DETAILED ACTION
Status of the Application
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 4, 2025, has been entered.
In the response, the Applicant amended claims 1, 10, 11, and 20. Claims 1-20 are pending and currently under consideration for patentability.
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 .
Response to Amendments and Arguments
v Applicant’s arguments, with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered and are not persuasive. The rejections of claims 1-20 under 35 U.S.C. 101 have been maintained accordingly.
Applicant specifically argues that
1) “The Claims are Patent Eligible because the Claims Do Not Recite an Abstract Idea The Office Action alleges that the claims "fall within the 'certain methods of organizing human activity' subject matter grouping of abstract ideas… preprocessing and generating feature sets from heterogenous data does not involve any type of fundamental economic principle or practice or any type of commercial or legal interaction. For at least these reasons, the claims cannot be considered to recite a certain method of organizing human activity"
Examiner respectfully disagrees with Applicant’s first argument.
Generating a set of offer recommendations for a plurality of users (e.g., based on a set of generated features based on interaction data of the users) and generating and/or updating a model configured to generate uplift scores (a measure of impact of purchasing probability of users due to offers being presented) using a generated training dataset, are both advertising, marketing, or sales activities. Generating a set of offers of items (e.g., item discounts) is undeniably an advertising, marketing, or sales activities. Generating and updating a model that is configured to generate uplift scores for users is also an advertising, marketing, or sales activity, as the purpose of such a model is to identify optimal users to provide with offer recommendations (as evidenced by Applicant’s specification and claim 3). Doing so can help increase or maximize revenue for the entity generating the offer recommendations (e.g., per [0068] & [0072] & [0083] of Applicant’s published disclosure). The steps of preprocessing the obtained data, generating the set of features, and generating the training data set is part of this process. These steps are not “additional” elements in the claims, but are rather data manipulation/processing substeps of the overall business process. That the model is required to be a machine-learning model provides nothing more than mere instructions to implement an abstract idea on a generic computer and further serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. The focus of the claim as a whole is directed to a result or effect that itself is the abstract idea.
Applicant specifically argues that
2) “As noted above, the claims recite steps of "preprocess[ing] the set of heterogeneous data to obtain a reduced dataset having a common data format," and "generat[ing], using the reduced dataset, a set of features for a training dataset," which Applicant submits cannot practically be performed in the human mind. Accordingly, the claims do not recite any type of mental process and do not recite an abstract idea.
Examiner respectfully disagrees with Applicant’s second argument.
Applicant’s argument is conclusory, and is therefore not persuasive. Furthermore, a human being is capable of “preprocessing” a set of heterogenous data (e.g., using an “aggregation function” based on recency and frequency of interaction data of the plurality of historical users) to obtain a reduced dataset having a common data format. This amounts merely to performing calculations on the data to obtain a result (e.g., count values, sums, etc.). Furthermore, there is no limit on the amount of data being preprocessed.
Applicant specifically argues that
3) “…even if the claims are considered to recite an abstract idea (which is not conceded here), Applicant submits that the claims integrate any would-be abstract idea into a practical application…As explained in the Specification, with respect to obtaining and processing interaction data using machine-learning techniques, "due to the data being collected from disparate data sources and being heterogeneous in content, format, and type, AI-backed methods are not efficient, may require high processing power, and may not yield accurate results." See Specification para. [0003]… these improvements are recited in the claims, in particular in "preprocess[ing] the set of heterogenous data comprises generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users," and "generat[ing], using the reduced dataset, a set of features for a training dataset, the set of features generated based on the interaction data of the plurality of historical users." See amended independent claim 1. Accordingly, the claimed technology provides a technical improvement to conventional systems by enabling the automatic processing and formatting of heterogenous data
Examiner respectfully disagrees with Applicant’s third argument.
Applicant’s specification, claims, and arguments all refer to the “disparate data” that is “heterogenous” and of a different “data format” and/or “type” at an extremely high level of generality. There are little to no specifics given regarding what these formats are or how they are different, what their “types” are or how they are different, etc.. The claims themselves place no restriction on the heterogenous data being preprocessed, other than that it corresponds to “a plurality of historical users”, and has a “plurality of data formats”. Due, in part, to this lack of specificity, the validity of Applicant’s general assertion that this makes AI-backed methods “not efficient” or that they “may require high processing power” is indeterminate. This is a very high-level problem characterized with sweeping generalizations.
The alleged solution to this alleged problem is similarly broad and lacking is specificity. The solution to the problem alleged above appears to amount simply to pre-processing the data (i.e., don’t just use the raw data for generating recommendations of training/retraining a model). However, the specification provides a huge variety of potential pre-processing alternatives that are themselves described at a very high level of generality (e.g., “normalizing” it, “extracting features” from it, looking for patterns, finding a frequency of occurrences of features, “using data aggregators”, etc.). The claimed “solution” comprises a seemingly arbitrary pre-processing alternative from the specification, and is simply “preprocessing” such that the data now has a “common data format” and comprises “using an “aggregation function” based on recency and frequency of interaction data of the plurality of historical users”. No additional details are recited regarding the aggregation function or how the data is reduced to a common format. The specification appears to generally suggest (e.g., due to the lack of detail given) that “data aggregators such as a recency aggregator(s), a frequency aggregator(s), a change in frequency aggregator(s)” ([0054] of the published disclosure) were known pre-processing tools. Paragraph [0056] of the published disclosure at most suggests that “The data aggregators can include functions, operators, models, and/or objects that roll up and/or aggregate features based on a criterion (e.g., recency, frequency, etc.). For example, the data aggregators can include a recency aggregator that indicates a time since the last occurrence of a feature. In another example, the data aggregators can include a count aggregator that indicates the number of occurrences of a feature in a predetermined and/or selected time interval. In another example, the data aggregators can include a delta count aggregator that indicates a difference in the number of occurrences of a feature in a first time period compared to a number of occurrences of a feature in a second time period”. In other words, an aggregator simply counts a number of occurrences of one or more features in data, perhaps using with respect to different time windows. Due to the lack of details, particularly within the claim language itself, simply “preprocessing” such that the data now has a “common data format” and “using an “aggregation function” based on recency and frequency of interaction data of the plurality of historical users” does not appear to necessarily provide a solution to the problem above. For example, is it really more efficient to preprocess an unknown quantity of unspecified heterogeneous data having unspecified data formats by processing using at least an unknown “aggregation function” that is based on recency and frequency of interact data such that it now has a common format? The Examiner is not persuaded that this is in fact more efficient or requires less processing power. The Examiner is also not persuaded that the claims are directed to a particular solution, as the claimed solution does not appear to be inventive and as the claimed solution is recited at an extremely high level of generality. Finally, the steps of pre-processing the data, generating the features, generating the training data step, etc., are part of the abstract idea. They are non-technical steps that are not “additional” elements in the claims. Therefore, not only is the Examiner note persuaded that they amount to a specific solution to a technical problem, the alleged solution is not a technical one.
Applicant specifically argues that
4) “…Applicant respectfully submits that the claims are patent-eligible because they are conceptually similar to the patent-eligible claims evaluated in McRO… The claims (similar to the claim evaluated in McRO) recite a specific and limited set of computer- implemented rules, claimed as the specific "generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users" to improve an existing technology (e.g., computer-generated training datasets for interaction evaluation).
Examiner respectfully disagrees with Applicant’s fourth argument.
Generating a set of offer recommendations for a plurality of users is not a computer-related technology, and is not analogous to the specific computer-generated automatic lip synchronization and facial expression animation techniques of McRo. Furthermore, unlike the claims in McRo, the instant claims do not appear to cover a particular solution and/or recite a particular way to achieve a desired outcome. The recited solution of simply “preprocessing” the data such that the data now has a “common data format” and “using an “aggregation function based on recency and frequency of interaction data of the plurality of historical users” amounts to claiming the idea of a solution or outcome rather than a particular solution or a particular way to achieve a desired outcome. This is especially true in light of Applicant’s specification, which generally provides a large variety of potential pre-processing alternatives that are themselves described at a very high level of generality (e.g., “normalizing” it, “extracting features” from it, looking for patterns, finding a frequency of occurrences of features, “using data aggregators”, etc.). The claimed “solution” comprises a seemingly arbitrary pre-processing alternative selected from those suggested by the specification, and recites it at a high level of generality.
Applicant specifically argues that
5) “…Moreover, even if the claims are not considered to integrate any purported abstract idea into a practical application, Applicant submits that the claims recite additional elements that amount to significantly more than any purported abstract idea. In particular, the claims recite
unconventional operations that cannot be considered to routine or well-understood, such as "preprocess[ing] the set of heterogeneous data to obtain a reduced dataset having a common data format," where "preprocessing the set of heterogenous data comprises generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users," and "generat[ing], using the reduced dataset, a set of features for a training dataset, the set of features generated based on the interaction data of the plurality of historical users," as recited in amended independent claim 1.”
Examiner respectfully disagrees with Applicant’s fifth argument.
Search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016).
v Applicant’s arguments, with respect to the rejection of claims 1-20 under for Double Patenting have been fully considered and are not persuasive. The Double Patenting rejections of claims 1-20 under 35 U.S.C. 101 have been maintained accordingly. See updated rejections 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.
v Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claim(s) 11-20 is/are drawn to methods (i.e., a process), while claim(s) 1-10 is/are drawn to systems (i.e., a machine/manufacture). As such, claims 1-20 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 1 (representative of independent claim(s) 11) recites/describes the following steps;
obtain a set of heterogeneous data corresponding to a plurality of historical users, the set of heterogeneous data having a plurality of data formats
preprocess the set of heterogeneous data to obtain a reduced dataset having a common data format, wherein preprocessing the set of heterogenous data comprises generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users
generate, using the reduced dataset, a set of features for a training dataset, the set of features generated based on the interaction data of the plurality of historical users;
generate a set of offer recommendations for a plurality of users based on the set of features; and
generate the training dataset to include the set of features of the plurality of users and a set of elasticity scores corresponding to the plurality of users determined from interactions with the set of offer recommendation
update a model to generate uplift scores for users using the training dataset, the uplift scores representing an impact on purchasing probability due to offers being presented
These steps, under its broadest reasonable interpretation, describe or set-forth a process for generating a set of offer recommendations and for updating a model to generate uplift scores for users. More specifically, the process includes obtaining a set of heterogeneous data corresponding to a plurality of historical users, the set of heterogeneous data having a plurality of data formats; preprocessing the set of heterogeneous data (e.g., at least in part by using an aggregation function based on recency and frequency of interaction data of the plurality of historical users) to obtain a reduced dataset having a common data format, generating a set of features using the reduced set and based on interaction data of a plurality of users, generating a set of offer recommendations (e.g., product advertisements/offers/promotion/prices) for the plurality of users based on the generated set of features, and updating an uplift model based on a generated training dataset (which includes the set of features of the plurality of users and a set of elasticity scores corresponding to the plurality of users determined from interactions with the set of offer recommendation), which amounts to a fundamental economic principle or practice and/or a commercial or legal interactions (specifically, an advertising, marketing or sales activity or behavior). Generating a set of offers of items (e.g., item discounts) is undeniably an advertising, marketing, or sales activities. Generating and updating a model that is configured to generate uplift scores for users is also an advertising, marketing, or sales activity, as the purpose of such a model is to identify optimal users to provide with offer recommendations (as evidenced by Applicant’s specification and claim 3). Doing so can help increase or maximize revenue for the entity generating the offer recommendations (e.g., per [0068] & [0072] & [0083] of Applicant’s published disclosure). The steps of generating the training data set is part of this process. These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Additionally and/or alternatively, each of the above-recited steps, under their broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) preprocessing the set of heterogeneous data (e.g., at least in part by using an aggregation function based on recency and frequency of interaction data of the plurality of historical users) to obtain a reduced dataset having a common data format, generating a set of features using the reduced set and based on interaction data of a plurality of users, generating a set of offer recommendations (e.g., product advertisements/offers/promotion/prices) for the plurality of users based on the generated set of features, and updating an uplift model based on a generated training dataset (which includes the set of features of the plurality of users and a set of elasticity scores corresponding to the plurality of users determined from interactions with the set of offer recommendation), (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the “mental processes” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Independent claim(s) 11 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
Each of the depending claims likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis. Any element(s) recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim. The same reasoning is similarly applicable to the limitations in the remaining dependent claims, and their respective limitations are not reproduced here for the sake of brevity.
Step 2A - Prong Two:
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The claim(s) recite the additional elements/limitations of
“a system, comprising: one or more processors coupled to non-transitory memory, the one or more processors configured to” (independent claim 1)
“by one or more processors coupled to non-transitory memory… by the one or more processors… by the one or more processors” (independent claim 11)
“a machine-learning model” (independent claims 1 and 11)
“wherein the one or more processors are further configured to” (dependent claims 2-9)
“by the one or more processors” (dependent claims 12-19)
“the machine-learning model” (dependent claims 2, 4, 6, 12, 14, and 16)
“in a graphical user interface” (dependent claims 6 and 16)
“in the graphical user interface” (dependent claims 7 and 17)
The requirement to execute the claimed steps/functions using “a system, comprising: one or more processors coupled to non-transitory memory, the one or more processors configured to” (independent claim 1) and/or “by one or more processors coupled to non-transitory memory… by the one or more processors… by the one or more processors” (independent claim 11) and/or “wherein the one or more processors are further configured to” (dependent claims 2-9) and/or “by the one or more processors” (dependent claims 12-19) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s own disclosure explains that these elements may be embodied as a general-purpose computer (e.g., see paragraphs [0046]-[0051] “can include…a computing device 160, and/or a server 170…includes a memory…a communications interface…and a processor…random access memory (RAM) a read-only memory (ROM), a hard drive…and/or the like…can store…one or more software modules and/or code that includes instructions to cause the processor to execute one or more processes…processor can be…any suitable processing device…general-purpose processor, a central processing unit (CPU)…”, [0085]-[0090] “in some instances, the computing device can be/include, for example, a personal computer, a laptop, a smartphone…server…”, [0104]-[0108] “in some embodiments, the devices can be implemented on a single hardware device…or a software platform…can be performed by any processor or computer discussed and/or shown herein… “ and [0128]-[0129] “general-purpose processor…” of the published disclosure). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The requirement for the recited model to be “a machine-learning model” (independent claims 1 and 11) and “the machine-learning model” (dependent claims 2, 4, 6, 12, 14, and 16) provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The machine-learning model is used to generally apply the abstract idea without placing any limits on how the machine-learning model functions. Rather, these limitations only recite the outcome of “to generate update scores” and do not include any details about how the machine-learning model accomplishes these functions. That a machine is required to learn the model invokes computers or other machinery merely as a tool to perform an existing process (i.e., learn some statistical model/correlation). Furthermore, the machine-learning model is recited at a high level of generality. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recited additional element(s) of “in a graphical user interface” (dependent claims 6 and 16) and/or “in the graphical user interface” (dependent claims 7 and 17) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computing environments, such as distributed computing environments and/or the internet, where information is represented digitally, exchanged between computers over a network, and presented using graphical user interfaces. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recitation of “a machine-learning model” (independent claims 1 and 11) and/or “the machine-learning model” (dependent claims 2, 4, 6, 12, 14, and 16) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “a machine-learning model” limits the identified judicial exceptions to computing environments where models/correlations are learned using computers (i.e., machines), this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine-learned models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recited element(s) of “obtain a set of heterogeneous data corresponding to a plurality of historical users, the set of heterogeneous data having a plurality of data formats” (claims 1 and 11), even if considered to be an “additional” element for the purpose of the eligibility analysis, would simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional element(s) do are deemed “extra-solution” because all uses of the recited judicial exceptions require such data gathering, and because such data gathering steps have long been held to be insignificant pre/post-solution activity. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h) and (g)).
Furthermore, although the claims recite a specific sequence of computer-implemented functions, and although the specification suggests certain functions may be advantageous for various reasons (e.g., business reasons), the Examiner has determined that the ordered combination of claim elements (i.e., the claims as a whole) are not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. For example, Applicant’s as-filed specification suggests that it is advantageous for advertisers/business to analyze historical user data (e.g., interaction data of a plurality of users) to generate a set of features for a training data set, generate a set of offer recommendations for the plurality of users based on the set of features; and update a model (e.g., a model configured to generate uplift scores for users based on a set of elasticity scores determined from interactions with the set of offer recommendations, the uplift scores representing an impact on purchasing probability due to offers being presented), because doing so can help to effectively identify users that will react to an offer recommendation (e.g., reward/discount) in a way that is positive with respect to a desired performance indicator (e.g., engage with the offer, purchase a product, increase revenue) for targeting with the offer recommendation(s) (see, for example, paragraphs [0004] & [0068] & [0072] & [0093] of Applicant’s published disclosure). These are non-technical subjective business advantages/improvements. At most, the ordered combination of claim elements is directed to a non-technical improvement to an abstract idea itself (e.g., an improved way of generating a set of offer recommendations for users).
Dependent claims 10 and 20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 10 and 20 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). For example, claim 10 recites “wherein the interaction data of the plurality of users comprises heterogeneous data including at least one of multiple data types or originating from multiple data sources”. This is an abstract limitation which further sets forth the abstract idea encompassed by claim 10. This limitation is not an “additional element”, and therefore it is not subject to further analysis under Step 2A- Prong Two or Step 2B. The same logic applies to each of the other dependent claims, whose limitations are not being repeated here for the sake of brevity and clarity.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)
As discussed above in “Step 2A – Prong 2”, the requirement to execute the claimed steps/functions using “a system, comprising: one or more processors coupled to non-transitory memory, the one or more processors configured to” (independent claim 1) and/or “by one or more processors coupled to non-transitory memory… by the one or more processors… by the one or more processors” (independent claim 11) and/or “wherein the one or more processors are further configured to” (dependent claims 2-9) and/or “by the one or more processors” (dependent claims 12-19) and/or “a machine-learning model” (independent claims 1 and 11) and/or “the machine-learning model” (dependent claims 2, 4, 6, 12, 14, and 16) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recited additional element(s) of “in a graphical user interface” (dependent claims 6 and 16) and/or “in the graphical user interface” (dependent claims 7 and 17) and/or “a machine-learning model” (independent claims 1 and 11) and/or “the machine-learning model” (dependent claims 2, 4, 6, 12, 14, and 16) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, the recited element(s) of “obtain a set of heterogeneous data corresponding to a plurality of historical users, the set of heterogeneous data having a plurality of data formats” (claims 1 and 11), even if considered to be an “additional” element for the purpose of the eligibility analysis, would simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). These additional element(s), taken individually or in combination, additionally amount to well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, appended to the judicial exception. These additional elements, taken individually or in combination, are well-understood, routine and conventional to those in the field of advertising/marketing. These limitations therefore do not qualify as “significantly more”. (see MPEP 2106.05(d)). This conclusion is based on a factual determination. The determination that receiving data/messages over a network is well-understood, routine, and conventional is supported by Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), and MPEP 2106.05(d)(II), which note the well-understood, routine, conventional nature of receiving data/messages over a network. Furthermore, Examiner takes Official Notice that these steps were well-understood, routine, and conventional at the effective filing date of the claimed invention. Furthermore, the lack of technical detail/description in Applicant’s own specification provides implicit evidence that these steps were well-understood, routine, and conventional.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, generally link the abstract idea to a particular technological environment or field of use, append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity), and appended with well-understood, routine and conventional activities previously known to the industry.
Dependent claims 10 and 20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 10 and 20 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea identified by the Examiner to which each respective claim is directed).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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v Claims 1-20 are rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 1-18 of US Patent No. 12,033,177 (corresponding to co-pending US Application No. 18/372,616) in view of Ly (US PG Pub 2022/0156235, May 19, 2022). Although the conflicting claims are not identical, they are not patentably distinct from each other. Each of the instant claims is anticipated by at least one claim of US Patent No. 12,033,177, with the exception of the newly added features of “obtain a set of heterogeneous data corresponding to a plurality of historical users, the set of heterogeneous data having a plurality of data formats” and “preprocess the set of heterogeneous data to obtain a reduced dataset having a common data format, wherein preprocessing the set of heterogenous data comprises generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users”. The exact limitations of each of these claims are not being reproduced here for clarity and brevity, as the Examiner believes the remaining anticipation would be self-evident to a PHOSITA. However, Ly discloses “obtain a set of heterogeneous data corresponding to a plurality of historical users, the set of heterogeneous data having a plurality of data formats, preprocess the set of heterogeneous data to obtain a reduced dataset having a common data format, wherein preprocessing the set of heterogenous data comprises generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users” ([0085] & [0135]-[0140]). It would have been obvious to modify the claims of US Patent No. 12,033,177 to include these features taught by Ly because doing so can ensure the data is in a format more usable for machine learning ([0004]-[0008], [0071] & [0085] & [0135]-[0140]). It is further noted that Applicant has previously filed a Terminal Disclaimer for each of the previous patents in the family chain.
v Claims 1-20 are rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 1-20 of US Patent No. 11,803,871 (corresponding to co-pending US Application No. 17/545,221) in view of Ly (US PG Pub 2022/0156235, May 19, 2022). Although the conflicting claims are not identical, they are not patentably distinct from each other. Each of the instant claims is anticipated by at least one claim of US Patent No. 11,803,871, with the exception of the newly added features of “obtain a set of heterogeneous data corresponding to a plurality of historical users, the set of heterogeneous data having a plurality of data formats” and “preprocess the set of heterogeneous data to obtain a reduced dataset having a common data format, wherein preprocessing the set of heterogenous data comprises generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users”. The exact limitations of each of these claims are not being reproduced here for clarity and brevity, as the Examiner believes the anticipation would be self-evident to a PHOSITA. However, Ly discloses “obtain a set of heterogeneous data corresponding to a plurality of historical users, the set of heterogeneous data having a plurality of data formats, preprocess the set of heterogeneous data to obtain a reduced dataset having a common data format, wherein preprocessing the set of heterogenous data comprises generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users” ([0085] & [0135]-[0140]). It would have been obvious to modify the claims of US Patent No. 11,803,871 to include these features taught by Ly because doing so can ensure the data is in a format more usable for machine learning ([0004]-[0008], [0071] & [0085] & [0135]-[0140]). It is further noted that Applicant has previously filed a Terminal Disclaimer for each of the previous patents in the family chain.
Indication of Novel and Non-Obvious Subject Matter
Independent claims 1 and 11 recite novel and non-obvious subject matter. Each of the dependent claims similarly recite novel and non-obvious subject matter by virtue of their dependency on one of these claims.
The following is an examiner’s statement of reasons for indication of novel and non-obvious subject matter:
The closest prior art of record is Mena (U.S. PG Pub No. 2007/0011224, January 11, 2007 - hereinafter "Mena”); Valentine et al. (U.S. PG Pub No. 2011/0131079, June 2, 2011 - hereinafter "Valentine”); Xu et al. (U.S. PG Pub No. 2016/0189207 June 30, 2016 - hereinafter "Xu”); Hines et al. (U.S. Patent No. 8,170,823 May 1, 2012- hereinafter "Hines”); Vitaladevuni et al. (U.S. Patent No. 10,354,184 July 16, 2019- hereinafter "Vitaladevuni”); Fano et al. (U.S. PG Pub No. 2005/0189414, September 1, 2005 - hereinafter "Fano”); Michaud et al. (U.S. PG Pub No. 2010/0191570 July 29, 2010 - hereinafter "Michaud”); Fahner et al. (U.S. PG Pub No. 2012/0158474 June 21, 2012); Friedman et al. (U.S. PG Pub No. 2020/0234365, July 23, 2020); Jai et al. (U.S. PG Pub No. 2020/0134628, April 30, 2020); Zheng et al. (U.S. Patent No. 9,208,444, December 8, 2015); Lei et al. (U.S. PG Pub No. 2021/0334830 October 28, 2021); Ly (US PG Pub 2022/0156235, May 19, 2022).; Pande et al. (U.S. PG Pub No. 2020/0250734 August 6, 2020); and “Modeling the Distribution of Price Sensitivity and Implications for Optimal Retail Pricing” (Blattberg, Robert C. et al., published in Journal of Business and Economic Statistics, February 1995)
Mena discloses clustering/segmenting customers based on customer transaction data and using a neural network to generate predictive scores for customer propensity to purchase in response to promotional offers and further segmenting the customers base on their predicted propensity to buy/respond to marketing offers and identifying target users based thereon for generating/transmitting a recommendation offer.
Valentine discloses clustering/segmenting customers based on customer transaction data and calculating a set of elasticity scores for one or more segments of users using machine learning algorithms.
Xu discloses training a neural network based on historic offers provided to historic users and a subset of the historic offers that were accepted by the historic users, the neural network trained using customer data to output uplift scores for users based on their associated data.
Hines discloses training a model to generate uplift scores using elasticity scores as input. Discloses iteratively updating the model using responses to promotions, and corresponding elasticity scores calculated based on these responses.
Vitaladevuni discloses training a neural network to determine a user’s purchase probability based on changes in product price, and based user-specific learned pricing sensitivities as input.
Fano discloses calculating, using the set of elasticity scores, a threshold for identifying various customer segments.
Michaud discloses presenting a graphical indication of a distribution of elasticity score among a set of users.
Fahner discloses a machine learned model which calculates a score (CEI) representative of a lift due to a coupon for respective users, which factors in the amount of the discount and takes into consideration the user’s price sensitivities ([0035]-[0036], [0022], [0025]). Model is trained based on historical offer acceptances.
Friedman discloses wherein the set of elasticity scores is used to calculate a threshold for identifying the segments ([0050] “….assigning said customer to one of said plurality of price-sensitivity segments…taking into account said price-sensitivity score…each of price-sensitivity segments is stored as a plurality of price-sensitivity thresholds…over the range of possible price-sensitivity scores”).
Jai discloses ranking, by the processor, each feature within the set of features, wherein the processor uses a subset of the set of features in accordance with their respective ranking to generate the graph ([0070] feature rankings presented to user to select features to use).
Zheng discloses presenting histograms showing the distribution of various customer-scores within a particular customer segment in order to provide retailers additional insights regarding the distribution of certain scores within a particular customer segment (Fig 3B)
Lei discloses pre-processing heterogeneous customer data, using ML to extract significant predictor features related to purchase propensity and demand, generating various clusters/segments using the extracted features, and running additional ML models on top of the extracted features and clusters/segments to derive propensity/demand scores for various customer subsets for use in deploying optimized promotions for certain customer subsets.
Ly discloses preprocessing heterogeneous data from disparate sources using data aggregation function to reduce dimensionality of the data in preparation for machine learning.
Pande discloses preprocessing heterogeneous data from disparate sources using data aggregation function to reduce dimensionality of the data in preparation for machine learning.
“Modeling the Distribution of Price Sensitivity and Implications for Optimal Retail Pricing” discloses deriving price sensitivity values for various households/segments and determining the distribution of these sensitivity values and using these insights to increase revenue by more optimally pricing their products.
As per Claims 1 and 11, the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest the specific combination of “preprocess the set of heterogeneous data to obtain a reduced dataset having a common data format, wherein preprocessing the set of heterogenous data comprises generating the reduced dataset using an aggregation function based on recency and frequency of interaction data of the plurality of historical users…generate a set of offer recommendations for a plurality of users based on the set of features; generate the training dataset to include the set of features of the plurality of users and a set of elasticity scores corresponding to the plurality of users determined from interactions with the set of offer recommendations; and update a machine-learning model to generate uplift scores for users using the training dataset, the uplift scores representing an impact on purchasing probability due to offers being presented.”
Applicant’s claims and original disclosure inform the broadest reasonable interpretation of the claimed uplift scores and elasticity scores. There are several examples in the prior art of systems configured to generate such elasticity scores for segments of customers using various machine learning models (e.g. neural networks), to generate such uplift scores for customers segments using various machine learning models (e.g. neural networks), and/or to generate elasticity scores and uplift scores for various customer segments, and/or to use uplift scores and/or elasticity scores to segment customers and/or to identify targets for marketing campaigns. However, while individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features in the way that is claimed (e.g., updating a model to generate/output uplift scores based at least in part on a set of elasticity scores as input (e.g., a numeric value representing a magnitude of change in purchasing probability for a respective user relative to a magnitude of change in price per the specification), and where the generated uplift scores are specifically representative of an impact on purchasing probability for one or more users due to offers being presented to the one or more users), other than with impermissible hindsight
Claims 2-10 and 12-19 depend upon claims 1 and 11 and have all the limitations of claims 1 and 11 and are novel and non-obvious for the same reason.
Conclusion
No claim is allowed
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES M DETWEILER whose telephone number is (571)272-4704. The examiner can normally be reached on Monday-Friday from 8 AM to 5 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached at telephone number (571)-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES M DETWEILER/Primary Examiner, Art Unit 3621
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