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 .
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation "the authorization message" in line 4. There is insufficient antecedent basis for this limitation in the claim. For purposes of examination, the limitation will be viewed as “an authorization message”.
Claims 12-17 are dependent on claim 11, therefore same rationale applies.
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.
Step 1: Claims 1-19 are a method type claim. Claim 20 is a system claim. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 1: Step 2A Prong 1:
grouping... the plurality of features into a plurality of groups based on an impact of each feature of the plurality of features on the first task and the at least one second task; (mental process – of grouping features into groups can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... an overall accuracy score, a first task accuracy score, and at least one second task accuracy score based on inputting the testing data set to the first multi-task learning model; (mental process – of determining an overall accuracy score and accuracy score of the task can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
adjusting... the feature score of each respective feature of the plurality of features based on a respective grouping of the plurality of groupings associated with the respective feature and at least one of the overall accuracy score, the first task accuracy score, the at least one second task accuracy score, or a combination thereof to provide an adjusted feature score for the respective feature (mental process – of adjusting the feature score of each feature based on the grouping can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer-implemented method, comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
receiving, with at least one processor, a first multi-task learning model associated with a first task and at least one second task; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
receiving, with the at least one processor, a testing data set comprising a plurality of testing data items for the first multi-task learning model, each testing data item comprising a plurality of elements, each element of the plurality of elements associated with a respective feature of a plurality of features; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
applying, with the at least one processor, feature reduction evaluation (FRE) based on the first multi-task learning model and the testing data set to provide a feature score for each feature of the plurality of features; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer-implemented method, comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
receiving, with at least one processor, a first multi-task learning model associated with a first task and at least one second task; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
receiving, with the at least one processor, a testing data set comprising a plurality of testing data items for the first multi-task learning model, each testing data item comprising a plurality of elements, each element of the plurality of elements associated with a respective feature of a plurality of features; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
applying, with the at least one processor, feature reduction evaluation (FRE) based on the first multi-task learning model and the testing data set to provide a feature score for each feature of the plurality of features; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 20: is rejected under the same rational of claim 1. Claim 20 only recites the additional elements of A system, comprising: at least one processor; and at least one non-transitory computer-readable medium including one or more instructions that, when executed by the at least one processor, direct the at least one processor to.. which is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f).
Regarding claim 2: Step 2A Prong 1:
further comprising selecting... a subset of the plurality of features based on the adjusted feature score for each respective feature of the plurality of features (mental process – of selecting subset of the plurality of features based on the adjusted feature score can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 3: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
further comprising training, with the at least one processor, a second multi-task learning model based on the subset of the plurality of features (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 4: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
further comprising communicating, with the at least one processor, the adjusted feature score for each respective feature of the plurality of features to a remote computing device(This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 5: Step 2A Prong 1:
wherein grouping the plurality of features into a plurality of groups comprises: (mental process – of grouping features into groups can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
grouping... the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score (mental process – of grouping features into groups can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training, with the at least one processor, a second multi-task learning model based on a subset of the testing data set; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
applying, with the at least one processor, FRE based on the second multi- task learning model and the subset of the testing data set to provide a first impact score for each feature of the plurality of features on the first task and at least one second impact score for each feature of the plurality of features on the at least one second task; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 6: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the second multi-task learning model comprises an input layer, a first plurality of hidden layers associated with the first task, an output layer associated with the first task, at least one second plurality of hidden layers associated with the at least one second task, and at least one output layer associated with the at least one second task (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
Regarding claim 7: Step 2A Prong 1:
wherein grouping the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score comprises: (mental process – of grouping the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
ranking... the plurality of features based on the first impact score of each feature of the plurality of features to provide a first ranking of the plurality of features; (mental process – of ranking the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a first subset of features based on a first top portion of the first ranking of the plurality of features; (mental process – of determining a first subset of features based on a first top portion of the first ranking of the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a second subset of features comprising features of the plurality of features not in the first subset of features; (mental process – of determining a second subset of features comprising features of the plurality of features not in the first subset of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
ranking... the plurality of features based on the at least one second impact score of each feature of the plurality of features to provide at least one second ranking of the plurality of features; (mental process – of ranking the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... at least one third subset of features based on at least one second top portion of the at least one second ranking of the plurality of features; (mental process – of determining the third subset of features based on at least one second top portion of the at least one second ranking of the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... at least one fourth subset of features comprising features of the plurality of features not in the at least one third subset of features; and (mental process – of determining the fourth subset of features comprises features of the plurality of features not in the third subset of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
grouping... the plurality of features based on the first subset of features, the second subset of features, the at least one third subset of features, and the at least one fourth subset of features (mental process – of grouping features based on the first, second, third, fourth subset of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 8: Step 2A Prong 1:
wherein grouping the plurality of features based on the first subset of features, the second subset of features, the at least one third subset of features, and the at least one fourth subset of features comprises: (mental process – of grouping the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a first group of the plurality of features based on the first subset and the at least one third subset; (mental process – of determining a first group of the plurality of features based on the first and third subset can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a second group of the plurality of features based on the first subset and the at least one fourth subset; (mental process – of determining a second group of the plurality of features based on the first and fourth subset can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a third group of the plurality of features based on the second subset and the at least one third subset; and (mental process – of determining a third group of the plurality of features based on the second and third subset can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... a fourth group of the plurality of features based on the second subset and the at least one fourth subset (mental process – of determining a fourth group of the plurality of features based on the second and fourth subset can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 9: Step 2A Prong 1:
wherein adjusting the feature score of each respective feature of the plurality of features comprises: (mental process – of adjusting the feature score of each respective feature of the plurality of features can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
adjusting... the feature score of each respective feature of the first group of the plurality of features based on the overall accuracy score to provide the adjusted feature score for the respective feature of the first group of the plurality of features; (mental process – of adjusting the feature score can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
adjusting... the feature score of each respective feature of the second group of the plurality of features based on the overall accuracy score and the at least one second task accuracy score to provide the adjusted feature score for the respective feature of the second group of the plurality of features; (mental process – of adjusting the feature score can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
adjusting... the feature score of each respective feature of the third group of the plurality of features based on the overall accuracy score and the first task accuracy score to provide the adjusted feature score for the respective feature of the third group of the plurality of features; and (mental process – of adjusting the feature score can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
adjusting... the feature score of each respective feature of the fourth group of the plurality of features based on the overall accuracy score, the first task accuracy score, and the at least one second task accuracy score to provide the adjusted feature score for the respective feature of the fourth group of the plurality of features (mental process – of adjusting the feature score can be performed by the human mind with the aid of pen and paper (e.g., judgement )).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 10: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the first task comprises generating, based on an authorization request, a first prediction associated with a likelihood of a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 11: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the at least one second task comprises at least one of generating, based on the authorization request, a second prediction associated with when the at least one clearing message will be received after the authorization message, generating, based on the authorization request, a third prediction associated with a number of clearing messages of the at least one clearing message, or any combination thereof (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 12: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the first prediction comprises a first score (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
Regarding claim 13: Step 2A Prong 1:
generating, (mental process – of generating scores associated with a probability can be performed by the human mind with the help of pen and paper (e.g., judgement )).
Steps 2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving, with the at least one processor, the authorization request from at least one of a merchant system or an acquirer system; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
..with the at least one processor, based on the authorization request... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
communicating, with the at least one processor, the enhanced authorization request to an issuer system (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving, with the at least one processor, the authorization request from at least one of a merchant system or an acquirer system; ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
..with the at least one processor, based on the authorization request... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
communicating, with the at least one processor, the enhanced authorization request to an issuer system ( This is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 14: Step 2A Prong 1:
wherein generating the first score comprises: (mental process – of generating a first score can be performed by the human mind with the help of pen and paper (e.g., judgement)).
determining... a first plurality of elements based on the authorization request, each element of the first plurality of elements associated with a first respective feature of the plurality of features; and (mental process – of determine a first plurality of elements can be performed by the human mind with the help of pen and paper (e.g., judgement)).
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...with the at least one processor... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
inputting, with the at least one processor, the first plurality of elements to the first multi-task learning model to generate the first score associated with the likelihood of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 15: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
further comprising determining, with the at least one processor, based on the authorization request, that the issuer system is enrolled in a program before generating the first score (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 16: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein generating the first score, inserting the first score into the at least one field of the authorization request to provide the enhanced authorization request... (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
...and communicating the enhanced authorization request are in response to determining that the issuer is enrolled in the program (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of receiving or transmitting data over a network. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 17: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the issuer system determines to post a transaction associated with the authorization request to an account before receiving the clearing message corresponding to the authorization request based on the first score in the enhanced authorization request satisfying a threshold (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 18: See rejection of claim 13, same rational applies.
Regarding claim 19: Step 2A Prong 1: None.
Steps 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the machine learning model comprises at least one of a deep neural network (DNN), a multi- task learning model, or any combination thereof (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
Claim Rejections - 35 USC § 103
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.
Claims 1-3 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dong et al. US 2019/0156211 A1 (hereinafter Dong) as disclosed in the IDS filled 04/13/2022 in view of Chen et al. US 2020/0334520 A1 (hereinafter Chen) in further view of Kartoun et al. US 2021/0342735 A1 (hereinafter Kartoun).
Regarding claim 1:
Dong teaches A computer-implemented method, comprising: ( Dong [0006] teaches a computer implemented method).
receiving, with at least one processor, a first multi-task learning model associated with a first task and at least one second task; ( Dong [0006] teaches obtaining a “deep neural network” and [0059] teaches “A DNN based neural network may apply multi - task learning to form a trained DNN based neural model” which inheritably will have multiple task associated with the model such as first task and second task. Further, [0108] teaches using “a processor to carry out aspects of the present invention”).
receiving, with the at least one processor, a testing data set... ( Dong Fig. 8 element 810 teaches obtaining “testing data” and element 820 teaches obtaining features of the testing data. Further, [0089] teaches “testing data may be obtained by the DNN, the testing data being different from the training data [and] features of the testing data may be obtained from a feature output interface”. To add, [0108] teaches using “a processor to carry out aspects of the present invention”)
grouping, with the at least one processor, the plurality of features into a plurality of groups based on an impact of each feature of the plurality of features on the first task and the at least one second task; ( Dong [0054] teaches “the feature extracted by the trained deep neural network based model can be used for clustering/classification accurately” and [0089] teaches the feature of the testing data being grouped such that “at least one operation including clustering/classification may be performed based on the obtained features of the testing data. The clustering/classification may be implemented by a regular clustering/classification method, and thus, a learnt clustering /classification result may be obtained”. To add, [0108] teaches using “a processor to carry out aspects of the present invention”).
determining, with the at least one processor, an overall accuracy score, a first task accuracy score, and at least one second task accuracy score based on inputting the testing data set to the first multi-task learning model; ( Dong [0090] teaches “at least one accuracy value (overall accuracy score) including clustering accuracy/classification accuracy of the testing data may be calculated. Usually, the accuracy value may be determined based on the actual classification and the classification result learnt”. Further, it teaches “how to calculate a specific value based on the actual label and the estimated result may be obvious to a person in the art , details of which may be omitted for sake of concise of the specification”. To add, [0108] teaches using “a processor to carry out aspects of the present invention”).
adjusting, with the at least one processor, the feature score of each respective feature of the plurality of features based on a respective grouping of the plurality of groupings associated with the respective feature and at least one of the overall accuracy score, the first task accuracy score, the at least one second task accuracy score, or a combination thereof to provide an adjusted feature score for the respective feature ( Dong Fig. 7 and [0079] teaches adjusting “the entire neural network as a whole” and [0081] teaches the DNN can be adjusted via back propagation (BP). Moreover, [0082] teaches a classification loss and reconstruction loss can be determined based on the training data which these losses can represent the overall accuracy of the model. Furthermore, [0083] teaches the adjustment is based on the features such as the label and unlabeled data).
Dong does not teach ...testing data set comprising a plurality of testing data items for the first multi-task learning model, each testing data item comprising a plurality of elements, each element of the plurality of elements associated with a respective feature of a plurality of features; determining a first task accuracy score, and at least one second task accuracy score based on inputting the testing data set to the first multi-task learning model; applying, with the at least one processor, feature reduction evaluation (FRE) based on the first multi-task learning model and the testing data set to provide a feature score for each feature of the plurality of features; and adjusting, with the at least one processor, the feature score of each respective feature of the plurality of features.
Nevertheless, Chen teaches the following:
...testing data set comprising a plurality of testing data items for the first multi-task learning model, each testing data item comprising a plurality of elements, each element of the plurality of elements associated with a respective feature of a plurality of features; ( Chen [0020] teaches the multi task learning model can receive input data (testing data) , that comprises a “plurality of elements” such as presentation of a query, a document, an image, audio, or radar sample).
determining, with the at least one processor, an overall accuracy score, a first task accuracy score, and at least one second task accuracy score based on inputting the testing data set to the first multi-task learning model ( Chen [0040] teaches Equation 4, where K outputs are being averaged in order to obtain an averaging score ( overall accuracy). Further, Chen [0049] teaches obtaining a relevance score (accuracy score). “Generally, the relevance ranking layer can receive two inputs... and output relevance scores” this suggest that the two inputs being received by the ranking layer can be a first and second task for which a relevant score (accuracy score) is being obtained. To add, Chen [0090] teaches a processor that provided processing capabilities).
Chen is also in the same field of endeavor as Dong (Multi-Tasking Learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of receiving a multi-tasking learning model and the testing data comprising plurality of elements, as being disclosed and taught by Chen, in the system taught by Dong to yield the potential benefit of training a given multi-task machine learning model “to perform well at tasks even when there is not a great deal of task-specific training data” ( Chen [0054]).
Chen does not disclose applying, with the at least one processor, feature reduction evaluation (FRE) based on the first multi-task learning model and the testing data set to provide a feature score for each feature of the plurality of features; and adjusting, with the at least one processor, the feature score of each respective feature of the plurality of features.
However, Kartoun teaches the following:
applying, with the at least one processor, feature reduction evaluation (FRE) based on the first multi-task learning model and the testing data set to provide a feature score for each feature of the plurality of features; and ( Kartoun [0013] teaches performing enhanced feature selection (feature reduction evaluation) in order to “reduce the number of feature used to train the model without negatively impacting the models’ accuracy” and [0033] teaches a feature selection module that is used to perform feature selection, can be used to “rank the features “according to probability value. In addition, [0034] teaches “processing the case-control subset to obtain selection score for each feature in a dataset, and a final subset of features may be selected to train the model”. The “selection score” can be view as the “feature score”).
adjusting, with the at least one processor, the feature score of each respective feature of the plurality of features... ( Kartoun [0049] teaches adjusting the selection score (feature score) for each identified feature).
Kartoun is also in the same field of endeavor as Dong and Chen (machine leaning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of feature selection and adjusting the feature scores as being disclosed and taught by Kartoun, in the system taught by Dong and Chen to yield the predictable results of “increase processing efficiency by reducing the number of features that are processed by a predictive model, thereby reducing the total number of computational operations required to forecast an outcome” (Kartoun [0014]).
Regarding claim 2:
Dong, Chen and Kartoun teach The computer-implemented method of claim 1. Kartoun specifically teaches further comprising selecting, with the at least one processor, a subset of the plurality of features based on the adjusted feature score for each respective feature of the plurality of features (Kartoun [0025] teaches a “Feature subset module 130 processes an input dataset containing features and outcomes to identify different subsets of features for use in subpopulation analysis” and Fig. 3, teaches element 330 where the selection score (feature score) of each identifies feature is being adjusted element 330 and teaches selecting “features based on selection score” element 360). Regarding claim 3:
Dong, Chen and Nevitt teach The computer-implemented method of claim 2. Chen specifically teaches further comprising training, with the at least one processor, a second multi-task learning model based on the subset of the plurality of features ( Chen [0073] teaches in some implementation a student model can be trained “using teacher for only a subset of the task specific layers” this implies the student model ( second multi-task learning model) is trained using a subset of features that the have been obtained from the plurality of features).
Regarding claim 20: is rejected under the same rationale of claim 1. Claim 20 only recites the additional element of A system, comprising: at least one processor; and at least one non-transitory computer-readable medium including one or more instructions that, when executed by the at least one processor, direct the at least one processor to.. for which Dong [0001] teaches a computer - implemented method , system and computer program product to extract features using multi - task learning and [0114] teaches a processor to execute the instructions.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Dong, Chen, Kartoun in further view of Neveitt et al. US 8,078,617 B1 (hereinafter Neveitt).
Regarding claim 4:
Dong, Chen and Kartoun teach The computer-implemented method of claim 1.
Neither Dong, Chen and Kartoun teach further comprising communicating, with the at least one processor, the adjusted feature score for each respective feature of the plurality of features to a remote computing device.
Nevertheless, Neveitt teaches the following:
further comprising communicating, with the at least one processor, the adjusted feature score for each respective feature of the plurality of features to a remote computing device (Neveitt Fig. 5, element 170 teaches “select a subset of the plurality of documents having the highest adjusted scores” and element 172 teaches “Output the selected subset of documents”. Someone ordinary in the skilled in the art will arrive to the conclusion that such output will communicated on an external output device such as display coupled to a highspeed interference ( Neveitt col. 21:56-57)).
Neveitt is also in the same field of endeavor as Dong, Chen and Kartoun (feature extraction). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of communicating using an output device the adjusted features scores, as being disclosed and taught by Neveitt, in the system taught by Dong, Chen and Kartoun to yield the predictable results of improve user experience by selecting relevant features in a short amount of time (Neveitt col. 3:37-41).
Claims 5-9 are rejected under 35 U.S.C. 103 as being unpatentable over Dong, Chen, Kartoun in further view of Blumstein et al. US 2021/0390458 A1 (hereinafter Blumstein).
Regarding claim 5:
Dong, Chen, Kartoun teach The computer-implemented method of claim 1. Dong specifically teaches wherein grouping the plurality of features into a plurality of groups comprises... (Dong [0054] teaches “the feature extracted by the trained deep neural network based model can be used for clustering/classification accurately” and [0089] teaches the feature of the testing data being grouped such that “at least one operation including clustering/classification may be performed based on the obtained features of the testing data. The clustering/classification may be implemented by a regular clustering/classification method, and thus, a learnt clustering /classification result may be obtained”).
Dong does not suggest training, with the at least one processor, a second multi-task learning model based on a subset of the testing data set; applying, with the at least one processor, FRE based on the second multi- task learning model and the subset of the testing data set to provide a first impact score for each feature of the plurality of features on the first task and at least one second impact score for each feature of the plurality of features on the at least one second task; and grouping, with the at least one processor, the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score.
Nevertheless, Chen teaches the following:
training, with the at least one processor, a second multi-task learning model based on a subset of the testing data set; ( Chen [0073] teaches in some implementation a student model can be trained “using teacher for only a subset of the task specific layers” this implies the student model ( second multi-task learning model) is trained using a subset of features that the have been obtained from the plurality of features).
Chen is also in the same field of endeavor as Dong (Multi-Tasking Learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of training a second multi-task learning model based on a subset of the testing data set, as being disclosed and taught by Chen, in the system taught by Dong to yield the potential benefit of training a given multi-task machine learning model “to perform well at tasks even when there is not a great deal of task-specific training data” ( Chen [0054]).
Chen does not suggest applying, with the at least one processor, FRE based on the second multi- task learning model and the subset of the testing data set to provide a first impact score for each feature of the plurality of features on the first task and at least one second impact score for each feature of the plurality of features on the at least one second task; and grouping, with the at least one processor, the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score.
However, Kartoun teaches the following:
applying, with the at least one processor, FRE based on the second multi- task learning model and the subset of the testing data set to provide a first impact score for each feature of the plurality of features on the first task and at least one second impact score for each feature of the plurality of features on the at least one second task; and ( Kartoun [0013] teaches performing enhanced feature selection (feature reduction evaluation) in order to “reduce the number of feature used to train the model (second multi- task learning model) without negatively impacting the models’ accuracy” and [0033] teaches a feature selection module that is used to perform feature selection, can be used to “rank the features” according to probability value and teaches it can be used to determine the “statistical significance” (impact) of each feature. In addition, [0034] teaches “processing the case-control subset to obtain selection score for each feature in a dataset, and a final subset of features may be selected to train the model”. The “selection score” can be view as the “feature score”).
Kartoun is also in the same field of endeavor as Dong and Chen (machine leaning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of feature selection and adjusting the feature scores as being disclosed and taught by Kartoun, in the system taught by Dong and Chen to yield the predictable results of “increase processing efficiency by reducing the number of features that are processed by a predictive model, thereby reducing the total number of computational operations required to forecast an outcome” (Kartoun [0014]).
Kartoun does not teach grouping, with the at least one processor, the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score.
However, Blumstein teaches the following:
grouping, with the at least one processor, the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score
( Blumstein [ 0087] “feature impact score may refer to a score (e.g., a value)” and [0237] teaches feature selection and suggest grouping being performed by the feature selection module that select features based on their important and impact score).
Blumstein is also in the same field of endeavor as Dong, Chen, Kartoun (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of feature selection, grouping and impact scores, as being disclosed and taught by Blumstein, in the system taught by Dong, Chen and Kartoun to yield the predictable results of “efficiently search the space of relational spatial feature candidates, such that search efficiently converges upon the most useful feature candidates (e.g., the candidates with the highest feature impact scores and/or feature importance scores)” (Blumstein [0228]).
Regarding claim 6:
Dong, Chen, Kartoun and Blumstein teach The computer-implemented method of claim 5. Dong specifically teaches wherein the ( Dong [0007] teaches “a deep neural network ( DNN ) , the deep neural network comprising at least one hidden layer. Someone, ordinary in the skilled of the art will recognize that a neural network with a hidden layer will inheritably comprise of an input layer as well as an output layer. Furthermore, Dong [0059] teaches “A DNN based neural network may apply multi - task learning to form a trained DNN based neural model during the procedure of FIG . 4” this suggest that the DNN will also have input, hidden and output layers associated with the respective task).
Dong does not suggest the DNN is second multi-task learning model.
Nevertheless, Chen teaches the following:
...a second multi-tasking learning model... (Chen [0073] teaches in some implementation a student model can be trained).
Regarding claim 7:
Dong, Chen, Kartoun and Blumstein teach The computer-implemented method of claim 5. Kartoun specifically teaches determining, with the at least one processor, a second subset of features comprising features of the plurality of features not in the first subset of features; determining, with the at least one processor, at least one third subset of features based on at least one second top portion of the at least one second ranking of the plurality of features; determining, with the at least one processor, at least one fourth subset of features comprising features of the plurality of features not in the at least one third subset of features; and ( Kartoun [0026] teaches a feature subset module and [0027] teaches “feature subset module 130 generates a predetermined or defined number of subsets of features. Alternatively, feature subset module 130 may exhaustively assign features until there are no remaining unassigned features in a dataset. Feature subset module 130 may identify a subset of features for each unique combination of features” this suggest the feature subset module can create based on various criteria or configuration subset of features).
Dong, Chen and Kartoun do not suggest wherein grouping the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score comprises: ranking, with the at least one processor, the plurality of features based on the first impact score of each feature of the plurality of features to provide a first ranking of the plurality of features; ranking, with the at least one processor, the plurality of features based on the first impact score of each feature of the plurality of features to provide a first ranking of the plurality of features; determining, with the at least one processor, a first subset of features based on a first top portion of the first ranking of the plurality of features; ranking, with the at least one processor, the plurality of features based on the at least one second impact score of each feature of the plurality of features to provide at least one second ranking of the plurality of features; grouping, with the at least one processor, the plurality of features based on the first subset of features, the second subset of features, the at least one third subset of features, and the at least one fourth subset of features.
Nevertheless, Blumstein teaches the following:
wherein grouping the plurality of features into the plurality of groups based on the first impact score and the at least one second impact score comprises: ( Blumstein [0237] teaches grouping features based on impact and importance scores).
ranking, with the at least one processor, the plurality of features based on the first impact score of each feature of the plurality of features to provide a first ranking of the plurality of features; ( Blumstein [0133] teaches ranking features by their feature importance and [0274] teaches a processor).
determining, with the at least one processor, a first subset of features based on a first top portion of the first ranking of the plurality of features; (Blumstein [0131] teaches determining the feature importance and selecting a subset of features based on the N most important feature candidate having the importance score above a threshold and [0274] teaches a processor).
ranking, with the at least one processor, the plurality of features based on the at least one second impact score of each feature of the plurality of features to provide at least one second ranking of the plurality of features; ( Blumstein [0133] teaches ranking features by their feature importance and [0274] teaches a processor).
grouping, with the at least one processor, the plurality of features based on the first subset of features, the second subset of features, the at least one third subset of features, and the at least one fourth subset of features ( Blumstein [ 0087] “feature impact score may refer to a score (e.g., a value)” and [0237] teaches feature selection and suggest grouping being performed by the feature selection module that select features based on their important and impact score. To add, Blumstein [0274] teaches a processor).
Regarding claim 8:
Dong, Chen, Kartoun and Blumstein teach The computer-implemented method of claim 7. Kartoun specifically teaches wherein grouping the plurality of features based on the first subset of features, the second subset of features, the at least one third subset of features, and the at least one fourth subset of features comprises: determining, with the at least one processor, a first group of the plurality of features based on the first subset and the at least one third subset; determining, with the at least one processor, a second group of the plurality of features based on the first subset and the at least one fourth subset; determining, with the at least one processor, a third group of the plurality of features based on the second subset and the at least one third subset; and determining, with the at least one processor, a fourth group of the plurality of features based on the second subset and the at least one fourth subset ( Kartoun [0027] teaches “feature subset module 130 may exhaustively assign features until there are no remaining unassigned features in a dataset. Feature subset module 130 may identify a subset of features for each unique combination of features).
Regarding claim 9:
Dong, Chen, Kartoun and Blumstein teach The computer-implemented method of claim 8. Dong specifically teaches wherein adjusting the feature score of each respective feature of the plurality of features comprises: adjusting, with the at least one processor, the feature score of each respective feature of the first group of the plurality of features based on the overall accuracy score to provide the adjusted feature score for the respective feature of the first group of the plurality of features; adjusting, with the at least one processor, the feature score of each respective feature of the second group of the plurality of features based on the overall accuracy score and the at least one second task accuracy score to provide the adjusted feature score for the respective feature of the second group of the plurality of features; adjusting, with the at least one processor, the feature score of each respective feature of the third group of the plurality of features based on the overall accuracy score and the first task accuracy score to provide the adjusted feature score for the respective feature of the third group of the plurality of features; and adjusting, with the at least one processor, the feature score of each respective feature of the fourth group of the plurality of features based on the overall accuracy score, the first task accuracy score, and the at least one second task accuracy score to provide the adjusted feature score for the respective feature of the fourth group of the plurality of features ( Dong Fig. 7 and [0079] teaches adjusting “the entire neural network as a whole” and [0081] teaches the DNN can be adjusted via back propagation (BP). Furthermore, [0083] teaches the adjustment is based on the features such as the label and unlabeled data).
Claims 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Dong, Chen, Kartoun in further view of Carlson et al. US 2012/0259784 A1 (hereinafter Carlson) in further view of Bohanan et al. US 2017/0228726 A1 (hereinafter Bohanan) in further view of Basu et al. US 2014/0188710 A1 (hereinafter Basu).
Regarding claim 10:
Duong, Chen and Kartoun teach The computer-implemented method of claim 1. Chen specifically teaches wherein the first task comprises generating, based on an authorization request, a first prediction associated with a likelihood of a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request (Chen [0006] teaches a teacher model having “a first task-specific layer that performs a first task, and a second task-specific layer that performs a second task” and [0020] teaches the multi-tasking machine learning model can receive input and produce task-specific output (predictions). This suggest, the model is able to generate a first output ( first prediction) associated with the first task which can be seen in Fig 1. Element 108 “Task Specific Outputs”. Furthermore, Chen [0062] teaches “the selected teacher instances can output different values representing assessments of each labeled data instance, e.g., the probabilities of each possible label” that is the model can generate output (prediction) that are associated with a likelihood (probabilities) of a class).
Neither Duong, Kartoun or Chen suggest based on an authorization request... the first prediction is associated with a likelihood of a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request.
Carlson teaches the following:
...based on an authorization request... ( Carlson [0016] teaches receiving an authorization request).
Carlson is also in the same field of endeavor as Duong, Chen and Kartoun (information technology). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of authorization request, as being disclosed and taught by Carlson, in the system taught by Duong, Chen and Kartoun to yield the predictable results of “increase the speed and reliability with which users are alerted to suspicious or potentially fraudulent use of user account” ( Carlson [0009]).
Though Carlson teaches receiving an authorization request and the authorization request comprising transaction data ([0016]), Carlson does not suggest ...a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request.
However, Bohanan teaches the following:
...a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request ( Bohanan [0005] In various embodiments, the system may match the first transaction amount to the second transaction amount. The system may also transmit the time-based token to the customer device in response to the first transaction amount matching the second transaction amount).
Bohanan is also in the same field of endeavor as Duong, Chen, Kartoun and Carlson (information technology). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of transaction amount matching, as being disclosed and taught by Bohanan in the system taught by Duong, Chen, Kartoun and Carlson to yield the predictable results of “fully or partially prevent fraudulent transactions using a short-range transmission technology” ( Bohanan [0014]).
While Bohanan does not teaches at least one clearing message corresponding to the authorization request.
Nevertheless, Basu teaches the following:
... at least one clearing message corresponding to the authorization request ( Basu [0058] teaches a “clearing request module 262 is configured to facilitate the clearing stage of a payment transaction” and [0034] teaches a “clearing request message may include a transaction identifier that identifies the transaction for which clearing and/or settlement is being requested”).
Basu is also in the same field of endeavor as Duong, Chen, Kartoun, Carlson and Bohanan (information technology). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of clearing request module as being disclosed and taught by Basu, in the system taught by Duong, Chen, Kartoun, Carlson and Bohanan to yield the predictable results of facilitating the clearing stage of a payment transaction ( Badu [0054]).
Regarding claim 11:
Dong, Chen, Kartoun, Carlson, Bohanan, Basu teach The computer-implemented method of claim 10. Chen specifically teaches wherein the at least one second task comprises at least one of generating, based on the authorization request, a second prediction associated with when the at least one clearing message will be received after the authorization message, generating, based on the authorization request, a third prediction associated with a number of clearing messages of the at least one clearing message, or any combination thereof (Chen [0006] teaches a teacher model (multi task machine learning model) having “a first task-specific layer that performs a first task, and a second task-specific layer that performs a second task” and [0020] teaches the multi-tasking machine learning model can receive input and produce task-specific output (predictions). This suggest, the model is able to generate a second output ( second prediction) associated with the second task which can be seen in Fig 1. Element 108 “Task Specific Outputs).
Dong, Chen, Kartoun, Carlson, Bohanan do not suggest ...associated with when the at least one clearing message will be received after the authorization message...
Nevertheless, Basu teaches the following:
...associated with when the at least one clearing message will be received after the authorization message... ( Basu [0058] teaches it is possible for the authorization for a transaction being performed before initiation of the clearing and settlement process, this suggest that the clearing message could be received after the authorization).
Regarding claim 12:
Dong, Chen, Kartoun, Carlson, Bohanan, Basu teach The computer-implemented method of claim 10. Chen specifically teaches wherein the first prediction comprises a first score ( Chen Fig. 7 teaches the Task Specific Outputs (predictions) element 312 comprises relevance scores output element 704).
Regarding claim 13:
Dong, Chen, Kartoun, Carlson, Bohanan, Basu teach The computer-implemented method of claim 12, further comprising. Chen specifically teaches generating, with the at least one processor, based on the authorization request, the first score associated with the likelihood... (Chen [0006] teaches a teacher model having “a first task-specific layer that performs a first task, and a second task-specific layer that performs a second task” and [0020] teaches the multi-tasking machine learning model can receive input and produce task-specific output (predictions). This suggest, the model is able to generate a first output ( first prediction) associated with the first task which can be seen in Fig 1. Element 108 “Task Specific Outputs”. Furthermore, Chen [0062] teaches “the selected teacher instances can output different values representing assessments of each labeled data instance, e.g., the probabilities of each possible label” that is the model can generate output (prediction) that are associated with a likelihood (probabilities) of a class. To add, Chen [0090] teaches a processor that provided processing capabilities).
Chen does not teach ...based on an authorization request; receiving, with the at least one processor, the authorization request from at least one of a merchant system or an acquirer system; inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and communicating, with the at least one processor, the enhanced authorization request to an issuer system; ...of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request.
However, Carlson teaches the following:
...based on an authorization request... ( Carlson [0016] teaches receiving an authorization request).
receiving, with the at least one processor, the authorization request from at least one of a merchant system or an acquirer system; ( Carlson [0016] teaches reeving an authorization request and [0048] teaches the server computer can receive messages “from merchant , acquires and issuers”, this suggest the ability to receive authorization request (messages)).
inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and ( Carlson [0030] teaches the scores are “determined from the transaction data from an authorization request” and [0054] teaches the score can take into consideration data about the transaction such as the amount).
communicating, with the at least one processor, the enhanced authorization request to an issuer system ( Carlson [0030] teaches communicating by “ automatically sensing a transaction notification message to cell phone ...or other consumer device, based on the transaction score” and [0066] teaches a processor that “can implement the instructions”).
Carlson does not suggest ...of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request;
Nevertheless, Bohanan teaches the following:
...of the first transaction amount in the authorization request matching the second transaction amount ( Bohanan [0005] teaches matching the first transaction amount to the second transaction amount and teaches transmitting the time-based token to the costumer device in response to the first transaction amount matching the second transaction amount).
While Bohanan does not teaches at least one clearing message corresponding to the authorization request.
Nevertheless, Basu teaches the following:
...at least one clearing message corresponding to the authorization request ( Basu [0034] teaches a “clearing request message may include a transaction identifier that identifies the transaction for which clearing and/or settlement is being requested”).
Regarding claim 14:
Dong, Chen, Kartoun, Carlson, Bohanan, Basu teach The computer-implemented method of claim 13. Chen specifically teaches wherein generating the first score comprises: ( Chen Fig. 7 teaches the Task Specific Outputs (predictions) element 312 comprises relevance score output element 704).
determining, with the at least one processor, a first plurality of elements based on the authorization request, each element of the first plurality of elements associated with a first respective feature of the plurality of features; and ( Chen [0020] teaches the multi task learning model can receive input data (testing data) , that comprises a “plurality of elements” such as presentation of a query, a document, an image, audio, or radar sample).
inputting, with the at least one processor, the first plurality of elements to the first multi-task learning model to generate the first score associated with the likelihood of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request ( Chen [0020] teaches the multi-tasking machine learning model can receive input that comprises a “plurality of elements” such as presentation of a query, a document, an image, audio, or radar sample and produce task-specific output (predictions). This suggest, the model is able to generate a first output ( first prediction) associated with the first task which can be seen in Fig 1. Element 108 “Task Specific Outputs” that comprises “relevance Score Output” element 704 which can be view as first score . Furthermore, Chen [0062] teaches “the selected teacher instances can output different values representing assessments of each labeled data instance, e.g., the probabilities of each possible label” that is the model can generate output (prediction) that are associated with a likelihood (probabilities) of a class. To add, Chen [0090] teaches a processor that provided processing capabilities).
Chen does not suggest ...a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request.
However, Bohanan teaches the following:
...a first transaction amount in the authorization request matching a second transaction amount in at least one clearing message corresponding to the authorization request ( Bohanan [0005] teaches matching the first transaction amount to the second transaction amount and teaches transmitting the time-based token to the costumer device in response to the first transaction amount matching the second transaction amount).
While Bohanan does not teaches at least one clearing message corresponding to the authorization request.
Nevertheless, Basu teaches the following:
... at least one clearing message corresponding to the authorization request ( Basu [0034] teaches a “clearing request message may include a transaction identifier that identifies the transaction for which clearing and/or settlement is being requested”).
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dong, Chen, Kartoun Carlson, Bohanan, Basu in further view of Hammad et al. US 2010/0274688 A1 (hereinafter Hammad).
Regarding claim 15:
Dong, Chen, Kartoun, Carlson, Bohanan and Basu teach The computer-implemented method of claim 13.
Dong, Chen, Kartoun, Carlson, Bohanan and Basu do not suggest further comprising determining, with the at least one processor, based on the authorization request, that the issuer system is enrolled in a program before generating the first score.
However, Hammad teaches the following:
further comprising determining, with the at least one processor, based on the authorization request, that the issuer system is enrolled in a program before generating the first score (Hammad [0007] teaches “receiving an authorization request message,... analyzing the authorization request message by a server computer to determine if the portable device is enrolled in a transaction alert program”).
Hammad is also in the same field of endeavor as Dong, Chen, Kartoun Carlson, Bohanan and Basu (information technology). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of enrollment, as being disclosed and taught by Hammad, in the system taught by Dong, Chen, Kartoun Carlson, Bohanan and Basu to yield the predictable results of “provide for greater control over the authorization process by an approving entity... [and] ...also improve the security of the transaction by providing additional authentication data to an issuer” ( Hammad [0006]).
Regarding claim 16:
Dong, Chen, Kartoun, Carlson, Bohanan, Basu, and Hammad teach The computer-implemented method of claim 15. Carlson specifically teaches wherein generating the first score, inserting the first score into the at least one field of the authorization request to provide the enhanced authorization request, and communicating the enhanced authorization request are in response to determining that the issuer is enrolled in the program ( Carlson [0030] teaches the score is “determine from the transaction data from an authorization request” and [0054] teaches the score can take into account data about the transaction such as the amount. Furthermore, [0030] teaches communicating by “automatically sending a transaction notification message to cell phone, ...or other consumer device, based on the transaction score”).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Dong, Chen, Kartoun Carlson, Bohanan, Basu in further view of Brickell et al. US 2016/0125396 A1 (hereinafter Brickell).
Regarding claim 17:
Dong, Chen, Kartoun, Carlson, Bohanan, Basu teach The computer-implemented method of claim 13. Carlson specifically teaches ...based on the first score in the enhanced authorization request satisfying a threshold ([0055] an advance module compares the score to a threshold).
While neither Carlson , Dong, Chen, Kartoun, Bohanan or Basu teaches wherein the issuer system determines to post a transaction associated with the authorization request to an account before receiving the clearing message corresponding to the authorization request...
Nevertheless Brickell teaches the following:
wherein the issuer system determines to post a transaction associated with the authorization request to an account before receiving the clearing message corresponding to the authorization request based on the first score in the enhanced authorization request satisfying a threshold... ( Brickell [0057] teaches the issuer system “approves the transaction based on the validity of the received token, the validity of the user computing device 110 identifier, and any other relevant considerations for approving a transaction authorization request. In other example embodiments, the issuer system denies a payment authorization request. In an example embodiment, the issuer system transmits a notice of approval or denial of the payment authorization request to the payment processing system and/or the user computing device”. This suggest the ability to post a transaction associated with an authorization request).
Brickell is also in the same field of endeavor as Dong, Chen, Kartoun, Carlson, Bohanan, and Basu (information technology). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of post transaction, as being disclosed and taught by Brickell, in the system taught by Dong, Chen, Kartoun, Carlson, Bohanan and Basu to yield the predictable results of “improving the security of transactions in which tokenized payment card information is provisioned on a user computing device” (Brickell [0001]).
Claims 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chen, Carlson, Bohanan and Basu.
Regarding claim 18:
A computer-implemented method, comprising: ( Chen [0004] teaches a method that can be performed on a computing device, therefore a computer implemented method).
generating, with the at least one processor, based on the authorization request, the first score associated with the likelihood... (Chen [0006] teaches a teacher model having “a first task-specific layer that performs a first task, and a second task-specific layer that performs a second task” and [0020] teaches the multi-tasking machine learning model can receive input and produce task-specific output (predictions). This suggest, the model is able to generate a first output ( first prediction) associated with the first task which can be seen in Fig 1. Element 108 “Task Specific Outputs”. Furthermore, Chen [0062] teaches “the selected teacher instances can output different values representing assessments of each labeled data instance, e.g., the probabilities of each possible label” that is the model can generate output (prediction) that are associated with a likelihood (probabilities) of a class. To add, Chen [0090] teaches a processor that provided processing capabilities).
Chen does not teach ...based on an authorization request; receiving, with the at least one processor, the authorization request from at least one of a merchant system or an acquirer system; inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and communicating, with the at least one processor, the enhanced authorization request to an issuer system; ...of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request.
However, Carlson teaches the following:
...based on an authorization request... ( Carlson [0016] teaches receiving an authorization request).
receiving, with the at least one processor, the authorization request from at least one of a merchant system or an acquirer system; ( Carlson [0016] teaches reeving an authorization request and [0048] teaches the server computer can receive messages “from merchant , acquires and issuers”, this suggest the ability to receive authorization request (messages)).
inserting, with the at least one processor, the first score into at least one field of the authorization request to provide an enhanced authorization request; and ( Carlson [0030] teaches the scores are “determined from the transaction data from an authorization request” and [0054] teaches the score can take into consideration data about the transaction such as the amount).
communicating, with the at least one processor, the enhanced authorization request to an issuer system ( Carlson [0030] teaches communicating by “ automatically sensing a transaction notification message to cell phone ...or other consumer device, based on the transaction score” and [0066] teaches a processor that “can implement the instructions”).
Carlson is also in the same field of endeavor as Chen (information technology). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of an authorization request being received as well as inserting scores and communicating the authorization request, as being disclosed and taught by Carlson, in the system taught by Chen to yield the predictable results of “increase the speed and reliability with which users are alerted to suspicious or potentially fraudulent use of user account” ( Carlson [0009]).
Carlson does not suggest ...of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request;
Nevertheless, Bohanan teaches the following:
...of the first transaction amount in the authorization request matching the second transaction amount in the at least one clearing message corresponding to the authorization request; ( Bohanan [0005] teaches matching the first transaction amount to the second transaction amount and teaches transmitting the time-based token to the costumer device in response to the first transaction amount matching the second transaction amount).
Bohanan is also in the same field of endeavor as Chen and Carlson (information technology). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of transaction amount matching, as being disclosed and taught by Bohanan in the system taught by Chen and Carlson to yield the predictable results of “fully or partially prevent fraudulent transactions using a short-range transmission technology” ( Bohanan [0014]).
While Bohanan does not teaches at least one clearing message corresponding to the authorization request.
Nevertheless, Basu teaches the following:
...at least one clearing message corresponding to the authorization request ( Basu [0034] teaches a “clearing request message may include a transaction identifier that identifies the transaction for which clearing and/or settlement is being requested”).
Basu is also in the same field of endeavor as Chen, Carlson and Bohanan (information technology). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of clearing request module as being disclosed and taught by Basu, in the system taught by Chen, Carlson and Bohanan to yield the predictable results of facilitating the clearing stage of a payment transaction ( Badu [0054]).
Regarding claim 19:
Chen, Carlson, Bohanan and Basu teach The computer-implemented method of claim 18. Chen specifically teaches wherein the machine learning model comprises at least one of a deep neural network (DNN), a multi- task learning model, or any combination thereof ( Chen [0020] and Fig. 1 teaches a multi-task machine learning model element 100).
Conclusion
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/G.G.F./Examiner, Art Unit 2127
/ASHISH THOMAS/Supervisory Patent Examiner, Art Unit 2142
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