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 the Application
This is a Non-Final Action in response to the claims filled on 06/23/2025.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/26/2025 and 08/20/2025 are being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-6, 11-17 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5 and 7 of U.S. Patent No. 12,340,383 in view of Simkoff (US Patent 10,255,550).
Regarding claims 1, 13 and 20, US 12,340,383 discloses:
obtaining, from one or more sources, a set of source data associated with a specified parcel of real property (obtaining, from one or more sources, a plurality of data points associated with a specified parcel of real property);
processing the set of source data; based on the processing of the set of source data, (i) identifying potentially-open mortgages associated with the specified parcel of real property and (ii) for each potentially-open mortgage that is identified, extracting a corresponding set of mortgage data (extracting specific mortgage information from the plurality of data points, comprising: i) identifying each mortgage recorded against the parcel; ii) identifying an indication of whether the mortgage is open);
for each identified potentially-open mortgage:
providing, as input to a first machine learning model, the corresponding set of mortgage data and thereby causing the first machine learning model to generate a predicted likelihood that the identified potentially-open mortgage is open, wherein the first machine learning model is trained by applying a machine learning process to a first set of training data comprising data records that each contains (i) a respective set of mortgage data for a respective mortgage associated with a parcel of real property that is distinct from the specified parcel of real property and (ii) a respective label indicating whether the respective mortgage is closed or open (providing, as input to a machine learning model, the specific mortgage information corresponding to the set of potentially open mortgages, wherein the machine learning model is trained using a training set comprising a collection of data points and labels for a set of real property parcels distinct from the specified parcel of real property, wherein each real property parcel of the training set includes information about each mortgage attached to the parcel; generating, by the machine learning model, a prediction for each potentially open mortgage in the set of potentially open mortgages, wherein the prediction indicates a likelihood that a potentially open mortgage attached to the specified parcel of real property is actually open; );
evaluating whether the predicted likelihood that the identified potentially-open mortgage is open satisfies a threshold value (for each potentially open mortgage: determining whether the prediction for the potentially open mortgage satisfies a threshold value); and
based on the evaluating, making a respective prediction of whether the identified potentially-open mortgage is closed or open (determining the potentially open mortgage to be closed or open based on whether or not the prediction satisfies the threshold value);
evaluating the respective predictions of whether the identified potentially-open mortgages are closed or open to determine whether the specified parcel of real property is within a risk tolerance (using the determination of each potentially open mortgage as either closed or open to determine a risk tolerance for the parcel of real property and,); and
based on the evaluating, either (i) automatically allowing a title evaluation process to proceed for the specified parcel of real property without further evaluation or (ii) determining that further evaluation is required (based on the risk tolerance, determining whether to manually resolve open mortgages or allow a title evaluation process to proceed for the parcel of real property.).
US Patent 12,340,383 discloses a system for determining a prediction of whether an identified potentially-open mortgage is closed or open to further determine whether the specified parcel of real property is within a risk tolerance in order to manually resolve open mortgages or allow a title evaluation process to proceed for the parcel of real property.
However, US Patent 12,340,383 does not explicitly disclose:
providing, as input to a second machine learning model, at least a subset of the processed set of source data and thereby causing the second machine learning model to generate a predicted likelihood that the specified parcel of real property has a title defect, wherein the second machine learning model is trained by applying a machine learning process to a second set of training data;
evaluating the predicted likelihood that the specified parcel of real property has a title defect to determine whether the specified parcel of real property is within a risk tolerance; and
based on the evaluating, responsively causing a notification to be presented to a user of the computing platform via a graphical user interface (GUI).
Simkoff is introduced to cure such deficiency and disclosed that it is well-known in title evaluation processes to predict a risk of a tittle defect for a real estate property.
Simkoff further teaches:
providing, as input to a second machine learning model, at least a subset of the processed set of source data and thereby causing the second machine learning model to generate a predicted likelihood that the specified parcel of real property has a title defect, wherein the second machine learning model is trained by applying a machine learning process to a second set of training data (Col. 2 lines 7-10 “A machine learning model can be used to predict a risk of a title defect for a parcel or real property using data examples of other parcels of real property to train the model.” Col. 5 lines 51-54 “The machine learning model 308 can be trained to take as input a large variety of data types. However, the machine learning model 308 does not need to have all data types input to generate a prediction.”);
evaluating the predicted likelihood that the specified parcel of real property has a title defect to determine whether the specified parcel of real property is within a risk tolerance (Col. 6 lines 45-53 “In some implementations, the threshold is set based upon an analysis of multiple factors. For example, a collection of historical data can be used to determine an historical occurrence and magnitude for the parameter. In the case of title defects, this can include past occurrences of title defects and the value of the resulting claims. Few instances of significant defects can lead, for example, to a higher threshold level of risk being acceptable. Updated information can be used to revise the threshold.” Col. 7 line s41-49 “As described above with respect to decision engine 310, the decision can be based on a comparison of the magnitude of the prediction parameter to a threshold value. The threshold value can involve an assessment of acceptable risk based on the prediction. Parameter values that fail to satisfy the threshold value can be denied issuance of title insurance or can be flagged for a full title search to determine whether a title defect actually exits in the parcel of real property.”); and
based on the evaluating, responsively causing a notification to be presented to a user of the computing platform via a graphical user interface (GUI) (Col. 7 lines 50-52 “The system outputs the results (412). The output result can include transmitting or displaying a result to one or more users of the system.” See also Col. 13 lines 4-9 “Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to predict a likelihood that a property has a title defect since such modification in US Patent 12,340,383 is merely a combination of prior art elements well-known in the art that provide the well-known benefit of reducing time and effort needed to make a first pass at decisions regarding title risk and insurance for a property as disclosed by Simkoff, Col.2 lines 5-10.
Regarding claim 2, Claim 5 of US Patent 12,340,383 further disclose wherein the threshold value is specified based on historical information on title defects resulting from unaccounted for open mortgages and a value of corresponding title insurance claims (Claim 5, wherein the threshold value is specified based on historical information on title defects resulting from unaccounted for open mortgages and a value of corresponding title insurance claims.).
Regarding claims 3 and 14, Simkoff further teaches training the first machine learning model by applying the machine learning process to the first set of training data, wherein the respective sets of mortgage data contained within the first set of training data are derived based on statistical and retail-history data for the respective parcels of real property (Col. 3 lines 30-38 “For example, in some implementations, the training data includes data values associated with a number of distinct parcels of real property. The data values for each parcel of real property can cover a variety of data including statistical data about the property itself, e.g., size, age, composition, a retail history for the property…).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to train a machine learning model by applying a machine learning process to a first set of training data, wherein a respective sets of mortgage data contained within the first set of training data are derived based on statistical and retail-history data for the respective parcels of real property since modification in US Patent 12,340,383 is merely a combination of prior art elements well-known in the art that provide the well-known benefit of cover a variety of data including statistical data about the property thereby improving decisions regarding title risk and insurance for a property as disclosed by Simkoff, Col. 3 lines 32-35.
Regarding claims 4 and 15, Claim 7 of US Patent 12,340,383 further disclose adjusting the threshold value based on a determination that predictions of whether potentially-open mortgages are closed or open have a higher-than-expected error rate (Claim 7, “determining that, for a plurality of evaluated parcels of real property, that a higher number of errors in determining open mortgages than expected occurs; and adjusting the threshold value for determining the potentially open mortgage to be closed.”).
Regarding claims 5 and 16, Simkoff further teaches further teaches:
evaluating an accuracy of the first machine learning model using a set test data comprising data records for parcels of real property for which training data records were not included in the first set of training data;
based on the evaluating, determining that the accuracy of the first machine learning model is deficient; and
in response to determining that the accuracy of the first machine learning model is deficient, retraining the first machine learning model using additional training data records that each contains (i) a respective set of mortgage data for a respective mortgage associated with a parcel of real property that is distinct from the specified parcel of real property and (ii) a respective label indicating whether the respective mortgage is closed or open (Col. 2 lines 1-4 “In response to identifying inaccuracies in one or more predictions, adjusting the machine learning model based on updated training data, ”, Col. 3 lines 54-57 “The training of the model can be an iterative process that adjusts features and associated weights to some specified degree of accuracy relative to the known parameter values.”, Col. 10 lines 7-19 “The system determines whether to update the model based on the comparison (708). For example, if the comparison results in a variance from the predicted and actual values that is greater than a specified allowed variance, then the model can be updated to try to improve the predictions. For example, additional training data can be acquired and used to retrain the machine learning model. In particular, the parcels of real property in which the actual values for the parameter were obtained can be used as additional training data so that the updated model generates more accurate predictions for ( data type, value) pairs input to the model” ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to evaluate the accuracy and retrain the model based on such evaluation since modification in US Patent 12,340,383 represents an improvement to provide the well-known benefit of maintaining an updated model based on identified variances as disclosed by Simkoff, Col. 10 lines 7-19.
Regarding claims 6 and 17, Simkoff further teaches further teaches:
wherein the set of source data comprises data records indicating dates when mortgages were recorded against the specified parcel of real property, dates of sales of the specified parcel of real property, and dates when mortgages were removed from the specified parcel of real property (Col. 3 lines 30-42 “For example, in some implementations, the training data includes data values associated with a number of distinct parcels of real property. The data values for each parcel of real property can cover a variety of data including statistical data about the property itself, e.g., size, age, composition, a retail history for the property, e.g., prior dates of sale, and characterizations of property condition, e.g., from an appraisal. In some implementations, the data can also include information associated with past purchaser and sellers of the parcel including credit information, property tax information, geographic information, crime data, and or other relevant data associated with the parcel”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include wherein the set of source data comprises data records indicating dates when mortgages were recorded against the specified parcel of real property, dates of sales of the specified parcel of real property, and dates when mortgages were removed from the specified parcel of real property since modification in US Patent 12,340,383 is merely a combination of prior art elements well-known in the art that provide the well-known benefit of cover a variety of data including statistical data about the property thereby improving decisions regarding title risk and insurance for a property as disclosed by Simkoff, Col. 3 lines 32-35.
Regarding claim 11, Simkoff further teaches further teaches:
wherein the obtained set of source data comprises electronic documents, and wherein processing the obtained set of source data comprises:
identifying content within the electronic documents by applying optical character recognition to the electronic documents; and filtering the identified content based on a set of keywords (Col. 5 lines 30-37, “In some implementations, the obtained training data includes unstructured content that that is processed to extract particular data. For example, optical character recognition can be used to identify content of a document which can be filtered based on identifying particular terms identified in the document. For example, if a document is recognized as having “sq ft” the system can pair this data type with a numerical value identified in close proximity.”, ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include wherein the obtained set of source data comprises electronic documents, and wherein processing the obtained set of source data comprises: identifying content within the electronic documents by applying optical character recognition to the electronic documents; and filtering the identified content based on a set of keywords since modification in US Patent 12,340,383 is merely a combination of prior art elements well-known in the art that provide the well-known benefit of identify text that matches particular data types used by the system as disclosed by Simkoff Col. 8 lines 12-15.
Regarding claim 12, Simkoff further teaches further teaches:
wherein the obtained set of source data includes personal information of one or more particular individuals, and wherein processing the obtained set of source data comprises: identifying and anonymizing the personal information of the one or more particular individuals (Col. 10 lines 20-30 “In some implementations of the above described techniques, some of the obtained data can be associated with particular individuals. The techniques can be implemented to protect individual privacy and include suitable controls on access to the information. For example, the personal information of a prospective buyer of a parcel of real property can be used in response to received consent from the prospective buyer. In some cases, identifiable information of individual can also be anonymized using a suitable technique and appropriate safeguards placed to protect the personal information.”).
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 claims are directed to an abstract idea without significantly more.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claims are directed to at least one potentially eligible category of subject matter (i.e., process and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-20 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls under the “Certain Methods Of Organizing Human Activity” and “Mental Processes” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106 since the claims set forth steps that recite fundamental economic principles or practices (including hedging, insurance, mitigating risk) and concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Claims 1 and 13 and 20 recites the abstract idea of evaluating tittle risk for a parcel of real property as part of generating title insurance policy in a real estate transaction [007]. In claim 1, this idea is described by the following claim steps:
obtaining, from one or more sources, a set of source data associated with a specified parcel of real property;
processing the set of source data;
based on the processing of the set of source data, (i) identifying potentially-open mortgages associated with the specified parcel of real property and (ii) for each potentially-open mortgage that is identified, extracting a corresponding set of mortgage data;
for each identified potentially-open mortgage:
generate a predicted likelihood that the identified potentially-open mortgage is open, by analyzing a first set of data comprising data records that each contains (i) a respective set of mortgage data for a respective mortgage associated with a parcel of real property that is distinct from the specified parcel of real property and (ii) a respective label indicating whether the respective mortgage is closed or open;
evaluating whether the predicted likelihood that the identified potentially-open mortgage is open satisfies a threshold value; and
based on the evaluating, making a respective prediction of whether the identified potentially-open mortgage is closed or open;
generate a predicted likelihood that the specified parcel of real property has a title defect;
evaluating the respective predictions of whether the identified potentially-open mortgages are closed or open and the predicted likelihood that the specified parcel of real property has a title defect to determine whether the specified parcel of real property is within a risk tolerance;
and based on the evaluating, either (i) allowing a title evaluation process to proceed for the specified parcel of real property without further evaluation or (ii) determining that further evaluation is required and responsively causing a notification to be presented to a user.
This idea falls within the Certain Methods of Organizing Human Activity grouping of abstract ideas because it is directed towards fundamental economic principles or practices when evaluating title risk for a parcel of real property as part of generating a title insurance policy in a real estate transaction (See [007]). The noted abstract idea is also directed to Mental Processes because the claims are directed to observation, evaluation and a determination based on the evaluation of the data (evaluating the respective predictions of whether the identified potentially-open mortgages are closed or open and the predicted likelihood that the specified parcel of real property has a title defect to determine whether the specified parcel of real property is within a risk tolerance.).
Because the above-noted limitations recite steps falling within the Certain Methods Of Organizing Human Activity and Mental Processes abstract idea groupings of the MPEP 2106, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry.
Therefore, because the limitations above set forth activities falling within the Certain Methods Of Organizing Human Activity and Mental Processes abstract idea groupings described in the MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Claim 13 and 20 recites similar limitations as claim 1 and is therefore determined to recite the same abstract idea.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements that fail to integrate the abstract idea into a practical application are:
a first machine learning model;
a second machine learning model;
automatically allowing a title evaluation to proceed;
a computing platform;
a graphical user interface (GUI);
at least one processor;
at least one non-transitory computer-readable medium;
program instructions stored on the at least one non-transitory computer-readable medium that, when executed by the at least one processor, cause the computing platform to perform functions;
However, using a computer environment such as a processor, and a memory and other recited computer elements amounts to no more than generally linking the use of the abstract idea to a particular technological environment. Evaluating tittle risk for a parcel of real property as part of generating title insurance policy in a real estate transaction can reasonably be performed by pencil and paper until limited to a computerized environment by requiring a processor and a memory to perform the steps.
These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and alternatively serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As noted above, the claims as a whole merely describes a method, computer system, and computer program product that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
For the reasons identified with respect to Step 2A, prong 2, claims 1, 13 and 20 fail to recite additional elements that amount to an inventive concept. For example, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a commercial or legal interaction or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)). In addition, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-12 and 14-19 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above.
Dependent claim 2 further limits the abstract idea by introducing wherein the threshold value is specified based on historical information on title defects resulting from unaccounted for open mortgages and a value of corresponding title insurance claims. Evaluating whether a value satisfies a threshold value is a process that could be performed manually until limited by a processor. Further embellishing that the invention is capable of processing information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claims 3 and 14 further limits the abstract idea by linking the judicial exception to a particular field of use by introducing the limitation training the first machine learning model by applying the machine learning process to the first set of training data, wherein the respective sets of mortgage data contained within the first set of training data are derived based on statistical and retail-history data for the respective parcels of real property. The examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.). Therefore the claims are also non-statutory subject matter.
Dependent claims 4 and 15 further limits the abstract idea by introducing adjusting the threshold value based on a determination that predictions of whether potentially-open mortgages are closed or open have a higher-than-expected error rate. Evaluating whether a value satisfies a threshold value is a process that could be performed manually until limited by a processor. Further embellishing that the invention is capable of processing information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claim 5 and 16 further limits the abstract idea by introducing the limitations evaluating an accuracy of the first machine learning model using a set test data comprising data records for parcels of real property for which training data records were not included in the first set of training data; based on the evaluating, determining that the accuracy of the first machine learning model is deficient; and in response to determining that the accuracy of the first machine learning model is deficient, retraining the first machine learning model using additional training data records that each contains (i) a respective set of mortgage data for a respective mortgage associated with a parcel of real property that is distinct from the specified parcel of real property and (ii) a respective label indicating whether the respective mortgage is closed or open. The examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.). Therefore the claims are also non-statutory subject matter.
Dependent claims 6-7 and 17-18 further limits the abstract idea by introducing the limitations wherein the set of source data comprises data records indicating dates when mortgages were recorded against the specified parcel of real property, dates of sales of the specified parcel of real property, and dates when mortgages were removed from the specified parcel of real property; for each potentially-open mortgage that is identified, the corresponding set of mortgage data comprises data indicating an instrument number, a dollar amount, a principal amount, a grantee, and a grantor. Further describing the data used by the system does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claims 8-12 and 19 further limits the abstract idea by introducing the limitations directed to identifying, discarding, filtering and anonymizing data. However such processes are processes that could be performed manually until limited by a processor. Further embellishing that the invention is capable of processing information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide high level of generality computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information see MPEP 2106.
Prior Art
Regarding claims 1-20 and with respect to the prior art, the closes prior art of record, Simkoff (US Patent 10,255,550), does not teach or fairly suggest (by itself or in combination) an invention as claimed. Specifically the closest prior art of record, does not disclose:
identifying potentially-open mortgages associated with the specified parcel of real property and for each potentially-open mortgage that is identified, extracting a corresponding set of mortgage data;
for each identified potentially-open mortgage:
providing, as input to a first machine learning model, the corresponding set of mortgage data and thereby causing the first machine learning model to generate a predicted likelihood that the identified potentially-open mortgage is open, wherein the first machine learning model is trained by applying a machine learning process to a first set of training data comprising data records that each contains (i) a respective set of mortgage data for a respective mortgage associated with a parcel of real property that is distinct from the specified parcel of real property and (ii) a respective label indicating whether the respective mortgage is closed or open;
evaluating whether the predicted likelihood that the identified potentially-open mortgage is open satisfies a threshold value; and
based on the evaluating, making a respective prediction of whether the identified potentially-open mortgage is closed or open; and
evaluating the respective predictions of whether the identified potentially-open mortgages are closed or open to determine whether the specified parcel of real property is within a risk tolerance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A. E. Márquez-Chamorro, M. Resinas and A. Ruiz-Cortés, "Predictive Monitoring of Business Processes: A Survey," in IEEE Transactions on Services Computing, vol. 11, no. 6, pp. 962-977, 1 Nov.-Dec. 2018,
A. Dubey, T. Parida, A. Birajdar, A. K. Prajapati and S. Rane, "Smart Underwriting System: An Intelligent Decision Support System for Insurance Approval & Risk Assessment," 2018 3rd International Conference for Convergence in Technology (I2CT), Pune, India, 2018, pp. 1-6
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA C SANTOS-DIAZ whose telephone number is (571)272-6532. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/MARIA C SANTOS-DIAZ/Primary Examiner, Art Unit 3629
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