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 .
Status of the Claims
Claims 1-20 filed on 2 OCT 2024 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12 JAN 2026 has been considered by the examiner.
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.
Claim(s) 1, 3-6, 9, 11-14, and 16-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Amiri et al. (US 2023/0318725 A1, cited by applicant in IDS dated 12 JAN 2026).
Regarding claim 1, Amiri et al. discloses:
A method for generating a radiofrequency map (Amiri et al. heatmap - ¶ [0091]), comprising:
receiving, via processing circuitry (Amiri et al. location server 160, Fig. 1), a set (Amiri et al. measurements 406a, …, 406n, Fig. 4) of received signal strength indicator (RSSI) data (Amiri et al. “For RAT-dependent position methods location measurements may include one or more of a Received Signal Strength Indicator (RSSI)…” - ¶ [0057]) from a first device in an indoor environment based on signals received by the first device from one or more transmitters in the indoor environment (Amiri et al. “A location of the UE 105… may be defined… by reference to a point, area, or volume indicated on a map, floor plan or building plan.” - ¶ [0045]);
receiving, via the processing circuitry, sensor data from the first device in the indoor environment (Amiri et al. “The UE 105 can further include sensor(s) 1040… some of which may be used to obtain position-related measurements and/or other information.” - ¶ [0135]);
determining, via the processing circuitry, location data (Amiri et al. “the position data 404a may correspond to a measured location of the wireless device.” - ¶ [0082]) of the first device in the indoor environment based on the sensor data (Amiri et al. “The UE 105 can further include sensor(s) 1040… some of which may be used to obtain position-related measurements and/or other information.” - ¶ [0135]); and
generating, via the processing circuitry, a map of predicted RSSI data corresponding to locations in the indoor environment (Amiri et al. “Such heatmap data may represent a collection of predictions at various locations, and may include indications of the one or more predicted wireless measurements with respect to corresponding locations of the RF environment associated with the wireless network.” - ¶ [0091]) using an artificial intelligence model (Amiri et al. trained model 602, Fig. 6), the artificial intelligence model being trained on training data (Amiri et al. “The input layer 504 may be configured to receive external data. The external data may be training data from a database (e.g., the database 410 of Fig. 4).” - ¶ [0086]), the training data including the received set of RSSI data and the location data (Amiri et al. “The database 410 may be a data structure configured to store the aforementioned position data and wireless measurements data…” - ¶ [0084]).
Regarding claim 3, Amiri et al. discloses:
The method of Claim 1, wherein the sensor data includes accelerometer data, gyroscope data, and/or imaging data (Amiri et al. “The UE 105 can further include sensor(s) 1040. Sensor(s) 1040 may comprise, without limitation, one or more inertial sensors and/or other sensors (e.g., accelerometer(s), gyroscope(s), camera(s), magnetometer(s), altimeter(s), microphone(s), proximity sensor(s), light sensor(s), barometer(s), and the like)…” - ¶ [0135]).
Regarding claim 4, Amiri et al. discloses:
The method of Claim 1, wherein the one or more transmitters are Bluetooth emitters (Amiri et al. “a Bluetooth® beacon using a Bluetooth protocol” - ¶ [0050]), WiFi emitters (Amiri et al. Wi-Fi communication protocols - ¶ [0050]), ultra-wideband (UWB) signal emitters, or Zigbee signal emitters.
Regarding claim 5, Amiri et al. discloses:
The method of Claim 1, further comprising receiving, via the processing circuitry, a second set of RSSI data from a second device in the indoor environment based on signals received by the second device, the training data further including the second set of RSSI data (Amiri et al. “many UEs (e.g., hundreds, thousands, millions, etc.) may utilize the 5G NR positioning system 200.” - ¶ [0043]).
Regarding claim 6, Amiri et al. discloses:
The method of Claim 1, wherein the generating the map of predicted RSSI data includes classifying the sensor data using a machine learning classifier (Amiri et al. “the trained machine learning model 612 may include at least one classifier 616a and at least one regressor 618a. As used herein, a classifier may refer to an algorithm or a module configured to categorize data into one or more of a set of classes (e.g., yes or no, 1 or 0).” - ¶ [0096]) and interpolating the set of RSSI data corresponding to the classified sensor data (Amiri et al. “the trained machine learning model 612 may include at least one classifier 616a and at least one regressor 618a…As used herein, a regressor may refer to an algorithm or a module configured to predict continuous values (e.g., wireless measurements).” - ¶ [0096]).
Regarding claim 9, Amiri et al. discloses:
A device comprising:
processing circuitry (Amiri et al. location server 160, Fig. 1) configured to
receive a set (Amiri et al. measurements 406a, …, 406n, Fig. 4) of received signal strength indicator (RSSI) data (Amiri et al. “For RAT-dependent position methods location measurements may include one or more of a Received Signal Strength Indicator (RSSI)…” - ¶ [0057]) from a first device in an indoor environment based on signals received by the first device from one or more transmitters in the indoor environment (Amiri et al. “A location of the UE 105… may be defined… by reference to a point, area, or volume indicated on a map, floor plan or building plan.” - ¶ [0045]),
receive sensor data from the first device in the indoor environment (Amiri et al. “The UE 105 can further include sensor(s) 1040… some of which may be used to obtain position-related measurements and/or other information.” - ¶ [0135]),
determine location data (Amiri et al. “the position data 404a may correspond to a measured location of the wireless device.” - ¶ [0082]) of the first device in the indoor environment based on the sensor data (Amiri et al. “The UE 105 can further include sensor(s) 1040… some of which may be used to obtain position-related measurements and/or other information.” - ¶ [0135]), and
generate a map of predicted RSSI data corresponding to locations in the indoor environment (Amiri et al. “Such heatmap data may represent a collection of predictions at various locations, and may include indications of the one or more predicted wireless measurements with respect to corresponding locations of the RF environment associated with the wireless network.” - ¶ [0091]) using an artificial intelligence model (Amiri et al. trained model 602, Fig. 6), the artificial intelligence model being trained on the received set of RSSI data and the location data (Amiri et al. “The input layer 504 may be configured to receive external data. The external data may be training data from a database (e.g., the database 410 of Fig. 4).” - ¶ [0086]; “The database 410 may be a data structure configured to store the aforementioned position data and wireless measurements data…” - ¶ [0084]).
Regarding claim 11, the same cited section and rationale as claim 3 is applied.
Regarding claim 12, the same cited section and rationale as claim 4 is applied.
Regarding claim 13, the same cited section and rationale as claim 6 is applied.
Regarding claim 14, Amiri et al. discloses:
A non-transitory computer-readable storage medium for storing computer-readable instructions (Amiri et al. “a computer-readable apparatus including a storage medium storing computer-readable and/or computer-executable instructions” - ¶ [0105]) that, when executed by a computer (Amiri et al. “hardware (e.g., processor)” - ¶ [0105]), cause the computer to perform a method, the method comprising:
receiving a set (Amiri et al. measurements 406a, …, 406n, Fig. 4) of received signal strength indicator (RSSI) data (Amiri et al. “For RAT-dependent position methods location measurements may include one or more of a Received Signal Strength Indicator (RSSI)…” - ¶ [0057]) from a first device in an indoor environment based on signals received by the first device from one or more transmitters in the indoor environment (Amiri et al. “A location of the UE 105… may be defined… by reference to a point, area, or volume indicated on a map, floor plan or building plan.” - ¶ [0045]);
receiving sensor data from the first device in the indoor environment (Amiri et al. “The UE 105 can further include sensor(s) 1040… some of which may be used to obtain position-related measurements and/or other information.” - ¶ [0135]);
determining location data (Amiri et al. “the position data 404a may correspond to a measured location of the wireless device.” - ¶ [0082]) of the first device in the indoor environment based on the sensor data (Amiri et al. “The UE 105 can further include sensor(s) 1040… some of which may be used to obtain position-related measurements and/or other information.” - ¶ [0135]); and
generating a map of predicted RSSI data corresponding to locations in the indoor environment (Amiri et al. “Such heatmap data may represent a collection of predictions at various locations, and may include indications of the one or more predicted wireless measurements with respect to corresponding locations of the RF environment associated with the wireless network.” - ¶ [0091]) using an artificial intelligence model (Amiri et al. trained model 602, Fig. 6), the artificial intelligence model being trained on training data (Amiri et al. “The input layer 504 may be configured to receive external data. The external data may be training data from a database (e.g., the database 410 of Fig. 4).” - ¶ [0086]), the training data including the received set of RSSI data and the location data (Amiri et al. “The database 410 may be a data structure configured to store the aforementioned position data and wireless measurements data…” - ¶ [0084]).
Regarding claim 16, the same cited section and rationale as claim 3 is applied.
Regarding claim 17, the same cited section and rationale as claim 4 is applied.
Regarding claim 18, the same cited section and rationale as claim 5 is applied.
Regarding claim 19, Amiri et al. discloses:
The non-transitory computer-readable storage medium of Claim 14, wherein the generating the map of predicted RSSI data includes classifying the location data (Amiri et al. “The trained machine learning model 602 may be configured to receive input data 604, which may include position data indicative of a location of a UE 105.” - ¶ [0094]; Fig. 6B) and interpolating the received set of RSSI data based on the classified location data (Amiri et al. “the trained machine learning model 612 may include at least one classifier 616a and at least one regressor 618a…As used herein, a regressor may refer to an algorithm or a module configured to predict continuous values (e.g., wireless measurements).” - ¶ [0096]).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2, 10 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amiri et al. (US 2023/0318725 A1, cited by applicant in IDS dated 12 JAN 2026) in view of Kim et al. (WO 2022/004950 A1, cited by applicant in IDS dated 12 JAN 2026).
Regarding claim 2, Amiri et al. discloses:
[Note: what is not explicitly taught by Amiri et al. has been struck-through]
The method of Claim 1
Kim et al. discloses:
The method of Claim 1, wherein the sensor data includes light detection and ranging (LIDAR) data (Kim et al. 3D LiDar - ¶ [0002]).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Kim et al. into the invention of Amiri et al. to yield the invention of claim 2 above. Both Amiri et al. and Kim et al. are considered analogous arts to the claimed invention as they both disclose using machine learning for positioning wireless devices and generating maps. Amiri et al. discloses the invention of claim 2. However, Amiri et al. fails to explicitly disclose wherein the sensor data includes light detection and ranging (LIDAR) data. This feature is disclosed by Kim et al. where a machine learning-based indoor map generation system uses LIDAR sensors (Kim et al. ¶ [0001]). The combination of Amiri et al. and Kim et al. would be obvious with a reasonable expectation of success to utilize “3D LiDar and indoor positioning technology simultaneously to build data in real time by combining indoor map cloud data of a service space with location information… thereby reducing collection and construction costs and eliminating the need for separate mapping and position correction.” (Kim et al. ¶ [0027]) .
Regarding claim 10, the same cited section and rationale as claim 2 is applied.
Regarding claim 15, the same cited section and rationale as claim 2 is applied.
Claim(s) 7-8 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amiri et al. (US 2023/0318725 A1, cited by applicant in IDS dated 12 JAN 2026) in view of Parikh et al. (US 2022/0141619 A1).
Regarding claim 7, Amiri et al. discloses:
[Note: what is not explicitly taught by Amiri et al. has been struck-through]
The method of Claim 1
Parikh et al. discloses:
identifying an area of signal interference in the indoor environment based on the map of predicted RSSI data (Parikh et al. “The computer models are machine learning (ML) models that can be dynamically updated with additional RF data in response to physical changes in the environment, failure or changes to the RF signal sources, addition of new IRFSS, or other changes.” - ¶ [0014]; “The changed RSSI data values from a transmission occurring subsequent to the addition of the wall is added to the data model. In subsequent object location operations, the updated data model will then reflect the changed RF environment and the object can still be reliably located.” - ¶ [0112]).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Parikh et al. into the invention of Amiri et al. to yield the invention of claim 7 above. Both Amiri et al. and Parikh et al. are considered analogous arts to the claimed invention as they both disclose positioning wireless devices using predictive models and RSSI data. Amiri et al. discloses the invention of claim 1. However, Amiri et al. fails to explicitly disclose identifying an area of signal interference in the indoor environment based on the map of predicted RSSI data. This feature is disclosed by Parikh et al. where “The changed RSSI data values from a transmission occurring subsequent to the addition of the wall is added to the data model. In subsequent object location operations, the updated data model will then reflect the changed RF environment and the object can still be reliably located.” (Parikh et al. ¶ [0112]). The combination of Amiri et al. and Parikh et al. would be obvious with a reasonable expectation of success to improve “the computational result with repeated iterations of the computations using additional sets of data” (Parikh et al. ¶ [0065]).
Regarding claim 8, Amiri et al. discloses:
[Note: what is not explicitly taught by Amiri et al. has been struck-through]
The method of Claim 1, further comprising receiving, via the processing circuitry, a target set of RSSI data from a second device in the indoor environment based on signals received by the second device and predicting a location of the second device in the indoor environment based on the target set of RSSI data and the map of predicted RSSI data using a support vector machine.
Parikh et al. discloses:
receiving, via the processing circuitry, a target set of RSSI data from a second device in the indoor environment based on signals received by the second device and predicting a location of the second device in the indoor environment based on the target set of RSSI data and the map of predicted RSSI data using a support vector machine (Parikh et al. “The SVM accesses data in the survey database and/or training database 730, and creates one or more SVM data models that are used for object location prediction, based on RSSI values provided by a tag 25 that are activated to transmit its tag data package containing such RSSI values.” - ¶ [0172]).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Parikh et al. into the invention of Amiri et al. to yield the invention of claim 8 above. Both Amiri et al. and Parikh et al. are considered analogous arts to the claimed invention as they both disclose positioning wireless devices using predictive models and RSSI data. Amiri et al. discloses the invention of claim 1. However, Amiri et al. fails to explicitly disclose receiving, via the processing circuitry, a target set of RSSI data from a second device in the indoor environment based on signals received by the second device and predicting a location of the second device in the indoor environment based on the target set of RSSI data and the map of predicted RSSI data using a support vector machine. This feature is disclosed by Parikh et al. where “The SVM accesses data in the survey database and/or training database 730, and creates one or more SVM data models that are used for object location prediction, based on RSSI values provided by a tag 25 that are activated to transmit its tag data package containing such RSSI values.” (Parikh et al. ¶ [0172]). The combination of Amiri et al. and Parikh et al. would be obvious with a reasonable expectation of success to improve “the computational result with repeated iterations of the computations using additional sets of data” (Parikh et al. ¶ [0065]).
Regarding claim 20, the same cited section and rationale as claim 8 is applied.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAOMI M WOLFORD whose telephone number is (571)272-3929. The examiner can normally be reached Monday - Friday, 8:30 am - 4:30 pm EST.
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NAOMI M. WOLFORD
Examiner
Art Unit 3648
/N.M.W./ Examiner, Art Unit 3648
19 JUN 2026
/RESHA DESAI/ Supervisory Patent Examiner, Art Unit 3648
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