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 “Claims 1-21 were rejected under 35 U.S.C. § 101 for the reasons noted in the Office Action. Claims 1, 11, and 21 have been amended as shown above. During the Examiner Interview, Examiner Brown felt that the subject matter of the amendments moved the claims closer to eligibility under 35 U.S.C. § 101. The Applicant submits that, as amended, the claims are eligible under 35 U.S.C. § 101 and requests that the rejection be withdrawn.,” Examiner respectfully disagrees. The limitations being performed “by one or more AI models,” or recite retraining the 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).
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. The claims overcome the prior art of record such that none of the cited prior art references can be applied to form the basis of a 35 USC 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 USC 101, as set forth. Accordingly, the 35 USC 103 rejection has been withdrawn.
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 distribution point including a suggested location within the underserved area; measuring post-implementation ATM usage data at an ATM installed at the suggested location.” 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,” “wherein the one or more Al models implement: generative Al that employs feedback loops that allow for capture of temporal dependencies and recognition of past inputs; and a hierarchical structure of a plurality of neurons where connections between the plurality of neurons have associated weights,” “analyzing, by the one or more Al models, the integrated data to determine an underserved area,” “determining, using the one or more Al models, an updated ATM distribution point,” “ retraining the one or more Al models using the post-implementation ATM usage data as a labeled feedback signal for the feedback loop, the labeled feedback signal being indicative of accuracy of the determined underserved area; and updating, based on the retraining, the associated weights of the connections between the plurality of neurons of the one or more Al models that determine subsequent underserved areas from subsequent integrated data.” 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,” “wherein the one or more Al models implement: generative Al that employs feedback loops that allow for capture of temporal dependencies and recognition of past inputs; and a hierarchical structure of a plurality of neurons where connections between the plurality of neurons have associated weights,” “analyzing, by the one or more Al models, the integrated data to determine an underserved area,” “determining, using the one or more Al models, an updated ATM distribution point,” “ retraining the one or more Al models using the post-implementation ATM usage data as a labeled feedback signal for the feedback loop, the labeled feedback signal being indicative of accuracy of the determined underserved area; and updating, based on the retraining, the associated weights of the connections between the plurality of neurons of the one or more Al models that determine subsequent underserved areas from subsequent integrated data.” 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.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: The claims overcome the prior art of record such that none of the cited prior art references can be applied to form the basis of a 35 USC 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 USC 101, as set forth above.
The closest prior art of the record discloses:
Chowdhury et al. (“Location Optimization of ATM Networks,” 2017) discloses 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 for use by the one or more Al models, the external data received from outside a financial institution associated with an ATM, analyzing, by the one or more Al models, the integrated data to determine an underserved area; determining, using the one or more Al models, an updated ATM distribution point configured to cover the underserved area. However, Chowdhury fails to explicitly teach or disclose 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: wherein the one or more Al models implement: generative Al that employs feedback loops that allow for capture of temporal dependencies and recognition of past inputs; and a hierarchical structure of a plurality of neurons where connections between the plurality of neurons have associated weights; generating an output comprising the updated ATM distribution point, the updated ATM distribution point including a suggested location within the underserved area; measuring post-implementation ATM usage data at an ATM installed at the suggested location; retraining the one or more Al models using the post-implementation ATM usage data as a labeled feedback signal for the feedback loop, the labeled feedback signal being indicative of accuracy of the determined underserved area; and updating, based on the retraining, the associated weights of the connections between the plurality of neurons of the one or more Al models that determine subsequent underserved areas from subsequent integrated data.
Khan et al. (“A GIS Based Approach to Manage Spatial Distribution and Location of Financial Services: A Case Study of ATM Services,” 2018) discloses 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 distribution point including a suggested location within the underserved area. However, Khan fails to explicitly teach or disclose wherein the one or more Al models implement: generative Al that employs feedback loops that allow for capture of temporal dependencies and recognition of past inputs; and a hierarchical structure of a plurality of neurons where connections between the plurality of neurons have associated weights; measuring post-implementation ATM usage data at an ATM installed at the suggested location; retraining the one or more Al models using the post-implementation ATM usage data as a labeled feedback signal for the feedback loop, the labeled feedback signal being indicative of accuracy of the determined underserved area; and updating, based on the retraining, the associated weights of the connections between the plurality of neurons of the one or more Al models that determine subsequent underserved areas from subsequent integrated data.
San Juan et al. ("A data-driven target-oriented robust optimization framework: bridging machine learning and optimization under uncertainty." May 30, 2024) discloses wherein the one or more Al models implement: Al that employs feedback loops that allow for capture of temporal dependencies and recognition of past inputs; and a hierarchical structure of a plurality of neurons where connections between the plurality of neurons have associated weights; measuring post-implementation ATM usage data; retraining the one or more Al models using ATM usage data as a labeled feedback signal for the feedback loop, the labeled feedback signal being indicative of accuracy; updating, based on the retraining, the associated weights of the connections between the plurality of neurons of the one or more Al models. However, San Juan fails to explicitly teach or disclose wherein the one or more Al models implement: generative Al that employs feedback loops; measuring post-implementation ATM usage data at an ATM installed at the suggested location; retraining the one or more Al models using the post-implementation ATM usage data as a labeled feedback signal for the feedback loop, the labeled feedback signal being indicative of accuracy of the determined underserved area; and updating, based on the retraining, the associated weights of the connections between the plurality of neurons of the one or more Al models that determine subsequent underserved areas from subsequent integrated data.
Mattison et al. (US 12394283 B1) discloses wherein the one or more Al models implement: generative Al that employs feedback loops; retraining the one or more Al models using ATM usage data as a labeled feedback signal for the feedback loop, the labeled feedback signal being indicative of accuracy. However, Mattison fails to explicitly teach or disclose measuring post-implementation ATM usage data at an ATM installed at the suggested location; retraining the one or more Al models using the post-implementation ATM usage data, the labeled feedback signal being indicative of accuracy of the determined underserved area; and updating, based on the retraining, the associated weights of the connections between the plurality of neurons of the one or more Al models that determine subsequent underserved areas from subsequent integrated data.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cella et al. (US 20220366494 A1) discloses utilizing a generative learning model and retraining the weights of output neurons based on feedback, as well as receiving real time monitoring data from ATMs
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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