DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on October 17, 2025, for the application with serial number 18/533,051.
Claims 1, 11, and 20 are amended.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement filed on October 17, 2025, has been considered.
Interview
The Examiner acknowledges the interview conducted on September 23, 2025, in which proposed amendments were discussed.
Response to Remarks/Amendments
35 USC §101 Rejections
Amendments to the claims changed the scope of the claims, necessitating an updated subject matter eligibility analysis. The updated analysis is set forth in the rejection, below. Contrary to the Applicant’s assertions, determining where to build or install network infrastructures is not a practical application of an abstract idea. A determination of where to build said infrastructure is a business decision that does not amount to significantly more than an abstract idea. The decision is not an improvement to a technology or technical field; at least because, as recited in the rejection below, the decision is a business decision intended to optimize profit and prevent customer churn. No apparent improvement to networking hardware is recited in the claims. The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Abed and Lall references, which are cited in the prior art rejection of the independent claims, below. The Applicant’s arguments are moot in light of the newly cited references.
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.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 1-20 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: presenting a geolocation as a candidate for building or installing wireless network infrastructure (as evidenced by exemplary independent claim 1: “presenting the at least one geolocation as a candidate location for building or installing wireless network infrastructure”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “identifying one or more features;” “predicting, based on the one or more features . . . , one or more parameters;” “determining a score;” “identifying multiple locations with particular data usage patterns;” “identifying from the multiple geolocations, at least one geolocation with a data usage level that satisfies a first threshold condition;” “aggregating respective scores;” “determining whether the aggregated score satisfies a second threshold condition;” and “presenting the at least one geolocation as a candidate location for building or installing wireless network infrastructure.” The steps are all steps for managing personal behavior related to the abstract idea of presenting a geolocation as a candidate for building or installing wireless network infrastructure that, when considered alone and in combination, are part of the abstract idea of presenting a geolocation as a candidate for building or installing wireless network infrastructure. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of presenting a geolocation as a candidate for building or installing wireless network infrastructure. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes determining locations for installing network infrastructure to prevent customer churn and maintain an optimal level of subscribers.
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (no hardware is recited in independent claim 1; a computing system with a memory and processors in independent claim 11; and machine-readable storage devices in independent claim 20). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of machine learning models, but the abstract idea of presenting a geolocation as a candidate for building or installing wireless network infrastructure is generally linked to a machine learning algorithm for implementation. Therefore, the machine learning merely amounts to a technological environment that does not provide a practical application or significantly more than the recited abstract idea. See MPEP §2106.05(h). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 11-13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 6917816 B2 to Abed et al. (hereinafter ‘ABED’) in view of US 8355945 B1 to Lall et al. (hereinafter ‘LALL’).
Claim 1 (Currently Amended)
ABED discloses a method comprising: for each wireless subscriber in a plurality of wireless subscribers (see abstract; Wired and wireless access technologies can be modeled and evaluated. In an exemplary implementation for modeling and evaluating fixed wireless access networks, the network planning tool comprises a market scenario planner, a cluster analysis tool, a hub sector planner and a network architecture planner.
ABED does not specifically disclose, but LALL discloses, identifying one or more features corresponding to the wireless subscriber associated with a wireless service provider (see abstract; identify subscribers that have churned. Various sector profile data is captured for sectors serving the churners),
predicting, based on the one or more features corresponding to the wireless subscriber and using one or more machine learning models, one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider (see col 2, ln 11-21 and col 2, ln 59-col 3, ln 2; customer profile data could be used to attempt to predict sectors that may produce churners. According to an embodiment of the invention, a neural network could be used to determine the probabilities that a sector would be among a top threshold percentile of churning sectors, where a churning sector is a sector that produces churners), and
ABED further discloses determining a score that is indicative of an expected profitability associated with the wireless subscriber based on the one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider (see abstract and col 5, ln 21-56; a cluster analysis tool for potential hub placement. Include a record for each end-user building in the market and, for each building, indicate characteristics such as: building address, building size, latitude, longitude, tenant SIC codes, tenant revenue, number of tenant employees, building type (e.g., commercial, multi-tenant, warehouse storage, educational or government), and the initial demand estimates for various telecommunication services. The market scenario planner 210 allows the user to optionally filter the original set of buildings from the market-specific data 220 based on certain parameters, such as building type, number of tenants or employees, minimum demand levels, or minimum projected revenues, to generate a list of target customers 230);
identifying multiple geolocations with particular data usage patterns based on the respective one or more features corresponding to the plurality of wireless subscribers (see col 3, ln 38-51 and col 6, ln 9-25; the hub sector planner analyzes the hub assignments generated by the cluster analysis tool (i.e., the hub locations, covered buildings and their access type) and allocates each building in a given hub to a particular sector. Evaluate the possible hub locations by evaluating, e.g., minimum demand thresholds, bandwidth capacity of the selected vendor and an appropriate rain radius for the area for the different technologies);
identifying from the multiple geolocations, at least one geolocation with a data usage level that satisfies a first threshold condition by at least a subset of the wireless subscribers (see col 3, ln 28-38; the serving radius of the selected radio technology and vendor provide a collection of hub sites that cover the maximum amount of demand subject to user-configurable upper and lower bandwidth thresholds. See also col 6, ln 9-24; evaluate minimum demand thresholds for hub locations);
aggregating respective scores determined for wireless subscribers in the subset of the wireless subscribers (see col 9, ln 53-ln 67; once the expense and revenue information is available, the "Financial" capability of the network architecture planner 250 further allows the service provider to evaluate the scenario with known business measures. These include: Cash Flow, Balance Sheet, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), and Net Income. In this manner, the network planner can evaluate a specific network configuration scenario. See also claim 1; a list of target customers and corresponding revenue forecast);
determining whether the aggregated score satisfies a second threshold condition; and responsive to determining that the aggregated score satisfies the second threshold condition (see col 5,ln 43-56; the market scenario planner 210 allows the user to optionally filter the original set of buildings from the market-specific data 220 based on certain parameters, such as building type, number of tenants or employees, minimum demand levels, or minimum projected revenues, to generate a list of target customers 230),
presenting the at least one geolocation as a candidate location for building or installing wireless network infrastructure to improve network performance and/or capacity in the at least one geolocation (see col 5, ln 57-col 6, ln 8; the cluster analysis tool 300 processes the target customer list 230 from the market scenario planner 210 and determines an optimal location for the hub sites, their associated customer buildings and the detailed access method per building).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
Claim 2 (Original)
The combination of ABED and LALL discloses the method as set forth in claim 1.
ABED does not specifically disclose, but LALL discloses, wherein the one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider comprise:(i) at least one future payment from the wireless subscriber,
ii) at least one future cost associated with providing services to the wireless subscriber, and
(iii) at least one future churn probability associated with the wireless subscriber (see col 2, ln 59-61; determine a probability that a particular sector is likely to produce churners).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
Claim 3 (Original)
The combination of ABED and LALL discloses the method as set forth in claim 1.
ABED does not specifically disclose, but LALL discloses, wherein the one or more features corresponding to the wireless subscriber comprise historical payments made by the wireless subscriber, historical data usage (see col 2, ln 37-42; time-based data and per-call data can be referred to generally as network usage data), historical costs associated with providing services to the wireless subscriber, a type of device of the wireless subscriber, a type of data plan associated with the wireless subscriber, a longevity of a business relationship with the wireless subscriber, and/or demographic features of the wireless subscriber.
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
Claim 11 (Currently Amended)
ABED discloses a computing system comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operations (see col 5, ln 5-20; the network planning tool 200 includes a processor 205 and a data storage device 208. The processor implements the method) comprising: for each wireless subscriber in a plurality of wireless subscribers (see abstract; Wired and wireless access technologies can be modeled and evaluated. In an exemplary implementation for modeling and evaluating fixed wireless access networks, the network planning tool comprises a market scenario planner, a cluster analysis tool, a hub sector planner and a network architecture planner.
ABED does not specifically disclose, but LALL discloses, identifying one or more features corresponding to the wireless subscriber associated with a wireless service provider (see abstract; identify subscribers that have churned. Various sector profile data is captured for sectors serving the churners),
predicting, based on the one or more features corresponding to the wireless subscriber and using one or more machine learning models, one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider (see col 2, ln 11-21 and col 2, ln 59-col 3, ln 2; customer profile data could be used to attempt to predict sectors that may produce churners. According to an embodiment of the invention, a neural network could be used to determine the probabilities that a sector would be among a top threshold percentile of churning sectors, where a churning sector is a sector that produces churners).
ABED further discloses determining a score that is indicative of an expected profitability associated with the wireless subscriber based on the one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider (see abstract and col 5, ln 21-56; a cluster analysis tool for potential hub placement. Include a record for each end-user building in the market and, for each building, indicate characteristics such as: building address, building size, latitude, longitude, tenant SIC codes, tenant revenue, number of tenant employees, building type (e.g., commercial, multi-tenant, warehouse storage, educational or government), and the initial demand estimates for various telecommunication services. The market scenario planner 210 allows the user to optionally filter the original set of buildings from the market-specific data 220 based on certain parameters, such as building type, number of tenants or employees, minimum demand levels, or minimum projected revenues, to generate a list of target customers 230);
identifying multiple geolocations with particular data usage patterns based on the respective one or more features corresponding to the plurality of wireless subscribers (see col 3, ln 38-51 and col 6, ln 9-25; the hub sector planner analyzes the hub assignments generated by the cluster analysis tool (i.e., the hub locations, covered buildings and their access type) and allocates each building in a given hub to a particular sector. Evaluate the possible hub locations by evaluating, e.g., minimum demand thresholds, bandwidth capacity of the selected vendor and an appropriate rain radius for the area for the different technologies);
identifying from the multiple geolocations, at least one geolocation with a data usage level that satisfies a first threshold condition by at least a subset of the wireless subscribers (see col 3, ln 28-38; the serving radius of the selected radio technology and vendor provide a collection of hub sites that cover the maximum amount of demand subject to user-configurable upper and lower bandwidth thresholds. See also col 6, ln 9-24; evaluate minimum demand thresholds for hub locations);
aggregating respective scores determined for wireless subscribers in the subset of the wireless subscribers (see col 9, ln 53-ln 67; once the expense and revenue information is available, the "Financial" capability of the network architecture planner 250 further allows the service provider to evaluate the scenario with known business measures. These include: Cash Flow, Balance Sheet, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), and Net Income. In this manner, the network planner can evaluate a specific network configuration scenario. See also claim 1; a list of target customers and corresponding revenue forecast);
determining whether the aggregated score satisfies a second threshold condition; and responsive to determining that the aggregated score satisfies the second threshold condition (see col 5,ln 43-56; the market scenario planner 210 allows the user to optionally filter the original set of buildings from the market-specific data 220 based on certain parameters, such as building type, number of tenants or employees, minimum demand levels, or minimum projected revenues, to generate a list of target customers 230),
presenting the at least one geolocation as a candidate location for building or installing wireless network infrastructure to improve network performance and/or capacity in the at least one geolocation (see col 5, ln 57-col 6, ln 8; the cluster analysis tool 300 processes the target customer list 230 from the market scenario planner 210 and determines an optimal location for the hub sites, their associated customer buildings and the detailed access method per building).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
Claim 12 (Original)
The combination of ABED and LALL discloses the computing system as set forth in claim 11.
ABED does not specifically disclose, but LALL discloses, wherein the one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider comprise:(i) at least one future payment from the wireless subscriber,
(ii) at least one future cost associated with providing services to the wireless subscriber, and
(iii) at least one future churn probability associated with the wireless subscriber (see col 2, ln 59-61; determine a probability that a particular sector is likely to produce churners).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
Claim 13 (Original)
The combination of ABED and LALL discloses the computing system as set forth in claim 11.
ABED does not specifically disclose, but LALL discloses, wherein the one or more features corresponding to the wireless subscriber comprise historical payments made by the wireless subscriber, historical data usage (see col 2, ln 37-42; time-based data and per-call data can be referred to generally as network usage data), historical costs associated with providing services to the wireless subscriber, a type of device of the wireless subscriber, a type of data plan associated with the wireless subscriber, a longevity of a business relationship with the wireless subscriber, and/or demographic features of the wireless subscriber ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
Claim 20 (Currently Amended)
ABED discloses one or more machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processing devices to perform operations (see col 5, ln 5-20; the network planning tool 200 includes a processor 205 and a data storage device 208. The processor implements the method) comprising: for each wireless subscriber in a plurality of wireless subscribers (see abstract; Wired and wireless access technologies can be modeled and evaluated. In an exemplary implementation for modeling and evaluating fixed wireless access networks, the network planning tool comprises a market scenario planner, a cluster analysis tool, a hub sector planner and a network architecture planner.
ABED does not specifically disclose, but LALL discloses, identifying one or more features corresponding to the wireless subscriber associated with a wireless service provider (see abstract; identify subscribers that have churned. Various sector profile data is captured for sectors serving the churners),
predicting, based on the one or more features corresponding to the wireless subscriber and using one or more machine learning models, one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider (see col 2, ln 11-21 and col 2, ln 59-col 3, ln 2; customer profile data could be used to attempt to predict sectors that may produce churners. According to an embodiment of the invention, a neural network could be used to determine the probabilities that a sector would be among a top threshold percentile of churning sectors, where a churning sector is a sector that produces churners).
ABED further discloses determining a score that is indicative of an expected profitability associated with the wireless subscriber based on the one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider (see abstract and col 5, ln 21-56; a cluster analysis tool for potential hub placement. Include a record for each end-user building in the market and, for each building, indicate characteristics such as: building address, building size, latitude, longitude, tenant SIC codes, tenant revenue, number of tenant employees, building type (e.g., commercial, multi-tenant, warehouse storage, educational or government), and the initial demand estimates for various telecommunication services. The market scenario planner 210 allows the user to optionally filter the original set of buildings from the market-specific data 220 based on certain parameters, such as building type, number of tenants or employees, minimum demand levels, or minimum projected revenues, to generate a list of target customers 230);
identifying multiple geolocations with particular data usage patterns based on the respective one or more features corresponding to the plurality of wireless subscribers (see col 3, ln 38-51 and col 6, ln 9-25; the hub sector planner analyzes the hub assignments generated by the cluster analysis tool (i.e., the hub locations, covered buildings and their access type) and allocates each building in a given hub to a particular sector. Evaluate the possible hub locations by evaluating, e.g., minimum demand thresholds, bandwidth capacity of the selected vendor and an appropriate rain radius for the area for the different technologies);
identifying from the multiple geolocations, at least one geolocation with a data usage level that satisfies a first threshold condition by at least a subset of the wireless subscribers (see col 3, ln 28-38; the serving radius of the selected radio technology and vendor provide a collection of hub sites that cover the maximum amount of demand subject to user-configurable upper and lower bandwidth thresholds. See also col 6, ln 9-24; evaluate minimum demand thresholds for hub locations);
aggregating respective scores determined for wireless subscribers in the subset of the wireless subscribers (see col 9, ln 53-ln 67; once the expense and revenue information is available, the "Financial" capability of the network architecture planner 250 further allows the service provider to evaluate the scenario with known business measures. These include: Cash Flow, Balance Sheet, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), and Net Income. In this manner, the network planner can evaluate a specific network configuration scenario. See also claim 1; a list of target customers and corresponding revenue forecast);
determining whether the aggregated score satisfies a second threshold condition; and responsive to determining that the aggregated score satisfies the second threshold condition (see col 5,ln 43-56; the market scenario planner 210 allows the user to optionally filter the original set of buildings from the market-specific data 220 based on certain parameters, such as building type, number of tenants or employees, minimum demand levels, or minimum projected revenues, to generate a list of target customers 230),
presenting the at least one geolocation as a candidate location for building or installing wireless network infrastructure to improve network performance and/or capacity in the at least one geolocation (see col 5, ln 57-col 6, ln 8; the cluster analysis tool 300 processes the target customer list 230 from the market scenario planner 210 and determines an optimal location for the hub sites, their associated customer buildings and the detailed access method per building).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
Claim(s) 4, 5, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 6917816 B2 to ABED et al. in view of US 8355945 B1 to LALL et al. as applied to claim 1 above, and further in view of US 20210350395 A1 to Avinash Dorle et al. (hereinafter ‘AVINASH DORLE’).
Claim 4 (Original)
The combination of ABED and LALL discloses the method as set forth in claim 1.
ABED further discloses comprising: classifying the wireless subscriber into one of a plurality of cohorts based on the one or more features corresponding to the wireless subscriber (see abstract; a cluster analysis tool).
The combination of ABED and LALL does not specifically disclose, but AVINASH DORLE discloses, selecting the one or more machine learning models for use based on the classification of the wireless subscriber (see ¶[0062]; the second artificial intelligence component may implement various supervised learning models for determination of the prospect churn value for each of the plurality of prospect clusters).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). AVINASH DORLE discloses intelligent prospect assessment that determines churn probability of subscribers (see ¶[0063], where various machine models are applied to clusters to predict churn. It would have been obvious to include the machine learning models as taught by AVINASH DORLE in the system executing the method of ABED with the motivation to identify clusters that are likely to churn.
Claim 5 (Original)
The combination of ABED and LALL discloses the method as set forth in claim 1.
The combination of ABED and LALL does not specifically disclose, but AVINASH DORLE discloses, wherein the one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider correspond to a length of time between 1 day and 48 months subsequent to the predicting (see ¶[0129]; days of contract lifecycle. See also ¶[0059]; years a prospect may have been operational)).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). AVINASH DORLE discloses intelligent prospect assessment that determines churn probability of subscribers (see ¶[0063], where various machine models are applied to clusters to predict churn. It would have been obvious to include the machine learning models as taught by AVINASH DORLE in the system executing the method of ABED with the motivation to identify clusters that are likely to churn.
Claim 14 (Original)
The combination of ABED and LALL discloses the computing system as set forth in claim 11.
ABED further discloses comprising: classifying the wireless subscriber into one of a plurality of cohorts based on the one or more features corresponding to the wireless subscriber (see abstract; a cluster analysis tool).
The combination of ABED and LALL does not specifically disclose, but AVINASH DORLE discloses, selecting the one or more machine learning models for use based on the classification of the wireless subscriber (see ¶[0062]; the second artificial intelligence component may implement various supervised learning models for determination of the prospect churn value for each of the plurality of prospect clusters).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). AVINASH DORLE discloses intelligent prospect assessment that determines churn probability of subscribers (see ¶[0063], where various machine models are applied to clusters to predict churn. It would have been obvious to include the machine learning models as taught by AVINASH DORLE in the system executing the method of ABED with the motivation to identify clusters that are likely to churn.
Claim 15 (Original)
The combination of ABED and LALL discloses the computing system as set forth in claim 11.
The combination of ABED and LALL does not specifically disclose, but AVINASH DORLE discloses, wherein the one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider correspond to a length of time between 1 day and 48 months subsequent to the predicting (see ¶[0129]; days of contract lifecycle. See also ¶[0059]; years a prospect may have been operational)).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). AVINASH DORLE discloses intelligent prospect assessment that determines churn probability of subscribers (see ¶[0063], where various machine models are applied to clusters to predict churn. It would have been obvious to include the machine learning models as taught by AVINASH DORLE in the system executing the method of ABED with the motivation to identify clusters that are likely to churn.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 6917816 B2 to ABED et al. in view of US 8355945 B1 to LALL et al. as applied to claim 1 above, and further in view of US 7526434 B2 to Sharp (hereinafter ‘SHARP’).
Claim 6 (Original)
The combination of ABED and LALL discloses the method as set forth in claim 1.
The combination of ABED and LALL does not specifically disclose, but SHARP discloses, wherein the score that is indicative of the expected profitability associated with the wireless subscriber corresponds to a length of time between 1 day and 48 months subsequent to determining the score (see col 11, ln 53-col 12, ln 4; Customer Lifetime Value profit reduces it to the individual customer to show the net present value of future profits to be received from the average customer gained in year one over a period of several years).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). SHARP discloses marketing management that includes customer lifetime value measured annually over a period of several years. It would have been obvious to include the lifetime value measurement over a period of years as taught by SHARP in the system executing the method of ABED with the motivation to assess profitability and lifetime value of customers.
Claim 16 (Original)
The combination of ABED and LALL discloses the computing system as set forth in claim 11.
The combination of ABED and LALL does not specifically disclose, but SHARP discloses, wherein the score that is indicative of the expected profitability associated with the wireless subscriber corresponds to a length of time between 1 day and 48 months subsequent to determining the score (see col 11, ln 53-col 12, ln 4; Customer Lifetime Value profit reduces it to the individual customer to show the net present value of future profits to be received from the average customer gained in year one over a period of several years).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). SHARP discloses marketing management that includes customer lifetime value measured annually over a period of several years. It would have been obvious to include the lifetime value measurement over a period of years as taught by SHARP in the system executing the method of ABED with the motivation to assess profitability and lifetime value of customers.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 6917816 B2 to ABED et al. in view of US 8355945 B1 to LALL et al. as applied to claim 1 above, and further in view of US 20230275973 A1 to Sakamoto et al. (hereinafter ‘SAKAMOTO’).
Claim 7 (Original)
The combination of ABED and LALL discloses the method as set forth in claim 1.
ABED does not specifically disclose, but LALL discloses, wherein the one or more machine learning models are trained using data about other wireless subscribers (see col 2, ln 11-21 and col 2, ln 59-col 3, ln 2; customer profile data could be used to attempt to predict sectors that may produce churners. According to an embodiment of the invention, a neural network could be used to determine the probabilities that a sector would be among a top threshold percentile of churning sectors, where a churning sector is a sector that produces churners),
The combination of ABED and LALL does not specifically disclose, but SAKAMOTO discloses, wherein the data comprises historical payments made by the other wireless subscribers (see ¶[0192]; the churn prediction component may include payment information).
ABED does not specifically disclose, but LALL discloses, historical data usage (see col 2, ln 37-42; time-based data and per-call data can be referred to generally as network usage data).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
The combination of ABED and LALL does not specifically disclose, but SAKAMOTO discloses,, historical costs associated with providing services to the other wireless subscribers (see ¶[0126]; operating costs), churn rates of the other wireless subscribers (see ¶[0223] and [0281]; observed churn rates) a type of device of the other wireless subscribers, a type of data plan associated with the other wireless subscribers (see ¶[0053]; type of service), a longevity of a business relationship with the other wireless subscribers (see ¶[0207]; length of subscription lifespan), and/or demographic features of the other wireless subscribers (see ¶[0200]; demographics).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). SAKAMOTO discloses churn prediction with a machine learning model that considers multiple training factors. It would have been obvious to include the trained machine learning model as taught by SAKAMOTO in the system executing the method of ABED with the motivation to determine target customers.
Claim 17 (Original)
The combination of ABED and LALL discloses the computing system as set forth in claim 11.
ABED does not specifically disclose, but LALL discloses, wherein the one or more machine learning models are trained using data about other wireless subscribers (see col 2, ln 11-21 and col 2, ln 59-col 3, ln 2; customer profile data could be used to attempt to predict sectors that may produce churners. According to an embodiment of the invention, a neural network could be used to determine the probabilities that a sector would be among a top threshold percentile of churning sectors, where a churning sector is a sector that produces churners),
The combination of ABED and LALL does not specifically disclose, but SAKAMOTO discloses, wherein the data comprises historical payments made by the other wireless subscribers (see ¶[0192]; the churn prediction component may include payment information).
ABED does not specifically disclose, but LALL discloses historical data usage (see col 2, ln 37-42; time-based data and per-call data can be referred to generally as network usage data).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). LALL discloses identifying and ranking churn sectors in a wireless network to determine sectors requiring additional resources (see abstract), where customer profile data is used in a neural network to model and predict customer patterns. It would have been obvious for one of ordinary skill in the art at the time of invention to include customer profile data in a neural network as taught by LALL in the system executing the method of ABED with the motivation to model customer clusters and determine appropriate location of network components to optimize revenue and income.
The combination of ABED and LALL does not specifically disclose, but SAKAMOTO discloses, historical costs associated with providing services to the other wireless subscribers (see ¶[0126]; operating costs), churn rates of the other wireless subscribers (see ¶[0223] and [0281]; observed churn rates) a type of device of the other wireless subscribers, a type of data plan associated with the other wireless subscribers (see ¶[0053]; type of service), a longevity of a business relationship with the other wireless subscribers (see ¶[0207]; length of subscription lifespan), and/or demographic features of the other wireless subscribers (see ¶[0200]; demographics).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). SAKAMOTO discloses churn prediction with a machine learning model that considers multiple training factors. It would have been obvious to include the trained machine learning model as taught by SAKAMOTO in the system executing the method of ABED with the motivation to determine target customers.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 6917816 B2 to ABED et al. in view of US 8355945 B1 to LALL et al. as applied to claim 1 above, and further in view of US 20200234365 A1 to Friedman et al. (hereinafter ‘FRIEDMAN’).
Claim 9 (Original)
The combination of ABED and LALL discloses the method as set forth in claim 1.
The combination of ABED and LALL does not specifically disclose, but FRIEDMAN discloses, further comprising determining a value at risk associated with the wireless subscriber, wherein determining the value at risk is based on the determined score and a predicted line churn probability associated with the wireless subscriber (see ¶[0032]; the term “customer-lifetime-value score” is an umbrella term comprising the “value-at-risk score”. Churn is a possible indicator of customer dissatisfaction, more attractive offers from competition, and also relates to the customer lifetime. Related, the “customer lifetime” refers to the time spent by customers in the customer base of the issuer. Hereby, the average customer lifetime may preferably be equal to the inverse of the churn probability, e.g. a churn probability of 0.2 implies an average customer lifetime of 5 years).
ABED discloses analyzing and designing various network configuration scenarios that includes modeling of network scenarios and clusters to target customers and determine the appropriate location of network components (see abstract and col 2, ln 11-27). FRIEDMAN discloses allocating discounts, where discounts may be given based on churn rate, which is based on customer lifetime, and customer lifetime value is synonymous with value at risk. It would have been obvious for one of ordinary skill in the art at the time of invention to include the value at risk as taught by FRIEDMAN in the system executing the method of ABED with the motivation to assess churn probability of customers and target customers.
Claim 19 (Original)
The combination of ABED and LALL discloses the computing system as set forth in claim 11.
The combination of ABED and LALL does not specifically disclose, but FRIEDMAN discloses, further comprising determining a value at risk associated with the wireless subscriber, wherein determining the value at risk is based on the determined score and a predicted line churn probability associated with the wireless subscriber (see ¶[0032]; the term “customer-lifetime-value score” is an umbrella term comprising the “value-at-risk score”. Churn is a possible indicator of customer dissatisfaction, more attractive offers from competition, and also relates to the customer lifetime. Related, the “customer lifetime” refers to the time spent by customers in the customer base of the issuer. Hereby, the average customer lifet