/DIANE D MIZRAHI/Primary Examiner, Art Unit 2647 DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/24/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
The use of the term Wi-Fi and Bluetooth, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shin (US Publication No. 20250093159 and Shin hereinafter).
Regarding Claim 1, Shin discloses a method for performing data pooling and analysis (i.e. the method includes in response to obtaining the location data, determining an on-user device status for the each of the first and second computing devices. In the example method, the method includes in response to determining an on-user device status for each of the first and second computing devices, obtaining inertial measurement unit (IMU) sensor data from each of the first and second computing devices) Para [0015], comprising: receiving, by a hub (Figure 10, element 1010), sensor data streamed from a plurality of personal electronic devices worn by a user (i.e. Data flow 100 can include prediction pipeline 108 obtaining sensor data from devices. For example, prediction pipeline 108 can obtain first device sensor data 102, second device sensor data 104, and third device sensor data. First device, second device, and third device can be any kind of computing device. For example, the computing devices can be wearable computing devices or pseudo-wearable computing devices. By way of example the devices can include smartphones, smart watches, earbuds, fitness trackers, laptop computers, tablets, or any other computing device. The computing devices can include sensors.) Para [0043]; performing a first analysis of the sensor data using a first data fusion to determine instantaneous aspects of the movement of the user (i.e. the computing system can translate IMU sensor data to location data indicative of a location with three degrees of freedom (e.g., xyz coordinates (cartesian coordinates)). FIG. 3B depicts an example graphical representation of trajectories 312, 314, 316, and 320 in cartesian coordinates. The computing system can combine IMU sensor data directly or can transform the sensor data from IMU sensor data to data with three degrees of freedom (e.g., xyz coordinates).) Para [0048]; performing a second analysis of the instantaneous aspects using a second data fusion (i.e. fusion layer 522 can include obtaining data from IMU streaming 514 from the plurality of devices at data obtaining step 602. In fusion layer 522, the computing system can translate the data from IMU streaming 514 into location data represented in three degrees of freedom. This can include data associated with first device 614, second device 616, and Nth device 618.) Para [0060] to determine behavioral aspects of the movement of the user over time (i.e. The computing system (e.g., via the prediction pipeline) can perform a fusion of the sensor data to generate an output (e.g., output 110). Output can include location prediction data (e.g., location prediction data 112) or predicted trajectory (e.g., predicted trajectory 425).) Para [0048]; determining an actionable result according to the second analysis (i.e. Location prediction data 112 can include a predicted location within an indoor environment (e.g., a target location). The computing system can process location prediction data 112 to perform actions.) Para [0044]; and performing one or more operations based on the actionable result (i.e. The actions can include serving content (e.g., to one of the user devices or a third-party device), facilitating provision of emergency services, or controlling devices (e.g., turning on lights, speakers, TV, or other IoT devices).) Para [0044].
Regarding Claim 13, Shin suggests all the limitations of claim 1 in system form rather than method form. Further Shin discloses a system for performing data pooling and analysis (i.e. computing system 1000 that generates, trains, or uses machine learned models to predict indoor localization of user devices associated with a user) Para [0111], comprising: a plurality of personal electronic devices worn by a user, the plurality of personal electronic devices configured to generate sensor data with respect to the user (i.e. an example system for indoor localization based on multiple device sensors, including one or more processors and one or more memory device storing instructions that are executable to cause the one or more processors to perform operations) Para [0004]; and a hub device in wireless communication with the plurality of personal electronic devices (i.e. Primary computing device 1010 can be any type of computing device, such as for example, a mobile computing device (e.g., smartphone or tablet). Client computing system 902 can be any type of computing device, such as, for example, a mobile computing device (e.g., smartphone or tablet), a wearable computing device, a personal computing device (e.g., laptop or desktop), a gaming console or controller, an embedded computing device, or any other type of computing device.) Para [0112]. Therefore, the rejection of claim 1 applies equally as well to the limitations of claim 13.
Regarding Claim 25, Shin suggests all the limitations of claim 1 in CRM form rather than method form. Further Shin discloses computer readable medium (i.e. the one or more memory devices can include one or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations) Para [0004]. Therefore, the rejection of claim 1 applies equally as well to the limitations of claim 25.
Regarding Claim 2 and Claim 14, Shin discloses all the limitations of claim 1 and 13, respectively, as discussed above. Further Shin discloses wherein the sensor data includes inertial measurement unit (IMU) data from one or more IMU sensors of the plurality of personal electronic devices (i.e. the computing devices can be wearable computing devices or pseudo-wearable computing devices. By way of example the devices can include smartphones, smart watches, earbuds, fitness trackers, laptop computers, tablets, or any other computing device. The computing devices can include sensors. The sensors can include inertial measurement unit (IMU) sensors,) Para [0043].
Regarding Claim 3 and Claim 15, Shin discloses all the limitations of claim 2 and 14, respectively, as discussed above. Further Shin discloses wherein the IMU data includes acceleration and/or velocity information with respect to movement of the user (i.e. The sensors can include inertial measurement unit (IMU) sensors, ambient light sensors, accelerometer (e.g., 3-axis accelerometer), altimeter (detects change in height), optical hear rate sensor (detect heart beats per minute), SpO2 monitor (e.g., to measure blood oxygen levels), bioimpedance sensor(s), proximity sensor (e.g., to save battery and wake display when needed), compass, GPS, gyroscope, gesture sensors, ultraviolet (UV) sensor, magnetometer, electrodermal activity sensor, skin temperature sensor, accelerometer(s), gyroscope(s), magnetometer(s), global positioning system (GPS), heart rate sensor(s) (e.g., electrode sensor or photodiode), pedometer(s) (e.g., electrical, mechanical, or microelectromechanical), pressure sensor(s) (e.g., strain gauges), optical sensors, audio sensors, or other sensors.) Para [0043].
Regarding Claim 4 and Claim 16, Shin discloses all the limitations of claim 1 and 13, respectively, as discussed above. Further Shin discloses wherein the sensor data includes radio frequency (RF) channel information data from one or more RF transmitters of the plurality of personal electronic devices (i.e. radio frequency identification (RFID)) Para [0038].
Regarding Claim 5 and Claim 17, Shin discloses all the limitations of claim 4 and 16, respectively, as discussed above. Further Shin discloses wherein the RF channel information data indicates one or more of distances between pairs of the plurality of personal electronic devices and channel state information (CSI) representative of the environment between the plurality of personal electronic devices (i.e. IMU streaming 514 can include, in response to determining a device is on the user (e.g., an on-user device status), obtaining IMU sensor data associated with the user. By way of example, by processing first device sensor data 502, second device sensor data 504, and third device sensor data 506 the computing system can determine that first device, second device, and third device are located on a user. In response, the computing system can obtain IMU sensor data associated with the devices (e.g., first device IMU sensor data 516, second device IMU sensor data 518, or third device IMU sensor data 520)) Para [0058].
Regarding Claim 6 and Claim 18, Shin discloses all the limitations of claim 1 and 13, respectively, as discussed above. Further Shin discloses wherein the instantaneous aspects include one or more of user traits, posture, actions, and/or activities (i.e. a user may be provided with controls allowing the user to make an election as to both if and when systems, programs, or features described herein may enable collection of user information (e.g., information about a user's activity or a user's current location)) Para [0071] and (i.e. The sensors can include inertial measurement unit (IMU) sensors, ambient light sensors, accelerometer (e.g., 3-axis accelerometer), altimeter (detects change in height), optical hear rate sensor (detect heart beats per minute), SpO2 monitor (e.g., to measure blood oxygen levels), bioimpedance sensor(s), proximity sensor (e.g., to save battery and wake display when needed), compass, GPS, gyroscope, gesture sensors, ultraviolet (UV) sensor, magnetometer, electrodermal activity sensor, skin temperature sensor, accelerometer(s), gyroscope(s), magnetometer(s), global positioning system (GPS), heart rate sensor(s) (e.g., electrode sensor or photodiode), pedometer(s) (e.g., electrical, mechanical, or microelectromechanical), pressure sensor(s) (e.g., strain gauges), optical sensors, audio sensors, or other sensors.) Para [0043].
Regarding Claim 7 and Claim 19, Shin discloses all the limitations of claim 1 and 13, respectively, as discussed above. Further Shin discloses wherein the behavioral aspects include one or more of user intent determination (i.e. obtaining data indicative of the colocation of the first and second computing devices within the target subzone of the target location) Para [0084] and/or person identification (i.e. radio frequency identification (RFID)) Para [0038].
Regarding Claim 8 and Claim 20, Shin discloses all the limitations of claim 7 and 19, respectively, as discussed above. Further Shin discloses wherein the actionable result includes authorizing the user to access a device or location based on the user intent determination and the person identification (i.e. obtaining data indicative of the colocation of the first and second computing devices within the target subzone of the target location, transmitting data which instructs a user interface of the first computing device to provide a content item for display. For instance, a computing system can, in response to obtaining data indicative of the colocation of the first and second computing devices within the target subzone of the target location, transmit data which instructs a user interface of the first computing device to provide a content item for display. As described herein, the content item for display can be associated with a third-party (e.g., content provider, advertiser). By way of example, the content item can be related to the target subzone. For example, a target subzone can be associated with a section of store with a specific t-shirt brand. The content item could be associated with additional information for the t-shirt brand, a coupon for the shirt, or an advertisement associated with the shirt. In some implementations, the target subzone can be a portion of a museum. In some implementations, the content item can be associated with the portion of the museum (e.g., a particular historical display, piece of art, etc.). In some implementations, the content item can be a selectable user interface element (e.g., that can be selected by a user to perform an action such as turn on or control a light, TV, speaker, or other device).) Para [0084].
Regarding Claim 9 and Claim 21, Shin discloses all the limitations of claim 1 and 13, respectively, as discussed above. Further Shin discloses wherein the plurality of personal electronic devices include one or more of headphones, a biometric device, a smart watch, and/or a mobile phone (i.e. the computing devices can be wearable computing devices or pseudo-wearable computing devices. By way of example the devices can include smartphones, smart watches, earbuds, fitness trackers, laptop computers, tablets, or any other computing device. The computing devices can include sensors.) Para [0043].
Regarding Claim 10 and Claim 22, Shin discloses all the limitations of claim 1 and 13, respectively, as discussed above. Further Shin discloses identifying, by the hub, the plurality of personal electronic devices based on advertisement messages sent by the respective personal electronic devices (see whole Figure 10; i.e. Data flow 100 can include prediction pipeline 108 obtaining sensor data from devices. For example, prediction pipeline 108 can obtain first device sensor data 102, second device sensor data 104, and third device sensor data.) Para [0043]; receiving, to the hub, device information messages from the plurality of personal electronic devices, the device information messages indicating device-specific interfaces and capabilities of the respective personal electronic devices (i.e. the computing system can determine that the user is located within the target subzone. In response to determining that the user is located within the target subzone, the computing system can perform a content selection process and provide one or more content items for display via an interface of a user device (e.g., graphical user interface of a phone, speaker of an earbud, graphical user interface of a smartwatch).) Para [0041]; and sending, by the hub, configurations to the plurality of personal electronic devices, the configurations indicating a cadence for receiving the sensor data and/or information on which elements of the sensor data is to be provided to the hub (i.e. a user may be provided with controls allowing the user to make an election as to both if and when systems, programs, or features described herein may enable collection of user information (e.g., information about a user's activity or a user's current location), and if the user is sent content or communications from a server (i.e. sent from the hub to the personal devices). In addition, certain data may be treated in one or more ways before it is stored or used) Para [0071].
Regarding Claim 11 and Claim 23, Shin discloses all the limitations of claim 1 and 13, respectively, as discussed above. Further Shin discloses broadcasting, by the hub, a device query message broadcast requesting that the plurality of personal electronic devices send device sensor information messages to the hub (i.e. a user may be provided with controls allowing the user to make an election as to both if and when systems, programs, or features described herein may enable collection of user information (e.g., information about a user's activity or a user's current location), and if the user is sent content or communications from a server.) Para [0071]; receiving, by the hub, the device sensor information messages requested from the plurality of personal electronic devices (i.e. The computing system can use the predicted location of the user (e.g., and the sensor data from sensor database 924) to send a request to server computing system 904 for one or more content items (e.g., message, image, video, selectable user interface element, advertisement). The computing system can cause the one or more suggested content items to be retrieved, generated, or presented to a user via a user interface of a device (e.g., user device).) Para [0094]; and continuing to receive periodic device sensor information messages from the plurality of personal electronic devices (i.e. data or instructions include routines, programs, objects, components, data structures, or the like that perform particular tasks or implement particular data types) Para [0124] and (i.e. The training computing system 906 can include a model trainer 960 that trains the machine-learned models 920 or 926 stored at the client computing system 902 or the server computing system 904 using various training or learning techniques, such as, for example, backwards propagation of errors…a loss function can be backpropagated through the model(s) to update one or more parameters of the model(s) (e.g., based on a gradient of the loss function). Gradient descent techniques can be used to iteratively (i.e. periodic) update the parameters over a number of training iterations.) Para [0099].
Regarding Claim 12 and Claim 24, Shin discloses all the limitations of claim 11 and 23, respectively, as discussed above. Further Shin discloses wherein the plurality of personal electronic devices defer sending the periodic device sensor information messages if there is a conflict with protocol messages being sent or received by the plurality of personal electronic devices (i.e. certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user's identity may be treated so that no personally identifiable information can be determined for the user, or a user's geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over what information is collected about the user, how the information is used, and what information is provided to the user.) Para [0071].
Pertinent Prior Art
The prior art made of record is considered pertinent to applicant's disclosure.
Tiwari e al. (US Publication No. US 20210118255 A1) “SEAMLESS ACCESS CONTROL SYSTEM USING WEARABLE” (April 22, 2021) is directed to a seamless access control system includes a local access assembly, a wearable mobile device, a storage medium, and a processor. The assembly is adapted to operate between access and no-access states, and includes a controller to effect actuation between the states, and a signal transceiver. The wearable mobile device is worn by a user, and includes an inertial measurement unit (IMU) sensor system configured to detect a performed inherent gesture performed by the user, and being at least a portion of a user exercise to gain entry. The storage medium and the processor are configured to receive the performed inherent gesture and execute an application to compare the performed inherent gesture to the preprogrammed inherent gesture, and thereby conditionally output a command signal to the local access assembly via the transceiver to effect actuation between states, and prior to completion of the user exercise to gain entry.
Dhekne et al. (US 20230358848 A1) “ON-BODY SENSOR SYSTEM AND METHOD FOR AUTOMATIC INTERPRETATION OF VISUAL BODY SIGNALS” (November 9, 2023) is directed to capturing visual body signals using distance measurements among different parts of the body and for providing classification for them. The exemplary system and method can be employed to generate the classification and provide a second source of communication of the classification to supplement the visual cues provided by such body motion or positioning.
Shin (US 20250093159 A1) “Indoor Localization Based On Multiple Device Sensors” (March 20, 2025) is directed to obtaining location data associated with a first and second computing device. Example method can include determining an on-user device status for the each of the first and second computing devices. Example method can include obtaining inertial measurement unit (IMU) sensor data from each of the first and second computing devices. Example method can include inputting the IMU sensor data from each of the first and second computing devices into a machine learned model. Example method can include obtaining, from the machine learned model, output data indicative of a predicted location. Example method can include comparing the output data indicative of the predicted location to data indicative of a location of a target subzone. Example method can include transmitting data which instructs a user interface of the first computing device to provide a content item for display.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Iyonda L. Lewis whose telephone number is (571)272-4440. The examiner can normally be reached Monday - Friday 8:00am - 4:00pm.
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/DIANE D MIZRAHI/Primary Examiner, Art Unit 2647
/IYONDA L LEWIS/Patent Examiner, Art Unit 2647
Iyonda.Lewis@USPTO.gov
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