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 Objections
Claim 8 objected to because of the following informalities: line 4, reads "service data a defined geographic area", for examining purposes, the examiner interpreted the following "service data in a defined geographic area". Appropriate correction is required.
Claim 12 objected to under 37 CFR 1.75 as being a substantial duplicate of claim 11. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). Applicant is advised that should claim 11 be found allowable, claim 12 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of co-pending Application No. 17/983878 (Indrieri et al., US20240155362A1) in view of Ahmed et al. (US-11729636-B1). Although the claims at issue are not identical, they are patentably indistinct from each other because of the following:
With respect to claim 1, claim 1 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per claim 1,
A computerized method comprising: generating a first plurality of clusters within a defined geographic region using one or more metrics obtained from a set of telecommunications service data;
utilizing a hierarchical clustering algorithm and a first linkage distance, generating a second plurality of clusters from the first plurality of clusters;
utilizing the hierarchical clustering algorithm and a second linkage distance, generating a third plurality of clusters from the second plurality of clusters;
for each cluster in the third plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type;
and ranking each cluster in the third plurality of clusters relative to one another.
As per claim 1,
A computerized method comprising: receiving telecommunication service data within a defined geographic area;
identifying a plurality of new site deployment locations within the defined geographic area;
identifying a buffer area for each of the plurality of new site deployment locations;
identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area for each of the plurality of new site deployment locations;
filtering the first plurality of data sets from the telecommunication service data, wherein a second plurality of data sets in the telecommunication service data remains;
generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets;
for each cluster in the plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type;
and ranking each cluster in the plurality of clusters relative to one another.
The features of claim 1 that are not present in claim 1 of the co-pending application are the use of a “hierarchical clustering algorithm”.
However, in an analogous art, Ahmet et al teaches a computing device that can implement a machine learning algorithm that can implement various clustering techniques, to include hierarchical clustering. (Ahmed et al., Detailed Description, par.14; a computing device can implement a clustering component to receive input data representing one or more of: network configuration data, network user data, location data and/or map data. The clustering component can implement one or more clustering techniques (e.g., k-means, k-medians, k-prototype, expectation maximization (EM), hierarchical clustering, etc.) to cluster the input data. In some examples, the clustering component represents a machine learned model that processes the input data independent of flattening and/or weighing the input data (e.g., the model can implement a k-prototype algorithm or similar algorithm that enables clustering of numerical data and categorical data). In some examples, the clustering techniques employed by the model are based at least in part on least dissimilarity between cells).
Accordingly, the prior art references teach all of the claimed elements. The combination of the known elements is achieved by the combination of the machine learning method of Ahmed et al. to generate cluster by hierarchy with the machine learning method of the Co-pending application to identify possible options for new site deployment locations.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the hierarchical elements of Ahmed et al. with the elements for site identification taught by the co-pending application to filter or rank the feasibility of new cell deployment sites based on preestablished metrics.
With respect to claim 2, claim 2 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 2,
The computerized method of claim 1, wherein the telecommunication service data comprises values for Reference Signal Received Power (RSRP) measurements, coverage metrics, uncovered population data, sector metrics, downlink backhaul speed, or a combination thereof.
As per Claim 2,
The computerized method of claim 1, wherein the telecommunication service data comprises values for Reference Signal Received Power (RSRP) measurements, coverage metrics, uncovered population data, sector metrics, downlink backhaul speed, or a combination thereof.
As underlined, claim 2 from the instant application is identical to claim 2 of the co-pending application. There are no patentably distinct elements between them, therefore claim 2 is rejected.
With respect to claim 3, claim 1 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 3,
The computerized method of claim 1 further comprising: receiving the telecommunication service data within the defined geographic area;
identifying a plurality of new site deployment locations within the defined geographic area, each of the plurality of new site deployment locations comprising a buffer area;
identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area for each of the plurality of new site deployment locations;
and filtering the first plurality of data sets from the telecommunication service data.
As per Claim 1,
A computerized method comprising: receiving telecommunication service data within a defined geographic area;
identifying a plurality of new site deployment locations within the defined geographic area;
identifying a buffer area for each of the plurality of new site deployment locations;
identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area for each of the plurality of new site deployment locations;
filtering the first plurality of data sets from the telecommunication service data, wherein a second plurality of data sets in the telecommunication service data remains;
generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets;
for each cluster in the plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type;
and ranking each cluster in the plurality of clusters relative to one another.
As underlined, claim 3 from the instant application is identical or analogous to claim 1 of the co-pending application. Merely rearranging the order of the claim does not provide patentable distinct elements to the claim, therefore claim 3 is rejected.
With respect to claim 4, claim 4 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 4,
The computerized method of claim 1, wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location.
As per Claim 4,
The computerized method of claim 1, wherein, for each cluster in the plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location.
As underlined, claim 4 from the instant application is identical or analogous to claim 4 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 4 is rejected.
With respect to claim 5, claim 5 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 5,
The computerized method of claim 1, wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1 GHz.
As per Claim 5,
The computerized method of claim 1, wherein, for each cluster in the plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1GHz.
As underlined, claim 5 from the instant application is identical or analogous to claim 5 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 5 is rejected.
With respect to claim 6, claim 6 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 6,
The computerized method of claim 1, wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises: determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster.
As per Claim 6,
The computerized method of claim 1, wherein, for each cluster in the plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises: determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster.
As underlined, claim 6 from the instant application is identical or analogous to claim 6 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 6 is rejected.
With respect to claim 7, claim 7 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 7,
The computerized method of claim 1 wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises: determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network.
As per Claim 7,
The computerized method of claim 1 wherein, for each cluster in the plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises: determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network.
As underlined, claim 7 from the instant application is identical or analogous to claim 7 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 7 is rejected.
With respect to claim 8, claim 8 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 8,
One or more non-transitory computer-readable media storing instructions that when executed via one or more processors performs a computerized method, the media comprising: via one or more processors, without user intervention: receiving telecommunication service data a defined geographic area; generating a first plurality of clusters within a defined geographic region using one or more metrics obtained from telecommunications service data; utilizing a hierarchical clustering algorithm and a first linkage distance, generating a second plurality of clusters from the first plurality of clusters; utilizing the hierarchical clustering algorithm and a second linkage distance, generating a third plurality of clusters from the second plurality of clusters; for each cluster in the third plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type; and ranking each cluster in the third plurality of clusters relative to one another.
As per Claim 8,
One or more non-transitory computer-readable media storing instructions that when executed via one or more processors performs a computerized method, the media comprising: via one or more processors, without user intervention: receiving telecommunication service data a defined geographic area; identifying a plurality of new site deployment locations within the defined geographic area; identifying a buffer area for each of the plurality of new site deployment locations; identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer are for each of the plurality of new site deployment locations; filtering the first plurality of data sets from the telecommunication service data, wherein a second plurality of data sets in the telecommunication service data remains; generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets; for each cluster in the plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type; and ranking each cluster in the plurality of clusters relative to one another.
The features of claim 8 that are not present in claim 8 of the co-pending application are the use of a “hierarchical clustering algorithm”.
However, in an analogous art, Ahmet et al., teaches a computing device that can implement a machine learning algorithm that can implement various clustering techniques, to include hierarchical clustering. (Ahmed et al., Detailed Description, par.14; a computing device can implement a clustering component to receive input data representing one or more of: network configuration data, network user data, location data and/or map data. The clustering component can implement one or more clustering techniques (e.g., k-means, k-medians, k-prototype, expectation maximization (EM), hierarchical clustering, etc.) to cluster the input data. In some examples, the clustering component represents a machine learned model that processes the input data independent of flattening and/or weighing the input data (e.g., the model can implement a k-prototype algorithm or similar algorithm that enables clustering of numerical data and categorical data). In some examples, the clustering techniques employed by the model are based at least in part on least dissimilarity between cells).
Accordingly, the prior art references teach all of the claimed elements. The combination of the known elements is achieved by the combination of the machine learning method of Ahmed et all to generate cluster by hierarchy with the machine learning method of the Co-pending application to identify possible options for new site deployment locations.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the hierarchical elements of Ahmed et al. with the elements for site identification taught by the co-pending application to filter or rank the feasibility of new cell deployment sites based on preestablished metrics.
With respect to claim 9, claim 9 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 9,
The media of claim 8 further comprising, via the one or more processors without user intervention, generating and causing display of a graphical user interface, the graphical user interface displaying the third plurality of clusters within the defined geographic region and the specific location within each of the third plurality of clusters for deployment of the solution type determined, each of the third plurality of clusters being visually coded to distinguish each cluster from one another, and the specific location for each of the third plurality of clusters being visually coded to indicate a particular solution type for deployment.
As per Claim 9,
The media of claim 8 further comprising, via the one or more processors without user intervention, generating and causing display of a graphical user interface, the graphical user interface displaying the plurality of clusters within the defined geographic region and the specific location within each of the plurality of clusters for deployment of the solution type determined, each of the plurality of clusters being visually coded to distinguish each cluster from one another, and the specific location for each of the plurality of clusters being visually coded to indicate a particular solution type for deployment.
As underlined, claim 9 from the instant application is identical or analogous to claim 9 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 9 is rejected.
With respect to claim 10, claim 8 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 10,
The media of claim 8 further comprising: receiving the telecommunication service data within the defined geographic area; identifying a plurality of new site deployment locations within the defined geographic area, each of the plurality of new site deployment locations comprising a buffer area; identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area for each of the plurality of new site deployment locations; and filtering the first plurality of data sets from the telecommunication service data.
As per Claim 8,
One or more non-transitory computer-readable media storing instructions that when executed via one or more processors performs a computerized method, the media comprising: via one or more processors, without user intervention: receiving telecommunication service data a defined geographic area; identifying a plurality of new site deployment locations within the defined geographic area; identifying a buffer area for each of the plurality of new site deployment locations; identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer are for each of the plurality of new site deployment locations; filtering the first plurality of data sets from the telecommunication service data, wherein a second plurality of data sets in the telecommunication service data remains; generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets; for each cluster in the plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type; and ranking each cluster in the plurality of clusters relative to one another
As underlined, claim 10 from the instant application is identical or analogous to claim 8 of the co-pending application. Merely rearranging the order of the claim does not provide patentable distinct elements to the claim, therefore claim 10 is rejected.
With respect to claim 11, claim 11 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per claim 11,
The media of claim 8 wherein determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location.
As per claim 11,
The media of claim 8 wherein determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location
As underlined, claim 11 from the instant application is identical or analogous to claim 11 of the co-pending application. Providing no patentable distinct elements to the claim, therefore claim 11 is rejected.
With respect to claim 12, claim 12 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per claim 12,
The media of claim 8, wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location.
As per claim 12,
The media of claim 8, wherein, for each cluster in the plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location.
As underlined, claim 12 from the instant application is identical or analogous to claim 12 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 12 is rejected.
With respect to claim 13, claim 13 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 13,
The media of claim 8, wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1 GHz.
As per Claim 13,
The media of claim 8, wherein, for each cluster in the plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1 GHz.
As underlined, claim 13 from the instant application is identical or analogous to claim 13 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 13 is rejected.
With respect to claim 14, claim 14 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 14,
The media of claim 8, wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster.
As per Claim 14,
The media of claim 8, wherein, for each cluster in the plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster.
As underlined, claim 14 from the instant application is identical or analogous to claim 14 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 14 is rejected.
With respect to claim 15, claim 15 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 15,
The media of claim 8, wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network.
As per Claim 15,
The media of claim 8, wherein, for each cluster in the plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises, via the one or more processors without user intervention: determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network.
As underlined, claim 15 from the instant application is identical or analogous to claim 15 of the co-pending application. A “third plurality” is an obvious reference to claim 1 “generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets” providing no patentable distinct elements to the claim, therefore claim 15 is rejected.
With respect to claim 16, claim 16 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 16,
A system comprising: a database storing telecommunications service data for a defined geographic area; a telecommunication network communicatively coupled to the database; and one or more processors communicatively coupled to the telecommunication network, the one or more processors configured to: generating a first plurality of clusters within the defined geographic region using one or more metrics obtained from the telecommunications service data; utilizing a hierarchical clustering algorithm and a first linkage distance, generating a second plurality of clusters from the first plurality of clusters; utilizing the hierarchical clustering algorithm and a second linkage distance, generating a third plurality of clusters from the second plurality of clusters; for each cluster in the third plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type; and ranking each cluster in the third plurality of clusters relative to one another.
As per Claim 16,
A system comprising: a database storing telecommunication service data for a defined geographic area; a telecommunication network communicatively coupled to the database; and one or more processors communicatively coupled to the telecommunication network, the one or more processors configured to: receiving telecommunication service data for a defined geographic area from the database; identifying a plurality of new site deployment locations within the defined geographic area; identifying a buffer area for each of the plurality of new site deployment locations; identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area for each of the plurality of new site deployment locations; filtering the first plurality of data sets from the telecommunication service data, wherein a second plurality of data sets in the telecommunication service data remains; generating a plurality of clusters within the defined geographic region using one or more metrics obtained from the second plurality of data sets; for each cluster in the plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type; and ranking each cluster in the plurality of clusters relative to one another.
The features of claim 16 that are not present in claim 16 of the co-pending application are the use of a “hierarchical clustering algorithm”.
However, in an analogous art, Ahmet et al., teaches a computing device that can implement a machine learning algorithm that can implement various clustering techniques, to include hierarchical clustering. (Ahmed et al., Detailed Description, par.14; a computing device can implement a clustering component to receive input data representing one or more of: network configuration data, network user data, location data and/or map data. The clustering component can implement one or more clustering techniques (e.g., k-means, k-medians, k-prototype, expectation maximization (EM), hierarchical clustering, etc.) to cluster the input data. In some examples, the clustering component represents a machine learned model that processes the input data independent of flattening and/or weighing the input data (e.g., the model can implement a k-prototype algorithm or similar algorithm that enables clustering of numerical data and categorical data). In some examples, the clustering techniques employed by the model are based at least in part on least dissimilarity between cells).
Accordingly, the prior art references teach all of the claimed elements. The combination of the known elements is achieved by the combination of the machine learning method of Ahmed et all to generate cluster by hierarchy with the machine learning method of the Co-pending application to identify possible options for new site deployment locations.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the hierarchical elements of Ahmed et al. with the elements for site identification taught by the co-pending application to filter or rank the feasibility of new cell deployment sites based on preestablished metrics.
With respect to claim 17, claim 17 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 17,
The system of claim 16, wherein the one or more processors are further configured to: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active site.
As per Claim 17,
The system of claim 16, wherein the one or more processors are further configured to: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active site.
As underlined, claim 17 from the instant application is identical or analogous to claim 17 of the co-pending application. Providing no patentable distinct elements to the claim, therefore claim 17 is rejected.
With respect to claim 18, claim 18 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 18,
The system of claim 16, wherein the one or more processors are further configured to: determining whether the active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1 GHz.
As per Claim 18,
The system of claim 16, wherein the one or more processors are further configured to: determining whether the active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1 GHz.
As underlined, claim 18 from the instant application is identical or analogous to claim 18 of the co-pending application. Providing no patentable distinct elements to the claim, therefore claim 18 is rejected.
With respect to claim 19, claim 19 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 19,
The system of claim 16, wherein the one or more processors are further configured to: determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster.
As per Claim 19,
The system of claim 16, wherein the one or more processors are further configured to: determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster.
As underlined, claim 19 from the instant application is identical or analogous to claim 19 of the co-pending application. Providing no patentable distinct elements to the claim, therefore claim 19 is rejected.
With respect to claim 20, claim 20 of co-pending application No. 17/983878 has the similarity limitations as underlined below:
Instant Application
Co-Pending Application No. 17/983878
As per Claim 20,
The system of claim 16, wherein the one or more processors are further configured to: determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network.
As per Claim 20,
The system of claim 16, wherein the one or more processors are further configured to: determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network
As underlined, claim 20 from the instant application is identical or analogous to claim 20 of the co-pending application. Providing no patentable distinct elements to the claim, therefore claim 20 is rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-3, 6, 8, 10 and 14 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process that can be performed with the aid of pen and paper. This judicial exception is not integrated into a practical application because the application as claimed depicts the process of collecting, analyzing and organizing data which is analogous to a mental process . The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the core process consists of collecting telecommunication data, evaluating it to form and refine clusters, judging solution types and locations and forming an opinion on rankings. Steps analogous to a network planner mentally grouping network coverage on a map, refining groups based on distances and making decisions to prioritize coverage based on a set hierarchy or criterion.
Regarding Claim 1, it recites:
A computerized method comprising:
generating a first plurality of clusters within a defined geographic region using one or more metrics obtained from a set of telecommunications service data;
utilizing a hierarchical clustering algorithm and a first linkage distance, generating a second plurality of clusters from the first plurality of clusters;
utilizing the hierarchical clustering algorithm and a second linkage distance, generating a third plurality of clusters from the second plurality of clusters;
for each cluster in the third plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type;
ranking each cluster in the third plurality of clusters relative to one another.
According to USPTO guidelines, a claim is directed to non-statutory subject matter (Process, machine, manufacture or composition of matter) if:
Step 1: Do the claims fall within one of the statutory categories? YES, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES, the claim falls within the mental process groupings of abstract ideas because they cover the concepts performed in the human mind, including observation, evaluation, judgement and opining. The claim limitations of collecting and analyzing data (observation and evaluation), then generate a cluster by taking linkage distance into account (judgement) to rank them with respect to each other (opinion).
Step-2A (Prong-2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite an improvement to the functioning of a computer or the network. The method described to generate clusters from collected data and determine a “solution type” by ranking the clusters relative to one another is a mental assessment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with respect to step-2A Prong-2, the additional elements in the claim amount to no more than mere instructions to apply the exception using a computer.
The claim and its associated limitations recite a method to a mental process that is being perform on a computer is not patent eligible as the method is not tied to a hardware improvement, see Gottschalk v. Benson where the courts recognized that simply implementing a mathematical principle (namely an algorithm) on a physical machine, namely a computer, was not a patentable application of that principle.
Regarding Claim 2, it recites:
The computerized method of claim 1,
wherein the telecommunication service data comprises values for Reference Signal Received Power (RSRP) measurements, coverage metrics, uncovered population data, sector metrics, downlink backhaul speed, or a combination thereof.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES, the claim falls within the mental process groupings of abstract ideas because they cover the concepts performed in the human mind, including observation, and evaluation. The claim limitation of collecting and analyzing data (observation and evaluation), such as RSRP measurements, coverage metrics etc., amounts to mere data collection.
Step-2A (Prong-2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite an improvement to the functioning of a computer or the network. The method described to limit the data collected to RSRP measurements, coverage metrics, uncovered population etc., this limitation amounts to data collection using a generic computer component.
Step-2B: Does the claim provide an inventive concept? No, as discussed with respect to step-2A Prong-2, the additional elements in the claim amount to no more than mere instructions to gather data using a computer.
The claim uses the structure of claim 1 and adds specific telecom metrics for mental observation without adding patent eligibility. Therefore claim 2 is not patent eligible as the method is not tied to a hardware improvement.
Regarding Claim 3, it recites:
The computerized method of claim 1 further comprising:
receiving the telecommunication service data within the defined geographic area;
identifying a plurality of new site deployment locations within the defined geographic area, each of the plurality of new site deployment locations comprising a buffer area;
identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area for each of the plurality of new site deployment locations; and
filtering the first plurality of data sets from the telecommunication service data.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES, the claim falls within the mental process groupings of abstract ideas because they cover the concepts performed in the human mind, including observation, evaluation, and judgement. The claim limitations of collecting and analyzing data (observation and evaluation) and filtering data sets (judgment) are analogous to a mental process to analyze and exclude data sets based on a set criterion.
Step-2A (Prong-2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite an improvement to the functioning of a computer or the network. The method described amounts to collecting and filtering data, using a generic computer component.
Step-2B: Does the claim provide an inventive concept? No, as discussed with respect to step-2A Prong-2, the additional elements in the claim amount to no more than mere instructions to gather and filtering data using a computer component.
The claim uses the structure of claim 1 and adds the collection and identification of data within an area and filtering by location to exclude data points. Steps that constitute a mental process to collect, analyze and exclude data. Therefore claim 3 is not patent eligible as the method is not tied to a hardware improvement.
Regarding Claim 6, it recites:
The computerized method of claim 1, wherein, for each cluster in the third plurality of clusters, determining the solution type and the specific location within the cluster for deployment of the solution type comprises:
determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES, the claim falls within the mental process groupings of abstract ideas because they cover the concepts performed in the human mind, including observation, evaluation, and judgement. The claim limitations of making a determination to provide service to a geographic area (judgment) and filtering the information based on an active lease-agreement status (observation and evaluation) is a mental assessment performed using a generic computer component that can be performed using pen and paper.
Step-2A (Prong-2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite an improvement to the functioning of a computer or the network. The method described amounts to mere instructions using a generic computer component to determine service coverage in a predetermined area.
Step-2B: Does the claim provide an inventive concept? No, as discussed with respect to step-2A Prong-2, the additional elements in the claim amount to no more than mere instructions to determine the feasibility of service coverage.
The claim uses the structure of claim 1 and adds coverage determination based on locations that are covered by an active lease agreement, analogous to a mental judgement. Therefore claim 6 is not patent eligible as the method is not tied to a hardware improvement.
Regarding Claim 8, it recites;
One or more non-transitory computer-readable media storing instructions that when executed via one or more processors performs a computerized method, the media comprising:
via one or more processors, without user intervention:
receiving telecommunication service data a defined geographic area;
generating a first plurality of clusters within a defined geographic region using one or more metrics obtained from telecommunications service data;
utilizing a hierarchical clustering algorithm and a first linkage distance, generating a second plurality of clusters from the first plurality of clusters;
utilizing the hierarchical clustering algorithm and a second linkage distance, generating a third plurality of clusters from the second plurality of clusters;
for each cluster in the third plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type; and
ranking each cluster in the third plurality of clusters relative to one another.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a non-transitory readable media (machine) and a series of steps (process). Therefore, it is a process for a machine.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES, the claim falls within the mental process groupings of abstract ideas because they cover the concepts performed in the human mind, including observation, evaluation, judgement and opinion. The claim limitations of collecting and analyzing data (observation and evaluation), then generate a cluster by taking linkage distance into account (judgement) to rank them with respect to each other (opinion).
Step-2A (Prong-2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite an improvement to the functioning of a computer or the network. The method described to generate clusters from collected data and determine a “solution type” by ranking the clusters relative to one another is a mental assessment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with respect to step-2A Prong-2, the additional elements in the claim amount to no more than mere instructions to gather data using a computer. The term, non-transitory computer readable media is analogous to software stored in the memory of a computer, which perform instructions that could otherwise be performed by doing a mental assessment using pen and paper.
The claim and its associated limitations recite a method to a mental process that is being perform on a computer is not patent eligible as the method is not tied to a hardware improvement. see Gottschalk v. Benson where the courts recognized that simply implementing a mathematical principle (namely an algorithm) on a physical machine, namely a computer, was not a patentable application of that principle.
Regarding Claim 10, it recites;
The media of claim 8 further comprising:
receiving the telecommunication service data within the defined geographic area;
identifying a plurality of new site deployment locations within the defined geographic area, each of the plurality of new site deployment locations comprising a buffer area;
identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area for each of the plurality of new site deployment locations;
and filtering the first plurality of data sets from the telecommunication service data.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a non-transitory readable media (machine) and a series of steps (process). Therefore, it is a process for a machine.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES, the claim falls within the mental process groupings of abstract ideas because they cover the concepts performed in the human mind, including observation, evaluation, and judgement. The claim limitations of collecting and analyzing data (observation and evaluation) and filtering data sets (judgment) are analogous to a mental process to analyze and exclude data sets based on a set criterion.
Step-2A (Prong-2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite an improvement to the functioning of a computer or the network. The method described amounts to collecting and filtering data, using a generic computer component.
Step-2B: Does the claim provide an inventive concept? No, as discussed with respect to step-2A Prong-2, the additional elements in the claim amount to no more than mere instructions to gather and filtering data using a computer component.
The claim uses the structure of claim 8 and adds the collection and identification of data within an area and filtering by location to exclude data points. Steps that constitute a mental process to collect, analyze and exclude data. Therefore claim 10 is not patent eligible as the method is not tied to a hardware improvement.
Regarding Claim 14, it recites;
The media of claim 8 further comprising:
determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a non-transitory readable media (machine) and a series of steps (process). Therefore, it is a process for a machine.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES, the claim falls within the mental process groupings of abstract ideas because they cover the concepts performed in the human mind, including observation, evaluation, and judgement. The claim limitations of making a determination to provide service to a geographic area (judgment) and filtering the information based on an active lease-agreement status (observation and evaluation) is a mental assessment performed using a generic computer component that can be performed using pen and paper.
Step-2A (Prong-2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite an improvement to the functioning of a computer or the network. The method described amounts to mere instructions using a generic computer component to determine service coverage in a predetermined area.
Step-2B: Does the claim provide an inventive concept? No, as discussed with respect to step-2A Prong-2, the additional elements in the claim amount to no more than mere instructions to determine the feasibility of service coverage.
The claim uses the structure of claim 8 and adds coverage determination based on locations that are covered by an active lease agreement, analogous to a mental judgement. Therefore claim 14 is not patent eligible as the method is not tied to a hardware improvement.
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.
Claim(s) 1 -20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al. (US-11729636-B1), in view of Sofuoglu (US-10448261-B2) as applied to claims above.
Regarding Claim 1, Ahmed et al. discloses a computerized method comprising: generating a first plurality of clusters within a defined geographic region using one or more metrics obtained from a set of telecommunications service data (Ahmed et al., Detailed Description, par. 9; This application relates to techniques for clustering network elements of a telecommunications network. For instance, a machine learned model can cluster cells based on a combination of network data, user equipment data, location data, and map data. Clusters output from the machine learned model can be compared to each other to identify an underperforming cell, to generate recommendations that improve performance of a cell, and/or to determine performance benchmarks for cells or networks across different geographical regions); Utilizing the hierarchical clustering algorithm and a second linkage distance, generating a third plurality of clusters from the second plurality of clusters (Ahmed et al., Detailed Description, par.14; a computing device can implement a clustering component to receive input data representing one or more of: network configuration data, network user data, location data and/or map data. The clustering component can implement one or more clustering techniques (e.g., k-means, k-medians, k-prototype, expectation maximization (EM), hierarchical clustering, etc.) to cluster the input data. In some examples, the clustering component represents a machine learned model that processes the input data independent of flattening and/or weighing the input data (e.g., the model can implement a k-prototype algorithm or similar algorithm that enables clustering of numerical data and categorical data). In some examples, the clustering techniques employed by the model are based at least in part on least dissimilarity between cells); for each cluster in the third plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type (Ahmed et al., Detailed Description, par. 12; The model may also or instead generate network recommendations that optimize network performance (e.g., increases the Quality of Signal versus not implementing the model)).
Ahmed et al. does not teach the ranking of each cluster in the third plurality of clusters relative to one another, however, Sofuoglu discloses a method to calculate coverage degree per LTE network bins. Making a comparison of the unified weak coverage indication cost metric that can be used to rank the bins from worst to best coverage degree (Sofuoglu, figs. 3 and 8, par. 41).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to combine the method of Ahmed et al. to integrate a machine learning algorithm that can perform cluster analysis based on hierarchy or other means to provide solution recommendations with Sofuoglu’s method to rank and identify clusters of weak LTE coverage using different key performance indicators such as linkage distances to improve network deployment accuracy in areas with weak network coverage.
Regarding Claim 2, Sofuoglu et al further discloses the method of claim 1, wherein the telecommunication service data comprises values for Reference Signal Received Power (RSRP) measurements, coverage metrics, uncovered population data, sector metrics, downlink backhaul speed, or a combination thereof (Sofuoglu, Detailed Description, fig. 3, par. 25; weak coverage symptom events in the worst-performing sectors are geolocated. The weak coverage symptom events can include UE Context IRAT relocation/redirection events (e.g., when a UE is redirected from LTE to UMTS due to UE received signal strength being below a set threshold) and abnormal call releases (e.g., due to dropped calls as a result of poor RSRP/RSRQ) as indicated in cell MDT (minimization of drive tests)/trace data. Each weak coverage symptom event can be correlated with a geographical location of the UE (e.g., based on GPS data from the UE or by another geolocation mechanism). As a result, geographical bins (e.g., 50 m×50 m) in the subset of the target area can be produced that will contain a mapping of geolocated MDT (minimization of drive tests)/trace events including but not limited to the count of IRAT redirection events per bin, the count of abnormal call releases (e.g., on the LTE network) per bin, and geolocated UE reported Radio Resource Control (RRC) Measurement Reports (MRs) which will be used later to form an interaction table. The geolocated MRs (RSRP, RSRQ, PCI triplet(s) as reported from UE with RRC signaling) contains UE received signal levels from each measured EUTRAN cell such as Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ)).
Regarding Claim 3, Sofuoglu further teaches the method of claim 1 further comprising: receiving the telecommunication service data within the defined geographic area(Sofuoglu, Summary, par. 7; method further comprises dividing each worst-performing multi-RAT sector into a plurality of geographical bins.); identifying a plurality of new site deployment locations within the defined geographic area, each of the plurality of new site deployment locations comprising a buffer area; identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area data for each of the plurality of new site deployment locations (Sofuoglu, Detailed description, figs. 3, 7 and 9, par 40; In step 313, weak coverage symptom events are identified and geolocated. As discussed above, weak coverage symptom events can include UE Context IRAT relocation/redirection events (e.g., when a UE is redirected from LTE to UMTS due to UE received signal strength being below a set threshold) and abnormal call releases (e.g., due to dropped calls as a result of poor RSRP/RSRQ) as indicated in cell MDT (minimization of drive tests)/trace data. Each weak coverage symptom event can be correlated with a geographical location of the UE (e.g., based on GPS data from the UE or by another geolocation mechanism). For example, the MDT standard includes an option for Global Navigation Satellite Systems (GNSS)-based location reporting from UEs. The availability of GNSS location information is subject to UE capability and/or UE implementation. Additionally, any CCO solution requiring location information will result in additional power consumption of the UE due to the need to run its positioning components. Alternatively, an inherent geolocation mechanism can be provided by correlating site/cell/antenna layout/configuration with collected MDT/trace data that does not include location information. In either case, geographical bins (e.g., 50 m×50 m) in the subset of the target area can be produced that will contain a mapping of geolocated MDT (minimization of drive tests)/trace events including but not limited to the count of IRAT events per bin, the count of abnormal call releases per bin, and geolocated MRs (e.g., as discussed above)); and filtering the first plurality of data sets from the telecommunication service data locations (Sofuoglu, Detailed description, fig. 9 par. 50; Combinations of any of the foregoing can be filtered/removed from the list of cells designated for configuration parameter updates. In some embodiments, the filtering (step 332) can occur before the new configuration parameters are generated (step 331)). Examiner notes, the use of a buffer area, as described by the spec “predefined radius” identified for new site deployments is an obvious variation of Sofouglu’s method to identify locations with weak coverage.
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Regarding claim 4, Sofuoglu further teaches the method of claim 1 further comprises: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location (Sofuoglu, Detailed description, par 29; A coverage shrink action can include an electrical tilt increase (e.g., downtilt) of the antenna for the LTE cell and/or cell power decrease (e.g., reference power and/or power boost reduction) of the LTE cell. A coverage extension action can include an electrical tilt decrease (e.g., uptilt) of the antenna for the LTE cell and/or cell power increase (e.g., reference power and/or power boost increase) of the LTE cell. The degree of change per iteration can be configurable by the operator as a step size parameter (e.g., +/−2 degree etilt, 1 dB RS power, power boost index change by 1) together with minimum and/or maximum boundary settings (e.g., maximum electrical tilt supported by an antenna may differ and can be stored in site/cell/antenna database, minimum and maximum RS power setting allowed per cell)).
Regarding claim 5, Ahmed et al further teaches determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1 GHz. (Ahmed et al., Detailed Description, par. 11; the machine learned model (also referred to as “the model”) receives data representing user and network attributes in different geographic regions, and outputs clusters of cells that can be further processed in various ways to improve throughput or overall quality of networks in the different geographical regions. The machine learned model can cluster cells based on network configuration data (e.g., bandwidth, security, antennae height, or antennae type, etc.), network user data (e.g., type of user session: FTP, call, etc.), location data (e.g., position of each user relative to a network antennae, location of the network relative to a coordinate system, etc.), and/or geographical data (e.g., elevation of a user equipment (UE) relative to network antennae, map data showing one or more of: a road, a building, a highway, urban area, residential area, etc.)). Examiner notes, it is common knowledge for a person of ordinary skill in the art that telecommunication technologies such as 2G, 3G, 4G and 5G commonly operate below 1 GHz particularly in low coverage areas.
Regarding Claim 6, Ahmed et al. further teaches, determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster (Ahmed et al., Detailed Description, par 18; The clustering techniques may also or instead be used to determine performance benchmarks for cells or networks across different geographical regions. For example, by identifying how similarly configured networks perform in different areas (e.g., independent of seasonality, land use, etc.), a network in a first location can be used to set more realistic and achievable benchmarks for performance of the network in the first location or a second location).
Regarding Claim 7, Sofuoglu further teaches determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network (Sofuoglu, fig. 9, par 50; some cells can be removed from the list of cells designated for configuration parameter updates due to temporary cell availability problems (e.g. backhaul related problems) during the observation period). Examiner notes, taking proximity to a fiber optic network (backhaul) into account is standard practice when deploying new cell sites, therefore a person of ordinary skill in the art would have been able to use an algorithm to filter possible new cell sites based on a stablished distance from a fiber tower.
Regarding Claim 8, Ahmed et al. discloses, One or more non-transitory computer-readable media storing instructions that when executed via one or more processors performs a computerized method, the media comprising: via one or more processors, without user intervention (Ahmed et al. fig. 7, par. 64; The memory 702 can further include non-transitory computer-readable media, such as volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data): receiving telecommunication service data a defined geographic area; generating a first plurality of clusters within a defined geographic region using one or more metrics obtained from telecommunications service data(Ahmed et al. Detailed Description, par. 10; the machine learned model (also referred to as “the model”) receives data representing user and network attributes in different geographic regions, and outputs clusters of cells that can be further processed in various ways to improve throughput or overall quality of networks in the different geographical regions); utilizing a hierarchical clustering algorithm and a first linkage distance, generating a second plurality of clusters from the first plurality of clusters; utilizing the hierarchical clustering algorithm and a second linkage distance, generating a third plurality of clusters from the second plurality of clusters (Ahmed et al, Detailed Description, par. 14; a computing device can implement a clustering component to receive input data representing one or more of: network configuration data, network user data, location data and/or map data. The clustering component can implement one or more clustering techniques (e.g., k-means, k-medians, k-prototype, expectation maximization (EM), hierarchical clustering, etc.) to cluster the input data); for each cluster in the third plurality of clusters, determining a solution type (Ahmed et al., Detailed Description, par. 12; The model may also or instead generate network recommendations that optimize network performance (e.g., increases the Quality of Signal versus not implementing the model)).and a specific location within the cluster for deployment of the solution type (Ahmed et al. Detailed Description, par. 9; Clusters output from the machine learned model can be compared to each other to identify an underperforming cell, to generate recommendations that improve performance of a cell, and/or to determine performance benchmarks for cells or networks across different geographical regions).
Ahmed et al. does not teach ranking of each cluster in the third plurality of clusters relative to one another, however, Sofuoglu discloses a method to calculate coverage degree per LTE network bins. Making a comparison of the unified weak coverage indication cost metric that can be used to rank the bins from worst to best coverage degree (Sofuoglu, figs. 3 and 8, par. 41).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to combine the system of Ahmed et al. to integrate a machine learning algorithm that can perform cluster analysis based on hierarchy or other means to provide solution recommendations with Sofuoglu’s method to rank and identify clusters of weak LTE coverage using different key performance indicators such as linkage distances to improve network deployment accuracy in areas with weak network coverage.
Regarding Claim 9. Ahmed et al. further teaches generating and causing display of a graphical user interface, the graphical user interface displaying the third plurality of clusters within the defined geographic region and the specific location within each of the third plurality of clusters for deployment of the solution type determined (Ahmed et al., Detailed Description, par. 42; the computing device 106 can store the output data 308 (along with associated metadata describing the cluster) in a database or other memory which enables cluster data determined over time to be searched and/or accessed based on user-specified network characteristics, network parameters, and the like (e.g., such as via a user interface on a display device), each of the third plurality of clusters being visually coded to distinguish each cluster from one another, and the specific location for each of the third plurality of clusters being visually coded to indicate a particular solution type for deployment (Ahmed et al. figs 3-4, par. 37; the first cluster 302 and the second cluster 304 are associated with map data indicating a street, highway, and other map features. In some examples, the clustering component 128 can determine a cluster type for the first cluster 302 and the second cluster 304 based at least in part on the map data. Example cluster types can be based at least in part on geographical information system (GIS) data and indicate at least one of: dense urban, urban, suburban, rural, barren, highway, forest, roadway, park, educational campus, airport, or commercial area, though other types are contemplated). It is common practice to display (in a screen or other media) the cluster results in a map (GUI) as observed in figures 3 and 4. Therefore it would have been reasonable for a person of ordinary skill in the art before the effective filing to use the combine the teachings of Ahmed et al. to generate network recommendations based on the clusters output with Sofuoglu’s method to determine week network coverage spots to be displayed on a computers screen (GUI), to facilitate access from the user.
Regarding Claim 10, Sofuoglu further teaches receiving the telecommunication service data within the defined geographic area; identifying a plurality of new site deployment locations within the defined geographic area, each of the plurality of new site deployment locations comprising a buffer area (Sofuoglu, Summary, par. 7 method further comprises dividing each worst-performing multi-RAT sector into a plurality of geographical bins.); identifying a first plurality of data sets in the telecommunication service data that correspond to the buffer area for each of the plurality of new site deployment locations (Sofuoglu, Detailed description, figs. 3, 7 and 9, par 40; In step 313, weak coverage symptom events are identified and geolocated. As discussed above, weak coverage symptom events can include UE Context IRAT relocation/redirection events (e.g., when a UE is redirected from LTE to UMTS due to UE received signal strength being below a set threshold) and abnormal call releases (e.g., due to dropped calls as a result of poor RSRP/RSRQ) as indicated in cell MDT (minimization of drive tests)/trace data. Each weak coverage symptom event can be correlated with a geographical location of the UE (e.g., based on GPS data from the UE or by another geolocation mechanism). For example, the MDT standard includes an option for Global Navigation Satellite Systems (GNSS)-based location reporting from UEs. The availability of GNSS location information is subject to UE capability and/or UE implementation. Additionally, any CCO solution requiring location information will result in additional power consumption of the UE due to the need to run its positioning components. Alternatively, an inherent geolocation mechanism can be provided by correlating site/cell/antenna layout/configuration with collected MDT/trace data that does not include location information. In either case, geographical bins (e.g., 50 m×50 m) in the subset of the target area can be produced that will contain a mapping of geolocated MDT (minimization of drive tests)/trace events including but not limited to the count of IRAT events per bin, the count of abnormal call releases per bin, and geolocated MRs (e.g., as discussed above)); and filtering the first plurality of data sets from the telecommunication service data(Sofuoglu, Detailed description, fig. 9 par. 50; Combinations of any of the foregoing can be filtered/removed from the list of cells designated for configuration parameter updates. In some embodiments, the filtering (step 332) can occur before the new configuration parameters are generated (step 331)). Examiner notes, the use of a buffer area, as described by the spec “predefined radius” identified for new site deployments is an obvious variation of Sofouglu’s method to identify locations with weak coverage.
Regarding claim 11, Sofuoglu further teaches determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location (Sofuoglu, Detailed description, par 29; A coverage shrink action can include an electrical tilt increase (e.g., downtilt) of the antenna for the LTE cell and/or cell power decrease (e.g., reference power and/or power boost reduction) of the LTE cell. A coverage extension action can include an electrical tilt decrease (e.g., uptilt) of the antenna for the LTE cell and/or cell power increase (e.g., reference power and/or power boost increase) of the LTE cell. The degree of change per iteration can be configurable by the operator as a step size parameter (e.g., +/−2 degree etilt, 1 dB RS power, power boost index change by 1) together with minimum and/or maximum boundary settings (e.g., maximum electrical tilt supported by an antenna may differ and can be stored in site/cell/antenna database, minimum and maximum RS power setting allowed per cell)).
Regarding claim 12, Sofuoglu further teaches determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location (Sofuoglu, Detailed description, par 29; A coverage shrink action can include an electrical tilt increase (e.g., downtilt) of the antenna for the LTE cell and/or cell power decrease (e.g., reference power and/or power boost reduction) of the LTE cell. A coverage extension action can include an electrical tilt decrease (e.g., uptilt) of the antenna for the LTE cell and/or cell power increase (e.g., reference power and/or power boost increase) of the LTE cell. The degree of change per iteration can be configurable by the operator as a step size parameter (e.g., +/−2 degree etilt, 1 dB RS power, power boost index change by 1) together with minimum and/or maximum boundary settings (e.g., maximum electrical tilt supported by an antenna may differ and can be stored in site/cell/antenna database, minimum and maximum RS power setting allowed per cell)).
Regarding claim 13, Ahmed et al further teaches determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1 GHz. (Ahmed et al., Detailed Description, par. 11; the machine learned model (also referred to as “the model”) receives data representing user and network attributes in different geographic regions, and outputs clusters of cells that can be further processed in various ways to improve throughput or overall quality of networks in the different geographical regions. The machine learned model can cluster cells based on network configuration data (e.g., bandwidth, security, antennae height, or antennae type, etc.), network user data (e.g., type of user session: FTP, call, etc.), location data (e.g., position of each user relative to a network antennae, location of the network relative to a coordinate system, etc.), and/or geographical data (e.g., elevation of a user equipment (UE) relative to network antennae, map data showing one or more of: a road, a building, a highway, urban area, residential area, etc.)). Examiner notes, it is common knowledge for a person of ordinary skill in the art that telecommunication technologies such as 2G, 3G, 4G and 5G commonly operate below 1 GHz particularly in low coverage areas.
Regarding claim 14, Ahmed et al. further teaches, determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster (Ahmed et al., Detailed Description, par 18; The clustering techniques may also or instead be used to determine performance benchmarks for cells or networks across different geographical regions. For example, by identifying how similarly configured networks perform in different areas (e.g., independent of seasonality, land use, etc.), a network in a first location can be used to set more realistic and achievable benchmarks for performance of the network in the first location or a second location).
Regarding claim 15, Sofuoglu further teaches determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network (Sofuoglu, fig. 9, par 50; some cells can be removed from the list of cells designated for configuration parameter updates due to temporary cell availability problems (e.g. backhaul related problems) during the observation period). Examiner notes, taking proximity to a fiber optic network (backhaul) into account is standard practice when deploying new cell sites, therefore a person of ordinary skill in the art would have been able to use an algorithm to filter possible new cell sites based on a stablished distance from a fiber tower.
Regarding claim 16, Ahmed et al. discloses a system comprising: a database storing telecommunications service data for a defined geographic area; a telecommunication network communicatively coupled to the database; and one or more processors communicatively coupled to the telecommunication network (Ahmed et al., Detailed Description, par. 41; the computing device 106 can store the output data 308 (along with associated metadata describing the cluster) in a database or other memory which enables cluster data determined over time to be searched and/or accessed based on user-specified network characteristics, network parameters, and the like (e.g., such as via a user interface on a display device)), the one or more processors configured to: generating a first plurality of clusters (Ahmed et al., Detailed Description, par 41; can implement a first clustering algorithm to generate an initial cluster(s) based on the input data 306) within the defined geographic region using one or more metrics obtained from the telecommunications service data (Ahmed et al., Detailed Description, par. 10; receives data representing user and network attributes in different geographic regions, and outputs clusters of cells that can be further processed in various ways to improve throughput or overall quality of networks in the different geographical regions.); utilizing a hierarchical clustering algorithm and a first linkage distance, generating a second plurality of clusters from the first plurality of clusters; utilizing the hierarchical clustering algorithm and a second linkage distance, generating a third plurality of clusters from the second plurality of clusters (Ahmed et al., Detailed Description, par. 14; The clustering component can implement one or more clustering techniques (e.g., k-means, k-medians, k-prototype, expectation maximization (EM), hierarchical clustering, etc.) to cluster the input data); for each cluster in the third plurality of clusters, determining a solution type and a specific location within the cluster for deployment of the solution type(Ahmed et al. Detailed Description, par. 9; Clusters output from the machine learned model can be compared to each other to identify an underperforming cell, to generate recommendations that improve performance of a cell, and/or to determine performance benchmarks for cells or networks across different geographical regions).
Ahmed et al. does not teach ranking of each cluster in the third plurality of clusters relative to one another, however, Sofuoglu discloses a method to calculate coverage degree per LTE network bins. Making a comparison of the unified weak coverage indication cost metric that can be used to rank the bins from worst to best coverage degree (Sofuoglu, figs. 3 and 8, par. 41).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to combine the system of Ahmed et al. to integrate a machine learning algorithm that can perform cluster analysis based on hierarchy or other means to provide solution recommendations with Sofuoglu’s method to rank and identify clusters of weak LTE coverage using different key performance indicators such as linkage distances to improve network deployment accuracy in areas with weak network coverage.
Regarding claim 17, Sofuoglu further teaches the method of claim 1 further comprises: determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by modifying antenna tilt at the active deployment location (Sofuoglu, Detailed description, par 29; A coverage shrink action can include an electrical tilt increase (e.g., downtilt) of the antenna for the LTE cell and/or cell power decrease (e.g., reference power and/or power boost reduction) of the LTE cell. A coverage extension action can include an electrical tilt decrease (e.g., uptilt) of the antenna for the LTE cell and/or cell power increase (e.g., reference power and/or power boost increase) of the LTE cell. The degree of change per iteration can be configurable by the operator as a step size parameter (e.g., +/−2 degree etilt, 1 dB RS power, power boost index change by 1) together with minimum and/or maximum boundary settings (e.g., maximum electrical tilt supported by an antenna may differ and can be stored in site/cell/antenna database, minimum and maximum RS power setting allowed per cell)).
Regarding claim 18, Ahmed et al further teaches determining whether an active site can provide service coverage to an area within the defined geographic area corresponding to the cluster by utilizing a low band frequency at or below 1 GHz. (Ahmed et al., Detailed Description, par. 11; the machine learned model (also referred to as “the model”) receives data representing user and network attributes in different geographic regions, and outputs clusters of cells that can be further processed in various ways to improve throughput or overall quality of networks in the different geographical regions. The machine learned model can cluster cells based on network configuration data (e.g., bandwidth, security, antennae height, or antennae type, etc.), network user data (e.g., type of user session: FTP, call, etc.), location data (e.g., position of each user relative to a network antennae, location of the network relative to a coordinate system, etc.), and/or geographical data (e.g., elevation of a user equipment (UE) relative to network antennae, map data showing one or more of: a road, a building, a highway, urban area, residential area, etc.)). Examiner notes, it is common knowledge for a person of ordinary skill in the art that telecommunication technologies such as 2G, 3G, 4G and 5G commonly operate below 1 GHz particularly in low coverage areas.
Regarding claim 19, Ahmed et al. further teaches, determining whether an active lease-agreement site can provide service coverage to an area within the defined geographic area corresponding to the cluster (Ahmed et al., Detailed Description, par 18; The clustering techniques may also or instead be used to determine performance benchmarks for cells or networks across different geographical regions. For example, by identifying how similarly configured networks perform in different areas (e.g., independent of seasonality, land use, etc.), a network in a first location can be used to set more realistic and achievable benchmarks for performance of the network in the first location or a second location).
Regarding claim 20, Sofuoglu further teaches determining whether deployment of a new small cell can provide service coverage to an area within the defined geographic area corresponding to the cluster based on a predefined proximity threshold of the cluster relative to a fiber-optic network (Sofuoglu, fig. 9, par 50; some cells can be removed from the list of cells designated for configuration parameter updates due to temporary cell availability problems (e.g. backhaul related problems) during the observation period). Examiner notes, taking proximity to a fiber optic network (backhaul) into account is standard practice when deploying new cell sites, therefore a person of ordinary skill in the art would have been able to use an algorithm to filter possible new cell sites based on a stablished distance from a fiber tower.
It is noted that any citations to specific pages, columns, lines or figures in the prior art references and any interpretation of the reference should not be considered limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to on having ordinary skill in the art. See MPEP 2123.
Other Reference of Importance Not Relied for this Application
The following prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure:
Tapia (US-10440503-B2), Machine learning-based geolocation and hotspot area identification, 2019-10-08.
Gupta (US-11729631-B2), System and method of automatic outdoor small cell planning, 2023-08-15.
Chandrasekaran (US-12089069-B1) Coverage improvement for 5G new radio wireless communication network to automatically adjust cell properties to improve coverage and capacity, 2024-09-10.
Conclusion
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/MRCM/ Examiner, Art Unit 2649
/YUWEN PAN/ Supervisory Patent Examiner, Art Unit 2649