DETAILED ACTION
Status of the Application
In response filed on November 19, 2025, the Applicant amended claims 1, 3, 7, 10, 11, 13, 17, and 20; and cancelled claims 5, 6, 8, 9, 15, 16, 18, and 19. Claims 1-4, 7, 10-14, 17, and 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 has amended the abstract of the specification to correct minor informalities identified in the previous action. The amended Abstract is accepted, and this objection has been withdrawn accordingly.
v Applicant has amended the claims to correct each of the informalities identified in the previous action. These objections have been withdrawn accordingly.
v With respect to the rejection of claims 1-4, 7, 10-14, 17, and 20 under 35 U.S.C. §112 (b), Applicant has appropriately amended the claims. The claims have been amended such that they no longer recite that which was identified as being indefinite. These rejections of claims 1-4, 7, 10-14, 17, and 20 under 35 U.S.C. §112 (b) have been withdrawn.
v With respect to the rejection of claims 1-4, 7, 10-14, 17, and 20 under 35 U.S.C. §103, Applicant has appropriately amended the claims. Applicant amended Independent claims 1 and 11 to include the limitations of dependent claim 9 (as well as each of the limitations recited in each intervening claim) identified as being novel and non-obvious in the previous action. The rejections of claims 1-4, 7, 10-14, 17, and 20 under 35 U.S.C. §103 have been withdrawn accordingly.
v Applicant’s arguments, with respect to the rejection of claims 1-4, 7, 10-14, 17, and 20 under 35 U.S.C. 101 have been fully considered and are not persuasive. The rejections of claims 1-4, 7, 10-14, 17, and 20 under 35 U.S.C. 101 have been maintained accordingly.
Applicant specifically argues that
1) “Applicant respectfully submits that: i) the elements in relation to the interaction of the system's use of the machine learning library when generating contextual feedback forms and updating the corpus of information in the machine learning library with received contextual feedback form data (supported at least at paragraphs 105 to 111 and Figure 9), and ii) the addition of the limitations in claims 5, 6, 8 and 9 (and corresponding claims 15, 16, 18 and 19) in relation to the automatic inclusion of a user-defined parameter of past contextual feedback forms into a selection item in future contextual feedback forms, integrate any alleged judicial exception into a practical application and/or ensure that the claims amount to significantly more than the judicial exception. Applicant respectfully submits that the contemporaneous and automatic inclusion of a user-defined parameter found in a set number of past received contextual feedback form data into a selection item in a next contextual feedback form is functionality that provides significantly more than any alleged abstract idea of providing feedback forms. These contextual feedback forms are generated based upon high volume of feedback data and customer transaction records received anonymously from varied touchpoints. This ability to dynamically add selection items based upon user-defined parameters cannot be performed manually in any meaningful, non-trivial, manner. The advantage of converting user-defined parameters into selection items is more than a business advantage. It is an actual technical improvement in relation to the generation and inclusion of selection items in contextual feedback forms.”
Examiner respectfully disagrees with Applicant’s first argument.
Applicant’s discussion of “contemporaneous and automatic inclusion…” and “high volume of feedback data” is incommensurate with what is actually claimed. The instant claims do not involve or require contemporaneous and automatic inclusion of user-defined parameters. To the extent that one could argue user-defined parameters are included in subsequent forms automatically, this would appear to merely be a result of a general-purpose computer being programmed to iteratively provide this functionality. Such automation amounts to mere instructions to apply the abstract idea using a programmed general-purpose computer (see MPEP 2106 “mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” does not result in an improvement in computer-functionality). Furthermore, there is nothing in the claims to suggest or require a “high volume” of data being processed.
The high-level requirement for the library to be “a machine learning library” 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 library is used to generally apply the abstract idea without placing any limits on how the machine learning library functions. Rather, these limitations only recite the outcome of “obtain…maintain…detect changes…update…a lookup table…” and do not include any details about how these functions are accomplished. 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 claimed process, which includes the ability to add selection items to feedback forms based upon user-defined parameters, does not result in a technical improvement. Applicant’s assertion that the there is “an actual technical improvement in relation to the generation and inclusion of selection items in contextual feedback forms” is conclusory, and is therefore not persuasive. That a general-purpose computer is programmed to implement the claimed process in combination with a “machine learning” library, or that the review forms may take a digital form, does not mean the claims are 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. Applicant’s published specification suggests that it is advantageous to implement the claimed business process because doing so can help to provide customers with product/service review forms that are semantically linked to the subject being reviewed and that include rating options associated with more relevant product/service parameters as determined from historic user reviews (see, for example, Applicant’s published disclosure at paragraphs [0105]-[0110]). These are non-technical 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 process for analyzing/generating product and service review forms). See In re Mohapatra, 842 F. App’x 635, 638 (Fed. Cir. 2021 - A claim does not “cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit.”). Even if the steps/formulas provide a useful business outcome, that is not enough for eligibility. See Univ. of Fla. Research Found., Inc. v.. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019 - the automation of data synthesis technology and device drivers for different bedside machines did not render the claims any less abstract even if the automation resulted in “life altering consequences”);
Applicant specifically argues that
2) “Applicant also respectfully submits that the use of a machine learning library is more than an "apply it" function in a generic computer. Rather, machine learning, or any other artificial intelligence, is significantly more than an "apply it" function. The machine learning library recognizes the set number of uses of a user-defined parameter and converts that parameter into a selection item for future contextual feedback forms. This technical ability is more than "apply it" functionality. "Apply it" functionality is limited to direct instructions that dictate an order or procedure of a pre-determined algorithm. Machine learning is significantly different from such "apply it" procedural functionality. The machine learning library allows for context (e.g., parameters in the context of a particular good or service) to be used when detecting changes (e.g., use of user-defined parameters) and automatically converting such changes in contextual feedback forms (e.g., user-defined parameters into selection items). Such context is not a feature in traditional "apply it" functionality in a generic computer..”
Examiner respectfully disagrees with Applicant’s second argument.
The general assertion that “machine learning, or any other artificial intelligence, is significantly more than an "apply it" function” is not accurate. There are many examples of inventions that recite or involve machine learning in various capacities yet are found patent ineligible under 35 U.S.C. §101. Applicant’s claims explicitly state that the steps/functions of the claims are the result of executing “a sequence of instructions…by the at least one processor”. As for the machine-learning library, the claims merely state the library is “machine learning” with no additional details regarding the machine learning, and without placing any limits on how the machine learning library functions. Rather, the claims only recite the outcome of “obtain…maintain…detect changes…update…a lookup table…” and do not include any details about how these functions are accomplished. 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 library is used to generally apply the abstract idea without placing any limits on how the machine learning library functions. Rather, these limitations only recite the outcome of “obtain…maintain…detect changes…update…a lookup table…” and do not include any details about how these functions are accomplished. 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)).
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) 1-4, 7, 10-14, 17, and 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) 11-14, 17, and 20 is/are drawn to methods (i.e., a process), while claim(s) 1-4, 7, and 10 is/are drawn to systems (i.e., a machine/manufacture). As such, claims 1-4, 7, 10-14, 17, and 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;
a transaction database configured to store customer-owned customer transaction records associated with customer accounts;
a reviews database configured to store customer reviews;
obtain, from the one or more data stores, at least one attribute associated with a customer transaction record;
obtain, from the one or more data stores, at least one keyword associated with the at least one attribute;
obtain…at least one contextual review form parameter associated with the at least one keyword, the at least one contextual review form parameter logically associated with a type of product or service listed in the customer transaction record,
maintain a corpus of information…from at least one of contextual review form parameters used in past received reviews, or contextual review form parameters defined or selected by users when customizing review forms;
detect changes made by reviewers to past contextual review form parameters for a product or service listed in the customer transaction record; and
update, responsive to the changes, a lookup table of keywords and associated contextual review form parameters;
generate a contextual review form comprising at least one parameter field comprising a choice of different parameters logically associated with the type of product or service listed in the customer transaction record;
send, to…the customer…the contextual review form;
receive, from…the customer…contextual review form data;
send a user-choice parameter selected from at least one parameter field in the contextual review form data
and store the contextual review form data, wherein the corpus of information maintained…is updated responsive to the storing of the contextual review form data
wherein: the at least one parameter field comprises an option for a user to define a user-defined parameter
update a lookup table in the…library to include a new user-defined parameter; and
include the new user-defined parameter as a selection option in parameter fields of future contextual review forms when a set number of received contextual review form data comprises the new user-defined parameter
These steps, under its broadest reasonable interpretation, describe or set-forth a business process for generating product/service review forms (i.e., contextual review forms) comprising product/service rating questions or options (i.e., at least one review form parameter) that are determined based at least in part on historic changes/selections past customers have made to review forms associated with the type of product/service being reviewed (e.g., so that the review form presented to the user asks the user about a product/service trait past customers felt the need to provide feedback on when reviewing this type of product). More specifically, the a business process for generating product/service review forms includes aggregating and storing transaction data records and customer reviews, aggregating/storing data including contextual review form parameters used/chosen by customers in past reviews when customizing reviews, detecting changes/customization made by customers to past review form parameters for reviews associated with products/services in the transaction records, maintaining/updating a table of keywords and associated review form parameters, obtaining an attribute associated with a customer transaction record, obtaining a keywords associated with the at least one attribute, obtaining at least one contextual review form parameter associated with the at least one keyword based at least in part on the type of product/service, generating a contextual review form comprising the at least one parameter field comprising a choice of different parameters logically associated with the type of product or service listed in the customer transaction record, sending the customer the contextual review form, receiving review from data from the customer, updating the aggregated/stored review form data, and wherein: the at least one parameter field comprises an option for a user to define a user-defined parameter, and wherein the process also comprises updating a lookup table in the library to include a new user-defined parameter; and including the new user-defined parameter as a selection option in parameter fields of future contextual review forms when a set number of received contextual review form data comprises the new user-defined parameter. This business process amounts to a commercial or legal interactions (specifically, an advertising, marketing or sales activity or behavior; business relations). 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) performing one or more concepts 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…the system comprising: at least one processor; and a memory comprising one or more data stores, including…and a sequence of instructions which when executed by the at least one processor configure the at least one processor to…wherein…the at least one processor is configured to…” (claim 1)
“from a machine learning library…wherein the machine learning library is configured to… to the machine learning library… by the machine learning library…in the machine learning library” (claim 1)
“to a mobile device or a computer associated with the customer account…from the mobile device or computer associated with the customer account…” (claims 1 and 11)
“computer-implemented…by a server processor…by the server processor…by the server processor… by the server processor… by the server processor… by the server processor… by the server processor……by the server processor…” (claim 11)
“from a machine learning library…by the machine learning library…by the machine learning library…by the machine learning library…to the machine learning library… by the machine learning library……in the machine learning library” (claim 11)
“wherein the at least one processor is configured to…” (claim 3)
“by the server processor” (claims 13)
The requirement to execute the claimed steps/functions using “a system…the system comprising: at least one processor; and a memory comprising one or more data stores, including…and a sequence of instructions which when executed by the at least one processor configure the at least one processor to…wherein…the at least one processor is configured to…” (claim 1) and/or “computer-implemented…by a server processor…by the server processor…by the server processor… by the server processor… by the server processor… by the server processor… by the server processor……by the server processor…” (claim 11) and/or “wherein the at least one processor is configured to…” (claim 3) or “by the server processor” (claims 13) 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 “additional” elements may be embodied as a general-purpose computer (e.g., the published specification at paragraphs [0066]-[0074] “may be an electronic device…processor and a memory…can be, for example, any type of general-purpose microprocessor…any type of computer memory” and [0181]-[0190] “computing device, such as a server…The embodiments of the devices, systems and methods described herein may be implemented in a combination of both hardware and software. These embodiments may be implemented on programmable computers, each computer including at least one processor, a data storage system (including volatile memory or non-volatile memory or other data storage elements or a combination thereof), and at least one communication interface…a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions…implemented by physical computer hardware, including computing devices, servers, receivers, transmitters, processors, memory, displays, and networks. The embodiments described herein provide useful physical machines and particularly configured computer hardware arrangements”). 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 recitation of “from a machine learning library…wherein the machine learning library is configured to… to the machine learning library… by the machine learning library…in the machine learning library” (claim 1) and/or “from a machine learning library…by the machine learning library…by the machine learning library…by the machine learning library…to the machine learning library… by the machine learning library……in the machine learning library” (claim 11) 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 library is used to generally apply the abstract idea without placing any limits on how the machine learning library functions. Rather, these limitations only recite the outcome of “obtain…maintain…detect changes…update…a lookup table…” and do not include any details about how these functions are accomplished. 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 “to a mobile device or a computer associated with the customer account…from the mobile device or computer associated with the customer account…” (claims 1 and 11) 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 “from a machine learning library…wherein the machine learning library is configured to… to the machine learning library… by the machine learning library…in the machine learning library” (claim 1) and/or “from a machine learning library…by the machine learning library…by the machine learning library…by the machine learning library…to the machine learning library… by the machine learning library……in the machine learning library” (claim 11) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “a machine learning library” limits the identified judicial exceptions to performing the functions of “obtain…maintain…detect changes…update…a lookup table…” using machine learning in some way, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) 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)).
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 published specification suggests that it is advantageous to implement the claimed business process because doing so can help to provide customer’s with product/service review forms that are semantically linked to the subject being reviewed and that include rating options associated with more relevant product/service parameters as determined from historic user reviews (see, for example, Applicant’s published disclosure at paragraphs [0105]-[0110]). These are non-technical 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 process for analyzing/generating product and service review forms).
Dependent claims 2, 4, 7, 10, 12, 14, 17, and 20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 2, 4, 7, 10, 12, 14, 17, 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 2 recites “wherein the at least one parameter field comprises a selection of parameters previously used in past reviews of the type of product or service listed in the customer transaction record”. This is an abstract limitation which further sets forth the abstract idea encompassed by claim 2. 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…the system comprising: at least one processor; and a memory comprising one or more data stores, including…and a sequence of instructions which when executed by the at least one processor configure the at least one processor to…wherein…the at least one processor is configured to…” (claim 1) and/or “computer-implemented…by a server processor…by the server processor…by the server processor… by the server processor… by the server processor… by the server processor… by the server processor……by the server processor…” (claim 11) and/or “wherein the at least one processor is configured to…” (claim 3) or “by the server processor” (claims 13) 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 recitation of “from a machine learning library…wherein the machine learning library is configured to… to the machine learning library… by the machine learning library…in the machine learning library” (claim 1) and/or “from a machine learning library…by the machine learning library…by the machine learning library…by the machine learning library…to the machine learning library… by the machine learning library……in the machine learning library” (claim 11) 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 “to a mobile device or a computer associated with the customer account…from the mobile device or computer associated with the customer account…” (claims 1 and 11) 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 recitation of “from a machine learning library…wherein the machine learning library is configured to… to the machine learning library… by the machine learning library…in the machine learning library” (claim 1) and/or “from a machine learning library…by the machine learning library…by the machine learning library…by the machine learning library…to the machine learning library… by the machine learning library……in the machine learning library” (claim 11) also 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)).
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, and generally link the abstract idea to a particular technological environment or field of use.
Dependent claims 2, 4, 7, 10, 12, 14, 17, and 20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 2, 4, 7, 10, 12, 14, 17, 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).
Indication of Novel and Non-Obvious Subject Matter
Independent claims 1 and 11 recite novel and non-obvious subject matter. 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 Morsberger (U.S. PG Pub No. 2010/0324971, December 23, 2010 - hereinafter "Morsberger”); Montoya (U.S. PG Pub No. 2017/0068967, March 9, 2017 - hereinafter "Montoya”); Mizrahi et al. (U.S. PG Pub No. 2006/0155513 July 13, 2006 - hereinafter "Mizrahi”); Moskowitz et al. (U.S. PG Pub No. 2002/0198788 December 26, 2022 - hereinafter " Moskowitz”); Jeffs et al. (U.S. PG Pub No. 2014/0143157 May 22, 2014 - hereinafter “Jeffs”); and “Smart credit cards are coming. Here’s what you need to know” (Profis, Sharon; published May 25, 2015 at https://www.cnet.com/tech/mobile/smart-credit-cards-swyp-plastc-stratos-coin/).
Morsberger discloses generating contextual feedback forms based on customer transaction records and tracking/storing results from the generated surveys .
Montoya discloses generating contextual feedback forms and enabling respondents to change or add questions and answers to the surveys and wherein the system tracks/stores these modifications or additions such that subsequent surveys can be modified to include these modified/new questions or answers such that the subsequent surveys can better reflect/capture possible views.
Mizrahi discloses generating contextual feedback forms including tracking free form answers to past questions and generating closed-formed versions of these questions for subsequent surveys including the most popular answers as selectable answer options in the subsequent surveys.
Moskowitz discloses generating contextual feedback forms comprising questions based on attributes and keywords extracted from transaction records.
Jeffs discloses generating contextual feedback forms comprising questions based on analysis of previous discussions about content such that the questions are related to identified issues of known significance.
“Smart credit cards are coming. Here’s what you need to know” teaches smart cards and smart wallets including tokenization and loyalty integration.
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 "generate a contextual review form comprising at least one parameter field comprising a choice of different parameters logically associated with the type of product or service listed in the customer record…the at least one parameter field comprises an option for a user to define a user-defined parameter; the at least one processor is configured to: update a lookup table in the machine learning library to include a new user-defined parameter; and include the new user-defined parameter as a selection option in parameter fields of future contextual review forms when a set number of received contextual review form data comprises the new user-defined parameter" in combination with each of the other limitations of the claims. Examiner notes that a broadest reasonable interpretation of a set number of received contextual review form data comprise the new user-defined parameter does not comprise including the most popular user-defined parameters or the top-N most included new user-defined parameters. A set number is a specific predetermined/threshold number or quantity, consistent with Applicant’s published disclosure at paragraphs [0123]-[0126]). The claim language requires the processor to include the new user defined parameter as an option when a set number of received contextual review form data comprise the new user-defined parameter.
Conclusion
No claim is allowed
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 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