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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
The following FINAL Office Action is in response to communication filed on 7/15/2026.
Priority
The Examiner has noted the Applicant claiming Priority from Provisional Application 63/615,953 filed 12/9/2023.
Status of Claims
Claims 1-20 are currently pending.
Claims 1, 7, 15 are currently amended.
Claims 1-20 are currently under examination and have been rejected as follows.
Claim Objections
Claim 1 is objected to for the following informality.
Claim 1 recites: “...determines the potential consumer is likely to purchase at least one of the product or the service base on the trigger event…”, [bolded emphasis added].
Claim 1 is recommended to recite: “...determines the potential consumer is likely to purchase at least one of the product or the service based on the trigger event…”
Appropriate correction is required.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Response to Amendment
The previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
The previously pending rejections under 35 USC 102 are withdrawn in view of the amendments.
New grounds for rejection 35 USC 103 are applied as necessitated by the amendments.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Response to Arguments
Regarding Applicant’s remarks pertaining to 35 USC 101:
Step 2A Prong 1:
Applicant argues on page 9 of remarks 7/15/2026:
“…the Office alleges claims 1-20 are directed to an abstract idea of a method of organizing human activity and a mental process…. Applicant respectfully disagrees.
“The Office Action characterizes the claims as directed to "advertising, marketing or sales activities or behaviors". While the claimed notification may relate to a product or service, the focus of the claims is the technical processing by which the system determines when to generate the notification. To do this, the claims recite a specific computer-implemented processing architecture that uses weighted variables, sentiment analysis output, machine learning based trigger event identification to determine purchase likelihood, and ideal communication timing.”
Examiner respectfully disagrees. Examiner submits determining when to generate a notification to a consumer is central to promotional activities in marketing (see MPEP 2106.04(a)(2) II B, In re Maucorps and OIP Techs., Inc. v. Amazon.com, Inc.), which is an abstract idea. Executing the functions of the abstract idea on a computer does not preclude the claims from being directed to a judicial exception, but impact on eligibility by the additional computer-based elements are then evaluated at Step 2A Prong 2 and Step 2B.
Applicant argues beginning on page 9 of remarks 7/15/2026:
“Furthermore, these operations are not merely human mental steps. They are computer implemented data-processing operations performed using a machine learning model sentiment analysis. As in McRO, the claims recite a specific computational approach rather than a mental process performed on a computer.”
Examiner respectfully disagrees. McRo improved the operation of the computer system in a specific way, namely use of particular rules to set morph weights and transitions through phonemes, to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters, and thus were not directed to an abstract idea. In the present case, the claims define a specific computational approach for determining when to send a marketing promotion to consumers. The claims are not a technical improvement to the operation of a computer but the removal of human labor that is just automating a task and not patent eligible (see In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Step 2A Prong 2:
Applicant argues on page 10 of remarks 7/15/2026:
“…As amended, the claims do not merely invoke a generic machine learning model to perform marketing. Instead, the claimed elements recite a specific sequence of data processing operations.
“This specific sequence of processing operations are illustrated by amended method claim 7….”
Examiner respectfully disagrees. Independent claim 7 as amended presents the newly added additional computer-based element “interactive user interface”. This additional element, along with the original “computer”, “user device”, and “machine learning model” perform functions such as receiving and transmitting data, identifying events, predicting likelihood of consumer purchase of product or service, and generating and presenting notifications. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of analyzing data, calculating statistics, communicating and presenting data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i).
Applicant argues on page 11 of remarks 7/15/2026:
“The claimed elements of amended independent claim 1 recite an integration into practical application by meaningfully limiting the claim to a particular machine learning and natural language processing implementation. The claimed elements are directed to a specific technical processing pipeline that transforms received text/audio/user interface data into weighted variables, sentiment analysis output, trigger event determinations, purchase likelihood determinations, and an ideal time-controlled notification.”
Examiner respectfully disagrees. The additional element “machine learning model” language and “natural language processing” (not present in the claims) merely require execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirements amount to mere instructions to implement the abstract idea on a computer, and, therefore, are not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea.
Step 2B:
Applicant argues beginning on page 11 of remarks 7/15/2026:
“For example, the ordered combination of steps recited in amended independent claim 1 provides a technological solution to the technological problem (e.g., predicting demand related to products and services to determine an optimum time to initiate communication with a user). Furthermore, the specific ordered combination of elements supplies an inventive concept.”
Examiner respectfully disagrees. Examiner submits that predicting demand related to products and services to determine an optimum time to initiate communication with a user is fundamentally and entrepreneurial problem as opposed to a technological one. Improvements to addressing this challenge, even with a specific ordered combination of elements, would not necessarily demonstrate an improvement to the computer or technology itself.
Applicant argues beginning on page 12 of remarks 7/15/2026:
“Applicant's amended claimed elements do not merely ‘apply it on a computer’. The claims recite a particular combination of data transformations and control logic that produces a timed, model-driven notification.”
Examiner respectfully disagrees. The claims as amended still narrow the additional computer-based elements to capabilities such as receive, identify, process, determine, include, and be trained by various forms of data such as text, audio, selections, products, services, life events, consumer demographic and behavior data, recommendations, promotions, explanations, comparisons, consumer characteristics, advertisements, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Because the claims do not sufficiently demonstrate an improvement to computer technology, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B).
Accordingly, the previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Regarding Applicant’s remarks pertaining to 35 USC 102:
Applicant argues beginning on page 12 of remarks 7/15/2026:
“The amended claims include processing data using sentiment analysis to determine whether the potential consumer is ready to purchase, see Subject Application paragraph [0018]. In contrast, Vanderveld determines that a consumer is ready to purchase based on a classification of a consumer and attributes of the consumer, see Vanderveld [Abstract], but does not use sentiment analysis, e.g., analyzing text or audio to determine an emotional tone of the consumer, to determine whether the user is ready to purchase a product or a service.”
Examiner considers Applicant’s argument but respectfully finds the argument moot on new grounds of rejection. Examiner points to art reference Singh et al. US 20240161146 A1, hereinafter Singh which cures the deficiencies of Vanderveld. See Singh at ¶ [0032], [0045] which disclose the sentiment analysis and interactive interface with text or audio input, respectively. Citations and additional details are included in the 103 rejection section below.
Applicant argues on page 13 of remarks 7/15/2026:
“Furthermore, Vanderveld does not teach Applicant's added feature, ‘determines an ideal time to communicate information about at least one of the product or the service to the potential customer’.”
Examiner respectfully disagrees. Additional support from primary reference Vanderveld discloses the claim limitations as amended at Fig 3; and ¶ [0121], [0093], and [0094]. Citations and additional details are included in the 103 rejection section below.
Accordingly, new grounds for rejection 35 USC 103 are applied as necessitated by the amendments.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-6 are directed to a system or machine which is a statutory category.
Claims 7-14 are directed to a method or process which is a statutory category.
Claims 15-20 are directed to a non-transitory computer-readable media or article of manufacture which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth advertising, marketing or sales activities or behaviors including: “receive data associated with a potential consumer of at least one of a product or a service… the data including input provided by the potential consumer using… audio”, “identifies from the data including the input, one or more variables associated with a trigger event”, “generate a sentiment analysis output indicating whether the potential consumer is likely to purchase at least one of the product or the service based on the event”, “applies… weights to variables based on the importance of the variables that indicate a purchase likelihood”, “identifies the trigger event based on the weighted variables”, “determines the potential consumer is likely to purchase at least one of the product or the service base on the trigger event and the sentiment analysis output”, “determines an ideal time to communicate information about the at least one of the product or the service to the potential consumer by predicting at least one of a consumer life event, behavior, or attitude that precedes a product or service search”, “generate, in response to determining the ideal time, a notification associated with at least one of the product or the service”, “transmit the notification to the user... to be presented”. Predicting consumer future behavior based on consumer data and current consumer behavior, including consumer feedback, and sending product or service information based on the current behavior falls within marketing or sales activities or behaviors, which are commercial or legal interactions under the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II). Additionally, the claims recite, describe or set forth concepts performed in the human mind (including observation, evaluation, and judgement) including: “identify an event based on the data” as an example of observation, and “predict the potential consumer is likely to purchase at least one of the product or the service based on the event” as an example of evaluation or judgement. Observing consumer behavior and predicting future behavior, including consumer feedback, based on the current behavior falls within Mental Processes1 (MPEP 2106.04(a)(2) III). Examiner also points to MPEP2106.04(a)(2) III C finding that computer aided processes such as: 1. Performing a mental process on a generic computer, 2. Performing a mental process in a computer environment, 3. Using a computer as a tool to perform a mental process can still be considered to recite a mental process. Accordingly, the claims recite an abstract idea.
Step 2A Prong Two: Independent claims 1, 7, 15 recite the following additional elements: “provider system”, “user device”, “interactive user interface”, “network”, “input systems”, “output systems”, “demand prediction system”, “machine learning model”, “notification generation system”, “computer”, “non-transitory computer-readable storage media”, “computer-executable instructions”, and “computing system”. The functions of these additional elements include examples such as receiving and transmitting data, identifying events, predicting likelihood of consumer purchase of product or service, and generating and presenting notifications. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of analyzing data, calculating statistics, communicating and presenting data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
The additional element “machine learning model” language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claims 2-6, 8-14, 16-20 do not appear to provide any further additional computer-based elements, let alone for such additional computer-based elements to integrate the abstract idea into practical application (Step 2A Prong Two) or providing significantly more (Step 2B).
Further, dependent claims 2-6, 8-14, 16-20 merely incorporate the additional elements recited in claims 1, 7, 15 along with further narrowing of the abstract idea of claims 1, 7, 15 and their execution of the abstract idea. Specifically, the dependent claims narrow the “provider system”, “user device”, “network”, “input systems”, “output systems”, “demand prediction system”, “machine learning model”, “notification generation system”, “computer”, “non-transitory computer-readable storage media”, “computer-executable instructions”, and “computing system” to capabilities such as receive, include, and be trained by various forms of data such as selections, products, services, life events, consumer demographic and behavior data, recommendations, promotions, explanations, comparisons, consumer characteristics, advertisements, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1-20 are reasoned to be patent ineligible.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will contain bracketed comments preceded by an “EN” that will denote an examiner note. This will be placed to further explain a rejection.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Vanderveld et al. US 20230252521 A1, hereinafter Vanderveld in view of
Singh et al. US 20240161146 A1, hereinafter Singh. As per,
Regarding claim 1: Vanderveld teaches:
A system comprising:
a provider system in communication with a user device over a network, the user device having one or more input systems and one or more output systems, the provider system configured to receive data associated with a potential consumer of at least one of a product or a service [..] (See Vanderveld Fig. 3: server or provider system receives clickstream data about consumer and products/services as output from consumer devices, and provider system sends fulfillment data and marketing communication as input to consumer devices. ¶ [0036]: As used herein, the term ‘clickstream data’ refers to electronic information indicating content viewed, accessed, edited, or retrieved by consumers. [Also see Fig. 1 and related text]);
a demand prediction system having a machine learning model (Vanderveld ¶ [0003]: …assessing and analyzing consumers based on a machine learning model. Mid-¶ [0141]: The selected first attributes are used to generate a decision tree for predicting whether the first consumer will make a purchase within a pre-specified time period… based on a machine learning algorithm), the demand prediction system:
identifies, from the data including the input, one or more variables associated with a trigger event (Vanderveld ¶ [0114]: In some implementations, the process 500 may optionally continue with determining that a first event associated with the first consumer occurred (506). ¶ [0123]: In some implementations, predictions may be used to analyze events that may affect consumer value. For example, by analyzing interactions, by a particular consumer, with customer service, a return on investment (ROI) [EN: variable] may be determined for the particular consumer. Further, a customer lifetime value [EN: variable] may be a metric that can be optimized for product experimentation),
processes the data [..] indicating whether the potential consumer is likely to purchase at least one of the product or the service (Vanderveld mid-¶ [0114]: …In response to determining that the first event occurred, the process 500 may determine based on the updated first values for the one or more first attributes, an updated prediction value that indicates an updated programmatically expected number of purchases by the first consumer (508). ¶ [0108]: As described, the score may represent a probability or likelihood that a consumer will make a purchase or accept a promotion. A cutoff score or percentage may be specified by the promotional and marketing service performing process 400. For example, a cutoff score of 0.7 or higher may result in a prediction indicating that the consumer will make a purchase);
applies, using the machine learning model, weights to variables based on the importance of the variables that indicate a purchase likelihood (Vanderveld ¶ [0125]: FIGS. 11A-11D shows lists of example attributes, for predicting consumer behaviors, and an associated ranking indicating a measure of importance of each attribute for a plurality of cohorts…. Stage 1 may be for predicting whether a consumer will make a purchase during a pre-specified period. Stage 2 may be for predicting a programmatically expected number of purchases a consumer will make during the pre-specified period. End-¶ [0126]: For example, weights may be associated with attributes. Higher weights may be assigned to higher ranking attributes. Similarly, lower weights may be assigned to lower ranking attributes. Mid-¶ [0140]: The top attributes for each cohort may be determined according to random forest machine learning algorithm),
identifies the trigger event based on the weighted variables using the machine learning model (Vanderveld ¶ [0003]: This specification relates to assessing and analyzing consumers based on a machine learning model. ¶ [0114]: In some implementations, the process 500 may optionally continue with determining that a first event associated with the first consumer occurred (506). [Also see Figs. 4-5 and related text]);
determines the potential consumer is likely to purchase at least one of the product or the service base on the trigger event [..] using the machine learning model (Vanderveld mid-¶ [0114]: In response to determining that the first event occurred, the process 500 may determine based on the updated first values for the one or more first attributes, an updated prediction value that indicates an updated programmatically expected number of purchases by the first consumer (508)), and
determines an ideal time to communicate information about the at least one of the product or the service to the potential consumer by predicting at least one of a consumer life event, behavior, or attitude that precedes a product or service search (Vanderveld ¶ [0051]: In some embodiments, the merchant self-service indicators are a result of analytics that allow for generation of promotions that are ideal for the particular merchant's circumstances. For example, the merchant self-service indicators may be used to identify optimal promotions for the particular merchant based on… the date or season of the year…. Further, predictions of consumer behavior may be used in combination with the merchant attributes above to identify whether a promotion for a specific merchant is likely to satisfy a particular consumer belonging to a particular cohort…. Accordingly, the optimal promotions for the particular merchant may be provided to consumers that are likely to be satisfied with the optimal promotion. ¶ [0085]: The cohort management circuitry 210 may send and/or receive data from binary promotion prediction circuitry 212 and/or number of promotion prediction circuitry 214…. Consumers having a normalized score above the received cutoff score may be identified as consumers that are likely to make, at least, one purchase during the pre-specified period… the cohort management circuitry 210 may… train and use a machine learning model for predicting consumer behavior); and
a notification generation system configured to generate, in response to determining the ideal time, a notification associated with at least one of the product or the service, the provider system configured to transmit the notification to the user device to cause the notification to be presented using the one or more output systems (See Vanderveld Fig. 3: Electronic Marketing Communication transmitted from server to Consumer Devices. ¶ [0121]: In some implementations, the process 700 may optionally continue with providing an advertisement to the first consumer… based, at least in part, on the first prediction. Mid-[0093]:… the server 302 may determine promotions, goods, and services that are more likely to be of interest to a particular consumer or group of consumers based on clickstream data… ¶ [0094]: …the server 302 may note that the consumer has an interest in a particular hobby (e.g., skiing) based on electronic marketing information associated with the consumer (e.g., a browser cookie that indicates they frequently visit websites that provide snowfall forecasts for particular ski resorts), and offer promotions associated with that hobby (e.g., a promotion offering discounted ski equipment rentals or lift tickets)).
Although Vanderveld teaches the provider system receiving data associated with a potential consumer of a product or a service, Vanderveld does not specifically teach receiving the data via an interactive user interface including text or audio input or generating consumer sentiment analysis based on the input.
However, Singh in analogous are of creating promotions based on consumer behavior teaches or suggests:
[..] receive data associated with a potential consumer of at least one of a product or a service via an interactive user interface of a user device, the data including input provided by the potential consumer using at least one of text or audio (Singh mid-¶ [0045]: Other kinds of input devices 714 can be used to provide for interaction with a consumer as well, such as a tactile input device, visual input device, audio input device, or brain-computer interface device. For example, feedback provided to the consumer can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the consumer can be received in any form, including acoustic, speech, tactile, or brain wave input. Exemplary output devices 716 include display devices, such as an LCD (liquid crystal display) monitor, for displaying information to the consumer);
[..]
processes the data using sentiment analysis to generate a sentiment analysis output indicating whether the potential consumer is likely to purchase at least one of the product or the service (Singh mid-¶ [0032]: In some embodiments, step 504 includes evaluating a psychographic data for the consumer, the psycho graphic data obtained from an online survey answered by the consumer. In some embodiments, step 504 includes evaluating a sentiment analysis about the consumer from a social network interaction of the consumer. In some embodiments, step 504 includes selecting a product and a value added to the targeted offer based on a likelihood that the consumer will purchase the product via the channel for the consumer to redeem the targeted offer. In some embodiments, step 504 includes determining a likelihood that the consumer will activate the targeted offer via the in-store redemption or the online redemption);
[..]
determines the potential consumer is likely to purchase at least one of the product or the service base on [..] the sentiment analysis output [..] (Singh mid-¶ [0032]: In some embodiments, step 504 includes evaluating a psychographic data for the consumer, the psycho graphic data obtained from an online survey answered by the consumer. In some embodiments, step 504 includes evaluating a sentiment analysis about the consumer from a social network interaction of the consumer. In some embodiments, step 504 includes selecting a product and a value added to the targeted offer based on a likelihood that the consumer will purchase the product via the channel for the consumer to redeem the targeted offer. In some embodiments, step 504 includes determining a likelihood that the consumer will activate the targeted offer via the in-store redemption or the online redemption).
Singh and Vanderveld are found as analogous art of creating promotions based on consumer behavior. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Vanderveld’s consumer behavior prediction system and method to have included Singh’s teachings around receiving data via an interactive user interface including text or audio input and generating consumer sentiment analysis based on the input. The benefit of these additional features would have increased the likelihood of consumers redeeming promotions (Singh ¶ [0017]-[0018]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Vanderveld in view of Singh (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of creating promotions based on consumer behavior. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Vanderveld in view of Singh above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 7, 15: Vanderveld teaches:
A computer implemented method comprising: (claim 7) /
One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising: (claim 15)
receiving data associated with a potential consumer (claims 7, 15) of at least one of a product or a service (claim 7 only) [..] (See Vanderveld Fig. 3: server or provider system receives clickstream data about consumer and products/services as output from consumer devices, and provider system sends fulfillment data and marketing communication as input to consumer devices. ¶ [0036]: As used herein, the term ‘clickstream data’ refers to electronic information indicating content viewed, accessed, edited, or retrieved by consumers. [Also see Fig. 1 and related text]);
identifying, from the data including the input, one or more variables associated with a trigger event (Vanderveld ¶ [0114]: In some implementations, the process 500 may optionally continue with determining that a first event associated with the first consumer occurred (506). ¶ [0123]: In some implementations, predictions may be used to analyze events that may affect consumer value. For example, by analyzing interactions, by a particular consumer, with customer service, a return on investment (ROI) [EN: variable] may be determined for the particular consumer. Further, a customer lifetime value [EN: variable] may be a metric that can be optimized for product experimentation);
processing the data [..] indicating whether the potential customer is ready to purchase at least one of the product or the service (Vanderveld mid-¶ [0114]: …In response to determining that the first event occurred, the process 500 may determine based on the updated first values for the one or more first attributes, an updated prediction value that indicates an updated programmatically expected number of purchases by the first consumer (508). ¶ [0108]: As described, the score may represent a probability or likelihood that a consumer will make a purchase or accept a promotion. A cutoff score or percentage may be specified by the promotional and marketing service performing process 400. For example, a cutoff score of 0.7 or higher may result in a prediction indicating that the consumer will make a purchase);
applying, by a machine learning model, weights to variables derived from the data based on the importance of the variables that indicate a purchase likelihood (Vanderveld ¶ [0125]: FIGS. 11A-11D shows lists of example attributes, for predicting consumer behaviors, and an associated ranking indicating a measure of importance of each attribute for a plurality of cohorts…. Stage 1 may be for predicting whether a consumer will make a purchase during a pre-specified period. Stage 2 may be for predicting a programmatically expected number of purchases a consumer will make during the pre-specified period. End-¶ [0126]: For example, weights may be associated with attributes. Higher weights may be assigned to higher ranking attributes. Similarly, lower weights may be assigned to lower ranking attributes. Mid-¶ [0140]: The top attributes for each cohort may be determined according to random forest machine learning algorithm);
identifying the trigger event (claim 7) / identifying an event (claim 15) based on the weighted variables using the machine learning model (Vanderveld ¶ [0003]: This specification relates to assessing and analyzing consumers based on a machine learning model. ¶ [0114]: In some implementations, the process 500 may optionally continue with determining that a first event associated with the first consumer occurred (506)));
determining (claim 7) / predicting (claim 15) the potential consumer is likely to purchase at least one of the product or the service (claim 7) / a product or service (claim 15) based on the trigger event (claim 7) / event (claim 15) [..] using the machine learning model (Vanderveld mid-¶ [0114]: …In response to determining that the first event occurred, the process 500 may determine based on the updated first values for the one or more first attributes, an updated prediction value that indicates an updated programmatically expected number of purchases by the first consumer (508). ¶ [0108]: As described, the score may represent a probability or likelihood that a consumer will make a purchase or accept a promotion. A cutoff score or percentage may be specified by the promotional and marketing service performing process 400. For example, a cutoff score of 0.7 or higher may result in a prediction indicating that the consumer will make a purchase);
determining an ideal time to communicate information about the at least one of the product or the service to the potential customer by predicting at least one consumer life event, behavior, or attitude that precedes a product or service search (Vanderveld ¶ [0051]: In some embodiments, the merchant self-service indicators are a result of analytics that allow for generation of promotions that are ideal for the particular merchant's circumstances. For example, the merchant self-service indicators may be used to identify optimal promotions for the particular merchant based on… the date or season of the year…. Further, predictions of consumer behavior may be used in combination with the merchant attributes above to identify whether a promotion for a specific merchant is likely to satisfy a particular consumer belonging to a particular cohort…. Accordingly, the optimal promotions for the particular merchant may be provided to consumers that are likely to be satisfied with the optimal promotion. ¶ [0085]: The cohort management circuitry 210 may send and/or receive data from binary promotion prediction circuitry 212 and/or number of promotion prediction circuitry 214…. Consumers having a normalized score above the received cutoff score may be identified as consumers that are likely to make, at least, one purchase during the pre-specified period… the cohort management circuitry 210 may… train and use a machine learning model for predicting consumer behavior);
generating, in response to determining the ideal time, a notification associated with the (claim 7) / at least one of the (claim 15) product or the service; and causing the notification to be presented using one or more output systems (claim 7) / output\ systems of a user device (claim 15) (See Vanderveld Fig. 3: Electronic Marketing Communication transmitted from server to Consumer Devices. ¶ [0121]: In some implementations, the process 700 may optionally continue with providing an advertisement to the first consumer… based, at least in part, on the first prediction. Mid-[0093]:… the server 302 may determine promotions, goods, and services that are more likely to be of interest to a particular consumer or group of consumers based on clickstream data… ¶ [0094]: …the server 302 may note that the consumer has an interest in a particular hobby (e.g., skiing) based on electronic marketing information associated with the consumer (e.g., a browser cookie that indicates they frequently visit websites that provide snowfall forecasts for particular ski resorts), and offer promotions associated with that hobby (e.g., a promotion offering discounted ski equipment rentals or lift tickets)).
Although Vanderveld teaches the provider system receiving data associated with a potential consumer of a product or a service, Vanderveld does not specifically teach receiving the data via an interactive user interface including text or audio input or generating consumer sentiment analysis based on the input.
However, Singh in analogous are of creating promotions based on consumer behavior teaches or suggests:
receiving data associated with a potential consumer (claims 7, 15) of at least one of a product or a service (claim 7 only) via an interactive user interface of a user device, the data including input provided by the potential consumer using at least one of text or audio (Singh mid-¶ [0045]: Other kinds of input devices 714 can be used to provide for interaction with a consumer as well, such as a tactile input device, visual input device, audio input device, or brain-computer interface device. For example, feedback provided to the consumer can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the consumer can be received in any form, including acoustic, speech, tactile, or brain wave input. Exemplary output devices 716 include display devices, such as an LCD (liquid crystal display) monitor, for displaying information to the consumer);
[..]
processing the data using sentiment analysis to generate sentiment analysis output indicating whether the potential customer is ready to purchase at least one of the product or the service (Singh mid-¶ [0032]: In some embodiments, step 504 includes evaluating a psychographic data for the consumer, the psycho graphic data obtained from an online survey answered by the consumer. In some embodiments, step 504 includes evaluating a sentiment analysis about the consumer from a social network interaction of the consumer. In some embodiments, step 504 includes selecting a product and a value added to the targeted offer based on a likelihood that the consumer will purchase the product via the channel for the consumer to redeem the targeted offer. In some embodiments, step 504 includes determining a likelihood that the consumer will activate the targeted offer via the in-store redemption or the online redemption);
[..]
determining (claim 7) / predicting (claim 15) the potential consumer is likely to purchase at least one of the product or the service (claim 7) / a product or service (claim 15) based on [..] the sentiment analysis output [..] (Singh mid-¶ [0032]: In some embodiments, step 504 includes evaluating a psychographic data for the consumer, the psycho graphic data obtained from an online survey answered by the consumer. In some embodiments, step 504 includes evaluating a sentiment analysis about the consumer from a social network interaction of the consumer. In some embodiments, step 504 includes selecting a product and a value added to the targeted offer based on a likelihood that the consumer will purchase the product via the channel for the consumer to redeem the targeted offer. In some embodiments, step 504 includes determining a likelihood that the consumer will activate the targeted offer via the in-store redemption or the online redemption).
Singh and Vanderveld are found as analogous art of creating promotions based on consumer behavior. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Vanderveld’s consumer behavior prediction system and method to have included Singh’s teachings around receiving data via an interactive user interface including text or audio input and generating consumer sentiment analysis based on the input. The benefit of these additional features would have increased the likelihood of consumers redeeming promotions (Singh ¶ [0017]-[0018]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Vanderveld in view of Singh (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of creating promotions based on consumer behavior. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Vanderveld in view of Singh above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 2, 8, 16: Vanderveld / Singh teaches all the limitations of claims 1, 7, 15 above.
Vanderveld further teaches:
receiving a selection via one or more input systems through interaction by the potential consumer, the selection indicating at least one of the product or the service the potential consumer desires to purchase (Vanderveld ¶ [0037]: As used herein, the term "transaction data" refers to electronic information indicating that a transaction is occurring or has occurred via either a merchant or the promotion and marketing service. Transaction data may also include information relating to the transaction. For example, transaction data may include consumer payment or billing information, consumer shipping information, items purchased by the consumer, a merchant rewards account number associated with the consumer, the type of shipping selected by the consumer for fulfillment of the transaction, or the like. ¶ [0036]: …the clickstream data may include various other consumer interactions, including without limitation, mouse over events and durations, the amount of time spent by the consumer viewing particular content, the rate at which impressions of particular content result in sales associated with that content… the frequency of impressions for particular content….).
Regarding claims 3, 10, 18: Vanderveld / Singh teaches all the limitations of claims 1, 7, 15 above.
Vanderveld further teaches:
wherein the data includes at least one of a life event, brand loyalty, a buying habit, an internet search habit, digital behavior, a life-time value, age, race, ethnicity, gender, income level, education level, employment status, occupation, homeownership, zip code, location, number of accidents, number of insurance claims, age of home, value of home, age of car, value of car, location density, years of being a customer, profitability, or a credit score (Vanderveld mid-¶ [0036]: For example, the clickstream data may include various other consumer interactions, including without limitation, mouse over events and durations, the amount of time spent by the consumer viewing particular content [EN: search habit], the rate at which impressions of particular content result in sales associated with that content [EN: buying habit], demographic information associated with each particular consumer [EN: age, race, ethnicity, gender, income level, education level], data indicating other content accessed by the consumer (e.g., browser cookie data) [EN: digital behavior], the time or date on which content was accessed, the frequency of impressions for particular content, associations between particular consumers or consumer demographics and particular impressions, and/or the like. [Also see table of consumer attributes for behavior prediction at ¶ [0137]).
Regarding claims 4, 11, 19: Vanderveld / Singh teaches all the limitations of claims 1, 7, 15 above.
Vanderveld further teaches:
wherein the notification includes at least one of a plurality of selectable products or services, a recommended additional product or additional service, an explanation of at least one of the product or the service, or a comparison with users with similar characteristics as the potential consumer (Vanderveld mid-¶ [0042]: These offering parameters may include parameters, bounds, considerations and/or the like that outline or otherwise define the terms, timing, constraints, limitations, rules or the like under which the promotion [EN: recommendation] is sold, offered, marketed, or otherwise provided to consumers [EN: explanation of product or service]. ¶ [0132]: It should also be understood that consumers may have associated data indicating one or more categories, sub-categories, location, hyper-locations, prices or the like [EN: to recommend additional products or services]. For example, a consumer, may be identified as a consumer that is interested in categories such as "beauty, wellness, and healthcare," "Food and drink," "Leisure Offers and Activities." Similarly, a consumer may be identified as a consumer interested in promotions within a pre-specified price range).
Regarding claims 5, 12, 20: Vanderveld / Singh teaches all the limitations of claims 1, 7, 15 above.
Vanderveld further teaches:
wherein the notification is at least one of an advertisement for the product or the service, a recommendation for the product or the service, or a promotion of at least one of the product or the service (Vanderveld ¶ [0046]: As used herein, the term "electronic marketing communication" [EN: notification] refers to any electronically generated information content provided by the promotion and marketing service to a consumer for the purpose of marketing a promotion, good, or service to the consumer. Electronic marketing communications may include any email, short message service (SMS) message, web page, application interface, or the like, electronically generated for the purpose of attempting to sell or raise awareness of a product, service, promotion, or merchant to the consumer).
Regarding claims 9, 17: Vanderveld / Singh teaches all the limitations of claims 8, 16 above.
Vanderveld further teaches:
providing at least one of the product or the service to the potential consumer based on the selection (Vanderveld ¶ [0092]: As a result of transactions performed between the one or more consumer devices 304 and the server 302, the server 302 may provide fulfillment data to the consumer devices. The fulfillment data may include information indicating whether the transaction was successful, the location and time the product will be provided to the consumer, instruments for redeeming promotions purchased by the consumer, or the like).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Conclusion
The following art is made of record and considered pertinent to Applicant’s disclosure:
Stone; Peter et al. US 20120005028 A1, Ad auction optimization.
Chu; Yea Jane et al. US 20150332296 A1, Predicting customer value.
Xu; Jin et al. US 20200027103 A1, Prioritization System for Products Using a Historical Purchase Sequence and Customer Features.
Fano, Andrew E. et al. US 20050189415 A1, System for individualized customer interaction.
NILAKANTA; HAEMA et al. US 20240412252 A1, Personalized ranking of promotional items.
CHUNG; Wook Jin et al. US 20100241498 A1, Dynamic advertising platform.
MANFIELD; Matthew et al. US 20220101378 A1, System and method for disseminating information to consumers.
McColeman; Ryan et al. US 20240428314 A1, Determining purchase suggestions for an online shopping concierge platform.
Langdon; Daniel et al. US 10832281 B1, Systems, apparatus, and methods for providing promotions based on consumer interactions.
Chennavasin; Don Albert et al. US 10832290 B1, Method and system for providing electronic marketing communications for a promotion and marketing service.
SINGH ZUBIN et al. WO 2022204483 A1, System to select in-store or e-commerce channels for optimized delivery of creative media to consumers.
Chaudhuri, Neha, et al. "On the platform but will they buy? Predicting customers' purchase behavior using deep learning." Decision Support Systems 149 (2021): 113622. https://doi.org/10.1016/j.dss.2021.113622
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REED M. BOND whose telephone number is (571) 270-0585. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/REED M. BOND/Examiner, Art Unit 3624
August 20, 2026
/HAMZEH OBAID/Primary Examiner, Art Unit 3624
1 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
Prosecution Insights