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 .
DETAILED ACTION
1. This office action is in response to an amendment received on 12/1/25.
2. Claims 1-3, 10-13, 18-20 are amended.
3. Claims 1-20 are pending.
RESPONSE TO ARGUMENTS
Applicant argues#1
Double Patenting Rejection
Claims 1-20 are rejected on the ground of obviousness type double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 11,948,214 and nonstatutory double patenting as being unpatentable over claims 1-20 of copending application, U.S. Patent Application No. 18/607,385. Office Action. The Applicant respectfully notes that the claims have been amended to change their respective scope, and as such, the double patenting rejections may no longer apply. The Examiner is invited to contact Applicant's undersigned representative if the presented claims are otherwise in condition for allowance to discuss whether a terminal disclaimer might still be warranted.
Examiner Response
Examiner respectfully disagrees.
The limitations from (claims 1-20) from the instant application are still rejected under the doctrine of obviousness type patenting over claims 1-13 of US Patent 11,948,214.
Claim 2 & Claim 6 of the dependent claim recite the same limitations that are present in the amendments to independent claims 1,10, 18 of the instant application.
The limitations from (claims 1-20) from the instant application are still rejected under the doctrine of obviousness type patenting over claims 1, 3-20 of copending application 18/607,385, herein the ‘385 app.
The amendments to claims 1, 9, 17 of the *385 app are present in the amendments to independent claims 1,10, 18 of the instant application.
The limitations from (claims 1-20) from the instant application are still rejected under the doctrine of obviousness type patenting over claims 1-20 of copending application 18/393,298 herein the ‘298 app.
The amendments to claims 1, 9, 17 of the *385 app are present in the amendments to independent claims 1,10, 18 of the instant application.
The rejection is maintained.
Applicant argues#2
Regarding Step 1 of the Alice analysis, the present claims concern at least a process and/or a machine and thus recite a patent-eligible subject matter under 35 U.S.C. § 101.
Regarding Step 2A Prong 1 of the Alice analysis, the present claims are not directed to an abstract idea.
The Office Action argues that the claims The Office Action argues that the claim limitations "cover performance of the limitation as certain methods of organizing human activity." Id. at 6. Claim 1 currently recites in part as follows: establishing a connection to at least one third-party application to continuously receive, over an expense network, real-time market data associated with a specific region sent over a communication network at a net lease management server configured to communicate with at the least one third-party application, wherein the market data includes expenses that
include fixed costs and variable costs, and net lease terms; storing the market data received from the expense network in expenses database, wherein the market data is used as training data for a machine-learning algorithm; generating a machine-learning model based on the market data, wherein the machine-learning model identifies one or more patterns in the expenses and one or more weights based on historical net lease terms using a machine-learning algorithm; continuously updating the machine-learning model based on continuously updating market data; generating, by the enhancement module, one or more stress scenarios based on the market data, wherein the one or more stress scenarios selects a multiplier based on the weights from the machine-learning model and predicts an amount of future expenses based on the identified patterns in expenses from the machine-learning model in varied scenarios, wherein the machine-learning model is updated by the updating market data to initialize
a new stress scenario; generating, based upon the determination and over the
communication network, an instruction to trigger a transfer of a difference between the sufficient backstop amount and an accounting at the backstop database to the backstop database.
The recited limitations find support in the Specification as filed, which describes the expense network that is "connected to a plurality of third-party networks to compile the market data" to "continuously update the market data." Specification, [0056]. Such continuously updated data is used by the machine-learning algorithm to determine "predicted future expenses calculate based on historical trend data that impacts the fixed costs and variable costs." Id. at [0026]. The machine learning model may be trained using "extracted historical data" to determine the weights for selecting a "predicted amount between an upper bound and a lower bound" and retrained with new market data to initialize one or more new stress scenarios. Id. at [0079].
Rather than relating to a commercial interaction or steps for conducting a commercial interaction between potential tenants and landlords for the management of a leased property, the claims are directed to establishing communication with various third-party networks to continuously receive data in real-time, using the data to train and generate a machine-learning model, updating the machine-learning model with continuously updating data, and generating stress scenarios based on the machine-learning model to predict future expenses "more accurately attuned to a changing economy." Id. at [0026].
Thus, the claims are not directed to organizing human activity or abstract ideas and the claims qualify as patent-eligible under Step 2A, Prong One.
Examiner Response
Examiner respectfully disagrees.
The claim limitations have been identified that are reciting the identified abstract idea and the claims were properly grouped in the abstract category of a commercial interaction, see the section 101 rejection below.
The limitations (receive, real-time market data associated with a specific region, wherein the market data includes expenses that include fixed costs and variable costs, and net lease terms; storing the market data received, identifies one or more patterns in the expenses and one or more weights based on historical net lease terms; generating, one or more stress scenarios based on the market data, wherein the one or more stress scenarios selects a multiplier and predicts an amount of future expenses based on the identified patterns in expenses, to initialize a new stress scenario; generating, based upon the determination an instruction to trigger a transfer of a difference between the sufficient backstop amount and an accounting at the backstop to the backstop) is part of the identified abstract idea.
The additional elements (the expense network, communication network, net lease management server, third party application, storage configured to store instructions, one or more processors configured to execute the instructions, a net lease module, a reserve module, an enhancement module, a single reserve database, a backstop database, owner module, manage module, non-transitory computer readable medium, establishing a connection to a third party application, generating a machine learning model and continuously updating a machine leaning model based on continuously updated data) are recited at a high level of generality and are being used in their ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f),
Applicant argues#3
Regarding Step 2A Prong 2 of the Alice analysis, the present claims implement a practical application. With respect to the practical application inquiry, the Office Action argues that the additional elements are "recited at a high-level of generality" and "do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea." Office Action at 8. The Applicant respectfully disagrees.
Rather than merely using a computer as a tool to perform an abstract idea, the claims recite technical improvement to current state of accounting by utilizing machine-learning models to process data received from third party networks, forecast future expenses, and trigger a transfer of funds based on the forecast to ensure that there is adequate reserve amount to account for predicted expenses in the future, integrating various networks, modules, and databases into a practical application.
Examiner Response
Examiner respectfully disagrees.
Applicant is pointed to MPEP 2106.05(a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field [R-07.2022]:
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.
During examination, the examiner should analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement.
Paras 30,79 of the instant specification are reproduced below:
[0030] In some cases, a machine-learning model is used to output the set of net lease terms. The machine-learning model may determine the weights based on training data including past net lease terms associated with the one or more regions.
[0079] In some cases, a machine-learning model may be used to initialize the one or more stress scenarios. The machine-learning model may select the predicted amount between an upper bound and a lower bound and a multiplier between a multiplier upper bound and a multiplier lower bound based on weights set by training data including the extracted historical data. The machine-learning model may retrain new extracted historical data. For example, when time passes, new data about a more current economic landscape may be available. The machine-learning model may be retrained to initialize one or more new stress scenarios. The machine-learning model may select a new predicted amount between a new upper bound and a new lower bound and a new multiplier between a new multiplier upper bound and a new multiplier lower bound based on new weights set by new training data including the new extracted historical data.
It can be seen from the instant specification that there is no technical explanation of the asserted improvement (the use of machine learning model) and reflected in the claims. The additional element( the machine learning model) is recited at a high level of generality, operating in its ordinary capacity and as such are being used as a tool to implement the identified abstract idea.
Therefore there are no additional elements in the claims that are indicative of integration into a practical application.
The rejection is maintained.
Applicant argues#4
Further, the claims affect a transformation or reduction by transforming raw market data received from different third party networks into one or more stress scenarios that predict future expenses and determine sufficiency of reserve funds to account for various scenarios.
Rather than a generic computer, the claimed concept is used in conjunction with a particular machine that is integral to the claim. At least the 'enhancement module' utilizes the data received from the 'expense network' that is connected to 'at least one third-party application' to generate a machine-learning model to generate one or more stress scenarios based on the stored data in 'expenses database,' which are working in conjunction to perform the function of the claims and thus are integral to the claims. The claims are therefore patent-eligible under Step 2A, Prong 2.
Examiner Response
Examiner respectfully disagrees.
The (enhancement module, expense network, third party application and machine learning model) are not particular machines.
Applicant is pointed to the MPEP:
2106.05(b)Particular Machine [R-07.2022]
I. THE PARTICULARITY OR GENERALITY OF THE ELEMENTS OF THE MACHINE OR APPARATUS
The particularity or generality of the elements of the machine or apparatus, i.e., the degree to which the machine in the claim can be specifically identified (not any and all machines). One example of applying a judicial exception with a particular machine is Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86, 40 USPQ 199 (1939). In this case, a mathematical formula was employed to use standing wave phenomena in an antenna system. The claim recited the particular type of antenna and included details as to the shape of the antenna and the conductors, particularly the length and angle at which they were arranged. 306 U.S. at 95-96; 40 USPQ at 203. Another example is Eibel Process, in which gravity (a law of nature or natural phenomenon) was applied by a Fourdrinier machine (which was understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web. Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923).
It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions). If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)
II. WHETHER THE MACHINE OR APPARATUS IMPLEMENTS THE STEPS OF THE METHOD
Integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. See CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) ("We are not persuaded by the appellant's argument that the claimed method is tied to a particular machine because it ‘would not be necessary or possible without the Internet.’ . . . Regardless of whether "the Internet" can be viewed as a machine, it is clear that the Internet cannot perform the fraud detection steps of the claimed method"). For example, as described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015) (explaining that in order for a machine to add significantly more, it must "play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly").
In the instant claims, (enhancement module, expense network, third party application and machine learning models), are recited a high level of generality and are operating in their ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f)
Therefore there are no additional elements in the claim that are indicative of integration into a practical application.
The rejection is maintained.
Applicant argues#5
Regarding Step 2B of the Alice analysis, Applicant's claims include an inventive concept. The Office Action argues that "the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception" and "there are no additional elements recited in the claim beyond the judicial exception." Id. at 9. These arguments in the Office Action are stated as conclusions, with no evidence provided. Without highly specific programming (e.g., such as provided by video game code), a general-purpose computer cannot provide mapping, normalizing, and synchronizing data between different third party server systems and the platform server, let alone provide for the specific type of metagame experience recited herein. MPEP § 2106.07(a)(III) requires that in step 2B:
Examiner should not assert that an additional element (or combination of elements) is well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
(A) A citation to an express statement in the Specification or to a statement made by an applicant during prosecution that demonstrates the well- understood, routine, conventional nature of the additional element(s). ...A finding that an element is well-understood, routine, or conventional cannot be based only on the fact that the Specification is silent with respect to describing
such element.
(B) A citation to one or more of the court decisions discussed in MPEP § 2106.05(d)(II) as noting the well-understood, routine, conventional nature of
the additional element(s)....
(C) A citation to a publication that demonstrates the well-understood, routine,
conventional nature of the additional element(s)....
(D) A statement that the examiner is taking official notice of the well- understood, routine, conventional nature of the additional element(s). (emphasis added).
The Office Action fails to "expressly support" its "rejection in writing" with any of the types of evidence listed in MPEP 2106.07(a)(III). Thus, the Office Action fails to show conventionality of any of the elements in the Applicant's claims.
At least 'establishing a connection to at least one third-party application to continuously receive, over an expense network, real-time market data associated with a specific region sent over a communication network at a net lease management server configured to communicate with at the least one third-party application, wherein the market data includes expenses that include fixed costs and variable costs, and net lease terms,''storing the market data received from the expense network in expenses database, wherein the market data is used as training data for a machine-learning algorithm,''generating a machine-learning model based on the market data, wherein the machine-learning model identifies one or more patterns in the expenses and one or more weights based on historical net lease terms using a machine-learning algorithm,''continuously updating the machine- learning model based on continuously updating market data,''generating, by the enhancement module, one or more stress scenarios based on the market data, wherein the19
one or more stress scenarios selects a multiplier based on the weights from the machine- learning model and predicts an amount of future expenses based on the historical data identified patterns in expenses from the machine-learning model in varied scenarios, wherein the machine-learning model is updated by the updating market data to initialize a new stress scenario,' and 'generating, based upon the determination and over the communication network, an instruction to trigger a transfer of a difference between the sufficient backstop amount and an accounting at the backstop database to the backstop database' are not routine or conventional.
The Office Action fails to further consider whether each and every element outside of the purported abstract idea-individually or in every ordered combination-adds significantly more so as to qualify as an inventive concept under the second part of the Alice analysis, thereby failing to establish the lack thereof with clear and convincing evidence as required by Berkheimer. Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018). In particular, the Office Action fails to consider the additional elements in combination in a way that deviates from what is routine or conventional.
Based on the foregoing, the Office Action fails to establish lack of patent-eligible subject matter under Section 101. Accordingly, Applicant respectfully requests reconsideration and withdrawal of the 35 U.S.C. § 101 rejection.
Examiner Response
Applicant misapprehends when a Berkheimer analysis is required under current examination policy. Simply put, Examiner is not required under current Examination policy to evaluate under Step 2B, whether additional elements constitute “well-understood, routine, and conventional activities,” [“WURC activities”] unless an additional element(s) were found to be insignificant extra-solution activity in Step 2A, Prong 2. MPEP § 2106.05(d)(I). Here, the condition precedent was not met and the Non-Final Office Action determined the additional elements were no more than mere instructions to apply the abstract idea exception using a computer. MPEP § 2106.05(f). Thus, Examiner was not required to determine a Berkheimer analysis. MPEP § 2106.05(d)(I). (See Section 101 rejection below).
The rejection is maintained.
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 obviousness- type 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 Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
3. Claims 1-20 are rejected under the judicially created doctrine of obviousness type double patenting as being unpatentable over claims 1-13 of US Patent 11,948,214, herein the *214 patent. 4. Although the conflicting claims are not identical, they are not patentably distinct from each other because both the scope and function of the instant invention and the *214 patent are the same and the claimed limitations are almost identical.
5. Claims 1-20 are provisionally rejected on the grounds of non-statutory double patenting as being unpatentable over claims 1, 3-20 of application no. 18/607,385 (the reference application), herein the *385 application and claims 1-20 of application no. 18/393,298 (the reference application), herein the *298 application. Although the claims at issue are not identical, they are not patentable distinct from each other because both the claims of the instant invention and the claims of both the *298 application and the *385 are the same except for minor changes to the claim language. However, the scope of the claims and function of the claimed invention are identical to both the *298 and *385 applications.
Claim Rejections- 35 U.S.C § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10,18 are directed to a method, system and computer readable medium which are statutory categories of invention. (Step 1: YES).
Representative Claim 1 recites the limitations of:
A computer-implemented method of automating a residential net lease management tool with a credit enhancement module, comprising:
establishing a connection to at least one third-party application to continuously receive over an expense network, real-time market data associated with a specific region sent over a communication network at a net lease management server configured to communicate with the at least one third-party application, wherein the market data includes expenses that include fixed costs and variable costs, and net lease terms;
storing the market data received from the expense network in expenses database, wherein the market data is used as training data for a machine learning algorithm;
generating a machine-learning model based on the market data, wherein the machine-learning model identifies one or more patterns in the expenses and one or more weights based on historical net lease terms using a machine learning algorithm;
continuously updating the machine-learning model based on continuously updating market data;
initiating, by a net lease module, a reserve module;
generating, by the reserve module, net lease parameters for the specific region based on a calculated profitability evaluation based on the market data received via the expense network, wherein the calculated profitability evaluation determines a threshold margin based on a percentage of an average rental rate and average fixed costs in the specific region;
initiating, by the net lease module, an owner module;
identifying, by the owner module, properties that fall within the net lease parameters generated by the reserve module;
initiating, by the net lease module, a manage module;
determining, by the manage module, fixed costs and variable costs based on data associated with at least one of the identified properties and extracted data points from stored invoice data;
generating, by the reserve module, a set of net lease terms for a residential net lease tenant, wherein the set of net lease terms is associated with the at least one of the properties identified by the net lease module, based on inputs including the fixed costs and variable costs determined by the manage module, wherein weights are assigned to each input;
initiating, by the net lease module, an enhancement module;
generating, by the enhancement module, one or more stress scenarios based on the market data, wherein the one or more stress scenarios selects a multiplier based on the weights from the machine learning model and predicts an amount of future expenses based on the identified patterns in expenses from the machine-learning model in varied scenarios, wherein the machine-learning model is updated by the updating market data to initialize a new stress scenario;
determining, by the enhancement module, that a backstop database or a single reserve database does not have a sufficient backstop amount to cover the multiplier of the predicted amount based on results of the stress scenario; and
generating, based upon the determination and over the communication network, an instruction to trigger a transfer of a difference between the sufficient backstop amount and an accounting at the backstop database to the backstop database.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity.
The claim recites elements that are in bold above, which covers performance of the limitation as a commercial interaction, steps for conducting a commercial interaction between potential tenants and landlords for the management of a leased property, (e.g., receive real-time market data associated with a specific region, wherein the market data includes expenses that include fixed costs and variable costs, and net lease terms; storing the market data; identifies one or more patterns in the expenses and one or more weights based on historical net lease terms; generating, net lease parameters for the specific region based on a calculated profitability evaluation based on the market data, wherein the calculated profitability evaluation determines a threshold margin based on a percentage of an average rental rate and average fixed costs in the specific region; identifying, properties that fall within the net lease parameters; determining, fixed costs and variable costs based on data associated with at least one of the identified properties and extracted data points from stored invoice data; generating, a set of net lease terms for a residential net lease tenant, wherein the set of net lease terms is associated with the at least one of the properties identified, based on inputs including the fixed costs and variable costs; generating, one or more stress scenarios based on the market data, wherein the one or more stress scenarios selects a multiplier based on the weights and predicts an amount of future expenses based on the identified patterns in expenses in varied scenarios to initialize a new stress scenario; determining, that a backstop or a single reserve does not have a sufficient backstop amount to cover the multiplier of the predicted amount based on results of the stress scenario; generating based upon the determination, an instruction to trigger a transfer of a difference between the sufficient backstop amount and an accounting at the backstop to the backstop)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a Commercial Interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Claims 10, 18 are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 1, 10,18 includes the following additional elements:
-An expense network
-A communication network
-A net lease management server
-A third party application
-A storage configured to store instructions
-One or more processors configured to execute the instructions
-A net lease module
-A reserve module
-An enhancement module
-A single reserve database
-A backstop database
-An owner module
-A manage module
- A non-transitory computer readable medium
-Establishing a connection to a third party application
-Generating a machine learning model using a machine learning algorithm and continuously updating the machine learning model based on updated data
The expense network, communication network, net lease management server, third party application, storage configured to store instructions, one or more processors configured to execute the instructions, a net lease module, a reserve module, an enhancement module, a single reserve database, a backstop database, owner module, manage module, non-transitory computer readable medium, establishing a connection to a third party application, generating a machine learning model using a machine learning algorithm and continuously updating the machine learning model based on updated data are recited at a high level of generality and are being used in their ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
Therefore claims 1, 10,18 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited in the claim beyond the judicial exception.
Mere instructions to implement an abstract idea, on or with the use of generic computer components, or even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 1,10, 18 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-9, 11-17, 19-20 further define the abstract idea that is present in their respective independent claims 1,10, 18 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above.
Claims 2-4, 11-13, 19-20 further defines the identified abstract idea as recited in claims 1,10,18. The additional element of the “retraining of the machine learning model” is recited a high level of generality, operating in their ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f)
Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims (2-9, 11-17, 19-20) are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
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 MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 1/10/2026