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 .
Claims 1-15 are pending.
Response to Arguments
Regarding 35 U.S.C. 101:
Applicant’s amendments and arguments regarding the rejection of claims 11-15 under 35 U.S.C. 101 have been fully considered and are found to be partially persuasive. The rejections of claims 11-15 under 35 U.S.C. 101 are withdrawn as the judicial exception of determining/selecting thread execution times is integrated into a practical application through the additional limitation reciting execution of a future processor thread for the selected thread execution time period.
Regarding: Prior Art Rejections:
Applicant’s amendments and arguments regarding the rejection of claims 1-15 under 35 U.S.C. 103 have been fully considered and are moot due to new grounds of rejection necessitated by amendment.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-6, 8-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. US 20120323760 A1 in view of Zeng et al. US 20160042405 A1 in view of Plondke et al. US 20100242041 A1 in view of Whiteley et al. US 11580514 B1.
Regarding claim 1, Lee teaches the invention substantially as claimed including:
A system for determining a difference in performance for a software program processing input data for a plurality of processor threads based on different termination time periods (Fig 9; [0110] The system may send this time period value to the prediction system to use a t-month model to classify all active loans according to an appropriate model and determine the probability of whether each active loan will default during the period t; Examiner notes: the performance of the active loans (i.e., threads) are predicted at each time (i.e., each termination time period) over a t-month period. The differences are noted in the graph of Fig 9), the system comprising:
one or more processors; and a non-transitory, computer-readable medium comprising instructions that, when executed by the one or more processors, cause operations ([0004] a processor and a computer-readable storage medium) comprising:
receiving information for a processor thread of the plurality of processor threads processed by a software program, the information including input data for the processor thread and corresponding performance data, of the software program, with respect to a first termination time period for terminating the processor thread ([0004] the processor to implement a method that receives a loan data set. The loan data set includes a first set of data relating to a set of loans that are in a default status and second set of data relating to loans that are in a non-default status; Examiner notes: loans defaulting/not defaulting are associated with their respective original loan terms (i.e., termination time period));
processing, using the software program, the input data for the processor thread to generate corresponding performance data with respect to a second termination time period for terminating the processor thread (Abstract: The system uses a set of data about loans that are in a default status and loans that are in a non-default status to train a set of loan models. The loan models include at least one model for a defaulted loan and at least one model for a non-defaulted loan; [0054] The system may then select a future time period of interest 58, such as one month, two months, three months, or another period… The system will then predict 60, or determine a probability, whether the target loan will move into a default state within the prospective time period of interest. The system may repeat this for multiple loans; [0081] To predict the likelihood of default of an active loan, as noted above the system will first classify the loan in question to one of the available models. It may then determine a probability of default in the near future (e.g., an upcoming time period such as one months, three months, or another period) using parameters of the model to which the target loan is classified), the processing including:
extracting, for the processor thread from the plurality of processor threads, first input from the input data ([0053] the system may monitor data 54 for any active loan of interest--referred to in this document as a target loan or active loan--and analyze the data to classify 56 the target loan in accordance with one of the models); and
processing, using the software program, for the processor thread, the first input to generate performance data, of the software program, with respect to the second termination time period ([0054] The system will then predict 60, or determine a probability, whether the target loan will move into a default state within the prospective time period of interest);
determining a performance difference for the processor thread processed by the software program with respect to the first and second termination time periods, including:
extracting, for the processor thread of the plurality of processor threads, performance data, of the software program, with respect to the first termination time period (Examiner notes: the system utilizes the model (i.e., performance data for first termination time period) to make predictions for future loan terms);
determining a performance difference for the processor thread based on the performance data, of the software program, with respect to the first termination time period and the performance data, of the software program, with respect to the second termination time period ([0054] The system may then select a future time period of interest 58, such as one month, two months, three months, or another period. The selection may be made automatically according to a default, randomly, or in response to a user selection or other command. The system will then predict 60, or determine a probability, whether the target loan will move into a default state within the prospective time period of interest. The system may repeat this for multiple loans and generate an alert 62 as to loans that are likely to move into a default state during the time period of interest; Fig 9 difference in default risks over time; [0110] The system may return a report to the user showing information (such an account number, identification code, or borrower name) for all loans whose risk of default exceeds a threshold probability during the time period t. Optionally, if the report is provided via an interactive display (such as a computer monitor with input devices), the user can select one of the loans to receive a report of the borrower's historic payment data. This data can be shown in text format, or a graphic format such as that shown in FIG. 9); Examiner notes: the system can repeat the prediction process for multiple periods, the system determines the difference between probability of defaulting between different time periods);
Lee does not explicitly teach the different termination time periods for handling an exception and when an exception is detected, wherein the first termination time period comprises a first elapsed time between detection of the exception and termination of the processor thread; wherein the second termination time period comprises a second elapsed time between detection of the exception and termination of the processor thread.
However, Zeng teaches different termination time periods for handling an exception and when an exception is detected, wherein the first termination time period comprises a first elapsed time between detection of the exception and termination of the processor thread; wherein the second termination time period comprises a second elapsed time between detection of the exception and termination of the processor thread (Fig 4; Method 400 in at least [0039]-[0048]; [0048] these factors can also be used to determine 435 a duration of the grace period. Generally, the more recently funds have been received, the larger the total amount of funds received, and the stronger the commitment to the advertising campaign (as suggested by the factors above), the more likely an advertiser is to pay an overdue balance. Therefore, it is more likely that a grace period will be granted and the longer the duration of the grace period.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined Zeng’s grace period duration determining with the existing system. A person of ordinary skill in the art would have been motivated to make this combination to provide the resulting system with the advantage of recourse in relationships between consumers in good standing and providers (see Zeng [0004] A suspension of advertising services is also detrimental to adverting providers. For unintentional account delinquencies, suspension of a delinquent account can damage a relationship with an advertiser that is typically in good standing and/or an advertiser that has a long relationship with the advertiser provider. Furthermore, when advertisements are no longer served due to an account suspension, the advertising provider risks losing advertising revenue that cannot be regained; [0005] Because the failure to pay an account balance may be unintentional, it would be beneficial to determine whether suspension of the account can be delayed).
While Lee discloses the cited processes being performed using a processor, Lee and Zeng do not explicitly teach the respective threads being computing processor threads.
However, Plondke teaches the claimed threads being processor threads ([0003] Typically, in a multithreaded or multiprocessor system with multiple central processing units (CPUs), a specific task is bound to a specific hardware thread or CPU and a single-processor scheduler algorithm is run on each hardware thread or CPU independently; [0005] Each thread of the set of threads executes a respective task of a set of executing tasks; Examiner notes: each target loan of Lee is able to be analyzed/processed using a respective thread of a set of threads).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined Plondke’s multithreading processing of tasks with the system of Lee and Zeng to obtain a processor that analyzes/processes loan terms using processor threads. A person of ordinary skill in the art would have been motivated to make this combination to provide Lee and Zeng’s system with the ability to manage, prioritize, and process multiple loans on a single processor (see Plondke [0006] a multithreaded processor configured to execute a plurality of threads such that a plurality of executing threads are running the highest priority tasks. The multithreaded processor is configured to schedule tasks such that executing tasks have a priority at least as high as a highest priority of all ready tasks; [0008] One particular advantage provided by disclosed embodiments is that tasks are scheduled so that the highest priority threads are executed with reduced disturbance from interrupts. Because a low priority thread receives interrupts, raising a reschedule interrupt automatically reschedules the low priority thread without disturbing the highest priority threads. Additionally, an external interrupt will interrupt the lowest priority thread rather than the highest priority threads).
Lee, Zeng, and Plondke do not explicitly teach based on the performance difference, selecting the first termination time period or the second termination time period for a future processor thread processed by the software program; and executing the future processor thread for a time period, wherein the time period is the termination time period selected.
However, Whiteley teaches based on the performance difference, selecting the first termination time period or the second termination time period for a future thread processed by the software program; and executing the future processor thread for a time period, wherein the time period is the termination time period selected. (Col 33 25-39 the merchant component 120 can determine an amount of time for deferral. In at least one example, based at least in part on determining that a deferred payment option is available (i.e., “yes” at operation 2704), the merchant component 120 can determine a length of time associated with the deferral based at least in part on the risk score. For example, a customer determined to be less risky may be permitted to remit payment at a time after a customer determined to be riskier. That is, in at least one example, a customer associated with a risk score below the threshold, or associated with a first range of risk scores, may be associated with a longer amount of time for deferring payment than a customer associated with a risk score that meets or exceeds the threshold, or is associated with a second range of risk scores; Examiner notes: by providing a longer/shorter deferral period for a customer based on a risk assessment, the loan is being executed for the determined length of time of the deferral);
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined Whiteley’s determining of a time length for payment deferral with the system of Lee, Zeng, and Plondke resulting in a system that opts for using deferred payment periods based on a loan holder’s probability of defaulting. A person of ordinary skill in the art would have been motivated to make this combination to provide Lee, Zeng, and Plondke’s system with the advantage of dynamically changing policies based on risk analysis (see Whiteley Col 29 65 – Col 30 5 Such a risk analysis can leverage machine-trained models and a portion of the customer data 134 associated with the customer 108 to determine a risk associated with whether the customer 108 is likely to remit payment via a deferred payment. In some examples, a customer determined to be less risky may be permitted to remit payment at a time after a customer determined to be riskier).
Regarding claim 3, Lee, Zeng, Plondke and Whiteley teach the system of claim 1.
Whiteley further teaches wherein determining a performance difference for the processor thread processed by the software program with respect to the first and second termination time periods comprises: determining a first minimum based on performance data, of the software program, for the processor thread with respect to the first termination time period; determining a second minimum based on performance data, of the software program, for the processor thread with respect to the first termination time period; and determining the performance difference for the processor thread with respect to the first and second termination time periods based on the first minimum and the second minimum (Col 34 58-64 the merchant component 120 can determine the first merchant based on identifying a merchant with a shortest waiting time and/or a waiting time below a threshold, a lowest occupancy and/or an occupancy below a threshold, a merchant with a lowest number of transactions that have been and/or are being processed).
Regarding claim 4, it is the method with elements of claim 1. Therefore, it is rejected for the same corresponding reasons as claim 1.
Regarding claims 5 and 6, they are the method of claim 4 with elements from claim 1. Therefore, they are rejected for the same corresponding reasons as claim 1.
Regarding claim 8, it is the method of claim 4 with elements of claim 3. Therefore, it is rejected for the same corresponding reasons as claim 3.
Regarding claim 9, Lee, Zeng, Plondke and Whiteley teach the system of claim 4.
Lee further teaches wherein the input data for the processor thread includes dynamic data associated with a time period and the corresponding performance data, of the software program, is associated with the time period ([0020] The monitoring system is dynamic in a sense that any loan's probability of default may change as new information about the loan is added to the historical data).
Regarding claim 10, Lee, Zeng, Plondke and Whiteley teach the system of claim 4.
Lee further teaches wherein the input data for the processor thread includes static data associated with a particular time and the corresponding performance data, of the software program, is associated with the particular time ([0007] analyzing, for each loan in the loan data set, observed data over a historic time period).
Regarding claims 11, it is the non-transitory, computer-readable medium with elements of claim 1. Therefore, it is rejected for the same corresponding reasons as claim 1.
Regarding claim 12 and 13, they are the non-transitory, computer-readable medium of claim 11 with elements of claim 1. Therefore, it is rejected for the same corresponding reasons as claim 1.
Regarding claim 15, it is the non-transitory, computer-readable medium of claim 11 with elements from claim 3. Therefore, it is rejected for the same corresponding reasons as claim 3.
Claims 2, 7, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. US 20120323760 A1 in view of, Zeng et al. US 20160042405 A1 in view of Plondke et al. US 20100242041 A1 in view of Whiteley et al. US 11580514 B1 in further view of An et al. US 8433631 B1.
Regarding claim 2, Lee, Zeng, Plondke, and Whiteley teach the system of claim 1.
Lee, Zeng, Plondke, and Whiteley do not explicitly teach wherein determining a performance difference for the plurality of threads processed by the software program with respect to the first and second termination time periods comprises: determining a first mean, a first median, or a first mode based on performance data for the plurality of threads with respect to the first termination time period; determining a second mean, a second median, or a second mode based on performance data for the plurality of threads with respect to the second termination time period; and determining the performance difference for the plurality of threads with respect to the first and second termination time periods based on: the first mean, the first median, or the first mode; and the second mean, the second median, or the second mode.
However, An teaches wherein determining a performance difference for the processor thread processed by the software program with respect to the first and second termination time periods comprises: determining a first mean, a first median, or a first mode based on performance data, of the software program, for the processor thread with respect to the first termination time period; determining a second mean, a second median, or a second mode based on performance data, of the software program, for the processor thread with respect to the second termination time period (); and determining the performance difference for the processor thread with respect to the first and second termination time periods based on: the first mean, the first median, or the first mode; and the second mean, the second median, or the second mode (Fig 6; calculating a performance difference for the study group, the performance difference comprising the difference between the average over the study group of the loan-specific performance variable value for each individual loan in the study group and the average over the study group of the estimated performance value for each individual loan in the study group, Col 4 11-17).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined An’s calculating and differencing of average performance data with the system of Lee, Zeng, Plondke, and Whiteley. A person of ordinary skill in the art would have been motivated to make this combination to provide Lee, Zeng, Plondke, and Whiteley’s system with the advantage of analyzing performance data to inform loan policy decisions (see An Col 2 40-55 the lending institution may desire to evaluate the performance of a newly created loan product and/or loans that are still in the early stages of repayment (e.g., loans that are still in the first or second year of repayment). The particular group of loans to be evaluated might include, e.g., loans from a particular lender, loans serviced by a particular loan servicer, or loans with the same or similar product characteristics. Because only a minimal amount of payment history has been compiled, it is often desirable for the lending institution to characterize the performance of this particular group of loans relative to the performance of a comparable group of loans with known payment history. The results of the comparison may then be used, for example, to assess and manage risk associated with the particular group of loans, or to make proper adjustment in the underwriting and pricing policies).
Regarding claim 7, it is the method of claim 4 with elements from claim 2. Therefore, it is rejected for the same corresponding reasons as claim 2.
Regarding claim 14, it is the non-transitory, computer-readable medium of claim 11 with elements from claim 2. Therefore, it is rejected for the same corresponding reasons as claim 2.
Conclusion
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to HARRISON LI whose telephone number is (703) 756-1469. The
examiner can normally be reached Monday-Friday 9:00am-5:30pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing
using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is
encouraged to use the USPTO Automated Interview Request (AIR) at
http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
supervisor, Aimee Li can be reached on (571) 272-4169. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.L./
Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195