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 .
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-2, 12-14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karapantelakis et al. (Karapantelakis), U.S. Publication No. 2025/0193768 in view of Stark et al. (Stark), U.S. Publication No. 2024/0356816.
Regarding Claim 1, Karapantelakis discloses a computer-
implemented method for predicting one or more Quality of Service (QoS) parameters associated with a wireless network, the method comprising:
obtaining a target location for predicting the one or more QoS parameters (i.e., UE parameters may comprise any one or combination of the following parameters: a location for the UE, a predicted throughput of the UE, coverage of a TN at the location for the UE, and a Quality of Service, QoS, requirement of the UE; see paragraphs [0078]-[0079]);
determining characteristics of one or more wireless assets in a region associated with the target location (i.e., the location for the UE may be a current location, or a prediction of where the UE will be located at a future time point (e.g., corresponding to the time interval, described above). For example, a prediction of the location of the UE may be determined from location and mobility characteristics of the UE. E.g., by obtaining a current location and a bearing (direction of movement). Bearing and velocity can both be extracted from the recent history of handovers from cell to cell, information contained already in the mobility management function; see paragraph [0079]);
obtaining geospatial features for the region associated with the target location (i.e., handover history such as, serving cell ID, latitude, longitude, etc. described in paragraph [0081]);
applying a machine learning model to predict the one or more QoS parameters at the target location based on the characteristics of the one or more wireless assets and the geospatial features for the region, the machine learning model based in part on a physics based model that models wireless signal propagation of the wireless assets given the geospatial features, and the machine learning model based in part on a data driven model learned from historical measured operational data associated with the wireless network (i.e., throughput of the UE may be predicted using ML. For example, the method 200 may comprise obtaining a predicted throughput of the UE by: predicting the throughput using a first model trained using a ML process that predicts the throughput at a predetermined future time interval, based on previous throughput of the UE…see paragraph [0085]); and
outputting the one or more QoS parameters to a user interface (i.e., output data described in paragraph [0086]).
Karapantelakis fails to teach its machine learning model/processes are implemented as a hybrid machine learning model.
Stark discloses its machine learning model/processes are implemented
as a hybrid machine learning model. (in other words, receiving data for predicting the quality of service of the communication service and processing the data for predicting the quality of service of the communication service by a hybrid machine learning model to generate a prediction of the quality of service (QOS) of the communication service; see abstract).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Stark’s invention cited to bridge the gap by expressly teaching a hybrid machine learning model for QoS prediction with Karapantelakis’ invention at paragraph [0072] further teaching that different types of ML models take different forms for improving prediction of the quality of service to further improve and prevent a drop in quality of service thereby improving communication service (see paragraph [0008] of Stark).
Regarding Claim 13, Karapantelakis discloses a non-transitory computer-readable storage medium (i.e., FIG. 13 shows a carrier 1300 containing a computer program 106; 606; 806. A carrier may be an electronic signal, optical signal, radio signal or computer readable storage medium; see paragraphs [0196]-[0197]) storing instructions for predictions one or more Quality of Service (QoS) parameters associated with a wireless network, the instructions when executed by one or more processors causing the one or more processors to perform steps including:
obtaining a target location for predicting the one or more QoS parameters (i.e.,
UE parameters may comprise any one or combination of the following parameters: a location for the UE, a predicted throughput of the UE, coverage of a TN at the location for the UE, and a Quality of Service, QoS, requirement of the UE; see paragraphs [0078]-[0079]);
determining characteristics of one or more wireless assets in a region associated
with the target location (i.e., the location for the UE may be a current location, or a prediction of where the UE will be located at a future time point (e.g., corresponding to the time interval, described above). For example, a prediction of the location of the UE may be determined from location and mobility characteristics of the UE. E.g., by obtaining a current location and a bearing (direction of movement). Bearing and velocity can both be extracted from the recent history of handovers from cell to cell, information contained already in the mobility management function; see paragraph [0079]);
obtaining geospatial features for the region associated with the target location
(i.e., handover history such as, serving cell ID, latitude, longitude, etc. described in paragraph [0081]);
applying a machine learning model to predict the one or more QoS parameters at
the target location based on the characteristics of the one or more wireless assets and the geospatial features for the region, the machine learning model based in part on a physics based model that models wireless signal propagation of the wireless assets given the geospatial features, and the machine learning model based in part on a data driven model learned from historical measured operational data associated with the wireless network (i.e., throughput of the UE may be predicted using ML. For example, the method 200 may comprise obtaining a predicted throughput of the UE by: predicting the throughput using a first model trained using a ML process that predicts the throughput at a predetermined future time interval, based on previous throughput of the UE…see paragraph [0085]); and
outputting the one or more QoS parameters to a user interface (i.e., output data
described in paragraph [0086]).
Karapantelakis fails teach to its machine learning model/processes are implemented as a hybrid machine learning model.
Stark discloses its machine learning model/processes are implemented
as a hybrid machine learning model. (in other words, receiving data for predicting the quality of service of the communication service and processing the data for predicting the quality of service of the communication service by a hybrid machine learning model to generate a prediction of the quality of service (QOS) of the communication service; see abstract).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Stark’s invention cited to bridge the gap by expressly teaching a hybrid machine learning model for QoS prediction with Karapantelakis’ invention at paragraph [0072] further teaching that different types of ML models take different forms for improving prediction of the quality of service to further improve and prevent a drop in quality of service thereby improving communication service (see paragraph [0008] of Stark).
Regarding Claim 20, Karapantelakis discloses a computer system (in other words, a computer program product comprising a computer readable medium, the computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor; see paragraph [0193]) comprising:
one or more processors (in other words, a computer program product comprising a computer readable medium, the computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor; see paragraph [0193]); and a
non-transitory computer-readable storage medium (i.e., FIG. 13 shows a carrier 1300 containing a computer program 106; 606; 806. A carrier may be an electronic signal, optical signal, radio signal or computer readable storage medium; see paragraphs [0196]-[0197]) storing instructions for predicting one or more Quality of Service (QoS) parameters associated with a wireless network, the instructions when executed by the one or more processors causing the one or more processors to perform steps including:
obtaining a target location for predicting the one or more QoS parameters (i.e.,
UE parameters may comprise any one or combination of the following parameters: a location for the UE, a predicted throughput of the UE, coverage of a TN at the location for the UE, and a Quality of Service, QoS, requirement of the UE; see paragraphs [0078]-[0079]);
determining characteristics of one or more wireless assets in a region associated
with the target location (i.e., the location for the UE may be a current location, or a prediction of where the UE will be located at a future time point (e.g., corresponding to the time interval, described above). For example, a prediction of the location of the UE may be determined from location and mobility characteristics of the UE. E.g., by obtaining a current location and a bearing (direction of movement). Bearing and velocity can both be extracted from the recent history of handovers from cell to cell, information contained already in the mobility management function; see paragraph [0079]);
obtaining geospatial features for the region associated with the target location
(i.e., handover history such as, serving cell ID, latitude, longitude, etc. described in paragraph [0081]);
applying a machine learning model to predict the one or more QoS parameters at
the target location based on the characteristics of the one or more wireless assets and the geospatial features for the region, the machine learning model based in part on a physics based model that models wireless signal propagation of the wireless assets given the geospatial features, and the machine learning model based in part on a data driven model learned from historical measured operational data associated with the wireless network (i.e., throughput of the UE may be predicted using ML. For example, the method 200 may comprise obtaining a predicted throughput of the UE by: predicting the throughput using a first model trained using a ML process that predicts the throughput at a predetermined future time interval, based on previous throughput of the UE…see paragraph [0085]); and
outputting the one or more QoS parameters to a user interface (i.e., output data
described in paragraph [0086]).
Karapantelakis fails teach to its machine learning model/processes are implemented as a hybrid machine learning model.
Stark discloses its machine learning model/processes are implemented
as a hybrid machine learning model. (in other words, receiving data for predicting the quality of service of the communication service and processing the data for predicting the quality of service of the communication service by a hybrid machine learning model to generate a prediction of the quality of service (QOS) of the communication service; see abstract).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Stark’s invention cited to bridge the gap by expressly teaching a hybrid machine learning model for QoS prediction with Karapantelakis’ invention at paragraph [0072] further teaching that different types of ML models take different forms for improving prediction of the quality of service to further improve and prevent a drop in quality of service thereby improving communication service (see paragraph [0008] of Stark).
Regarding Claims 2 and 14, Karapantelakis and Stark disclose the computer-implemented method and non-transitory computer-readable storage medium as described above. Karapantelakis further discloses wherein obtaining the target location comprises obtaining, from a user interface, at least one of: a set of geospatial coordinates (i.e., a coverage map can be a list of bounding boxes, i.e., polygons with edges expressed as <latitude, longitude> tuples); see paragraph [0083]), a street address, and a selected position in a map view.
Regarding Claim 12, Karapantelakis and Stark disclose the computer-implemented method as described above. Karapantelakis fails to disclose wherein the one or more QoS parameters comprises at least one of: RSRP, SINR, download (D/L) speed, upload (U/L) speed. Stark discloses wherein the one or more QoS parameters comprises at least one of: RSRP, SINR, download (D/L) speed, upload (U/L) speed (see paragraph [0039]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Stark’s invention with Karapantelakis’ invention for improving prediction of the quality of service to further improve and prevent a drop in quality of service thereby improving communication service (see paragraph [0008] of Stark).
Claim(s) 3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karapantelakis and Stark in view of Karaoguz et al. (Karaoguz), U.S. Publication No. 2009/0300688.
Regarding Claims 3 and 15, Karapantelakis and Stark disclose the computer-
implemented method and non-transitory computer-readable storage medium as described above. Karapantelakis and Stark fail to disclose wherein obtaining the geospatial features comprises: obtaining satellite map image data from a map data source; and processing the satellite map image data to identify one or more obstacles in the region that impact wireless signal propagation. Karaoguz discloses wherein obtaining the geospatial features comprises: obtaining satellite map image data from a map data source; and processing the satellite map image data to identify one or more obstacles in the region that impact wireless signal propagation (i.e., QoS map identifying geographic features such as buildings 1208 and 1214, monument 1210, and road 1206 as described in paragraphs [0058]-[0060]… The format of the streamed multimedia content is similar/same as that that is serviced by the limited access networks, e.g., cable networks, satellite networks.; see paragraphs [0006] and [0008]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Karaoguz’s invention with Karapantelakis’ and Stark’s invention for supporting reliability and quality in communication networks as described throughout Karaoguz.
Claim(s) 4-5, 10 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karapantelakis, Stark and Karaoguz in view of Consoli et al. (Consoli) U.S. Publication No. 2022/0110024.
Regarding Claims 4 and 16, Karapantelakis, Stark and Karaoguz disclose the computer-implemented method and non-transitory computer-readable storage medium as described above. Karapantelakis, Stark and Karaoguz fail to disclose wherein processing the satellite map image data comprises: applying a machine learning model trained to identify and characterize the one or more obstacles. Consoli discloses wherein processing the satellite map image data comprises: applying a machine learning model trained to identify and characterize the one or more obstacles (see paragraph [0091]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Karapantelakis’, Stark’s and Karaoguz’s invention with Consoli’s invention to preserve safety as described throughout Consoli.
Regarding Claims 5 and 17, Karapantelakis, Stark and Karaoguz disclose the computer-implemented method and non-transitory computer-readable storage medium as described above. Karapantelakis, Stark and Karaoguz fail to disclose wherein identifying the one or more obstacles comprises identifying at least one of: a building, a tree, foliage, a manmade structure, and a geological feature. Consoli discloses wherein identifying the one or more obstacles comprises identifying at least one of: a building, a tree, foliage, a manmade structure (i.e., network 100; see figure 2), and a geological feature. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Karapantelakis’, Stark’s and Karaoguz’s invention with Consoli’s invention to preserve safety as described throughout Consoli.
Regarding Claim 10, Karapantelakis, Stark and Karaoguz disclose the computer-implemented method as described above. Karapantelakis, Stark and Karaoguz fail to disclose further comprising: dependent on the one or more QoS parameters predicted for the target location, generating a recommended subscription service associated with the wireless network; presenting the recommended subscription service in the user interface; and facilitating enrollment of an existing or prospective customer in the recommended subscription service. Consoli discloses further comprising: dependent on the one or more QoS parameters predicted for the target location, generating a recommended subscription service associated with the wireless network; presenting the recommended subscription service in the user interface; and facilitating enrollment of an existing or prospective customer in the recommended subscription service (see paragraph [0144]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Karapantelakis’, Stark’s and Karaoguz’s invention with Consoli’s invention to preserve safety as described throughout Consoli.
Claim(s) 7 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karapantelakis and Stark in view of Pulleti et al. (Pulleti), U.S. Publication No. 2015/0350921.
Regarding Claims 7 and 19, Karapantelakis and Stark disclose the
computer-implemented method and non-transitory computer-readable storage medium as described above. Karapantelakis further discloses wherein the region associated with the target location comprises representing an area around a line-of-sight of a receiver at the target location (see paragraph [0081]). Karapantelakis and Stark fail to disclose a Fresnel zone. Pulleti discloses a Fresnel zone (see paragraph [0028]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Karapantelakis’ and Stark’s invention with Pulleti’s invention to prevent unreliable networks as discussed throughout Pulleti.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karapantelakis and Stark in view of Brodeur et al. (Brodeur), U.S. Publication No. 2024/0015547.
Regarding Claim 9, Karapantelakis and Stark disclose the computer-implemented method as described above. Karapantelakis and Stark fail to disclose wherein outputting the one or more QoS parameters comprises: generating a map overlay that represents different values of the one or more QoS parameters at different locations using a color-coding scheme. Brodeur discloses wherein outputting the one or more QoS parameters comprises: generating a map overlay that represents different values of the one or more QoS parameters at different locations using a color-coding scheme (see paragraph [0099]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Brodeur’s invention with Karapantelakis’ and Stark’s invention for preventing interruption of communication services as described throughout Brodeur.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karapantelakis and Stark in view of Salkintzis et al. (Salkintzis), U.S. Publication No. 2025/0056270.
Regarding Claim 11, Karapantelakis and Stark disclose the computer-
implemented method as described above. Karapantelakis and Stark fail to disclose further comprising: performing a comparison of the one or more QoS parameters predicted for the target location to measured QoS parameters experienced by an existing customer; identifying a subscriber service issue based on the comparison; and facilitating resolution of the subscriber service issue for that customer. Salkintzis discloses further comprising: performing a comparison of the one or more QoS parameters predicted for the target location to measured QoS parameters experienced by an existing customer; identifying a subscriber service issue based on the comparison; and facilitating resolution of the subscriber service issue for that customer (see paragraphs [0170]-[0173]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Salkintzis’ invention with Karapantelakis’ and Stark’s invention for preventing latency and battery consumption as described throughout Salkintzis.
Allowable Subject Matter
Claims 6, 8 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANTELL LAKETA HEIBER whose telephone number is (571)272-0886. The examiner can normally be reached on M-F from 9am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy, can be reached at telephone number 571-272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/SHANTELL L HEIBER/Primary Examiner, Art Unit 2645
January 13, 2026