DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Preliminary Amendment filed on 3/24/2023. Claims 1-15 and 21-27 are pending in the case. Claims 1 and 21-22 are independent claims.
Claim Objections
Claims 1, 5-6, 10, 14, 21-22, 24, and 27 are objected to because of the following informalities:
Claims 1 and 21-22 recite “the predicted first payment date” where “the first predicted payment date” was apparently intended.
Claim 1 recites “the predicted second payment date” where “the second predicted payment date” was apparently intended.
Claim 5 recites alternative (vi) which is a substantial duplicate of alternative (i).
Claims 6, 24, and 27 recite “feature” where “features” was apparently intended.
Claims 10 and 14 recite “a respective invoice” where “respective invoices” was apparently intended.
Claims 10 and 14 recite “the respective invoice” where “the respective invoices” was apparently intended.
Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 9 is rejected under 35 U.S.C. § 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. The phrase “different to, or the same as” covers all possibilities. Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
Claim Rejections - 35 U.S.C. § 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 1-15 and 21-27 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determining a dataset of historical financial record data related to an entity set, the entity set comprising one or more entities having a common attribute, and wherein the historical financial record data comprises an actual payment date or an indication of voiding for each of a plurality of invoices associated with one or more entities of the entity set” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Yes, the limitation “determining a first model of payment behaviour of the entity set, the first model configured to predict a date of payment of an invoice by an entity” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a second model of payment behaviour of the entity set, the second model configured to predict a date of payment of an invoice by the entity, and the second model being different to the first model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a first predicted payment date for each of the plurality of invoices associated with the dataset of historical financial record data using the first model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a second predicted payment date for each of the plurality of invoices associated with the dataset of historical financial record data using the second model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a first error metric associated with the first model, wherein the first error metric is based on a first difference measure between the actual payment date and the predicted first payment date for each of the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “determining a second error metric associated with the second model, wherein the second error metric is based on a second difference measure between the actual payment date and the predicted second payment date for each of the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “selecting a designated prediction model from a set of prediction models based on corresponding error metrics of the respective prediction models, the set comprising at least the first model and the second model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “deploying the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “deploying the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “deploying the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “deploying the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 2:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the plurality of invoices are invoices issued by (i) a particular issuing entity or (ii) a group of issuing entities having a common attribute” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the plurality of invoices are invoices issued by (i) a particular issuing entity or (ii) a group of issuing entities having a common attribute” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 3:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein each of the plurality of invoices are associated with a same first entity as an invoice addressee” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein each of the plurality of invoices are associated with a same first entity as an invoice addressee” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 4:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein at least the first model of the first and second models is a univariate model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 5:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the univariate model predicts invoice payment dates for a first entity as being any one of: (i) a particular number of days after the issue date or the due date; (ii) a particular date of a month of the issue date or due date; (iii) a next day of a week after the issue date or due date; (iv) a next business day of the week after the issue date or the due date; (v) a predefined day of a predefined week of a month after the issue date or the due date; and (vi) a specific number of days after the issue date or the due date” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the univariate model predicts invoice payment dates for a first entity as being any one of: (i) a particular number of days after the issue date or the due date; (ii) a particular date of a month of the issue date or due date; (iii) a next day of a week after the issue date or due date; (iv) a next business day of the week after the issue date or the due date; (v) a predefined day of a predefined week of a month after the issue date or the due date; and (vi) a specific number of days after the issue date or the due date” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 6:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determining values for a plurality of first feature for each of the respective plurality of invoices associated with the dataset” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Yes, the limitation “providing, as an input to the multivariate model, the values of the plurality of first feature associated with the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “predicting, as an output, a second payment date for the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 7:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the second model comprises a first sub model configured to predict an invoice payment date for an invoice that is not overdue” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the second model comprises a second sub model configured to predict an invoice payment date for an invoice that is overdue” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the first features provided to a first sub model are different to, or the same as, the first features provided to the second sub model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 10:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the values for the plurality of first features are derived from a respective invoice and/or accounting information associated with an entity addressee of the respective invoice” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 11:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the multivariate model is implemented using a random forest regression model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the multivariate model is implemented using a random forest regression model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “wherein the multivariate model is implemented using a random forest regression model” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the multivariate model is implemented using a random forest regression model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the multivariate model is implemented using a random forest regression model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “wherein the multivariate model is implemented using a random forest regression model” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 12:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determining a third model of payment behaviour of the entity set, the third model configured to predict a probability of non-payment of an invoice associated with the entity set” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a probability score of non-payment of each of the plurality of invoices associated with the dataset of historical financial record data using the third model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a third error metric associated with the third model, wherein the third error metric is indicative of an accuracy of the probability score relative to whether or not the invoice was paid” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “wherein the set of prediction models from which the designated prediction model is selected comprises the third model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 13:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determining values for a plurality of second features for each of the respective plurality of invoices associated with the dataset” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Yes, the limitation “providing, as an input to a multivariate model, the values of the plurality of second features associated with the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “predicting, as an output, the probability score of non-payment of each of the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 14:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the values for the plurality of second features are derived from a respective invoice and/or accounting information associated with an entity addressee of the respective invoice” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the third model is implemented using logistic regression or a random forest classifier” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the third model is implemented using logistic regression or a random forest classifier” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “wherein the third model is implemented using logistic regression or a random forest classifier” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the third model is implemented using logistic regression or a random forest classifier” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the third model is implemented using logistic regression or a random forest classifier” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “wherein the third model is implemented using logistic regression or a random forest classifier” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 21:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determine a dataset of historical financial record data related to an entity set, the entity set comprising one or more entities having a common attribute, and wherein the historical financial record data comprises an actual payment date or an indication of voiding for each of a plurality of invoices associated with one or more entities of the entity set” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Yes, the limitation “determine a first model of payment behaviour of the entity set, the first model configured to predict a date of payment of an invoice by an entity” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determine a second model of payment behaviour of the entity set, the second model configured to predict a date of payment of an invoice by the entity, and the second model being different to the first model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determine a first predicted payment date for each of the plurality of invoices associated with the dataset of historical financial record data using the first model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determine a second predicted payment date for each of the plurality of invoices associated with the dataset of historical financial record data using the second model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determine a first error metric associated with the first model, wherein the first error metric is based on a first difference measure between the actual payment date and the predicted first payment date for each of the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “determine a second error metric associated with the second model, wherein the second error metric is based on a second difference measure between the actual payment date and the second predicted payment date for each of the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “select a designated prediction model from a set of prediction models based on corresponding error metrics of the respective prediction models, the set comprising at least the first model and the second model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “one or more processors” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “memory comprising computer executable instructions, which when executed by the one or more processors, cause the computing device” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “deploy the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “deploy the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “one or more processors” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “memory comprising computer executable instructions, which when executed by the one or more processors, cause the computing device” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “deploy the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “deploy the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 22:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a manufacture.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determining a dataset of historical financial record data related to an entity set, the entity set comprising one or more entities having a common attribute, and wherein the historical financial record data comprises an actual payment date or an indication of voiding for each of a plurality of invoices associated with one or more entities of the entity set” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Yes, the limitation “determining a first model of payment behaviour of the entity set, the first model configured to predict a date of payment of an invoice by an entity” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a second model of payment behaviour of the entity set, the second model configured to predict a date of payment of an invoice by the entity, and the second model being different to the first model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a first predicted payment date for each of the plurality of invoices associated with the dataset of historical financial record data using the first model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a second predicted payment date for each of the plurality of invoices associated with the dataset of historical financial record data using the second model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determining a first error metric associated with the first model, wherein the first error metric is based on a first difference measure between the actual payment date and the predicted first payment date for each of the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “determining a second error metric associated with the second model, wherein the second error metric is based on a second difference measure between the actual payment date and the second predicted payment date for each of the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “selecting a designated prediction model from a set of prediction models based on corresponding error metrics of the respective prediction models, the set comprising at least the first model and the second model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform operations” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “deploying the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “deploying the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform operations” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “deploying the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “deploying the designated prediction model for predicting payment behaviour for the entity set” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 23:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein at least the first model of the first and second models is a univariate model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 24:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determine values for a plurality of first feature for each of the respective plurality of invoices associated with the dataset” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Yes, the limitation “provide, as an input to the multivariate model, the values of the plurality of first feature associated with the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “predict, as an output, a second payment date for the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 25:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determine a third model of payment behaviour of the entity set, the third model configured to predict a probability of non-payment of an invoice associated with the entity set” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determine a probability score of non-payment of each of the plurality of invoices associated with the dataset of historical financial record data using the third model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “determine a third error metric associated with the third model, wherein the third error metric is indicative of an accuracy of the probability score relative to whether or not the invoice was paid” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “wherein the set of prediction models from which the designated prediction model is selected comprises the third model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 26:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a manufacture.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein at least the first model of the first and second models is a univariate model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 27:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a manufacture.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determining values for a plurality of first feature for each of the respective plurality of invoices associated with the dataset” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that the dataset may be a handwritten ledger.
Yes, the limitation “providing, as an input to the multivariate model, the values of the plurality of first feature associated with the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Yes, the limitation “predicting, as an output, a second payment date for the plurality of invoices” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Note that a model may be as simple as a linear equation, decision tree, lookup table, etc.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-15 and 21-27 are rejected under 35 U.S.C. § 103 as being unpatentable over James et al. (US 2020/0265512 A1, hereinafter James) in view of Mor et al. (US 2021/0125087 A1, hereinafter Mor).
As to independent claim 1, James teaches a method comprising:
determining a dataset of historical financial record data related to an entity set, the entity set comprising one or more entities having a common attribute (“customer datasets often include features like customer id, income, etc.,” paragraph 0032 lines 6-7), and wherein the historical financial record data comprises an actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) or an indication of voiding for each of a plurality of invoices associated with one or more entities of the entity set (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices);
determining a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) model of payment behaviour of the entity set, the first model configured to predict a [behavior] of payment of an invoice by an entity (“developing a set of models using a machine learning module to predict performance of the borrower based on the business objective determination,” paragraph 0015 lines 13-15);
determining a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) model of payment behaviour of the entity set, the second model configured to predict a [behavior] of payment of an invoice by the entity (“developing a set of models using a machine learning module to predict performance of the borrower based on the business objective determination,” paragraph 0015 lines 13-15), and the second model being different to the first model (“a number of classifiers are tested,” paragraph 0049 lines 6-7);
determining a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) predicted payment [behavior] for each of the plurality of invoices associated with the dataset of historical financial record data using the first model (“To assess model performance the data can be partitioned into a training set and a validation or test set. Training set used to construct the classifiers and the test set is used to assess their performance,” paragraph 0049 lines 1-4);
determining a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) predicted payment [behavior] for each of the plurality of invoices associated with the dataset of historical financial record data using the second model (“To assess model performance the data can be partitioned into a training set and a validation or test set. Training set used to construct the classifiers and the test set is used to assess their performance,” paragraph 0049 lines 1-4);
determining a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) error metric associated with the first model, wherein the first error metric is based on a first difference measure between the actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) and the predicted first payment [behavior] for each of the plurality of invoices (“Performance may be determined by using a receiver operating characteristic curve that is created by plotting the true positive rate against the false positive rate at various threshold setting. Accuracy may be measured by the area under the receiver operating characteristic (ROC) curve, which measures accuracy of the model. Yet another testing approach is logarithmic loss that measures the performance of a classification model where the prediction input is a probability value between zero and one,” paragraph 0049 lines 9-18);
determining a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) error metric associated with the second model, wherein the second error metric is based on a second difference measure between the actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) and the predicted second payment [behavior] for each of the plurality of invoices (“Performance may be determined by using a receiver operating characteristic curve that is created by plotting the true positive rate against the false positive rate at various threshold setting. Accuracy may be measured by the area under the receiver operating characteristic (ROC) curve, which measures accuracy of the model. Yet another testing approach is logarithmic loss that measures the performance of a classification model where the prediction input is a probability value between zero and one,” paragraph 0049 lines 9-18);
selecting a designated prediction model from a set of prediction models based on corresponding error metrics of the respective prediction models, the set comprising at least the first model and the second model (“If a number of classifiers are tested, then in the model selection stage the machine learning model development module 115 can choose the classifier that performed best on the test set,” paragraph 0049 lines 6-9); and
deploying the designated prediction model for predicting payment behaviour for the entity set (“deploying accurate machine learning models,” paragraph 0033 lines 12-13).
James does not appear to expressly teach a method wherein the predicted payment behaviors are predicted payment dates.
Mor teaches a method wherein the predicted payment behaviors are predicted payment dates (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” paragraph 0065 lines 6-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the payment behaviors of James to comprise the payment dates of Mor. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely predicting payment dates (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” Mor paragraph 0065 lines 6-9). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 2, the rejection of claim 1 is incorporated. James/Mor further teaches a method wherein the plurality of invoices are invoices issued by (i) a particular issuing entity (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6) or (ii) a group of issuing entities having a common attribute.
As to dependent claim 3, the rejection of claim 1 is incorporated. James/Mor further teaches a method wherein each of the plurality of invoices are associated with a same first entity as an invoice addressee (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices).
As to dependent claim 4, the rejection of claim 1 is incorporated. James/Mor further teaches a method wherein at least the first model of the first and second models is a univariate model (“these processing times are treated as target values for model building during the training phase,” Mor paragraph 0034 lines 5-7), and wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” James paragraph 0038 lines 28-30).
As to dependent claim 5, the rejection of claim 4 is incorporated. James/Mor further teaches a method wherein the univariate model predicts invoice payment dates for a first entity as being any one of:
(i) a particular number of days after the issue date or the due date (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” Mor paragraph 0065 lines 6-9);
(ii) a particular date of a month of the issue date or due date;
(iii) a next day of a week after the issue date or due date;
(iv) a next business day of the week after the issue date or the due date;
(v) a predefined day of a predefined week of a month after the issue date or the due date; and
(vi) a specific number of days after the issue date or the due date (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” Mor paragraph 0065 lines 6-9).
As to dependent claim 6, the rejection of claim 1 is incorporated. James/Mor further teaches a method wherein at least the second model is a multivariate model, and the method further comprises:
determining values for a plurality of first feature (“the business objective determination module 131 weighs a number of features,” James paragraph 0038 lines 1-2) for each of the respective plurality of invoices associated with the dataset (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices); and
providing, as an input to the multivariate model, the values of the plurality of first feature (“the business objective determination module 131 weighs a number of features,” James paragraph 0038 lines 1-2) associated with the plurality of invoices (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices); and
predicting, as an output, a second payment date for the plurality of invoices (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” Mor paragraph 0065 lines 6-9).
As to dependent claim 7, the rejection of claim 6 is incorporated. James/Mor further teaches a method wherein the second model comprises a first sub model configured to predict an invoice payment date for an invoice that is not overdue (“produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion,” James paragraph 0046 lines 12-15).
As to dependent claim 8, the rejection of claim 6 is incorporated. James/Mor further teaches a method wherein the second model comprises a second sub model configured to predict an invoice payment date for an invoice that is overdue (“produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion,” James paragraph 0046 lines 12-15).
As to dependent claim 9, the rejection of claim 8 is incorporated. James/Mor further teaches a method wherein the first features provided to a first sub model are different to, or the same as, the first features provided to the second sub model (this covers all possibilities).
As to dependent claim 10, the rejection of claim 6 is incorporated. James/Mor further teaches a method wherein the values for the plurality of first features are derived from a respective invoice and/or accounting information associated with an entity addressee of the respective invoice (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices).
As to dependent claim 11, the rejection of claim 6 is incorporated. James/Mor further teaches a method wherein the multivariate model is implemented using a random forest (“produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees,” James paragraph 0046 lines 12-14) regression model (“various regression algorithms such as linear regression, GLM, SVR, GPR, ensemble methods, decision trees, and neural networks may be used for supervised learning,” James paragraph 0048 lines 9-12, emphasis added).
As to dependent claim 12, the rejection of claim 1 is incorporated. James/Mor further teaches a method comprising:
determining a third (“a number of classifiers are tested,” paragraph 0049 lines 6-7) model of payment behaviour of the entity set, the third model configured to predict a probability of non-payment of an invoice associated with the entity set; and
determining a probability score of non-payment (“Key Performance Indicators (e.g., First Payment Default etc.),” James paragraph 0037 lines 12-13) of each of the plurality of invoices associated with the dataset of historical financial record data using the third model (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices);
determining a third (“a number of classifiers are tested,” paragraph 0049 lines 6-7) error metric associated with the third model, wherein the third error metric is indicative of an accuracy of the probability score (“Performance may be determined by using a receiver operating characteristic curve that is created by plotting the true positive rate against the false positive rate at various threshold setting. Accuracy may be measured by the area under the receiver operating characteristic (ROC) curve, which measures accuracy of the model. Yet another testing approach is logarithmic loss that measures the performance of a classification model where the prediction input is a probability value between zero and one,” James paragraph 0049 lines 9-18) relative to whether or not the invoice was paid (“Key Performance Indicators (e.g., First Payment Default etc.),” James paragraph 0037 lines 12-13); and
wherein the set of prediction models from which the designated prediction model is selected comprises the third model (“If a number of classifiers are tested, then in the model selection stage the machine learning model development module 115 can choose the classifier that performed best on the test set,” James paragraph 0049 lines 6-9).
As to dependent claim 13, the rejection of claim 12 is incorporated. James/Mor further teaches a method comprising:
determining values for a plurality of second features (“the business objective determination module 131 weighs a number of features,” James paragraph 0038 lines 1-2) for each of the respective plurality of invoices associated with the dataset (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices); and
providing, as an input to a multivariate model, the values of the plurality of second features (“the business objective determination module 131 weighs a number of features,” James paragraph 0038 lines 1-2) associated with the plurality of invoices (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices); and
predicting, as an output, the probability score of non-payment of each of the plurality of invoices (“Key Performance Indicators (e.g., First Payment Default etc.),” James paragraph 0037 lines 12-13).
As to dependent claim 14, the rejection of claim 13 is incorporated. James/Mor further teaches a method wherein the values for the plurality of second features are derived from a respective invoice and/or accounting information associated with an entity addressee of the respective invoice (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices).
As to dependent claim 15, the rejection of claim 12 is incorporated. James/Mor further teaches a method wherein the third model is implemented using logistic regression (“various regression algorithms such as linear regression, GLM, SVR, GPR, ensemble methods, decision trees, and neural networks may be used for supervised learning,” James paragraph 0048 lines 9-12, emphasis added) or a random forest classifier (“produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees,” James paragraph 0046 lines 12-14).
As to independent claim 21, James teaches a computing device comprising:
one or more processors (“processors,” paragraph 0026 line 7); and
memory comprising computer executable instructions, which when executed by the one or more processors, cause the computing device (“Modules may also be implemented in software for execution by various types of processors. Modules or portions of a module that are implemented in software, may be stored on one or more computer readable storage media,” paragraph 0026 lines 5-9) to:
determine a dataset of historical financial record data related to an entity set, the entity set comprising one or more entities having a common attribute (“customer datasets often include features like customer id, income, etc.,” paragraph 0032 lines 6-7), and wherein the historical financial record data comprises an actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) or an indication of voiding for each of a plurality of invoices associated with one or more entities of the entity set (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices);
determine a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) model of payment behaviour of the entity set, the first model configured to predict a [behavior] of payment of an invoice by an entity (“developing a set of models using a machine learning module to predict performance of the borrower based on the business objective determination,” paragraph 0015 lines 13-15);
determine a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) model of payment behaviour of the entity set, the second model configured to predict a [behavior] of payment of an invoice by the entity (“developing a set of models using a machine learning module to predict performance of the borrower based on the business objective determination,” paragraph 0015 lines 13-15), and the second model being different to the first model (“a number of classifiers are tested,” paragraph 0049 lines 6-7);
determine a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) predicted payment [behavior] for each of the plurality of invoices associated with the dataset of historical financial record data using the first model (“To assess model performance the data can be partitioned into a training set and a validation or test set. Training set used to construct the classifiers and the test set is used to assess their performance,” paragraph 0049 lines 1-4);
determine a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) predicted payment [behavior] for each of the plurality of invoices associated with the dataset of historical financial record data using the second model (“To assess model performance the data can be partitioned into a training set and a validation or test set. Training set used to construct the classifiers and the test set is used to assess their performance,” paragraph 0049 lines 1-4);
determine a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) error metric associated with the first model, wherein the first error metric is based on a first difference measure between the actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) and the predicted first payment [behavior] for each of the plurality of invoices (“Performance may be determined by using a receiver operating characteristic curve that is created by plotting the true positive rate against the false positive rate at various threshold setting. Accuracy may be measured by the area under the receiver operating characteristic (ROC) curve, which measures accuracy of the model. Yet another testing approach is logarithmic loss that measures the performance of a classification model where the prediction input is a probability value between zero and one,” paragraph 0049 lines 9-18);
determine a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) error metric associated with the second model, wherein the second error metric is based on a second difference measure between the actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) and the second predicted payment [behavior] for each of the plurality of invoices (“Performance may be determined by using a receiver operating characteristic curve that is created by plotting the true positive rate against the false positive rate at various threshold setting. Accuracy may be measured by the area under the receiver operating characteristic (ROC) curve, which measures accuracy of the model. Yet another testing approach is logarithmic loss that measures the performance of a classification model where the prediction input is a probability value between zero and one,” paragraph 0049 lines 9-18);
select a designated prediction model from a set of prediction models based on corresponding error metrics of the respective prediction models, the set comprising at least the first model and the second model (“If a number of classifiers are tested, then in the model selection stage the machine learning model development module 115 can choose the classifier that performed best on the test set,” paragraph 0049 lines 6-9); and
deploy the designated prediction model for predicting payment behaviour for the entity set (“deploying accurate machine learning models,” paragraph 0033 lines 12-13).
James does not appear to expressly teach a device wherein the predicted payment behaviors are predicted payment dates.
Mor teaches a device wherein the predicted payment behaviors are predicted payment dates (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” paragraph 0065 lines 6-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the payment behaviors of James to comprise the payment dates of Mor. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely predicting payment dates (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” Mor paragraph 0065 lines 6-9). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to independent claim 22, James teaches a computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform operations (“Modules may also be implemented in software for execution by various types of processors. Modules or portions of a module that are implemented in software, may be stored on one or more computer readable storage media,” paragraph 0026 lines 5-9) including:
determining a dataset of historical financial record data related to an entity set, the entity set comprising one or more entities having a common attribute (“customer datasets often include features like customer id, income, etc.,” paragraph 0032 lines 6-7), and wherein the historical financial record data comprises an actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) or an indication of voiding for each of a plurality of invoices associated with one or more entities of the entity set (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices);
determining a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) model of payment behaviour of the entity set, the first model configured to predict a [behavior] of payment of an invoice by an entity (“developing a set of models using a machine learning module to predict performance of the borrower based on the business objective determination,” paragraph 0015 lines 13-15);
determining a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) model of payment behaviour of the entity set, the second model configured to predict a [behavior] of payment of an invoice by the entity (“developing a set of models using a machine learning module to predict performance of the borrower based on the business objective determination,” paragraph 0015 lines 13-15), and the second model being different to the first model (“a number of classifiers are tested,” paragraph 0049 lines 6-7);
determining a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) predicted payment [behavior] for each of the plurality of invoices associated with the dataset of historical financial record data using the first model (“To assess model performance the data can be partitioned into a training set and a validation or test set. Training set used to construct the classifiers and the test set is used to assess their performance,” paragraph 0049 lines 1-4);
determining a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) predicted payment [behavior] for each of the plurality of invoices associated with the dataset of historical financial record data using the second model (“To assess model performance the data can be partitioned into a training set and a validation or test set. Training set used to construct the classifiers and the test set is used to assess their performance,” paragraph 0049 lines 1-4);
determining a first (“a number of classifiers are tested,” paragraph 0049 lines 6-7) error metric associated with the first model, wherein the first error metric is based on a first difference measure between the actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) and the predicted first payment [behavior] for each of the plurality of invoices (“Performance may be determined by using a receiver operating characteristic curve that is created by plotting the true positive rate against the false positive rate at various threshold setting. Accuracy may be measured by the area under the receiver operating characteristic (ROC) curve, which measures accuracy of the model. Yet another testing approach is logarithmic loss that measures the performance of a classification model where the prediction input is a probability value between zero and one,” paragraph 0049 lines 9-18);
determining a second (“a number of classifiers are tested,” paragraph 0049 lines 6-7) error metric associated with the second model, wherein the second error metric is based on a second difference measure between the actual payment date (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” paragraph 0038 lines 28-30) and the second predicted payment [behavior] for each of the plurality of invoices (“Performance may be determined by using a receiver operating characteristic curve that is created by plotting the true positive rate against the false positive rate at various threshold setting. Accuracy may be measured by the area under the receiver operating characteristic (ROC) curve, which measures accuracy of the model. Yet another testing approach is logarithmic loss that measures the performance of a classification model where the prediction input is a probability value between zero and one,” paragraph 0049 lines 9-18);
selecting a designated prediction model from a set of prediction models based on corresponding error metrics of the respective prediction models, the set comprising at least the first model and the second model (“If a number of classifiers are tested, then in the model selection stage the machine learning model development module 115 can choose the classifier that performed best on the test set,” paragraph 0049 lines 6-9); and
deploying the designated prediction model for predicting payment behaviour for the entity set (“deploying accurate machine learning models,” paragraph 0033 lines 12-13).
James does not appear to expressly teach a medium wherein the predicted payment behaviors are predicted payment dates.
Mor teaches a medium wherein the predicted payment behaviors are predicted payment dates (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” paragraph 0065 lines 6-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the payment behaviors of James to comprise the payment dates of Mor. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely predicting payment dates (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” Mor paragraph 0065 lines 6-9). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 23, the rejection of claim 21 is incorporated. James/Mor further teaches a device wherein at least the first model of the first and second models is a univariate model (“these processing times are treated as target values for model building during the training phase,” Mor paragraph 0034 lines 5-7), and wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” James paragraph 0038 lines 28-30).
As to dependent claim 24, the rejection of claim 21 is incorporated. James/Mor further teaches a device wherein at least the second model is a multivariate model, and wherein the computer executable instructions, when executed, cause the computing device to:
determine values for a plurality of first feature (“the business objective determination module 131 weighs a number of features,” James paragraph 0038 lines 1-2) for each of the respective plurality of invoices associated with the dataset (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices); and
provide, as an input to the multivariate model, the values of the plurality of first feature (“the business objective determination module 131 weighs a number of features,” James paragraph 0038 lines 1-2) associated with the plurality of invoices (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices); and
predict, as an output, a second payment date for the plurality of invoices (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” Mor paragraph 0065 lines 6-9).
As to dependent claim 25, the rejection of claim 21 is incorporated. James/Mor further teaches a device wherein the computer executable instructions, when executed, cause the computing device to:
determine a third (“a number of classifiers are tested,” paragraph 0049 lines 6-7) model of payment behaviour of the entity set, the third model configured to predict a probability of non-payment of an invoice associated with the entity set; and
determine a probability score of non-payment (“Key Performance Indicators (e.g., First Payment Default etc.),” James paragraph 0037 lines 12-13) of each of the plurality of invoices associated with the dataset of historical financial record data using the third model (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices);
determine a third (“a number of classifiers are tested,” paragraph 0049 lines 6-7) error metric associated with the third model, wherein the third error metric is indicative of an accuracy of the probability score (“Performance may be determined by using a receiver operating characteristic curve that is created by plotting the true positive rate against the false positive rate at various threshold setting. Accuracy may be measured by the area under the receiver operating characteristic (ROC) curve, which measures accuracy of the model. Yet another testing approach is logarithmic loss that measures the performance of a classification model where the prediction input is a probability value between zero and one,” James paragraph 0049 lines 9-18) relative to whether or not the invoice was paid (“Key Performance Indicators (e.g., First Payment Default etc.),” James paragraph 0037 lines 12-13); and
wherein the set of prediction models from which the designated prediction model is selected comprises the third model (“If a number of classifiers are tested, then in the model selection stage the machine learning model development module 115 can choose the classifier that performed best on the test set,” James paragraph 0049 lines 6-9).
As to dependent claim 26, the rejection of claim 22 is incorporated. James/Mor further teaches a medium wherein at least the first model of the first and second models is a univariate model (“these processing times are treated as target values for model building during the training phase,” Mor paragraph 0034 lines 5-7), and wherein the dataset of historical financial record data further comprises an issue date and/or a due date of each of the plurality of invoices associated with the dataset (“A continuous variable can be numeric or date/time. For example, the date and time a payment is received,” James paragraph 0038 lines 28-30).
As to dependent claim 27, the rejection of claim 22 is incorporated. James/Mor further teaches a medium wherein at least the second model is a multivariate model, and the operations further include:
determining values for a plurality of first feature (“the business objective determination module 131 weighs a number of features,” James paragraph 0038 lines 1-2) for each of the respective plurality of invoices associated with the dataset (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices); and
providing, as an input to the multivariate model, the values of the plurality of first feature (“the business objective determination module 131 weighs a number of features,” James paragraph 0038 lines 1-2) associated with the plurality of invoices (“The lender has the borrowers’ incomes and the monthly repayment amount of each loan,” James paragraph 0033 lines 5-6 – a particular borrower has a “monthly” plurality of invoices); and
predicting, as an output, a second payment date for the plurality of invoices (“Payment delay risk is calculated at step 518, as the number of days between the predicted payment date and the payment due date,” Mor paragraph 0065 lines 6-9).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
US 2020/0349641 A1 disclosing testing and selecting models for payment behavior
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/Ryan Barrett/
Primary Examiner, Art Unit 2148