DETAILED ACTION
Notice of Pre-AIA or AIA Status
[1] The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
[2] This communication is in response to the amendment filed 29 January 2026. It is noted that this application benefits from Provisional Patent Application Serial No. 63/117,843 filed 24 November 2020. Claims 16 and 20 have been cancelled. Claims 1, 5-7, 11, 14, 17, and 18 have been amended. Claims 1-15 and 17-19 are pending.
Response to Remarks/Amendment
[3] Applicant's remarks filed 29 January 2026 have been fully considered and are addressed as follows:
[i] In response to rejection(s) of claim(s) 1-15 and 17-19 under 35 U.S.C. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 29 September 2025, Applicant provides the following remarks:
"… Applicant directs discussion of the rejection under 35 U.S.C. 101 to Step 2A Prong Two of the Alice/Mayo test…a memorandum provided by the Commissioner for Patents, dated August 4, 2025…provides that ‘the analysis should take into consideration all the claim limitations and how these limitations interact and impact each other when evaluating whether the exception is integrated into a practical application. The August 4th Memo further provides that an important consideration in determining whether a claim improves technology or a technical field is "the extent to which the claim covers a particular solution to a problem or a particular way to achieve the desired outcome, as opposed to merely claiming the idea of a solution or outcome’… "
Applicant further remarks:
“…the present specification describes the invention ‘such that the improvement would
be apparent to one of ordinary skill in the art" by providing a detailed discussion that "identifies
a technical problem’ and ‘identifies technical improvements realized by the claim over the prior
art’… Paragraph [0045] provides a specific example that ‘leads and showroom visit datasets are typically populated by data predominantly input by employees of a dealership," and "it is often the case that a sale is recorded without an accompanying data point from one of these pre-sale steps that technically must occur.’…Paragraph [0045] of the specification further explains that ‘[e]very sale technically has a lead associated with it; however, if an employee doesn't record what that lead is, in a vacuum, instances can and do arise where sales came from leads.’”
Applicant remarks:
"…Thus, the specification identifies the technical problem as relating to data inconsistencies
that lead to inaccurate, unreliable product information predictions using conventional means. The
specification further sufficiently identifies a specific means to realize the technical improvements
realized by the claim, namely the use of training a plurality of machine-learned models on
differing subsets of data from a training dataset that allow for a model with the highest accuracy
to be selected to provide predictions for a particular outcome variable… "
Applicant additionally remarks:
"…As explained in the August 4th Memo, the claimed invention covers a particular solution
to the stated problem and a particular way to achieve the desired outcome. Applicant is not
merely claiming the idea of a solution or outcome. Rather, Applicant claims, among other
limitations, training a plurality of MLMs on different subsets of data to identify and use a most
accurate MLM to generate a predicted outcome variable based on historic outcome variables.
One skilled in the art would readily understand that the claimed invention and the specification are directed to maximizing the accuracy of predictive models by using differing subsets of data
from a dataset for each of the MLMs that are trained to predict the outcome variable… "
In response, Examiner respectfully disagrees. With respect to considerations under Eligibility Step 2A prong 2: (See MPEP 2106.04(d)):
As presented by amendment, additional elements of claim 1 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject matter include: “computer system comprising one or more processors” and the “machine learning models” including the training and testing processes. Dependent claims 5-11 further include “a first user interface” and a “second user interface”. Claims 14-15 and 17-19 further include “a memory” and “computer-executable instructions”.
Claim 1 as presented by amendment includes a further step engaging the previously recited “first MLM” by further specifying “…generating, by inputting actual values of the first outcome variable over an immediately preceding period of time into the first MLM, a predicted value of the first outcome variable at a future point in time…”.
With respect to the identification of the machine learning models, Examiner notes the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register on 17 July 2024. In particular, Examiner respectfully maintains that Example 47, claim 2 is operative in the maintained rejection of the amended claims. While Applicant notes that, as per the August 4th memo, claim 2 was found not to be directed to an abstract idea based on an absence of a recitation of mathematics associated with the use and training of the recited machine learning models. However, the claims as presented recite limitations which direct the claimed invention to mental processes and/or organization of human activities under Step 2A prong 1. Thus, mathematics associated with the MLM processes are not required at Step 2A prong 1.
With respect to the limitations preform using or by the recited MLMs, the instant recitations of “training machine learning models” and “testing machine learning models” are analogous to the training of an artificial neural network based on input data and receiving continuous training data of Examiner 47. The amended limitation specifying “…inputting actual values of the first outcome variable over an immediately preceding period of time into the first MLM, a predicted value of the first outcome variable…”, is limited to a designation of inputs, i.e., actual values of an outcome variable, and recitation of output, i.e., predicted value of the first outcome variable, absent any specificity as to functions provided by the generic MLM model. Reasonably, the training data and feedback data are limited to mere data gathering and generating an output at a high level of generality and, by extension, are reasonably understood to constitute insignificant extra solution activity (See MPEP 2106.05(g)). The recited training process is limited to a recitation of the inputs and outputs to be applied to an undefined training process absent any technical specificity regarding actual training and/or testing. As the amended limitation present MLM functions in a unspecified manner and is limited to recitation of inputs and outputs to the unspecified model, the newly added limitation presents an additional example of a generic use of a machine learning model akin to the using the model of Example 47. Accordingly, the recited machine-learning processes and associated training/testing are reasonably understood to be generic/known machine-learning models/algorithms as the limitations fail to specify any technical steps in obtaining the results other than to state that general models are trained and tested for relative accuracy.
[ii] Applicant’s remarks directed to previous rejection(s) of claim(s) 1-15 and 17-19 as under 35 U.S.C. 103 as being unpatentable as set forth in the previous Office Action mailed 29 September 2025 have been fully considered and are moot in light of newly added grounds of rejection presented below.
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.
[4] Previous rejection(s) of claims 1-15 and 17-19 under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly has/have not been overcome by the amendments to the subject claims and is/are maintained. The revised statement of rejection presented below is necessitated by amendment and addresses the present amendments to the pending claims.
The following analysis is based on the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024 and further clarified in the Reminders on Evaluating Subject Matter Eligibility of claims under 35 U.S.C. 101 guidance memorandum published 4 August 2025. Claim(s) 1-15 and 17-19 as a whole is/are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might serve to impede, rather than promote, innovation. Still, inventions that integrate the building blocks of human ingenuity into something more by applying the abstract idea in a meaningful way are patent eligible (See MPEP 2106.04).
Consistent with the findings of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ineligible abstract ideas are defined in groups, namely: (1) Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations; (2) Mental Processes (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions); and (3) Certain Methods of Organizing Human Activity. Groupings of Certain Methods of Organizing Human Activity include three sub-categories within the group, namely: (1) fundamental economic principles or practices; (2) commercial or legal interactions (e.g., agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); (3) managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions) (See MPEP 2106.04(a).
Eligibility Step 1: Four Categories of Statutory Subject Matter (See MPEP 2106.03): Independent claims 1, 14, and 18 are directed to a method, a system, and non-transitory computer-readable storage medium, respectively, and are reasonably understood to be properly directed to one of the four recognized statutory classes of invention designated by 35 U.S.C. 101; namely, a process or method, a machine or apparatus, an article of manufacture, or a composition of matter. While the claims, generally, are directed to recognized statutory classes of invention, each of method/process, system/apparatus claims, and computer-readable media/articles of manufacture are subject to additional analysis as defined by the Courts to determine whether the particularly claimed subject matter is patent-eligible with respect to these further requirements. In the case of the instant application, each of claims 1, 14, and 18 are determined to be directed to ineligible subject matter based on the following analysis/guidance:
Eligibility Step 2A prong 1: (See MPEP 2106.04): In reference to claim 1, the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do/does not amount to significantly more than an abstract idea. The claim(s) is/are directed to the abstract idea of predicting commercial outcomes given a set of product and sales inputs using generic models, which is reasonably considered to be method of Organizing Human Activity. In particular, the general subject matter to which the claims are directed consist of a sequence of steps intended to predict a quantity of vehicles sold given a set of inputs including vehicle information, inventory, and/or customer visits and responses to advertisements and marketing efforts using generic models and to further adjust/train the predictive models, which is an ineligible concept of Organizing Human Activity, namely: commercial interactions (e.g., agreements in the form of advertising, marketing, or sales activities or behaviors, and business relations).
In support of Examiner’s conclusion, Examiner respectfully directs Applicant’s attention to the claim limitations of representative claim 1. In particular, claim 1 as presented by amendment includes:
“…determining…a first outcome variable to be predicted by a…model…the first outcome variable associated with a product…”, “…testing, using historical data that identifies historical values for the first outcome variable, each…[model]…to determine an accuracy for each [model]…”, and “…identifying, for use in making predictions, a first [model] based on the testing…”
Considered in light of the supportive disclosure, the claimed outcome variables are reasonably understood to be forms of commercial outcomes such as predicted vehicle demand or sales and the recited inputs are reasonably understood to be consumer -related marketing inputs, such as customer interactions with marketing efforts, e.g., dealership visits etc. Accordingly, considered as an ordered combination, the steps/functions of claim 1 are reasonably considered to be representative of the inventive concept and are further reasonably understood to be series of actions or activities directed to a general process of predicting commercial outcomes given a set of product and sales inputs using generic models, which is an ineligible concept of Organizing Human Activity, namely: commercial interactions (e.g., advertising, marketing, or sales activities or behaviors, and business relations, e.g., predicting or forecasting vehicle sales based on marketing inputs such as showroom visits) (See MPEP 2106.04(a)(2)).
Further limitations are directed to ineligible processes/functions which are performable by Human Mental Processing and/or or by a human using pen and paper (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011).
The courts have previously identified subject matter limited to steps/processes performable by Human Mental Processing and/or by a human using pen and paper to be ineligible abstract ideas (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011). Lastly, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for a recitation of generic computer components, then the claim is still to be grouped as a mental process unless the limitation cannot practically be performed in the human mind (See MPEP 2106.04(a)(2)).
With respect to functions/steps limited to processes performable by Human Mental Processing and/or by a human using pen and paper, representative claim 1 as presented by amendment recites:
“…determining…a first outcome variable to be predicted by a…model…the first outcome variable associated with a product…”, “…testing, using historical data that identifies historical values for the first outcome variable, each…[model]…to determine an accuracy for each [model]…”, and “…identifying, for use in making predictions, a first [model] based on the testing…”
Respectfully, absent further clarification of the processing steps executed by the recited computer system and associated machine learning processes, one of ordinary skill in the art would readily be relied upon to determine a target outcome variable, observe accuracies of a number of generic models applied to inputs to predict the outcome variable, and identify a most accurate model to predict the outcome variable by employing the human mental processing (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011) (“a method that can be performed by human thought alone is merely an abstract idea and is not patent eligible under 35 U.S.C 101).
The technical elements identified in claim 1 are limited to the recited: “computer system comprising one or more processors” and the “machine learning models” including the training and testing processes. Dependent claims 5-11 further include “a first user interface” and a “second user interface”. Claims 14-17 and 18-20 further include “a memory” and “computer-executable instructions”.
With respect to these potential additional elements, the claimed “computer system/processes” is/are identified as determining variables to be predicted by a machine-learning model. The claimed “machine-learning models” are identified as being trained and tested using input variables to generate/predict an outcome variable. The “interfaces” and associated “display device” are identified as generating imagery that identifies values of the outcome variables. The “memory” is identified as being coupled to the processors and the “executable-instructions” are identified in a general manner as executing the recited functions/steps of each of the independent claims. These technical elements and the recited functions constitute technical features which have been considered at each step of Examiner’s analysis but are determined to constitute generic computing structures executing generic computing functions previously identified by the courts, as further analyzed under Step 2A prong 2 and Step 2B below.
Eligibility Step 2A prong 2: (See MPEP 2106.04(d)): Under step 2A prong two, Examiners are to consider additional elements recited in the claim beyond the judicial exception and evaluate whether those additional elements integrate the exception into a practical application. Further, to be considered a recitation of an element which integrates the judicial exception into a practical application, the additional elements must apply, rely on, or use the judicial exception in a manner that imposes meaningful limits on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
Additional elements of claim 1 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject matter include: “computer system comprising one or more processors” and the “machine learning models” including the training and testing processes. Dependent claims 5-11 further include “a first user interface” and a “second user interface”. Claims 14-15 and 17-19 further include “a memory” and “computer-executable instructions”.
Claim 1 as presented by amendment includes a further step engaging the previously recited “first MLM” by further specifying “…generating, by inputting actual values of the first outcome variable over an immediately preceding period of time into the first MLM, a predicted value of the first outcome variable at a future point in time…”.
With respect to the identification of the machine learning models, Examiner notes the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register on 17 July 2024. In particular, Examiner respectfully directs Applicant’s attention to Example 47, claim 2. Specifically, the instant recitations of “training machine learning models” and “testing machine learning models” are analogous to the training of an artificial neural network based on input data and receiving continuous training data of Examiner 47. The amended limitation specifying “…inputting actual values of the first outcome variable over an immediately preceding period of time into the first MLM, a predicted value of the first outcome variable…”, is limited to a designation of inputs, i.e., actual values of an outcome variable, and recitation of output, i.e., predicted value of the first outcome variable, absent any specificity as to functions provided by the generic MLM model. Reasonably, the training data and feedback data are limited to mere data gathering and generating an output at a high level of generality and, by extension, are reasonably understood to constitute insignificant extra solution activity (See MPEP 2106.05(g)). The recited training process is limited to a recitation of the inputs and outputs to be applied to an undefined training process absent any technical specificity regarding actual training and/or testing. AS the amended limitation present MLM functions in a unspecified manner and is limited to recitation of inputs and outputs to the unspecified model, the newly added limitation presents an additional example of a generic use of a machine learning model akin to the using the model of Example 47. Accordingly, the recited machine-learning processes and associated training/testing are reasonably understood to be generic/known machine-learning models/algorithms as the limitations fail to specify any technical steps in obtaining the results other than to state that general models are trained and tested for relative accuracy.
With respect to the above noted functions attributable to the identified additional elements, MPEP 2106.05 stipulates that: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f); Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g); and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) serve as indications that the use of the technology recited does not indicate integration into a practical application of the judicial exception.
With respect to these potential additional elements, the claimed “computer system/processes” is/are identified as determining variables to be predicted by a machine-learning model. The claimed “machine-learning models” is identified as being trained and tested using input variables to generate/predict an outcome variable. The “interfaces” and associated “display device” are identified as generating imagery that identifies values of the outcome variables. The “memory” is identified as being coupled to the processors and the “executable-instructions” are identified in a general manner as executing the recited functions/steps of each of the independent claims.
Each of the above noted limitations states a result (e.g., variables are determined, generic models are tested to predict outcomes, data outputs are displayed, data and instructions are stored etc.) as associated with a respective “processor”, “machine learning model” or “instructions and memory”. Beyond the general statement that models are trained, processes/determination are made “by a processor”, and predictions are made and displayed, the limitations provide no further clarification with respect to the functions performed by the “computer system/processor” and “training of models” in producing the claimed result. A recitation of “by a computer system” or “by a model”, absent clarification of particular processing steps executed by the underlying technology to produce the result are reasonably understood to be an equivalent of “apply it”. The identified functions performed by the recited technology are limited to: (1) receiving and sending data via a computer network (e.g., requests, predictions and input and outcome values); (2) storing and retrieving information and data from a generic computer memory (e.g., models and data); and (3) performing mental observations and judgements using the obtaining information/data (e.g., determining outcome variables and observing tested accuracies of models and identifying a model based on the observed/relative accuracies of the models) (See MPEP 2106.05(f)).
Accordingly, claim 1 is reasonably understood to be conducting standard, and formally manually performed process of predicting commercial outcomes given a set of product and sales inputs by identifying a most accurate predictive model based on testing and an observed relative accuracy of generic predictive models and applying the most accurate model product and sales inputs. The identified functions of the recited additional elements reasonably constitute a general linking of the abstract idea to a generic technological environment, e.g., generic devices capable of processing and displaying data. The claimed predicting commercial outcomes given a set of product and sales inputs by identifying a most accurate predictive model based on testing and an observed relative accuracy of generic predictive models and applying the most accurate model product and sales inputs benefits from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception.
Eligibility Step 2B: (See MPEP 2106.05): Analysis under step 2B is further subject to the Revised Examination Procedure responsive to the Subject Matter Eligibility Decision in Berkheimer v. HP, Inc. issued by the United States Patent and Trademark Office (19 April 2018). Examiner respectfully submits that the recited uses of the underlying computer technology constitute well-known, routine, and conventional uses of generic computers operating in a network environment. In support of Examiner’s conclusion that the recited functions/role of the computer as presented in the present form of the claims constitutes known and conventional uses of generic computing technology, Examiner provides the following:
In reference to the Specification as originally filed, Examiner notes paragraphs [0069]-[0075]. In the noted disclosure, the Specification provides listings of generic computing systems, e.g., a general computing platform including exemplary servers, network configurations and various processor configuration which are identified as capable and interchangeable for performing the disclosed processes. The disclosure does not identify any particular modifications to the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that this disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
While the above noted disclosure serves to provide sufficient explanation of technical elements required to perform the inventive method using available computing technology, the disclosure does not appear to identify any particular modifications or inventive configurations of the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that the disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Further, absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
The claims specify that the above identified generic computing structures and associated functions/routines include: a “computer system/processes” determining variables to be predicted by a machine-learning model, “machine-learning models” being trained and tested using input variables to generate/predict an outcome variable, “interfaces” and associated “display devices” generating imagery that identifies values of the outcome variables, and “executable-instructions” are identified in a general manner as executing the recited functions/steps of each of the independent claims. Claim 1 as presented by amendment includes a further step engaging the previously recited “first MLM” by further specifying “…generating, by inputting actual values of the first outcome variable over an immediately preceding period of time into the first MLM, a predicted value of the first outcome variable at a future point in time…”. The amended limitation is limited to a designation of inputs, i.e., actual values of an outcome variable, and recitation of output, i.e., predicted value of the first outcome variable, absent any specificity provided regarding functions performed by the generic MLM model. While Examiner acknowledges that the noted limitations are computer-implemented, Examiner respectfully submits that, in aggregate (e.g., “as a whole”) they do not amount to significantly more than the abstract idea/ineligible subject matter to which the claimed invention is primarily directed.
While utilizing a computer, the claimed invention is not rooted in computer technology nor does it improve the performance of the underlying computer technology. The computer-implemented features of the claimed invention noted above are reasonably limited to: (1) receiving and sending data via a computer network (e.g., requests, predictions and input and outcome values); (2) storing and retrieving information and data from a generic computer memory (e.g., models and data); and (3) performing mental observations and judgements using the obtaining information/data (e.g., determining outcome variables and observing tested accuracies of models and identifying a model based on the observed/relative accuracies of the models) (See MPEP 2106.05(f)).
The above listed computer-implemented functions are distinguished from the generic data storage, retrieval, transmission, and data manipulation/processing capacities of the generic systems identified in the Specification solely by the recited identification of particular data elements that are of utility to a user performing the specific method of predicting commercial outcomes given a set of product and sales inputs by identifying a most accurate predictive model based on testing and an observed relative accuracy of generic predictive models and applying the most accurate model product and sales inputs. In summary, the computer of the instant invention is facilitating non-technical aims, i.e., predicting commercial outcomes given a set of product and sales inputs by identifying a most accurate predictive model based on testing and an observed relative accuracy of generic predictive models and applying the most accurate model product and sales inputs, because it has been programmed to store, retrieve, and transmit specific data elements and/or instructions that is/are of utility to the user. The non-technical functions of predicting commercial outcomes given a set of product and sales inputs by identifying a most accurate predictive model based on testing and an observed relative accuracy of generic predictive models and applying the most accurate model product and sales inputs benefit from the use of computer technology, but fail to improve the underlying technology.
In support, the courts have previously found that utilization of a computer to receive or transmit data and communications over a network and/or employing generic computer memory and processor capacities store and retrieve information from a computer memory are insufficient computer-implemented functions to establish that an otherwise unpatentable judicial exception (e.g. abstract idea) is patent eligible. With respect to the determinations of the Courts regarding using a computer for sending and receiving data or information over a computer network and storing and retrieving information from computer memory, see at least: 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; sending messages over a network OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); receiving and sending information 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); 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 and see performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) with respect to the performance of repetitive calculations does not impose meaningful limits on the scope of the claims.
Independent claims 14 and 18, directed to an apparatus/system and computer-executable instructions stored on computer-readable media for performing the method steps are rejected for substantially the same reasons, in that the generically recited computer components in the apparatus/system and computer readable media claims add nothing of substance to the underlying abstract idea.
Dependent claims 2-13, 15, 17, and 19 when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claimed invention is not directed to an abstract idea.
In accordance with all relevant considerations and aligned with previous findings of the courts, the technical elements imparted on the method that would potentially provide a basis for meeting a “significantly more” threshold for establishing patent eligibility for an otherwise abstract concept by the use of computer technology fail to amount to significantly more than the abstract idea itself. For further guidance and authority, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al. 573 U.S.____ (2014)) (See MPEP 2106).
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.
[5] Claim(s) 1, 12, 14-15, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable Price et al. (United States Patent Application Publication No. 2021/0350307 hereinafter “Price”) in view of Joseph et al. (United States Patent Application Publication No. 2020/0184494 hereinafter ‘Joseph’), and further in view of Padmanabhan et al. (United States Patent Application Publication No. 2021/0103858 hereinafter ‘Padmanabhan’).
With respect to (currently amended) claim 1, Price discloses method comprising: determining, by a computer system comprising one or more processor devices, a first outcome variable to be predicted by a machine-learning model (MLM), the first outcome variable associated with a product (Price et al.; paragraphs [0015] [0046]-[0047] [0052]; See at least training a machine learning model to recommend sales associated for a vehicle sale with the objective of maximizing customer satisfaction and sales); training, using product information that comprises values for each of a plurality of different input variables, a plurality of MLMs to predict the first outcome variable, each MLM utilizing a different set of input variables of the plurality of different input variables (Price et al.; paragraphs [0009] [0046]-[0048] [0052]; See at least training of models based on a selection of inputs. Each iteration and use of different inputs reasonably constitute generating a plurality of models based on distinct inputs); testing, using historical data that identifies historical values for the outcome variable, each MLM to determine an accuracy for each MLM (Price et al.; paragraphs [0047] [0052]; See at least accuracy and switching or adapting models based on accuracy); and identifying, for use in making predictions, a first MLM based on the testing (Price et al.; paragraphs [0052] [0072]; See at least selection of model based on accuracy of recommending sale associate to maximize customer satisfaction and sales).
With respect to training and testing a plurality of MLMs, while Examiner notes that Price discloses an iterative training process using distinct input sets to generate multiple iterations of a machine-learning model, which are reasonably a form of multiple machine-learning models, Price fails to expressly state that a plurality of machine learning models are tested.
However, as evidenced by Joseph, it is well-known in the art to test multiple different machine learning models, determine a most accurate model to predict a specified commercial outcome variable, e.g., forecasted product demand, and apply the best suited model to input datasets to predict the target outcome (Joseph et al.; paragraphs [0019] [0033] [0037]-[0040]; See at least model testing of multiple machine learning models and selection of most accurate/best model to predict product demand).
Claim 1 has been amended to further recite elements of previously presented claim 6 including “…inputting actual values of the first outcome variable over an immediately preceding period of time into the first MLM…” and further modifies the noted limitation of previous claim 6 to further applying the inputted actual values in a step “…generating, by inputting actual values of the first outcome variable over an immediately preceding period of time into the first MLM, a predicted value of the first outcome variable at a future point in time…”.
With respect to this element, Price discloses generating and training machine-learning models to predict an outcome variable of maximizing customer satisfaction and sales (Price et al.; paragraphs [0015] [0046]-[0047] [0052]; See at least training a machine learning model to recommend sales associated for a vehicle sale with the objective of maximizing customer satisfaction and sales). While the iterative variations of the model of Price utilize differing input data variables/set to generate a prediction of an outcome variable in the form of customer satisfaction/sales, Price fails to include a step in which value of an outcome in a historical period preceding a present period are input to generate a prediction of the outcome variable, e.g., sales, for a future defined time period.
However, as evidenced by Padmanabahn, it is well-known in the art to auto-select machine learning models from a plurality of candidate models based on a tested predictive accuracy of the models with respect to defined outcome. In particular, Padmanabhan discloses generating, by inputting actual values of the first outcome variable over an immediately preceding period of time into the first MLM, a predicted value of the first outcome variable at a future point in time (Padmanabhan et al.; paragraphs [0014] [0050]-[0051] [0054] [0057] [0063]-[0064]; See at least candidate models trained and tested for accuracy in predicting an event outcome using distinct data categories. See further input conditions and historical, i.e., preceding, outcome of events as inputs to the models to predict outcome, e.g., sales, as a future date range).
Regarding the combination that includes Joseph, it would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the iterative development and training of machine-learning models of Price by further including the well-known process of comparatively testing multiple types of machine-learning models to predict a same commercial outcome and select a best model for the relevant commercial outputs based on the observed testing results as taught by Joseph. The instant invention is directed to a system and method of predicting successful vehicle sales practices based on known marketing inputs. As Price discloses the use of generating predictive outputs based on machine-learning models in the context of a system and method for predicting successful vehicle sales practices based on known marketing inputs and Joseph similarly discloses the utility of comparatively testing multiple types of machine-learning models to predict a same commercial outcome in the context of a system and method for predicting commercial product demand based on known marketing inputs, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by the simple substitution of one known element for another to obtain the predictable result of improving commercial marketing and sales efforts and overall business efficiency by proportionally and optimally directing business resources based on optimized predictive business forecasts.
Regarding the combination that further includes Padmanabahn, it would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the iterative development and training of machine-learning models of Price by further including the well-known process of comparatively testing multiple types of machine-learning models to predict an event outcome from different data categories and utilize the most accurate predictive models to predict the outcome at a future time based on input known outcome for past/preceding events as taught by Padmanabahn. The instant invention is directed to a system and method of predicting successful vehicle sales practices based on known marketing inputs. As Price discloses the use of generating predictive outputs based on machine-learning models in the context of a system and method for predicting successful vehicle sales practices based on known marketing inputs and Padmanabahn similarly discloses the utility of utilizing the most accurate predictive models to predict the outcome at a future time based on input known outcome for past/preceding events in the context of a system and method for predicting commercial product demand based on known marketing inputs, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by the simple substitution of one known element for another to obtain the predictable result of improving commercial marketing and sales efforts and overall business efficiency by proportionally and optimally directing business resources based on optimized predictive business forecasts.
With respect to claim 12, Price discloses method wherein the product information comprises customer leads information comprising a plurality of customer lead variables comprising, for each respective customer lead of a plurality of customer leads, one or more of: a vehicle information variable that identifies a vehicle associated with the respective customer lead, a customer lead date variable that identifies a date of the customer lead, and a sold date variable that identifies a date the customer corresponding to the customer lead purchased the vehicle (Price et al.; paragraphs [0035]-[0038]; See at least customer visits and lead information. See further customer vehicle preference information).
Claims 14-15 and 18-19 substantially repeat the subject matter addressed above with respect to claim 1 as directed to the enabling system. With respect to these elements, Price discloses enabling the disclosed method employing analogous systems and executable instructions (See at least Price et al. paragraphs [0031]-[0033]). Accordingly, claims 14-15 and 18-19 are rejected under the applied teachings, conclusions obviousness, and rationale to modify as discussed above with respect to claim 1.
[6] Claim(s) 2-7, 10-11, 13, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price et al. (United States Patent Application Publication No. 2021/0350307 hereinafter “Price”) in view of Joseph et al. (United States Patent Application Publication No. 2020/0184494 hereinafter ‘Joseph’), and further in view of Padmanabhan et al. (United States Patent Application Publication No. 2021/0103858 hereinafter ‘Padmanabhan’), as applied to claim 1 above, and further in view of Zackrone (United States Patent Application Publication No. 2018/0285925).
With respect to claim 2, Price discloses a method wherein the product information comprises inventory information comprising a plurality of vehicle inventory variables comprising, for each respective vehicle of a plurality of vehicles, one or more of: a year variable that identifies a manufacture year of the respective vehicle, a make variable that identifies a make of the respective vehicle, a model variable that identifies a model of the respective vehicle, trim information that identifies a trim of the respective vehicle, vehicle identification number (VIN) information that identifies a VIN of the respective vehicle, color information that identifies a color of the respective vehicle, transmission information that identifies a transmission of the respective vehicle, a dealership variable that identifies a dealership of the respective vehicle, a condition variable that identifies a new or used condition of the respective vehicle, a drivetrain variable that identifies a drivetrain of the respective vehicle, a fuel type variable that identifies a fuel type of the respective vehicle, a price variable that identifies a price of the respective vehicle, and a lowest advertised price variable that identifies a lowest advertised price of the respective vehicle (Price et al.; paragraphs [0043]-[0046]; See at least vehicle information including inventory by dealership).
With respect to the outcome variable comprising a quantity of vehicles sold for a vehicle dealership at a future date, Price discloses an outcome of customer satisfaction and likelihood of a particular sale associated making a future sale with a particular customer. While Price is predicting a sale, Price fails to apply the model to calculating a quantity of vehicles sold at a future date.
However, as evidenced by Zackrone, it is well-known in the art to apply predictive forecasting models to customer/audience lead generation data such as showroom or website visit numbers to predict of forecast future vehicle sales over a defined time period (Zackrone; paragraphs [0040]-[0043]; See at least predictive modelling/forecasting of sales over defined time period. See output of quantity of sales that are expected at an end time associated with a time duration).
Regarding the combination that further includes Zackrone, it would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the predictive outputs based on machine-learning models of Price by further including the well-known utilization of forecasting models to predict a number of relevant commercial outputs based on marketing and sales activities as taught by Zackrone. The instant invention is directed to a system and method of predicting successful vehicle sales practices based on known marketing inputs. As Price discloses the use of generating predictive outputs based on machine-learning models in the context of a system and method for predicting successful vehicle sales practices based on known marketing inputs and Zackrone similarly discloses the utility of utilizing of forecasting models to predict a number of relevant commercial outputs based on marketing and sales activities in the context of a system and method for predicting successful vehicle sales practices based on known marketing inputs, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by the simple substitution of one known element for another to obtain the predictable result of improving vehicle sales performance and profitability by accurately identifying a gap between marketing and lead generation and a defined sales goal (Zackrone; paragraph [0024]).
With respect to claim 3, Price discloses a method wherein the inventory information comprises information about vehicle inventory at a plurality of different dealerships (Price et al.; paragraphs [0043]-[0050]; See at least dealership information and selection of dealership having vehicle information corresponding to customer preferences).
With respect to claim 4, Price discloses a method wherein the product information comprises only the inventory information (Price et al.; paragraphs [0043]-[0046]; See at least vehicle information from inventory).
With respect to claims 5-11, while Price discloses generating and training machine-learning models to predict/recommend sales associated with vehicles to maximize customer satisfaction with a future sale and displaying the output data on interfaces associated with customers and/or dealerships, Price fails to provide inputs/forecasts defining sales objectives or goals for a future defined time period.
However, with respect to (currently amended) claim 5, Zackrone discloses a method further comprising: receiving, by the computer system, a request for a prediction of the first outcome variable at a future point in time (Zackrone; paragraphs [0036]-[0040]; See at least inputs of sales goals defined by a duration of time); generating first user interface imagery that includes information that identifies the actual values of the first outcome variable over the immediately preceding period of time and that identifies the predicted value of the first outcome variable at the future point in time (Zackrone; paragraphs [0031] [0032] [0036] [0040]; See at least forecasts generated for specified time periods by first computing device); and presenting the first user interface imagery on a display device (Zackrone; paragraphs [0032] [0040]; See at least generation of data online via website, i.e., imagery).
With respect to claim 5 and the combination that includes Zackrone, the conclusions of obviousness and rationale to modify as applied to claim 2 above are applicable to claim 5 and are herein incorporated by reference.
With respect to (currently amended) claim 6, Zackrone discloses a method further comprising: prior to generating the predicted value, determining the actual values of the first outcome variable over the immediately preceding period of time (Zackrone; paragraphs [0031]-[0032] [0036]; See at least generation of sales forecasts in an ongoing manner utilizing actual data preceding a current time and forecasted data to a defined end time).
With respect to claim 6 and the combination that includes Zackrone, the conclusions of obviousness and rationale to modify as applied to claim 2 above are applicable to claim 6 and are herein incorporated by reference.
With respect to (currently amended) claim 7, Zackrone discloses a method further comprising: receiving, from the first MODULE/MODEL, a plurality of predicted values of the first outcome variable, the plurality of predicted values corresponding to a plurality of future points in time between a current point in time and the future point in time (Zackrone; paragraphs [0031]-[0036]; See at least generation of forecasts covering time intervals), and wherein the first user interface imagery identifies the plurality of predicted values and identifies the plurality of future points in time (Zackrone; paragraphs [0031] [0036] [0040]; See at least forecasts generated for future time intervals).
With respect to claim 7 and the combination that includes Zackrone, the conclusions of obviousness and rationale to modify as applied to claim 2 above are applicable to claim 7 and are herein incorporated by reference.
With respect to claim 10, Zackrone discloses a method wherein the first outcome variable comprises a quantity of vehicles sold, and further comprising: inputting, by the computer system, the predicted value of the quantity of vehicles sold into a showroom visits goal MODULE/MODEL trained to predict a value for a showroom visits goal outcome variable that identifies a quantity of showroom visits necessary to sell a designated quantity of vehicles (Zackrone; paragraphs [0024]-[0025] [0049]-[0051]; See at least predictions of sales goal achievability based on audience goals as measured by showroom and website visits); receiving, from the showroom visits goal MODULE/MODEL, a predicted showroom visits goal value based on the predicted value of the quantity of vehicles sold (Zackrone; paragraphs [0024]-[0025] [0050][0051]; See at least correlation between audience measures, i.e., visitation, and sales); generating second user interface imagery that includes information that identifies the predicted showroom visits goal value (Zackrone; paragraphs [0024]-[0025] [0050][0051]; See at least correlation between audience measures, i.e., visitation, and sales); and presenting the second user interface imagery on the display device (Zackrone; paragraphs [0032] [0040]; See at least generation of data online via website, i.e., imagery).
With respect to claim 10 and the combination that includes Zackrone, the conclusions of obviousness and rationale to modify as applied to claim 2 above are applicable to claim 10 and are herein incorporated by reference.
With respect to (currently amended) claim 11, Zackrone discloses a method further comprising: receiving, by the computer system, a request for a prediction of a showroom visits outcome variable at the future point in time (Zackrone; paragraphs [0049] [0050]; See at least cacluation of audience gaols and visitor volume); determining actual values that identify showroom visits over the immediately preceding period of time (Zackrone; paragraphs [0025] [0031] [0032] [0050]; See at least audience/visit goals. See inputting of audience data by time intervals); inputting the actual values that identify showroom visits over the immediately preceding period of time into a predicted showroom visits MODULE/MODEL trained to predict a quantity of showroom visits at the future point in time (Zackrone; paragraphs [0025] [0031] [0032] [0050]; See at least audience/visit goals. See inputting of audience data by time intervals); receiving, from the predicted showroom visits MODULE/MODEL, a predicted showroom visits value (Zackrone; paragraphs [0024]-[0025] [0050]-[0051]; See at least audience goals predicted visitation); generating second user interface imagery that includes information that identifies actual values of the showroom visits over the immediately preceding period of time and that identifies the predicted showroom visits value at the future point in time (Zackrone; paragraphs [0050] [0051]; See at least calculation of audience gap based on actual visits and goal visits); and presenting the second user interface imagery on the display device (Zackrone; paragraphs [0032] [0040]; See at least generation of data online via website, i.e., imagery).
With respect to claim 11 and the combination that includes Zackrone, the conclusions of obviousness and rationale to modify as applied to claim 2 above are applicable to claim 11 and are herein incorporated by reference.
With respect to claim 13, Price discloses an outcome of customer satisfaction and likelihood of a particular sale associated making a future sale with a particular customer. While Price is predicting a sale, Price fails to apply the model to calculating a quantity of vehicles sold at a future date.
However, Zackrone discloses a method wherein the first outcome variable comprises one of a quantity of sales, a quantity of showroom visits, a quantity of leads, and a quantity of web page activity (Zackrone; paragraphs [0050] [0051]; See at least sales forecasts and correlated audience goals).
With respect to claim 13 and the combination that includes Zackrone, the conclusions of obviousness and rationale to modify as applied to claim 2 above are applicable to claim 13 and are herein incorporated by reference.
[7] Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Price et al. (United States Patent Application Publication No. 2021/0350307 hereinafter “Price”) in view of Joseph et al. (United States Patent Application Publication No. 2020/0184494 hereinafter ‘Joseph’), and further in view of Padmanabhan et al. (United States Patent Application Publication No. 2021/0103858 hereinafter ‘Padmanabhan’), in view of Zackrone (United States Patent Application Publication No. 2018/0285925), as applied to claims 2/5 above, and further in view of Examiner’s Official Notice.
With respect to claim 8, while both Price and Zackrone disclose displaying output forecasts/metrics on a display, neither specify a graph.
However, Examiner takes Official Notice that it is well-known in the art to utilize a graph or chart to represent data over a timeline.
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the display of predictive outputs based on machine-learning models of Price by further including the well-known data display format of a graph as evidenced by Examiner’s Official Notice. One of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by the simple substitution of one known element for another to obtain the predictable result of improving clarity of a data presentation using a well-known visual display technique to present a timeline of data.
With respect to claim 9, Price discloses a method wherein the first user interface imagery further comprises information that identifies a target goal of the quantity of vehicles sold for a month, a predicted quantity of vehicles to be sold for the month and an actual quantity of vehicles sold for the month on a current date (Zackrone; paragraphs [0032] [0036]-[0040] [0042]; See at least setting sales goals for defined time periods).
Conclusion
[8] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cited NON-PATENT Literature:
Kulkarni et al., Fusion of Selection Parameters for Customer Behavior Prediction based on Web Usage Mining, 2020-04-01, 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA) (2020, Page(s): 198-201): Relevant Teachings: Kulkarni discloses a system/method that analyzes customer clickstream data/web activity to predict purchasing patterns. The publication establishes at least analysis of customer behavior in online retail environments is commonly applied to direct marketing and sales lead strategies.
Cited PATENT Literature:
Patel et al., MACHINE LEARNING MODEL SELECTION AND EXPLANATION FOR MULTI-DIMENSIONAL DATASETS, United States Patent Application Publication No. 2022/0067591, paragraphs [0014]-[0015] [0079] [0084]: Relevant Teachings: Patel discloses a system/method that includes steps/functions accessing different dimensions of training data t assess performance of candidate MLMs with respect to accessed training data.
Talagala et al., MACHINE LEARNING ABSTRACTION, United States Patent Application Publication No. 2019/0108417, paragraphs [0050]-[0056]: Relevant Teachings: Talagala discloses a system/method that includes steps/functions training a plurality of MLMs and further assessing the accuracy of trained MLMs with respect to subsets of input data.
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 ROBERT D RINES whose telephone number is (571)272-5585. The examiner can normally be reached M-F 9am - 5pm.
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/ROBERT D RINES/Primary Examiner, Art Unit 3625
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