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 .
The following is a Final Office Action in response to communications received October 29, 2025. No Claim(s) have been canceled. Claims 1-5, 9, 11-15 and 19-20 have been amended. No new claims have been added. Therefore, claims 1-20 are pending and addressed below.
Priority
Application 18/607118 filed 03/15/2024 3.
Applicant Name/Assignee: Wells Fargo Bank, N.A.
Inventor(s): Strader Matthew; Parks, Diane; Rashid, Pamela
Response to Amendment/Arguments
Drawings
Applicant’s amendments in response to the objection set forth in the previous Office Action for failing to comply with 37 CFR 1.84(p)(5) is sufficient to overcome the objection. The examiner withdraws the objection to the drawings.
Claim Rejections - 35 USC § 101
Applicant's arguments filed 10/29/2025 have been fully considered but they are not persuasive.
In the remarks applicant recites the limitations arguing the claim limitations are not directed toward the abstract category of mental concepts or methods of organizing human activity. With respect to mental concepts, the claimed subject matter cannot reasonably be performed using the human mind. Applicant argues the human mind is not capable of “determining a payoff plan for at least one item of inventory items, by applying the first data, second data and third data to …models trained to generate an optimized payoff plan” and “causing …implementation of the payoff plan automatically through a computing the system of the first entity”. The claimed features cannot be performed in the human mind because the mind cannot use “one or more machine learning models”. Applicant’s argument is not persuasive. The human mind is capable of performing the operations performed by the model claimed including “determining a payoff plan by applying data to generate an optimized payoff plan” and to “implement the payoff plan”, the machine learning model and computing system merely automates the processes that can be performed using the mental process. The examiner maintains the step 2A prong 1 rejection of the claimed subject matter being directed toward abstract category of mental concepts.
In the remarks applicant argues that the claimed subject matter does not fall within any of the sub-categories of the abstract category of methods of organizing human activity and pointing to MPEP 2106.04(a)(2), MPEP 2106.04(a)(2)(II). Applicant submits that the claimed subject matter, “receiving …an update to the …inventory items upon a sale of …inventory item”, “determining …a payoff plan for the …item of the …inventory items…to generate an optimized payoff plan”, “causing…implementation of the payoff plan…” which is not directed toward commercial interactions. The examiner respectfully disagrees. The claimed subject matter is explicitly directed toward collecting inventory sale data and analyzing plurality of data for determining an optimized payoff plan and then implementing the payoff plan --- a commercial activity/interaction. Commercial interactions/activity includes various forms of transactions including sales of goods. In the realm of commercial activities/interactions payoff plans are essential for managing financial obligations associated with business transactions. The examiner maintains the claimed subject matter when considered as a whole is directed toward commercial interactions/activities. The rejection is maintained.
In the remarks applicant argues the amended claimed subject matter under step 2a prong 2, integrates any alleged abstract idea into a practical application. Applicant’s response does not provide facts, evidence or arguments in the response that the previous Office action rejection under step 2A prong 2 is overcome by the claimed subject matter. (see MPEP 2145). Applicant’s argument amount to a general allegation that the claimed define patent eligible subject matter without specifically pointing to how the language of the claims are patent eligible under step 2A prong 2. The rejection is maintained.
Claim Rejections - 35 USC § 103
Applicant's arguments filed 10/29/2025 have been fully considered but they are not persuasive.
In the remarks applicant argues the prior art references fail to teach that amended limitations “retrieving,…first data relating to one or more inventory items of the first entity, the first data including a current amount of the one or more inventory items”. The examiner respectfully disagrees. The prior art reference Ambrose teaches para 0057 wherein the prior art teaches input data include factors such as item categories associated with items and teaches multiple item involved in transaction where item factors do not indicate transaction likely, para 0196 wherein the prior art teaches stored data accessible and may be extracted from databases to predict trends and behavior patterns, para 0245-0246 wherein the prior art teaches inventory management services which provide inventory tracking and reporting including data associated with a quantity of each item the merchant has available in inventory, para 0335 wherein the prior art teaches inventory database storing data associated with a quantity of each item merchant has available to the merchant. The rejection is maintained.
In the remarks applicant argues the prior art references fail to teach that amended limitations “retrieving…the one or more inventory items, second data relating to the one or more inventory items and including a product or a product type corresponding to the one or more inventory items”. The examiner respectfully disagrees. The prior art Ambrose teaches: para 0022 wherein the prior art teaches generating of shared channel which can include data including item type within which multi-user transaction can be facilitated; para 0054-0055 wherein the prior art teaches model utilizing transaction data including item types that have been predetermined and item types determined based on user input data, para 0063 wherein the prior art teaches generation of channel for receiving data including item type and other transaction data, para 0079 wherein the prior art teaches generation of shared channel include item type, para 0175 wherein the prior art teaches machine learning model utilized data indicating geolocation item type and other conditions, para 0198-0199 wherein the prior art teaches training dataset can include item types. The rejection is maintained.
In the remarks applicant argues the prior art references fail to teach that amended limitations “identifying…third data … the third data relating to a financing of the one or more inventory item”. The examiner respectfully disagrees see at least para 0199 wherein the prior art teaches training dataset may include past use of installment plans for a given user. The rejection is maintained.
In the remarks applicant argues the prior art references fail to teach that amended limitations “receiving… an update to the one or more inventory items upon a sale of at least one inventory item of the one or more inventory items”. The examiner respectfully disagrees. The prior art Ambrose teaches para 0057-0059 wherein the prior art teaches once transaction has been identified selectable element may be updated, customized or modified for example additional user or product may be added, para 0081-0082 wherein the prior art teaches interface may be updated to include transaction status indicator such as details of transaction, item details, item portions, payment details, installment information. The rejection is maintained.
In the remarks applicant argues the prior art references fail to teach that amended limitations “in response to receiving the update upon the sale of the at least one inventory item of the one or more inventory items, determining, by the provider computing system, a payoff plan for the at least one item of the one or more inventory items according to the first data, the second data and the third data”. The examiner respectfully disagrees. The prior art Ambrose teaches para 0081-0082 wherein the prior art teaches interface may be updated to include transaction status indicator such as details of transaction, item details, item portions, payment details, installment 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 instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to Claim(s) 1-10:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 1 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. Method claim 1 recites a method steps (1) retrieving data for input (2) retrieving second data for input (3) identifying third data to financing of input (4) receiving data update (5)determining a payoff plan to generate [intended use] optimized payoff plan (6) causing implementation of pay off plan. The claimed limitations which under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation “by one or more first servers of computing system, via first connection from API of one or more second servers.” That is, other than reciting the step “causing implementation of payoff plan and performing the method steps “by one or more first servers of computing system, via first connection from API of one or more second servers,” nothing in the claim precludes the limitations from being reasonably performed using mental processes. The Court(s) have held that processes which could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary”. The recited computer components do not make the claim less abstract because the recited limitations could be performed by humans without a computer.(see MPEP 2106.04(a) (2) III C
The claimed physical structures are generic computer components and tools to perform the mental processes. The computer components are recited at a high level of generality and merely automates functions that could reasonable be performed using mental concepts, therefore acting as a generic computer to perform the abstract idea.
The steps recite steps that can easily be performed in the human mind as mental processes because the steps of receiving, retrieving and identifying data which mimics mental processes of observation. Additional mental process include the steps of identifying and determining mimic mental processes of analysis. Therefore, the limitations, mimic human thought processes of observation, evaluation and opinion, and communication of result which, where the data interpretation is perceptible only in the human mind. See In re TLl Commc'ns LLC Patent Litig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. latric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)
Furthermore, when considered as a whole the claimed subject matter is directed toward commercial activity where data is financial related data is received, retrieved and analyzed to determine an optimized pay off plan. It is clear from the Specification (including the claim language) that claim 1 focuses on an abstract idea, and not on an improvement to technology and/or a technical field. The Specification is titled “SYSTEMS AND METHODS FOR DATA ANALYTICS FOR INITIATING PAYOFFS,” and discloses, in the Background section, providers accessing inventory and payment patterns do not provide a holistic view of patterns and trends in different levels in supply chains. The background states that missing information may provide insights when making payoff. The specification discloses that the focus of the invention is to provide a means for automatic payoff of outstanding loans from financial institutions that they have against retail products (vehicles and/or heavy equipment), for a user to “select an appropriate product or service that is beneficial for the user’s personal circumstances” (Spec 0015) The Specification describes being able to apply payment preferences and/or choose to delay payments on any particular loan as payoff options when placing in to consideration user’s circumstances.
This concept can be found in the abstract category of commercial interactions. These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of mental processes and methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the claims fail to provide indications of patent eligible subject matter that integrate the alleged abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include “one or more first servers of a provider computing system”, “application programing interface (API)”, “one or more second servers”.
The claimed “one or more servers” applied to perform the operations of “retrieving…first data” via a first connection from an API
The claimed “provider computing system” applied to perform the operations “retrieving …second data” via one or more connections form one or more second API’s of one or more third servers associated with providers of inventory items”; applied to perform the operations “receiving …an update to the one or more inventory items upon a sale of inventory item…” via the first connection from one or more second servers.
The claimed one or more servers applied for “retrieving” and the providing computing system applied for “retrieving”, “receiving” operations when viewed, according to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) as insignificant extra solution activity. The court decisions have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
The claim limitations “retrieving” and “receiving” are recited at a high level of generality without details of technical implementation and thus are insignificant extra solution activity.
The claimed “provider computing system” applied to perform the operation of “identifying …third data of one or more first servers; applied to perform the operation “determining …a payoff plan for the …item of the one or more inventory items…”; applied to perform the operation “causing …implementation of the payoff plan through a computing system…” are recited at a high-level of generality such that it amounts to no more than applying the exception using generic computer components for the purpose of analyzing financial transaction data to determine and implement a payoff plan in response to the sale of inventory which is a commercial activity. The claim limitations and specification lacks technical disclosure on what how the adjustments of weight to address error in the calculated output that does not meet a threshold is a technical problem related to ML algorithms or the recited servers, systems and or API applied for communication. The analysis finds that the claimed limitations are not directed toward an attempt provide a technical solution to a technical problem rather than a solution to a problem found in the abstract idea (predicted output results versus actual outputted results). Taking the claim elements separately, the operation performed by the computer components applied by the method at each step of the process is purely in terms of results desired and devoid of implementation of details. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application). The claim limitations when considered individually fail to provide any indications of patent eligible subject matter, according to MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h).
When the claims are taken as a whole, as an ordered combination, the combination of limitations 1-2 and 3 are an insignificant extra solution business related activity of financial related data collection and analyzed. The combination of limitations 1-3 and 4-5 is directed toward receiving data updates and the collected data is analyzed in order to generate an optimized payment plan-a business practice. The combinations of parts is not directed toward any technical process or technological technique or technological solution to a problem rooted in technology.
Accordingly, when the claims are taken as a whole, as an ordered combination, the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea, but instead are directed toward the implementation of a pay off plan which is a business practice. This is because the claimed subject matter fails to provide additional elements or combination or elements to apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The functions recited in the claims recite the concept of receiving and retrieving data, identifying data, determining, pay off plan and causing pay off plan to be implemented which is a process directed toward a business practice.
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments apply what generic computer functionality in the related arts. The steps are still a combination made to collect that for analysis in order to generate and determine a pay off plan that is implemented and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes that goes beyond merely confining the abstract idea in a particular technological environment, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The analysis finds no indication in the claim language that the structure and/or the manner in which a computer system operates is changed in any way. Nor do we find any such indication elsewhere in the record. The Specification describes as discussed above, that the focus of the invention is to calculate and generate a payoff plan based on the data received that is analyzed. The claim provides no technical details regarding how the “determining” operation of the ML model is performed. Instead, similar to the claims at issue in Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332 (Fed. Cir. 2017), “the claim language . . . provides only a result-oriented solution with insufficient detail for how a computer accomplishes it. Our law demands more.” Intellectual Ventures, 850 F.3d at 1342 (citing Elec. Power Grp. LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)). The claimed limitations are not "truly drawn to a specific" computer technology or to a particular technical process for improving technology or to solve a problem rooted in technology or any other indications of patent eligibility under step 2A prong 2, but rather is directed toward the method of determining a payoff plan in response to the sale of inventory and causing the determined payoff plan to be implemented. Simply reciting the use of a computer to execute a process or algorithm that can be performed the commercial activity/interaction does not change the analysis. The claim limitations when considered individually or as an ordered combination is determined not to meet the Alice/Mayo 2A test.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional element recited in the claim include one or more servers of a provider computing system and a connection from an API of one or more second serves and one or more second API’s of one or more third servers which are applied to receive and/or retrieve data and thus is applied to gather data. The provider computer system is further applied to perform the operations of identifying data of the first one or more servers relating to financing of an input, receiving an update to the input, determining a payoff plan according to data collected where the applied data is provided to a ML model to generate an optimized payoff and cause the payoff plan to be implemented through a generic computing system of a first entity. Although the claim language recites “ML models trained” the limitations are silent with respect to a technical process for training the model. The term “trained” is so high level as imply a mere “programming” of a model for the intended purposes of generating an optimized payoff using data ingested into the model. The specification also does not recite a particular process for training models, rather the specification discloses that errors determined by a comparator may be used to adjust weights so that the model learns over time and the model may be “trained” using backpropagation algorithms as an example. The back propagation algorithm propagates the error signal where the error signal may calculate an iteration of each pair of training inputs and associated actual outputs calculated error signal such that the weights adapt based on the amount of error. The error is minimized using a loss function which may include any of different mathematical functions. (spec para 0051). The specification further discloses the weighting coefficients of the ML model may be tuned to reduce error amount minimizing the differences between the predicted output and actual output. The model is adjusted until the error is within a threshold value. (Spec para 0052). The specifications disclosure that the training the model is based on data inputted where the model it trained using backpropagation and loss functions in order to reduce error where the weights applied to the data in the analysis is to reduce the different between predicted and actual value outputted and not for the purpose of solving a problem within the model itself or to improve upon how ML models are trained. The specification focuses on the mathematical processes that could be applied in order to get an output predicted value within a threshold of an actual value in order to reduce the error in the analysis based on data received.
Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. Using a servers and computer to collect, analyze and output a result and further cause a pay off plan to be implemented ----are some of the most basic functions of computer technology. Limitations referenced in Alice that are not enough to qualify as “significantly more” include “apply it” (or an equivalent) with an abstract idea, mere instructions to implement the abstract idea on a computer or requiring no more than a generic compute to perform generic computer functions that are well understood activities known to the industry. As a result, none of the computer elements recited by the method claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers. .. . The claim limitations do not recite that any of the “devices” perform more than a high level generic function ... . None of the limitations recite technological implementation details for any of these steps, but instead recite only results desired to be achieved by any and all possible means. .. . Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these computer functions are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) Absent a possible narrower construction of the terms “receiving”, “retrieving”, “identifying”, “determining” and “causing” ... are functions can be achieved by any general purpose computer without special programming"). None of these activities are used in some unconventional manner nor do any produce some unexpected result. Applicants do not contend they invented any of these activities. In short, each step does no more than require a generic computer to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
With respect to the ML model the specification discloses:
“…applying the first data, second data and third data to one or more machine learning models trained to generate an optimized payoff plan…” (para 0003, 0006-0007)
“…Supervised learning is a method of training a machine learning model given input-output pairs. An input-output pair is an input with an associated known output (e.g., an expected output)., (para 0041)
The specification para 0042-0047 and 0049-0050 discloses the use that can be applied using a ML model in a business process and what data can be acted upon in the analysis
[0042] Machine learning model 204 may be trained on known input-output pairs such that the
machine learning model 204 can learn how to predict known outputs given known inputs. Once
the machine learning model 204 has learned how to predict known input-output pairs, the
machine learning model 204 can operate on unknown inputs to predict an output.
[0043] The machine learning model 204 may be trained based on general data and/or granular
data (e.g., data based on a specific user, data based on a specific entity, etc.) such that the
machine learning model 204 may be trained specific to a particular user and/or entity.
[0044] Training inputs 202 and actual outputs 210 may be provided to the machine learning
model 204. Training inputs 202 may include accounts receivable data, accounts payable data,
account balance data, liquid asset data, illiquid asset data, and the like. Actual outputs 210 may
include payoff strategies (e.g., on-time payments, deferred payments, a scheduled payment plan,
refinance opportunities, and the like), user feedback (e.g., whether a customer, customer
relationship manager, or other specialist ranked ( or scored) the payment strategy as successful or unsuccessful, whether the customer, customer relationship manager, or the like ranked ( or
scored) the payment strategy as aggressive, conservative or moderate), actual future accounts
receivable data, actual future accounts payable data, actual future account balance data, actual
future liquid asset data, actual future illiquid asset data, and the like.
[0045] The inputs 202 and actual outputs 210 may be received from historic enterprise resource
130 data from any of the data repositories. For example, a data repository of an enterprise
resource 130 may contain an account balance of an entity one year ago. The data repository may
also contain data associated with the same account six months ago and/or data associated with
the same account currently. Thus, the machine learning model 204 may be trained to predict
future account balance information (e.g., account balance information one year into the future or
account balance information six months into the feature) based on the training inputs 202 and
actual outputs 210 used to train the machine learning model 204.
[0046] In an embodiment, a first machine learning model 204 may be trained to predict data
associated with a payoff strategy for an entity based on current entity enterprise resource 130
data. For example, the first machine learning model 204 may use the training inputs 202 (e.g.,
accounts receivable data, accounts payable data, account balance data, liquid asset data, illiquid
asset data, and the like.) to predict outputs 206 (e.g., future accounts receivable data, future
accounts payable data, future account balance data, future liquid asset data, future illiquid asset
data, and the like), by applying the current state of the first machine learning model 204 to the
training inputs 202. The comparator 208 may compare the predicted outputs 206 to actual
outputs 210 (e.g., actual future accounts receivable data, actual future accounts payable data,
actual future account balance data, actual future liquid asset data, actual future illiquid asset data, and the like) to determine an amount of error or differences. For example, the future predicted accounts receivable data (e.g., predicted output 206) may be compared to the actual accounts receivable data (e.g., actual output 710).
[0047] In other embodiments, a second machine learning model 204 may be trained to generate
one or more payment strategies for the entity based on the predicted data of the payoff strategy
for the entity. For example, the second machine learning model 204 may use the training inputs
202 (e.g., future accounts receivable data, future accounts payable data, future account balance
data, future liquid asset data, future illiquid asset data, and the like) to predict outputs 206 (e.g., a
probable success of a predicted on-time payment, a probable success of a predicted deferred
payment, a probable success of a predicted scheduled payment plan, a probable success of a
predicted refinance opportunity, and the like) by applying the current state of the second machine
learning model 204 to the training inputs 202. The comparator 208 may compare the predicted
outputs 206 to actual outputs 210 (e.g., a selected on-time payment, a selected deferred payment, a selected scheduled payment plan, a selected refinance opportunity, and the like) to determine an amount of error or differences.
[0049] In some embodiments, a single machine leaning model 204 may be trained to make one
or more recommendations to the user based on current user data received from enterprise
resources 130. That is, a single machine leaning model may be trained using the training inputs
202 (e.g., accounts receivable data, accounts payable data, account balance data, liquid asset
data, illiquid asset data, and the like) to predict outputs 206 (e.g., a probable success of a
predicted on-time payment, a probable success of a predicted deferred payment, a probable
success of a predicted scheduled payment plan, a probable success of a predicted refinance
opportunity, and the like) by applying the current state of the machine learning model 204 to the
training inputs 202. The comparator 208 may compare the predicted outputs 206 to actual
outputs 210 (e.g., a selected on-time payment, a selected deferred payment, a selected scheduled payment plan, a selected refinance opportunity, and the like) to determine an amount of error or differences. The actual outputs 210 may be determined based on historic data associated with the payoff strategy recommendations given to the user (e.g., determined by an enterprise manager or other specialist).
[0050] Training the machine learning model 204 with the data from the enterprise resources 130
allows the machine learning model 204 to learn, and benefit from, the interplay between the
current and future states of the user/entity and enterprise resource 130 data. For example, training the machine learning model to predict a future account balance with accounts receivable input data may result in improved accuracy of the future account balance. Conventional approaches may predict a future account balance information algorithmically, without consideration of other factors that may affect the future account balance such as accounts receivable data. Generally, machine learning models are configured to learn the dependencies between various inputs. Accordingly, the machine learning model 204 learns the dependencies between the enterprise resource 130 data and other data/factors of the user, resulting in improved predictions over predictions that are determined individually and/or independently.
The specification para 0051-0052 discloses the training of the model based on inputted data where the training includes mathematical calculations using such mathematical algorithms including back propagation, loss functions where the weights applied in the analysis are adjusted in order to reduce the difference between the predicted values which include error meet a determine threshold between actual values in order to optimize calculated results.
[0051] During training, the error (represented by error signal 212) determined by the comparator
208 may be used to adjust the weights in the machine learning model 204 such that the machine
learning model 204 changes (or learns) over time. The machine learning model 204 may be
trained using a backpropagation algorithm, for instance. The backpropagation algorithm operates
by propagating the error signal 212. The error signal 212 may be calculated each iteration (e.g.,
each pair of training inputs 202 and associated actual outputs 210), batch and/or epoch, and
propagated through the algorithmic weights in the machine learning model 204 such that the
algorithmic weights adapt based on the amount of error. The error is minimized using a loss
function. Non-limiting examples of loss functions may include the square error function, the
root mean square error function, and/or the cross-entropy error function.
[0052] The weighting coefficients of the machine learning model 204 may be tuned to reduce the
amount of error, thereby minimizing the differences between (or otherwise converging) the
predicted output 206 and the actual output 210. The machine learning model 204 may be trained
until the error determined at the comparator 208 is within a certain threshold ( or a threshold
number of batches, epochs, or iterations have been reached). The trained machine learning model 204 and associated weighting coefficients may subsequently be stored in memory 116 or other data repository (e.g., a database) such that the machine learning model 204 may be employed on unknown data (e.g., not training inputs 202). Once trained and validated, the machine learning model 204 may be employed during a testing (or an inference phase). During testing, the machine learning model 204 may ingest unknown data to predict future data (e.g., accounts receivable, accounts payable, 401k data, IRA data, account balance, and the like).
The specification discloses that the computer elements can comprise any number of computing systems without distinction over other systems in the art
[0018] Referring to FIG 1, a block diagram of a computing system 100 is shown, according to an
exemplary embodiment. In brief overview, the computing system 100 is shown to include a
provider computing system 110 communicably coupled to an artificial intelligence (Al) system
200, one or more enterprise resources 130, at least one entity computing system 140 (shown as
one entity computing system 140, but there may be any number of entity computing systems
140), and at least one third-party system 150. The computing system 100 may be affiliated with,
controlled or maintained by, or otherwise provided by a financial institution, such as a bank. As
described in greater detail below, the provider computing system 110 may be configured to
retrieve, from an enterprise resource 130 via the entity computing system 140, first data relating
to an input of a first entity (e.g., the first entity being associated with the entity computing system
140). For example, the input of the first entity may refer to a number of cars in stock at a motor
vehicle dealership. The provider computing system 110 may be configured to retrieve second
data relating to the input (e.g., from an enterprise resource 130 via the entity computing system
140 of an entity that may be a provider of the input). Continuing with the example where the
first entity is a motor vehicle dealership, the provider of the input may include a manufacturer, a
supplier, or a wholesaler of the cars and/or of one or more parts associated with the cars in stock
at the motor vehicle dealership. The provider computing system 110 may be configured to
identify third data relating to a financing of the input of the first entity (e.g., from the provider
computing system 110, from the entity computing system 140, from a third-party data source,
etc.). The provider computing system 110 may receive an update to the input (e.g., from the
entity computing system 140). Responsive to the update, the provider computing system 110
may be configured to determine a payoff plan for the updated input based on the first data, the
second data, and the third data (e.g., using the AI system 200). The provider computing system
110 may be further configured to cause implementation of the payoff plan automatically (e.g.,
through the entity computing system 140 associated with the first entity).
[0019] The provider computing system 110 is shown to include a controller 112. The controller
112 includes a processing circuit 114, having a processor 115 and a memory 116. The controller
112 may also include, and the processing circuit 114 may be communicably coupled to, a
communications interface 113 such that the processing circuit 114 may send and receive content
and data via the communications interface 113. As such, the controller 112 may be structured to
communicate via one or more networks 105 with other devices and/or applications. The
computing system 100 is shown to include the enterprise resources 130 including a plurality of
enterprise resource planning (ERP) applications 132, dealer management system (DMS)
applications 134, and point of sale (POS) applications 136. The computing system 100 is also
shown to include the entity computing system 140 accessing an enterprise resource 130 (which
may be one of the enterprise resources 130). In some embodiments, the controller 112, the
enterprise resources 130, and the entity computing system 140 may be communicably coupled
and configured to exchange data over the network 105, which may include one or more of the
Internet, cellular network, Wi-Fi, Wi-Max, a proprietary banking network, a proprietary retail or
service provider network, or other type of wired or wireless network. The controller 112 may be
configured to transmit, receive, exchange, or otherwise provide data to one or more of the
enterprise resources 130. The controller 112 is shown to include an application programming
interface (API) gateway circuit 119. The API gateway circuit 119 may be configured to facilitate
the transmission, receipt, and/or exchange of data between the controller 112 and the enterprise
resources 130.
-:
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-10 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 2 is directed toward input of first entity one or more units of input or bulk quantity input- limiting data received- insignificant activity. Dependent claim 3 is directed toward input of first entity comprises financing from an institution- limiting inputted data to financial data- insignificant extra solution activity. Dependent claim 4 is directed toward input identified as product identifier- limiting inputted data to identifiers- insignificant extra solution activity. Dependent claim 5 is directed toward second data content- non-functional descriptive subject matter. Dependent claim 6 is directed toward transmitting analytics – insignificant extra solution activity. Dependent claim 7 is directed toward first and second entities belong to same entity category- a business practice. Dependent claim 8 is directed toward analytics of entities comprises allowing second entities to filter analytics- data manipulation. Dependent claim 9 is directed toward contextual information- non-functional descriptive subject matter. Dependent claim 10 is directed toward analytics comprise industry performance, sales performance of product type, sales performance of product and financial report- non-functional descriptive subject matter.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-10 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claim(s) 11-19:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a system, as in independent Claim 11 and the dependent claims. Such systems fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The operations of system claim 11 corresponds to steps of method claim 1. Therefore, claim 11 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The operations of system claim 11 corresponds to steps of method claim 1. Therefore, claim 11 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a provider computing system comprising a processing circuit, comprising one or more processors and memory, the memory storing instructions–is purely functional and generic. Nearly every computer system for implementing a process will include a “processor” capable of performing the basic computer functions -of “receive”, “retrieve”, “identify”, “determine …by applying data to generate…plan” and “cause…implementation of pay off plan” - As a result, none of the hardware recited by the system claims offers a meaningful limitation beyond generally linking the use of the abstract idea to a particular technological environment, that is, implementation via computers.
The operations of system claim 11 corresponds to steps of method of claim 1. Therefore, claim 11 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
Evidence includes:
Electric Power Group;
With respect to the ML model the specification discloses:
“…applying the first data, second data and third data to one or more machine learning models trained to generate an optimized payoff plan…” (para 0003, 0006-0007)
“…Supervised learning is a method of training a machine learning model given input-output pairs. An input-output pair is an input with an associated known output (e.g., an expected output)., (para 0041)
The specification para 0042-0047 and 0049-0050 discloses the use that can be applied using a ML model in a business process and what data can be acted upon in the analysis
[0042] Machine learning model 204 may be trained on known input-output pairs such that the machine learning model 204 can learn how to predict known outputs given known inputs. Once the machine learning model 204 has learned how to predict known input-output pairs, the machine learning model 204 can operate on unknown inputs to predict an output.
[0043] The machine learning model 204 may be trained based on general data and/or granular data (e.g., data based on a specific user, data based on a specific entity, etc.) such that the machine learning model 204 may be trained specific to a particular user and/or entity.
[0044] Training inputs 202 and actual outputs 210 may be provided to the machine learning
model 204. Training inputs 202 may include accounts receivable data, accounts payable data, account balance data, liquid asset data, illiquid asset data, and the like. Actual outputs 210 may include payoff strategies (e.g., on-time payments, deferred payments, a scheduled payment plan, refinance opportunities, and the like), user feedback (e.g., whether a customer, customer relationship manager, or other specialist ranked ( or scored) the payment strategy as successful or unsuccessful, whether the customer, customer relationship manager, or the like ranked ( or scored) the payment strategy as aggressive, conservative or moderate), actual future accounts receivable data, actual future accounts payable data, actual future account balance data, actual future liquid asset data, actual future illiquid asset data, and the like.
[0045] The inputs 202 and actual outputs 210 may be received from historic enterprise resource 130 data from any of the data repositories. For example, a data repository of an enterprise resource 130 may contain an account balance of an entity one year ago. The data repository may also contain data associated with the same account six months ago and/or data associated with the same account currently. Thus, the machine learning model 204 may be trained to predict future account balance information (e.g., account balance information one year into the future or account balance information six months into the feature) based on the training inputs 202 and actual outputs 210 used to train the machine learning model 204.
[0046] In an embodiment, a first machine learning model 204 may be trained to predict data
associated with a payoff strategy for an entity based on current entity enterprise resource 130 data. For example, the first machine learning model 204 may use the training inputs 202 (e.g., accounts receivable data, accounts payable data, account balance data, liquid asset data, illiquid asset data, and the like.) to predict outputs 206 (e.g., future accounts receivable data, future accounts payable data, future account balance data, future liquid asset data, future illiquid asset data, and the like), by applying the current state of the first machine learning model 204 to the training inputs 202. The comparator 208 may compare the predicted outputs 206 to actual outputs 210 (e.g., actual future accounts receivable data, actual future accounts payable data, actual future account balance data, actual future liquid asset data, actual future illiquid asset data, and the like) to determine an amount of error or differences. For example, the future predicted accounts receivable data (e.g., predicted output 206) may be compared to the actual accounts receivable data (e.g., actual output 710).
[0047] In other embodiments, a second machine learning model 204 may be trained to generate one or more payment strategies for the entity based on the predicted data of the payoff strategy for the entity. For example, the second machine learning model 204 may use the training inputs 202 (e.g., future accounts receivable data, future accounts payable data, future account balance data, future liquid asset data, future illiquid asset data, and the like) to predict outputs 206 (e.g., a probable success of a predicted on-time payment, a probable success of a predicted deferred payment, a probable success of a predicted scheduled payment plan, a probable success of a predicted refinance opportunity, and the like) by applying the current state of the second machine learning model 204 to the training inputs 202. The comparator 208 may compare the predicted outputs 206 to actual outputs 210 (e.g., a selected on-time payment, a selected deferred payment, a selected scheduled payment plan, a selected refinance opportunity, and the like) to determine an amount of error or differences.
[0049] In some embodiments, a single machine leaning model 204 may be trained to make one or more recommendations to the user based on current user data received from enterprise resources 130. That is, a single machine leaning model may be trained using the training inputs 202 (e.g., accounts receivable data, accounts payable data, account balance data, liquid asset data, illiquid asset data, and the like) to predict outputs 206 (e.g., a probable success of a predicted on-time payment, a probable success of a predicted deferred payment, a probable success of a predicted scheduled payment plan, a probable success of a predicted refinance opportunity, and the like) by applying the current state of the machine learning model 204 to the training inputs 202. The comparator 208 may compare the predicted outputs 206 to actual outputs 210 (e.g., a selected on-time payment, a selected deferred payment, a selected scheduled payment plan, a selected refinance opportunity, and the like) to determine an amount of error or differences. The actual outputs 210 may be determined based on historic data associated with the payoff strategy recommendations given to the user (e.g., determined by an enterprise manager or other specialist).
[0050] Training the machine learning model 204 with the data from the enterprise resources 130 allows the machine learning model 204 to learn, and benefit from, the interplay between the current and future states of the user/entity and enterprise resource 130 data. For example, training the machine learning model to predict a future account balance with accounts receivable input data may result in improved accuracy of the future account balance. Conventional approaches may predict a future account balance information algorithmically, without consideration of other factors that may affect the future account balance such as accounts receivable data. Generally, machine learning models are configured to learn the dependencies between various inputs. Accordingly, the machine learning model 204 learns the dependencies between the enterprise resource 130 data and other data/factors of the user, resulting in improved predictions over predictions that are determined individually and/or independently.
The specification para 0051-0052 discloses the training of the model based on inputted data where the training includes mathematical calculations using such mathematical algorithms including back propagation, loss functions where the weights applied in the analysis are adjusted in order to reduce the difference between the predicted values which include error meet a determine threshold between actual values in order to optimize calculated results.
[0051] During training, the error (represented by error signal 212) determined by the comparator
208 may be used to adjust the weights in the machine learning model 204 such that the machine
learning model 204 changes (or learns) over time. The machine learning model 204 may be
trained using a backpropagation algorithm, for instance. The backpropagation algorithm operates
by propagating the error signal 212. The error signal 212 may be calculated each iteration (e.g.,
each pair of training inputs 202 and associated actual outputs 210), batch and/or epoch, and
propagated through the algorithmic weights in the machine learning model 204 such that the
algorithmic weights adapt based on the amount of error. The error is minimized using a loss
function. Non-limiting examples of loss functions may include the square error function, the
root mean square error function, and/or the cross-entropy error function.
[0052] The weighting coefficients of the machine learning model 204 may be tuned to reduce the
amount of error, thereby minimizing the differences between (or otherwise converging) the
predicted output 206 and the actual output 210. The machine learning model 204 may be trained
until the error determined at the comparator 208 is within a certain threshold ( or a threshold
number of batches, epochs, or iterations have been reached). The trained machine learning model 204 and associated weighting coefficients may subsequently be stored in memory 116 or other data repository (e.g., a database) such that the machine learning model 204 may be employed on unknown data (e.g., not training inputs 202). Once trained and validated, the machine learning model 204 may be employed during a testing (or an inference phase). During testing, the machine learning model 204 may ingest unknown data to predict future data (e.g., accounts receivable, accounts payable, 401k data, IRA data, account balance, and the like).
The specification discloses that the computer elements can comprise any number of computing systems without distinction over other systems in the art
[0018] Referring to FIG 1, a block diagram of a computing system 100 is shown, according to an
exemplary embodiment. In brief overview, the computing system 100 is shown to include a
provider computing system 110 communicably coupled to an artificial intelligence (Al) system
200, one or more enterprise resources 130, at least one entity computing system 140 (shown as
one entity computing system 140, but there may be any number of entity computing systems
140), and at least one third-party system 150. The computing system 100 may be affiliated with,
controlled or maintained by, or otherwise provided by a financial institution, such as a bank. As
described in greater detail below, the provider computing system 110 may be configured to
retrieve, from an enterprise resource 130 via the entity computing system 140, first data relating
to an input of a first entity (e.g., the first entity being associated with the entity computing system
140). For example, the input of the first entity may refer to a number of cars in stock at a motor
vehicle dealership. The provider computing system 110 may be configured to retrieve second
data relating to the input (e.g., from an enterprise resource 130 via the entity computing system
140 of an entity that may be a provider of the input). Continuing with the example where the
first entity is a motor vehicle dealership, the provider of the input may include a manufacturer, a
supplier, or a wholesaler of the cars and/or of one or more parts associated with the cars in stock
at the motor vehicle dealership. The provider computing system 110 may be configured to
identify third data relating to a financing of the input of the first entity (e.g., from the provider
computing system 110, from the entity computing system 140, from a third-party data source,
etc.). The provider computing system 110 may receive an update to the input (e.g., from the
entity computing system 140). Responsive to the update, the provider computing system 110
may be configured to determine a payoff plan for the updated input based on the first data, the
second data, and the third data (e.g., using the AI system 200). The provider computing system
110 may be further configured to cause implementation of the payoff plan automatically (e.g.,
through the entity computing system 140 associated with the first entity).
[0019] The provider computing system 110 is shown to include a controller 112. The controller
112 includes a processing circuit 114, having a processor 115 and a memory 116. The controller
112 may also include, and the processing circuit 114 may be communicably coupled to, a
communications interface 113 such that the processing circuit 114 may send and receive content
and data via the communications interface 113. As such, the controller 112 may be structured to
communicate via one or more networks 105 with other devices and/or applications. The
computing system 100 is shown to include the enterprise resources 130 including a plurality of
enterprise resource planning (ERP) applications 132, dealer management system (DMS)
applications 134, and point of sale (POS) applications 136. The computing system 100 is also
shown to include the entity computing system 140 accessing an enterprise resource 130 (which
may be one of the enterprise resources 130). In some embodiments, the controller 112, the
enterprise resources 130, and the entity computing system 140 may be communicably coupled
and configured to exchange data over the network 105, which may include one or more of the
Internet, cellular network, Wi-Fi, Wi-Max, a proprietary banking network, a proprietary retail or
service provider network, or other type of wired or wireless network. The controller 112 may be
configured to transmit, receive, exchange, or otherwise provide data to one or more of the
enterprise resources 130. The controller 112 is shown to include an application programming
interface (API) gateway circuit 119. The API gateway circuit 119 may be configured to facilitate
the transmission, receipt, and/or exchange of data between the controller 112 and the enterprise
resources 130.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 12-19 these dependent claim have also been reviewed with the same analysis as independent claim 11. Dependent claim 12 is directed toward input of first entity one or more units of input or bulk quantity input- limiting data received- insignificant activity. Dependent claim 13 is directed toward input of first entity comprises financing from an institution- limiting inputted data to financial data- insignificant extra solution activity. Dependent claim 14 is directed toward input identified as product identifier- limiting inputted data to identifiers- insignificant extra solution activity. Dependent claim 15 is directed toward second data content- non-functional descriptive subject matter. Dependent claim 16 is directed toward transmitting analytics – insignificant extra solution activity. Dependent claim 17 is directed toward first and second entities belong to same entity category- a business practice. Dependent claim 18 is directed toward transmitting analytics to entities comprises allowing second entities to filter analytics- data manipulation and insignificant extra solution activity. Dependent claim 19 is directed toward contextual information- non-functional descriptive subject matter.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 11. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 12-19 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claim(s) 20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a non-transitory computer readable medium, as in independent Claim 20. Such mediums fall under the statutory category of "manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The instructions of medium claim 20 corresponds to steps of method claim 1. Therefore, claim 20 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The instructions of medium claim 20 corresponds to steps of method claim 1. Therefore, claim 20 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a non-transitory computer readable medium storing instructions executed by one or more processors –is purely functional and generic. Nearly every non-transitory medium comprising instructions will include a “processor” capable of performing the basic computer instructions -of “receive”, “retrieve”, “identify”, “determine …by applying data to generate…plan” and “cause…implementation of pay off plan” - Even though the claim is directed to a manufacture, the claim is not "truly drawn to a specific" computer readable medium, but rather is directed toward storing instructions for collecting data that is analyze to produce a result related to a business practice (e.g. pay out plan for a financed product). The claimed subject matter has not met the burden to demonstrate that claim 20 is "truly drawn to a specific" computer readable medium, rather than to the underlying method of collecting and analyzing data in order to generate a pay off plan for a financed product. As a result, none of the hardware recited by the medium claim offers a meaningful limitation beyond generally linking the use of the abstract idea to a particular technological environment, that is, implementation via computers.
Since all computer instructions must be stored in a memory to be of any use in the real world, simply applying a manufacture does not distinguish the abstract idea into patent eligibility.
The operations of system claim 11 corresponds to steps of method of claim 1. Therefore, claim 11 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
Evidence includes:
Electric Power Group;
With respect to the ML model the specification discloses:
“…applying the first data, second data and third data to one or more machine learning models trained to generate an optimized payoff plan…” (para 0003, 0006-0007)
“…Supervised learning is a method of training a machine learning model given input-output pairs. An input-output pair is an input with an associated known output (e.g., an expected output)., (para 0041)
The specification para 0042-0047 and 0049-0050 discloses the use that can be applied using a ML model in a business process and what data can be acted upon in the analysis
[0042] Machine learning model 204 may be trained on known input-output pairs such that the
machine learning model 204 can learn how to predict known outputs given known inputs. Once
the machine learning model 204 has learned how to predict known input-output pairs, the
machine learning model 204 can operate on unknown inputs to predict an output.
[0043] The machine learning model 204 may be trained based on general data and/or granular
data (e.g., data based on a specific user, data based on a specific entity, etc.) such that the
machine learning model 204 may be trained specific to a particular user and/or entity.
[0044] Training inputs 202 and actual outputs 210 may be provided to the machine learning
model 204. Training inputs 202 may include accounts receivable data, accounts payable data,
account balance data, liquid asset data, illiquid asset data, and the like. Actual outputs 210 may
include payoff strategies (e.g., on-time payments, deferred payments, a scheduled payment plan,
refinance opportunities, and the like), user feedback (e.g., whether a customer, customer
relationship manager, or other specialist ranked ( or scored) the payment strategy as successful or unsuccessful, whether the customer, customer relationship manager, or the like ranked ( or
scored) the payment strategy as aggressive, conservative or moderate), actual future accounts
receivable data, actual future accounts payable data, actual future account balance data, actual
future liquid asset data, actual future illiquid asset data, and the like.
[0045] The inputs 202 and actual outputs 210 may be received from historic enterprise resource
130 data from any of the data repositories. For example, a data repository of an enterprise
resource 130 may contain an account balance of an entity one year ago. The data repository may
also contain data associated with the same account six months ago and/or data associated with
the same account currently. Thus, the machine learning model 204 may be trained to predict
future account balance information (e.g., account balance information one year into the future or
account balance information six months into the feature) based on the training inputs 202 and
actual outputs 210 used to train the machine learning model 204.
[0046] In an embodiment, a first machine learning model 204 may be trained to predict data
associated with a payoff strategy for an entity based on current entity enterprise resource 130
data. For example, the first machine learning model 204 may use the training inputs 202 (e.g.,
accounts receivable data, accounts payable data, account balance data, liquid asset data, illiquid
asset data, and the like.) to predict outputs 206 (e.g., future accounts receivable data, future
accounts payable data, future account balance data, future liquid asset data, future illiquid asset
data, and the like), by applying the current state of the first machine learning model 204 to the
training inputs 202. The comparator 208 may compare the predicted outputs 206 to actual
outputs 210 (e.g., actual future accounts receivable data, actual future accounts payable data,
actual future account balance data, actual future liquid asset data, actual future illiquid asset data, and the like) to determine an amount of error or differences. For example, the future predicted accounts receivable data (e.g., predicted output 206) may be compared to the actual accounts receivable data (e.g., actual output 710).
[0047] In other embodiments, a second machine learning model 204 may be trained to generate
one or more payment strategies for the entity based on the predicted data of the payoff strategy
for the entity. For example, the second machine learning model 204 may use the training inputs
202 (e.g., future accounts receivable data, future accounts payable data, future account balance
data, future liquid asset data, future illiquid asset data, and the like) to predict outputs 206 (e.g., a
probable success of a predicted on-time payment, a probable success of a predicted deferred
payment, a probable success of a predicted scheduled payment plan, a probable success of a
predicted refinance opportunity, and the like) by applying the current state of the second machine
learning model 204 to the training inputs 202. The comparator 208 may compare the predicted
outputs 206 to actual outputs 210 (e.g., a selected on-time payment, a selected deferred payment, a selected scheduled payment plan, a selected refinance opportunity, and the like) to determine an amount of error or differences.
[0049] In some embodiments, a single machine leaning model 204 may be trained to make one
or more recommendations to the user based on current user data received from enterprise
resources 130. That is, a single machine leaning model may be trained using the training inputs
202 (e.g., accounts receivable data, accounts payable data, account balance data, liquid asset
data, illiquid asset data, and the like) to predict outputs 206 (e.g., a probable success of a
predicted on-time payment, a probable success of a predicted deferred payment, a probable
success of a predicted scheduled payment plan, a probable success of a predicted refinance
opportunity, and the like) by applying the current state of the machine learning model 204 to the
training inputs 202. The comparator 208 may compare the predicted outputs 206 to actual
outputs 210 (e.g., a selected on-time payment, a selected deferred payment, a selected scheduled payment plan, a selected refinance opportunity, and the like) to determine an amount of error or differences. The actual outputs 210 may be determined based on historic data associated with the payoff strategy recommendations given to the user (e.g., determined by an enterprise manager or other specialist).
[0050] Training the machine learning model 204 with the data from the enterprise resources 130
allows the machine learning model 204 to learn, and benefit from, the interplay between the
current and future states of the user/entity and enterprise resource 130 data. For example, training the machine learning model to predict a future account balance with accounts receivable input data may result in improved accuracy of the future account balance. Conventional approaches may predict a future account balance information algorithmically, without consideration of other factors that may affect the future account balance such as accounts receivable data. Generally, machine learning models are configured to learn the dependencies between various inputs. Accordingly, the machine learning model 204 learns the dependencies between the enterprise resource 130 data and other data/factors of the user, resulting in improved predictions over predictions that are determined individually and/or independently.
The specification para 0051-0052 discloses the training of the model based on inputted data where the training includes mathematical calculations using such mathematical algorithms including back propagation, loss functions where the weights applied in the analysis are adjusted in order to reduce the difference between the predicted values which include error meet a determine threshold between actual values in order to optimize calculated results.
[0051] During training, the error (represented by error signal 212) determined by the comparator
208 may be used to adjust the weights in the machine learning model 204 such that the machine
learning model 204 changes (or learns) over time. The machine learning model 204 may be
trained using a backpropagation algorithm, for instance. The backpropagation algorithm operates
by propagating the error signal 212. The error signal 212 may be calculated each iteration (e.g.,
each pair of training inputs 202 and associated actual outputs 210), batch and/or epoch, and
propagated through the algorithmic weights in the machine learning model 204 such that the
algorithmic weights adapt based on the amount of error. The error is minimized using a loss
function. Non-limiting examples of loss functions may include the square error function, the
root mean square error function, and/or the cross-entropy error function.
[0052] The weighting coefficients of the machine learning model 204 may be tuned to reduce the
amount of error, thereby minimizing the differences between (or otherwise converging) the
predicted output 206 and the actual output 210. The machine learning model 204 may be trained
until the error determined at the comparator 208 is within a certain threshold ( or a threshold
number of batches, epochs, or iterations have been reached). The trained machine learning model 204 and associated weighting coefficients may subsequently be stored in memory 116 or other data repository (e.g., a database) such that the machine learning model 204 may be employed on unknown data (e.g., not training inputs 202). Once trained and validated, the machine learning model 204 may be employed during a testing (or an inference phase). During testing, the machine learning model 204 may ingest unknown data to predict future data (e.g., accounts receivable, accounts payable, 401k data, IRA data, account balance, and the like).
The specification discloses that the computer elements with respect to non-transitory computer readable mediums
[0019] The provider computing system 110 is shown to include a controller 112. The controller
112 includes a processing circuit 114, having a processor 115 and a memory 116. The controller
112 may also include, and the processing circuit 114 may be communicably coupled to, a
communications interface 113 such that the processing circuit 114 may send and receive content
and data via the communications interface 113. As such, the controller 112 may be structured to
communicate via one or more networks 105 with other devices and/or applications. The
computing system 100 is shown to include the enterprise resources 130 including a plurality of
enterprise resource planning (ERP) applications 132, dealer management system (DMS)
applications 134, and point of sale (POS) applications 136. The computing system 100 is also
shown to include the entity computing system 140 accessing an enterprise resource 130 (which
may be one of the enterprise resources 130). In some embodiments, the controller 112, the
enterprise resources 130, and the entity computing system 140 may be communicably coupled
and configured to exchange data over the network 105, which may include one or more of the
Internet, cellular network, Wi-Fi, Wi-Max, a proprietary banking network, a proprietary retail or
service provider network, or other type of wired or wireless network. The controller 112 may be
configured to transmit, receive, exchange, or otherwise provide data to one or more of the
enterprise resources 130. The controller 112 is shown to include an application programming
interface (API) gateway circuit 119. The API gateway circuit 119 may be configured to facilitate
the transmission, receipt, and/or exchange of data between the controller 112 and the enterprise
resources 130.
[0080] An exemplary system for implementing the overall system or portions of the
embodiments might include a general purpose computing computers in the form of computers,
including a processing unit, a system memory, and a system bus that couples various system
components including the system memory to the processing unit. Each memory device may
include non-transient volatile storage media, non-volatile storage media, non-transitory storage
media (e.g., one or more volatile and/or non-volatile memories), etc. In some embodiments, the
non-volatile media may take the form of ROM, flash memory (e.g., flash memory such as
NAND, 3D NAND, NOR, 3D NOR, etc.), EEPROM, MRAM, magnetic storage, hard discs,
optical discs, etc. In other embodiments, the volatile storage media may take the form of RAM,
TRAM, ZRAM, etc. Combinations of the above are also included within the scope of machine readable
media. In this regard, machine-executable instructions include, for example, instructions
and data which cause a general purpose computer, special purpose computer, or special purpose
processing machines to perform a certain function or group of functions. Each respective
memory device may be operable to maintain or otherwise store information relating to the
operations performed by one or more associated circuits, including processor instructions and
related data (e.g., database components, object code components, script components, etc.), in
accordance with the example embodiments described herein.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over CA 3207828 A1 by Ambrose et al. (Ambrose), and further in view of CA 2074337 A1 by Highbloom et al (Highbloom)
In reference to Claim 1:
Ambrose teaches:
(Currently Amended) A method ((Ambrose) in at least para 0040) comprising:
retrieving, by one or more first servers of a provider computing system via a first connection from an application programming interface (API) of one or more second servers associated with a first entity, first data relating to one or more inventory items of the first entity, the first data including a current amount of the one or more inventory items ((Ambrose) in at least FIG. 1; FIG. 5; para 0025-0026 wherein the prior art teaches a first user to initiate a transaction via the first user interface and the interface may present user profile; para 0029, para 0048, para 0051, para 0057 wherein the prior art teaches input data include factors such as item categories associated with items and teaches multiple item involved in transaction where item factors do not indicate transaction likely, para 0124, para 0133, para 0196 wherein the prior art teaches stored data accessible and may be extracted from databases to predict trends and behavior patterns, para 0198, para 0245-0246 wherein the prior art teaches inventory management services which provide inventory tracking and reporting including data associated with a quantity of each item the merchant has available in inventory, para 0254, para 0262, para 0279-0281, para 0319, para 0327, para 0335 wherein the prior art teaches inventory database storing data associated with a quantity of each item merchant has available to the merchant);
retrieving, by the provider computing system via one or more second connections from one or more second APIs of one or more third servers associated with providers of the one or more inventory items, second data relating to the one or more inventory items and including a product or product type corresponding to the one or more inventory items ((Ambrose) in at least FIG. 1; FIG. 5; para 0022 wherein the prior art teaches generating of shared channel which can include data including item type within which multi-user transaction can be facilitated; para 0027, para 0048 wherein the prior art teaches a private and open/public API(s), para 0051, para 0054-0055 wherein the prior art teaches model utilizing transaction data including item types that have been predetermined and item types determined based on user input data, para 0063 wherein the prior art teaches generation of channel for receiving data including item type and other transaction data, para 0079 wherein the prior art teaches generation of shared channel include item type, para 0124, para 0133, para 0175 wherein the prior art teaches machine learning model utilized data indicating geolocation item type and other conditions, para 0198-0199 wherein the prior art teaches training dataset can include item types, para 0242, para 0246, para 0254, para 0279-0281, para 0319, para 0327);
identifying, by the provider computing system, third data of the one or more first servers, the third data relating to a financing of the one or more inventory items of the first entity ((Ambrose) in at least para 0025-0026, para 0029, para 0051, para 0054-0056-0058, para 0062, para 0074, para 0080, para 0096-0097, para 0124, para 0199-0200, para 0226-0229, para 0246, para 0254, para 0319, para 0348);
receiving, by the provider computing system via the first connection from the one or more second servers, an update to the one or more inventory items upon a sale of at least one inventory item or the one or more inventory items ((Ambrose) in at least para 0023, para 0030, para 0036, para 0057-0059 wherein the prior art teaches once transaction has been identified selectable element may be updated, customized or modified for example additional user or product may be added, para 0065, para 0081-0082 wherein the prior art teaches interface may be updated to include transaction status indicator such as details of transaction, item details, item portions, payment details, installment information);
in response to receiving the update upon the sale of the at least one inventory item of the one or more inventory items, determining, by the provider computing system, a payoff plan for the at least one item of the one or more inventory items, according to the first data, the second data, and the third data, the provider computing system determining the payoff plan by applying the first data, the second data, and the third data to one or more machine learning models trained to generate an optimized payoff plan ((Ambrose) in at least FIG. 3B wherein the prior art illustrates timeline slider, price of item, number of installments options to input and amount of payment for payoff; FIG. 6A-B wherein the prior art illustrates identifying participants, requesting/retrieving participant behavior, determine installment offers based on data for each participant; FIG. 9; para 0023-0025, para 0032-0033, para 0037, para 0045, para 0048, para 0054-0056, para 0070-0072, para 0083, para 0081-0082 wherein the prior art teaches interface may be updated to include transaction status indicator such as details of transaction, item details, item portions, payment details, installment information. para 0090-0091, para 0108, para 0116, para 0175, para 0200); and
causing, by the provider computing system, implementation of the payoff plan automatically through a computing system of the first entity ((Ambrose) in at least FIG. 6A-B; para 0033, para 0072, para 0090, para 0142-0156).
Although the prior art Ambrose does not differentiate data second data or second from one or more second servers or third data from one or more third servers, the prior art does teach different data from different sources received via different plurality of data. Accordingly the prior art provides some teaching, suggestion or motivation and based on knowledge generally available that different servers of different data sources are commonly applied to send data for input, making it obvious to one of ordinary skill in the art to arrive to the claimed limitation with a reasonable expectation of success. (see KSR).
Highbloom teaches and provides supporting evidence:
retrieving, by one or more first servers of a provider computing system … first data relating to one or more inventory items of the first entity the first data including a …[listing] of the one or more inventory items ((Highbloom) in at least Abstract; page 3 lines 15-35, page 4 lines 5-16, page 8 lines 25- page 9 lines 1-3, lines 22-25, page 12 lines 12-119 page 20 lines 9-32, page 22 lines 21-25 wherein the prior art teaches tabling listing makes/model retrieved from memory, page 26 lines 1-page 27 lines 1-14 wherein the prior art teaches listing of all models of products stored within memory)
retrieving, by the provider computing system via one or more second connections … with providers of the one or more inventory items, second data relating to the one or more inventory items and including a product or product type corresponding to the one or more inventory items ((Highbloom) in at least Abstract; page 3 lines 15-35, page 4 lines 17-30, page 5 lines 5-9, page 7 lines 25-page 8 lines 1-9, page 17 lines 8-24, page 20 lines 9-30, page 20 lines 13-19, page 22 lines 21-25 wherein the prior art teaches table listing makes/model retrieved from memory) ;
identifying, by the provider computing system, third data of the one or more first servers, the third data relating to a financing of the one or more inventory items of the first entity ((Highbloom) in at least Abstract; page 4 lines 15-30, page 21 lines 10-21);
receiving, by the provider computing system via the first connection from the one or more second servers, an update to the one or more inventory items upon a sale of at least one inventory item of the one or more inventory items ((Highbloom) in at least page 17 lines 1-7, page 19 lines 18-22, page 20 lines 33-page 21 lines 1-9, page 22 lines 21-25 wherein the prior art teaches table listing makes/model retrieved from memory)
According to KSR, known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives of other market forces if the variations are predictable to one of ordinary skill in the art. The scope and content of the prior art references Ambrose and Highbloom both refer to financing products and that the financing can be between business (Ambrose para 0226-0227) and/or customers and therefore include similar financing requirements such as financial data collection related to products financed and other data for analyzing financial data, however Highbloom focuses on dealership floor plan financing where the prior art Ambrose focuses on traditional financing between entities. The prior art Highbloom provides design incentives/market forces for financing which requires the item financed to be paid off when the merchant has financed the item sold which provides incentives to incorporate the teaching of Ambrose with Highbloom to analyze financial data related to the item of Highbloom on options for paying of an item with a loan. Accordingly, the differences between the claimed invention and the prior art were encompassed in known variations or in a principle known in the prior art. Based on the teaching of the prior art references and common sense rationale of applying known variations to modify a process is obvious, one of ordinary skill in the art, in view of the identified design incentives or other market forces, could have implemented the claimed variation of the prior art, and the claimed variation would have been predictable to one of ordinary skill in the art.
Both Ambrose and Highbloom teach collecting and inputting for analysis loan related data and loan related repayment data. Highbloom teaches the motivation receiving financial information from different sources so that the financing information can be compared in order to identify particular items/inventory items financed that are secured as collateral and its financial debt status can be verified, so as to determine after an item is sold in order to determine whether a debt is repaid. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify and identify the data source of the data collected for input in loan repayment analysis of Ambrose to include specific sources for loan related financial information of Highbloom since Highbloom teaches the motivation receiving financial information from different sources so that the financing information can be compared in order to identify particular items/inventory items financed that are secured as collateral and its financial debt status can be verified, so as to determine after an item is sold in order to determine whether a debt is repaid.
In reference to Claim 2:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 1. Ambrose further discloses the limitations of dependent claim 2
(Currently amended) The method of claim 1 (see rejection of claim 1 above),
wherein the one or more inventory items of the first entity further comprises at least one of one or more individual units of the one or more inventory items or a bulk quantity of the one or more inventory items((Ambrose) in at least FIG. 1; FIG. 5; para 0025-0026 wherein the prior art teaches a first user to initiate a transaction via the first user interface and the interface may present user profile; para 0029, para 0048, para 0051, para 0057 wherein the prior art teaches input data include factors such as item categories associated with items and teaches multiple item involved in transaction where item factors do not indicate transaction likely, para 0124, para 0133, para 0196 wherein the prior art teaches stored data accessible and may be extracted from databases to predict trends and behavior patterns, para 0198, para 0245-0246 wherein the prior art teaches inventory management services which provide inventory tracking and reporting including data associated with a quantity of each item the merchant has available in inventory, para 0198).
In reference to Claim 3:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 1. Ambrose further discloses the limitations of dependent claim 3
(Currently amended) The method of claim 1, wherein the financing of the one or more inventory items of the first entity (see rejection of claim 1 above) further comprises
at least one of financing from a provider institution or a third-party financial institution ((Ambrose) in at least para 0024, para 0032, para 0221, para 0227).
In reference to Claim 4:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 1. Ambrose further discloses the limitations of dependent claim 4
(Currently Amended) The method of claim 1, wherein the one or more inventory items (see rejection of claim 1 above),
Ambrose does not explicitly teach:
are identified by a serial number or a vehicle identification number (VIN).
Highbloom teaches:
are identified by a serial number or a vehicle identification number (VIN). ((Highbloom) in at least page 8 lines 25-page 9 lines 1-3, page 20 lines 10-21)
According to KSR, simple substitution is an obvious common sense rationale. The prior art Ambrose contained a first data for input that differed from the claimed first data input by the substitution of specific data for input with generic first data inputted. The prior art Highbloom provides evidence that such data for input and its use is known in the art in financial analysis. Accordingly, based on the teaching of both Ambrose and Highbloom, one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable.
Both Ambrose and Highbloom teach collecting financing of items related data. Highbloom teaches the motivation of the first data included in the financing information for financing sources to be a unique identification code (VIN) which is associated with the item financed in order to be able to compare the item uniquely identified with similar financed items which is acquired when processing the financing of a loan and in the determination of whether the loan has been paid. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify and identify the first data received for input in loan repayment analysis of Ambrose to include a VIN as taught by Highbloom since Highbloom teaches the motivation of the first data included in the financing information for financing sources to be a unique identification code (VIN) which is associated with the item financed in order to be able to compare the item uniquely identified with similar financed items which is acquired when processing the financing of a loan and in the determination of whether the loan has been paid.
In reference to Claim 5:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 1. Ambrose further discloses the limitations of dependent claim 5
(Currently Amended) The method of claim 1, wherein the third data (see rejection of claim 1 above) further comprises
at least one of an early payoff, an outstanding loan, and a turnaround related to the input. ((Ambrose) in at least FIG. 3A-B; para 0088-0099, 0187, para 0346)
In reference to Claim 6:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 1. Ambrose further discloses the limitations of dependent claim 6
(Original) The method of claim 1 (see rejection of claim 6 above), wherein the method further comprises
transmitting, by the provider computing system, analytics based on the first data and the second data to one or more second entities, the one or more second entities each having an account enrolled at a provider institution ((Ambrose) in at least para 0063-0064, para 0221, para 0237, 0239).
In reference to Claim 7:
The combination of Ambrose and Highbloom discloses the limitations of dependent claim 6. Ambrose further discloses the limitations of dependent claim 7
(Original) The method of claim 6 (see rejection of claim 6 above),
wherein the one or more second entities and the first entity belong to a same entity category.((Ambrose) in at least FIG. 6B wherein the prior art illustrates process entailing user A and B for determining installment plan; para 0012, para 0023 wherein the prior art teaches multi-user transaction with shared cart joining transaction; para 0026-0028)
In reference to Claim 8:
The combination of Ambrose and Highbloom discloses the limitations of dependent claim 6. Ambrose further discloses the limitations of dependent claim 8
(Original) The method of claim 6 (see rejection of claim 6 above), wherein transmitting the analytics to the one or more second entities further comprises
allowing the one or more second entities to, upon receiving the analytics based on the first data and the second data, filter the analytics based on contextual information. ((Ambrose) in at least para 0023, para 0027, para 0055, para 0057, para 0124-0125, para 0174, para 0199)
In reference to Claim 9:
The combination of Ambrose and Highbloom discloses the limitations of dependent claim 8. Ambrose further discloses the limitations of dependent claim 9
(Currently Amended) The method of claim 8 (see rejection of claim 8 above), wherein the contextual information further comprises
at least one of an entity category, a geographical region, an inventory item category, or a particular inventory item. ((Ambrose) in at least para 0022 wherein the prior art teaches generating of shared channel which can include data including item type within which multi-user transaction can be facilitated; para 0023, para 0027, para 0054-0055 wherein the prior art teaches model utilizing transaction data including item types that have been predetermined and item types determined based on user input data, para 0057, para 0063 wherein the prior art teaches generation of channel for receiving data including item type and other transaction data, para 0079 wherein the prior art teaches generation of shared channel include item type, para 0124-0125, para 0133, para 0174, para 0175 wherein the prior art teaches machine learning model utilized data indicating geolocation item type and other conditions, para 0198-0199 wherein the prior art teaches training dataset can include item types)
In reference to Claim 11:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 11.
The provider computing system claim 11 functional processes correspond to the method steps of method claim 1. The additional limitations recited in claim 10 that go beyond the limitations of claim 1 include the provider computing system components to perform the operation that correspond to claim 1 include the structure comprising:
processing circuit comprising one or more processors and memory, the memory storing instructions that, when executed, cause the processing circuit ((Ambrose) in at least FIG. 10-14; para 0204-0205; para 0245-0246, para 0257, para 0299) to perform the operations corresponding to claim 1.
Therefore, claim 11 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 12:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 11. Ambrose further discloses the limitations of dependent claim 12
The operations of Machine claim 12 corresponds to steps of method claim 2. Therefore, claim 12 has been analyzed and rejected as previously discussed with respect to claim 2
In reference to Claim 13:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 11. Ambrose further discloses the limitations of dependent claim 13
The operations of Machine claim 13 corresponds to steps of method claim 3. Therefore, claim 13 has been analyzed and rejected as previously discussed with respect to claim 3
In reference to Claim 14:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 11. Ambrose further discloses the limitations of dependent claim 14
The operations of Machine claim 14 corresponds to steps of method claim 4. Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 4.
In reference to Claim 15:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 11. Ambrose further discloses the limitations of dependent claim 15
The operations of Machine claim 15 corresponds to steps of method claim 5. Therefore, claim 15 has been analyzed and rejected as previously discussed with respect to claim 5
In reference to Claim 16:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 11. Ambrose further discloses the limitations of dependent claim 16
The operations of Machine claim 16 corresponds to steps of method claim 6. Therefore, claim 16 has been analyzed and rejected as previously discussed with respect to claim 6
In reference to Claim 17:
The combination of Ambrose and Highbloom discloses the limitations of dependent claim 16. Ambrose further discloses the limitations of dependent claim 17
The operations of Machine claim 17 corresponds to steps of method claim 7. Therefore, claim 17 has been analyzed and rejected as previously discussed with respect to claim 7
In reference to Claim 18:
The combination of Ambrose and Highbloom discloses the limitations of dependent claim 16. Ambrose further discloses the limitations of dependent claim 18
The operations of Machine claim 18 corresponds to steps of method claim 8. Therefore, claim 18 has been analyzed and rejected as previously discussed with respect to claim 8
In reference to Claim 19:
The combination of Ambrose and Highbloom discloses the limitations of dependent claim 18. Ambrose further discloses the limitations of dependent claim 19
The operations of Machine claim 19 corresponds to steps of method claim 9. Therefore, claim 19 has been analyzed and rejected as previously discussed with respect to claim 9
In reference to Claim 20:
The combination of Ambrose and Highbloom discloses the limitations of independent claim 20.
The instructions of manufacture claim 20 processor executed operations correspond to the method steps of method claim 1. The additional limitations recited in claim 20 that go beyond the limitations of claim 1 include the non-transitory computer-readable medium storing instructions executed by one or more processors of a processing circuit ((Ambrose) in at least FIG. 11; para 0046, para 0048, para 0093, para 0246)to perform the operation that correspond to claim 1 . Therefore, claim 20 has been analyzed and rejected as previously discussed with respect to claim 1.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over CA 3207828 A1 by Ambrose et al. (Ambrose), in view of CA 2074337 A1 by Highbloom et al (Highbloom) as applied to claim 6 above, and further in view of Floor Plan Lending by Comptroller’s Handbook (Handbook)
In reference to Claim 10:
The combination of Ambrose and Highbloom discloses the limitations of dependent claim 6. Ambrose further discloses the limitations of dependent claim 10
(Original) The method of claim 6 (see rejection of claim 6 above), wherein the analytics further comprise
Ambrose does not explicitly teach:
at least one of an industry performance, a product-type sales performance, a product sales performance, and a financial report.
Handbook teaches:
at least one of an industry performance, a product-type sales performance, a product sales performance, and a financial report.((Handbook) in at least page 6 “Advance Rates” wherein the prior art teaches measuring dealers average monthly sales performance in units under dealer agreement.)
Both Ambrose and Handbook are directed toward financial lending endeavors where financial data is collected in order to determine the credit risk which includes transaction history (see Ambrose para 0228) and Handbook page 6. Handbook teaches the motivation that determining the “rate of travel” helps determine the typical inventory, average cost of each unit in order to determine the amount to lend, which requires a payoff based on agreements when products are sold. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the data collected and analyzed for determining a payoff installment plan criteria as taught by Ambrose to include data such as “product-type sales performance or a product sales performance” for example as taught by Handbook since Handbook teaches the motivation that determining the “rate of travel” helps determine the typical inventory, average cost of each unit in order to determine the amount to lend which requires a payoff based on agreements when products are sold.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Patent No. 12,045,875 B2 by Tomich; WO 2007/017874 A2 by Shavit et al.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 MARY M GREGG whose telephone number is (571)270-5050. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Behncke can be reached at 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARY M GREGG/Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695