How Google’s Patent Portfolio Secures Section 101-Eligibility by Patenting Ambient Intelligence Syst

How Google's Patent Portfolio Secures Section 101-Eligibility by Patenting Ambient Intelligence Systems, Not Abstract AI

White Paper by anovIP

Executive Summary

Under 35 U.S.C. §101, many AI and software patents fail because they are framed as abstract ideas—data analysis, pattern recognition, or automation of human tasks—without sufficient grounding in technical systems. A review of recently published patent applications assigned to Google (Dec 2025–Jan 2026) shows a markedly different strategy.

Google consistently designs and claims inventions as ambient intelligence systems—where sensing, identity, media processing, power management, networking, and machine learning are inseparably embedded into devices and environments. Even when advanced ML models (CNNs, diffusion models) are involved, the claims emphasize how intelligence is executed within real-world constraints, often on-device.

This white paper evaluates Google's patent categories under the Alice/Mayo framework and explains why Google's portfolio is structurally resilient under Section 101.

1. Identity, Authentication & Trust Systems

(Biometrics, cryptography, and device-bound identity)

Representative publications

US 20260031997 – Authenticating a User by Matching a Trusted Identification Credential with On-Device Identity Verification in Generating an Authentication Token

Publication Number: US20260031997A1

Publication Date: January 29, 2026

Applicant: Google LLC

Abstract: This document describes systems and techniques for authenticating a user by matching a trusted identification credential with on-device identity verification in generating an authentication token. For example, a request to verify an identity of a current user of the first communications device is received from a second communications device. A trusted identification credential associated with stored biometric information of a designated user is accessed. A sensor of the first communications device is used to collect captured biometric information of the current user. A verification is performed to determine if the captured biometric information matches the stored biometric information. Responsive to determining that the captured biometric information matches the stored biometric information, an authentication token is generated including a cryptographically signed assertion that includes the nonce received from the second communications device and an assertion confirming the match between the captured biometric information and the stored biometric information.

First Indepedendent Claim: A method for authenticating a user of a first communications device to a second communications device, the method comprising:

by the first communications device: receiving, from the second communications device, a request to verify an identity of a current user of the first communications device, the request including a nonce;

accessing a trusted identification credential stored on the first communications device, the trusted identification credential comprising a derived identification credential associated with stored biometric information of a designated user;

using a sensor of the first communications device to collect captured biometric information of the current user;

performing a verification, entirely on the first communications device, to determine if the captured biometric information matches the stored biometric information; and

in response to determining that the captured biometric information matches the stored biometric information: generating an authentication token, the authentication token comprising a cryptographically signed assertion that includes the nonce received from the second communications device, the assertion confirming the match between the captured biometric information and the stored biometric information and being signed using a private key associated with the trusted identification credential; and

transmitting the authentication token to the second communications device to authenticate the current user as being the designated user.

US 20260030330 – Authentication Using Active Acoustic Sensing

Publication Number: US20260030330A1

Publication Date: January 29, 2026

Applicant: Google LLC

Abstract: Techniques and apparatuses are described that perform authentication using active acoustic sensing. During active acoustic sensing, a hearable transmits and receives at least one ultrasound signal, which propagates within a person's ear canal. The ultrasound signal contains information that is related to the vocalization as well as additional contextual information in how the person created the vocalization using their body and how the vocalization travels, via bone conduction, from the person's vocal chords to their ear canal. With active acoustic sensing, the hearable can generate an ultrasound-based voice signature based on the ultrasound signal and directly perform authentication based on the ultrasound-based voice signature. In some cases, authentication can be performed using a combination of the ultrasound-based voice signature and a voice signature. With active acoustic sensing, the hearable can realize a target spoof acceptance rate and a target false acceptance rate to provide a desired level of security.

First Indepedendent Claim: A method comprising:

transmitting, during a first time period, an ultrasound transmit signal that propagates within at least a portion of an ear canal of a person;

receiving, during the first time period, an ultrasound receive signal, the ultrasound receive signal representing a version of the ultrasound transmit signal with one or more characteristics modified based on the propagation within the ear canal and based on the person speaking during at least a portion of the first time period;

generating an ultrasound-based voice signature based on the ultrasound receive signal, the ultrasound-based voice signature comprising a voice component and a physiological component; and

authenticating the person based on the ultrasound-based voice signature.

US 20260012336 – Piggybacking Multiple Receivers on a Cryptographic Value

Publication Number: US20260012336A1

Publication Date: January 8, 2026

Applicant: Google LLC

Abstract: Techniques and apparatuses are described for piggybacking multiple receivers on a cryptographic value. In example aspects, an agent generates a reusable key based on a cryptographic value that is also known by the owner. To establish secure communications to the owner through multiple receivers, the reusable key acts as a public key for the decryption performed by the second receiver. Additionally, the reusable key acts as a public key for the encryption performed by the first receiver. In this sense, the first receiver and the second receiver are piggybacking on the cryptographic value generated by the transmitter. This piggybacking enables a first message to be transmitted by the agent with a single group element. With the single group element, the first message can be readily transmitted using wireless communication technologies with relatively small message sizes, such as Bluetooth™ low energy, without using fragmentation.

First Indepedendent Claim: A method performed by an agent, the method comprising:

establishing a shared secret with an owner of the agent;

generating a cryptographic value using a pseudorandom function and the shared secret;

generating a reusable key based on the cryptographic value; and

broadcasting a message comprising the reusable key, the reusable key to be used by a first receiver for encryption and forwarded by the first receiver to a second receiver, the reusable key to be used by the second receiver for decryption.

Section 101 analysis

Why this category is strong

Courts routinely uphold patents where identity verification is tied to physical sensing, device hardware, or cryptographic system design, rather than abstract "verifying identity."

anovIP insight:

Google avoids claiming identity decisions and instead claims identity mechanisms grounded in physics and cryptography.

2. Intelligent Media Processing & Generative Imaging Systems

(Pipelines, not creative outcomes)

Representative publications

US 20260030806 – Generating a Group Photo That Includes a Photographer

Publication Number: US20260030806A1

Publication Date: January 29, 2026

Applicant: Google LLC

Abstract: A user device receives a request to generate a composite image. The media application a first image that includes one or more first subjects. The media application determines a previous pose of the user device associated with capture of the first image. The media application segments the one or more first subjects from the first image. The media application generates one or more overlays that correspond to the one or more first subjects based on segmenting the one or more first subjects. The media application displays the one or more overlays on a viewfinder of the user device to provide guidance for a user to capture a second image based on a comparison of a current pose of the user device to the previous pose of the user device. The media application generates the composite image.

First Indepedendent Claim: A computer-implemented method comprising:

receiving, at a user device, a request to generate a composite image;

receiving a first image that includes one or more first subjects;

determining a previous pose of the user device associated with capture of the first image;

segmenting the one or more first subjects from the first image;

generating one or more overlays that correspond to the one or more first subjects based on segmenting the one or more first subjects;

displaying the one or more overlays on a viewfinder of the user device to provide guidance for a user to capture a second image based on a comparison of a current pose of the user device to the previous pose of the user device, wherein the second image includes one or more second subjects; and

generating the composite image that includes the one or more first subjects and the one or more second subjects.

US 20260024165 – Depth of Field Modification in Images Using Machine Learning

Publication Number: US20260024165A1

Publication Date: January 22, 2026

Applicant: Google LLC

Abstract: Implementations described herein relate to modifying depth of field in images using machine learning. In some implementations, a computer-implemented method for training a machine learning model includes generating an input training image that is a composition of multiple images captured in focus stacks at different lens focus positions and camera distances. A corresponding ground truth image is generated from merged images in particular focus stacks. A convolutional neural network (CNN) machine learning (ML) model receives the input training image and outputs an output image that adjusts blurriness in the input training image to simulate a target depth of field. The CNN ML model is updated based on comparison of the output image and the ground truth image. The CNN ML model can include a depth CNN that performs an implicit depth estimation for features of the input image, and a deconvolution CNN that adjusts the blurriness.

First Indepedendent Claim: A computer-implemented method comprising:

obtaining a plurality of images captured with a camera, each image depicting a respective scene of a plurality of scenes, wherein for each scene, multiple focus stacks of images are captured, wherein individual images in each focus stack are captured at a respective lens focus position of a plurality of lens focus positions of the camera, and wherein individual focus stacks of the multiple focus stacks for the scene are captured at a respective distance of a plurality of distances of the camera to the scene;

selecting a target depth of field that is associated with a particular f-stop of a simulated camera;

generating, by one or more processors, an input training image that is an input image composition of particular images that are at least portions of the plurality of images, wherein each of the particular images is from a respective particular focus stack of the multiple focus stacks for a different scene of the plurality of scenes;

determining, by the one or more processors, a merged image for each particular focus stack, wherein determining the merged image is based on, for each particular focus stack, merging two or more images in the particular focus stack in a focus stacking operation based on the target depth of field to determine the merged image for the particular focus stack;

generating, by the one or more processors, a ground truth image that is a ground truth composition of the merged images, wherein the ground truth composition corresponds to the input image composition;

providing the input training image and the target depth of field as inputs to a convolutional neural network (CNN) machine learning (ML) model;

outputting, by the CNN ML model, an output image, wherein the output image is obtained by adjusting blurriness in the input training image to obtain the target depth of field; and

updating, by the one or more processors, the CNN ML model based on a loss value determined based on comparison of the output image and the ground truth image, wherein the updating comprises adjusting one or more parameters of the CNN ML model based on the loss value.

US 20260011061 – Restyling Images Using a Diffusion Model with Text Conditioning and a Depth Map

Publication Number: US20260011061A1

Publication Date: January 8, 2026

Applicant: Google LLC

Abstract: A media application receives an initial image, user input that selects one or more objects in the initial image, and a textual request to generate an output image that modifies the one or more selected objects in the initial image. The media application generates a user-selected mask that includes object pixels corresponding to the one or more selected objects. A diffusion model receives the textual request to generate the output image, a depth map, and the user-selected mask, where the diffusion model is trained to generate output pixels that are not associated with a human subject. The diffusion model outputs the output image that satisfies the textual request.

First Indepedendent Claim: A computer-implemented method to generate an image based on a textual request, the method comprising:

receiving an initial image, user input that selects one or more objects in the initial image, and a textual request to generate an output image that modifies the one or more selected objects in the initial image;

generating a user-selected mask that includes object pixels corresponding to the one or more selected objects;

providing, as input to a diffusion model, the textual request to generate the output image, a depth map, and the user-selected mask, wherein the diffusion model is trained to generate output pixels for the output image that are not associated with a human subject; and

generating, with the diffusion model, the output image that satisfies the textual request.

US 20250390998 – Generative Photo Uncropping and Recomposition

Publication Number: US20250390998A1

Publication Date: December 25, 2025

Applicant: Google LLC

Abstract: A media application receives an input image that includes a subject. The media application segments the subject from the input image. The media application generates, based on segmenting the subject, a subject mask that includes subject pixels associated with the subject. The media application determines, based on the subject mask, whether a portion of the subject is cut off by one or more borders of the input image. Responsive to the portion of the subject not being cut off, the media application provides the input image and the subject mask as input to an inpainter machine-learning model. The media application generates, with the inpainter machine-learning model, an output image that extends one or more borders of the input image by adding inpainted pixels to the input image.

First Indepedendent Claim: A computer-implemented method to uncrop an input image, the method comprising:

receiving an input image that includes a subject;

segmenting the subject from the input image;

generating, based on segmenting the subject, a subject mask that includes subject pixels associated with the subject;

determining, based on the subject mask, whether a portion of the subject is cut off by one or more borders of the input image;

responsive to the portion of the subject not being cut off by the one or more borders, providing the input image and the subject mask as input to an inpainter machine-learning model; and

generating, with the inpainter machine-learning model, an output image that extends one or more borders of the input image by adding inpainted pixels to the input image.

US 20260010974 – Performing Segmentation of Objects in Media Items Based on User Input

Publication Number: US20260010974A1

Publication Date: January 8, 2026

Applicant: Google LLC

Abstract: A media application receives user input that indicates one or more objects to be erased from a media item. The media application translates the user input to a bounding box. The media application provides a crop of the media item based on the bounding box to a segmentation machine-learning model. The segmentation machine-learning model outputs a segmentation mask for one or more segmented objects in the crop of the media item and a corresponding segmentation score that indicates a quality of the segmentation mask.

First Indepedendent Claim: A computer-implemented method comprising:

receiving, via a user interface, one or more strokes from a user that indicate one or more objects to be erased from an input image;

translating the one or more strokes to an oriented bounding box based on an orientation of at least one of the one or more strokes, wherein edges of the oriented bounding box are not parallel to edges of the input image;

providing a crop of the input image based on the oriented bounding box to a segmentation machine-learning model;

outputting, with the segmentation machine-learning model, a segmentation mask for one or more segmented objects in the crop of the input image; and

inpainting a portion of the input image that corresponds to the segmentation mask to obtain an output image.

Section 101 analysis

Why this category is strong

Federal Circuit precedent supports eligibility where ML improves image processing pipelines, not artistic expression.

anovIP insight:

Google patents media intelligence workflows, not the aesthetics of generated content—significantly reducing §101 risk.]

3. Ambient Audio & Multi-User Perception

(Sound as a spatial system)

Representative publications

US 20260025629 – Spatial Aliasing Reduction for Multi-Speaker Channels

Publication Number: US20260025629A1

Publication Date: January 22, 2026

Applicant: Google LLC

Abstract: Various arrangements for reducing auditory spatial aliasing for a user are detailed herein. A first delay filter may be set that delays output of a first audio signal by a first duration to a speaker of a device compared to a second speaker. A second delay filter may also be set that delays output of a second audio signal by a second duration. The first and second audio signals can be output by the speakers.

First Indepedendent Claim: A system for reducing auditory spatial aliasing, the system comprising:

a first stereo channel comprising a first delay filter that delays output of a first audio signal by a first duration to a first speaker of the first stereo channel;

a second stereo channel comprising a second delay filter that delays output of a second audio signal by a second duration to a second speaker of the second stereo channel; and

an anti-aliasing profile datastore, wherein the anti-aliasing profile datastore stores a plurality of anti-aliasing profiles used to define the first duration and the second duration based on a state of the system.

US 20250391411 – Multi-User Warm Words

Publication Number: US20250391411A1

Publication Date: December 25, 2025

Applicant: Google LLC

Abstract: A method includes detecting a presence of multiple users within an environment of an assistant-enabled device (AED) and obtaining, for each user, a respective active set of warm words that each specify a respective action for a digital assistant to perform. Based on each respective active set of warm words, the method also includes executing a warm word arbitration routine to enable a final set of warm words for detection by the AED. Here, the final set of warm words includes warm words selected from the respective active set of warm words. While the final set of warm words are enabled, the method also includes receiving audio data corresponding to an utterance captured by the AED, detecting a warm word from the final set of warm words in the audio data, and instructing the digital assistant to perform the respective action specified by the detected warm word.

First Indepedendent Claim: A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations comprising:

executing a digital assistant on an assistant-enabled device (AED), the digital assistant configured to: display a graphical user interface on a screen of an assistant-enabled device; and present, in the graphical user interface, a list of available warm words;

receiving, in the graphical user interface, a first user input indication indicating selection of a corresponding warm word from the list of available warm words to add to an active set of warm words, each respective warm word in the active set of warm words specifying a respective action for the digital assistant to perform when the respective warm word is detected in streaming audio captured by the AED;

for the corresponding warm word added to the active set of warm words responsive to the user input indication indicating selection of the corresponding warm word, receiving an audio sample of the user speaking the corresponding warm word added to the active set of warm words;

receiving audio data corresponding to an utterance captured by the AED;

detecting, in the audio data, based on the audio sample of the user speaking the corresponding warm word, the corresponding warm word from the active set of warm words; and

instructing the digital assistant to perform the respective action specified by the corresponding warm word detected in the audio data.

Section 101 analysis

Why this category is strong

Audio and spatial sound patents tied to signal timing and multi-device coordination are widely viewed as technical.

anovIP insight:

Google treats sound as a distributed system problem, not a voice feature.

4. Smart Home Systems Under Power & Safety Constraints

(Graceful degradation as a technical effect)

Representative publications

US 20260022856 – Smart Home Device Feature Set Selection Based on Power Source Availability

Publication Number: US20260022856A1

Publication Date: January 22, 2026

Applicant: Google LLC

Abstract: Feature sets of smart home devices may be activated based on whether they can be supported by a selected power sourcing method. For example, thermostats may use a technique known as "power stealing" in order to steal power from the HVAC system. Different algorithms and techniques may be used for efficiently stealing power from the HVAC system, each of which may provide different levels of power to the thermostat at different times. The smart home device may test an external system to determine which power stealing methods are compatible, then select predetermined feature sets that are compatible with the available power stealing methods.

First Indepedendent Claim: A thermostat comprising:

a power wire connector for controlling a heating, ventilation, and air conditioning (HVAC) function of an HVAC system;

a return wire connector for the HVAC function;

one or more switching elements configured to operate in: a first operating state in which the one or more switching elements create a connection between the power wire connector and the return wire connector to activate the HVAC function; and a second operating state in which the one or more switching elements interrupt the connection between the power wire connector and the return wire connector; and

one or more processors programmed to perform operations comprising: controlling a timing of the one or more switching elements to steal power from the HVAC according to at least a first power-stealing method or a second power-stealing method;

selecting a power stealing method from at least the first power-stealing method or the second power-stealing method based at least in part on a characteristic of the HVAC system; and

selecting a set of functions from a plurality of functions of the thermostat to be active, wherein the set of functions are compatible with the power stealing method.

US 20260022857 – Interleaved Sampling Power Calibration for Power Stealing in Smart Home Devices

Publication Number: US20260022857A1

Publication Date: January 22, 2026

Applicant: Google LLC

Abstract: Smart home devices may use a technique known as "power stealing" in order to steal power from an external environmental system. For example, thermostats may steal power from an HVAC system. Different algorithms and techniques may be used for efficiently stealing power from the HVAC system, each of which may provide different levels of power to the thermostat at different times. The smart home device may test an external system to determine which power stealing methods are compatible and calibrate various power stealing parameters. A calibration routine may sample at a plurality of discrete intervals while increasing a test load to determine a maximum current limit and an optimal power stealing method.

First Indepedendent Claim: A thermostat comprising:

a power stealing circuit configured to steal power from a power wire connector for controlling a heating, ventilation, and air conditioning (HVAC) function of an HVAC system, wherein an output of the power stealing circuit provides power to operational systems of thermostat;

an adjustable test load coupled to the output of the power stealing circuit;

one or more processors configured to perform operations comprising: switching the output of the power stealing circuit from the operational systems of the thermostat to the adjustable test load;

causing the power stealing circuit to steal power from the HVAC system with different loads provided by the adjustable test load;

sampling a voltage provided by the power stealing system at the different loads provided by the adjustable test load; and

calibrating operation of the power stealing circuit based the voltage provided by the power stealing circuit at the different loads provided by the adjustable test load.

US 20260022849 – Battery Fault Detection Using Temperature Measurements in Smart Home Devices

Publication Number: US20260022849A1

Publication Date: January 22, 2026

Applicant: Google LLC

Abstract: A battery pack may use an integrated temperature sensor to monitor a temperature of a battery cell during charging and discharging. However, if the integrated temperature sensor fails, a smart home device may continue to charge and discharge the battery outside of its approved temperature range. This may lead to both safety and performance concerns. To identify a failed integrated temperature sensor, the device may leverage any additional temperature sensors that are located in the device. These temperature sensors may be used to externally measure or estimate the battery temperature. If a sufficient deviation between the measurements of these external temperature sensors and the measurements from the integrated temperature sensor is detected, the device may use the comparison of these temperature measurements to determine that the integrated temperature sensor may be malfunctioning. The device may then change its operational state in response to maintain performance and safety.

First Indepedendent Claim: A thermostat comprising:

a battery pack disposed inside of a housing of the thermostat, wherein the battery pack comprises a battery cell and an integrated temperature sensor in the battery pack;

one or more temperature sensors disposed inside of the housing of the thermostat and outside of the battery pack; and

one or more processors that are programmed to perform operations comprising: receiving a first temperature measurement from the integrated temperature sensor of the battery pack;

receiving a second temperature measurement from the one or more temperature sensors disposed inside the housing of the thermostat and outside of the battery pack;

comparing the first temperature measurement with the second temperature measurement; and

determining whether the integrated temperature sensor is malfunctioning based at least in part on comparing the first temperature measurement with the second temperature measurement.

Section 101 analysis

Why this category is strong

Patents improving device reliability, safety, and power management are classic examples of eligible subject matter.

anovIP insight:

Google patents survivability under constraints, a highly defensible technical outcome.

5. Device Health, Thermal Intelligence & Longevity

(Self-aware computing systems)

Representative publications

US 20260023600 – Self-Adjusting Aware Thermal Control of a Semiconductor Device

Publication Number: US20260023600A1

Publication Date: January 22, 2026

Applicant: Google LLC

Abstract: Aspects of self-adjusting aware thermal control of a semiconductor device are disclosed. For example, a central unit may be coupled with an element of the semiconductor device and one or more temperature controllers configured to sequentially apply throttling steps to thermally control the element. The throttling steps are sequentially applied based on individual throttling tables. The central unit has access to the individual throttling tables and may access a current performance state of the element. The central unit may command one or more of the temperature controllers to throttle the element based on the current performance state of the element. The central unit may command one or more of the temperature controllers to apply a throttling step to the element based on throttling steps previously applied to the element. The temperature controllers may include memory to store a current throttling status of the element communicated by the central unit.

First Indepedendent Claim: A system comprising:

a first temperature controller configured to throttle an element of a semiconductor device;

a first throttling table, the first temperature controller configured to sequentially apply throttling steps to the element based on the first throttling table; and

a central unit coupled with the first temperature controller and the element, the central unit configured to dynamically control, via the first controller, the throttling steps applied to the element for thermal control of the semiconductor device.

US 20260023417 – Two-Stage Thermal Throttling

Publication Number: US20260023417A1

Publication Date: January 22, 2026

Applicant: Google LLC

Abstract: Techniques and apparatuses are described that implement two-stage thermal throttling. In some examples, two-stage thermal throttling of a mobile device is achieved using a main controller and an auxiliary controller. The auxiliary controller can be a proportional controller that monitors a temperature and a rate of change of the temperature of the mobile device during operations. When a first temperature threshold and a threshold rate of increase of the temperature are exceeded by the mobile device, the auxiliary controller can throttle a metric of the device to slow down the rate of increase of the temperature. After the temperature has exceeded a second temperature threshold, the auxiliary controller can hand off control to the main controller, which can further throttle the metric of the device or one or more additional metrics of the device.

First Indepedendent Claim: A method comprising:

obtaining, by an auxiliary controller of a device, first thermal information regarding a first temperature of the device and a first rate of increase of the first temperature;

determining that the first temperature has exceeded a first temperature threshold and that the first rate of increase of the first temperature has exceeded a threshold rate of increase;

throttling, by the auxiliary controller, a metric of the device to a first level to adjust the first rate of increase of the first temperature to a second rate of increase in response to the first temperature threshold and the threshold rate of increase being exceeded;

obtaining, by the auxiliary controller, second thermal information regarding a second temperature of the device;

determining that the second temperature of the device has exceeded a second temperature threshold;

sending the second thermal information and historical data regarding actions of the auxiliary controller to a main controller of the device in response to the second temperature threshold being exceeded;

handing off thermal control of the device to the main controller from the auxiliary controller in response to the second temperature of the device exceeding the second temperature threshold; and

throttling, by the main controller, the metric of the device to a second level to adjust the second temperature of the device.

Section 101 analysis

Why this category is strong

Thermal and power control patents improve hardware operation, placing them among the lowest-risk §101 domains.

anovIP insight:

Google claims how chips protect themselves, not abstract resource allocation.

6. Sensing Humans Without Contact

(Health and safety as system intelligence)

Representative publication

US 20260000345 – At-Home Contactless Fetal Movement Tracking

Publication Number: US20260000345A1

Publication Date: January 1, 2026

Applicant: Google LLC

Abstract: Various arrangements for performing contactless fetal movement tracking are detailed herein. User input can first be received from an expectant mother requesting that contactless fetal movement monitoring be performed. A state analysis on the expectant mother may be performed to determine that the expectant mother is present and static in a bed at which the contactless fetal movement tracking device is pointed. Fetal movement tracking may be performed using radar data received from a radar sensor of the contactless fetal movement tracking device while the state analysis indicates that the expectant mother is present and static in the bed. A fetal tracking report may then be presented based on the performed fetal movement tracking.

First Indepedendent Claim: A contactless fetal movement tracking device, comprising:

a housing;

a touchscreen electronic display housed by the housing;

a radar sensor housed by the housing, the radar sensor aimed such that when the housing is placed bedside, movement of an expectant mother within a bed is detected; and

a processing system housed by the housing, comprising one or more processors, that receives data from the radar sensor and the touchscreen electronic display, and outputs data to the touchscreen electronic display for presentation, wherein the processing system is configured to: receive user input, via the touchscreen electronic display, requesting that contactless fetal movement monitoring be performed;

in response to the user input, perform a state analysis on the expectant mother to determine that the expectant mother is present and static in the bed;

while the state analysis indicates that the expectant mother is present and static in the bed, perform fetal movement tracking using radar data received from the radar sensor; and

cause a fetal tracking report to be presented by the touchscreen electronic display based on the performed fetal movement tracking.

Section 101 analysis

Why this category is strong

Non-invasive sensing patents grounded in physical signal acquisition are consistently upheld.

anovIP insight:

Google avoids claiming medical conclusions; it claims measurement systems.

7. Network Intelligence & QoS Control

(Context-aware connectivity)

Representative publications

US 20250392926 – Enhancement of Quality of Service for Communications-Intensive Applications by Automatically Associating the Applications with Premium Access

Publication Number: US20250392926A1

Publication Date: December 25, 2025

Applicant: Google LLC

Abstract: This document describes systems and techniques for enhancing Quality of Service (QoS) of communications-intensive applications by automatically associating the applications with premium access. For example, a connectivity service provides applications with standard access or premium access to communications services, the premium access operable to provide increased bandwidth or reduced latency. A premium access controller associated with the connectivity service identifies the communications-intensive application for association with the premium access and a connectivity manager interacts with the applications to identify the communications-intensive application and to cause the premium access controller of the connectivity service to automatically associate the communications-intensive application with the premium access.

First Indepedendent Claim: A communications system comprising:

a connectivity service configured to provide one or more applications with standard access or premium access to communications services, the premium access operable to provide at least one of increased bandwidth and reduced latency, the one or more applications including a communications-intensive application configured to provide enhanced functionality when connected with the premium access;

a premium access controller associated with the connectivity service, the premium access controller being configured to identify the communications-intensive application for association with the premium access; and

a connectivity manager configured to interact with the applications to identify the communications-intensive application and to cause the premium access controller of the connectivity service to automatically associate the communications-intensive application to the premium access.

US 20260025753 – Determining a Central Node for Reporting Sensor Data

Publication Number: US20260025753A1

Publication Date: January 22, 2026

Applicant: Google LLC

Abstract: Techniques and devices for determining a central node for reporting sensor data are described for an electronic device that inserts ranges between nodes in the wireless network into a Euclidean distance matrix (EDM) and decodes the EDM to generate a global topology for the nodes in the wireless network. The electronic device sums, for each node in the wireless network, events detected by each node during a predetermined time period and performs a kernel density filtering of the sums of the detected events over a two-dimensional space of the global topology. The electronic device calculates a product of Gaussian distributions calculated during the kernel density filtering and selects the node that is spatially closest to a peak of the product of Gaussian distributions as the central node for event reporting.

First Indepedendent Claim: A method for selecting a central node for event reporting in a wireless network, the method comprising:

inserting, by an electronic device, ranges between a plurality of nodes in the wireless network into a Euclidean distance matrix (EDM);

decoding, by the electronic device, the EDM to generate a global topology for the plurality of nodes in the wireless network;

summing, by the electronic device and for each node in the plurality of nodes in the wireless network, events detected by each node in the plurality of nodes during a predetermined time period;

performing, by the electronic device, a kernel density filtering of the sums of the detected events over a two-dimensional space of the global topology;

calculating, by the electronic device, a product of Gaussian distributions calculated by the kernel density filtering; and

selecting the node that is spatially closest to a peak of the product of Gaussian distributions as the central node for event reporting.

Section 101 analysis

Why this category is strong

Network optimization patents that improve latency, bandwidth, or coordination are typically §101-eligible.

anovIP insight:

Google patents network behavior driven by context, not business policies.

Portfolio-Level Section 101 Assessment

Conclusion: Why Google's Patents Endure Under Section 101

Google does not ask the patent system to protect AI models or creative outputs.

It asks the patent system to protect intelligent systems that sense, decide, and adapt within real environments.

Abstract AI fails. Ambient intelligence survives.

By embedding machine learning into devices, sensors, power systems, networks, and privacy-preserving architectures, Google has built a patent portfolio that is both technologically forward-looking and legally resilient under Section 101.

How anovIP Can Assist

At anovIP, we help clients building AI platforms, smart devices, and ambient computing systems adopt the same Section 101-ready strategy demonstrated by Google.

Our support includes:
a) Early-stage §101 eligibility assessments for AI and ML inventions
b) Claim strategies that emphasize system architecture, physical inputs, and technical effect
c) Invention harvesting across sensors, OS layers, ML pipelines, and networks
d) Prosecution and litigation-readiness reviews for AI-heavy portfolios

For organizations operating at the intersection of AI, devices, privacy, and real-world sensing, anovIP ensures that innovation is not only powerful—but defensible for the long term.