9Notice 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 . The following is a Final Office Action. Claims 54-73 are pending and rejected below.
Applicant’s Amendments
Applicant’s amendments are acknowledged.
Applicant’s Arguments
Applicant’s arguments with respect to 101 rejection has been fully considered but is non-persuasive.
Applicant argues, “...the claims at issue involve using "an optimization model" that analyzes a plurality of temporal goals and a set of constraints to "generate a set of multi-dimensional actions with predicted action values" across a plurality of channels. See, e.g., claim 54. Such sophisticated analysis "cannot be practically performed in the human mind." 2025 Guidance at 2, citing 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence published on July 17, 2024 (89 FR 58128). Therefore, the claims are not directed to a mental process.
Examiner responds using an optimization model which analyzes goals/ constraints and generates action (under BRI-outputted actions) can still be performed in the human mind.
Applicant argues, “The claims do not recite or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols and are not directed to a mathematical concept...”
Examiner responds using an optimization model and generated action outputs with values (ie. set of multi-dimensional actions with predicted action values) can be considered mathematical correlations/relationships.
Applicant argues, “The claims are also not directed to organizing a human activity. As set forth in the specification, the instant application relates to "machine-learned predictions and user-defined strategies to maximize/achieve at least one long term goal and a plurality of short term goals that enhance communication effectiveness and engagement between one or more actor types (e.g. pharma reps) and one or more target entities (e.g. HCPs), across a multitude of channels over one or more time periods." Published Application (US20240362673A1) at [0003]. The claims do not recite economic principles, fundamental economic practices or management of personal behavior, and as such are not directed to "organizing human activity."
Examiner responds generating actions that will be used for commercial entities to communicate (company actors and target entities) over channels can be considered organizing human activities as it relates to marketing activities or behaviors and managing personal behavior (including social activities, teaching, and following rules or instructions).
Applicant’s arguments with respect to 103 rejection has been fully considered but is non-persuasive.
Applicant argues Phillips does not disclose: "providing the plurality of temporal goals and a set of constraints to an optimization model comprising an objective function, wherein the set of constraints comprise action-type constraints, channel constraints, channel capacity constraints, pacing constraints, and/or channel fatigue constraints across a plurality of channels, wherein the plurality of channels is used to enable or facilitate communications between one or more actor types and the one or more target entities.....Phillips and Ferguson fail to teach or suggest these elements of claim 54. Specifically, the sections of Phillips cited by the Office disclose using the action plan module 102 to dynamically update certain displayed actions 142 in response to a user's input provided by manipulating certain values such as a count, cost, goal, ROI, confidence, or other attribute/machine learning parameter to satisfy a goal 150 or outcome. See e.g., Phillips at 84. Phillips neither provides a "plurality of temporal goals and a set of constraints to an optimization model comprising an objective function" nor does this reference offer any teaching or suggestion of using the "plurality of channels [] to enable or facilitate communications between one or more actor types and the one or more target entities," as recited in claim 54. Ferguson also does not cure these deficiencies of Phillips.
Examiner responds Phillips disclose “one or more goals” (“goals” (par.58: a user may set one more results or goals).... Once the...goals...are set by a user; See Also 0080 - the action plan module 102 may predict multiple outcomes 150 or goals 150 for a single action 142) with the goals including short-term or long-term
(par.72: A goal may include... a short-term goal, a long-term goal} (different timescale categories), the temporal goals and a set of constraints are provided to an optimization model comprising an objective function (0084 - the user adjusted “count, cost, goal, ROI, confidence, or other attribute”(constraints, temporal goals) are machine learning parameters; 0061-0065 – the machine learning provides prediction through regression and classification which includes support vector machines (with objection function)) wherein the set of constraints comprise action-type constraints, channel constraints, channel capacity constraints, pacing constraints, and/or channel fatigue constraints across a plurality of channels (fig. 1C: action plan 140 comprises actions 142 related to different dimensions of contact channels (ie. email, buy search term, banner ad, etc.) thus, the constraints apply to the action/ channel/ channel capacity dimensions)) wherein the plurality of channels is used to enable or facilitate communications between one or more actor types and the one or more target entities (fig. 1C: the action plan 140 comprises actions 142 related to different dimensions of contact channels (ie. email, buy search term, banner ad, etc;); (for communications between actor/target); par.44: An action plan, as used herein, comprises a set of one or more recommended actions which a client 104... an action plan may include identifiers for one or more target subjects for the recommended actions, such as customer identifiers, email addresses, mailing addresses, phone numbers, or the like (ie. for communication between actor/ target)
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 54-73 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 54-73 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically Claims 54-73 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
Step 54 of the Alice/Mayo analysis is directed to determining whether or not the claims fall within a statutory class. Based on a facial reading of the claim elements, Claims 54-73 fall within a statutory class of process, machine, manufacture, or composition of matter.
With respect to Step 2A Prong One of the framework, the claims recite an abstract idea. Claim 54 includes limitations reciting functionality for optimizing a marketing campaign, including limitations that:
defining a plurality of temporal goals for attainment based at least in part on the input data....
providing the plurality of temporal goals and a set of constraints to an optimization model comprising an objective function, wherein the set of constraints comprise action-type constraints, channel constraints, channel capacity constraints, pacing constraints, and/or channel fatigue constraints across a plurality of channels...
using the optimization model to generate a set of multi-dimensional actions with predicted action values for at least a subset of the plurality of channels, wherein the predicted action values collectively maximize an impact value of the objective function while balancing a feasibility of the plurality of temporal goals with respect to one another.
which is an abstract idea reasonably categorized as
Mental processes, because each of the limitations describes concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Certain methods of organizing human activity – because the limitations describe marketing activities or behaviors, or managing personal behavior (including social activities, teaching, and following rules or instructions)
Mathematical concept, because the use of the optimization model to generate a set of multi-dimensional actions with predicted action values describes mathematical relationships
Similarly, Claims 55-73 further narrow the same abstract concept identified above related to mental processes, certain methods of organizing human activity, and mathematical concepts. As a result, Claims 55-73 recite an abstract idea related to mental processes, certain methods of organizing human activity, and mathematical concepts under Step 2A Prong One.
With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claims 54 includes no additional elements that are not directed to the abstract idea under Step 2A Prong One of the framework.
Claims 55-73 do not include any additional elements. As a result, Claims 54-73 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 54 includes no additional elements that are not directed to the abstract idea under Step 2A Prong One of the framework.
Claims 55-73 do not include any additional elements. As a result, Claims 54-73 do not amount to significantly more than the abstract idea.
Accordingly, Claims 54-73 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 54-57, 59-62, 64-73 are rejected under 35 U.S.C. 103 as being unpatentable over Applicant’s cited art Phillips (2014/358828) in view of Ferguson (20030033194)
Regarding Claim 54: Phillips discloses A method comprising:
receiving input data associated with promotion of a product to one or more target entities; claim 1: A method for an action plan;
par.58: a user may set one more results or goals to their desired outcomes or states, and the action plan module 102 may present a recommendation or action plan to accomplish the desired outcomes or states;
par.72: A goal, as used herein, may include a desired, intended, or selected
outcome or result of one or more actions or, action plans, or other events,
par.76: the action plan module 102 has determined and displayed that to achieve the goal. a user should send a buy-one-get-one {(BOGO) free email to a target set 130 {e.g.. from a customer list and/or database)):
defining a plurality of temporal goals for attainment based at least in part on the input data, wherein the plurality of temporal goals is classified into at least two categories of different timescales; (par.58: a user may set one more results or goals to their desired outcomes or states, and the action plan module 102 may present a recommendation or action plan to accomplish the desired outcomes or states: Once the results, goals, or other parameters are set by a user, the presented action plan may indicate to the user what actions to take, how many times to take an action, when to take an action, one or more target subjects of an action, or the like.
par.72: A goal may include a business goal (e.g.. a sales goal, a marketing goal. a corporate goal ... a short-term goal, a long-term goal} (different timescale categories);
providing the plurality of temporal goals and a set of constraints to an optimization model comprising an objective function, (par.84: The action plan module 102 may dynamically update the displayed actions 142, the displayed machine learning parameters 144, and/or the displayed goal 150 or outcome of the action plan 140 in response io user input manipulating or adjusting one of the values 142, 144, 148, 150. For example, if a user changes a value for a count, cost, goal, ROI, confidence, or other attribute/machine learning parameter 144, such as decreasing the displayed action count 144 for the “email 20% off" action, (goals and constraints) an action plan module 102 may update attributes/machine learning parameters for other actions 142, such as increasing action counts lor one or more other actions 142, in an optimal machine learning manner, to satisfy the goal 150 or outcome.
par 45: the machine learning that the action plan module 102 uses to generate an action plan for a client 104 may be generated by a machine learning factory or predictive compiler using data from the client 104 for a specific goal ... the action plan module 102 may deliver to a business or other client 102 a set of optimal actions to best achieve or move toward a specific goal; par.63: prediction may be applied through at least two general techniques: Regression and Classification; par.65: Classification methods may include ... Support Vector Machines which comprise an objective function;},
wherein the set of constraints comprise action-type constraints, channel constraints, channel capacity constraints, pacing constraints, and/or channel fatigue constraints across a plurality of channels, par.79; fig. 1C:146: and a slider 146 graphical user interface element corresponding to the machine learning parameter 144 is displayed, allowing a user to adjust the “count” machine learning parameter (an action within the contact channels in Figure 1C)
par.84: The action plan module 102 may dynamically update the displayed actions 142, the displayed machine learning parameters 144, and/or the displayed goal 150 or outcome of the action plan 140 in response of user input manipulating or adjusting one of the values 142, 144, 148, 150),
fig. 1C: the action plan 140 comprises actions 142 related to different dimensions of contact channels (ie. email, buy search term, banner ad, etc.))
wherein the plurality of channels is used to enable or facilitate communications between one or more actor types and the one or more target entities;
fig. 1C: the action plan 140 comprises actions 142 related to different dimensions of contact channels (ie. email, buy search term, banner ad, etc;);
par.44: An action plan, as used herein, comprises a set of one or more recommended actions which a client 104, such as a user, a business entity, or the like, may take to achieve or move closer toward a predefined goal. In certain embodiments, an action plan may include identifiers for one or more target subjects for the recommended actions, such as customer identifiers, email addresses, mailing addresses, phone numbers, or the like. An action plan, in a further embodiment, includes a timing indicator for each included action. In this manner, in certain an action plan may include the who {e.g.. target subjects), the what (e.g., one or more recommended actions), and the when (e.g., one or more timing indicators embodiments,) for a user or other client 104 to accomplish the why {e.g.. the associated goal).
and using the optimization model to generate a set of multi-dimensional actions with predicted action values for at least a subset of the plurality of channels, while balancing a feasibility of the plurality of temporal goals with respect to one another.
par.43: An action plan module 102 may determine one or more action plans for the one or more clients 104 using machine learning
fig. 1C: the action plan 140 comprises actions 142 related to different dimensions of contact channels (email, buy search term, banner ad, etc;);
par.79: the action plan module 102 displays an action pian 140 with a plurality of actions 142, each with associated machine learning parameters 144 such as confidence metrics;
par. 84: The action plan module 102 may dynamically update the displayed actions 142, the displayed machine learning parameters 144, and/or the displayed goal 150 or outcome of the action plan 140 in response to user input manipulating or adjusting one of the values 142, 144, 148, 150. For example, if a usar changes a
value for a count, cost, goal, ROY, confidence, or other attribute/machine learning
parameter 144, such as decreasing the displayed action count 144 for the “email
20% off action, an action plan module 102 may update attributes/machine learning parameters for other actions 142, such as increasing action counts for one or more other actions 142, in an optimal machine learning manner, to satisfy the goal 150 or outcome. }.
Because in 0058 one or more temporal goals are set, under BRI both are balanced
Phillips does not explicitly state: wherein the predicted action values collectively maximize an impact value of the objective function
Ferguson in analogous art discloses an objective function is input into an “optimizer” that outputs decision variables 40 which have values optimized for the goal specified by the objective function 39 subject to the constraint(s). [0151] The optimization process may comprise inputting the information related to the e-commerce transaction into at least one predictive model to generate one or more action variables. The action variables may comprise predictive user behaviors corresponding to the information. The action variables, as well as other data, such as constraints and an objective function, may then be input into an optimizer, which then may generate the one or more inducements to be presented to the user.
See 0188 and 0192- In one embodiment, the action variables 44 generated by the predictive model(s) 43 may be used to formulate constraint(s) 38 and the objective function 39 via formulas. As shown in FIG. 7b, a data calculator 45 may generate the constraint(s) 38 and objective function 39 using the action variables 44 and potentially other data and variables. In one embodiment, the formulas used to formulate the constraint(s) 38 and objective function 39 may include financial formulas such as formulas for determining net operating income over a certain time period. The constraint(s) 38 and objective function 39 may be input into an optimizer 47, which may comprise, for example, a custom-designed process or a commercially available "off the shelf" product. The optimizer may then generate the optimal decision variables 40 which have values optimized for the goal specified by the objective function 39 and subject to the constraint(s) 38. A further understanding of the optimization process 35 and the optimizer 47 may be gained from the references "An Introduction to Management Science: Quantitative Approaches to Decision Making", by David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams, West Publishing Co. (1991); and "Fundamentals of Management Science" by Efraim Turban and Jack R. Meredith, Business Publications, Inc. (1988).
Phillips is directed to an action plan generated by machine learning. Ferguson is directed to an electronic commerce system for on-line training of a non-linear model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Phillips regarding directing an action plan generated by machine learning, to have include Ferguson’s maximized impact value of an objective function because both inventions utilize e-commerce business planning using machine-learning. Further, 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.
Regarding Claim 55, Phillips discloses: The method of claim 54, wherein the set of multi-dimensional actions with predicted action values are generated based on one or more of the following: a value of the one or more target entities, a relative impact value of the actions, a probability of success of the actions, or a timing value of the actions.
See Figure 1C, [0082] Supported manipulations to a displayed object 148, in the depicted embodiment, are each associated with a machine learning parameter 144 and each different object 148 is associated with a different action 142 of the action plan 140. For example, a size of a displayed object 148 may correspond to a size of a result set or target count for a machine learning parameter 144, a vertical position (e.g., relative to the percentage scale of the Y axis) of a displayed object 148 may correspond to an action count for a machine learning parameter 144 or the like, a horizontal position (e.g., relative to the time scale of the X axis) of a displayed object 148 may correspond to an execution time or date for a machine learning parameter 144 such as an action 142 or the like, an opacity, color, shading, and/or border of a displayed object 142 may correspond to a confidence metric for a machine learning parameter 144 or the like.
[0105] In one embodiment, the recommended action module 204, in cooperation with the machine learning module 202 and/or the pre-compute module 208, may determine a list of target subjects for a recommended action, as part of an action plan, with target subjects ordered by confidence metrics associated with the target subjects. Providing an action plan with a list of target subjects for each recommended action ordered by confidence metric, in certain embodiments, allows a user to perform a recommended action first with the target subjects with the highest likelihood of success and to decide at what point in the list to stop performing the action (e.g., when the confidence metric falls below a threshold, based on balancing a cost for the action with a confidence metric, or the like).
[0112] The action plan interface module 206, in certain embodiments, may be configured to display one or more attributes of a data set used by the machine learning to determine the one or more machine learning results and one or more impact metrics for each displayed attribute, one or more of which may be stored by the pre-compute module 208 in a results data structure, or the like. For example, the action plan interface module 206 may display a list, ranking, and/or statistics associated with specific features, instances, or other attributes indicating how predictive an attribute was (e.g., an impact), what contribution an attribute made to a result, a frequency that an attribute occurred, a coverage or range for an attribute in the data set, or the like.
Regarding Claim 56, Phillips discloses: The method of claim 54, wherein the one or more actor types comprise one or more enumerated actors including sales representatives, district managers, or medical science liaisons that are associated with the promotion of the product to the one or more target entities. [0049] For example, the action plan module 102 may present an action plan, machine learning inputs, results, and/or other parameters for a business to a business person, sales person, or another user. Because, in certain embodiments, the action plan module 102 presents the action plan in a dynamic, experiential manner, using an interactive data visualization or the like, the action plan module 102 may facilitate understanding of the meaning of the presented action plan and associated data, without burdening the user with the minutia and complexity of the literal underlying data. The action plan module 102 may present an action plan, machine learning inputs, results, predictions, and/or other parameters in a manner that communicates business meaning to a user, allowing the user to navigate and recognize patterns in enterprise data, thereby determining optimal actions for the business.
Regarding Claim 57, Phillips discloses: The method of claim 56. Phillips does not explicitly state: Ferguson discloses: wherein the one or more actor types comprise automated systems including marketing automation systems or web portal management systems. ([0172] The optimization process used to generate the e-commerce site configuration is described above with reference to FIG. 5, but in this embodiment of the invention, the information input into the predictive model is the vendor information, and the optimized decision variables comprise the e-commerce site configuration parameters. Examples of the constraints in this embodiment may comprise the number of products displayed, the number of colors employed simultaneously on the page, or limits on the values of sale discounts. The objective function represents a given desired commercial goal of the e-commerce vendor, such as increased profits, increased sales of a particular product or product line, increased traffic to the e-commerce site, etc. Further detailed description of the optimization process may be found below, with reference to FIGS. 7a and 7b.
[0173] Once the optimizer has solved the objective function, in step 32, the resulting configuration parameters may be applied to the e-commerce site. In other words, the e-commerce site may be configured, modified, or generated based on the configuration parameters produced by the optimization process. Thus a designer may change one or more of a color, layout, or content of the e-commerce site. In an alternate embodiment, the optimized configuration parameters may be applied to the e-commerce site automatically by software designed for that purpose which may reside on the e-commerce server. In this way, the e-commerce site may in large part be configured without the need for direct human involvement) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Phillips’ actor types to have include Ferguson’s web portal management systems, 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.
Regarding Claim 59, Phillips discloses: The method of claim 54, wherein the input data comprises market data, strategies, analytics, context, campaigns or models from one or more sources. (0061- Predictive analytics is the study of past performance, or patterns, found in historical and transactional data to identify behavior and trends in future events, using machine learning or the like. This may be accomplished using a variety of statistical techniques including modeling, machine learning, data mining, or the like.
0087 - the machine learning module 202 may process or analyze different instances of data, such as historic data of a business or other user (e.g., sales data, customer data, or the like), simulated data, projected data, estimated data, and/or a combination of several of the above. In certain embodiments, the machine learning module 202 may process or analyze a user's own data, historical or otherwise, together with simulated, estimated, or projected data to fill in holes or missing data. The machine learning module 202 may determine simulated, estimated, or projected data to fill-in or complete data from a user based on the data from the user, by recognizing patterns in the data, fitting one or more functions to the data, or the like. In other embodiments, the machine learning module 202 may fill in missing data using substantially each permutation of the missing data (e.g., each possible data value, each value at fixed increments between minimum and maximum values, or the like).
Regarding Claim 60, Phillips discloses: The method of claim 59, wherein the one or more sources comprise databases for customer relationship management (CRM), marketing automation systems, website target management systems, customer data platforms (CDP), market segmentation, sales, a pharmaceutical company's proprietary databases, and/or third party provided/generated data. (0087 - the machine learning module 202 may process or analyze different instances of data, such as historic data of a business or other user (e.g., sales data, customer data, or the like), simulated data, projected data, estimated data, and/or a combination of several of the above. In certain embodiments, the machine learning module 202 may process or analyze a user's own data, historical or otherwise, together with simulated, estimated, or projected data to fill in holes or missing data. The machine learning module 202 may determine simulated, estimated, or projected data to fill-in or complete data from a user based on the data from the user, by recognizing patterns in the data, fitting one or more functions to the data, or the like. In other embodiments, the machine learning module 202 may fill in missing data using substantially each permutation of the missing data (e.g., each possible data value, each value at fixed increments between minimum and maximum values, or the like).
Regarding Claim 61, Phillips discloses: The method of claim 54, wherein the plurality of temporal goals comprises at least one long-term goal on a first time scale ranging from about six to twelve months, and a plurality of short-term goals on a second timescale that is daily, weekly, or monthly. (0126 - if the recommended action module 204 provides recommended actions for discrete intervals (e.g., an action for each week (weekly), for each month (monthly), for each year (interpreted as twelve months)), an action function may be used to provide action recommendations at a finer granularity.
par.58: a user may set one or more results or goals to their desired outcomes or states, and the action plan module 102 may present a recommendation or action plan to accomplish the desired outcomes or states: Once the results, goals, or other parameters are set by a user....
Regarding Claim 62, Phillips discloses: The method of claim 54, wherein the plurality of temporal goals is expressed in a form of actions and/or responses to actions on the different timescales. (Figure 1C expresses the temporal goals across the circles representing actions)
Regarding Claim 64, Phillips discloses: The method of claim 57, wherein the set of multi-dimensional actions are designed to complement each other so as to enable cross-channel optimization across the subset of channels based at least in part on an execution capacity of the enumerated actors, capacity limits set of the automated systems, utilization or consumption of content, or usefulness/relevance of the content. (par.79: adjusting parameter related to number of emails to be generated; fig.1C, 0044- actions are related to different contact channels (ie. email, buy search term, banner ad, etc;);
Regarding Claim 65, Phillips discloses: The method of claim 54, wherein the optimization model is configured to iteratively search for actions to maximize expected action values by applying and varying the plurality of constraints and parameters to (1) influence the predicted action values and (2) monitor changes in the predicted action values. [0147] In one embodiment, the machine learning compiler module 302 may generate a machine learning ensemble 222 for each possible combination of features from which the feature selector module 304 may select. In a further embodiment, the machine learning compiler module 302 may begin generating machine learning ensembles 222 with a minimal number of features, and may iteratively increase the number of features used to generate machine learning ensembles 222 until an increase in effectiveness or usefulness of the results of the generated machine learning ensembles 222 fails to satisfy a feature effectiveness threshold. By increasing the number of features until the increases stop being effective, in certain embodiments, the machine learning compiler module 302 may determine a minimum effective set of features for use in a machine learning ensemble 222, so that generation and use of the machine learning ensemble 222 is both effective and efficient. The feature effectiveness threshold may be predetermined or hard coded, may be selected by a client 104 as part of a new ensemble request or the like, may be based on one or more parameters or limitations, or the like.
[0148] During the iterative process, in certain embodiments, once the feature selector module 304 determines that a feature is merely introducing noise, the machine learning compiler module 302 excludes the feature from future iterations, and from the machine learning ensemble 222. In one embodiment, a client 104 may identify one or more features as required for the machine learning ensemble 222, in a new ensemble request or the like. The feature selector module 304 may include the required features in the machine learning ensemble 222, and select one or more of the remaining optional features for inclusion in the machine learning ensemble 222 with the required features.
Regarding Claim 66, Phillips discloses: The method of claim 65, wherein the plurality of parameters comprises one or more of the following: cost, impact, urgency, recency, channel propensity, physical proximity, representative (rep) engagement, time to engage, account priority, or factor priority. 0072- For example, in the depicted embodiment, the supported attributes or values for the features 128, 130, 132, 134, 136, 138 vary based on a selected goal, such as a zip code (e.g., a customer zip code, a store zip code), an age group (e.g., a customer age group, a target age group), a dollar amount (e.g., a purchase amount, a profit amount, a goal amount, an action cost, a budget), an integer value (e.g., a target group size, a number of sales, a number of actions), or the like. In certain embodiments, a goal may comprise business goals or desired outcomes, such as goals to double sales, to increase repeat purchases, or the like. A goal, as used herein, may include a desired, intended, or selected outcome or result of one or more actions or, action plans, or other events. A goal may include a business goal (e.g., a sales goal, a marketing goal, a corporate goal, an IT goal, a customer service goal, or the like) a personal goal, a medical goal, a fitness goal, a political goal, an organization goal, a team goal, an economic goal, a short-term goal, a long-term goal, a custom goal, a predefined goal, or another type of goal, based on a context in which the action plan module 102 operate.
Regarding Claim 67, Phillips discloses: The method of claim 65, wherein the optimization model is configured to search through a range of predicted action values for the plurality of channels when the plurality of parameters is being varied subject to the set of constraints, in order to identify a subset of predicted action values that forms a basis for the set of multi-dimensional actions. [0052] The action plan module 102 may process each instance within the data set to generate a new set of predictive metrics (e.g., machine learning results). The action plan module 102 may perform this processing of data iteratively for each instance, deriving a new set of predictive metrics or other machine learning results for each iteration. The accumulation of each of the predictive metrics or machine learning results gathered after processing each of the different instances by the action plan module 102 may populate a table or other results data structure of predictive, machine learning information (e.g., machine learning inputs, machine learning results, and/or other machine learning parameters) that is pre-processed and readily accessible by the action plan module 102 for presentation to a user with direct correlation between the various actions of an action plan.
[0053] The table or other results data structure may include up to millions, billions, trillions, or more of predictions or other machine learning results which form a search space that the action plan module 102 may filter to identify actions with higher possible degrees of value and/or better predicted outcomes than other predictions in the search space (e.g., a lower cost, a higher return on investment (ROI), an achieved goal, or the like). For example, the action plan module 102 may select N actions from the search space with outcomes or predicted results having the highest values, actions from the search space with outcomes or predicted results above a predefined threshold, or the like. This table or other results data structure may accommodate user interaction with the predictive metrics presented by the action plan module 102 in a substantially real-time manner, allowing the action plan module 102 to determine and/or update an action plan.
Regarding Claim 68, Phillips discloses: The method of claim 54, further comprising generating a campaign comprising the multi-dimensional actions, wherein the multi-dimensional actions are ordered along a timescale of the campaign. Figure 1C, 0044- months are ordered for each action (ie. actions that use email, mailing, phone, etc;);
0082- Supported manipulations to a displayed object 148, in the depicted embodiment, are each associated with a machine learning parameter 144 and each different object 148 is associated with a different action 142 of the action plan 140. For example, a size of a displayed object 148 may correspond to a size of a result set or target count for a machine learning parameter 144, a vertical position (e.g., relative to the percentage scale of the Y axis) of a displayed object 148 may correspond to an action count for a machine learning parameter 144 or the like, a horizontal position (e.g., relative to the time scale of the X axis) of a displayed object 148 may correspond to an execution time or date for a machine learning parameter 144 such as an action 142 or the like, an opacity, color, shading, and/or border of a displayed object 142 may correspond to a confidence metric for a machine learning parameter 144 or the like.
Regarding Claim 69, Phillips discloses: The method of claim 68, wherein the timescale comprises a start date and an end date for performing the multi-dimensional actions of the campaign. (Figure 1-C – the timescales extends across each circle)
Regarding Claim 70, Phillips discloses: The method of claim 68, further comprising providing the campaign to an actor of the one or more actor types, wherein the actor is prompted to perform the multi-dimensional actions of the campaign. (Figure 1C, 0078-0079 – under BRI, presented the action plan along the months timeline to the customer “prompts” the customer to start the campaign)
Regarding Claim 71, Phillips discloses: The method of claim 70. Phillips does not explicitly state: Ferguson discloses: further comprising, responsive to the prompting, performing at least one action of the multi-dimensional actions of the campaign to accomplish at least one temporal goal of the plurality of temporal goals. (0172-0173-....Examples of the constraints in this embodiment may comprise the number of products displayed, the number of colors employed simultaneously on the page, or limits on the values of sale discounts..... Once the optimizer has solved the objective function, in step 32, the resulting configuration parameters may be applied to the e-commerce site. In other words, the e-commerce site may be configured, modified, or generated based on the configuration parameters produced by the optimization process. Thus a designer may change one or more of...content of the e-commerce site. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Phillips’ prompting include Ferguson’s performance of an action, 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.
Regarding Claim 72, Phillips discloses: The method of claim 71, further comprising determining a progress of the actor in accomplishing the at least one temporal goal of the plurality of temporal goals. ([0078] FIG. 1C – when a user changes “count” number in action plan which updates the other numbers....
In other embodiments, the action plan module 102 may display action plans 140 (e.g., machine learning inputs, results, and/or other parameters) as one or more other geometric shapes (e.g., squares, triangles, rectangles, ovals, stars, or the like); as a graphical metaphor or image for a user, a business, or a product (e.g., an icon, a logo, a product image, or the like); and/or as a dynamic interactive graph, chart, or plot (e.g., a pie chart, bar chart, histogram, line chart, tree chart, scatter plot, or the like) that dynamically reform or readjust with new values for machine learning inputs, results, and/or other parameters based on user input, as an updated action plan 140.
Regarding Claim 73, Phillips discloses: The method of claim 72, further comprising analyzing the progress of the actor for integration into omni-channel business intelligence. (Examiner notes everything after “for” is directed to intended use and given limited patentable weight; [0078] FIG. 1C – when a user changes “count” number in action plan which updates the other numbers)
Claims 58 are rejected under 35 U.S.C. 103 as being unpatentable over Phillips (2014/358828) in view of Ferguson (20030033194) in view of Applicant’s cited art RXPrism (WO 2019207456)
Regarding Claim 58, Phillips discloses: The method of claim 55. Phillips does not explicitly state: RXPrism discloses this limitation: wherein the product comprises one or more drug products or medical devices, and wherein the one or more target entities comprise one or more healthcare providers (HCPs), healthcare organizations (HCOs), or healthcare institutional accounts. (Background - Lifesciences organizations inducing pharmaceutical companies, biotechnology companies, medical device companies etc. These companies deploy medical representative, medical science liaisons and other sales & marketing professionals (collective called hereinafter as ‘customer engagement professionals’) for engaging their healthcare customers like Healthcare professionals (HCPs), Patients and Care givers for various activities. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Phillips’ in view of Ferguson’s products and target entities to include RXPrism’s medical devices and HCP’s, 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.
Overcoming Prior Art
Claim 63 would be allowable if the claims were written to overcome 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. [AltContent: rect]
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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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/Scott Ross/
Examiner - Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623