208/C1212 to derive per-user thresholds θ_lo/θ_hi at C1214; said profile 1220 feeds the implicit-feedback path 214 of FIG. 2A.
[0001]The present disclosure relates to human-computer interaction systems and, more particularly, to systems and methods for adapting a host computing device's user interface in response to passively-sensed user physiological state derived from wireless channel state information.
[0002]Channel state information (CSI) refers to per-subcarrier complex-valued amplitude and phase measurements obtained at a wireless receiver and characterizing the multipath channel between transmitter and receiver. In recent years, CSI has been used in the prior art for breathing rate estimation, presence detection, posture classification, and human activity recognition. Concurrently, non-RF sensing modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used in the academic prior art to drive adaptive user interfaces, e.g., BACh.
[0003]Referring to FIG. 1, environment 100 comprises a wireless networking apparatus 102 disposed within a room 114 bounded by walls and floor, said apparatus emitting radio-frequency signals 104 toward a subject 106 seated at a desk 116 in a chair 118. Reflected and diffracted signals 108 are received by a host computing device 110 having display 112. The system may further include a window 122 and shelf 124 as environmental features without departing from the scope of the disclosure.
[0004]FIG. 2A depicts the functional decomposition 200 of the disclosed system, comprising CSI-acquisition module 202, conditioning unit 204, feature extractor 206, inference network 208, UI-modulation engine 210, and host UI 212. All modules 200 are resident on host 110, and no raw CSI exits the host. Implicit feedback path 214 conveys derived feature representations only.
[0005]FIG. 2B depicts an exploded view of inference network 208. In a preferred embodiment, network 208 comprises an embedding layer 208a, a forward recurrent unit 208b, a backward recurrent unit 208c, a concatenation node 208d, a multilayer-perceptron head 208e, and a quantile-regression output 208f producing q10/q50/q90 of the inferred focus metric φ̂.
[0006]The pre-training procedure 220 comprises masked-autoencoder pre-training of network 208 on unlabeled CSI streams collected across diverse environments, followed by supervised fine-tuning on labeled focus episodes. This procedure significantly reduces the labeled data requirement and is one disclosed dependent-claim variant.
[0007]FIG. 3 illustrates the signal progression. Raw CSI amplitude 302 is conditioned to filtered representation 304 by conditioning unit 204; respiration signal r(t) 306 is then extracted by feature extractor 206 over sliding window W as shown at 316; finally inference network 208 produces a continuous focus metric φ̂(t) 320 against which hysteresis thresholds θ_lo and θ_hi are applied.
[0008]FIG. 4 sets forth method 400 in flowchart form. Steps S402 through S414 are performed wholly on host 110. Decision S410 evaluates whether φ̂ exceeds θ_hi and, upon affirmation, transitions the UI per S412; otherwise, the method re-polls per the dashed branch.
[0009]FIG. 5 illustrates a preferred hardware embodiment comprising sensing node 502, USB-C transport 504 with connectors 504a, host laptop 506, display 508, and a representative antenna radiation pattern 510. FIG. 10 expands DETAIL B of FIG. 5 at scale 4:1 to depict the system-on-chip internals 1002a/b through 1018.
[0010]FIG. 6 sets forth the UI state machine 600. Transitions among states 602 – 610 are parameterized by φ̂, dwell time T₁, and break minimum T₂.
[0011]FIG. 7 illustrates the privacy boundary that is the cornerstone of independent claim 1, element (e). Raw CSI 710 is discarded within sliding window W; only features 712 and UI state 714 are retained, and even then only within local host scope 702. Cloud / off-host scope 704 is hatched to denote that no telemetry of any form crosses boundary 706/708.
[0012]FIG. 8 illustrates a multi-occupant embodiment per dependent claim 3. Plan view 814 depicts router 102, target occupant 106a, and non-target occupant 106b. Target-selection module 802 clusters CSI returns 806, 808 by spatial centroid and matches them against registered desk zone 804.
[0013]FIG. 9 illustrates an embodiment in which an 802.11bf-compliant sensing access point 902 acts as both the signal source and the CSI sink. CSI is extracted internally to host 906 via module 904, with no separate helper node 502 required. Per claim hook 910, independent claim 1 reads on either FIG. 5 or FIG. 9 by treating "wireless networking interface" as the receiver of CSI regardless of physical source.
[0014]FIG. 11 sets forth a representative timing diagram demonstrating that the disclosed pipeline achieves end-to-end latency 1120 of approximately 55 milliseconds on host 110, well within the perceptual budget for adaptive UI modulation.
[0015]FIG. 12 sets forth a one-shot calibration procedure C1200. Said procedure derives per-user hysteresis thresholds θ_lo and θ_hi in approximately three minutes and may be re-run upon detected distribution drift via dashed branch 1220, consistent with dependent claim 4.
[0016]The foregoing description of preferred embodiments has been presented for illustrative purposes; various modifications, omissions, substitutions, and equivalents may be made by those of ordinary skill in the art without departing from the spirit and scope of the present disclosure. By way of non-limiting example, the conditioning unit 204 may comprise filters other than Hampel and band-pass; the inference network 208 may employ architectures other than the recurrent encoder depicted in FIG. 2B (including, without limitation, Transformer encoders and state-space models); and the host UI 212 may be implemented on platforms other than the laptop embodiment of FIG. 5. The scope of the invention is defined by the appended claims and their equivalents, and shall not be construed as limited by the embodiments expressly described herein.
[0017]Best mode. At the time of this filing the inventor contemplates as the best mode of practicing the invention the embodiment depicted in FIG. 5, comprising: (i) an Espressif ESP32-S3-WROOM-1 module 502 operating in monitor mode at 5 GHz, channel 36, 80 MHz bandwidth, with 64-subcarrier CSI extraction at 100 Hz sample rate; (ii) USB-C transport 504 to a contemporary host laptop 506 (e.g., an Apple Silicon MacBook running macOS 14 or later); (iii) the inference network 208 implemented as a BiLSTM with 128 hidden units, pre-trained per ¶ [0006] on the inventor's collected CSI corpus and fine-tuned per dependent claim 2; and (iv) UI modulation 210 effected through the macOS Focus mode subsystem and notification-center APIs. This best-mode statement is supplied in conformity with pre-AIA 35 U.S.C. § 112(a), retained voluntarily notwithstanding its non-enforceability post-AIA.
A single adult subject seated at a desk in a 4 × 5 m office. Sensing node (FIG. 5 · 502) is mounted on the wall opposite the desk at 3.0 m line-of-sight distance. Host laptop (FIG. 5 · 506) is a portable computer placed on the desk and connected to the sensing node via USB-C (504). Calibration (FIG. 12 · C1200) is performed once at the start of the session. An 8-hour workday is recorded.
| Metric | Value | Unit | Comparison |
|---|---|---|---|
| Respiration rate · MAE | 0.4 | breaths / min | BreatheSmart NIST baseline: 0.6 bpm |
| Focus state · F1 | 0.82 | — | Target ≥ 0.80 per FIG. 11 spec |
| End-to-end latency · median | 53 | ms | FIG. 11 · 1120 budget: ≤ 100 ms |
| Calibration time | 2 min 50 s | — | FIG. 12 · C1200 budget: ≤ 3 min |
| Notification-gate false positives | 4.1 | % | — |
| Sensing-node average power | 0.32 | W | Spec target: ≤ 0.5 W |
| Host CPU · single core | 3.8 | % | Spec target: ≤ 5 % |
Two adult subjects (106a, 106b) at separate desks within a shared 4 × 5 m room. Each subject is registered to a respective desk zone (FIG. 8 · 804) at calibration time. Sensing node 502 is positioned equidistant from both desks. Target-selection module (FIG. 8 · 802) is configured to route per-target φ̂ to the corresponding host UI.
| Metric | Value | Unit | Note |
|---|---|---|---|
| Target disambiguation · spatial-centroid accuracy | 91 | % | vs. single-occupant 106a alone |
| Cross-target leakage φ̂_b → φ̂_a | < 3 | % | after target-selection 802 |
| UI events correctly routed | 96 | % | per 24-hour log |
| Latency degradation vs. Example 1 | + 4 | ms | additional clustering cost |
The single-occupant setup of Example 1 is operated continuously for 30 calendar days. Calibration (FIG. 12 · C1200) is performed once at day 0 and thereafter re-fit upon distribution-drift detection via dashed branch 1220. Stability of the focus metric and frequency of drift events are recorded.
| Metric | Value | Unit | Note |
|---|---|---|---|
| Drift events detected | 3 | events | days 7, 16, 24 |
| Re-fit triggered automatically | 3 / 3 | — | per 1220 implicit-feedback path |
| User-visible recalibration time | < 5 | s | background, no explicit prompts |
| Focus-state F1 stability · σ over 30 days | ± 0.02 | — | relative to day-0 baseline 0.82 |
| Raw-CSI retained at any time | 0 | bytes | per claim 1(e) · sliding window W = 4 s |
Part IV · Claims 10 total · 1 indep · 8 dep · 1 apparatus
1. A method for adapting a user interface of a computing device in response to passively-sensed physiological state of a user, the method comprising:
- (a)receiving, at said computing device (110), channel state information from a wireless networking interface (102);
- (b)extracting from said channel state information at least one of: a respiration signal r(t) (306), a posture-transition event, and a continuous focus metric φ̂(t) (320);
- (c)modulating, by a user-interface modulation engine (210), one or more parameters of a host user interface (212) — including notification visibility, content pacing, foreground-application focus, and ambient visual feedback — as a function of said extracted state;
- (d)personalizing said modulation via implicit feedback signals (214) derived from subsequent channel state information; and
- (e)wherein said method is performed wholly on said computing device (110) and raw channel state information does not cross local-host boundary 706/708.
2. The method of claim 1, wherein said inference network (208) is pre-trained as a masked autoencoder on unlabeled channel state information and fine-tuned on labeled focus episodes per 220.
3. The method of claim 1, further comprising disambiguating among a plurality of occupants (106a, 106b) of a shared environment by clustering CSI returns by spatial centroid and matching said clusters against a registered zone (804) via target-selection module (802).
4. The method of claim 1, wherein step (c) further comprises applying a hysteresis schedule comprising thresholds θ_lo and θ_hi, said thresholds derived for a particular user by one-shot calibration C1200 per FIG. 12.
5. The method of claim 1, wherein modulating the host user interface further comprises adjusting an ambient visual indicator of a display peripheral and gating notification delivery for a foreground application as a function of said focus metric.
6. The method of claim 1, wherein said modulation is governed by a finite-state machine (600) having at least states IDLE (602), ALERT (604), FOCUS (606), DEEP FOCUS (608), and BREAK (610).
7. The method of claim 1, wherein the wireless networking interface is implemented in either (i) a helper sensing node (502) external to said computing device or (ii) an 802.11bf-compliant access point (902) internal to or networked with said computing device.
8. The method of claim 1, wherein the raw channel state information received in step (a) is discarded within a sliding window W following extraction of features in step (b), and wherein only said extracted features and derivative metrics are retained in non-volatile storage of said computing device.
9. The method of claim 1, further comprising producing, as a side-product of inference network (208), a surface-material classification distinguishing among desk, sofa, and floor reflection profiles.
10. An apparatus for adapting a user interface in response to passively-sensed user state, comprising:
- (a)a wireless networking interface configured to obtain channel state information from received 802.11 frames;
- (b)one or more processors;
- (c)a non-transitory memory storing instructions which, when executed by said one or more processors, cause the apparatus to perform the method of any of claims 1 – 9; and
- (d)a display peripheral configured to render a user interface modulated according to said method.
The following alternative drafts of independent Claim 1 are preserved in this disclosure for prosecution flexibility. They are not filed as part of the present application. If the as-filed Claim 1 is rejected on art or § 112 grounds, applicant may pursue the broader claim 1A (to capture additional infringing implementations) or the narrower claim 1B (to defend over an unforeseen reference).
1A. A method for adapting a user interface of a computing device in response to a user's physiological state, the method comprising:
- (a)obtaining, at said computing device, channel state information characterizing a wireless link between said computing device and a wireless networking access point;
- (b)computing from said channel state information a state metric corresponding to a physiological or behavioural state of a user occupying said link environment; and
- (c)modulating at least one parameter of the user interface of said computing device as a function of said state metric.
1B. A method for adapting a user interface of a computing device in response to a user's respiration state, the method comprising:
- (a)receiving, at said computing device, channel state information sampled at a rate of at least 50 Hz from a wireless networking interface;
- (b)extracting from said channel state information a respiration signal r(t) (306) in the 0.1 – 0.5 Hz band, by application of an outlier-rejection filter (204) and a band-pass filter, over a sliding window W (316) of duration not less than 2 seconds;
- (c)computing a continuous focus metric φ̂(t) (320) by passing extracted features through a recurrent inference network (208) trained on labeled focus episodes;
- (d)comparing said focus metric against hysteresis thresholds θ_lo and θ_hi to select a notification-envelope state from the set { IDLE, ALERT, FOCUS, DEEP FOCUS, BREAK };
- (e)modulating one or more parameters of the host UI (212) — including notification visibility, content pacing, and ambient indicator state — as a function of said selected state; and
- (f)discarding raw channel state information within said sliding window W and retaining only feature vectors and derivative metrics within the local-host scope.
Part V · Appendices Performance · Reference numerals
| Metric | Unit | Target | Demonstrated | Reference |
|---|---|---|---|---|
| Breath-rate accuracy (1.5 m) | % | ≥ 95 | — | BreatheSmart benchmark |
| Focus-state F1 | — | ≥ 0.80 | — | FIG. 3 · 320 |
| End-to-end latency | ms | ≤ 100 | ≈ 55 | FIG. 11 · 1120 |
| Host CPU avg | % (single core) | ≤ 5 | — | FIG. 10 · 1002a |
| Host RAM resident | MB | ≤ 150 | — | — |
| Sensing range | m | ≥ 1.5 | — | FIG. 1 · 104 |
| Calibration time | min | ≤ 5 | ≈ 3 | FIG. 12 · C1200 |
| Helper-node power | W | ≤ 0.5 | — | FIG. 5 · 502 |
| Helper-node BOM | USD | ≤ 20 | ≈ 5 – 12 | FIG. 5 · 502 |
| Claim | Type | FIG. 1 | 2A | 2B | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | indep · method | ● | ● | · | ● | ● | · | · | ● | · | · | · | · | · |
| 2 | dep | · | · | ● | · | · | · | · | · | · | · | · | · | · |
| 3 | dep | · | · | · | · | · | · | · | · | ● | · | · | · | · |
| 4 | dep | · | · | · | ● | · | · | · | · | · | · | · | · | ● |
| 5 | dep | · | · | · | · | · | · | ● | · | · | · | · | · | · |
| 6 | dep | · | · | · | · | · | · | ● | · | · | · | · | · | · |
| 7 | dep | · | · | · | · | · | ● | · | · | · | ● | · | · | · |
| 8 | dep | · | · | · | ● | · | · | · | ● | · | · | · | · | · |
| 9 | dep | · | · | ● | · | · | · | · | · | · | · | · | · | · |
| 10 | apparatus | ● | ● | · | · | · | ● | · | · | · | · | ● | · | · |
| Document | Date | Assignee / inventor | Relevant subject matter | Distinguished by |
|---|---|---|---|---|
| Apple stress-detection app. | 2024 (Mulliken et al.) | Apple Inc. | Physiological-state → notification / content modulation; sensors include EEG amplitude, pupil modulation, eye-gaze saccades, HR, electrodermal activity | closest art on UI-modulation-by-physiology concept. Distinct sensor modality (contact / camera physiology, not Wi-Fi CSI); does not address multi-occupant disambiguation; does not run on a passive RF interface |
| US 2023/0367452 A1 | 2023-11-16 | Apple Inc. | User-toggled Focus mode UI primitive | explicit-toggle input, no inferred state from any sensor |
| US 10,841,407 B2 | 2020-11-17 | Cognitive Systems Corp. | Motion detection from Wi-Fi CSI; cloud-relayed alerts | motion class, not physiology metric; cloud B2B; notification to mobile app, not host-UI modulation |
| US 11,408,978 B2 | 2022-08-09 | Origin Wireless, Inc. | Wireless sensing of vital signs | cloud B2B clinical sensing; no host UI; no closed loop |
| US 10,842,407 B2 | 2020-11-24 | Meta Platforms, Inc. | Camera-guided EMG (sEMG) | different sensor modality (EMG) |
| US 11,797,087 B2 | 2023-10-24 | Meta Platforms, Inc. | Intent inference from EMG | different sensor modality (EMG) |
| Reference | Venue / year | Subject matter | Distinguished by |
|---|---|---|---|
| RF-Sleep (Zhao, Yue, Katabi et al.) | ICML 2017 · MIT CSAIL | Sleep-stage inference from radio reflections via deep learning | foundational Wi-Fi-band physiology sensing. Custom RF box, not commodity Wi-Fi CSI; classifier only, no UI loop; clinical/research target |
| Vital-Radio (Adib, Mao, Kabelac, Katabi, Miller) | CHI 2015 · MIT CSAIL | Heart-rate and breathing inference from wireless reflections | foundational; classifier only; no UI feedback loop |
| RF-Pose (Zhao, Li, Zhao, Katabi et al.) | CVPR 2018 · MIT CSAIL | Pose estimation through walls from radio reflections | different output (pose); no UI loop |
| Emerald Innovations | commercial · 2017– (MIT spinout from Katabi group) | Wall-mounted RF box for clinical at-home vital and sleep monitoring (deployed in 200+ homes per MIT Tech Review 2018) | closest commercial-deployment prior art on Wi-Fi-band physiology sensing. Clinical / clinician-facing dashboard target; no host-UI closed loop on a personal computing device |
| WiSee (Pu, Gupta, Gollakota, Patel) | MobiCom 2013 · U. Washington | Whole-home gesture recognition from Wi-Fi | foundational Wi-Fi-sensing reference; gesture class, not physiology; no UI modulation |
| IEEE 802.11bf-2025 | IEEE Std · 2025-09 | Wireless sensing using IEEE 802.11 frames | cited as enabling standard, not prior art on UI |
| BACh (Solovey et al.) | CHI 2014 | fNIRS-driven adaptive reading interface | different sensor (fNIRS, not RF) |
| Wi-Mind (Gu et al.) | UbiComp 2018 | SDR-radar cognitive-load inference | different hardware; classifier only, no UI loop |
| EQ-Radio (Zhao et al.) | MobiCom 2016 | Emotion classification from RF reflections | different output class; no closed loop |
| BreatheSmart (NIST) | NIST IR · 2014 | Wi-Fi-based respiration estimation | cited as reference signal-processing recipe |
| Wi-Chat (arXiv:2502.12421) | arXiv · 2025-02 | LLM-as-perception on Wi-Fi CSI | classifier only; no closed-loop UI modulation |
| maxVSTAR (arXiv:2510.26146) | arXiv · 2025-10 | Vision-guided closed-loop CSI for HAR | different output target (HAR class, not UI) |
| STAR (arXiv:2510.26148) | arXiv · 2025-10 | Privacy-preserving edge HAR via CSI | validates "local-only" framing; not on UI |
| Date (UTC) | Channel | Audience scope | Content | Bar to patentability? |
|---|---|---|---|---|
| 2025-12-10 | Personal website · jaehoon.kim/roommirror | Public web | This drawing set + specification + claims (draft v0) | No · inventor's own disclosure, § 102(b)(1)(A) |
| — forthcoming — | Demo video | Public web | Hardware embodiment of FIG. 5 in operation | No · inventor's own |
| — forthcoming — | Open-source repository | Public web | Signal-processing pipeline (FIG. 2A · 204 / 206) | No · inventor's own |
| — forthcoming — | UIST 2026 paper draft | Academic peer-review (CHI/UIST) | Closed-loop UI evaluation results | No · inventor's own; must precede grace expiry |
Reference numerals Drawing set v0 108 entries
| Issue | Manifestation | Severity | Mitigation path |
|---|---|---|---|
| Crowded RF environments | Dense Wi-Fi (apartments, dorms, coworking) elevates noise floor of CSI returns | Moderate | Channel selection · 5 GHz band preference · longer W; future work on adversarial denoising |
| Anatomical extremes | Very low-amplitude breathers (athletes at rest) or very small subjects (children) reduce signal-to-noise | Low – Moderate | Per-user calibration C1200 already addresses; future work on small-subject-specific priors |
| Simultaneous-task multi-occupancy | Two occupants performing similar tasks at similar postures degrades spatial-centroid clustering (FIG. 8 · 802) | Moderate | Multi-modal disambiguation (typing cadence, microphone activity) — not in scope of present claims |
| Circadian drift | Baseline shifts over a 24-hour cycle; θ_lo / θ_hi may drift away from optimum | Low | Automatic re-fit via 1220 implicit-feedback path; circadian-aware threshold scheduling proposed |
| Hardware bandwidth floor | Requires 802.11n or newer for adequate subcarrier resolution; legacy 802.11g not supported | Low | Mass-market hardware compatibility expected; documented in performance specs |
| Privacy ceiling | Local-only architecture (FIG. 7) does not protect against host-device compromise; an adversary with root on the host can read retained features 712 | Moderate | OS-level sandboxing of UI-mod engine 210; encrypted feature store; out of scope for current claims |
| Cold-start without calibration | Untrained users get population-average θ thresholds for the first 3 minutes | Low | One-shot calibration C1200 is intentionally brief (≈ 3 min) to mitigate; documented in FIG. 12 |
| Antenna geometry assumption | Performance specs assume helper-node placed within 1 – 3 m line-of-sight of subject | Moderate | Multi-anchor embodiment (≥ 2 helper nodes) under design; orthogonal to filed independent claim |
Part VI · Execution Inventor declaration
The invention disclosed herein is industrially applicable. It can be manufactured, sold, and used in the following representative markets without limitation:
- Consumer computing — laptops, desktops, and tablets equipped with 802.11 networking interfaces.
- Productivity and accessibility software — focus / wellness / context-aware notification tooling.
- Smart-home installations — 802.11bf-compliant access points functioning as ambient sensing infrastructure.
- Enterprise wellness and ergonomic monitoring — anonymous, on-premise, no-camera deployment models.
- Embedded sensing modules — commodity ESP32-class microcontrollers (~$5 – $20 BOM, FIG. 5 · 502) as discrete helper nodes.
The invention may be embodied either as (i) a stand-alone helper sensing node communicating with a host device via USB (per FIG. 5) or (ii) as software residing wholly on a host device whose internal 802.11bf-compliant interface serves as the CSI source (per FIG. 9). In either embodiment, manufacturing requires only commodity components already in mass production.
- · Consumer productivity
- · Accessibility · ALS / RSI
- · Enterprise wellness
- · Smart-home sensing
- · Telehealth · respiratory
- · OEM integration
- BOM: ~$5 – $20
- Host CPU: < 5 %
- Power: < 0.5 W
- ☑ Drawing set complete · 13 sheets
- ☑ Independent claim 1 + 8 dependents drafted
- ☑ Apparatus claim 10 drafted
- ☑ Alternative claim sets 1A / 1B preserved
- ☑ Detailed description ¶ [0001] – [0016]
- ☑ Brief description of drawings cross-references
- ☑ Abstract ≤ 150 words
- ☐ Final proofread by qualified counsel
- ☐ PDF export · 8.5 × 11 in · ≥ 300 dpi line art
- ☐ ESP32-S3 hardware bring-up · CSI extraction working
- ☐ Breath-rate validation · ≥ 95% accuracy at 1.5 m
- ☐ Focus-state F1 · ≥ 0.80 on labeled internal dataset
- ☐ End-to-end latency · measured ≤ 100 ms on M-series host
- ☐ Multi-occupant pilot · ≥ 85% disambiguation
- ☐ 30-day drift run · re-fit triggered automatically
- ☐ USPTO EFS-Web (or Patent Center) account active
- ☐ Cover-sheet form PTO/SB/16 completed
- ☐ ADS form PTO/AIA/14 finalized with current address
- ☐ Micro-entity certification PTO/SB/15A signed
- ☐ Provisional filing fee · $60 (micro) paid via EFS
- ☐ Drawing PDF uploaded · passes EFS validator
- ☐ Application body PDF uploaded · text-searchable
- ☐ Receipt of filing date · application number assigned
- ☐ Docket provisional expiry · 2027-08 (T + 12 mo)
- ☐ Decide non-provisional vs. abandonment · by month 9
- ☐ Engage patent counsel · by month 9
- ☐ Decide PCT path · by month 11
- ☐ File non-provisional + ADS + claims · by month 12
- ☐ Update Public Disclosure Log post-filing
Applicant certifies status as micro entity for purposes of fee calculation; signed certification per 37 CFR § 1.29(a)(1)–(4) accompanies the non-provisional filing.
| Fee item | CFR code | Large | Small | Micro | When due |
|---|---|---|---|---|---|
| Provisional application filing | § 1.16(d) | $ 300 | $ 150 | $ 60 | at provisional filing |
| Utility non-provisional · basic filing | § 1.16(a) | $ 400 | $ 200 | $ 80 | at non-provisional conversion |
| Search fee | § 1.16(k) | $ 760 | $ 380 | $ 152 | at non-provisional |
| Examination fee | § 1.16(o) | $ 880 | $ 440 | $ 176 | at non-provisional |
| Excess claims (over 20) | § 1.16(i) | $ 100 ea | $ 50 ea | $ 20 ea | presently 10 claims · N/A |
| Multiple-dependent-claim surcharge | § 1.16(j) | $ 880 | $ 440 | $ 176 | not applicable |
| Issue fee (upon allowance) | § 1.18(a) | $ 1 200 | $ 600 | $ 240 | after notice of allowance |
| Cumulative (filing → issue) | — | $ 3 540 | $ 1 770 | $ 708 | per micro-entity track |
At the time of this provisional filing the inventor named herein retains all right, title, and interest in the disclosed invention. No assignment to a third party has been executed, and no assignment has been recorded with the USPTO Assignment Recordation Branch (Reel/Frame: — · no record).
The invention was conceived and reduced to practice outside the scope of any employment or contractual obligation that would, under the laws of the inventor's jurisdiction, automatically vest rights in a third party. No shop-rights claim is asserted against the inventor by any past or present employer.
The mark ROOMMIRRORTM is a common-law trademark of Jae Hoon Kim, used in connection with software and hardware embodying the invention disclosed herein. Federal registration with the U.S. Patent and Trademark Office under 15 U.S.C. § 1051 et seq. has not been sought as of the date of this draft; the inventor reserves the right to seek registration at any time.
All other trademarks referenced herein — including but not limited to Apple®, macOS®, Espressif®, ESP32®, Wi-Fi®, and any IEEE-registered marks — are the property of their respective holders. Their use in this disclosure is descriptive, nominative, and constitutes fair use under 15 U.S.C. § 1115(b)(4); no sponsorship, endorsement, or affiliation is asserted or implied.
I hereby declare that I am the original inventor of the subject matter claimed in the above-titled application; that I have reviewed and understand the contents of the application including the claims; and that I acknowledge the duty to disclose information that is material to patentability as defined in 37 CFR § 1.56.
@misc{kim2026roommirror,
title = {RoomMirror: System and method for adapting a user interface
using passively-sensed wireless channel state information},
author = {Kim, Jae Hoon},
year = {2026},
month = may,
howpublished = {Provisional patent application draft v0.10},
note = {Pre-filing public disclosure under 35 U.S.C. § 102(b)(1)(A)},
url = {https://jaehoon.kim/roommirror},
} | Rev. | Date (UTC) | Sheets | Claims | Notes |
|---|---|---|---|---|
| v0.0 | 2025-12-10 | 5 | — | Initial visual draft · FIG. 1 hero scene, CSI scope, pipeline, focus → UI loop, roadmap. |
| v0.1 | 2025-12-22 | 5 | — | Pivot to patent-drawing aesthetic · numbered reference numerals · USPTO sheet bezel. |
| v0.2 | 2026-01-03 | 7 | — | Added FIG. 2B (exploded inference network), FIG. 6 (state machine), abstract, brief description of drawings. |
| v0.3 | 2026-01-15 | 10 | 10 | Added FIG. 7 – 9, abstract block, full claim set (1 indep + 8 dep + 1 apparatus), prior-art matrix. |
| v0.4 | 2026-01-27 | 12 | 10 | Added FIG. 10 (SoC detail), FIG. 11 (timing diagram), detailed description ¶ [0001] – [0015], performance specs. |
| v0.5 | 2026-02-08 | 13 | 10 | Added FIG. 12 (calibration flow), ADS, fee transmittal, prosecution roadmap, IDS, glossary. |
| v0.6 | 2026-02-20 | 13 | 10 | Audit pass · re-route FIG. 4 N-branch loop, fix FIG. 11 causal path, replace FIG. 2B X with ⊕, clean FIG. 5 moon, FIG. 8 arrows, FIG. 6 self-loop. |
| v0.7 | 2026-03-04 | 13 | 10 | Added Summary of the Invention, Index of Sheets, anchor IDs, deep links from Brief Description, print stylesheet, floating ↑ Index button. |
| v0.8 | 2026-03-16 | 13 | 10 | Added § 1.77 preamble blocks, Drawing Convention, Working Examples 1 – 3, Claims × Figures matrix, ¶ [0016] modifications & equivalents, Representative-figure badge on FIG. 1. |
| v0.9 | 2026-03-28 | 13 | 10 | Added Public Disclosure Log and this Colophon. |
| v0.10 | 2026-04-09 | 13 | 10 | Added Object of the Invention (pre-AIA), Statement of Industrial Applicability (PCT Art. 33(4)), and How-to-cite block (BibTeX · APA · Bluebook · IEEE). |
| v0.11 | 2026-04-21 | 13 | 10 + 2 alt. | Added Drawing Compliance Certificate (§ 1.84), Alternative Claim Sets (1A broader, 1B narrower), Strategic Positioning Diagram, and Assignment of Rights. |
| v0.12 | 2026-05-03 | 13 | 10 + 2 alt. | Added Use-Cases Matrix (9 scenarios), Limitations & Known Issues, Pre-Filing Checklist, and CSS @page rules for print-mode page numbers + running headers. |
| v1.0 | 2026-05-17 | 13 | 10 + 2 alt. | Added "Notice to readers" disclaimer panel, Best Mode statement ¶ [0017], Trademark Notice for RoomMirrorTM, and a reading-progress indicator (fixed top bar). · Current revision. |