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 .
This Office Action is in response to the Application filed May 16, 2024. Claims 1-20 are pending in this case.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 16, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 an abstract idea without significantly more.
In the instant case, claims 1-9 are directed toward a system, claims 10-18 are directed toward a method, and claims 19-20 are directed toward a nontransitory computer-readable medium. Therefore, the claims fall within the four statutory categories of invention.
The claims recite storing and comparing data.
Specifically, the claims recite generating trend map based on trend indicators, storing an association between data items, receiving a request for a report of data associations, retrieving data items, determining associated data items, and generating a report thereon, which is an abstract idea.
The claims recite a transaction which is grouped within the certain methods of organizing human activity grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)) because they involve storing and comparing transaction data.
Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional elements of the claims such as the machine learning system, processing circuits, and data structures merely implement the abstract idea. The use of a machine learning system, processing circuits, and data structures as a tool to implement the abstract idea does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using machine learning system, processing circuits, and data structures to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of storing and comparing data. As discussed above, taking the claim elements separately, the machine learning system, processing circuits, and data structures perform the steps or functions of storing an association between data items, receiving a request for a report of data associations, retrieving data items, determining associated data items, and generating a report thereon. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of storing and comparing data.
Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05 (f) & (h)). Therefore, the claims are not patent eligible.
Dependent claims, 2-9, 11-18, and 20 further describe the abstract idea of storing and comparing data. The dependent claims do not include additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Agrawal et al (US 2021/0279731)in view of Mody et al (US 2006/0195567)
Regarding claims 1, 10, and 19 –
Agrawal discloses a system (abs) comprising:
a first data structure configured to maintain a first dataset, the first dataset comprising, for a plurality of entities, an entity name, and an entity identifier; (par 5-6)
a second data structure configured to securely maintain a second dataset, the second dataset comprising, for the plurality of entities, one or more accounts associated with an entity; (par 5-6)
a machine learning (ML) system; (par 6) and
a processing circuit comprising one or more processors and memory storing instructions that, when executed, (par 79-80) cause the processing circuit to:
determine trends corresponding to the one or more accounts for a third-party entity of the plurality of entities and transaction types (par 79-81), wherein, to determine the trends, the processing circuit is configured to:
generate, by the ML system, a trend map associated with the third-party entity according to a set of trend indicators determined for the third-party entity, the trend map identifying trends corresponding to the one or more accounts for the third-party entity and transaction types (par 77, 79-81); and
store an association between the trend map and corresponding data item for the third-party entity in the first dataset; (abs, par 9, 13, 17)
retrieve, from an enterprise resource of the first entity, a transaction history for the plurality of transactions with the subset of third-party entities, the transaction history including identifying information relating to a respective third-party entity of the subset; (par 70-71)
determine, for each of the subset of third-party entities, the corresponding data item in the first dataset; (par 70-71) and
generate, by the ML system, the report according to the request, the report comprising a content item including information corresponding to the trend map for the subset of third-party entities. (par 73)
Mody discloses, as Agrawal does not specifically disclose, receive, from a user device associated with a first entity of the plurality of entities, a request for a report for a plurality of transactions of the first entity with a subset of third-party entities; (par 79)
It would be obvious to one of ordinary skill in the art to combine Agrawal with Mody in order to obtain a more user-friendly method and system.
Regarding claims 2, 11, and 19 –
Agrawal discloses a storage system configured to store the first data structure and the second data structure; (par 5-6) and
wherein the ML system comprises at least one first ML model trained to match third-party entities with corresponding data items of the first dataset and determine a predicted account of the one or more accounts for a corresponding transaction, and at least one second ML model trained to generate the content item identifying one or more recommendations. ( par 6, 8, 10)
Regarding claims 3 and 12 –
Agrawal discloses wherein the transaction history comprises a payment type for each of the plurality of transactions, (par 10) and wherein to generate the report, the ML system is configured to:
determine, based on to the trend map for the respective third-party entity of the plurality of entities, a previous payment type used in prior transactions of the respective third-party entity; (par 10, 14)
determine, based on a subset of the plurality of transactions between the first entity and the respective third-party entity, the payment type used for the subset of plurality of transactions; (par 10, 14) and
generate the report to identify usage by the respective third-party entity of the previous payment type. (par 73)
Regarding claims 4 and 13 –
Mody discloses transmitting, to a device corresponding to the respective third-party entity, an enrollment request for the previous payment type for one or more future transactions between the first entity and the respective third-party entity. (par 35)
It would be obvious to one of ordinary skill in the art to combine Agrawal with Mody in order to obtain a more user-friendly method and system.
Regarding claims 5 and 14 –
Agrawal discloses determining at least one of the one or more accounts for the respective third-party entity is maintained by the system of a provider; (par 70)
transmitting, to the user device of the first entity, a request to initiate a future payment, using the system of the provider, for a future payment type based on the report; (par 70)
transmitting, to the device of the respective third-party entity, the request to initiate the future payment, using the system of the provider, for the future payment type based on the report; (par 70, 75) and
responsive to receiving an acceptance of the request to initiate the future payment from at least one of the first entity or the respective third-party entity, processing an on-us payment corresponding to the future payment type by the provider. (par 70-75)
Regarding claims 6 and 15 –
Agrawal discloses wherein the report comprises, for the respective third-party entity, one or more identifiers corresponding to an account of the one or more accounts of the third-party entity maintained in the second data structure, the one or more identifiers indicating a transaction type used for transactions with the account. (par 10, 14)
Regarding claims 7 and 16 –
Agrawal discloses retrieving, from a respective enterprise resource of at least some of a plurality of entries, a plurality of data entries corresponding to transactions between a respective entity and a plurality of third-party entities, each data entry including transaction information for the transaction and identifying information relating to a respective third-party entity. (par 10. 14)
Regarding claims 8 and 17 –
Agrawal discloses wherein generating the trend map, responsive to retrieving the plurality of data entries, comprises the processing circuit being further configured to: determine, by the ML system, a match score between each third-party entity and a corresponding data item of the first dataset, based on the identifying information for the each data entry for the respective third-party entity; (par 79) and
determine, by the ML system, for a data entry of the plurality of entries, a trend indicator for the third-party entity, the trend indicator identifying an account and a transaction type. (par 79)
Regarding claims 9 and 18 –
Agrawal discloses responsive to the match score satisfying a threshold criteria:
generate a data item corresponding to the respective third-party entity for storage in the second data structure, the data item including information corresponding to an account used in a transaction with the third-party entity. (par 72, 81)
Conclusion
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/Cristina Owen Sherr/Examiner, Art Unit 3697
/JOHN W HAYES/Supervisory Patent Examiner, Art Unit 3697
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