DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The application has been examined. Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/12/2025, 07/12/2025, 10/12/2025, 01/03/2026, 02/28/2026, and 05/16/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being anticipated over claims 1-17 of the U.S. Patent No. 12,316,715. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claims 1-20 of the instant application and claims 1-17 of the U.S. Patent No. 12,316,715 is that the claims of the instant application discloses the scope of the invention to be broader than to the scope of the U.S. Patent No. 12,316,715.
Claim 1 is rejected on the ground of nonstatutory obviousness-type double patenting as being anticipated over claim 1 of the U.S. Patent No. 12,316,715. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 1 of the instant application and claim 1 of the U.S. Patent No. 12,316,715 is that the claims of the instant application discloses apparatus steps which are broader to the apparatus steps of the U.S. Patent No. 12,316,715.
Claim 9 is rejected on the ground of nonstatutory obviousness-type double patenting as being anticipated over claim 8 of the U.S. Patent No. 12,316,715. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 9 of the instant application and claim 8 of the U.S. Patent No. 12,316,715 is that the claims of the instant application discloses method steps which are broader to the method steps of the U.S. Patent No. 12,316,715.
Claim 17 is rejected on the ground of nonstatutory obviousness-type double patenting as being anticipated over claim 15 of the U.S. Patent No. 12,316,715. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference between claim 17 of the instant application and claim 15 of the U.S. Patent No. 12,316,715 is that the claims of the instant application discloses the non-transitory computer-readable storage medium implementing the method steps which are broader to the non-transitory computer-readable storage medium implementing the method steps of the U.S. Patent No. 12,316,715.
Claims Comparison Table
Instant Application:
19/194,038
U.S. Patent No. 12,316,715 B2
(common inventive entity and assignee)
Claim 1:
An apparatus comprising: a memory; and a processor configured to: train an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents; execute the AI model on identified objects located at a current geographic location of a source device and digital documents to identify document content related to the identified objects, wherein the AI model identifies the document content based on a software library of the AI model; extract the identified document content; generate a message related to the identified document content; and display the message within a chat window on a software application on the source device.
Claim 1:
An apparatus comprising: a memory; and a processor configured to: train an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents, receive global positioning system (GPS) coordinates of a current geographic location of a source device from a software application on the source device, identify objects located at the current geographic location of the source device, identify one or more cards stored within the software application on the source device, retrieve the digital documents associated with the one or more identified cards from a database, execute the trained AI model on the identified objects and the retrieved digital documents to identify the document content related to the identified objects, where the AI model identifies the document content based on a software library of the AI model, extract the identified document content and generate message content that includes the identified document content, and display a chat message with a description of the extracted document content within a chat window on the software application on the source device.
Claim 2:
The apparatus of claim 1, wherein the processor is configured to: identify one or more cards stored within the software application on the source device; and retrieve the digital documents associated with the one or more identified cards from a memory.
Claim 1:
An apparatus comprising: a memory; and a processor configured to: train an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents, receive global positioning system (GPS) coordinates of a current geographic location of a source device from a software application on the source device, identify objects located at the current geographic location of the source device, identify one or more cards stored within the software application on the source device, retrieve the digital documents associated with the one or more identified cards from a database, execute the trained AI model on the identified objects and the retrieved digital documents to identify the document content related to the identified objects, where the AI model identifies the document content based on a software library of the AI model, extract the identified document content and generate message content that includes the identified document content, and display a chat message with a description of the extracted document content within a chat window on the software application on the source device.
Claim 3:
The apparatus of claim 1, wherein the processor is configured to receive feedback about a description of the identified document content from the source device, generate a feedback record that includes the description of the identified document content and the received feedback, and train the AI model based on the generated feedback record.
Claim 2:
The apparatus of claim 1, wherein the processor is configured to receive feedback about the description of the extracted document content from the source device, generate a feedback record including the description of the extracted document content and the received feedback, and train the AI model based on the generated feedback record.
Claim 4:
The apparatus of claim 1, wherein the processor is configured to receive a query via the chat window, convert the query into a vector, identify a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and display the response via the chat window.
Claim 3:
The apparatus of claim 1, wherein the processor is configured to receive a query via the chat window, convert the query into a vector, identify a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and display the response via the chat window.
Claim 5:
The apparatus of claim 1, wherein the processor is configured to simultaneously push two or more chat messages with a description of the identified document content to the chat window on the source device.
Claim 4:
The apparatus of claim 1, wherein the processor is configured to simultaneously push two or more chat messages with the description of the extracted document content to the chat window on the source device.
Claim 6:
The apparatus of claim 1, wherein the AI model comprises a large language model (LLM) which is trained on retrieved digital documents mapped to one or more identified cards.
Claim 5:
The apparatus of claim 1, wherein the AI model comprises a large language model (LLM) which is trained on the retrieved digital documents mapped to the one or more identified cards.
Claim 7:
The apparatus of claim 1, wherein the processor is configured to output the message as an in-app message within a mobile application installed on the source device.
Claim 6:
The apparatus of claim 1, wherein the processor is configured to output the chat message as an in-app message within a mobile application installed on the source device.
Claim 8:
The apparatus of claim 1, wherein the processor is configured to detect a location associated with a host of the software application within a predetermined distance of the current geographic location of the source device and display a content associated with the detected location within the chat window on the source device.
Claim 7:
The apparatus of claim 1, wherein the processor is configured to detect a location associated with a host of the software application within a predetermined distance of the current geographic location of the user device and display a content associated with the detected location within the chat window on the source device.
Claim 9:
A method comprising: training an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents; executing the AI model on identified objects located at a current geographic location of a source device and digital documents to identify document content related to the identified objects, wherein the AI model identifies the document content based on a software library of the AI model; extracting the identified document content; generating a message related to the identified document content; and displaying the message within a chat window on a software application on the source device.
Claim 8:
A method comprising: training an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents; receiving global positioning system (GPS) coordinates of a current geographic location of a source device from a software application on the source device; identifying one or more cards stored within the software application on the source device; retrieving the digital documents associated with the one or more identified cards from a database; identifying objects located at the current geographic location of the source device; executing the trained AI model on the identified objects and the retrieved digital documents to identify the document content related to the identified objects, where the AI model identifies the document content based on a software library of the AI model; extracting the identified document content and generate message content that includes the identified document content; and displaying a chat message with a description of the extracted document content within a chat window on the software application on the source device.
Claim 10:
The method of claim 9, wherein the method further comprises: identifying one or more cards stored within the software application on the source device; and retrieving the digital documents associated with the one or more identified cards from a memory.
Claim 8:
A method comprising: training an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents; receiving global positioning system (GPS) coordinates of a current geographic location of a source device from a software application on the source device; identifying one or more cards stored within the software application on the source device; retrieving the digital documents associated with the one or more identified cards from a database; identifying objects located at the current geographic location of the source device; executing the trained AI model on the identified objects and the retrieved digital documents to identify the document content related to the identified objects, where the AI model identifies the document content based on a software library of the AI model; extracting the identified document content and generate message content that includes the identified document content; and displaying a chat message with a description of the extracted document content within a chat window on the software application on the source device.
Claim 11:
The method of claim 9, wherein the method further comprises receiving feedback about a description of the identified document content from the source device, generating a feedback record including the description of the identified document content and the received feedback, and training the AI model based on the generated feedback record.
Claim 9:
The method of claim 8, wherein the method further comprises receiving feedback about the description of the extracted document content from the source device, generating a feedback record including the description of the extracted document content and the received feedback, and training the AI model based on the generated feedback record.
Claim 12:
The method of claim 9, wherein the method further comprises receiving a query via the chat window, converting the query into a vector, identifying a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and displaying the response via the chat window.
Claim 10:
The method of claim 8, wherein the method further comprises receiving a query via the chat window, converting the query into a vector, identifying a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and displaying the response via the chat window.
Claim 13:
The method of claim 9, wherein the displaying comprises simultaneously displaying two or more chat messages with a description of the identified document content to the chat window on the source device.
Claim 11:
The method of claim 8, wherein the displaying comprises simultaneously displaying two or more chat messages with the description of the extracted document content to the chat window on the source device.
Claim 14:
The method of claim 9, wherein the AI model comprises a large language model (LLM) which is trained on retrieved digital documents mapped to one or more identified cards.
Claim 12:
The method of claim 8, wherein the AI model comprises a large language model (LLM) which is trained on the retrieved digital documents mapped to the one or more identified cards.
Claim 15:
The method of claim 9, wherein the displaying comprises outputting the message as an in-app message within a mobile application installed on the source device.
Claim 13:
The method of claim 8, wherein the displaying comprises outputting the chat message as in-app message within a mobile application installed on the user source device.
Claim 16:
The method of claim 9, wherein the method further comprises detecting a location associated with a host of the software application within a predetermined distance of the current geographic location of the source device and displaying a content associated with the detected location within the chat window on the source device.
Claim 14:
The method of claim 8, wherein the method further comprises detecting a location associated with a host of the software application within a predetermined distance of the current geographic location of the user device and displaying a content associated with the detected location within the chat window on the source device.
Claim 17:
A non-transitory computer-readable storage medium comprising instructions stored therein which when executed by a processor cause a computer to perform: training an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents; executing the AI model on identified objects located at a current geographic location of a source device and digital documents to identify document content related to the identified objects, wherein the AI model identifies the document content based on a software library of the AI model; extracting the identified document content; generating a message related to the identified document content; and displaying the message within a chat window on a software application on the source device.
Claim 15:
A non-transitory computer-readable storage medium comprising instructions stored therein which when executed by a processor cause a computer to perform: training an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents; receiving global positioning system (GPS) coordinates of a current geographic location of a source device from a software application on the source device; identifying one or more cards stored within the software application on the source device; retrieving the digital documents associated with the one or more identified cards from a database; identifying objects located at the current geographic location of the source device; executing the trained AI model on the identified objects and the retrieved digital documents to identify the document content related to the identified objects, where the AI model identifies the document content based on a software library of the AI model; extracting the identified document content and generate message content that includes the identified document content; and displaying a chat message with a description of the extracted document content within a chat window on the software application on the source device.
Claim 18:
The non-transitory computer-readable storage medium of claim 17, wherein the processor is further configured to perform: identifying one or more cards stored within the software application on the source device; and retrieving the digital documents associated with the one or more identified cards from a memory.
Claim 15:
A non-transitory computer-readable storage medium comprising instructions stored therein which when executed by a processor cause a computer to perform: training an artificial intelligence (AI) model using a neural network capability with object identifiers mapped to document content within digital documents; receiving global positioning system (GPS) coordinates of a current geographic location of a source device from a software application on the source device; identifying one or more cards stored within the software application on the source device; retrieving the digital documents associated with the one or more identified cards from a database; identifying objects located at the current geographic location of the source device; executing the trained AI model on the identified objects and the retrieved digital documents to identify the document content related to the identified objects, where the AI model identifies the document content based on a software library of the AI model; extracting the identified document content and generate message content that includes the identified document content; and displaying a chat message with a description of the extracted document content within a chat window on the software application on the source device.
Claim 19:
The non-transitory computer-readable storage medium of claim 17, wherein the processor is further configured to perform receiving feedback about a description of the identified document content from the source device, generating a feedback record including the description of the identified document content and the received feedback, and training the AI model based on the generated feedback record.
Claim 16:
The non-transitory computer-readable storage medium of claim 15, wherein the processor is further configured to perform receiving feedback about the description of the extracted document content from the source device, generating a feedback record including the description of the extracted document content and the received feedback, and training the AI model based on the generated feedback record.
Claim 20:
The non-transitory computer-readable storage medium of claim 17, wherein the processor is further configured to perform receiving a query via the chat window, converting the query into a vector, identifying a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and displaying the response via the chat window.
Claim 17:
The non-transitory computer-readable storage medium of claim 15, wherein the processor is further configured to perform receiving a query via the chat window, converting the query into a vector, identifying a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and displaying the response via the chat window.
Claim Objections
Claims 1, 6, 9, 14, and 17 are objected to because of the following informalities: lack of terminology consistency
Claim 1, line 6, recites “execute the AI model” and should be changed to -- execute the trained AI model --.
Similar changes are suggested for subsequent claims.
Claim 6, line 1, recites “claim 1” and should be changed to -- claim [[1]]2--.
Similar changes are suggested for subsequent claims.
Appropriate corrections are required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
Claims 1-3, 6-11, and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Whelan et al. (2022/0277323, hereinafter Whelan) in view of Baeuml et al. (2023/0074406, hereinafter Baeuml).
Regarding claim 1, Whelan discloses an apparatus comprising:
a memory (Whelan, para. 103); and
a processor (Whelan, para. 103) configured to:
train an artificial intelligence (AI) model using a neural network capability (Whelan discloses that the adaptive training and validation module 172 may provide candidate process data 174 and candidate input data 176 as inputs to execute training input module 166 of the training engine 162, which is enabled to validate the predictive capability and accuracy of the adaptively trained (train an AI model)) (Whelan, para. 86) with object identifiers mapped to document content within digital documents (Whelan discloses that the data records of account data 112B (document content) may include financial products, one or more identifiers (object identifiers) of the financial products (e.g., account identifiers, expiration data, card-security code, etc.), corresponding product identifiers, customer identifiers, additional information characterizing a balance or status, payments or amounts, etc.) (Whelan, para. 30);
execute the AI model on identified objects located at a source device (Whelan discloses that the adaptive training and validation module 172 may provide candidate process data 174 and candidate input data 176 as inputs to execute training input module 166 of the training engine 162, which is enabled to validate the predictive capability and accuracy of the adaptively trained (train an AI model)) (Whelan, para. 86) and digital documents to identify document content related to the identified objects (Whelan discloses that the data records may include customer profile data 112A, account data 112B, transaction data 112C, and/or digital-access data 114B) (Whelan, para. 44), wherein the AI model identifies the document content based on a software library of the AI model (Whelan discloses that the FI computing system 130 may generate model coefficients, parameters, thresholds, and other modelling data that collectively specify the trained machine learning or AI process and may store the generated model within the consolidated data store 144) (Whelan, para. 53); and
extract the identified document content (Whelan discloses that the consolidated data records are processed to obtain or extract one or more features values within the first subset 168A) (Whelan, para. 81).
Whelan does not explicitly disclose execute the AI model on identified objects located at a current geographic location of a source device; generate a message related to the identified document content; and display the message within a chat window on a software application on the source device.
In analogous art, Baeuml teaches execute the AI model on identified objects located at a current geographic location of a source device (Baeuml discloses that the detected movement, a location of the user may be predicted (execute the AI model), and this location may be assumed to be the user’s location when any content (identified objects) is caused to be rendered at the client device 110 and/or other computing device(s) based on the proximity of the client device 110 and/or other computing device(s) to the user’s location (current geographic location)) (Baeuml, para. 39);
generate a message related to the identified document content (Baeuml discloses that the display 680 of the client device 610 can include various system interface elements that may be interacted (generate a message) with by a user of the client device 610 to perform one or more actions (identified document content)) (Baeuml, para. 93); and
display the message within a chat window on a software application on the source device (Baeuml discloses that the user of the client device 610 (source device) is provided with a visual and/or audio presentation (display the message within the chat window) from a spoken utterance 660 to the automated assistant (software application)) (Baeuml, para. 102).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Baeuml related to execute the AI model located at a current geographic location, generate a message related to the identified document content, and display the message within a chat window and to combine with Whelan in order to decrease and conserve the computational resources at the client device and cause the dialog sessions to be reduced and concluded in a quicker and more efficient manner (Baeuml, para. 19).
Regarding claim 2, Whelan and Baeuml discloses the apparatus of claim 1, wherein the processor is configured to:
identify one or more cards stored within the software application on the source device (Whelan discloses that the consolidated data records may include data identifying and characterizing financial products (identify cards) held by the corresponding customer, digital platforms of the financial institution, ATMs, or digital platforms, etc.) (Whelan, para. 82); and
retrieve the digital documents associated with the one or more identified cards from a memory (Whelan discloses that the source data repository 113 that includes interaction data 114 that identify, and characterize discrete interactions between customers of the financial institution and retail locations in the geographic regions, which includes data records of branch access data 114A (e.g., unique customer identifier, bank branch, temporal data (time and date))) (Whelan, para. 34).
Regarding claim 3, Whelan and Baeuml discloses the apparatus of claim 1, wherein the processor is configured to receive feedback about a description of the identified document content from the source device (Whelan discloses that the user interaction provides the user with any feedback such as sensory, visual, audio, or tactile, where the computer can interact with a user by sending documents to and receiving documents in response to the request(s)) (Whelan, para. 166), generate a feedback record that includes the description of the identified document content and the received feedback (Whelan discloses that the customer profile data 112A includes information of the particular customer, data records that corresponds with elements of temporal data (time and date), and data records (generated feedback records) of temporal evolution in parameter values (description of identified content and received feedback)) (Whelan, para. 28), and train the AI model based on the generated feedback record (Whelan discloses that the Fl computing system 130 may ingest data records (generated feedback record) of the customer profile, account, transactions, branch-access, and/or digital access data from the source systems 110 for adaptive training of the ML or AI processes (train the AI model)) (Whelan, para. 50).
Regarding claim 6, Whelan and Baeuml discloses the apparatus of claim 1, wherein the AI model comprises a large language model (LLM) which is trained on retrieved digital documents mapped to one or more identified cards (Whelan discloses that the training datasets 170 (Large Language Models) include additional or alternate element of data extracted or obtained from the consolidated data records (identified cards) of the first subset, associated with the corresponding one of the customers) (Whelan, para. 81).
Regarding claim 7, Whelan and Baeuml discloses the apparatus of claim 1, wherein the processor is configured to output the message as an in-app message within a mobile application installed on the source device (Baeuml discloses that the client device 110 and/or the natural conversation system 120 can have one or more software applications (in-app message within a mobile application) be installed locally at the client device 110) (Baeuml, para. 40).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Baeuml related to output the message as in-app message within a mobile application and to combine with Whelan and Baeuml in order to decrease and conserve the computational resources at the client device and cause the dialog sessions to be reduced and concluded in a quicker and more efficient manner (Baeuml, para. 19).
Regarding claim 8, Whelan and Baeuml discloses the apparatus of claim 1, wherein the processor is configured to detect a location associated with a host of the software application within a predetermined distance of the current geographic location of the source device (Baeuml discloses that the users are provided with an opportunity to control whether programs or features collect user information (e.g., user’s preferences, users’ current geographic location), which a generalized geographic location (predetermined distance) is obtained and not a particular location of the user) (Baeuml, para. 115) and display a content associated with the detected location within the chat window on the source device (Baeuml discloses that the client device 610 being located at a particular location, the automated assistant provides a current time and spoken utterance and corresponding context of the dialog session) (Baeuml, para. 95).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Baeuml related to detect a location associated with a host of the software application within a predetermined distance and display a content and to combine with Whelan and Baeuml in order to decrease and conserve the computational resources at the client device and cause the dialog sessions to be reduced and concluded in a quicker and more efficient manner (Baeuml, para. 19).
Regarding claim 9, Whelan discloses a method comprising:
training an artificial intelligence (AI) model using a neural network capability (Whelan discloses that the adaptive training and validation module 172 may provide candidate process data 174 and candidate input data 176 as inputs to execute training input module 166 of the training engine 162, which is enabled to validate the predictive capability and accuracy of the adaptively trained (train an AI model)) (Whelan, para. 86) with object identifiers mapped to document content within digital documents (Whelan discloses that the data records of account data 112B (document content) may include financial products, one or more identifiers (object identifiers) of the financial products (e.g., account identifiers, expiration data, card-security code, etc.), corresponding product identifiers, customer identifiers, additional information characterizing a balance or status, payments or amounts, etc.) (Whelan, para. 30);
executing the AI model on identified objects located at a source device (Whelan discloses that the adaptive training and validation module 172 may provide candidate process data 174 and candidate input data 176 as inputs to execute training input module 166 of the training engine 162, which is enabled to validate the predictive capability and accuracy of the adaptively trained (train an AI model)) (Whelan, para. 86) and digital documents to identify document content related to the identified objects (Whelan discloses that the data records may include customer profile data 112A, account data 112B, transaction data 112C, and/or digital-access data 114B) (Whelan, para. 44), wherein the AI model identifies the document content based on a software library of the AI model (Whelan discloses that the FI computing system 130 may generate model coefficients, parameters, thresholds, and other modelling data that collectively specify the trained machine learning or AI process and may store the generated model within the consolidated data store 144) (Whelan, para. 53); and
extracting the identified document content (Whelan discloses that the consolidated data records are processed to obtain or extract one or more features values within the first subset 168A) (Whelan, para. 81).
Whelan does not explicitly disclose executing the AI model on identified objects located at a current geographic location of a source device; generating a message related to the identified document content; and displaying the message within a chat window on a software application on the source device.
In analogous art, Baeuml teaches executing the AI model on identified objects located at a current geographic location of a source device (Baeuml discloses that the detected movement, a location of the user may be predicted (execute the AI model), and this location may be assumed to be the user’s location when any content (identified objects) is caused to be rendered at the client device 110 and/or other computing device(s) based on the proximity of the client device 110 and/or other computing device(s) to the user’s location (current geographic location)) (Baeuml, para. 39);
generating a message related to the identified document content (Baeuml discloses that the display 680 of the client device 610 can include various system interface elements that may be interacted (generate a message) with by a user of the client device 610 to perform one or more actions (identified document content)) (Baeuml, para. 93); and
displaying the message within a chat window on a software application on the source device (Baeuml discloses that the user of the client device 610 (source device) is provided with a visual and/or audio presentation (display the message within the chat window) from a spoken utterance 660 to the automated assistant (software application)) (Baeuml, para. 102).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Baeuml related to execute the AI model located at a current geographic location, generate a message related to the identified document content, and display the message within a chat window and to combine with Whelan in order to decrease and conserve the computational resources at the client device and cause the dialog sessions to be reduced and concluded in a quicker and more efficient manner (Baeuml, para. 19).
Regarding claim 10, Whelan and Baeuml discloses the method of claim 9, wherein the method further comprises:
identifying one or more cards stored within the software application on the source device (Whelan discloses that the consolidated data records may include data identifying and characterizing financial products (identify cards) held by the corresponding customer, digital platforms of the financial institution, ATMs, or digital platforms, etc.) (Whelan, para. 82); and
retrieving the digital documents associated with the one or more identified cards from a memory (Whelan discloses that the source data repository 113 that includes interaction data 114 that identify, and characterize discrete interactions between customers of the financial institution and retail locations in the geographic regions, which includes data records of branch access data 114A (e.g., unique customer identifier, bank branch, temporal data (time and date))) (Whelan, para. 34).
Regarding claim 11, Whelan and Baeuml discloses the method of claim 9, wherein the method further comprises receiving feedback about a description of the identified document content from the source device (Whelan discloses that the user interaction provides the user with any feedback such as sensory, visual, audio, or tactile, where the computer can interact with a user by sending documents to and receiving documents in response to the request(s)) (Whelan, para. 166), generating a feedback record including the description of the identified document content and the received feedback (Whelan discloses that the customer profile data 112A includes information of the particular customer, data records that corresponds with elements of temporal data (time and date), and data records (generated feedback records) of temporal evolution in parameter values (description of identified content and received feedback)) (Whelan, para. 28), and training the AI model based on the generated feedback record (Whelan discloses that the Fl computing system 130 may ingest data records (generated feedback record) of the customer profile, account, transactions, branch-access, and/or digital access data from the source systems 110 for adaptive training of the ML or AI processes (train the AI model)) (Whelan, para. 50).
Regarding claim 14, Whelan and Baeuml discloses the method of claim 9, wherein the AI model comprises a large language model (LLM) which is trained on retrieved digital documents mapped to one or more identified cards (Whelan discloses that the training datasets 170 (Large Language Models) include additional or alternate element of data extracted or obtained from the consolidated data records (identified cards) of the first subset, associated with the corresponding one of the customers) (Whelan, para. 81).
Regarding claim 15, Whelan and Baeuml discloses the method of claim 9, wherein the displaying comprises outputting the message as an in-app message within a mobile application installed on the source device (Baeuml discloses that the client device 110 and/or the natural conversation system 120 can have one or more software applications (in-app message within a mobile application) be installed locally at the client device 110) (Baeuml, para. 40).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Baeuml related to output the message as in-app message within a mobile application and to combine with Whelan and Baeuml in order to decrease and conserve the computational resources at the client device and cause the dialog sessions to be reduced and concluded in a quicker and more efficient manner (Baeuml, para. 19).
Regarding claim 16, Whelan and Baeuml discloses the method of claim 9, wherein the method further comprises detecting a location associated with a host of the software application within a predetermined distance of the current geographic location of the source device (Baeuml discloses that the users are provided with an opportunity to control whether programs or features collect user information (e.g., user’s preferences, users’ current geographic location), which a generalized geographic location (predetermined distance) is obtained and not a particular location of the user) (Baeuml, para. 115) and displaying a content associated with the detected location within the chat window on the source device (Baeuml discloses that the client device 610 being located at a particular location, the automated assistant provides a current time and spoken utterance and corresponding context of the dialog session) (Baeuml, para. 95).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Baeuml related to detect a location associated with a host of the software application within a predetermined distance and display a content and to combine with Whelan and Baeuml in order to decrease and conserve the computational resources at the client device and cause the dialog sessions to be reduced and concluded in a quicker and more efficient manner (Baeuml, para. 19).
Regarding claim 17, Whelan discloses a non-transitory computer-readable storage medium (Whelan, para. 103) comprising instructions stored therein which when executed by a processor (Whelan, para. 103) cause a computer to perform:
training an artificial intelligence (AI) model using a neural network capability (Whelan discloses that the adaptive training and validation module 172 may provide candidate process data 174 and candidate input data 176 as inputs to execute training input module 166 of the training engine 162, which is enabled to validate the predictive capability and accuracy of the adaptively trained (train an AI model)) (Whelan, para. 86) with object identifiers mapped to document content within digital documents (Whelan discloses that the data records of account data 112B (document content) may include financial products, one or more identifiers (object identifiers) of the financial products (e.g., account identifiers, expiration data, card-security code, etc.), corresponding product identifiers, customer identifiers, additional information characterizing a balance or status, payments or amounts, etc.) (Whelan, para. 30);
executing the AI model on identified objects located at a source device (Whelan discloses that the adaptive training and validation module 172 may provide candidate process data 174 and candidate input data 176 as inputs to execute training input module 166 of the training engine 162, which is enabled to validate the predictive capability and accuracy of the adaptively trained (train an AI model)) (Whelan, para. 86) and digital documents to identify document content related to the identified objects (Whelan discloses that the data records may include customer profile data 112A, account data 112B, transaction data 112C, and/or digital-access data 114B) (Whelan, para. 44), wherein the AI model identifies the document content based on a software library of the AI model (Whelan discloses that the FI computing system 130 may generate model coefficients, parameters, thresholds, and other modelling data that collectively specify the trained machine learning or AI process and may store the generated model within the consolidated data store 144) (Whelan, para. 53); and
extracting the identified document content (Whelan discloses that the consolidated data records are processed to obtain or extract one or more features values within the first subset 168A) (Whelan, para. 81).
In analogous art, Baeuml teaches executing the AI model on identified objects located at a current geographic location of a source device (Baeuml discloses that the detected movement, a location of the user may be predicted (execute the AI model), and this location may be assumed to be the user’s location when any content (identified objects) is caused to be rendered at the client device 110 and/or other computing device(s) based on the proximity of the client device 110 and/or other computing device(s) to the user’s location (current geographic location)) (Baeuml, para. 39);
generating a message related to the identified document content (Baeuml discloses that the display 680 of the client device 610 can include various system interface elements that may be interacted (generate a message) with by a user of the client device 610 to perform one or more actions (identified document content)) (Baeuml, para. 93); and
displaying the message within a chat window on a software application on the source device (Baeuml discloses that the user of the client device 610 (source device) is provided with a visual and/or audio presentation (display the message within the chat window) from a spoken utterance 660 to the automated assistant (software application)) (Baeuml, para. 102).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Baeuml related to execute the AI model located at a current geographic location, generate a message related to the identified document content, and display the message within a chat window and to combine with Whelan in order to decrease and conserve the computational resources at the client device and cause the dialog sessions to be reduced and concluded in a quicker and more efficient manner (Baeuml, para. 19).
Regarding claim 18, Whelan and Baeuml discloses the non-transitory computer-readable storage medium of claim 17, wherein the processor is further configured to perform:
identifying one or more cards stored within the software application on the source device (Whelan discloses that the consolidated data records may include data identifying and characterizing financial products (identify cards) held by the corresponding customer, digital platforms of the financial institution, ATMs, or digital platforms, etc.) (Whelan, para. 82); and
retrieving the digital documents associated with the one or more identified cards from a memory (Whelan discloses that the source data repository 113 that includes interaction data 114 that identify, and characterize discrete interactions between customers of the financial institution and retail locations in the geographic regions, which includes data records of branch access data 114A (e.g., unique customer identifier, bank branch, temporal data (time and date))) (Whelan, para. 34).
Regarding claim 19, Whelan and Baeuml discloses the non-transitory computer-readable storage medium of claim 17, wherein the processor is further configured to perform receiving feedback about a description of the identified document content from the source device (Whelan discloses that the user interaction provides the user with any feedback such as sensory, visual, audio, or tactile, where the computer can interact with a user by sending documents to and receiving documents in response to the request(s)) (Whelan, para. 166), generating a feedback record including the description of the identified document content and the received feedback (Whelan discloses that the customer profile data 112A includes information of the particular customer, data records that corresponds with elements of temporal data (time and date), and data records (generated feedback records) of temporal evolution in parameter values (description of identified content and received feedback)) (Whelan, para. 28), and training the AI model based on the generated feedback record (Whelan discloses that the Fl computing system 130 may ingest data records (generated feedback record) of the customer profile, account, transactions, branch-access, and/or digital access data from the source systems 110 for adaptive training of the ML or AI processes (train the AI model)) (Whelan, para. 50).
Claims 4, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Whelan et al. (2022/0277323, hereinafter Whelan) in view of Baeuml et al. (2023/0074406, hereinafter Baeuml) as applied to claims 1, 9, and 17 above, and further in view of Batina et al. (2024/0289361, hereinafter Batina).
Regarding claim 4, Whelan and Baeuml discloses the apparatus of claim 1, but does not explicitly disclose wherein the processor is configured to receive a query via the chat window, convert the query into a vector, identify a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and display the response via the chat window.
In analogous art, Batina teaches wherein the processor is configured to receive a query via the chat window, convert the query into a vector (Batina discloses that the chatbot may present prompts for the user (receives a query) to identify attributes that the user likes (converts into a vector) and the system may determine features of the selection in the vector space based on the identified common attributes of selected products) (Batina, para. 45), identify a response that matches the converted query based on a comparison of the vector and a response that has been vectorized (Batina discloses that the generated enhancement data comprise text that results in a vector embedding to be used in a search) (Batina, para. 35), and display the response via the chat window (Batina discloses that the user interfaces may be updated in real-time based on the user interaction(s) (e.g., input of search query, user responses, etc.) with other of the user interfaces) (Batina, para. 96).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Batina related to receiving a query and converting to a vector, and display a response and to combine with Whelan and Baeuml in order to enhance the convenience of providing the most relevant products to the user (Batina, para. 45).
Regarding claim 12, Whelan and Baeuml discloses the method of claim 9, but does not explicitly disclose wherein the method further comprises receiving a query via the chat window, converting the query into a vector, identifying a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and displaying the response via the chat window.
In analogous art, Batina teaches wherein the processor is configured to receive a query via the chat window, convert the query into a vector (Batina discloses that the chatbot may present prompts for the user (receives a query) to identify attributes that the user likes (converts into a vector) and the system may determine features of the selection in the vector space based on the identified common attributes of selected products) (Batina, para. 45), identify a response that matches the converted query based on a comparison of the vector and a response that has been vectorized (Batina discloses that the generated enhancement data comprise text that results in a vector embedding to be used in a search) (Batina, para. 35), and display the response via the chat window (Batina discloses that the user interfaces may be updated in real-time based on the user interaction(s) (e.g., input of search query, user responses, etc.) with other of the user interfaces) (Batina, para. 96).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Batina related to receiving a query and converting to a vector, and display a response and to combine with Whelan and Baeuml in order to enhance the convenience of providing the most relevant products to the user (Batina, para. 45).
Regarding claim 20, Whelan and Baeuml discloses the non-transitory computer-readable storage medium of claim 17, but does not explicitly disclose wherein the processor is further configured to perform receiving a query via the chat window, converting the query into a vector, identifying a response that matches the converted query based on a comparison of the vector and a response that has been vectorized, and displaying the response via the chat window.
In analogous art, Batina teaches wherein the processor is further configured to perform receiving a query via the chat window, converting the query into a vector (Batina discloses that the chatbot may present prompts for the user (receives a query) to identify attributes that the user likes (converts into a vector) and the system may determine features of the selection in the vector space based on the identified common attributes of selected products) (Batina, para. 45), identifying a response that matches the converted query based on a comparison of the vector and a response that has been vectorized (Batina discloses that the generated enhancement data comprise text that results in a vector embedding to be used in a search) (Batina, para. 35), and displaying the response via the chat window (Batina discloses that the user interfaces may be updated in real-time based on the user interaction(s) (e.g., input of search query, user responses, etc.) with other of the user interfaces) (Batina, para. 96).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Batina related to receiving a query and converting to a vector, and display a response and to combine with Whelan and Baeuml in order to enhance the convenience of providing the most relevant products to the user (Batina, para. 45).
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Whelan et al. (2022/0277323, hereinafter Whelan) in view of Baeuml et al. (2023/0074406, hereinafter Baeuml) as applied to claims 1 and 9 above, and further in view of Fabian et al. (2024/0303423, hereinafter Fabian).
Regarding claim 5, Whelan and Baeuml discloses the apparatus of claim 1, but does not explicitly disclose wherein the processor is configured to simultaneously push two or more chat messages with a description of the identified document content to the chat window on the source device.
In analogous art, Batina teaches wherein the processor is configured to simultaneously push two or more chat messages with a description of the identified document content to the chat window on the source device (Fabian discloses that in the task pane 146 of the user experience 145, the application service 110 configures and displays suggested actions (simultaneously push two or more chat messages) based on the chat history and spreadsheet contextual information) (Fabian, para. 50).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Fabian related to simultaneously push two or more messages and to combine with Whelan and Baeuml in order to enhance the efficiency of providing the user with specific results or suggestions based receiving the user’s inputs (Fabian, para. 40).
Regarding claim 13, Whelan and Baeuml discloses the method of claim 9, but does not explicitly disclose wherein the displaying comprises simultaneously displaying two or more chat messages with a description of the identified document content to the chat window on the source device.
In analogous art, Batina teaches wherein the displaying comprises simultaneously displaying two or more chat messages with a description of the identified document content to the chat window on the source device (Fabian discloses that in the task pane 146 of the user experience 145, the application service 110 configures and displays suggested actions (simultaneously push two or more chat messages) based on the chat history and spreadsheet contextual information) (Fabian, para. 50).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to take the teachings of Fabian related to simultaneously push two or more messages and to combine with Whelan and Baeuml in order to enhance the efficiency of providing the user with specific results or suggestions based receiving the user’s inputs (Fabian, para. 40).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ma et al. (2023/0085061) discloses the updating response generation by artificial intelligence or chatbot.
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/ANDREW WOO/Examiner, Art Unit 2458
/UMAR CHEEMA/Supervisory Patent Examiner, Art Unit 2458
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