DETAILED ACTION
This communication is a first Office Action Non-Final rejection on the merits. Claims 1-20 as originally filed are currently pending and are considered below.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to ` without significantly more. The claim(s) recite(s):
retrieve a rule set, associated with an account, indicating one or more rules for associating a source, of a first source and a second source associated with the account, with an item based on a first level of data of the item;
retrieve information, associated with the account, indicating one or more records that indicate first data at a second level of data;
analyze, using a machine learning model, the first data in association with a record, of the one or more records, to determine second data, at the first level of data, for respective items of one or more items associated with the record;
determine, based on the rule set and the second data, sources, from the first source and the second source, associated with respective items of the one or more items;
modify, based on determining the sources to be associated with the respective items, the record to indicate the sources associated with the respective items; and
store, in a data structure associated with the account, the record indicating the sources associated with the respective items.
The steps of the method, as drafted, provide a process that, under its broadest reasonable
interpretation, covers concepts performed in the mind such as an observation or evaluation of which source should pay for a particular item based on a predetermined set of rules, which would include determining a first and second source for payment of items within a transaction (as claimed), for example, determining which account to use for the purchase of a personal or business item.
If a claim limitation, under its broadest reasonable interpretation, covers evaluation or observation, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application.
In particular, the claim recites the additional elements of one or memories, one or more processors, a machine learning model and a data structure. These elements are recited at a high‐level of generality (i.e., as a generic computer components performing generic computer functions of storing, retrieving data, and processing data) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
Accordingly, these additional element do not integrate the abstract idea into a practical
application because they do not impose any meaningful limits on practicing the abstract idea. Similarly, ne or memories, one or more processors, a machine learning model and a data structure would not be sufficient to amount to significantly more than the judicial exception. Mere instructions to
apply an exception using a generic computer component cannot provide an inventive concept.
The claim is patent ineligible.
A similar analysis is applied to claims 9 and 17 which recites essentially the same
abstract idea as in claim 1. Claim 17 includes the additional elements of non-transitory computer readable medium and one or more processors of a device. However, these elements are recited at a
high‐level of generality (i.e., as a generic processors performing a generic computer functions) such that
they amount to no more than mere instructions to apply the exception using generic computer
components. Accordingly, these additional elements do not integrate the abstract idea into a practical
application because they do not impose any meaningful limits on practicing the abstract idea. The claim
is directed to an abstract idea.
Similarly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium and one or more processors of a device amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception
using a generic computer component cannot provide an inventive concept. When considering the claim
as a whole, the claim is not patent eligible.
The dependent claims also are patent ineligible. For example, claims 4 and 19 include the step
of describing a rule within the set of predetermined rules further describes the observation and evaluation of items within a transactions. Claims 2‐3, 5-8, 9-16, 18 and 20 further describe the abstract idea with limitations directed to identifying parameters within the rules set such as location, time, transaction total, account selection and providing training data to a machine learning model.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Weidenmiller et al., U.S. Patent Application Publication 2016/0117650 in view of Griffith et al., U.S. Patent Application Publication 2020/0005270
As per Claim 1, Weidenmiller et al. discloses a system for using machine learning to analyze records, the system comprising:
one or more memories (Figure 20, Memory 22); and
one or more processors (Figure 20, Processing Unit 21), communicatively coupled to the one or more memories, configured to:
retrieve a scoring, associated with an account, indicating one or more rules for associating a source, of a first source and a second source associated with the account, with an item based on a first level of data of the item (pg.6, ¶ [0061] discusses the payment processing system 215 may, for each item in the transaction, score the payment accounts based on a classification of the item, information in the user profile, and/or contextual analysis of contextual information);
retrieve information, associated with the account, indicating one or more records that indicate first data at a second level of data (pg.6, ¶ [0061] discusses the payment processing system 215 may analyze the description of the item (e.g., from a catalogue that identifies the description of the item) and apply NLC techniques to the description of the item to classify the item);
analyze, using a machine learning model, the first data in association with a record, of the one or more records, to determine second data, at the first level of data, for respective items of one or more items associated with the record (pg.5, ¶ [0060] discusses the machine learning data and/or training data may include, for example, accounts historically used to pay for certain types of items (e.g., accounts the user has manually selected to pay for items, or accounts that the payment processing system 215 has automatically selected to pay for items);
determine, based on the rule set and the second data, sources, from the first source and the second source, associated with respective items of the one or more items (pg.6, ¶ [0066] discusses the payment accounts are scored for each item/classification of item, and the highest score payment
accounts are selected to pay for specific items);
modify, based on determining the sources to be associated with the respective items, the record to indicate the sources associated with the respective items (pg.7, ¶ [0068] discusses this "override" feature sends information identifying manually selected accounts back into learning model and training data set such that the payment processing system 215 may update its classification algorithms to more accurately classify this item (and related items) in the future); and
store, in a data structure associated with the account, the record indicating the sources associated with the respective items (pg.8, ¶ [0084] discusses if the user modifies or overrides an account selection, the payment processing system 215 may save the information identifying the override to refine the confidence scoring algorithm for future use).
Griffith et al. teaches a system and method capable of utilizing a machine learning algorithm to select a payment source account based on identified items within a transaction.
However, Griffith et al. fails to explicitly disclose a rule set.
Weidenmiller et al. teaches a rule set (pg.4, ¶ [0047] discusses smart engine that makes decisions based on a set of predetermined rules…¶[0049] discusses the administrator 104 can associate different rules for each of these different accounts).
Therefore it would have been obvious to one of ordinary skill in the art of transaction monitoring before the effective filing date of the claimed invention to modify the system of Griffith et al. to include the ability to utilize a predetermined rule set for payment account as taught by Weidenmiller et al. to provide a rule-based engine for managing expenses for organizations using cards issued to members of the organization. pg.2, ¶[0034]
As per Claim 2, Griffith discloses the system of claim 1, wherein the one or more processors are further configured to:
provide, to a user device, display information to cause a user interface to be displayed, wherein the user interface includes a display item indicating the sources associated with the respective items (pg.8, ¶ [0084] discusses prompting the user to confirm or modify account selections when the confidence score is below a threshold (step 640). For example, the payment processing system 215 may present account selections based on the accounts with the highest confidence scores for each item).
As per Claim 3, Griffith et al. discloses the system of claim 1, wherein the record is associated with a completed exchange that is associated with the one or more items (pg.8, ¶ [0077] discusses the payment processing system 215 may store training data, such as historical account payment selection data).
As per Claim 4, Griffith et al., discloses the system of claim 1, wherein the one or more rules indicate the source to be associated with the item based on at least one of:
a category of the item (pg.6, ¶ [0066] discusses "Water" matches the user's training data set indicating that the item should be classified as "beverage." Further, the payment processing system 215 may store training data that defines a criterion that items with the "beverage" classification are assigned to the user's personal credit card 123).
As per Claim 5, Griffith et al. discloses the system of claim 1. However, Griffith et al., fails to disclose wherein the one or more rules indicate the source to be associated with the item based on whether a sum of amounts of exchanges associated with the account, over a time interval, satisfies a threshold.
Weidenmiller et al., teaches wherein the one or more rules indicate the source to be associated with the item based on whether a sum of amounts of exchanges associated with the account, over a time interval, satisfies a threshold (pg.4, ¶ [0049] discusses there may be debit account rules 142 specifying the daily total amount of transactions that a cardholder can complete).
Therefore it would have been obvious to one of ordinary skill in the art of transaction monitoring before the effective filing date of the claimed invention to modify the system of Griffith et al., to include the ability to enforce rules regarding item-level transactions as taught by Weidenmiller et al., to provide a rule-based engine for managing expenses for organizations using cards issued to members of the organization. pg.2, ¶[0034]
As per Claim 6, Griffith et al. discloses the system of claim 1, wherein the one or more processors are further configured to: determine, based on the first data, a location associated with an exchange that is associated with the record (pg.1, ¶ [0014] discusses contextual information may include transaction location).
However, Griffith et al. fails to explicitly state wherein an application of the rule set to determine the sources associated with the respective items is based on the location being outside of or being inside of a geographic region.
Weidenmiller et al. teaches (pg.8, ¶ [0082] discusses the operation 1804 may also indicate
the acceptable range around the hotel that is associated with the event. In such a case, transactions occurring within this range are associated with the event. For example, such range may be within a radius of I mile around the hotel, the borough of Manhattan, the city of New York, etc.).
Therefore it would have been obvious to one of ordinary skill in the art of transaction monitoring before the effective filing date of the claimed invention to modify the system of Griffith et al., to include the ability to enforce rules regarding item-level transactions as taught by Weidenmiller et al., to provide a rule-based engine for managing expenses for organizations using cards issued to members of the organization. pg.2, ¶[0034]
As per Claim 7, Griffith et al. discloses the system of claim 1, wherein the one or more processors are further configured to:
obtain, from a user device associated with the account, permission to apportion charges of the account between the first source and the second source, wherein an application of the rule set to determine the sources associated with the respective items is based on obtaining the permission (pg.7, ¶[0068] discusses the payment processing system 215 may predict that "Printer Paper" is an office supply and should be charged to a company account. However, the user may know that the company does not cover office supplies at the user's home as those supplies are provided in the office and, thus, the user overrides this classification and manually selects the more appropriate account).
As per Claim 8, Griffith et al., discloses the system of claim 1, wherein the first source is associated with a first contribution account associated with the account and the second source is associated with a second contribution account associated with the account (Figure 4D, depicts a first and second source accounts).
As per Claim 9, Griffith et al. discloses a method for allocating sources to items associated with records, comprising:
retrieving, by a device, a scoring, associated with an account, indicating one or more rules for associating a source, of a first source and a second source associated with the account, with an item based on item-level data of the item (pg.6, ¶ [0061] discusses the payment processing system 215 may, for each item in the transaction, score the payment accounts based on a classification of the item, information in the user profile, and/or contextual analysis of contextual information);
retrieving, by the device, information, associated with the account, indicating one or more records that indicate first data at an exchange level of data (pg.6, ¶ [0061] discusses the payment processing system 215 may analyze the description of the item ( e.g., from a catalogue that identifies the description of the item) and apply NLC techniques to the description of the item to classify the item);
analyzing, by the device, the first data associated with a record, of the one or more records, to determine item-level data for respective items of one or more items associated with the record (pg.5, ¶ [0060] discusses the machine learning data and/or training data may include, for example, accounts historically used to pay for certain types of items (e.g., accounts the user has manually selected to pay for items, or accounts that the payment processing system 215 has automatically selected to pay for items);
determining, by the device and based on the rule set and the item-level data, sources, of the first source and the second source, associated with respective items of the one or more items (pg.6, ¶ [0066] discusses the payment accounts are scored for each item/classification of item, and the highest score payment accounts are selected to pay for specific items);
modifying, by the device and based on determining the sources to be associated with the respective items, the record to indicate the sources associated with the respective items (pg.7, ¶ [0068] discusses this "override" feature sends information identifying manually selected accounts back into learning model and training data set such that the payment processing system 215 may update its classification algorithms to more accurately classify this item (and related items) in the future); and
storing, by the device and in a data structure associated with the account, the record indicating the sources associated with the respective items (pg.8, ¶ [0084] discusses if the user modifies or overrides an account selection, the payment processing system 215 may save the information identifying the override to refine the confidence scoring algorithm for future use).
Griffith et al. teaches a system and method capable of utilizing a machine learning algorithm to select a payment source account based on identified items within a transaction.
However, Griffith et al. fails to explicitly disclose a rule set.
Weidenmiller et al. teaches a rule set (pg.4, ¶ [0047] discusses smart engine that makes decisions based on a set of predetermined rules…¶[0049] discusses the administrator 104 can associate different rules for each of these different accounts).
Therefore it would have been obvious to one of ordinary skill in the art of transaction monitoring before the effective filing date of the claimed invention to modify the system of Griffith et al. to include the ability to utilize a predetermined rule set for payment account as taught by Weidenmiller et al. to provide a rule-based engine for managing expenses for organizations using cards issued to members of the organization. pg.2, ¶[0034]
As per Claim 10, Griffith et al. discloses the method of claim 9, further comprising: obtaining, based on the sources associated with the respective items, a contribution toward a total resource amount associated with the account (pg.7, ¶ [0071] discusses when the user selects to automatically select the payment accounts ( e.g., in GUI 410) the payment processing system 215 may split a check in a restaurant for individual business accounts associated with different individuals).
As per Claim 11, Griffith et al. discloses the method of claim 10, wherein obtaining the contribution comprises:
obtaining, from the first source, a first contribution associated with a first portion of the total resource amount (Figure 4C, depicts a sales order with multiple items Water and Juice are classified as beverages and associated with a Personal Credit Card 123); and
obtaining, from the second source, a second contribution associated with a second portion of the total resource amount (Figure 4C, depicts a second account Gas Rewards Card for items classified as fuel),
wherein the first portion is based on amounts of first items, associated with the one or more records, determined to be associated with the first source based on the rule set and item-level data of the first items (pg.6, ¶ [0066] discusses "Water" matches the user's training data set indicating that the item should be classified as "beverage." Further, the payment processing system 215 may store training data that defines a criterion that items with the "beverage" classification are assigned to the user's personal credit card 123), and
wherein the second portion is based on amounts of second items, associated with the one or more records, determined to be associated with the second source based on the rule set and item-level data of the second items (pg.7, ¶ [0067] discusses "Gasoline" may not match the training data, yet a learning model implemented by the payment processing system 215 predicts that "Gasoline" is another name for "Fuel" and thus assigns an 85% probability that the user would wish to a gas rewards card).
As per Claim 12, Griffith et al. discloses the method of claim , wherein the record is associated with a completed exchange that is associated with the one or more items (pg.8, ¶ [0077] discusses the payment processing system 215 may store training data, such as historical account payment selection data).
As per Claim 13, Griffith discloses the method of claim 9, wherein the one or more rules indicate the source to be associated with the item based on stock keeping unit (SKU) level data of the item (pg.6, ¶ [0061] discusses a database may store the classification information for the item based on an item number or other item identifier1).
As per Claim 14, Griffith et al. discloses the method of claim 9, further comprising:
searching an item-level database using the first data (pg.6, ¶ [0061] discusses the payment processing system 215 may analyze the description of the item ( e.g., from a catalogue that identifies the description of the item) and apply NLC techniques to the description of the item to classify the item); and
obtaining the item-level data for the respective items based on searching the item-level database using the first data (pg.6, ¶ [0066] discusses "Water" matches the user's training data set indicating that the item should be classified as "beverage." Further, the payment processing system 215 may store training data that defines a criterion that items with the "beverage" classification are assigned to the user's personal credit card 123).
As per Claim 15, Griffith et al., discloses the method of claim 9, wherein analyzing the first data comprises:
providing, to a machine learning model, the first data (pg.5, ¶ [0060] discusses The machine learning data and/or training data may include, for example, accounts historically used to pay for certain types of items (e.g., accounts the user has manually selected to pay for items, or accounts that the payment processing system 215 has automatically selected to pay for items); and
obtaining, from the machine learning model, an output indicating the item-level data for the respective items (pg.7, ¶ [0067] discusses "Gasoline" may not match the training data, yet a learning model implemented by the payment processing system 215 predicts that "Gasoline" is another name for "Fuel" and thus assigns an 85% probability that the user would wish to a gas rewards card).
As per Claim 16, Griffith et al. discloses the method of claim 9. Griffith et al. further teaches a system and method to identify items within a transaction and the associated payment account. However, Griffith et al. fails to explicitly state a method further comprising:
determining, based on the first data, a time associated with an exchange that is associated with the record, wherein an application of the rule set to determine the sources associated with the respective items is based on the time being included in an allowable time period.
Wiedenmiller et al. teaches determining, based on the first data, a time associated with an exchange that is associated with the record, wherein an application of the rule set to determine the sources associated with the respective items is based on the time being included in an allowable time period (pg.10, ¶ [0105] discusses the administrator may use an input field 2220 to provide the starting date when the time-based funds are available and an input field 2222 to provide the ending date for the time-based funds).
Therefore it would have been obvious to one of ordinary skill in the art of transaction monitoring before the effective filing date of the claimed invention to modify the system of Griffith et al. to include the ability to provide a source account for purchases made within a time period as taught by Weidenmiller et al. to provide a rule-based engine for managing expenses for organizations using cards issued to members of the organization. pg.2, ¶[0034]
As per Claim 17, Griffith et al. discloses a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device to:
retrieve a rule set, associated with an account, indicating one or more rules for associating a source, of a first source and a second source associated with the account, with an item based on item-level data of the item (pg.6, ¶ [0061] discusses the payment processing system 215 may, for each item in the transaction, score the payment accounts based on a classification of the item, information in the user profile, and/or contextual analysis of contextual information);
retrieve information, associated with the account, indicating one or more records that indicate first data at an exchange-level of data (pg.6, ¶ [0061] discusses the payment processing system 215 may analyze the description of the item ( e.g., from a catalogue that identifies the description of the item) and apply NLC techniques to the description of the item to classify the item);
analyze the first data associated with a record, of the one or more records, to determine item-level data for respective items of one or more items associated with the record (pg.5, ¶ [0060] discusses the machine learning data and/or training data may include, for example, accounts historically used to pay for certain types of items (e.g., accounts the user has manually selected to pay for items, or accounts that the payment processing system 215 has automatically selected to pay for items);
determine, based on the rule set and the item-level data, sources, of the first source and the second source, associated with respective items of the one or more items (pg.6, ¶ [0066] discusses the payment accounts are scored for each item/classification of item, and the highest score payment
accounts are selected to pay for specific items);
modify, based on determining the sources to be associated with the respective items, the record to indicate the sources associated with the respective items (pg.7, ¶ [0068] discusses this "override" feature sends information identifying manually selected accounts back into learning model and training data set such that the payment processing system 215 may update its classification algorithms to more accurately classify this item (and related items) in the future); and
obtain, based on modifying the record, a contribution toward a total resource amount associated with the account (pg.8, ¶ [0084] discusses the payment processing system 215 may prompt the user to confirm or modify the selected accounts to charge, e.g., the accounts with the highest confidence scores),
wherein the contribution includes a first contribution of a first portion of the total resource amount from the first source and a second contribution of a second portion of the total resource amount from the second source (pg.8, ¶[0084] discusses determining a total amount to charge each selected account (step 650) and communicating with payment account servers to charge the accounts in accordance with the determined amounts… the payment processing system 215 may determine a total amount to charge each selected account by adding the amounts of each item associated with a selected account… if items 1, 2, and 3 have been selected to be charged to account A, the payment processing system 215 may add the price amounts of items 1, 2, and 3 and communicate with the payment account server 220 associated account A to charge account A with the sum of the prices of items 1, 2, and 3. If items 4, 5, and 6 have been selected to be charged to account B, the payment processing system 215 may add the price amounts of items 4, 5, and 6 and communicate with the payment account server 220 associated account B to charge account B with the sum of the prices of items 4, 5, and 6).
Griffith et al. teaches a system and method capable of utilizing a machine learning algorithm to select a payment source account based on identified items within a transaction.
However, Griffith et al. fails to explicitly disclose a rule set.
Weidenmiller et al. teaches a rule set (pg.4, ¶ [0047] discusses smart engine that makes decisions based on a set of predetermined rules…¶[0049] discusses the administrator 104 can associate different rules for each of these different accounts).
Therefore it would have been obvious to one of ordinary skill in the art of transaction monitoring before the effective filing date of the claimed invention to modify the system of Griffith et al. to include the ability to utilize a predetermined rule set for payment account as taught by Weidenmiller et al. to provide a rule-based engine for managing expenses for organizations using cards issued to members of the organization. pg.2, ¶[0034]
As per Claim 18, Griffith et al., discloses the non-transitory computer-readable medium of claim 17, wherein the first portion is based on amounts of first items, associated with the one or more records, determined to be associated with the first source based on the rule set and item-level data of the first items (pg.6, ¶ [0066] discusses "Water" matches the user's training data set indicating that the item should be classified as "beverage." Further, the payment processing system 215 may store training data that defines a criterion that items with the "beverage" classification are assigned to the user's personal credit card 123), and
wherein the second portion is based on amounts of second items, associated with the one or more records, determined to be associated with the second source based on the rule set and item-level data of the second items (pg.7, ¶ [0067] discusses "Gasoline" may not match the training data, yet a learning model implemented by the payment processing system 215 predicts that "Gasoline" is another name for "Fuel" and thus assigns an 85% probability that the user would wish to a gas rewards card).
As per Claim 19, Griffith et al. discloses the non-transitory computer-readable medium of claim , the one or more rules indicate the source to be associated with the item based on at least one of: a category of the item (pg.6, ¶ [0066] discusses "Water" matches the user's training data set indicating that the item should be classified as "beverage." Further, the payment processing system 215 may store training data that defines a criterion that items with the "beverage" classification are assigned to the user's personal credit card 123).
As per Claim 20, Griffith et al. discloses the non-transitory computer-readable medium of claim , wherein the one or more instructions, that cause the device to analyze the first data, cause the device to:
provide, to a machine learning model, the first data (pg.5, ¶ [0060] discusses The machine learning data and/or training data may include, for example, accounts historically used to pay for certain types of items (e.g., accounts the user has manually selected to pay for items, or accounts that the payment processing system 215 has automatically selected to pay for items); and
obtain, from the machine learning model, an output indicating the item-level data for the respective items (pg.7, ¶ [0067] discusses "Gasoline" may not match the training data, yet a learning model implemented by the payment processing system 215 predicts that "Gasoline" is another name for "Fuel" and thus assigns an 85% probability that the user would wish to a gas rewards card).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sood et al., U.S. Patent Application Publication 2020/0118137 discusses a transaction management service and method that can streamline transaction tracking and budget management. Transactions may be pre-approved based on inheritance and/or context-based authorization. The system can integrate a payment method, transaction tracking service, and a budget allocation service, among other operations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHFORD S HAYLES whose telephone number is (571)270-5106. The examiner can normally be reached M-F 6AM-4PM with Flex.
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/ASHFORD S HAYLES/Primary Examiner, Art Unit 3627
1 Examiner is construing an item number as a SKU, because an SKU is used to identify an item.