Patent Issued for Financial autopilot (USPTO 11127075): United Services Automobile Association

2021 OCT 12 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Hendry, Jason Paul (Selma, TX, US), Mahoney, Nathan (New Braunfels, TX, US), Silva, Luis Daniel (San Antonio, TX, US), Vijayvergia, Gunjan C. (San Antonio, TX, US), filed on September 27, 2019, was published online on September 21, 2021, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11127075 is assigned to United Services Automobile Association (San Antonio, Texas, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “A budget is a financial plan for a defined period of time, usually a year. It may also include planned sales volumes and revenues, resource quantities, costs and expenses, assets, liabilities and cash flows.

“Machine-learning can encompass a wide variety of different techniques that are used to train a machine to perform specific tasks without being specifically programmed to perform those tasks.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include the act of receiving a list of historic transactions for a user from a plurality of financial institutions. The methods can be used to create a holistic financial experience that brings together machine learning and personal financial management that enables the member to pay bills automatically, envelope savings, set and manage goals, manage the day to day needs including debt management, budgeting, money movement and advice. The methods include the act of identifying at least one predicted unexpected expense based on providing at least some of the historic transactions to a trained machine-learning model, the trained machine-learning model trained using historic transaction information for a plurality of other individuals. The methods include the act of identifying a savings plan to account for the unexpected expense. The method also includes the act of automatically transferring an amount based on the savings plan.

“Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. A system of one or more computers can be configured to perform particular actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

“The foregoing and other embodiments can each optionally include one or more of the following features, alone or in combination. The methods may include the acts of identifying a set of recurring transactions based on the list of historic transactions, identifying at least one transaction in the set of recurring transaction that is not being automatically paid, and scheduling an automatic payment based on the at least one transactions. The methods may include the acts of identifying one or more categories based on the list of historic transaction, and establishing a hierarchy among the one or more categories. The methods may include the acts of enabling a user to adjust the hierarchy between the categories. The methods also include the acts of creating a financial plan for the user based at least in part on a machine-learning model.

“Like reference numbers and designations in the various drawings indicate like elements.”

The claims supplied by the inventors are:

“1. A method implemented by a data processing system, comprising: receiving training data including (i) transaction histories of a plurality of users during a specific period of time, and (ii) for each of the plurality of users, data specifying unexpected expenses that occurred to the respective user during the specific period of time; training a neural network to predict an unexpected expense for a given transaction history using a supervised learning technique based on the training data, wherein the neural network is configured to receive as input the given transaction history and to process the input to generate an output that specifies a specified expense for the given transaction history, wherein the neural network comprises a plurality of artificial neurons that are connected through edges and are aggregated into a plurality of neural network layers comprising at least an input layer and an output layer, wherein each of the edges is configured to transmit a signal from one artificial neuron to another artificial neuron, and wherein an output of each of the plurality of artificial neurons is computed by a specified function of a sum of inputs of the artificial neuron in accordance with a plurality of weights; setting values of the plurality of weights based on the training of the neural network; receiving new data indicating a list of historic transactions of a particular user from a plurality of financial institutions; based on the list of historic transactions, creating a plurality of categories using a clustering algorithm; generating a hierarchy among the plurality of categories; processing, by the data processing system, the new data using the plurality of artificial neurons in the trained neural network in accordance with the values of the plurality of weights to identify at least one specified expense for the particular user, wherein the artificial neurons in the input layer are configured to receive the new data as input and the artificial neurons in the output layer are configured to generate output that identifies the at least one specified expense; determining a plan to account for the at least one specified expense, wherein the plan comprises deducting a payment for the at least one specified expense from a lowest ranked category in the hierarchy; and automatically transferring, by the data processing system, an amount from a first account of the particular user to a second account of the particular user based on the plan.

“2. The method of claim 1, further comprising: identifying a set of recurring transactions based on the list of historic transactions; identifying at least one transaction in the set of recurring transaction that is not being automatically paid; and scheduling an automatic payment based on the at least one transaction.

“3. The method of claim 2, wherein the scheduling is further based on output from the trained neural network.

“4. The method of claim 1, further comprising enabling a user to adjust the hierarchy among the plurality of categories.

“5. The method of claim 1, further comprising creating a financial plan for the particular user based at least in part on output from the trained neural network.

“6. A non-transitory computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving training data including (i) transaction histories of a plurality of users during a specific period of time, and (ii) for each of the plurality of users, data specifying unexpected expenses that occurred to the respective user during the specific period of time; training a neural network to predict an unexpected expense for a given transaction history using a supervised learning technique based on the training data, wherein the neural network is configured to receive as input the given transaction history and to process the input to generate an output that specifies a specified expense for the given transaction history, wherein the neural network comprises a plurality of artificial neurons that are connected through edges and are aggregated into a plurality of neural network layers comprising at least an input layer and an output layer, wherein each of the edges is configured to transmit a signal from one artificial neuron to another artificial neuron, and wherein an output of each of the plurality of artificial neurons is computed by a specified function of a sum of inputs of the artificial neuron in accordance with a plurality of weights; setting values of the plurality of weights based on the training of the neural network; receiving new data indicating a list of historic transactions of a particular user from a plurality of financial institutions; based on the list of historic transactions, creating a plurality of categories using a clustering algorithm; generating a hierarchy among the plurality of categories; processing the new data using the plurality of artificial neurons in the trained neural network in accordance with the values of the plurality of weights to identify at least one specified expense for the particular user, wherein the artificial neurons in the input layer are configured to receive the new data as input and the artificial neurons in the output layer are configured to generate output that identifies the at least one specified expense; determining a plan to account for the at least one specified expense, wherein the plan comprises deducting a payment for the at least one specified expense from a lowest ranked category in the hierarchy; and automatically transferring an amount from a first account of the particular user to a second account of the particular user based on the plan.

“7. The non-transitory computer storage medium of claim 6, the operations further comprising: identifying a set of recurring transactions based on the list of historic transactions; identifying at least one transaction in the set of recurring transaction that is not being automatically paid; and scheduling an automatic payment based on the at least one transaction.

“8. The non-transitory computer storage medium of claim 7, wherein the scheduling is further based on output from the trained neural network.

“9. The non-transitory computer storage medium of claim 6, the operations further comprising enabling a user to adjust the hierarchy among the plurality of categories.

“10. The non-transitory computer storage medium of claim 6, the operations further comprising creating a financial plan for the particular user based at least in part on output from the trained machine-learning model.

“11. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving training data including (i) transaction histories of a plurality of users during a specific period of time, and (ii) for each of the plurality of users, data specifying unexpected expenses that occurred to the respective user during the specific period of time; training a neural network to predict an unexpected expense for a given transaction history using a supervised learning technique based on the training data, wherein the neural network is configured to receive as input the given transaction history and to process the input to generate an output that specifies a specified expense for the given transaction history, wherein the neural network comprises a plurality of artificial neurons that are connected through edges and are aggregated into a plurality of neural network layers comprising at least an input layer and an output layer, wherein each of the edges is configured to transmit a signal from one artificial neuron to another artificial neuron, and wherein an output of each of the plurality of artificial neurons is computed by a specified function of a sum of inputs of the artificial neuron in accordance with a plurality of weights; setting values of the plurality of weights based on the training of the neural network; receiving new data indicating a list of historic transactions of a particular user from a plurality of financial institutions; based on the list of historic transactions, creating a plurality of categories using a clustering algorithm; generating a hierarchy among the plurality of categories; processing the new data using the plurality of artificial neurons in the trained neural network in accordance with the values of the plurality of weights to identify at least one specified expense for the particular user, wherein the artificial neurons in the input layer are configured to receive the new data as input and the artificial neurons in the output layer are configured to generate output that identifies the at least one specified expense; determining a plan to account for the at least one specified expense, wherein the plan comprises deducting a payment for the at least one specified expense from a lowest ranked category in the hierarchy; and automatically transferring an amount from a first account of the particular user to a second account of the particular user based on the plan.

“12. The system of claim 11, the operations further comprising: identifying a set of recurring transactions based on the list of historic transactions; identifying at least one transaction in the set of recurring transaction that is not being automatically paid; and scheduling an automatic payment based on the at least one transaction.

“13. The system of claim 12, wherein the scheduling is further based on output from the trained neural network.

“14. The system of claim 11, the operations further comprising enabling a user to adjust the hierarchy among the one or more categories.”

There are additional claims. Please visit full patent to read further.

URL and more information on this patent, see: Hendry, Jason Paul. Financial autopilot. U.S. Patent Number 11127075, filed September 27, 2019, and published online on September 21, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11127075.PN.&OS=PN/11127075RS=PN/11127075

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