Prosecution Insights
Last updated: April 19, 2026
Application No. 18/013,327

Indoor Localization Based on Multiple Device Sensors

Non-Final OA §103
Filed
Dec 28, 2022
Examiner
LEITE, PAULO ROBERTO GONZ
Art Unit
3663
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Google LLC
OA Round
3 (Non-Final)
52%
Grant Probability
Moderate
3-4
OA Rounds
3y 8m
To Grant
70%
With Interview

Examiner Intelligence

Grants 52% of resolved cases
52%
Career Allow Rate
44 granted / 85 resolved
At TC average
Strong +18% interview lift
Without
With
+17.8%
Interview Lift
resolved cases with interview
Typical timeline
3y 8m
Avg Prosecution
35 currently pending
Career history
120
Total Applications
across all art units

Statute-Specific Performance

§101
11.3%
-28.7% vs TC avg
§103
67.0%
+27.0% vs TC avg
§102
9.6%
-30.4% vs TC avg
§112
8.8%
-31.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 85 resolved cases

Office Action

§103
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 . Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 15, 2025, has been entered. Status of Claims This Office Action is in response to the Response to Final Rejection filed December 15, 2025. Claims 1-7, 9-12, 14-16, and 19-22, are presently pending and presented for examination. Response to Arguments Applicant’s arguments with respect to claims 1-7, 9-12, 14-16, and 19-22, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. An updated and detailed rejection follows below. Claim Rejections - 35 USC § 103 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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1-4, 7, 9-12, 14, and 19-22, are rejected under 35 U.S.C. 103 as being unpatentable over Santarone et al. (US 20220164492; hereinafter Santarone, of record in IDS), in view of Holman et al. (US 20150031294; hereinafter Holman, already of record), further in view of Chidlovskii et al. (US 20220155079; hereinafter Chidlovskii, already of record), further in view of Balan et al. (US 20190318501; hereinafter Balan), and further in view of Rowe et al. (US 20250130071; hereinafter Rowe, already of record). Regarding Claim 1, Santarone teaches A computing system, (Santarone: Paragraph [0600]) comprising: one or more processors; (Santarone: Paragraph [0600]) and one or more computer-readable media storing instructions that are executable to cause the one or more processors to perform operations (Santarone: Paragraph [0600]), the operations comprising: obtaining location data associated with a first computing device and a second computing device, (Santarone: Paragraph [0024], [0251], Fig. 6) the location data indicative of the first computing device and the second computing device each being at a target location; (Santarone: Paragraph [0251]; [0254]-[0258], [0285]-[0286], FIG. 6D (Element 644-646); “... it may be determined that an Agent and/or an associated Smart Device has access rights to the digital content. Access rights may include, by way of non-limiting example, multi-factor authorization, including whether an Agent is positioned within an area authorized for receipt of the digital content.”); ... 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; (Santarone: Paragraph [0256], [0259]) ... in response to obtaining data indicative of colocation of each of the first and second computing devices within the target subzone of the target location, ... (Santarone: Paragraph [0256]-[0258], [0285]-[0286], FIG. 6D (Element 644-646); “... it may be determined that an Agent and/or an associated Smart Device has access rights to the digital content. Access rights may include, by way of non-limiting example, multi-factor authorization, including whether an Agent is positioned within an area authorized for receipt of the digital content.”) Santarone does not teach ... in response to obtaining the location data, determining an on-user device status for the each of the first and second computing devices by obtaining sensor data associated with the first and second computing devices to determine whether the first and second devices are located on a user; ... inputting the IMU sensor data from each of the first and second computing devices into a machine learned model; processing, by the machine learned model, the IMU sensor data from the first and second computing devices to perform fusion of the sensor data to generate output data indicative of a predicted location, wherein the fusion comprises a noise reduction process to estimate the predicted location, wherein the predicted location comprises a noise-reduced three degrees of freedom result; comparing the output data indicative of the predicted location to data indicative of a location of a target subzone to determine that the user is located within a target subzone; and ... ...transmitting data which controls one or more devices located within the target subzone. However in the same field of endeavor, Holman teaches ... in response to obtaining the location data, determining an on-user device status for the each of the first and second computing devices by obtaining sensor data associated with the first and second computing devices to determine whether the first and second devices are located on a user; (Holman: Paragraph [0011], [0109], [0113]) ... It would be obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to modify the localization system of Santarone with the sensor data determination of Holman for the benefit of allowing wearable device systems to provide the same or similar functionalities provided by larger mobile devices. (Holman: Paragraph [0096]) Santarone, in view of Holman does not teach ... inputting the IMU sensor data from each of the first and second computing devices into a machine learned model; processing, by the machine learned model, the IMU sensor data from the first and second computing devices to perform fusion of the sensor data to generate output data indicative of a predicted location, wherein the fusion comprises a noise reduction process to estimate the predicted location, wherein the predicted location comprises a noise-reduced three degrees of freedom result; comparing the output data indicative of the predicted location to data indicative of a location of a target subzone to determine that the user is located within a target subzone; and ... ...transmitting data which controls one or more devices located within the target subzone. However in the same field of endeavor, Chidlovskii teaches ... inputting the IMU sensor data from each of the first and second computing devices into a machine learned model; (Chidlovskii: Paragraph [0089]-[0090], [0099]) processing, by the machine learned model, the IMU sensor data from the first and second computing devices to perform fusion of the sensor data to generate output data indicative of a predicted location; (Chidlovskii: Paragraph [0084], [0099], FIG. 1 & 2) comparing the output data indicative of the predicted location to data indicative of a location of a target subzone to determine that the user is located within a target subzone; (Chidlovskii: Paragraph [0099]-[0100]) and... It would be obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to modify the localization system of Santarone with the machine learned model operations of Chidlovskii for the benefit of better determining an absolute position of the portable electronic device in an accurate manner. (Chidlovskii: Paragraph [0027]) Santarone, in view of Holman, and further in view of Chidlovskii, does not teach ... ...wherein the fusion comprises a noise reduction process to estimate the predicted location, wherein the predicted location comprises a noise-reduced three degrees of freedom result; ... ...transmitting data which controls one or more devices located within the target subzone. However in the same field of endeavor, Balan teaches ... ... wherein the fusion comprises a noise reduction process to estimate the predicted location, (Balan: Paragraph [0024]-[0025], [0030]-[0031]) wherein the predicted location comprises a noise-reduced three degrees of freedom result; (Balan: Paragraph [0030]-[0031]; The system of Balan determines six degrees of freedom for the location of the device(s) which would include the three degrees of freedom described by the limitations of the claim.) ... It would be obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to modify the localization system of Santarone, Holman, and Chidlovskii, with the noise reduction system of Balan for the benefit of improving the accuracy of device tracking in a system. (Balan: Paragraph [0017], [0055]) Santarone, in view of Holman, further in view of Chidlovskii, and further in view of Balan, does not teach ...transmitting data which controls one or more devices located within the target subzone. However in the same field of endeavor, Rowe teaches ...transmitting data which controls one or more devices located within the target subzone. (Rowe: Paragraph [0047]; “...device 114 may communicate directly or via a network, and/or optionally through one or more other computing devices, such as server 118, with any IoT device(s) associated with the correlation and control or instruct the corresponding IoT device(s) to take the action identified by the correlation data.”) It would be obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to modify the localization system of Santarone, Holman, Chidlovskii, and Balan, with the control of devices within a subzone of Rowe for the benefit of recognizing the location of a person based on signals from an IMU described herein advantageously, accurately, and efficiently permit tracking of a person's location and/or path in, for example, indoor environments or environments where GNSS signals are attenuated. (Rowe: Paragraph [0049]) Regarding Claim 2, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computing system of claim 1, wherein the on-user device status for each of the first and second computing devices are indicative of each of the first and second computing devices being located on a user moving inside the target location. (Santarone: Paragraph [0471]-[0474]; The ancillary position determining device and smart device are both stated to be on the user’s person during operation.) Regarding Claim 3, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computing system of claim 1, wherein the IMU sensor data comprises accelerometer data and gyroscope data. (Chidlovskii: Paragraph [0143]) The motivation to combine Santarone, Holman, Chidlovskii, Balan, and Rowe, is the same as stated for Claim 1 above. Regarding Claim 4, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computing system of claim 3, wherein the machine learned model is configured to transform the accelerometer data and the gyroscope data to data cartesian coordinates indicative of an absolute location. (Chidlovskii: Paragraph [0089]-[0090], [0114], [0143]) The motivation to combine Santarone, Holman, Chidlovskii, Balan, and Rowe, is the same as stated for Claim 1 above. Regarding Claim 7, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computing system of claim 1, wherein the computing is performed on the first computing device. (Santarone: Paragraph [0082], [0600]; “A process is disclosed for determination of a position based upon wireless communication between a Node and/or Smart Device and with reference point transceivers. The process may be accomplished...for example via running an app on the Smart Device or as a service on a server accessible via the Internet.”) Regarding Claim 9, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computing system of claim 1, wherein the machine learned model comprises a neural network. (Chidlovskii: Paragraph [0091]) The motivation to combine Santarone, Holman, Chidlovskii, Balan, and Rowe, is the same as stated for Claim 1 above. Regarding Claim 10, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computing system of claim 1, wherein the machine learned model comprises a fully connected neural network. (Chidlovskii: Paragraph [0106]; “The implementation of the neural network module 268 of FIG. 3 involves three convolution layers and two max-pooling layers, followed by fully connected layers.”) The motivation to combine Santarone, Holman, Chidlovskii, Balan, and Rowe, is the same as stated for Claim 1 above. Regarding Claim 11, the claim is analogous to Claim 1 limitations and is therefore rejected under the same premise as Claim 1. Regarding Claim 12, the claim is analogous to Claim 2 limitations and is therefore rejected under the same premise as Claim 2. Regarding Claim 14, the claim is analogous to Claim 4 limitations and is therefore rejected under the same premise as Claim 4. Regarding Claim 19, the claim is analogous to Claim 9 limitations and is therefore rejected under the same premise as Claim 9. Regarding Claim 20, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computer-implemented method of claim 11, wherein the first device is a primary computing device (Santarone: Paragraph [0472]; Smart Phone 2230) and the second computing device is a secondary computing device, (Santarone: Paragraph [0472], FIG. 22C; Ancillary position determining device 2231. “A smart watch (ancillary position determining device 2231) that may be worn on an Agent's arm...”) and wherein the first device functions as a modem to facilitate transmission of data between the second device and a server computing system. (Santarone: Paragraph [0082], [0535]) Regarding Claim 21, the claim is analogous to Claim 1 limitations and is therefore rejected under the same premise as Claim 1. Regarding Claim 22, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computing system of claim 1, wherein the data which controls one or more devices located within the target subzone comprises data which causes at least one of: (i) turning on a light, (Rowe: Paragraph [0047]; “For example, pursuant to the foregoing example placing user 112 at LOI 106, smart bulb 144 and smart switch 148 can, according to the corresponding correlation data, be turned on automatically for the user.”) (ii) turning on a speaker, (iii) turning on a TV, (Santarone: Paragraph [0432]-[0433]) or (iv) turning on an internet of things (IoT) device. (Rowe: Paragraph [0047]; “...device 114 may communicate directly or via a network, and/or optionally through one or more other computing devices, such as server 118, with any IoT device(s) associated with the correlation and control or instruct the corresponding IoT device(s) to take the action identified by the correlation data.”) The motivation to combine Santarone, Holman, Chidlovskii, Balan, and Rowe, is the same as stated for Claim 1 above. Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, as applied to claim 1-4, 7-12, 14, and 18-21, above, and further in view of Sommer et al. (US 20200264006; hereinafter Sommer, already of record). Regarding Claim 5, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, teaches The computing system of claim 1,... Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, and further in view of Rowe, does not teach ...further comprising obtaining IMU sensor data from a third computing device. However in the same field of endeavor, Sommer teaches ...further comprising obtaining IMU sensor data from a third computing device. (Sommer: Paragraph [0049]) It would be obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Santarone, in view of Chidlovskii, further in view of Balan, and further in view of Rowe, with the third computing device of Sommer for the benefit of providing an interactive user experience for multiple applications. (Sommer: Paragraph [0003]) Regarding Claim 6, Santarone, in view of Holman, further in view of Chidlovskii, further in view of Balan, further in view of Rowe, and further in view of Sommer, teaches The computing system of claim 5, wherein the first computing device comprises a smartphone, (Santarone: Paragraph [0472]; Smart Phone 2230) the second computing device comprises a smartwatch, (Santarone: Paragraph [0472], FIG. 22C; Ancillary position determining device 2231. “A smart watch (ancillary position determining device 2231) that may be worn on an Agent's arm...”) and the third computing device comprises earbuds. (Sommer: Paragraph [0049]) The motivation to combine Santarone, Holman, Chidlovskii, Balan, Rowe, and Sommer, is the same as stated for Claim 5 above. Regarding Claim 15, the claim is analogous to Claim 5 limitations and is therefore rejected under the same premise as Claim 5. Regarding Claim 16, the claim is analogous to Claim 6 limitations and is therefore rejected under the same premise as Claim 6. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULO ROBERTO GONZALEZ LEITE whose telephone number is (571)272-5877. The examiner can normally be reached Mon-Fri: 8:00 am - 4:30 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached on 571-272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /P.R.L./Examiner, Art Unit 3663 /ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
Read full office action

Prosecution Timeline

Dec 28, 2022
Application Filed
Mar 13, 2025
Non-Final Rejection — §103
Jun 05, 2025
Interview Requested
Jun 18, 2025
Examiner Interview Summary
Jun 18, 2025
Applicant Interview (Telephonic)
Jun 20, 2025
Response Filed
Sep 10, 2025
Final Rejection — §103
Dec 09, 2025
Applicant Interview (Telephonic)
Dec 15, 2025
Examiner Interview Summary
Dec 15, 2025
Request for Continued Examination
Dec 28, 2025
Response after Non-Final Action
Jan 08, 2026
Non-Final Rejection — §103
Mar 30, 2026
Applicant Interview (Telephonic)
Apr 02, 2026
Examiner Interview Summary

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
52%
Grant Probability
70%
With Interview (+17.8%)
3y 8m
Median Time to Grant
High
PTA Risk
Based on 85 resolved cases by this examiner. Grant probability derived from career allow rate.

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