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 .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The Office Action fails to provide any statement linking the alleged abstract idea to the claims, instead offering nothing but blanket statements that the claims "cover" the abstract idea.,” Examiner respectfully disagrees. As can be seen below with respect to Step 2A, Prong 1, the claims recite several abstract limitations for consideration. These limitations do not recite any particular additional elements for consideration. These abstract limitations are related to generating an updated ATM distribution point, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors, as well as business relations. Therefore, Examiner respectfully disagrees with Applicant’s assertions and maintains that the claims recite abstract limitations.
Regarding Applicant’s assertion of “Ex Parte Desjardins at page 9. Here, even if portions of the claims could broadly read on aspects of a method of organizing human activity, the rejection in the Office Action only analyzes the claims at exactly that impermissible "high level of generality," for example alleging that the claims "implement an abstract idea using a computer in its ordinary capacity" on page 4 in a manner that is without any explanation. The claims are directed to a practical application that provides a technical solution to a technological problem.,” Examiner respectfully disagrees. The present claims do not provide an analogous improvement to the machine learning model (e.g. AI models). Examiner respectfully asserts that the claims are unlike the Des Jardins decision because the claims are directed to an abstract idea versus being directed to an improvement to computer functionality. The improvement of Des Jardins is related to catastrophic forgetting, whereas the instant claims merely use machine learning at a high level to perform an abstract idea. The present claims do not provide an analogous technical solution to that of Des Jardins because the claims do not “address challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” The machine learning model of the present claims is merely a tool to perform the abstract process of location determination for an ATM. An improvement to the identification of a service location would be an improvement to the abstract limitations for consideration under Step 2A, Prong 1 and not to the reinforcement learning model itself. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 103 rejection, Examiner has fully considered Applicant’s arguments and amendments. Regarding Applicant’s assertions in view of the prior art combination of the record, Examiner has introduced a new grounds of rejection. See the detailed rejection below.
Accordingly, the present claims are rejected under 35 USC 103.
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 1-21 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-10 are directed to a system, claims 11-20 are directed to a method, and claim 21 is directed to a machine-storage medium. Claim 21 is directed to a machine-storage medium. In at least [0190] of the instant specification, the medium is defined as “The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below.” Therefore, the computer readable medium is being interpreted in view of the definition as being a non-transitory computer readable medium. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 11, and 21 recite limitations related to generating an updated ATM distribution point, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors, as well as business relations. Claim 1 recites limitations, similarly recited in claims 11 and 21, including “collecting ATM usage data; integrating the collected ATM usage data with external data associated with a zip code of an ATM user to generate integrated data, the external data received from outside a financial institution associated with an ATM; analyzing, the integrated data to determine an underserved area; determining, an updated ATM distribution point configured to cover the underserved area; generating an output comprising the updated ATM distribution point, the updated ATM” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “by at least one hardware processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “by at least one hardware processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “by at least one hardware processor” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 2-7, 9-10, 12-17, and 19-20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 8 and 18 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Claims 1, 11, and 21 do not integrate the judicial exception into a practical application. Claim 1 is directed to a system comprising “one or more hardware processors of a machine; and at least one memory storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising.” Claim 11 is directed to a computer implemented method, which is performed “by at least one hardware processor” within the claim. Claim 21 is directed to “a machine-storage medium comprising instructions, which when executed by one or more artificial intelligence (AI) models on a computer, cause the one or more AI models to perform operations for selecting a location for automated teller machine (ATM) placement, the operations comprising,” which is recited in the preamble of the claim. Claim 1 further recites the additional elements of “generate integrated data for use by the one or more Al models,” “analyzing, by the one or more Al models, the integrated data to determine an underserved area,” and “determining, using the one or more Al models, an updated ATM distribution point.” The limitations being performed “by one or more AI models” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-7, 9-10, 12-17, and 19-20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claims 8 and 18 introduce the additional element of “the operations further comprising: providing a user interface to enable an operator of the financial institution to adjust the updated ATM distribution point based on qualitative data received by the financial institution.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in combination with the independent claims above, are not sufficient to prove integration into a practical application.
Step 2B: Claims 1, 11, and 21 do not comprise anything significantly more than the judicial exception. Claim 1 is directed to a system comprising “one or more hardware processors of a machine; and at least one memory storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising.” Claim 11 is directed to a computer implemented method, which is performed “by at least one hardware processor” within the claim. Claim 21 is directed to “a machine-storage medium comprising instructions, which when executed by one or more artificial intelligence (AI) models on a computer, cause the one or more AI models to perform operations for selecting a location for automated teller machine (ATM) placement, the operations comprising,” which is recited in the preamble of the claim. Claim 1 further recites the additional elements of “generate integrated data for use by the one or more Al models,” “analyzing, by the one or more Al models, the integrated data to determine an underserved area,” and “determining, using the one or more Al models, an updated ATM distribution point.” The limitations being performed “by one or more AI models” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 2-7, 9-10, 12-17, and 19-20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 8 and 18 introduce the additional element of “the operations further comprising: providing a user interface to enable an operator of the financial institution to adjust the updated ATM distribution point based on qualitative data received by the financial institution.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in combination with the independent claims above, are not anything significantly more than the judicial exception.
Accordingly, claims 1-21 are rejected under 35 USC 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5, 7, 11-15, 17, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhury et al. (“Location Optimization of ATM Networks,” 2017) in view of Khan et al. (“A GIS Based Approach to Manage Spatial Distribution and Location of Financial Services: A Case Study of ATM Services,” 2018).
Regarding claim 1, Chowdhury teaches a system for selecting a location for automated teller machine (ATM) placement using one or more artificial intelligence (Al) models (Pg. 5 teaches a system), the system comprising:
collecting ATM usage data (Pg. 1 teaches the various factors that a bank keeps account of while placing an ATM include, but is not limited to, popularity of the location, type of customer traffic, intensity of customer traffic, approximate number and type of transactions, service routes, and more; see also: Pg. 4);
integrating the collected ATM usage data with external data associated with a zip code of an ATM user to generate integrated data for use by the one or more Al models (Pgs. 1-2 teaches features were collected in order to construct the ideal prediction model, wherein the features include economic status of a given area, median home value of a given area, transportation modes used to estimate wealth, and more, wherein the data can be used to compute a measure of the wealth estimate for each zip code, which can be used to determine potential ATM locations, wherein Pg. 3 teaches a local model is designed to shed light on the local features of every zip-code, wherein some features govern ATM revenue generation on a county level, and these may be more abstract than the granular features at the county level, wherein the data is partitioned by county and labels are provided to each zip code, wherein Pg. 4 teaches the weights are assigned to respective features and are crucial in assigning a score to each zip-code, which are the building blocks used to sum up them up county wise to form the strategic advantage; see also: Pg. 5),
the external data received from outside a financial institution associated with an ATM (Pgs. 1-2 teaches features were collected in order to construct the ideal prediction model, wherein the features include economic status of a given area, median home value of a given area, transportation modes used to estimate wealth, and more, wherein the data can be used to compute a measure of the wealth estimate for each zip code, which can be used to determine potential ATM locations, wherein the various factors further include potential criminal activity, maintenance and power costs, utilities and infrastructure, and more; see also: Pgs. 3-5);
analyzing, by the one or more Al models, the integrated data to determine an underserved area (Pg. 1 teaches utilizing two concurrent prediction models including a local model that encodes spatial variance by considering highly energetic features in a given location and the global model that enforces the dominant trends in the entire data and serves as a feedback to the local model to prevent overfitting, wherein the models are trained using a synthetic objective function using the dominant features returned from the k-means clustering algorithm of the location model, wherein the results are obtained from the energetic features using the models, wherein Pg. 2 teaches two separate models are deployed including one for capturing global features and the other for capturing local features, wherein both the models are used to compute the weights for the features and the predictions shown in Fig. 2, wherein Pg. 3 teaches a local model is designed to shed light on the local features of every zip-code, wherein some features govern ATM revenue generation on a county level, and these may be more abstract than the granular features at the county level, wherein the data is partitioned by county and labels are provided to each zip code, wherein Pg. 4 teaches the weights are assigned to respective features and are crucial in assigning a score to each zip-code, which are the building blocks used to sum up them up county wise to form the strategic advantage; see also: Pg. 3);
determining, using the one or more Al models, an updated ATM distribution point configured to cover the underserved area (Pg. 5 teaches utilizing the hybrid approach that generates a weighted score summation of two models in order to maximize revenue if there is a venture for opening a new ATM network based on the calculated scores for the respective county regions, wherein the cost can be a limiting factor, wherein then one can optimize the function of the reward, taken as the county score, and the penalty, which is the cost of setting up an ATM, over a few counties, which can be used to device where to set up ATMs in order to reach maximum revenue, as well as in Pg. 1 teaches assigning accurate priority to the appropriate features in order to sort regions where maximum transactions are possible and forming an inference about the regions where placing ATMs will provide the maximum revenue, wherein one can maximize the number of transactions for the strategically placed ATM network based on the competition in the vicinity; see also: Pgs. 2-3).
However, Chowdhury does not explicitly teach one or more hardware processors of a machine; and at least one memory storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising: generating an output comprising the updated ATM distribution point, the updated ATM
From the same or similar field of endeavor, Khan teaches one or more hardware processors of a machine; and at least one memory storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising (Pg. 129 teaches building a GIS application system including a GIS database system including a basic spatial database and an integrated database, wherein the data can be collected from google earth and converted into a shapefile by using ArcGis10 software, as well as in Pg. 126 teaches utilizing statistical software in order to make quantitative analysis; see also: Pg. 128):
generating an output comprising the updated ATM distribution point (Pg. 138 teaches expanding banking services by constructing new ATMs in the most suitable unserved area, wherein Pg. 137 and Fig. 10 teach showing the suitable locations for new ATMs after running the location model, wherein a most suitable first and second location can be displayed that would be installed to cover the unserved area, wherein the new locations for ATMs are proposed by considering structural density and based on the study area, wherein new locations can be generated for the ATMs, as well as in Pg. 125 teaches identifying a suitable location for an ATM network, wherein the newly constructed ATM network can be constructed in the most suitable unserved area in order to reach customers’ convenience locations, wherein the GIS recommends the most suitable locations for ATMs for cities and towns; see also: Pgs. 129-130),
the updated ATM(Pg. 138 teaches expanding banking services by constructing new ATMs in the most suitable unserved area, wherein Pg. 137 and Fig. 10 teach showing the suitable locations for new ATMs after running the location model, wherein a most suitable first and second location can be displayed that would be installed to cover the unserved area, wherein the new locations for ATMs are proposed by considering structural density and based on the study area, wherein new locations can be generated for the ATMs, as well as in Pg. 125 teaches identifying a suitable location for an ATM network, wherein the newly constructed ATM network can be constructed in the most suitable unserved area in order to reach customers’ convenience locations, wherein the GIS recommends the most suitable locations for ATMs for cities and towns; see also: Pgs. 129-130).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Chowdhury to incorporate the teachings of Khan to include one or more hardware processors of a machine; and at least one memory storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising: generating an output comprising the updated ATM distribution point, the updated ATM
Regarding claims 11 and 21, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 11, Chowdhury teaches a computer-implemented method for selecting a location for automated teller machine (ATM) placement using one or more artificial intelligence (AI) models, the method comprising (Pg. 169 teaches performing the predictions and algorithms using software). Regarding claim 21, Chowdhury teaches a machine-storage medium comprising instructions, which when executed by one or more artificial intelligence (AI) models on a computer, cause the one or more AI models to perform operations for selecting a location for automated teller machine (ATM) placement, the operations comprising (Pg. 169 teaches performing the predictions and algorithms using software). Accordingly, claims 11 and 21 are rejected as being unpatentable over Chowdhury in view of Khan.
Regarding claims 2 and 12, the combination of Chowdhury and Khan teaches all the limitations of claims 1 and 11 above.
Chowdhury further teaches wherein collecting the ATM usage data further comprises: collecting the external data from a plurality of external data sources comprising at least one of demographic data, real estate availability data, economic indicator data (Pg. 2 teaches features were collected in order to construct the ideal prediction model, wherein the features include several demographic features including economic status of a given area, median home value of a given area, transportation modes used to estimate wealth, and more, wherein the model can attempt to find relations between several demographic features within the wealth estimate metric, wherein the model can also utilize the number of ATM transactions relative to the given location as an input, as well as in Pg. 3 teaches generating a local model that can provide granular features on a zip-code or county level, wherein Pg. 1 teaches the various factors to consider when placing an ATM includes customer traffic, intensity of traffic, approximate number and type of transactions, leasing space availability and costs, ATMs of competition in the vicinity, and more; see also: Pgs. 4-5).
Regarding claims 3 and 13, the combination of Chowdhury and Khan teaches all the limitations of claims 1 and 11above.
However, Chowdhury does not explicitly teach the operations further comprising: identifying customer ATM traffic patterns associated with existing ATM distribution points; identifying a potential partner store location; associating the customer ATM traffic patterns with the potential partner store location; and recommending, based on the associating, the potential partner store location for placement of the updated ATM distribution point based on the customer ATM traffic patterns.
From the same or similar field of endeavor, Khan further teaches the operations further comprising: identifying customer ATM traffic patterns associated with existing ATM distribution points (Pg. 125 teaches when opening a new ATM location, one has to consider the concentration of commercial areas, traffic patterns, workplace or living places of customers, and more, wherein the GIS-based approach can be utilized to identify suitable locations for an ATM network in a given location, wherein the ATM services network can expand their banking services through acquiring partners from existing ATM networks of other banks and simultaneously constructing new ATMs in the most suitable unserved areas, wherein this is how banks can expand their ATM network with less commitment cost, wherein the expansion can be performed by building/installing new ATM locations or by acquiring an existing partner or competitor network, wherein Pg. 127 teaches identifying a potential new branch location based on a number of factors including building availability, transportation costs to customers, location and market areas of competitors, and even the quality of life, wherein potential customer zones can be identified based on finding out the locations of nearby competitors, wherein Pg. 137 teaches banks can expand their ATM networks by partnering with a retailer, wherein Pg. 138 teaches expanding banking services by acquiring partners from existing ATM networks developed by other banks and simultaneously constructing new ATMs in the most suitable unserved area; see also: Pg. 128);
identifying a potential partner store location (Pg. 125 teaches when opening a new ATM location, one has to consider the concentration of commercial areas, traffic patterns, workplace or living places of customers, and more, wherein the GIS-based approach can be utilized to identify suitable locations for an ATM network in a given location, wherein the ATM services network can expand their banking services through acquiring partners from existing ATM networks of other banks and simultaneously constructing new ATMs in the most suitable unserved areas, wherein this is how banks can expand their ATM network with less commitment cost, wherein the expansion can be performed by building/installing new ATM locations or by acquiring an existing partner or competitor network, wherein Pg. 127 teaches identifying a potential new branch location based on a number of factors including building availability, transportation costs to customers, location and market areas of competitors, and even the quality of life, wherein potential customer zones can be identified based on finding out the locations of nearby competitors, wherein Pg. 137 teaches banks can expand their ATM networks by partnering with a retailer, wherein Pg. 138 teaches expanding banking services by acquiring partners from existing ATM networks developed by other banks and simultaneously constructing new ATMs in the most suitable unserved area; see also: Pg. 128);
associating the customer ATM traffic patterns with the potential partner store location (Pg. 125 teaches when opening a new ATM location, one has to consider the concentration of commercial areas, traffic patterns, workplace or living places of customers, and more, wherein the GIS-based approach can be utilized to identify suitable locations for an ATM network in a given location, wherein the ATM services network can expand their banking services through acquiring partners from existing ATM networks of other banks and simultaneously constructing new ATMs in the most suitable unserved areas, wherein this is how banks can expand their ATM network with less commitment cost, wherein the expansion can be performed by building/installing new ATM locations or by acquiring an existing partner or competitor network, wherein Pg. 127 teaches identifying a potential new branch location based on a number of factors including building availability, transportation costs to customers, location and market areas of competitors, and even the quality of life, wherein potential customer zones can be identified based on finding out the locations of nearby competitors, wherein Pg. 137 teaches banks can expand their ATM networks by partnering with a retailer, wherein Pg. 138 teaches expanding banking services by acquiring partners from existing ATM networks developed by other banks and simultaneously constructing new ATMs in the most suitable unserved area; see also: Pg. 128);
and recommending, based on the associating, the potential partner store location for placement of the updated ATM distribution point based on the customer ATM traffic patterns (Pg. 125 teaches when opening a new ATM location, one has to consider the concentration of commercial areas, traffic patterns, workplace or living places of customers, and more, wherein the GIS-based approach can be utilized to identify suitable locations for an ATM network in a given location, wherein the ATM services network can expand their banking services through acquiring partners from existing ATM networks of other banks and simultaneously constructing new ATMs in the most suitable unserved areas, wherein this is how banks can expand their ATM network with less commitment cost, wherein the expansion can be performed by building/installing new ATM locations or by acquiring an existing partner or competitor network, wherein Pg. 127 teaches identifying a potential new branch location based on a number of factors including building availability, transportation costs to customers, location and market areas of competitors, and even the quality of life, wherein potential customer zones can be identified based on finding out the locations of nearby competitors, wherein Pg. 137 teaches banks can expand their ATM networks by partnering with a retailer, wherein Pg. 138 teaches expanding banking services by acquiring partners from existing ATM networks developed by other banks and simultaneously constructing new ATMs in the most suitable unserved area; see also: Pg. 128).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Chowdhury and Khan to incorporate the further teachings of Khan to include the operations further comprising: identifying customer ATM traffic patterns associated with existing ATM distribution points; identifying a potential partner store location; associating the customer ATM traffic patterns with the potential partner store location; and recommending, based on the associating, the potential partner store location for placement of the updated ATM distribution point based on the customer ATM traffic patterns. One would have been motivated to do so in order to select a new ATM location confidently and reliably in a short time cycle (Khan, Pg. 125). By incorporating the teachings of Khan, one would have been able to generate a strategic plan that provides realistic results for determining unserved areas (Khan, Pg. 138).
Regarding claims 4 and 14, the combination of Chowdhury and Khan teaches all the limitations of claims 1 and 11 above.
Chowdhury further teaches the operations further comprising: employing predictive analytics to forecast demographic and economic changes affecting a potential ATM distribution point among a plurality of existing ATM distribution points (Pg. 2 teaches features were collected in order to construct the ideal prediction model, wherein the features include economic status of a given area, median home value of a given area, transportation modes used to estimate wealth, and more, wherein the model can attempt to find relations between several demographic features within the wealth estimate metric, wherein the model can also utilize the number of ATM transactions relative to the given location as an input, as well as in Pg. 3 teaches generating a local model that can provide granular features on a zip-code or county level; see also: Pgs. 1, 4-5);
and combining the predictive analytics and the integrated data to identify an optimal ATM distribution point based on the forecasted demographic and economic changes (Pg. 2 teaches features were collected in order to construct the ideal prediction model, wherein the features include economic status of a given area, median home value of a given area, transportation modes used to estimate wealth, and more, wherein the model can attempt to find relations between several demographic features within the wealth estimate metric, wherein the model can also utilize the number of ATM transactions relative to the given location as an input, as well as in Pg. 3 teaches generating a local model that can provide granular features on a zip-code or county level, wherein Pg. 5 teaches utilizing the local and global trend modeling data in order to determine a location for opening a new ATM; see also: Pgs. 1, 4).
Regarding claims 5 and 15, the combination of Chowdhury and Khan teaches all the limitations of claims 4 and 14 above.
Chowdhury further teaches the operations further comprising: scoring the potential ATM distribution point (Pg. 1 teaches introducing a model which provides a score to an ATM location that serves as an indicator of its relative likelihood of transactions, which is done by modeling spatially dynamic features, wherein Pg. 2 teaches the model can utilize zip code data and consider a total of eleven features for the global objective function in order to analyze the features associated with the wealth estimate for the given area, wherein a score can be computed for each ATM location; see also: Pgs. 3-5), the scoring comprising utilizing multi-criteria decision analysis to predict the optimal ATM distribution point (Pg. 1 teaches introducing a model which provides a score to an ATM location that serves as an indicator of its relative likelihood of transactions, which is done by modeling spatially dynamic features, wherein Pg. 2 teaches the model can utilize zip code data and consider a total of eleven features for the global objective function in order to analyze the features associated with the wealth estimate for the given area, wherein a score can be computed for each ATM location; see also: Pgs. 3-5).
Regarding claims 7 and 17, the combination of Chowdhury and Khan teaches all the limitations of claims 1 and 11 above.
Chowdhury further teaches wherein the generating the output comprising the updated ATM distribution point further comprises: employing an econometric model to estimate potential construction costs based on regional economic data associated with the updated ATM distribution point (Pg. 1 teaches various factors that a bank keeps account of when placing an ATM in their strategic ATM network includes leasing space availability and costs, utilities and infrastructure, maintenance and power costs, and more, wherein the companies also consider the cost of setting up the ATM in such locations and the amount of return that can be estimated, wherein Pg. 5 teaches from the score estimate of county regions, one can maximize revenue if there is a venture for opening a new ATM network, wherein the model utilizes the cost of setting up a new ATM in a particular region as a limiting factor in order to optimize the function of the reward and the penalty over a few counties in order to decide which public spots can establish ATMs in order to maximize revenue; see also: Pgs. 2-4).
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhury et al. (“Location Optimization of ATM Networks,” 2017) in view of Khan et al. (“A GIS Based Approach to Manage Spatial Distribution and Location of Financial Services: A Case Study of ATM Services,” 2018) in view of Qadrei et al. (“Allocation of Heterogeneous Banks’ Automated Teller Machines,” 2009).
Regarding claims 6 and 16, the combination of Chowdhury and Khan teaches all the limitations of claims 1 and 11 above.
However, Chowdhury does not explicitly teach the operations further comprising: monitoring the plurality of existing ATM distribution points to identify peak usage times; associating the peak usage times with customer wait times; and adjusting the scoring of the potential ATM distribution point based on the associating.
From the same or similar field of endeavor, Qadrei teaches the operations further comprising: monitoring the plurality of existing ATM distribution points to identify peak usage times (Pgs. 17-18 teach the ATM placement problem is modeled and defined mathematically, wherein a cost function is defined and all constraints are expressed mathematically, wherein the inputs into the problem include the ATM types, possible locations, maximum number of ATMs per location, distances between locations, maximum delay times, and weights, wherein Pg. 16 teaches finding a relationship between a certain area and an existing bank branch; see also: Pgs. 19-21);
associating the peak usage times with customer wait times (Pgs. 17-18 teach the ATM placement problem is modeled and defined mathematically, wherein a cost function is defined and all constraints are expressed mathematically, wherein the inputs into the problem include the ATM types, possible locations, maximum number of ATMs per location, distances between locations, and weights, wherein the input includes the maximum processing delay time for each ATM including both ATM service time to dispense cash and the customers’ waiting time in queue to use the ATM, wherein Pg. 19 teaches the performance constraint is utilized to ensure that the maximum processing delay time per location does not exceed a given value, which is the maximum processing delay time for each ATM, wherein Pg. 16 teaches finding a relationship between a certain area and an existing bank branch; see also: Pgs. 20-21);
and adjusting the scoring of the potential ATM distribution point based on the associating (Pgs. 17-18 teach the ATM placement problem is modeled and defined mathematically, wherein a cost function is defined and all constraints are expressed mathematically, wherein the inputs into the problem include the ATM types, possible locations, maximum number of ATMs per location, distances between locations, and weights, wherein the input includes the maximum processing delay time for each ATM including both ATM service time to dispense cash and the customers’ waiting time in queue to use the ATM, wherein Pg. 19 teaches the performance constraint is utilized to ensure that the maximum processing delay time per location does not exceed a given value, which is the maximum processing delay time for each ATM, wherein Pg. 16 teaches finding a relationship between a certain area and an existing bank branch, and wherein Pgs. 20-21 teach utilizing the mathematical calculations in order to determine the best assignment of heterogenous ATM machines at given bank locations).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Chowdhury and Khan to incorporate the teachings of Qadrei to include the operations further comprising: monitoring the plurality of existing ATM distribution points to identify peak usage times; associating the peak usage times with customer wait times; and adjusting the scoring of the potential ATM distribution point based on the associating. One would have been able to find the best assignment of heterogeneous ATM machines to bank locations by developing a genetic algorithm solver to search for good solutions (Qadrei, Pgs. 20-21).
Claim(s) 8-10 and 18- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhury et al. (“Location Optimization of ATM Networks,” 2017) in view of Khan et al. (“A GIS Based Approach to Manage Spatial Distribution and Location of Financial Services: A Case Study of ATM Services,” 2018) in view of Baldasare et al. (US 20200412133 A1).
Regarding claims 8 and 18, the combination of Chowdhury and Khan teaches all the limitations of claims 1 and 11 above.
However, Chowdhury does not explicitly teach the operations further comprising: providing a user interface to enable an operator of the financial institution to adjust the updated ATM distribution point based on qualitative data received by the financial institution.
From the same or similar field of endeavor, Baldasare teaches the operations further comprising: providing a user interface to enable an operator of the financial institution to adjust the updated ATM distribution point based on qualitative data received by the financial institution ([0165] teaches an exemplary user interface, wherein an operator may input that the location of the kiosk has been changed, wherein the operator may type in details about the location of the kiosk, wherein the system may prompt the operator to upload one or more photos of the new location of the kiosk using the interface, wherein [0187-0188] teach the system may provide data to operators regarding the installation of a kiosk in a new location via the interface, wherein the system can leverage business intelligence and perform analysis regarding kiosk usage in order to identify optimal installation venues, wherein [0181] teaches the control module can make a recommendation for a kiosk location change within a venue, wherein [0084] teaches an automated teller machine; see also: [0099, 0163-0164, 0186]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Chowdhury and Khan to incorporate the teachings of Baldasare to include the operations further comprising: providing a user interface to enable an operator of the financial institution to adjust the updated ATM distribution point based on qualitative data received by the financial institution. One would have been motivated to do so in order to improve a product offering and reduce costs by providing recommendations regarding the installation or removal of a kiosk in its current venue (Baldasare, [0186-0187]). By incorporating the teachings of Baldasare, one would have been able to help find new locations of kiosks based on low kiosk usage (Baldasare, [0164]).
Regarding claims 9 and 19, the combination of Chowdhury and Khan teaches all the limitations of claims 1 and 11 above.
However, Chowdhury does not explicitly teach wherein generating the output further comprises: monitoring a plurality of metrics associated with the ATM usage data in near real-time; and generating a data visualization for conveying a plurality of geo-spatial patterns associated with the updated ATM distribution point based on at least one of the plurality of metrics.
From the same or similar field of endeavor, Khan further teaches wherein generating the output further comprises: generating a data visualization for conveying a plurality of geo-spatial patterns associated with the updated ATM distribution point based on at least one of the plurality of metrics (Pgs. 130-131 teach generating visualizations related to existing ATM locations and their service area within the study area, wherein Fig. 3 displays the existing location and service area of all ATMs, wherein Pgs. 137-138 teach generating Fig. 10 that shows the suitable locations for new ATMs after running the location model, wherein the most suitable first and second locations can be derived, wherein an additional 36 new ATM booths can be installed to cover the unserved area, wherein the new locations are proposed by considering structural density and use within the studied area, wherein Pg. 125 teaches when opening a new ATM location, one has to consider the concentration of commercial areas, traffic patterns, workplace or living places of customers, and more, wherein the GIS-based approach can be utilized to identify suitable locations for an ATM network in a given location, wherein the ATM services network can expand their banking services through acquiring partners from existing ATM networks of other banks and simultaneously constructing new ATMs in the most suitable unserved areas; see also: Pgs. 132-134).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Chowdhury and Khan to incorporate the further teachings of Khan to include wherein generating the output further comprises: generating a data visualization for conveying a plurality of geo-spatial patterns associated with the updated ATM distribution point based on at least one of the plurality of metrics. One would have been motivated to do so in order to select a new ATM location confidently and reliably in a short time cycle (Khan, Pg. 125). By incorporating the teachings of Khan, one would have been able to generate a strategic plan that provides realistic results for determining unserved areas (Khan, Pg. 138).
However, the combination of Chowdhury and Khan does not explicitly teach monitoring a plurality of metrics associated with the ATM usage data in near real-time.
From the same or similar field of endeavor, Baldasare teaches monitoring a plurality of metrics associated with the ATM usage data in near real-time ([0164] teaches identifying a change in location of a kiosk because a given location may result in kiosk customers not being able to find the kiosk, which may lower kiosk usage, wherein [0181] teaches the control module can make a recommendation for a kiosk location change within a venue, wherein [0084] teaches an automated teller machine, as well as in [0187] teaches providing data to operators including removal of a kiosk from its current venue, wherein [0152] teaches a brand may pay for a kiosk to be at a given location, wherein the retailer may pay for data collected from kiosk usage, wherein [0179] teaches monitoring factors of the kiosk including time of day, user behavior, usage patterns at the kiosk, and other suitable factors, wherein [0098] teaches having access to a variety of real-time information including usage of the kiosk and more; see also: [0099, 0123, 0186]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Chowdhury and Khan to incorporate the teachings of Baldasare to include wherein generating the output further comprises: monitoring a plurality of metrics associated with the ATM usage data in near real-time. One would have been motivated to do so in order to improve a product offering and reduce costs by providing recommendations regarding the installation or removal of a kiosk in its current venue (Baldasare, [0186-0187]). By incorporating the teachings of Baldasare, one would have been able to help find new locations of kiosks based on low kiosk usage (Baldasare, [0164]).
Regarding claims 10 and 20, the combination of Chowdhury and Khan teaches all the limitations of claims 1 and 11 above.
However, Chowdhury does not explicitly teach the operations further comprising: identifying an underperforming existing ATM distribution point; and recommending removal of the underperforming existing ATM distribution point.
From the same or similar field of endeavor, Baldasare teaches the operations further comprising: identifying an underperforming existing ATM distribution point ([0164] teaches identifying a change in location of a kiosk because a given location may result in kiosk customers not being able to find the kiosk, which may lower kiosk usage, wherein [0181] teaches the control module can make a recommendation for a kiosk location change within a venue, wherein [0084] teaches an automated teller machine, as well as in [0187] teaches providing data to operators including removal of a kiosk from its current venue; see also: [0099, 0186]);
and recommending removal of the underperforming existing ATM distribution point ([0164] teaches identifying a change in location of a kiosk because a given location may result in kiosk customers not being able to find the kiosk, which may lower kiosk usage, wherein [0181] teaches the control module can make a recommendation for a kiosk location change within a venue, wherein [0084] teaches an automated teller machine, as well as in [0187] teaches providing data to operators including removal of a kiosk from its current venue; see also: [0099, 0186]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Chowdhury and Khan to incorporate the teachings of Baldasare to include the operations further comprising: identifying an underperforming existing ATM distribution point; and recommending removal of the underperforming existing ATM distribution point. One would have been motivated to do so in order to improve a product offering and reduce costs by providing recommendations regarding the installation or removal of a kiosk in its current venue (Baldasare, [0186-0187]). By incorporating the teachings of Baldasare, one would have been able to help find new locations of kiosks based on low kiosk usage (Baldasare, [0164]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kolls et al. (US 20220036268 A1) discloses identifying one or more locations which are unserved or underserved and providing financial services including an ATM
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sara G Brown whose telephone number is (469)295-9145. The examiner can normally be reached M-F 8:00 am- 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625