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
Claims 1, 3, 4, 7, 9, 11-13, 15, 17, 19 and 20 have been canceled.
Claims 2,5-6,8,10,14,16 and 18 are pending and have been examined herein.
Claim Rejections - 35 USC § 112
The rejection of claims 3 and 15 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite is withdrawn in view of claims 3 and 15 being cancelled by Applicant’s amendment.
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 2,5-6, 8, 10, 14, 16 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a system (claims 2, 5-6, 8 and 10) and a method (claims 14, 16, and 18) and thus fall into at least one statutory category enumerated in 35 U.S.C. § 101 (Step 1 – yes).
However, claim 2 (and claim 14, in substance) recites:
receiving voice commands from a user for performing an operation and returning audio messages to the user; process a voice command from the user to send units to an entity; verify that the user is authorized to send units; search for and identify the entity if the user is authorized to send units; ask the user to confirm the identified entity and the number of units to send; send units to the identified entity if the user confirms the identified entity and the number of units to send; train, using training test data, a neural network, the training including: iteratively predicting the factors from the training test data that correlate to processing voice commands for sending units, the predicting generating a prediction; testing and comparing, during each iteration, the prediction to a target variable; indicating, for each iteration and via a feedback loop, modifications to weights assigned to nodes
process a voice command , verify that the user is authorized to send units using the digital units transfer application; search for and identify the entity […] if the user is authorized to send units, searches for and identifies the entity in a phone contacts list; ask the user using the voice command application to confirm the identified entity and the number of units to send; and digitally send units to the identified entity if the user confirms the identified entity and the number of units to send, asks the user if the user wants to add a note to a message when sending the units to the identified entity.
This is an abstract idea that falls under the grouping of fundamental economic principles or practices including mitigating risks. (see MPEP § 2106.04(a)(2), subsections II) and commercial or legal interactions (marketing or sales activities or behaviors, and business relations) (see MPEP § 2106.04(a)(2), subsection II.B) or because it recites sending units from a user to an entity and verification that the user is authorized to send units; or Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III) because the claims recite comparing a prediction to a target variable. Under the broadest reasonable interpretation, “units” may be understood to include “money” as the Specification filed 4/23/2024 states at ¶[0001]: “This disclosure relates generally to a system and method for digitally sending money using a mobile device and, more particularly, to a system and method for digitally sending money to a person using a mobile device and voice commands through Siri or Google Assistant and a Zelle® pay application.”
This judicial exception is not integrated into a practical application because the additional elements include: a mobile device, a voice command application, a banking application, a digital units transfer application, a back-end server comprising a processor, communications interface, and memory device, and using/deploying a neural network.
The Applicant’s specification at ¶[0035] explains:
“[…] the mobile device may be or include a workstation, a server, or any other suitable device, including a set of servers, a cloud-based application or system, or any other suitable system, adapted to execute, for example any suitable operating system, including Linux, UNIX, Windows, macOS, iOS, Android and any other known operating system used on personal computers, central computing systems, phones, and other devices”;
and at ¶[0038] explains:
“As used herein, memory includes any computer readable medium to store data, code, or other information. The memory device 22 may include volatile memory, such as volatile RAM including a cache area for the temporary storage of data. The memory device 22 may also include non-volatile memory, which can be embedded and/or may be removable. The non-volatile memory can additionally or alternatively include an electrically erasable programmable read-only memory (EEPROM), flash memory or the like.”;
and at ¶[0040] explains:
“The processing device 20, and other processors described herein, generally include circuitry for implementing communication and/or logic functions of the mobile device 16. For example, the processing device 20 may include a digital signal processor, a microprocessor, and various analog to digital converters, digital to analog converters, and/or other support circuits.”
Thus, the additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the abstract idea using generic computer and computer networking components or amount to merely using a computer as a tool to perform the abstract idea or merely confine the use of the abstract idea to a particular technological environment (neural networks). See MPEP 2106.05(f) and MPEP 2106.05(h). Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Claim 2 includes training and using a neural network. The neural network and training steps are generic and standard steps of a machine learning model or a neural network. For example, YU (US 20180144346 A1 to YU; Seung-hak et al.) teaches all of the elements of the claims concerning neural networks as shown in the 35 U.S.C. § 103 rejection, below, and the claims recite no more than generic and conventional training steps in the field of neural networks/machine learning. Thus, this element is recited at a high level of generality and amounts to using a computer as a tool to carry out the abstract idea or merely confining the use of the abstract idea to a particular technological environment (neural networks). This does not integrate the abstract idea into a practical application or provide significantly more. The claim does not include additional elements, individually and in combination, that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components or merely using a computer as a tool to perform the abstract ideas amount to no more than mere instructions to apply the exception using generic computer and computer network components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept and confining the use of the abstract idea to a particular technological environment (neural networks) fails to add an inventive concept to the claims. See MPEP 2106.05(h). Thus, the claim is not patent-eligible.
Independent claim 14 recites substantially the same limitations as independent claim 2 but recites a method reciting the same functionality and thus does not integrate the abstract idea into a practical application or provide significantly more.
Claim 6 adds using a camera for face recognition or biometric input for fingerprint recognition. These are generic computer elements that carry out the abstract idea of mitigating risk and does not integrate the abstract idea into a practical application or provide significantly more.
Claims 8 and 16 recite searching for an entity and displaying multiple entitles if there are multiple entities with the same name and asking the user to identify the correct entity using a computer and display. This is implementing the abstract idea on generic computer components and does not integrate the abstract idea into a practical application or provide significantly more.
Claims 10 and 18 recite sending a confirmation to the user. This is part of the abstract idea and does not integrate the abstract idea into a practical application or provide significantly more.
Accordingly, each of claims 2,5-6, 8, 10, 14, 16 and 18 is rejected under 35 U.S.C. § 101 as being patent-ineligible.
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.
Claim(s) 2, 5-6, 8, 10, 14, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over YU (US 20180144346 A1 to YU; Seung-hak et al.) to KOLCHIN (US 11321709 B1 to Kolchin; Dmitriy).
Regarding claim(s) 2 and 14,
YU discloses:
A system/method for digitally sending units using a mobile device (YU: [0010]: A method and a device for sending money to an account of a recipient by using voice are provided; ¶[0039]: The device 10 may be, without limitation, a smart phone, a tablet PC, […], a mobile phone, a personal digital assistant (PDA), a laptop, a media player, […] consumer electronics, and other mobile […] computing devices),
said mobile device including a voice command application accessible on the mobile device and receiving voice commands from a user for performing an operation (YU: ¶[0034]: The device 10 may receive voice input from the user. The device 10 may include a microphone, which receives the user's voice. The device 10 may receive the voice input of the user via the microphone by executing, for example, a voice assistant application such as “S Voice” and controlling the executed application; ¶[0039]: The device 10 may include any kind of device capable of receiving voice input of a user and providing a reply message to the user; ¶[0035]: The device 10 may recognize the user's voice as indicated at item 1 in FIG. 1. The device 10 may analyze the voice to determine an intention of the user. For example, if the device receives a voice input of the user saying ‘send 100 million won to Samsung’, the device 10 may determine from the user's voice whether the user intends to send money. ; ¶[0112]: if the voice input or the sentence for learning is ‘Send 100 million won from A bank account to Samsung’, the learning entity is {user information: A bank, recipient information: Samsung, remittance amount: 100 million won, remittance instruction: proceed with remittance}.)
and returning audio messages to the user (YU:¶[0039]: The device 10 may include any kind of device capable of receiving voice input of a user and providing a reply message to the user; ¶[0119]: output the payment details by voice.),
said mobile device further including a banking application accessible on the mobile device that is a different application than the voice command application (YU: ¶[0005]: A user may access a financial service using a device executing an application provided by a bank. For example, the user may send money to an account of a recipient by using the device.; ¶[0034]: The device 10 may receive the voice input of the user via the microphone by executing, for example, a voice assistant application such as “S Voice” and controlling the executed application.; ¶[0049]: The processor 11 may control turning the microphone 14 on/off and analyze (e.g., by executing a voice analysis application) a voice input through the microphone 14.),
[…]
said system comprising: a back-end server including: at least one processor for processing data and information; a communications interface communicatively coupled to the at least one processor; and a memory device storing data and executable code that, when executed, causes the at least one processor to [claim 2] : (YU: [0177]:the second components 1502 and 1602 may be the server 2000 that stores a data analysis model; figure 14: mobile device 1000 and server 2000; ¶[0142]: the model learner 1310-4 may store the learned data recognition model in a memory of a server connected to the electronic device; [0064] The device 10 may search a contact list stored in memory 12 (or some other external memory) for a name identified as a recipient; ¶[0132]: the data obtainer 1310-1 may obtain data via an external device (e.g., a server) that communicates with a device; ¶[0142]: the model learner 1310-4 may store the learned data recognition model in a memory of a server connected to the electronic device (for example, the above-described device 10) via a wired or wireless network; ¶[0166]: the data obtainer 2310, the preprocessor 2320, the learning data selector 2330, the model learner 2340, and the model evaluator 2350 of the data learner 2300 of the server 2000 respectively correspond to the data obtainer 1310-1, the preprocessor 1310-2, the learning data selector 1310-3, the model learner 1310-4, and the model evaluator 1310-5; ¶[0038]: The device 10 may transmit user information, recipient information, and an amount of money to a bank server 20; ¶[0183]: second component 1502 may apply the received voice input or sentence to a data recognition model set to estimate the remittance intention of the user; ¶[0184]: the second component 1502 may obtain a recognition entity. For example, the recognition entity may include at least one of user information, recipient information (e.g., a name of a recipient), a remittance amount, and a remittance instruction.; ¶[0196]: 1602 may obtain a recognition entity. For example, the recognition entity may include, without limitation, at least one of payment means, a payment item, a payment method and a payment instruction; ¶[0177]: the first components 1501 and 1601 may be a general purpose processor, and the second components 1502 and 1602 may be an AI dedicated processor; [0040]: Also, the device 10 may communicate with other devices (not shown) over a network in order to use various types of context information.; The communication media typically include computer-readable instructions, a data structure, a program module, other pieces of data of a modulated signal, other transmission mechanisms, and arbitrary information delivery media.)
train, using training test data, a neural network to predict factors for processing voice commands to the mobile device for sending units, the training including (YU: ¶[0137]: The data recognition model may be, for example, a model based on a neural network. […] The data recognition model may include a plurality of network nodes having weights to simulate a neuron of a human neural network. […] The data recognition model may include, for example, a neural network model or a deep learning model developed from the neural network model. In the deep learning model, the plurality of network nodes may be located at different depths (or layers) and may exchange data according to a convolution connection relationship. For example, a model such as a Deep Neural Network (DNN), a Recurrent Neural Network (RNN), or a Bidirectional Recurrent Deep Neural Network (BRDNN) may be used as a data recognition model, but the present disclosure is not limited thereto; ¶[0109]: The data learner 1310 may learn the data recognition model by using a supervised learning method using a voice or a sentence and a learning entity as learning data.; [0118]: In an example embodiment, the data recognition model may be a model set to estimate an intention of the user to send money. In this case, the data recognizer 1320 may estimate the intention of the user to send money by applying the user's voice input or the sentence with which the user's voice is recognized to the data recognition model; figure 3: “pattern learning”; figure 12: “model learner”.):
iteratively predicting the factors from the training test data that correlate to processing voice commands to the mobile device for sending units, the predicting generating a prediction; testing and comparing, during each iteration, the prediction to a target variable (YU: [0144] The model evaluator 1310-5 may input evaluation data to the data recognition model, and if a recognition result output from the evaluation data does not satisfy a predetermined reference, the model evaluator 1310-5 may allow the model learner 1310-4 to learn again; [0145]: For example, when the number or a ratio of the evaluation data whose recognition result is not correct exceeds a preset threshold value in the recognition result of the learned data recognition model for the evaluation data, the model evaluator 1310-5 may evaluate the learned data recognition model as not satisfying the predetermined reference. For example, when the predetermined reference is defined as a ratio of 2%, when the learned data recognition model outputs an incorrect recognition result for evaluation data exceeding 20 pieces of evaluation data among a total of 1000 pieces of the evaluation data, the model evaluator 1310-5 may evaluate that the learned data recognition model is not suitable; figure 12: 1310-5 Model Evaluator);
indicating, for each iteration and via a feedback loop, modifications to weights assigned to nodes of the neural network to improve the neural network’s ability to predict the target variable and reduce error of the prediction (YU: ¶[0137]: The data recognition model may include a plurality of network nodes having weights to simulate a neuron of a human neural network. The plurality of network nodes may establish a connection relationship to simulate a synaptic activity of a neuron sending and receiving signals via synapse. The data recognition model may include, for example, a neural network model or a deep learning model developed from the neural network model.; ¶[0139] Also, the model learner 1310-4 may learn the data recognition model by using, for example, a learning algorithm including an error back-propagation method or a gradient descent method; ¶[1040]: Also, the model learner 1310-4 may learn the data recognition model through reinforcement learning, for example by using feedback on whether a result of determination of the situation based on the learning is correct.);
deploy the trained neural network; process a voice command from the user through the voice command application to send units to an entity using the deployed neural network; (YU: ¶[0195]: In operation 1617, the second component 1602 may apply the received voice or the sentence to a data recognition model set to estimate the payment intention of the user.; ¶[0196] As a result of application, in operation 1619, the second component 1602 may obtain a recognition entity; For example, the recognition entity may include, without limitation, at least one of payment means, a payment item, a payment method and a payment instruction; ¶[0137]: The data recognition model may be, for example, a model based on a neural network. […] The data recognition model may include a plurality of network nodes having weights to simulate a neuron of a human neural network. […] The data recognition model may include, for example, a neural network model or a deep learning model developed from the neural network model. In the deep learning model, the plurality of network nodes may be located at different depths (or layers) and may exchange data according to a convolution connection relationship. For example, a model such as a Deep Neural Network (DNN), a Recurrent Neural Network (RNN), or a Bidirectional Recurrent Deep Neural Network (BRDNN) may be used as a data recognition model, but the present disclosure is not limited thereto. [0142]: Further, when the data recognition model is learned, the model learner 1310-4 may store the learned data recognition model. In this case, the model learner 1310-4 may store the learned data recognition model in a memory of an electronic device (for example, memory 12 of the above-described device 10) including the data recognizer 1320; ¶ [0171] As another example, the recognition result provider 1320-4 of the device 1000 may receive the recognition model generated by the server 2000 from the server 2000 and determine the situation by using the received recognition model. In this case, the recognition result provider 1320-4 of the device 1000 may apply the data selected by the recognition data selector 1320-3 to the data recognition model received from the server 2000 to determine the situation; ¶[0171]: For example, when the selected data includes the user's voice input or the sentence with which the user's voice is recognized, the recognition result provider 1320-4 of the device 1000 may apply the selected data to a data recognition model set to estimate an intention of the user received from the server 2000 to obtain a recognition entity including the intention of the user; ¶[0172]: The processor 11 may determine a remittance intention of the user or a payment intention based on the recognition entity, and may perform a process for sending money or payment. Figure 14: deployment of “model learner”);
verify that the user is authorized to send units using the digital units transfer application (YU: ¶[0047] The bank server 20 may receive the authentication result and send the money to the recipient according to a received authentication result authenticating the remittance details; ¶[0047]: may send the money to the recipient if the user is authenticated as a legitimate user);
search for and identify the entity in the memory device if the user is authorized to send units using the digital units transfer application (YU: ¶[0070]: The bank server 20 may transmit the plurality of banks (or account numbers) registered in a name of a recipient when transmitting remittance details to the device 10. For example, if there are a plurality of account numbers registered in the name of the recipient, the device 10 may display the account numbers to the user on display 13 for the user to determine to which account number to send money. As above, the user may select any one of the displayed account numbers by voice or touch input. ; ¶[0080]: The bank server 20 may search for an account number of the recipient by using the name and the contact information of the recipient and transmit to the device 10 the remittance details including, without limitation, the name of the recipient, the account number, and the amount of money; ¶[0081]: the device 10 may approve the remittance details by using, without limitation, the user's voice input, fingerprint, iris scan, vein image, facial image, and/or a password. ; ¶[0047] The bank server 20 may receive the authentication result and send the money to the recipient according to a received authentication result authenticating the remittance details; ¶[0047]: may send the money to the recipient if the user is authenticated as a legitimate user);
wherein the at least one processor searches for and identifies the entity in a phone contacts list in the mobile device (YU: [0037] The device 10 may confirm the name of the recipient or a title and search for the name or the title stored in a contact list. For example, if the user inputs the recipient as ‘Samsung’, the device 10 may search for ‘Samsung’ in the contact list. For example, the device 10 may confirm a phone number of ‘Samsung’ in the contact list; ¶[0039]: The device 10 may be, without limitation, a smart phone,[…] a mobile phone, […], consumer electronics, and other mobile or non-mobile computing devices; ¶[0012]: retrieving contact information from a stored contact list based on a name of the recipient; ¶[0013]: retrieving contact information from a stored contact list based on a name of a recipient specified in the voice input; ¶[0064]: The device 10 may search a contact list stored in memory 12 […] for a name identified as a recipient; figure 2: device 10 contains memory 12; ¶[0048]: FIG. 2 is a block diagram illustrating the device 10 according to an example embodiment. Referring to FIG. 2, the device 10 may include a processor 11, a memory 12, a display 13, and a microphone 14; ¶[0142]: may store the […] data […] model in a memory of an electronic device (for example, memory 12 of the above-described device 10).);
ask the user using the voice command application to confirm the identified entity and the number of units to send (YU: figure4: will you send 100 million Won from A Bank account to “B Bank Account” “of Samsung”?, “Yes Send Money”, “Approve Remittance Details Through Voice […]”; ;¶[0070]: if there are a plurality of account numbers registered in the name of the recipient, the device 10 may display the account numbers to the user on display 13 for the user to determine to which account number to send money. As above, the user may select any one of the displayed account numbers by voice or touch input; ¶[0044]: The device 10 may display the remittance details. The device 10 may display the remittance details to allow the user to confirm whether the intention of the user's voice input and the remittance details coincide with each other.; ¶[0045]: The user may approve the remittance details. The user may input, for example, one or more of voice,[…] if the user wants to send money according to remittance details; ¶[0068]: [0068] The device 10 may display the two recipients on the display 13. The user may select either a 1st recipient or a 2.sup.nd recipient by voice input. For example, the user may select a recipient by inputting a voice such as ‘send money to the 1.sup.st one’ or ‘send money to Samsung Electronics’);
and digitally send units to the identified entity using the digital units transfer application if the user confirms the identified entity and the number of units to send (YU: ¶[0062]: The device 10 may transmit a message indicating that the remittance details are approved to the bank server 20; ¶[0047]: The bank server 20 may receive the authentication result and send the money to the recipient according to a received authentication result authenticating the remittance details as shown at item 7 in FIG. 1. The bank server 20 may send the money to the recipient if the user is authenticated as a legitimate user (and optionally send confirmation to the device 10 that the money is sent).
YU does not expressly disclose the following limitations, which KOLCHIN however, teaches:
said banking application including a digital units transfer application operable to digitally transfer units using the banking application (KOLCHIN: figure 9A: iWallet registration; figure 9B: iWallet, connect and verify your bank account; figure 10A: SubAcc Biz Corp has requested payment of $11.99; figure 10B: pay with secured- e-check; col. 24, ll. 45-52: (109) The system of the present invention is also configured to provide the ability for a checkwriter to write a check by voice (for Siri, Alexa, Google home and the like): “Hey Siri, write a check to XXX, amount XXX.”),
wherein the at least one processor asks the user if the user wants to add a note to a message when digitally sending the units to the identified entity using the voice command application (KOLCHIN: col. 24, ll. 45-52: The system of the present invention is also configured to provide the ability for a checkwriter to write a check by voice […]; ““Hey Siri, write a check to XXX, amount XXX.”; col. 24, ll. 15-33: info added to “memo area” of electronic check; col. 2, ll. 55-56: receiving input from the user corresponding to at least one of a plurality of check fields; col. 2, ll. 60-62: populating the virtual check based on the received input; col. 12, ll. 44-49: system provides access to “notes of the transaction”; col. 17, ll. 35-39: payer information such as service address, ID, name, invoice number, etc added for tracking purposes for payee; col. 18, ll. 65-67 to col. 19, ll. 1-11: (82) The system of the present invention is configured to provide a check writer with the ability to add tips as part of confirmation when writing a check, wherein the tips are charged through the ACH network as a separate transaction; an sms or email will be sent to the check writer to confirm the amount ; Right there on the same confirmation page, a check writer will be able to give tips as illustrated in FIG. 14.).
It would have been obvious to one of ordinary skill in the art before the time of filing to combine/modify the system/method of YU, which relates to a method and device for sending money using voice input (YU [0002]) with the technique of KOLCHIN, which relates to a system and computer-implemented methods for conducting secure electronic financial transactions (KOLCHIN col. 1, ll. 15-20), in order to allow conduct of electronic transactions in real-time (KOLCHIN col. 2, ll. 1-2) and improve security of financial transactions (KOLCHIN col. 3, ll. 57-59) and increase the speed of transactions (KOLCHIN col. 22, ll. 59-61).
Regarding claim(s) 5,
YU and KOLCHIN teaches the limitations of claim 2.
YU further teaches:
wherein the units are money (YU: ¶[0002]: The present disclosure generally relates to a method and device for sending money using voice input.).
Regarding claim(s) 6,
YU and KOLCHIN teaches the limitations of claim 2.
YU further discloses:
wherein the at least one processor verifies that the user is authorized to send units to entities using the digital units transfer application using a camera for face recognition or a biometric input for fingerprint recognition (YU: [0132]: The data obtainer 1310-1 may receive data through an input device (e.g., a microphone, a camera, a sensor, keyboard, or the like) of an electronic device; [0081]: In operation 760, the device 10 may approve (authenticate) the remittance details. The device 10 may approve the remittance details by using, without limitation, the user's voice input, fingerprint, iris scan, vein image, facial image, and/or a password; [0081]: The user may confirm the remittance details and use voice input to the device 10 to approve the remittance details or allow the device 10 recognize the iris, the fingerprint, and the like; [0045]: The user may approve the remittance details. The user may input, for example, one or more of […], a fingerprint, an iris scan, […], a face image, and a password if the user wants to send money according to remittance details.).
Regarding claim(s) 8,
YU and KOLCHIN teaches the limitations of claim 2.
YU further discloses:
wherein if the at least one processor searches for an entity and determines that there are multiple entities having the same name, the at least one processor will display the multiple entities on the mobile device and ask the user to identify the correct entity using the voice command application (YU: figure 5: select recipient by voice, display plurality of recipients (1 and 2 found by "name of Samsung"); figure 6: plurality of banks account numbers after finding "by name of Samsung" and voice selection of bank account number to send money to; [0064] The device 10 may search a contact list stored in memory 12 (or some other external memory) for a name identified as a recipient. If a plurality of recipients including the identified name are found in the contact list, the device 10 may display names of the plurality of found recipients on display 13. The user may select any one of the displayed names by voice input; [0070] The bank server 20 may transmit the plurality of banks (or account numbers) registered in a name of a recipient when transmitting remittance details to the device 10. For example, if there are a plurality of account numbers registered in the name of the recipient, the device 10 may display the account numbers to the user on display 13 for the user to determine to which account number to send money. As above, the user may select any one of the displayed account numbers by voice or touch input.).
Regarding claim(s) 10,
YU and KOLCHIN teaches the limitations of claim 2.
YU further discloses:
wherein the at least one processor sends a confirmation message to the user that the units have been sent (YU: [0086]: server 30 may transmit a payment completion message to the device 10 when payment is completed. The device 10 may display the payment completion message to notify the user that the payment has been completed normally.; [0047]; send confirmation to the device 10 that the money is sent).
Regarding claim(s) 16,
YU and KOLCHIN teaches the limitations of claim 14.
YU further discloses:
wherein if the method searches for an entity and determines that there are multiple persons having the same name, the method will display the multiple persons on the mobile device and ask the user to identify the correct person using the voice command application (YU: figure 5: select recipient by voice, display plurality of recipients (1 and 2 found by "name of Samsung"); figure 6: plurality of banks account numbers after finding "by name of Samsung" and voice selection of bank account number to send money to; [0064] The device 10 may search a contact list stored in memory 12 (or some other external memory) for a name identified as a recipient. If a plurality of recipients including the identified name are found in the contact list, the device 10 may display names of the plurality of found recipients on display 13. The user may select any one of the displayed names by voice input; [0070] The bank server 20 may transmit the plurality of banks (or account numbers) registered in a name of a recipient when transmitting remittance details to the device 10. For example, if there are a plurality of account numbers registered in the name of the recipient, the device 10 may display the account numbers to the user on display 13 for the user to determine to which account number to send money. As above, the user may select any one of the displayed account numbers by voice or touch input.).
Regarding claim(s) 18,
YU and KOLCHIN teaches the limitations of claim 14.
YU further discloses:
further comprising sending a confirmation message to the user that the money has been sent (YU: [0086]: server 30 may transmit a payment completion message to the device 10 when payment is completed. The device 10 may display the payment completion message to notify the user that the payment has been completed normally.; [0047]; send confirmation to the device 10 that the money is sent).
Response to Arguments
Applicant’s arguments, see pages 4-6, filed 11 November 2025, with respect to claims 2 and 14 have been fully considered but they are not persuasive. Applicant argues that claims train a model, collect data, and use that data to process voice commands to a mobile device for sending units and thus is not a fundamental economic principle. However, as shown in the 35 U.S.C. § 101 rejection above, the claims 2 and 14 have been amended to include the abstract idea of mental processes in addition to the abstract ideas of fundamental economic principles or commercial interactions. The additional elements are recited at a high level of generality and amount to no more than using a computer as a tool to perform the abstract idea.
Applicant points to Example 39 as evidence that the claims are directed to patent-eligible subject matter at pages 5-6 of Applicant’s arguments. This argument has been considered but Example 39 involves a claim that "does not recite any of the judicial exceptions enumerate in the 2019 PEG" and is eligible at step 2A - Prong 1 (Judicial Exception Recited --> No). In contrast, claim 2 and 14 recite at least the abstract idea of mitigating risk, commercial activities or behaviors, and mental processes as explained in the 35 USC 101 rejection, above. IN fact, Example 39 of Subject Matter Eligibility Examples: Abstract Ideas (published 7 January 2019) at page 9 states that “the claim does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people” in the analysis. Thus, Example 39 does not help answer the question of whether the instant claims integrate the abstract idea into a practical application and applicant's argument's are unpersuasive.
At pages 7-11, Applicant argues that the claims are eligible because they reflect an improvement in the function of a technology or technical field by improving predictability of the target variable functionality of the neural network by making adjustment to weights and thereby reducing the error amount and making predictability of target variables more accurate. This argument has been considered but is unpersuasive as the claims merely recite generic machine learning/neural network training steps and components and merely use a computer to perform the identified abstract ideas. Accordingly, claims 2 and 14 are different from USPTO Example 47 (Anomaly Detection) which was directed to an improvement in the technical field of network intrusion detection and recited steps that provided improvements to network security and represented an improvement in the functioning of a computer or technical field. In contrast, these claims merely recite using a computer as a tool to perform the abstract idea or merely confine the use of the abstract idea to a particular technological environment (neural networks).
At pages 11-14, Applicant argues that the claims are subject-matter eligible because they include limitations that are not well-understood, routine, or generic and process data and send units in an unconventional way (and points to Bascom for support). This argument has been considered, but is unpersuasive. Yu teaches all of the elements of the claims concerning neural networks as shown above and the claims recite no more than generic and conventional training steps in the field of neural networks/machine learning.
At pages 14 and 15, Applicant argues that YU does not disclose a phone contact list. This argument has been considered but is unpersuasive as YU discloses the following which teaches or suggests this limitation:
YU: [0037] The device 10 may confirm the name of the recipient or a title and search for the name or the title stored in a contact list. For example, if the user inputs the recipient as ‘Samsung’, the device 10 may search for ‘Samsung’ in the contact list. For example, the device 10 may confirm a phone number of ‘Samsung’ in the contact list; ¶[0039]: The device 10 may be, without limitation, a smart phone,[…] a mobile phone, […], consumer electronics, and other mobile or non-mobile computing devices; ¶[0012]: retrieving contact information from a stored contact list based on a name of the recipient; ¶[0013]: retrieving contact information from a stored contact list based on a name of a recipient specified in the voice input; ¶[0064]: The device 10 may search a contact list stored in memory 12 […] for a name identified as a recipient; figure 2: device 10 contains memory 12; ¶[0048]: FIG. 2 is a block diagram illustrating the device 10 according to an example embodiment. Referring to FIG. 2, the device 10 may include a processor 11, a memory 12, a display 13, and a microphone 14; ¶[0142]: may store the […] data […] model in a memory of an electronic device (for example, memory 12 of the above-described device 10).)
At page 15, Applicant argues that Kolchin only teaches adding information to physical check and is different than adding a note to a message when digitally sending money. This argument has been considered but is unpersuasive because KOLCHIN describes generating a virtual check and distinguishes between Physical and Virtual checks at least at column 17, lines 20-26: “The QR-code can be embedded into a physical check or a virtual check generated by the system of the present invention“ and upon generating the virtual check, the virtual check is display[ed] on at least one of a display of an electronic device of a user or the merchant at column 2, lines 50-55.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOLKO HAMERSKI whose telephone number is (571)270-7621. The examiner can normally be reached Monday-Friday 10:00 AM to 6:00 PM.
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BOLKO HAMERSKI
Examiner
Art Unit 3694
/BOLKO M HAMERSKI/Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
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