DETAILED ACTION
Response to Amendment
This Final office action is in response to Applicant’s amendment filed 2/19/2026. Claims 1, 13 and 20 have been amended. Claims 1, 4-13 and 20-28 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant's arguments filed 2/19/2026 have been fully considered but they are not persuasive.
The Examiner notes that the amendments of independent claim 20 seem to be the exact same amendments from Applicant’s response filed 11/10/2025. As a result, the claim will be examined accordingly.
The previous pending objection to claims 1 and 13 have been withdrawn.
Claim Objections
Claim 1 is objected to because of the following informalities: The current amendment seems to have deleted “updating, at the data store associated with the system: when a…”, without any corresponding notation. Appropriate correction is required.
Claim 20 is objected to because of the following informalities: The claims recite “the explainability data to indicate a change into a change into a third predetermined cohort”, which seems to be grammatically incorrect. Appropriate correction is required.
Claims 1, 13 and 20 are objected to because of the following informalities: The claims recite “the explainability data to indicate a change into a second predetermined cohort different from the first predetermined cohort and, and…”, which seems to be grammatically incorrect. Appropriate correction is required.
Claim 23 is objected to because of the following informalities: The claim is missing a period (“.”). Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-13 and 20-28 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Amended independent claims 1 and 13 recite “…first action element is determined, the explainability data to indicate a change into a second predetermined cohort different from the first predetermined cohort and, and when a second action element, different from the first action element, is determined…” (emphasis added).
There does not seem to be any support in the originally filed specification for a first action or second action element. Clarification is required.
Amended independent claim 20 recites “…updating, at the data store associated with the system: when a first threshold action element is satisfied, the explainability data to indicate a change into a second predetermined cohort different from the first predetermined cohort and, and when the first threshold action element is not satisfied, the explainability data to indicate a change into a change into a third predetermined cohort different from the first predetermined cohort and the second predetermined cohort…” (emphasis added).
There does not seem to be any support in the originally filed specification for a first threshold action element being satisfied or not satisfied, followed by a change in the predetermined cohort. Clarification is required.
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, 4-13 and 20-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, system claims 1, 4-12 and 21-28 are directed to a communications interface; a memory resource storing instructions; and at least one processor coupled to the communications interface and to the memory, method claim 13 is directed to a series of steps, and computer readable medium claim 20 is directed to storing instructions executed by a processor. Thus the claims are directed to a machine, process and manufacture, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite conversion management, including obtaining, implementing, generating, and sorting steps.
The limitations of obtaining, implementing, generating, and sorting, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite perform a first process during a first time period, the first process comprising: obtaining a first set of features of a set of users including a first set of one or more features of transaction data for each respective user of the set of users comprising a first set of elements of transaction data associated with the first time period prior to a second time period and a second set of elements of transaction data associated with the second time period and a first set of one or more features of engagement data for each respective user of the set of users comprising a first set of action elements obtained from a corresponding computing device in response to a first input responsive to a first actionable graphical user interface element, in a plurality of actionable graphical user interface elements displayed at the corresponding computing device during the first time period; generate as output a first plurality of conversion scores, each respective conversion scores score of the plurality of conversion scores being associated with a particular user of the set of users and characterizing a likelihood of a conversion event inputted by the corresponding user during the second time period; sorting each user identifier of the set of users into one of multiple predetermined cohorts, each respective predetermined cohort of the multiple predetermined cohorts representing a corresponding subrange in a predetermined range of conversion scores; for at least a first predetermined cohort of the multiple predetermined cohorts, implementing a second set of operations that generate explainability data associated with the first predetermined cohort, wherein the explainability data includes multiple subsets of values, each subset of values is associated with one of the set of features, and each value in the multiple subsets of values indicates a magnitude and a negative or positive sign of a contribution of a corresponding feature to conversion scores associated with at least the first predetermined cohort; identifying, based on the explainability data, a second actionable graphical user interface element in the plurality of actionable graphical user interface elements: storing, at a data store associated with the system, the explainability data;
perform a second process during the second time period, the second process comprising: obtaining a second set of features of the set of users including (i) a second set of one or more features of transaction data for each respective user of the set of users comprising a third set of elements of transaction data associated with the second time period and (ii) a second set of one or more features of engagement data for each respective user of the set of users comprising a second set of action elements obtained from the corresponding computing device or a different computing device associated with the respective user in response to a second input responsive to the second actionable graphical user interface element displayed at the corresponding computing device or the different computing device during the second time period; generate as output a second plurality of conversion scores, each respective conversion score of the second plurality of conversion scores being associated with a corresponding user of the set of users and characterizing the second input by the corresponding user responsive to the second actionable graphical user interface element and updating, at the data store associated with the system: when a first threshold action element is satisfied, the explainability data to indicate a change into a second predetermined cohort different from the first predetermined cohort and, and when the first threshold action element is not satisfied, the explainability data to indicate a change into a change into a third predetermined cohort different from the first predetermined cohort and the second predetermined cohort.
That is, other than reciting a memory resource storing instructions; and at least one processor coupled to memory, and a trained validated machine learning model, the claim limitations merely cover commercial interactions, including marketing or sales activities or behaviors, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include a memory resource storing instructions; and at least one processor coupled to memory, and a trained validated machine learning model. The memory resource storing instructions; and at least one processor coupled to memory, and a trained validated machine learning model in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a memory resource storing instructions; and at least one processor coupled to memory, and a trained validated machine learning model amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claims 4-7 recite an additional implementing step, and further describe the first and second set of operations. Claims 8 and 9 recite additional obtaining steps. Claims 10-12 recite additional determining and transmitting steps. Claims 21-23 further describe each corresponding subrange, the multiple predetermined cohorts, and the likelihood of the conversion event. Claims 24 and 25 further describe the first set of elements of transaction data, and recite additional determining and providing steps. Claims 26-28 further describes each respective predetermined cohort of the multiple predetermined cohorts, and recite additional communicating, receiving, and generating steps. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, a memory resource storing instructions; and at least one processor coupled to memory, and a trained validated machine learning model.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraph 17 of the specification, “FIG. 1 illustrates a block diagram of an example membership system 100 that includes membership computing device 102 (e.g., a server, such as an application server), external source system 103, a web server 104,data repository 116, multiple customer mobile computing devices 110, 112, and 114, and multiple computing systems 118, 120, and 122 operatively coupled over communication network 108. Membership computing device 102, web server 104, multiple customer mobile computing devices 110, 112, and 114, and multiple computing systems 118, 120, and 122 can each be any suitable computing device that includes any hardware or hardware and software combination for processing and handling information. For example, each can include one or more processors, one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more state machines, digital circuitry, or any other suitable circuitry. In addition, each can transmit data to, and receive data from, communication network 108.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4, 5, 8-13, 20 and 23-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al (US 20210406931 A1).
As per claim 1, Liu et al disclose a system comprising:
a memory resource storing instructions; and at least one processor coupled to the memory (i.e., server provider (SP) system 100 in which certain examples of the disclosed principles may be implemented. The example SP system 100 includes a network 111, client device 101, contextual marketing (CMM) device 106, and service provider devices 107-108, ¶ 0022), the at least one processor being configured to execute the instructions to:
train a machine learning model using a first set of feature data associated with a first prior time period, thereby forming a trained machine learning model (i.e., the status of the user, the age of the user's membership/trial, the subscribed platform, etc., may be used as training data to train machine learning models, ¶ 0040);
validate the trained machine learning model using a second set of feature data associated with a second prior time period before the first prior time period, thereby forming a trained validated machine learning model (i.e., ML framework may construct sequences of behavioral observations for the user and determining a loss label for model training and (once enough time passes for it to become available) monitor the operational model performance. For model training and calibration, data available in the event storage 306 or attribute/state vector storage 410 is split into independent sets for training, testing, and validation, ¶ 0049);
perform a first process during a first time period after the first prior time period, the first process comprising; obtaining a first set of features of a set of users including (i) a first set of one or more features of transaction data for each respective user of the set of users comprising a first set of elements of transaction data associated with the first time period prior to a second time period and a second set of elements of transaction data associated with the second time period (i.e., FIG. 3 shows one embodiment of an intake manager (IM) 300 configured to provide a framework for accessing raw or model produced data files that may include transactional and/or behavioral data for users of a service provider, ¶ 0036, wherein in a daily updated batch model the historical data can include any data received the day before. In contrast, current data is any data that is received and processed in real-time upon receipt of receiving said data. The data source 202 may store various historical data as well as current data including but not limited to product and/or service use behaviors, and the like, ¶ 0031)
and (ii) a first set of one or more features of engagement data for each respective user of the set of users comprising a first set of action elements obtained from a corresponding computing device in response to a first input responsive to a first actionable graphical user interface element, in a plurality of actionable graphical user interface elements, displayed at the corresponding computing device during the first time period; (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, or more generally, take any desired action on a platform interface, ¶ 0020, wherein The marketing campaigns sent to the user over the life of the user’s engagement with the service provider’s platform ages is noted and stored, ¶ 0061);
further inputting the first set of features to the trained validated machine learning model to generate as output a first plurality of conversion scores, each respective conversion scores score of the first plurality of conversion scores being associated with a particular user of the set of users and characterizing a likelihood of a conversion event inputted by the corresponding user during the second time period (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, ¶ 0020, wherein FIG. 2 shows one example of a contextual marketing architecture 200 that may be used to determine a propensity score indicating the likelihood that a user will convert and/or churn, ¶ 0028, wherein the status of the user, the age of the user's membership/trial, the subscribed platform, etc., may be used as training data to train machine learning models that may be used to determine the propensity score. The machine learning models may be updated based on daily updated state data, or the like, to compute a new propensity score, ¶ 0040);
sorting each user identifier of the set of users into one of multiple predetermined cohorts, each respective predetermined cohort of the multiple predetermined cohorts representing a corresponding subrange in a predetermined range of conversion scores (i.e., Decider sub-process 504 uses the rank ordered messaging for each user in conjunction with platform features to deliver the messaging to the user to reduce loss risk. For example, an initial propensity score can be used to assign a user to four bins, where one is the least likely to convert and four is the most likely to convert, ¶ 0057);
for at least a first predetermined cohort of the multiple predetermined cohorts, implementing a second set of operations that generate explainability data associated with the first predetermined cohort (i.e., CMM 500 uses the propensity score to indicate the likelihood that a user will convert and/or churn in accordance. For example, sub-processes 502 and 504 are directed towards making marketing decisions about what messaging content should be provided and when should that messaging content be received to increase the propensity score for the user, ¶ 0058), wherein:
the explainability data includes multiple subsets of values, each subset of values is associated with one of the set of features, and each value in the multiple subsets of values indicates a magnitude and a negative or positive sign of a contribution of a corresponding feature to conversion scores associated with at least the first predetermined cohort (i.e., The propensity score is recalculated based on each of the hypothetical scenarios. The largest propensity score gains are determined, and the top hypothetical scenario(s) that indicates the largest gain in propensity score is retained. The CMM 500 is operable to selectively prepare a message that is configured to reach a user, based at least in part on the service provider feature related to the retained top scenario. The service provider feature is any feature related to a hypothetical scenario of the user. For example, if a hypothetical scenario with a significant gain in propensity score includes the user updating his profile to include a business account, the service provider feature might include a feature related to linking business accounts, ¶ 0029, wherein the propensity score whose gains reach and/or exceed the predetermined threshold is identified. The top hypothetical scenario(s) that indicates the largest gain in propensity score is retained, ¶ 0051);
identifying, based on the explainability data, a second actionable graphical user interface element in the plurality of actionable graphical user interface elements (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, or more generally, take any desired action on a platform interface, ¶ 0020. Service provider devices 107-108 may provide various interfaces including, but not limited to, those described in more detail below in conjunction with FIG. 2, ¶ 0027, wherein the Predictive Models 600 are implemented to receive a propensity score, implement a simulation model that augments a user's actual actions with hypothetical scenarios they could take, receive a propensity score for each hypothetical scenario, and select at least one service provider feature in the hypothetical scenarios based in part on the propensity score. The propensity score is recalculated based on each of the hypothetical scenarios, ¶ 0029);
storing, at a data store associated with the system, the explainability data (i.e., The data source 202 may be configured to store such historical data as a customer's profile, including its billing history, platform subscriptions, feature information, content purchases, client device characteristics, and the like. Historical data is any data that is received and processed at some other time other than real time, ¶ 0031);
perform a second process during the second time period (i.e., The data may then be provided to common schema manager 400, which may compute various additional attributes, manage updates to state vectors for entities (users) within the system, further map raw data into a common schema, and determine a propensity score for the user based on the same, ¶ 0034), the second process comprising:
obtaining a second set of features of the set of users including (i) a second set of one or more features of transaction data for each respective user of the set of users comprising a third set of elements of transaction data associated with the second time period (i.e., FIG. 3 shows one embodiment of an intake manager (IM) 300 configured to provide a framework for accessing raw or model produced data files that may include transactional and/or behavioral data for users of a service provider, ¶ 0036, wherein in a daily updated batch model the historical data can include any data received the day before. In contrast, current data is any data that is received and processed in real-time upon receipt of receiving said data. The data source 202 may store various historical data as well as current data including but not limited to product and/or service use behaviors, and the like, ¶ 0031) and
(ii) a second set of one or more features of engagement data for each respective user of the set of users comprising a second set of action elements obtained from the corresponding computing device or a different computing device associated with the respective user in response to a second input responsive to the second actionable graphical user interface element displayed at the corresponding computing device or the different computing device during the second time period (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, or more generally, take any desired action on a platform interface, ¶ 0020, wherein The marketing campaigns sent to the user over the life of the user’s engagement with the service provider’s platform ages is noted and stored, ¶ 0061);
further inputting the second set of features to the trained validated machine learning model to generate as output a second plurality of conversion scores, each respective conversion score of the second plurality of conversion scores being associated with a corresponding user of the set of users and characterizing the second input by the corresponding user responsive to the second actionable graphical user interface element (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, ¶ 0020, wherein FIG. 2 shows one example of a contextual marketing architecture 200 that may be used to determine a propensity score indicating the likelihood that a user will convert and/or churn, ¶ 0028, wherein the status of the user, the age of the user's membership/trial, the subscribed platform, etc., may be used as training data to train machine learning models that may be used to determine the propensity score. The machine learning models may be updated based on daily updated state data, or the like, to compute a new propensity score, ¶ 0040) and
updating, at the data store associated with the system (For example, in a daily updated batch model the historical data can include any data received the day before. In contrast, current data is any data that is received and processed in real-time upon receipt of receiving said data. The data source 202 may store various historical data as well as current data including but not limited to product and/or service use behaviors, and the like, ¶ 0031);
when a first action element is determined, the explainability data to indicate a change into a second predetermined cohort different from the first predetermined cohort and, and when a second action element, different from the first action element, is determined, the explainability data to indicate a change into a third predetermined cohort different from the first predetermined cohort and the second predetermined cohort (i.e., After implementing a predictive model at block 712, process 700 proceeds to block 714 where a second propensity score is determined based on the hypothetical attribute of the user and/or the hypothetical state data of the user. It is determined whether the second propensity score reaches or exceeds a predetermined threshold. The first propensity score and the second propensity score may be determined daily. Process 700 proceeds to block 722 where message content is generated to deliver to the user based on the determined second propensity score and the service provider feature, ¶ 0068-0069).
As per claim 4, Liu et al disclose based on a conversion score associated with a first user of the set of users associated with the first predetermined cohort, implement a third set of operations that generates explainability data associated with the first user (i.e., the propensity score can be used to rank the predicted effectiveness of various messages and to define eligibility requirements that may be used by CMM 500 to direct marketing campaigns or that match appropriate offers, incentives, and messages to a select user, ¶ 0057).
As per claim 5, Liu et al disclose the first set of operations includes applying a trained and validated machine learning model to the set of features (i.e., the status of the user, the age of the user’s membership/trial, the subscribed platform, etc., may be used as training data to train machine learning models that may be used to determine the propensity score. The machine learning models may be updated based on daily updated state data, or the like, to compute a new propensity score, ¶ 0040).
As per claim 8, Liu et al disclose obtain one or more features of supplemental user data of the set of users; and wherein the implementation of the first set of operations that generate the output data is further based on the one or more features of supplemental user data (i.e., The data source 202 may be configured to store such historical data as a customer’s profile, including its billing history, platform subscriptions, feature information, content purchases, client device characteristics, and the like. Historical data is any data that is received and processed at some other time other than real time, ¶ 0031).
As per claim 9, Liu et al disclose obtain features of membership data of the set of users; and wherein the implementation of the first set of operations that generate the output data is further based on the features of membership data (i.e., Such user profile information may include, but is not limited to, type of user and/or behavioral information about the user. For example, the user might be determined to be a trial user, who is less than twenty-eight days into a trial membership of a specific platform. The user profile is subsequently used to predict the likelihood of the trial user to convert to a subscriber, ¶ 0024).
As per claim 10, Liu et al disclose determine at least a first activation of a plurality of activations associated with the first predetermined cohort, each of the plurality of activations being associated with a computing system of a plurality of computing systems (i.e., it may be decided to send a specific marketing campaign to high risk churners, and not to low risk churners. Moreover, the propensity score can be generated and updated as the user’s engagement with the service provider’s platform ages. The marketing campaigns sent to the user over the life of the user’s engagement with the service provider’s platform ages is noted and stored. The updated propensity score is used as indicia of experimental success for marketing experiments, ¶ 0061).
As per claim 11, Liu et al disclose for at least the first activation, transmit instructions to a first computing system associated with the first activation causing the first computing system to communicate data associated with the first activation with at least a computing device of a user associated with the first predetermined cohort (i.e., The output of the decider process includes a validated assignment of each user to a bin, or a control group, which is used by sub-process 506 to update various decision attributes, and by sub-process 508 to compose and send various messages to a user. Thus, the propensity score can be used to rank the predicted effectiveness of various messages and to define eligibility requirements that may be used by CMM 500 to direct marketing campaigns or that match appropriate offers, incentives, and messages to a select user, ¶ 0057).
As per claim 12, Liu et al disclose each activation of the plurality of activations being associated with each predetermined cohort of the multiple predetermined cohorts (i.e., The output of the decider process includes a validated assignment of each user to a bin, or a control group, which is used by sub-process 506 to update various decision attributes, and by sub-process 508 to compose and send various messages to a user. Thus, the propensity score can be used to rank the predicted effectiveness of various messages and to define eligibility requirements that may be used by CMM 500 to direct marketing campaigns or that match appropriate offers, incentives, and messages to a select user, ¶ 0057).
Claim 13 is rejected based upon the same rationale as the rejection of claim 1, since it is the method claim corresponding to the system claim.
Claim 20 is rejected based upon the same rationale as the rejection of claim 1, since it is the computer readable medium claim corresponding to the system claim.
As per claim 23, Liu et al disclose the likelihood of the conversion event comprise a utilization of the corresponding user associated with the actionable graphical user interface element displayed at the computing device during the first time period (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, or more generally, take any desired action on a platform interface, ¶ 0020, wherein The marketing campaigns sent to the user over the life of the user’s engagement with the service provider’s platform ages is noted and stored, ¶ 0061).
As per claim 24, Liu et al disclose the first set of elements of transaction data comprises one or more data elements identifying a physical location associated with the respective user during the first time period (i.e., The data source 202 may also provide information about a time when such communications occur, as well as a physical location for which a user might be connected to during a communication, ¶ 0031).
As per claim 25, Liu et al disclose the first set of elements of transaction data further comprises a plurality of orders by the set of users at the physical location (i.e., The data source 202 may store various historical data as well as current data including but not limited to product and/or service use behaviors, and the like. The data source 202 may also provide information about a time when such communications occur, as well as a physical location, ¶ 0031), and wherein the further inputting the set of features comprises (i) determining both (a) one or more item types purchased in each respective order in the plurality of orders and a corresponding customer identifier associated with the respective order (i.e., The data source 202 may be configured to store such historical data as a customer's profile, including its billing history, platform subscriptions, feature information, content purchases, client device characteristics, and the like…The data source 202 may store various historical data as well as current data including but not limited to product and/or service use behaviors, ¶ 0031) and (b) an inter-purchase interval between a first order associated with the corresponding customer identifier and a second order associated with the corresponding customer identifier different from the first order (i.e., The data source 202 may be configured to store such historical data as a customer's profile, including its billing history, platform subscriptions, feature information, content purchases, client device characteristics, and the like. Historical data is any data that is received and processed at some other time other than real time, ¶ 0031); and (i) providing, as input to the machine learning model, both (a) the one or more item types and the corresponding customer identifier to the machine learning model and (b) the inter-purchase interval (i.e., the status of the user, the age of the user's membership/trial, the subscribed platform, etc., may be used as training data to train machine learning models that may be used to determine the propensity score, ¶ 0040).
As per claim 26, Liu et al disclose the at least one processor is further configured to execute instructions to: for each respective predetermined cohort of the multiple predetermined cohorts, implement the second set of operations that generate corresponding explainability data associated with the respective predetermined cohort (i.e., CMM 500 uses the propensity score to indicate the likelihood that a user will convert and/or churn in accordance. For example, sub-processes 502 and 504 are directed towards making marketing decisions about what messaging content should be provided and when should that messaging content be received to increase the propensity score for the user, ¶ 0058), wherein: the corresponding explainability data includes multiple subsets of values, each subset of values is associated with one of the set of features, and each value in the multiple subsets of values indicates a magnitude and a negative or positive sign of a contribution of a corresponding feature to conversion scores associated with the respective predetermined cohort (i.e., The propensity score is recalculated based on each of the hypothetical scenarios. The largest propensity score gains are determined, and the top hypothetical scenario(s) that indicates the largest gain in propensity score is retained. The CMM 500 is operable to selectively prepare a message that is configured to reach a user, based at least in part on the service provider feature related to the retained top scenario. The service provider feature is any feature related to a hypothetical scenario of the user. For example, if a hypothetical scenario with a significant gain in propensity score includes the user updating his profile to include a business account, the service provider feature might include a feature related to linking business accounts, ¶ 0029, wherein the propensity score whose gains reach and/or exceed the predetermined threshold is identified. The top hypothetical scenario(s) that indicates the largest gain in propensity score is retained, ¶ 0051), generating, for each respective predetermined cohort having a corresponding explainability data that satisfies a threshold conversion score, one or more instructions to implement another actionable graphical user interface element for display at a respective computing device during the second time period (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, or more generally, take any desired action on a platform interface, ¶ 0020, wherein The marketing campaigns sent to the user over the life of the user’s engagement with the service provider’s platform ages is noted and stored, ¶ 0061).
As per claim 27, Liu et al disclose the at least one processor is further configured to execute instructions to: communicate, via a communication network, one or more instructions for displaying the explainability data for the at least the first predetermined cohort at a corresponding computing device associated with a third-party different from a respective user in the set of users; and receiving, via the communication, one or more annotations, inputted at the corresponding computing device, associated with the first predetermined cohort (i.e., Service provider devices 107-108 may include virtually any network computing device that is configured to provide, to CMM device 106, information including product usage characteristic information, user information, and/or other context information, including, for example, the number of bank accounts the user has added, the number of trips the user has reviewed, the ratio of business trips to personal trips, etc. In some embodiments, service provider devices 107-108 may provide various interfaces including, but not limited to, those described in more detail below in conjunction with FIG. 2, ¶ 0027).
As per claim 28, Liu et al disclose the at least one processor is further configured to execute instructions to: generate, for each respective predetermined cohort having a corresponding explainability data that satisfies a threshold conversion score, one or more instructions to implement another actionable graphical user interface element for display at a respective computing device during the second time period (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, or more generally, take any desired action on a platform interface, ¶ 0020, wherein The marketing campaigns sent to the user over the life of the user’s engagement with the service provider’s platform ages is noted and stored, ¶ 0061).
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US 20210406931 A1), in view of Liu et al (US 20210406743 A1).
As per claims 6 and 7, Liu et al does not disclose the trained and validated machine learning model is a light gradient boosted model and the second set of operations includes applying a SHAP model to the trained and validated machine learning model.
Liu et al ‘743 disclose Shapley can be implemented for feature importance analysis in a churn model. For each feature, a shap value is calculated for every feature value. The feature value is the average of this feature value’s marginal contribution across all permutations of other features. Shapley method is just one of several algorithms that can be implemented. In various example examples, different machine-learning tools are used. For example, Multinomial Naïve Bayes (MNB), Support Vector Machines (SVM), multinomial Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT), neural networks (NN), matrix factorization, and other tools may be used for generating loss risk models (¶ 0068).
Liu et al and Liu et al ‘743 are concerned with effective conversion management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the trained and validated machine learning model is a light gradient boosted model and the second set of operations includes applying a SHAP model to the trained and validated machine learning model in Liu et al, as seen in Liu et al ‘743, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US 20210406931 A1), in view of Raman et al (US 20210081468 A1).
As per claims 21 and 22, Liu et al does not disclose each corresponding subrange is a disjoint subrange in the predetermined range of conversion scores, and the multiple predetermined cohorts comprises at least 10 cohorts.
Raman et al disclose In some examples, a custom trait can be configured by an operator of an online service provider by specifying certain information, such as: a name or label for the trait (e.g., “Lifetime Value” or “NPS”); a data type (e.g., whether the trait is represented by a number, a Boolean value, a string, or a date); and/or a display type (e.g., a slider, checkbox, dropdown, multi select, range select, or text input box), ¶ 0041, wherein The predetermined range may be plus or minus 20%, 10%, 5%, 3%, 1%, 0.1%, or less than 0.1%, unless otherwise indicated (¶ 0096).
A “segment” can be or refer to a group of users that share a common set of traits. A user may belong to more than one segment, and there are generally no limits on the number of segments that may be created or the specific traits that can be combined to form a segment (¶ 0027).
Liu et al and Raman et al are concerned with effective conversion management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include each corresponding subrange is a disjoint subrange in the predetermined range of conversion scores, and the multiple predetermined cohorts comprises at least 10 cohorts in Liu et al, as seen in Raman et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Arguments
In the Remarks, Applicant argues that a system for performing a first process of identifying an actionable graphical user interface element based on a corresponding predetermined cohort and performing a second process of storing at the data store a change to the corresponding predetermined cohort of a user, as reflected by independent claim 1, does not fall into any of the "enumerated sub-groupings." In particular, techniques for training models, validating such trained models, and for performing a first process of identifying an actionable graphical user interface element based on a corresponding predetermined cohort and performing a second process of storing at the data store the changed to the corresponding predetermined cohort of a user, as recited by the claims, is not a method of organizing human activity. For instance, the system is not managing personal behavior or personal interactions between people. Moreover, the system does not manage social activities, teaching, or following rules or instructions. And because this grouping is "limited to activity that falls within the enumerated groupings," the claims do not fall within this grouping.
The currently amended independent claims provide a first process that obtains the feature data that includes the transaction data spanning both the first time period and the second time period as well as the engagement data obtained via the actionable graphical user interface element displayed at the computing device during the first time period in order to sort the set of users into the predetermined cohorts that each represent a corresponding subrange in the predetermined range of conversion scores that is further stored at a data store, and performing a second process that stores at the data store the indication of the change in explainability data when the first threshold action element is determined, such that network resources are conserved, and processing efficiency of the computer system is increased by not serving unnecessary content to users. This concept of processing efficiency and conservation of resources is recited in a specific manner that represents a technical improvement over systems that are configured to process and transmit recommended items in an unrestricted manner during the respective session at the website.
The Office Action finds that the independent claims do not add specific limitations other than what is well-understood, routing, or conventional activity. In response, Applicant notes that the use of a first process of identifying an actionable graphical user interface element based on a corresponding predetermined cohort and performing a second process of storing at the data store the indication of the change to the corresponding predetermined cohort of a user, as reflected by independent claim 1, represents an unconventional combination of features that confine the claims to a particular useful application under M.P.E.P § 2106.05(d). The Examiner respectfully disagrees.
As an initial point, regarding the 35 USC 101 rejection, while method claim 13 recites “computer-implemented” in the preamble, the body of the claim fails to recite any additional elements implementing the method steps (e.g., at least one processor, as seen in independent claims 1 and 20).
Contrary to Applicant’s assertion, the claim limitations merely cover commercial interactions, including marketing or sales activities or behaviors, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Specifically, as described in paragraph 4 of the specification, “The embodiments described herein are directed to a computing system that determines/predicts, for each user or customer of an e-commerce entity, a likelihood of an occurrence of a user conversion event. In various examples, a user conversion event may include, for a particular user of an online ecommerce platform, converting or upgrading their respective trial-membership status to a full membership status.”
“Additionally, the computing system may also determine factors or parameters that contributed to such a determination/prediction. Further, the computing system may utilize such determinations and predictions to determine which customers or user to communicate conversion/upgrade content items (e.g., content items that promote or encourage the conversion of the trial-membership status to a full membership status) to and what specific conversion/upgrade content items to communicate to the trial-membership customers or users (e.g., the computing system may, for a trial-membership user or customer that doesn't utilize or underutilizes scheduled deliveries, communicate content items related to a coupon for scheduled deliveries).”
As further described in paragraphs 22 and 23 of the specification, “For example, membership computing device 102 may compare the store features indicated by store data of a particular store and all the membership benefits that are provided by the associated e-commerce entity. Additionally, membership computing device 102 may determine which membership benefits may be available for the particular store based on which store features match the membership benefits that are provided by the associated e-commerce entity. For instance, membership computing device 102 may determine that fuel discounts (the membership benefits) are available to store 109 because store 109 has available fuel pumps. In such an example, the e- commerce entity, may, from another system or database (not shown in FIG. 1), transmit benefit data that identifies all the membership benefits that are provided by the e-commerce entity.”
“In some implementations, membership computing device 102 may determine whether a customer or a user of e-commerce entity is currently participating or not participating in a trial loyalty or membership program. In some examples, membership data generated by the e- commerce entity may include data identifying a plurality of users or customers of the e- commerce entity that are currently or have previously participated in the loyalty or membership program…Further, membership computing device 102 may determine which customers or users of e-commerce entity is currently participating in a trial loyalty or membership program based on the membership data and the current time and/or date.”
Moreover, as described in paragraph 40 of the specification, “In various implementations, membership computing device 102 may generate explainability data for trial-membership users that are more likely to join or participate in a loyalty or membership program of an e-commerce entity. In such implementations, membership computing device 102 may, for each trial-membership user of the e-commerce entity, associate with or place into one of a set of bins/cohorts, based on the conversion score of the corresponding trial-membership user or customer.”
Additionally, the claim language recites, inter alia, “sorting each user identifier of the set of users into one of multiple predetermined cohorts, each respective predetermined cohort of the multiple predetermined cohorts representing a corresponding subrange in a predetermined range of conversion scores; for at least a first predetermined cohort of the multiple predetermined cohorts, implementing a second set of operations that generate explainability data associated with the first predetermined cohort, wherein: the explainability data includes multiple subsets of values, each subset of values is associated with one of the set of features, and each value in the multiple subsets of values indicates a magnitude and a negative or positive sign of a contribution of a corresponding feature to conversion scores associated with at least the first predetermined cohort.”
As such, and contrary to Applicant’s assertion, the claim limitations merely cover commercial interactions, including marketing or sales activities or behaviors, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55. Besides the abstract idea, the claims include a memory resource storing instructions; and at least one processor coupled to memory, and a trained validated machine learning model.
The memory resource storing instructions; and at least one processor coupled to memory, and a trained validated machine learning model in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Even when viewed in combination, the additional elements in the claims do no more than use computer components as a tool (i.e., a memory resource storing instructions; and at least one processor coupled to memory, and a trained validated machine learning model). There is no change to the computers and/or other technology recited in the claims, thus the claims do not improve computer functionality or other technology. See, e.g., Trading Technologies Int’l v. IBG, Inc., 921 F.3d 1084, 1093 (Fed. Cir. 2019) (using a computer to provide a trader with more information to facilitate market trades improved the business process of market trading, but not the computer) and the cases discussed in MPEP 2106.05(a)(I), particularly FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095 (Fed. Cir. 2016) (accelerating a process of analyzing audit log data is not an improvement when the increased speed comes solely from the capabilities of a general-purpose computer) and Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055 (Fed. Cir. 2017) (using a generic computer to automate a process of applying to finance a purchase is not an improvement to the computer’s functionality). Accordingly, the claim as a whole does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Additionally, and importantly, the amended claim language, including “validate the trained machine learning model using a second set of feature data associated with a second prior time period before the first prior time period, thereby forming a trained validated machine learning model”, does not sufficiently describe the validation step or the purported results of the validation.
As recited in paragraph 75 of the specification, “In various examples, executed membership engine 308 may validate the predictive capability and accuracy of the adaptively trained machine learning process/model based on elements of ground truth data incorporated within the validation datasets, or based on one or more computed metrics (that is based on the generated elements of output data and corresponding ones of validation datasets), such as, but not limited to, computed precision values, computed recall values, and computed area under curve (AUC) for receiver operating characteristic (ROC) curves or precision-recall (PR) curves.”
Under step 2B of the analysis, the claims include, inter alia, a memory resource storing instructions; and at least one processor coupled to memory, and a machine learning model.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
Applicant also argues that Liu does not teach or suggest a first process of identifying an actionable graphical user interface element based on a corresponding predetermined cohort and performing a second process of updating the data store based on changes to the corresponding predetermined cohort of a user, as reflected by independent claim 1, as recited by the claims. The Examiner respectfully disagrees.
As discussed in the updated rejection, Liu et al indeed disclose Applicant’s claim language. Specifically, Liu et al disclose identifying, based on the explainability data, a second actionable graphical user interface element in the plurality of actionable graphical user interface elements (i.e., “Conversion” or non-conversion can be measured with respect to conversion optimization, or conversion rate optimization (CRO). CRO is a measurement of trial users that convert to subscribers, or more generally, take any desired action on a platform interface, ¶ 0020. Service provider devices 107-108 may provide various interfaces including, but not limited to, those described in more detail below in conjunction with FIG. 2, ¶ 0027, wherein the Predictive Models 600 are implemented to receive a propensity score, implement a simulation model that augments a user's actual actions with hypothetical scenarios they could take, receive a propensity score for each hypothetical scenario, and select at least one service provider feature in the hypothetical scenarios based in part on the propensity score. The propensity score is recalculated based on each of the hypothetical scenarios, ¶ 0029).
As a result, and contrary to Applicant’s assertion, Liu et al disclose augmenting the users actual actions, including “For example, the CMP 357 may perform the following tasks daily: generate a propensity score, implement a model that augments a user's actual actions with hypothetical scenarios they could take and select at least one service provider feature based in part on the propensity score” (¶ 0037).
Conclusion
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 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 ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao (Rob) Wu can be reached at (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 May 30, 2026
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