DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The following is Office Action on the merits in response to the communication received on 2/12/26.
Claim status:
Amended claims: 20
Canceled claims: none
Added New claims: None
Pending claims: 1-20
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is not directed to statutory subject matter. Specifically, the invention of claims 1-20 is directed to an abstract idea without significantly more.
Independent claims 1, 19 and 20 are directed to a device (claim 1), a method (claim 19) and one or more non-transitory machine-readable storage media (claim 20). Therefore on its face, each of claims 1, 19 and 20 is directed to a statutory category of invention under Step 1 of the 2019 PEG. However each of claims 1, 19 and 20 is also directed to an abstract idea without significantly more, under Step 2A (Prong One and Prong Two) and Step 2B of the 2019 PEG, which is a judicial exception to 35 U.S.C. 101, as detailed below. Using the language of independent claim 1 to illustrate the claim recites the limitations of, (i) determine for each asset category in a set of multiple asset categories, a set of macroeconomic variables that affect a credit loss for the corresponding asset category; (ii) obtain data indicative of a change to be applied to a selected macroeconomic variable of the set of macroeconomic variables; (iii) calculate, for each asset category determined to be affected by the selected macroeconomic variable, an estimated credit loss resulting from the change in the selected macroeconomic variable while excluding from the calculation one or more asset categories from the set of multiple asset categories that have been determined to not be affected by the selected macroeconomic variable; and (iv) present, the estimated credit loss under the broadest reasonable interpretation (BRI) covers methods of organizing human activity – fundamental economic principles or practices - mitigating risk but for the recitation of generic computers and generic computer components. (Independent claims 19 and 20 recite similar limitations and the analysis is the same).
That is, other than reciting a compute device, circuitry and a user interface nothing in the claim precludes the steps from being directed to organizing human activity – fundamental economic principles or practices - mitigating risk. If a claim limitation under its BRI, covers methods of organizing human activity but for the recitation of generic computers, then the limitations fall within the “methods of organizing human activity” grouping of abstract ideas. Therefore, claim 1 recites an abstract idea under Step 2A Prong One of the Revised Patent Subject Matter Eligibility Guidance 84 Fed.Reg 50 (“2019 PEG”).
This “methods of organizing human activity” is not integrated into a practical application under Step 2A prong Two of the 2019 PEG. In particular claim 1 recites the following additional elements of, a compute device, circuitry and a user interface. This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements – a compute device, circuitry and a user interface.
The compute device, circuitry and user interface are recited at a high-level or generality (i.e. as a generic computer performing generic computer functions) such that, they amount to no more than instructions to apply the abstract idea with a computer (see MPEP 2106.05(h). Accordingly these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under Step 2B of the 2019 PEG independent claim 1 does not include additional elements that are sufficient to amount to significantly more than the abstract idea. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a compute device, circuitry and a user interface, determine for each asset category in a set of multiple asset categories, a set of macroeconomic variables that affect a credit loss for the corresponding asset category; obtain data indicative of a change to be applied to a selected macroeconomic variable of the set of macroeconomic variables; calculate, for each asset category determined to be affected by the selected macroeconomic variable, an estimated credit loss resulting from the change in the selected macroeconomic variable while excluding from the calculation one or more asset categories from the set of multiple asset categories that have been determined to not be affected by the selected macroeconomic variable; and present, the estimated credit loss, amounts to instructions to apply the abstract idea with a computer. The claims are not patent eligible.
The dependent claims have been given the full two part analysis including analyzing the additional limitations both individually and in combination. The Dependent claim(s) when analyzed individually are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail to establish that the claim(s) are not directed to an abstract idea. The additional limitations of the dependent claim(s) when considered individually do not amount to significantly more than the abstract idea. Claims 2-18 merely further explain the abstract idea.
When viewed individually the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly claims 1-20 are ineligible.
Claim Rejections - 35 USC § 112
The Applicant’s arguments and amendments overcome the 112 Rejections, therefore, the Rejection(s) are moot.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-14 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over, Keyes (U.S. Pub. No. 2003/0212618), in view of Zarikian (U.S. Pub. No. 2009/0125439).
With respect to claims 1, 19 and 20:
Keyes teaches:
A compute device comprising: circuitry configured to: determine for each asset category in a set of multiple asset categories, a set of macroeconomic variables that affect a credit loss for the corresponding asset category (“According to another embodiment, at least one condition associated with a target business segment is determined, and a series of indicator input items is selected. A forecast model for the target business segment is then generated. Future conditions are predicted based on current indicator input items and the forecast model, and a score associated with an existing credit account is adjusted based on the prediction. According to still another embodiment, a potential credit deal is adjusted based on the prediction” (Keyes Pgh. [0015]) and “At 102, a target business segment is identified. The target business segment may be associated with, for example, an industry or an industry segment (e.g., manufacturing, construction, retail trade, services, and/or wholesale trade). Other examples may include agriculture, forestry, fishing, mining, transportation, communication, utility (e.g., electric gas, or sanitary services), finance, insurance, real estate, and public administration. Similarly, the target business segment may be associated with a market or market segment. According to some embodiments, the target business segment may be associated with a customer (e.g., a large customer) or group of customers. The target business segment might further be associated with a collateral type, a geographic location, and/or a customer type (e.g., the target business segment may be associated with small retail stores in the western United States)” (Keyes Pgh. [0039]) and “A series of potential indicator input items is identified at 106. That is, a number of items that might potentially be used by a forecast model for the target business segment may be identified. The potential indicator input items may be associated with any type of economic information, such as employment information, inflation information, equity information (e.g., stock prices), debt information (e.g., bond prices), construction information, backlog information, new order information, vacancy information, interest rate information, and/or money supply information. Other examples of potential indicator input items include payment and delinquency information (e.g., associated with existing loans). Note that the potential indicator input items could be associated with a particular industry or market (or segment)” Keyes Pgh. [0041]);
obtain data indicative of a change to be applied to a selected macroeconomic variable of the set of macroeconomic variables (“The processor 210 is in communication with an input device 230. The input device 230 may comprise, for example, a keyboard, a mouse or other pointing device, a microphone, and/or a touch screen. Such an input device 230 may be used, for example, by an operator to enter or select a series of indicator input values or conditions” Keyes Pgh. [0046]); and
calculate, for each asset category determined to be affected by the selected macroeconomic variable, an estimated credit loss resulting from the change in the selected macroeconomic variable while excluding from the calculation one or more asset categories from the set of multiple asset categories that have been determined to not be affected by the selected macroeconomic variable (“At 102, a target business segment is identified. The target business segment may be associated with, for example, an industry or an industry segment (e.g., manufacturing, construction, retail trade, services, and/or wholesale trade)” (Keyes Pgh. [0039]) and “At 110, future conditions are predicted for the target business segment based on current indicator input items and the forecast model. The prediction may then be applied to an existing commercial credit account, customer, or portfolio at 112. For example, a customer who would otherwise be assigned a high risk score (e.g., based on his or her past behavior) may be assigned a lower score if a forecast model predicts positive changes in the customer's business segment. Of course, if the forecast model instead predicts negative changes the customer may be assigned a higher score. According to another embodiment, the prediction is applied to a potential credit account (e.g., a potential commercial credit deal)” (Keyes Pgh. [0043]) and “At 904, a series of indicator input items are selected. For example, the leading indictor system 200 may select an appropriate series of items from the indicator input database 500” (Keyes Pgh. [0082]) and “In this way, the leading indicator system 200 may evaluate the potential impact of macroeconomic and market changes on portfolio performance and profitability. Moreover, a creditor may proactively identify business segments and/or customers that may be at risk and respond in an appropriate manner” Keyes Pgh. [0102]) (The Examiner interprets the claimed exclusion of asset categories from the calculation as the disclosed identification of a target business segment (and, therefore, exclusion of other business segments) for which the model predicts future conditions).
Further Keyes teaches one or more machine-readable storage media comprising a plurality of instructions stored thereon at paragraph [0049].
Keyes does not teach but Zarikian teaches:
present, in a user interface, the estimated credit loss (“Also included in the system 95 is a display device/input device 64 for receiving and displaying data. This display device/input device 64 may be, for example, a keyboard or pointing device that is used in combination with a monitor” (Zarikian Pgh. [0063]) and “In addition, the risk model adjustment module 900 of several embodiments may also output the credit decision, as shown in Step 905. This decision can be output independent of the adjusted credit risk score or in addition to the adjusted credit risk score. It will be apparent to one of ordinary skill in the art that the credit decision may be presented in a variety of different ways, as well as, obtained in a variety of different ways. For example, the credit decision may be sent in a print image or system image in various embodiments in the same fashion as the adjusted risk score is sent as described above” Zarikian Pgh. [0085]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.
With respect to claim 2:
Keyes teaches:
wherein to determine a set of macroeconomic variables that affect a credit loss for the corresponding asset category comprises to perform an analysis of historical effects of each of multiple macroeconomic variables on the corresponding asset category to identify a subset of the macroeconomic variables that affect a credit loss for the corresponding asset category (“The condition identifier 602 may be, for example, an alphanumeric code for a particular condition associated with one or more target business segments and the description 604 describes the condition. The values 606 indicate historic (i.e., past) values associated with the condition. For example, as shown by the second entry in FIG. 6, prior average restaurant sales values 606 are stored on a quarterly basis. Note that the condition database 600 may also store current or predicted values 606. As before, the “historic” values might be adjusted or revised (e.g., by an economic information service)” Keyes Pgh. [0067]).
With respect to claim 3:
Keyes does not teach but Zarikian teaches:
wherein to determine the set of macroeconomic variables comprises to perform an analysis of historical effects of each macroeconomic variable over multiple quarters (“In various embodiments, different ratios may be created for each series. For example, a ratio may be calculated as the inflation rate for the previous quarter over the one year average. This provides a measure of change in the economy and indicates the level of impact the econometric factor has on the model. These ratios are then used as independent attributes in the model. In various embodiments, economic factors may be trended over multiple time periods to gauge their direction and/or Velocity of change. For example, the Consumer Confidence Index may be trended over four consecutive quarters, and a value for the trend's direction and the trend's velocity be calculated. These values are then used as independent attributes in the model. In various embodiments, economic factors found to be leading indicators of changes in the macroeconomic risk environment may be lagged relative to the dependent variable by different periods of time” Zarikian Pgh. [0048]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.
With respect to claim 4:
Keyes does not teach but Zarikian teaches:
wherein to determine the set of macroeconomic variables comprises to perform a regression analysis (“Numerous statistical techniques for modeling are employed among various embodiments of the invention in order to determine the macroeconomic risk score, for example, logistic regression or other nonlinear techniques that use a neural network, decision tree, or score fusion” Zarikian Pgh. [0014]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.
With respect to claim 5:
Keyes teaches:
wherein to determine a set of macroeconomic variables that affect a credit loss for the corresponding asset category comprises to identify one or more macroeconomic variables from the set having at least a predefined threshold effect on the credit loss for the corresponding asset category (“In another embodiment, predictions generated by a forecast model are used to ensure compliance with credit policy rules and guidelines (e.g., rules established by a chief risk officer). For example, risk managers may be authorized to extend only a pre-determined amount of credit to customers having a threshold adjusted risk score. If the customer is seeking credit over that amount, the controller 1450 may automatically notify the risk manager's supervisor (e.g., a party who is authorized to extend larger amounts of credit)” Keyes Pgh. [0109]).
With respect to claim 6:
Keyes does not teach but Zarikian teaches:
wherein to determine for each asset category in a set of multiple asset categories, a set of macroeconomic variables that affect a credit loss for the corresponding asset category comprises to produce a matrix structure indicative of macroeconomic variables that affect each asset category (“In addition, in various embodiments of the invention, the model used to determine the individual's adjusted credit risk score may comprise evaluating the macroeconomic risk score and the individual's credit risk score using a matrix composed of macroeconomic risk scores and unadjusted credit risk scores. In other embodiments, the model is developed via a statistical technique” Zarikian Pgh. [0012]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.
With respect to claim 7:
Keyes teaches:
wherein to obtain data indicative of a change to be applied to a selected macroeconomic variable comprises to obtain a user-defined increase or decrease of the selected macroeconomic variable (“The processor 210 is in communication with an input device 230. The input device 230 may comprise, for example, a keyboard, a mouse or other pointing device, a microphone, and/or a touch screen. Such an input device 230 may be used, for example, by an operator to enter or select a series of indicator input values or conditions” Keyes Pgh. [0046]).
With respect to claim 8:
Keyes does not teach but Zarikian teaches:
wherein to obtain data indicative of a change to be applied to a selected macroeconomic variable comprises to predict a change to the selected macroeconomic variable based on a historical analysis of changes to the selected macroeconomic variable over time (“In various embodiments, different ratios may be created for each series. For example, a ratio may be calculated as the inflation rate for the previous quarter over the one year average. This provides a measure of change in the economy and indicates the level of impact the econometric factor has on the model. These ratios are then used as independent attributes in the model. In various embodiments, economic factors may be trended over multiple time periods to gauge their direction and/or Velocity of change. For example, the Consumer Confidence Index may be trended over four consecutive quarters, and a value for the trend's direction and the trend's velocity be calculated. These values are then used as independent attributes in the model. In various embodiments, economic factors found to be leading indicators of changes in the macroeconomic risk environment may be lagged relative to the dependent variable by different periods of time” Zarikian Pgh. [0048]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.
With respect to claim 9:
Keyes does not teach but Zarikian teaches:
wherein to predict the change to the selected macroeconomic variable comprises to predict the change with a machine learning model that has been trained to predict changes to macroeconomic variables based on historical economic data (“In various embodiments, different ratios may be created for each series. For example, a ratio may be calculated as the inflation rate for the previous quarter over the one year average. This provides a measure of change in the economy and indicates the level of impact the econometric factor has on the model. These ratios are then used as independent attributes in the model. In various embodiments, economic factors may be trended over multiple time periods to gauge their direction and/or Velocity of change. For example, the Consumer Confidence Index may be trended over four consecutive quarters, and a value for the trend's direction and the trend's velocity be calculated. These values are then used as independent attributes in the model. In various embodiments, economic factors found to be leading indicators of changes in the macroeconomic risk environment may be lagged relative to the dependent variable by different periods of time” Zarikian Pgh. [0048]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.)
With respect to claim 10:
Keyes teaches:
wherein to calculate the estimated credit loss comprises to store data indicative of the estimated credit loss resulting from the change in the selected macroeconomic variable separately from a dataset that is indicative of estimated credit losses to the multiple asset categories without the change applied to the selected macroeconomic variable (“The condition identifier 602 may be, for example, an alphanumeric code for a particular condition associated with one or more target business segments and the description 604 describes the condition. The values 606 indicate historic (i.e., past) values associated with the condition. For example, as shown by the second entry in FIG. 6, prior average restaurant sales values 606 are stored on a quarterly basis. Note that the condition database 600 may also store current or predicted values 606. As before, the “historic” values might be adjusted or revised (e.g., by an economic information service)” Keyes Pgh. [0067]).
With respect to claim 11:
Keyes does not teach but Zarikian teaches:
wherein to present the estimated credit loss comprises to present estimated credit losses for each asset category for multiple changes to the selected macroeconomic variable (“In addition, the risk model adjustment module 900 of several embodiments may also output the credit decision, as shown in Step 905. This decision can be output independent of the adjusted credit risk score or in addition to the adjusted credit risk score. It will be apparent to one of ordinary skill in the art that the credit decision may be presented in a variety of different ways, as well as, obtained in a variety of different ways. For example, the credit decision may be sent in a print image or system image in various embodiments in the same fashion as the adjusted risk score is sent as described above” Zarikian Pgh. [0085]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.
With respect to claim 12:
Keyes does not teach but Zarikian teaches:
wherein to present the estimated credit loss comprises to present estimated credit losses for each asset category for multiple changes to multiple macroeconomic variables (“In addition, the risk model adjustment module 900 of several embodiments may also output the credit decision, as shown in Step 905. This decision can be output independent of the adjusted credit risk score or in addition to the adjusted credit risk score. It will be apparent to one of ordinary skill in the art that the credit decision may be presented in a variety of different ways, as well as, obtained in a variety of different ways. For example, the credit decision may be sent in a print image or system image in various embodiments in the same fashion as the adjusted risk score is sent as described above” Zarikian Pgh. [0085]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.
With respect to claim 13:
Keyes teaches:
wherein to present the estimated credit loss resulting from the change in the selected macroeconomic variable comprises to present the estimated credit loss relative to an estimated credit loss in which the selected macroeconomic variable is not changed (“The condition identifier 602 may be, for example, an alphanumeric code for a particular condition associated with one or more target business segments and the description 604 describes the condition. The values 606 indicate historic (i.e., past) values associated with the condition. For example, as shown by the second entry in FIG. 6, prior average restaurant sales values 606 are stored on a quarterly basis. Note that the condition database 600 may also store current or predicted values 606. As before, the “historic” values might be adjusted or revised (e.g., by an economic information service)” Keyes Pgh. [0067]).
With respect to claim 14:
Keyes does not teach but Zarikian teaches:
wherein to present the estimated credit loss comprises to present multiple asset categories that the selected macroeconomic variable has been determined to affect, a relative magnitude of the estimated credit loss for each of the asset categories, and an aggregate impact on overall credit loss estimates across all asset categories in a portfolio (“In various embodiments, different ratios may be created for each series. For example, a ratio may be calculated as the inflation rate for the previous quarter over the one year average. This provides a measure of change in the economy and indicates the level of impact the econometric factor has on the model. These ratios are then used as independent attributes in the model. In various embodiments, economic factors may be trended over multiple time periods to gauge their direction and/or Velocity of change. For example, the Consumer Confidence Index may be trended over four consecutive quarters, and a value for the trend's direction and the trend's velocity be calculated. These values are then used as independent attributes in the model. In various embodiments, economic factors found to be leading indicators of changes in the macroeconomic risk environment may be lagged relative to the dependent variable by different periods of time” (Zarikian Pgh. [0048]) and “In addition, the risk model adjustment module 900 of several embodiments may also output the credit decision, as shown in Step 905. This decision can be output independent of the adjusted credit risk score or in addition to the adjusted credit risk score. It will be apparent to one of ordinary skill in the art that the credit decision may be presented in a variety of different ways, as well as, obtained in a variety of different ways. For example, the credit decision may be sent in a print image or system image in various embodiments in the same fashion as the adjusted risk score is sent as described above” Zarikian Pgh. [0085]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Zarikian’s teachings, in order “to keep overall delinquency and/or loss rates in line as economic factors change” Zarikian Abstract.
With respect to claim 16:
Keyes teaches:
wherein the circuitry is further configured to determine whether a target number of estimated credit losses have been calculated based on changes to the selected macroeconomic variable to enable streamlined prediction of credit losses for additional changes to the selected macroeconomic variable (“FIG. 13 illustrates a watch list display 1300 that may be provided via a risk manager device 1220 according to an embodiment of the present invention. In particular, the display 1300 includes a list of customers, accounts, or portfolios that have a high adjusted risk score (e.g., so that a manager may more closely monitor those customers). Note that the watch list and/or the adjusted risk scores are generated in accordance with a predicted outlook trend generated by a forecast model for each client's business segment (e.g., based on a series of leading indicator items). The predicted outlook trend may indicate, for example, where the industry is expected to be in terms of growth rates in employment at the end of a forecast horizon. The predicted outlook trend may be, for example, “above” trend (e.g., above an average range and therefore indicating that the industry is in a expansionary phase and/or the high part of a business cycle), at trend (e.g., within an average range associated with normal growth), or “below” trend (e.g., below an average range and there indicating that the industry is contracting and/or the low part of a business cycle)” Keyes Pgh. [0093]).
With respect to claim 17:
Keyes teaches:
wherein the circuitry is further configured to perform, in response to a determination that the target number of estimated credit losses have been calculated, streamlined prediction of credit losses for at least one additional change to the selected macroeconomic variable (“FIG. 13 illustrates a watch list display 1300 that may be provided via a risk manager device 1220 according to an embodiment of the present invention. In particular, the display 1300 includes a list of customers, accounts, or portfolios that have a high adjusted risk score (e.g., so that a manager may more closely monitor those customers). Note that the watch list and/or the adjusted risk scores are generated in accordance with a predicted outlook trend generated by a forecast model for each client's business segment (e.g., based on a series of leading indicator items). The predicted outlook trend may indicate, for example, where the industry is expected to be in terms of growth rates in employment at the end of a forecast horizon. The predicted outlook trend may be, for example, “above” trend (e.g., above an average range and therefore indicating that the industry is in a expansionary phase and/or the high part of a business cycle), at trend (e.g., within an average range associated with normal growth), or “below” trend (e.g., below an average range and there indicating that the industry is contracting and/or the low part of a business cycle)” Keyes Pgh. [0093]).
With respect to claim 18:
Keyes teaches:
wherein the circuitry is further configured to perform streamlined prediction of a credit loss by interpolating between previously calculated estimated credit losses (“FIG. 13 illustrates a watch list display 1300 that may be provided via a risk manager device 1220 according to an embodiment of the present invention. In particular, the display 1300 includes a list of customers, accounts, or portfolios that have a high adjusted risk score (e.g., so that a manager may more closely monitor those customers). Note that the watch list and/or the adjusted risk scores are generated in accordance with a predicted outlook trend generated by a forecast model for each client's business segment (e.g., based on a series of leading indicator items). The predicted outlook trend may indicate, for example, where the industry is expected to be in terms of growth rates in employment at the end of a forecast horizon. The predicted outlook trend may be, for example, “above” trend (e.g., above an average range and therefore indicating that the industry is in a expansionary phase and/or the high part of a business cycle), at trend (e.g., within an average range associated with normal growth), or “below” trend (e.g., below an average range and there indicating that the industry is contracting and/or the low part of a business cycle)” Keyes Pgh. [0093]).
Claim 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over, Keyes (U.S. Pub. No. 2003/0212618), in view of Zarikian (U.S. Pub. No. 2009/0125439) and Robida (U.S. Pub. No. 2007/0214076)
With respect to claim 15:
Keyes does not teach but Robida teaches:
wherein to present the estimated credit loss comprises to present the estimated credit loss in a dendogram (“In one embodiment, the computing system 100 executes the profile module 150, which is configured to analyze data received from one or more data sources and generate a profile model that is usable to assign individuals to groups. The groups to which individuals may be assigned may also be referred to as segments and the process of assigning accounts to particular segments may be referred to as segmentation. A segmentation structure may include multiple segments arranged in a tree configuration, wherein certain segments are parents, or children, of other segments. A segment hierarchy includes the segment to which an individual is assigned and each of the parent segments to the assigned segment. FIG. 7, described in detail below, illustrates a segmentation structure having multiple levels of segments to which individuals may be assigned. In one embodiment, the segments are each configured to be associated with individuals that each have certain similar attributes” (Robida Pgh. [0040]) and “In certain embodiments, the assignment of an individual to a particular segment may be a factor that was relevant in arriving at the risk score for the individual. Thus, in one embodiment, one or more adverse action codes provided to a customer may be related to the assignment of the individual to a particular segment, or to particular segments in the segment hierarchy. In one embodiment, the adverse action module 160 is configured to determine how many, if any, of a determined number of total adverse action codes should be allotted to various segments of the individuals segment hierarchy” Robida Pgh. [0041]).
It would have been obvious to one of ordinary skill of the art to have modified Keyes’ teachings to incorporate Robida’s teachings, in order to “generate a profile model that is usable to assign individuals to groups” Robida Pgh. [0040].
Response to Arguments
Applicant's arguments filed 2/12/26 have been fully considered but they are not persuasive.
35 USC § 101
The Applicant states “the claims are not directed to methods of organizing human activity and/or incorporate a practical application” (page 7). The Examiner disagrees with the sentence because the claims are an improvement of the abstract idea only. It is a business solution to a business problem of determining expected credit loss of a portfolio of asset categories under varying macroeconomic conditions. The applicant has not shown how the claims improve a computer or other technology, invoke a particular machine, transform matter, or provide more than a general link between the abstraction and the technology, MPEP 2106.05(a)-(c) & (e). The Examiner disagrees that the claims “impose concrete operational requirements on the system, thereby doing far more than confining an abstract idea to a conventional computer implementation” (page 10). The claims do not provide an improvement over prior systems and only add details (e.g., obtain data indicative of a change to be applied to a selected macroeconomic variable) to the abstract idea, they do not address a problem particular to the Internet and merely applies the abstract idea on a general computer. The amended claims make the abstract idea more specific, and determining expected credit loss of a portfolio of asset categories under varying macroeconomic conditions is not an unconventional activity. The invention uses conventional components arranged in a conventional manner to perform a conventional process. Applicant’s remarks about why these limitations provide a practical application fail to surface any technical improvement identified in the specification (the teaching at paragraph 19 regarding computational efficiencies is well- understood, routine and conventional) and, therefore this is not an inventive concept and significantly more.
35 USC § 112
The Applicant’s arguments and amendments overcome the 112 Rejections, therefore, the Rejection(s) are moot.
35 USC § 103
The amended claim language is taught in the references of record as indicated above in the Office action.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARLA HUDSON whose telephone number is (571)272-1063. The examiner can normally be reached M-F 9:30 a.m. - 5:30 p.m. ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett Sigmond can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.H./Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694