Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This action is in response to the original filing on 07/24/2024. Claims 1-20 are pending and have been considered below.
Double Patenting
3. 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); 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).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 12,093,919 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following mapping below. Each corresponding limitation is either identical or does not have a patentable, nonobvious distinction unless otherwise noted.
Instant Application 18/782,090
Patent No.: US 12,093,919 B2
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Claim Rejections – 35 USC § 103
4. 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.
5. Claims 1-7, 9-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Moon et al. (U.S. Patent Application Pub. No. US 20200236071 A1) in view of Schafer et al. (U.S. Patent Application Pub. No. US 20200097718 A1).
Claim 1: Moon teaches a system for automatically generating resource distributions (i.e. transaction system 130 may be associated with, or may be operated by, a financial institution that provides financial services to customers, such as, but not limited to user 101, and the financial institution may provide financial services to these customers that include, but are not limited to, an initiation and execution of one or more payment transactions (e.g., a peer-to-peer transaction, a digital bill-payment transaction, etc.) or one or more purchase transactions (e.g., involving digital or physical retailers); para. [0028, 0129]), the system comprising:
a parser comprising (i.e. NLP engine 144 may parse the received message data to identify one or more discrete linguistic elements (e.g., a word, a combination of morphemes, a single morpheme, etc.), and to generate contextual information that establishes the meaning or a context of one or more discrete linguistic elements; para. [0038]) a neural network (i.e. the one or more natural language processing algorithms may also include one or more artificial intelligence models, such as, but not limited to, an artificial neural network model, a recurrent neural network model, a Bayesian network model, or a Markov model; para. [0072]), a named entity recognition component (i.e. NLP engine 144 may determine that message 208 corresponds to a request to initiate an exchange of data (e.g., a peer-to-peer (P2P) transaction involving user 101 and “Mike”), and that message 208 specifies at least an incomplete portion of one or more parameter values that characterize the data exchange (e.g., a portion of an identifier of a counterparty to the P2P transaction (e.g., “Mike”) and data characterizing a product or service associated with the P2P transaction (e.g., the “hockey tickets”); para. [0071-0074]), an entity identifier component (i.e. NLP engine 144 may determine that message 208 corresponds to a request to initiate an exchange of data (e.g., a peer-to-peer (P2P) transaction involving user 101 and “Mike”), and that message 208 specifies at least an incomplete portion of one or more parameter values that characterize the data exchange (e.g., a portion of an identifier of a counterparty to the P2P transaction (e.g., “Mike”) and data characterizing a product or service associated with the P2P transaction (e.g., the “hockey tickets”); para. [0071-0074]), an entity sorter component (i.e. Examples of these transaction parameters include, but are not limited to, payer profile information, payee profile information (e.g., an identifier of the counterparty), source and destination accounts, and a transaction amount; para. [0034, 0035, 0077-0080]), and an assembler component (i.e. NLP engine 144 may package data identifying a particular type of the data exchange (e.g., the P2P transaction described herein) and data identifying the completely, or incompletely, specified ones of the parameter values of the data exchange (e.g., the portion of the counterparty identifier (e.g., “Mike”) and the data characterizing the product or service (e.g., the “hockey tickets”) into corresponding portions of contextual information 328; para. [0074]);
a decision engine (i.e. when executed by transaction system 130, transaction engine 148 can perform operations to initiate one or more exchanges of data, such as the exemplary payment or purchase transactions described herein, in accordance with one or more received, obtained, or dynamically determined, parameter values; para. [0044]);
a historical user information data structure comprising historical user data (i.e. predictive engine 146 may access customer database 132, and obtain first user data 342A, which characterizes user 101, and second user data 342B, which characterizes one or more additional users associated with transaction system 130 (e.g., additional customers of the financial institution that operates transaction system 130); para. [0083]);
a historical resource distribution information data structure comprising historical resource distribution data (i.e. predictive engine 146 may also access transaction database 134, and obtain first transaction data 344A, which characterizes prior exchanges of data involving user 101, and second transaction data 344B, which characterizes prior exchanges of data involving the additional users associated with transaction system 130; para. [0085]);
at least one non-transitory storage device (i.e. non-transitory memories that store data and/or software instructions, such as application repository 106; para. [0018]); and
at least one processing device coupled to the parser, the decision engine, the historical user information data structure, the historical resource distribution information data structure, and the at least one non-transitory storage device, wherein the at least one processing device is configured to (i.e. the one or more servers 160 may each include one or more processor-based computing devices, which may be configured to execute portions of the stored code or application modules to perform operations consistent with the disclosed embodiments; para. [0026]):
receive a text-based instruction (i.e. session data 302 may include message data 304, which represents message 208 provided by user 101 as an input to chatbot interface 200 of FIG. 2B (e.g., “I need to pay Mike for the hockey tickets”); para. [0068]);
preprocess, using the parser, the text-based instruction into text (i.e. convert the audio content into text corresponding to message 208 … a session management module 324 of chatbot engine 142 may receive session data 302, and may parse session data 302 to extract message data 304; para. [0064, 0070]);
access the neural network (i.e. the one or more natural language processing algorithms may also include one or more artificial intelligence models, such as, but not limited to, an artificial neural network model, a recurrent neural network model, a Bayesian network model, or a Markov model; para. [0072]), where the neural network has been trained using (i) and (ii) historical text-based instruction training data comprising historical text-based instructions and historical resource distributions generated in response to receiving the historical text-based instructions (i.e. artificial intelligence models can be trained against, and adaptively improved using, training data having a specified composition, which may be extracted from portions of customer database 132, transaction database 134, and/or a chatbot session database 136 … session log that includes raw or processed information that identifies and characterizes each of the messages exchanged programmatically between client device 102; para. [0040, 0085-0088]);
predict, using the named entity recognition component, predicted distribution entities and predicted distribution elements based on the text (i.e. NLP engine 144 may determine that message 208 corresponds to a request to initiate an exchange of data (e.g., a peer-to-peer (P2P) transaction involving user 101 and “Mike”), and that message 208 specifies at least an incomplete portion of one or more parameter values that characterize the data exchange (e.g., a portion of an identifier of a counterparty to the P2P transaction (e.g., “Mike”) and data characterizing a product or service associated with the P2P transaction (e.g., the “hockey tickets”); para. [0071-0074]);
identify, using the entity identifier component distribution entities and distribution elements from the predicted distribution entities and the predicted distribution elements (i.e. NLP engine 144 may determine that message 208 corresponds to a request to initiate an exchange of data (e.g., a peer-to-peer (P2P) transaction involving user 101 and “Mike”), and that message 208 specifies at least an incomplete portion of one or more parameter values that characterize the data exchange (e.g., a portion of an identifier of a counterparty to the P2P transaction (e.g., “Mike”) and data characterizing a product or service associated with the P2P transaction (e.g., the “hockey tickets”); para. [0071-0074]);
sort, using the entity sorter component, the distribution entities and the distribution elements into pre-defined distribution fields (i.e. the data records of digital interface database 138 may include elements of layout data that identify one or more discrete interface elements (e.g., fillable text boxes, etc.) included within the digital payment interface and position of these discrete interface elements within the digital payment interface, along with corresponding elements of metadata that identify values (or ranges of values) of transaction parameters associated with each of the discrete interface elements. Examples of these transaction parameters include, but are not limited to, payer profile information, payee profile information (e.g., an identifier of the counterparty), source and destination accounts, and a transaction amount; para. [0034, 0035, 0077-0080]);
assemble, using the assembler component, the distribution entities and the distribution elements in the pre-defined distribution fields into a structured resource distribution (i.e. NLP engine 144 may package data identifying a particular type of the data exchange (e.g., the P2P transaction described herein) and data identifying the completely, or incompletely, specified ones of the parameter values of the data exchange (e.g., the portion of the counterparty identifier (e.g., “Mike”) and the data characterizing the product or service (e.g., the “hockey tickets”) into corresponding portions of contextual information 328 … predictive engine 146 may perform operations that package each of the candidate values for the parameters that characterize the referenced data exchange, e.g., the P2P transaction described herein, into corresponding elements of candidate parameter value data 350; para. [0074, 0101, 0102, 0120]);
determine, using the decision engine, whether the structured resource distribution comprises actual distribution elements (i.e. NLP engine 144 may provide linguistic element data 326 and contextual information 328 as inputs to transaction engine 148 that, when executed by transaction system 130, performs any of the exemplary processes described herein to determine whether contextual information 328 includes parameter values sufficient to initiate and execute the corresponding type of data exchange, and if not, to dynamically predict values of one of more additional data-exchange parameters that, when combined within the parameter values within contextual information 328, facilitate an initiation and execute of the corresponding type of data exchange by transaction system 130; para. [0074, 0077-0081]);
determine, in response to determining that the structured resource distribution does not comprise the actual distribution elements (i.e. Based on comparison of identified parameter data 336 and metadata 338, the parameter determination module 334 may establish that identified parameter data 336 fails to include one or more of the minimum set of parameter values necessary to initiate and execute the particular type of data exchange, e.g., the P2P transaction. For example, parameter determination module 335 may determine that identified parameter data 336 fails to specify, among other things, the proper counterparty identifier (e.g., beyond the extracted first name “Mike”), the identifiers of the source and destination accounts, and the transaction amount, date, and time; para. [0081, 0082, 0090]), the actual distribution elements by accessing the historical user data in the historical user information data structure and the historical resource distribution data in the historical resource distribution information data structure (i.e. predictive engine 146 may access customer database 132, and obtain first user data 342A, which characterizes user 101, and second user data 342B, which characterizes one or more additional users associated with transaction system 130 (e.g., additional customers of the financial institution that operates transaction system 130); para. [0083, 0085, 0091, 0092]); and
generate, based on the actual distribution elements, a resource distribution (i.e. transaction engine 148 may perform operations that initiate the requested P2P transaction in accordance with corresponding ones of populated parameter values 476, e.g., an electronic transfer of $550.00 from the deposit account of user 101 (e.g., as specified by the source account identifier) to the deposit account of counterparty “Michael Jones” (e.g., as specified by the destination account identifier) on Oct. 14, 2018; para. [0129, 0156]).
Moon does not explicitly teach a convolution neural network; access the convolution neural network, where the convolution neural network has been trained using (i) annotated linguistic data.
However, Schafer teaches a parser comprising a convolution neural network (i.e. The machine learning model 122 may include, e.g., a convolutional neural network and/or a recurrent neural network; para. [0054]), a named entity recognition component, an entity identifier component (i.e. The named entity recognizer 130 may be configured to analyze the tagged map 124 and/or the estimated tag locations within the document 102 to recognize named entities 132, 134 within the recognized text 108. In certain implementations, the named entity recognizer 130 may analyze each word of the text 108 and evaluate the corresponding portions of the tagged map 124 (i.e., the corresponding tagged cells 126, 128) to identify named entities 132, 134; para. [0055]); access the convolution neural network, where the convolution neural network has been trained using (i) annotated linguistic data (i.e. After the named entity recognizer 608 recognizes named entities within the training documents and training text, the training system 602 may receive the recognized named entities (block 626). The named entities may be received in a predefined data structure, e.g., a list of named entities contained within the training document and training text. The training system 602 may then compare the recognized named entities with the labeled training text entities (block 628). Similar to the tagged feature map, the labeled training text may be stored in a training database along with a training image and training text and may identify the named entities within the training text which the named entity recognizer 608 was intended to identify; para. [0015, 0048, 0126, 0127]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Moon to include the feature of Schafer. One would have been motivated to make this modification because it improves the accuracy of named entity recognition by considering contextual and spatial relationships among textual elements.
Claim 2: Moon and Schafer teach the system of claim 1. Moon further teaches wherein the text-based instruction comprises at least one of an email message, an SMS message, recorded speech converted to text, text input to a chat function, or text recognized in an image (i.e. user 101 may provide input to fillable text box 204, e.g., via a miniaturized “virtual” keyboard presented within digital chatbot interface 200, that specifies message 208, e.g., “I need to pay Mike for the hockey tickets.”; para. [0062, 0063]).
Claim 3: Moon and Schafer teach the system of claim 1. Moon further teaches wherein the at least one processing device is configured to: when sorting the distribution entities, sort, based on surrounding words in the text-based instruction and syntactic roots (i.e. NLP engine 144 may identify one or more discrete linguistic elements (e.g., a word, a combination of morphemes, a single morpheme, etc.) within message data 304, and may establish a context and a meaning of combinations of the discrete linguistic elements, e.g., based on the identified discrete linguistic elements, relationships between these discrete linguistic elements, and relative positions of these discrete linguistic elements within message data 304; para. [0071]), the distribution entities and the distribution elements into the pre-defined distribution fields (i.e. metadata, that characterize a type or range of input data associated with each of the discrete interface elements … Examples of these transaction parameters include, but are not limited to, payer profile information, payee profile information (e.g., an identifier of the counterparty), source and destination accounts, and a transaction amount … parameter determination module 334 may receive contextual information 328, and may perform operations that parse contextual information 328 to extract transaction type data 332 and identified parameter data 336, which includes each of the complete or incomplete parameter values identified by NLP engine 144 within message data 304; para. [0034, 0035, 0077]).
Schafer further teaches sort, based on surrounding words in the text-based instruction and syntactic roots, the distribution entities and the distribution elements into the pre-defined distribution fields (i.e. The machine learning model may analyze more than one cell (e.g., a region of cells) at the same time to better integrate spatial information, such as which words are located near one another within the document … The syntactic properties 308 may include an indication of the typical or associated usage or associated words of the intersecting text 306 … . The tags whose locations are estimated at block 616 may be predetermined according to the document being analyzed, e.g., may be selected based on document type; para. [0043, 0047, 0068-0070, 0123]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Moon to include the feature of Schafer. One would have been motivated to make this modification because it improves the accuracy of named entity recognition by considering contextual and spatial relationships among textual elements.
Claim 4: Moon and Schafer teach the system of claim 1. Moon further teaches wherein the predicted distribution elements comprise at least one of a predicted sending source retainer, a predicted receiving source retainer, a predicted amount of resources to be distributed, a predicted type of resource distribution, a predicted type of resources to be distributed, a predicted date on which resources are to be distributed, a predicted frequency at which resources are to be distributed, a predicted type of sending source retainer, or a predicted type of receiving source retainer (i.e. predictive engine 146 may perform operations that establish $500 as a candidate parameter value for the missing transaction amount, and may establish October 15th as a candidate parameter value for the missing transaction data. Further, and based on the identified correlation, predictive engine 146 may also establish a tokenized (or actual) account number of user 101's account as a candidate parameter value for the source account, and establish a tokenized or actual account number of the additional account held by “Michael Jones” as a candidate parameter value for the destination account; para. [0090-0092, 0100, 0101]).
Claim 5: Moon and Schafer teach the system of claim 1. Moon further teaches wherein the at least one processing device is further configured to parse, using the convolutional neural network, the text-based instruction to determine additional information associated with the text-based instruction (i.e. NLP engine 144 may identify one or more discrete linguistic elements (e.g., a word, a combination of morphemes, a single morpheme, etc.) within message data 304, and may establish a context and a meaning of combinations of the discrete linguistic elements, e.g., based on the identified discrete linguistic elements, relationships between these discrete linguistic elements, and relative positions of these discrete linguistic elements within message data 304. In some instances, NLP engine 144 may generate linguistic element data 326, which includes each discrete linguistic elements, and contextual information 328 that specifies the established context or meaning of the combination of the discrete linguistic elements; para. [0071-0074]).
Moon does not explicitly teach the convolutional neural network.
However, Schafer further teaches parse, using the convolutional neural network, the text-based instruction to determine additional information associated with the text-based instruction (i.e. the machine learning model may include a convolutional neural network … the feature vector 304 includes text features such as the intersecting text 306, syntactic properties 308, orthographic properties 312, and spatial features such as the text location 314, and the text boundary 316 … The named entity recognizer 130 may be configured to analyze the tagged map 124 and/or the estimated tag locations within the document 102 to recognize named entities 132, 134 within the recognized text 108. In certain implementations, the named entity recognizer 130 may analyze each word of the text 108 and evaluate the corresponding portions of the tagged map 124 (i.e., the corresponding tagged cells 126, 128) to identify named entities 132, 134; para. [0047, 0055, 0056, 0068]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Moon to include the feature of Schafer. One would have been motivated to make this modification because it improves the accuracy of named entity recognition by considering contextual and spatial relationships among textual elements.
Claim 6: Moon and Schafer teach the system of claim 5. Moon further teaches wherein the additional information comprises at least one of a sender alias from which the text-based instruction was sent, a recipient alias to which the text-based instruction was sent, an entity associated with the resource distribution, a name of a user, an address of the user, or content of the text-based instruction (i.e. NLP engine 144 may determine that message 208 corresponds to a request to initiate an exchange of data (e.g., a peer-to-peer (P2P) transaction involving user 101 and “Mike”) … NLP engine 144 may package data identifying a particular type of the data exchange (e.g., the P2P transaction described herein) and data identifying the completely, or incompletely, specified ones of the parameter values of the data exchange (e.g., the portion of the counterparty identifier (e.g., “Mike”) and the data characterizing the product or service (e.g., the “hockey tickets”) into corresponding portions of contextual information 328; para. [0073, 0074, 0099]).
Claim 7: Moon and Schafer teach the system of claim 1. Moon further teaches wherein the at least one processing device is configured to: identify multiple resource distributions within the text-based instruction (i.e. NLP engine 144 may parse the received message data to identify one or more discrete linguistic elements (e.g., a word, a combination of morphemes, a single morpheme, etc.), and to generate contextual information that establishes the meaning or a context of one or more discrete linguistic elements; para. [0038]); and generate, for each resource distribution of the multiple resource distributions, a structured resource distribution (i.e. certain elements of the exchanged data may correspond to a request, by user 101, to initiate one or more exchanges of data involving one or more corresponding counterparties, and the elements of exchanged data may specify values for one or more parameters of these data exchanges … when executed by transaction system 130, transaction engine 148 can perform operations to initiate one or more exchanges of data, such as the exemplary payment or purchase transactions described herein, in accordance with one or more received, obtained, or dynamically determined, parameter values. In some instances, and as described herein, transaction engine 148 may perform operations that identify a minimum set of parameter necessary to initiate a corresponding data exchange, e.g., as specified within a corresponding portion of digital interface database 138; para. [0033-0035, 0044]).
Claim 9: Moon and Schafer teach the system of claim 1. Moon further teaches wherein the at least one processing device is configured to perform the resource distribution (i.e. transaction engine 148 may perform operations that initiate the requested P2P transaction in accordance with corresponding ones of populated parameter values 476, e.g., an electronic transfer of $550.00 from the deposit account of user 101 (e.g., as specified by the source account identifier) to the deposit account of counterparty “Michael Jones” (e.g., as specified by the destination account identifier) on Oct. 14, 2018; para. [0129]).
Claims 10-16 and 18-20 are similar in scope to Claims 1-7, 9 and are rejected under a similar rationale.
6. Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Moon in view of Schafer, and further in view of Albouyeh et al. (U.S. Patent Application Pub. No. US 20200265440 A1).
Claim 8: Moon and Schafer teach the system of claim 1. Moon further teaches wherein the at least one processing device is configured to provide, to user, the resource distribution for authorization (i.e. Referring to FIG. 3D, and when presented within chatbot session area 202 of chatbot interface 200, interface elements 372 may establish a new message 374 that include a deep link 376 to a populated payment interface that facilitates an initiation of the P2P transaction, and additional textual content 378 that prompts user 101 to select deep link 376 and confirm the pre-populated parameter values that characterize the P2P transaction, e.g., “Click here to pay Mike.”; para. [0103]).
Moon does not explicitly teach provide, to another user, the resource distribution for authorization.
However, Albouyeh teaches provide, to another user, the resource distribution for authorization (i.e. the system requests, in real time, approval of the transaction from a joint owner of the account involved in the transaction. In embodiments, a threshold is also established for transactions to determine if approval of the transaction by a joint owner of the account is required. In this manner, implementations of the invention provide joint account owners with the added security of particular classes of transactions requiring two-party approval; para. [0012]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Moon and Schafer to include the feature of Schafer. One would have been motivated to make this modification because it provides additional transaction control for transactions requiring multi-user authorization.
Claim 17 is similar in scope to Claim 8 and is rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Yu et al. (Pub. No. US 20180144346 A1), the operations include determining a payment intention of a user based on analyzing the received voice input; retrieving contact information from a stored contact list based on the name of the recipient; transmitting the name and the contact information of the recipient to a bank server together with an amount of money specified in the voice input; receiving remittance details from the bank server; and approving the remittance details. The device may analyze the received voice input by using an artificial intelligence (AI) algorithm.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/TAN H TRAN/Primary Examiner, Art Unit 2141
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