DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the application filed on 07/12/2024.
Claims 1-20 are currently pending and have been examined.
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 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In the instant case, claims 1, 12 and 19 are directed to a methods, and system. For the purposes of this analysis, representative claim 1 is addressed.
Claim 1 recites “contextual payment augmentation” which is a grouped under “Certain methods of organizing human activity — fundamental economic practices” in prong one of step 2A (MPEP 2106.04(a)). Claim 1 recites
A computer-implemented payment method, comprising:
receiving, by a server, a payment request from a client electronic computing device, the payment request including an amount of a payment;
generating, by the server, a prompt based on the amount;
feeding, by the server, the prompt to a generative artificial intelligence model (GAIM);
generating, by the GAIM and in response to the prompt, a text string that includes a number corresponding to the amount;
generating, by the server, first signals that cause the client electronic computing device to display the text string;
receiving, by the server, second signals from the client electronic computing device in response to a request to confirm the payment, the second signals indicating whether the payment has been confirmed.
The additional elements of claim 1 such as “by a server”, “client electronic computing device,”, “generating, by the server, a prompt based on the amount”, “feeding, by the server, the prompt to a generative artificial intelligence model (GAIM)”, “generating, by the server, first signals that cause the client electronic computing device to display the text string” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements amount to no more than mere instructions to apply the abstract idea of using generic computer components. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of pay request processing.
Hence, claims 1, 12 and 19 are not patent eligible.
Dependent claims 2-11, 13-17, and 20 recite additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more.
Claim 7 recites the additional elements of “ prior to receiving the payment request, training the GAIM to generate educational text strings, including the text string.” does no more than use a computer as a tool to perform an abstract idea and do no more than generally link the abstract idea to a particular field of use. Therefore, as it is no more than apply it does not improve the functioning of a computer, or improve other technology or technical field.
Claim 14 recites the additional elements of “wherein the feedback is provided, by the server, to the trained GAIM causing the trained GAIM to be further trained.” does no more than use a computer as a tool to perform an abstract idea and do no more than generally link the abstract idea to a particular field of use. Therefore, as it is no more than apply it does not improve the functioning of a computer, or improve other technology or technical field.
The claims as a whole do not amount to significantly more than the abstract idea itself. This is because the claims do not affect an improvement to another technology or technical field, the claims do not amount to an improvement to the functioning of a computer system itself, and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fort (US 2025/0322400 A1) in view of Singh (US 2025/0259178 A1)
Regarding claims 1, 12 and 19
A computer-implemented payment method, comprising: receiving, by a server, a payment request from a client electronic computing device, the payment request including an amount of a payment; (See at least Fort [0064] At step 905, the computing device may receive a request to dispute a transaction (e.g., purchase, payment, etc.) from a user device.)
generating, by the server, a prompt based on the amount; (See at least Fort [0047] In step 335, the computing device may receive, from the generative AI model and based on the one or more data points, a first narrative associated with the transaction. The first narrative may be displayed in a user-friendly manner (e.g., mobile compatibility, screen-readers, error messages, contrasting color scheme, etc.). The first narrative may provide context around the user's transaction or purchase to explain the what, where, how, and/or why around the transaction. An illustrative example of the first narrative may be: “On Tuesday, Dec. 5, 2023, Mary Smith purchased a red wool coat from via a mobile app on her mobile device while possibly commuting to work in Chicago given her location was tracked along the L train path to the downtown station. This coat had been trending on one or more social media sites and, given the temperature had dropped precipitously in the Great lakes region over the past few days, it was a prudent purchase.” This illustrative example of the first narrative showcases that the generative AI model obtained a plurality of data points, such as the date, the user's name, the item that was purchased, the location of the user at the time of the purchase, the merchant name, the model of the user's device used to perform the purchase, the current event at the time of the purchase, the current and recent weather, etc.)
feeding, by the server, the prompt to a generative artificial intelligence model (GAIM); (See at least Fort [0046] In step 330, the computing device may input the one or more data points into a generative AI model trained to generate narratives. The one or more data points may be cleaned (e.g., scrubbed) and/or preprocessed to remove inconsistencies and/or mitigate any missing values to ensure the data may be suitable for generating narratives. Additionally, or alternatively, the computing device may tokenize the one or more data points, for example, prior to inputting the one or more data points into the generative AI model. In another example, the computing device may encrypt, mask, or use another process of securing the one or more data points, especially when the one or more data points related to user information and/or personally identifiable information (PII). Utilizing data security processes may improve the security of the collected one or more data points, increase the efficiency in managing one or more data points, and minimize the impact of any potential data breach. Sensitive data may proactively be stored separately. As a result, residual costs may also decrease.)
generating, by the GAIM and in response to the prompt, a …corresponding to the amount; (See at least Fort [0040] In step 305, a computing device may train one or more machine learning models to generate narratives for transactions. The one or more machine learning models may comprise a generative AI model or a large language model (LLM). The generative AI model may be a publicly-available generative AI model, such as ChatGPT, Bard, M365 Copilot, Scribe, Jasper, etc. Additionally, or alternatively, the one or more machine learning models may be used interchangeably with a generative AI model. The generative AI model may be trained to generate narratives for transactions, for example, based on a plurality of information, such as personal, business, and/or environmental information. The generative AI model may be trained using supervised learning, unsupervised learning, back propagation, transfer learning, stochastic gradient descent, learning rate decay, dropout, max pooling, batch normalization, long short-term memory, skip-gram, or any equivalent deep learning technique. The one or more data points used to train the generative AI model may be further discussed in step 325. The one or more data points may be used interchangeably with data points, dataset, plurality of data points, etc. Examples of one or more data points used to train the generative AI model may include at least one of: a location of the transaction, a day of the transaction, price of items associated with the transaction, weather conditions at the time of the transaction, a total cost of the transaction, products that were part of the transaction, current event at the time of the transaction customer demographics, customer previous payments, servicing agent transcripts, merchant and business information, and story feedback from customer and agents. Once the one or more machine learning algorithms are trained to generate narratives associated with transactions, the one or more machine learning algorithms may be deployed, for example, as part of a mobile application, a browser plug-in, an on-demand service, etc.)
generating, by the server, first signals that cause the client electronic computing device to display the text string; (See at least Fort [0058] Following the affirmative selection 715, the computing device may send a narrative 720 associated with the transaction as shown. The user device may display the narrative to the user. For example, narrative 720 states, “On Tuesday, Dec. 5, 2023, Mary Smith purchased a red wool coat from StoreABC on her mobile device while possibly commuting to work in City X given her location was tracked along the L train path to the downtown station. This coat had been trending on social media and given the temperature had dropped precipitously in the Region X over the past few days, it was a prudent purchase.” Narrative 720 is an example of a narrative that may be generated by the one or more machine learning models (e.g., generative AI model) described herein. Additionally, or alternatively, the request 710 may be displayed by the merchant website on the checkout page or the confirmation page for example, once the user has completed the transaction. Additionally, or alternatively, the user may have opted to automatically generate a narrative for any purchases that the user may have made based on a plurality of predetermined factors, such that the user may not be required to provide affirmative selection 715 to generate narrative 720. For example, the user may indicate that narratives may be generated for any purchases made with a specific credit card or bank account, within a period of time, or from a specific store. The user may be able to make a variety of customizations to the narrative generation process.)
However Fort does not specifically teach: … text string that includes a number… and receiving, by the server, second signals from the client electronic computing device in response to a request to confirm the payment, the second signals indicating whether the payment has been confirmed.
However Singh teaches:
… text string that includes a number…(See at least Singh [0091] Once the generative AI model 204 has compared the one or more second parameters to the one or more first parameters, at step 420, the generative AI model 204 determines a legitimacy value associated with the first transaction based on the comparison of the one or more second parameters to the one or more first parameters, at step 422. The legitimacy value refers to a likelihood of the first transaction being fraudulent and/or containing one or more errors. In some embodiments, the legitimacy value includes a score (e.g., a number, a letter, a category, etc.) out of a predefined scale. The predefined scale may be determined by the provider institution and encoded into the AI system 200. The predefined scale may include any of a numerical range (e.g., 0-5, 0-10, 0-100, etc.), an alphabetical range (e.g., A, B, C, D, or F), a categorical range (e.g., very good, good, neutral, bad, very bad), and so on.)
receiving, by the server, second signals from the client electronic computing device in response to a request to confirm the payment, the second signals indicating whether the payment has been confirmed. (See at least Singh [0097] In some embodiments, the response may include approving the first request, at step 425a, or denying the first request, at step 425b. Approving the first request may further include processing the first transaction as indicated by the first request. In some embodiments, the provider institution computing system 110 may approve the first request if the first transaction receives a predefined legitimacy (e.g., the highest legitimacy value out of the predefined scale). In some embodiments, approving the first request includes transmitting the first request to a transfer service (e.g., to the transfer service computing system 130) for the transfer service to complete a transfer of funds as indicated by the first transaction. Denying the first request may further include failing to process the first transaction as indicated by the first request. For example, the provider institution computing system 110 may deny the first request if the first transaction receives a legitimacy value that is lower than the highest legitimacy value out of the predefined scale.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the payment narrative generating system of Fort view of method for securing transactions using a generative artificial intelligence model as taught by Singh in order to improve detection of fraudulent transactions (Singh [0015].
Regarding claim 2
wherein the prompt is generated by the server based on a contextual factor. (See at least Fort [0039] The role of the payment narrative generator may be to create a narrative (e.g., story) around a specific purchase that was made (e.g., transaction, purchase, etc.), with language and contextual details that explain the purchase thoroughly.)
Regarding claim 3
wherein the contextual factor is based on feedback received by the server to another text string generated in response to a previous payment request. (See at least Fort [0050] In step 350, the computing device may determine whether the response indicates an approval of the first narrative, an approval of the first narrative with edits, or a rejection of the first narrative. If the response is a rejection (i.e., “N”), the process may go back to step 335. A new narrative may be generated at step 335 and the approval process may repeat. If the response includes an approval, or an approval with edits (i.e., “Y”), the narrative may be stored, in step 355. Additionally, or alternatively, the response may consist of varying results depending on the prompts provided to the user device, as discussed in step 345.)
Regarding claim 4
wherein the contextual factor is based on a type of a good or a type of a service that the payment is for. (See at least Fort [0053] one or more products that were part of the transaction 445)
Regarding claim 5
wherein the contextual factor is based on a physical location of the client electronic computing device. (See at least Fort [0053] The example payment story training data points 415 may comprise at least one of a location of the transaction 420)
Regarding claim 6
generating, by the server, third signals that cause the client electronic computing device to display a feedback request related to the text string and; receiving, by the server, feedback about the text string from the client electronic computing device, the feedback being in response to the feedback request. (See at least Fort [0024] The database may record and make available the narratives generated for each purchase at a customer level. The database may be accessible to users via an application programming interface (API). Primary users may include customer servicing applications and digital consumer experiences (e.g., issue website, mobile app). Additional users may include analysts, researchers, and marketers. Customers and agents feedback loops may be useful with respect to the ongoing learning and training of the generative AI model. Inconsistencies and errors in produced stories, along with successful story descriptions, may be inputs for model training. Examples of the primary feedback loop channels are: in-app or site feedback mechanisms that customers may use to manually lodge a complaint (e.g., “Report a Problem Button”); transcribed customer servicing calls; and/or customer-agent text exchanges.)
Regarding claim 7
further comprising: prior to receiving the payment request, training the GAIM to generate educational text strings, including the text string. (See at least Fort [0040] In step 305, a computing device may train one or more machine learning models to generate narratives for transactions.)
Regarding claim 8
wherein the training includes feeding the GAIM user data associated with financial accounts from which payment requests, including the payment request, are made. (See at least Fort [0053] The one or more machine learning models discussed in process 300 (e.g., generative AI model 410) may collect a plurality of training data points (e.g., one or more data points) related to a transaction (e.g., purchase, payment, etc.) to generate an accurate and descriptive narrative associated with the transaction. FIG. 4 shows an example of one or more data points that may be used to train one or more machine learning models to generate narratives. The generative AI model 410 may be integral (e.g., part of) the payment story generator 405. The generative AI model 410 may comprise the one or more machine learning models trained in step 305, above. The example payment story training data points 415 illustrate the one or more data points that the generative AI model 410 may use to generate a narrative. The example payment story training data points 415 may comprise at least one of a location of the transaction 420, a day of the transaction 425, a price of one or more items associated with the transaction 430, weather conditions at the time of the transaction 435, a total cost of the transaction 440, one or more products that were part of the transaction 445, a current event at the time of the transaction 450, customer demographics 455, customer previous payments 460, servicing agent transcripts 465, merchant and business information 470, story feedback from customer and agents 475, and/or a time of the transaction 480. Once the generative AI model 410 generates a narrative associated with the transaction, the computing device may send the narrative from the payment story generator 405 to one or more user devices, as discussed above in process 300. The one or more data points shown in FIG. 4 serve as examples and do not represent the full set of data or an exhaustive list of data points that may be used to train the generative AI model 410. Additionally, or alternatively, the computing device may use any or none of the one or more data points to generate the narrative.
Regarding claims 9 and 18
wherein the user data includes data about education of users, data about employers of the users, data about family members of the users, data about geographic areas surrounding homes of the users, data about spending habits of the users, and data about feedback by the users to previous text strings generated in response to previous payment requests. (See at least Fort [0053] The one or more machine learning models discussed in process 300 (e.g., generative AI model 410) may collect a plurality of training data points (e.g., one or more data points) related to a transaction (e.g., purchase, payment, etc.) to generate an accurate and descriptive narrative associated with the transaction. FIG. 4 shows an example of one or more data points that may be used to train one or more machine learning models to generate narratives. The generative AI model 410 may be integral (e.g., part of) the payment story generator 405. The generative AI model 410 may comprise the one or more machine learning models trained in step 305, above. The example payment story training data points 415 illustrate the one or more data points that the generative AI model 410 may use to generate a narrative. The example payment story training data points 415 may comprise at least one of a location of the transaction 420, a day of the transaction 425, a price of one or more items associated with the transaction 430, weather conditions at the time of the transaction 435, a total cost of the transaction 440, one or more products that were part of the transaction 445, a current event at the time of the transaction 450, customer demographics 455, customer previous payments 460, servicing agent transcripts 465, merchant and business information 470, story feedback from customer and agents 475, and/or a time of the transaction 480. Once the generative AI model 410 generates a narrative associated with the transaction, the computing device may send the narrative from the payment story generator 405 to one or more user devices, as discussed above in process 300. The one or more data points shown in FIG. 4 serve as examples and do not represent the full set of data or an exhaustive list of data points that may be used to train the generative AI model 410. Additionally, or alternatively, the computing device may use any or none of the one or more data points to generate the narrative.
Regarding claim 10
Fort does not specifically teach: wherein the number included in the text string is not in a unit of currency.
However Singh teaches: [0091] Once the generative AI model 204 has compared the one or more second parameters to the one or more first parameters, at step 420, the generative AI model 204 determines a legitimacy value associated with the first transaction based on the comparison of the one or more second parameters to the one or more first parameters, at step 422. The legitimacy value refers to a likelihood of the first transaction being fraudulent and/or containing one or more errors. In some embodiments, the legitimacy value includes a score (e.g., a number, a letter, a category, etc.) out of a predefined scale. The predefined scale may be determined by the provider institution and encoded into the AI system 200. The predefined scale may include any of a numerical range (e.g., 0-5, 0-10, 0-100, etc.), an alphabetical range (e.g., A, B, C, D, or F), a categorical range (e.g., very good, good, neutral, bad, very bad), and so on.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the payment narrative generating system of Fort view of method for securing transactions using a generative artificial intelligence model as taught by Singh in order to improve detection of fraudulent transactions (Singh [0015].
Regarding claim 11
Fort does not teach: wherein the second signals indicate that that the payment is confirmed, the method further comprising: electronically initiating the payment by an electronic payment service device.
However Singh teaches: [0097] In some embodiments, the response may include approving the first request, at step 425a, or denying the first request, at step 425b. Approving the first request may further include processing the first transaction as indicated by the first request. In some embodiments, the provider institution computing system 110 may approve the first request if the first transaction receives a predefined legitimacy (e.g., the highest legitimacy value out of the predefined scale). In some embodiments, approving the first request includes transmitting the first request to a transfer service (e.g., to the transfer service computing system 130) for the transfer service to complete a transfer of funds as indicated by the first transaction. Denying the first request may further include failing to process the first transaction as indicated by the first request. For example, the provider institution computing system 110 may deny the first request if the first transaction receives a legitimacy value that is lower than the highest legitimacy value out of the predefined scale.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the payment narrative generating system of Fort view of method for securing transactions using a generative artificial intelligence model as taught by Singh in order to improve detection of fraudulent transactions (Singh [0015].
Regarding claim 13
receiving, by the client electronic computing device, feedback to the educational text string; and transmitting the feedback to the server. (See at least Fort [0060] A user may be presented with the opportunity to review and/or edit narrative before the narrative is stored. FIG. 8 shows an example of one or more interfaces prompting a user to review the narrative after the narrative has been generated. The user may have selected the option to generate a narrative (e.g., 715). Additionally, or alternatively, the narrative may be generated automatically. The one or more user interfaces shown in FIG. 8 may be displayed, for example, in association with steps 340-350, discussed above. The computing device may generate the narrative and prompt 810 the user to review the narrative. The user may provide an affirmative selection 815 (e.g., “Yes”) to indicate that the user would like to review the generated narrative. The narrative prompt 820 provides the option for a user to “Validate” or “Edit” the narrative.)
Regarding claim 14
wherein the feedback is provided, by the server, to the trained GAIM causing the trained GAIM to be further trained. (See at least Fort [0050] In step 350, the computing device may determine whether the response indicates an approval of the first narrative, an approval of the first narrative with edits, or a rejection of the first narrative. If the response is a rejection (i.e., “N”), the process may go back to step 335. A new narrative may be generated at step 335 and the approval process may repeat. If the response includes an approval, or an approval with edits (i.e., “Y”), the narrative may be stored, in step 355. Additionally, or alternatively, the response may consist of varying results depending on the prompts provided to the user device, as discussed in step 345.
Regarding claim 15
Fort does not specifically teach: further comprising transmitting the another input to the electronic payment service device.
However Singh teaches: [0097] In some embodiments, the response may include approving the first request, at step 425a, or denying the first request, at step 425b. Approving the first request may further include processing the first transaction as indicated by the first request. In some embodiments, the provider institution computing system 110 may approve the first request if the first transaction receives a predefined legitimacy (e.g., the highest legitimacy value out of the predefined scale). In some embodiments, approving the first request includes transmitting the first request to a transfer service (e.g., to the transfer service computing system 130) for the transfer service to complete a transfer of funds as indicated by the first transaction. Denying the first request may further include failing to process the first transaction as indicated by the first request. For example, the provider institution computing system 110 may deny the first request if the first transaction receives a legitimacy value that is lower than the highest legitimacy value out of the predefined scale.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the payment narrative generating system of Fort view of method for securing transactions using a generative artificial intelligence model as taught by Singh in order to improve detection of fraudulent transactions (Singh [0015].
Regarding claims 16 and 20
further comprising simultaneously displaying the confirmation request and the educational text string on a display of the client electronic computing device. (See at least Fort [0058] Following the affirmative selection 715, the computing device may send a narrative 720 associated with the transaction as shown. The user device may display the narrative to the user. For example, narrative 720 states, “On Tuesday, Dec. 5, 2023, Mary Smith purchased a red wool coat from StoreABC on her mobile device while possibly commuting to work in City X given her location was tracked along the L train path to the downtown station. This coat had been trending on social media and given the temperature had dropped precipitously in the Region X over the past few days, it was a prudent purchase.” Narrative 720 is an example of a narrative that may be generated by the one or more machine learning models (e.g., generative AI model) described herein. Additionally, or alternatively, the request 710 may be displayed by the merchant website on the checkout page or the confirmation page for example, once the user has completed the transaction. Additionally, or alternatively, the user may have opted to automatically generate a narrative for any purchases that the user may have made based on a plurality of predetermined factors, such that the user may not be required to provide affirmative selection 715 to generate narrative 720. For example, the user may indicate that narratives may be generated for any purchases made with a specific credit card or bank account, within a period of time, or from a specific store. The user may be able to make a variety of customizations to the narrative generation process.)
Regarding claim 17
from the client electronic computing device, contextual data associated with the payment request, wherein the educational text string is generated based on the contextual data. (See at least Fort [0039] The role of the payment narrative generator may be to create a narrative (e.g., story) around a specific purchase that was made (e.g., transaction, purchase, etc.), with language and contextual details that explain the purchase thoroughly.)
Prior Art of Record Not Currently Relied Upon
Kadhim (US 12,423,703 B1) Teaches: Real time fraud detection and intervention
Abdelrahman (US 2024/0330935 A1) Teaches: Data transfer across layer 2 networks
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY MARK JAMES whose telephone number is (571)272-5155. The examiner can normally be reached M-F 8:30am - 5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon can be reached at 571-270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GREGORY M JAMES/Examiner, Art Unit 3692
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
Prosecution Insights