DETAILED ACTION
This communication is a Final Office Action rejection on the merits. Claims 1-20 are currently pending and have been addressed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A method comprising: receiving an indication of a creation of a communication process flow comprising a plurality of actions that control electronic communications between an entity and a plurality of users, the communication process flow comprising a plurality of paths for the plurality of actions; receiving a first user input that indicates a goal for the plurality of paths of the communication process flow, the goal being based at least in part on data that is separate from the communication process flow; distributing the electronic communications to the plurality of users across the plurality of paths in accordance with an initial traffic allocation; monitoring, during execution of the communication process flow, each of the plurality of paths by evaluating result data for each respective path derived from the data stored within the data platform against the goal; adjusting, during execution of the communication process flow, a traffic allocation of a subset of the plurality of users to one or more paths of the plurality of paths based at least in part on the result data indicating that the one or more paths satisfy the goal at a higher frequency than a remaining one or more paths of the plurality of paths; and distributing a subset of the electronic communications to the subset of the plurality of users in accordance with the one or more paths. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “commercial or legal interactions.” In this case, monitoring a marketing campaign to analyze multiple variations of the campaign is a marketing activity (see at least Paragraph 0028 of the specification). If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: a data processing; and the goal being based at least in part on data stored within a data platform that is separate from the communication process flow; and a machine learning model.
The data processing is merely used to: receive an indication of a creation of a communication process flow including a set of multiple actions that control electronic communications between an entity and a set of multiple users, the communication process flow including a set of multiple paths for the set of multiple actions; and receive a first user input that indicates a goal for the set of multiple paths of the communication process flow (Paragraph 0079). The data platform is merely used to store a goal (Paragraph 0079). The machine learning is merely used to analyze the performance of a set of paths of the communication process flow that include different combinations of actions and action variations to route a subset of users via one or more paths based on a result of the one or more paths satisfying a goal. Therefore, a subset of the electronic communications may be distributed to the subset of users in accordance with the one or more paths (Paragraphs 0016 & & 0029-0030). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “data processing,” “data platform,” and “machine learning model” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Also, the data processing is considered “field of use” since it’s just used to receive inputs to analyze a communication process flow, but the technology is not improved (MPEP 2106.05h). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of creating multiple variations of a communication process flow. The specification shows that the data processing is merely used to: receive an indication of a creation of a communication process flow including a set of multiple actions that control electronic communications between an entity and a set of multiple users, the communication process flow including a set of multiple paths for the set of multiple actions; and receive a first user input that indicates a goal for the set of multiple paths of the communication process flow (Paragraph 0079). The data platform is merely used to store a goal (Paragraph 0079). The machine learning is merely used to analyze the performance of a set of paths of the communication process flow that include different combinations of actions and action variations to route a subset of users via one or more paths based on a result of the one or more paths satisfying a goal. Therefore, a subset of the electronic communications may be distributed to the subset of users in accordance with the one or more paths (Paragraphs 0016 & & 0029-0030). In this case, the claim lacked details as to how the machine learning operates (MPEP 2106.05f). Also, the step of “distributing a subset of the electronic communications to the subset of the plurality of users in accordance with the one or more paths” is considered a well-understood, routine, and conventional function since it's just “receiving or transmitting data over a network” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 11 is directed to a system at step 1, which is a statutory category. Claim 11 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 11 further recites: one or more processors coupled with the one or more memories – which are treated as just an explicit “processor/computer” for storing and executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these additional elements are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, the claim is not patent eligible.
Independent claim 16 is directed to an article of manufacture at step 1, which is a statutory category. Claim 16 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 16 further recites: a non-transitory computer-readable medium; and one or more processors – which are treated as just an explicit “processor/computer” for storing and executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these additional elements are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, the claim is not patent eligible.
Dependent claims 2-4, 6-7, 12-13, and 17-18 are not directed to any additional claim elements. Rather, these claims offer further descriptive functions of elements found in the independent claims and addressed above - such as to: receive an indication that the result data of the one or more paths has a higher likelihood of satisfying the goal relative to the remaining one or more paths of the plurality of paths of the communication process flow; receive respective weight indications for the two or more goals, wherein each goal of the two or more goals is assigned respective weight; and wherein the goal is specified as a Boolean expression. In this case, the main functions are merely used to: collect data (e.g., a respective weight of two or more goals) and analyze the data (e.g., an indication that the result of the one or more paths has a higher likelihood of satisfying the goal relative to a remaining one or more paths of the plurality of paths of the communication process flow). Those are functions that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 5, 14, and 19 are directed to additional function of the machine learning model. The machine learning model is further used to route/allocate the subset of the set of multiple users via the one or more paths of the set of multiple paths based on the result of the one or more paths satisfying the goal for the set of multiple paths, the second goal for the set of multiple paths, or both (Paragraph 0085). Merely stating that the step is performed by a computer component (e.g., a machine learning model) results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In this case, the claims lacked details as to how the machine learning operates (e.g., how the subset of the set of multiple users is selected). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is not patent eligible.
Dependent claims 8-10, 15, and 20 are directed to additional elements such as: a first user interface. The first user interface is merely used to: display a set of analytics corresponding to data associated with the result of the one or more paths satisfying the goal; receive an indication of one or more data records and one or more goal conditions; and receive multiple variations (Paragraphs 0088-0090). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, the user interface is considered “field of use” since it’s just used to receive inputs to create a communication process flow, but the user interface is not improved (MPEP 2106.05h). Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 5-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Singhai et al. (US 2021/0406935 A1).
Regarding claim 1 (Currently Amended), Singhai et al. discloses a method for data processing, comprising (Abstract, Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained; Paragraph 0037, data store 212 stores information or data received or generated via the various components of journey manager 202 and provides the various components with access to that information or data, as needed):
receiving an indication of a creation of a communication process flow comprising a plurality of actions that control electronic communications between an entity and a plurality of users, the communication process flow comprising a plurality of paths for the plurality of actions (Paragraph 0040, To generate a journey, a user, such as a marketer, may select or input events, controls, and/or responses. The events, controls, and/or responses may be presented via a graphical user interface in a workflow diagram to illustrate one or more workflow paths. Generally, the journey manager 202 enables a user (e.g., a marketer) to use events, controls (e.g., end, condition, timer), and/or responses (e.g., email, SMS) to design workflows for personalized engagements with audience members (e.g., customers));
receiving a first user input that indicates a goal for the plurality of paths of the communication process flow, the goal being based at least in part on data stored within a data platform that is separate from the communication process flow (Paragraph 0039, In embodiments, journeys may be related to a marketing workflow associated with a campaign. In this regard, a journey is developed or created in an effort to achieve a particular marketing goal. A marketing goal may be any goal related to marketing and is not intended to be limited herein. By way of example only, a marketing goal may be a financial goal (e.g., maximize spend), a consumer interaction goal (e.g., click-through rate, conversion rate), or the like; Paragraph 0068, In some cases, upon identifying and presenting a journey insight in the form of an audience segment(s) associated with a journey path(s), a user (e.g., marketer) can select or implement an intervention based on the journey insight. In this regard, a marketer may view the journey insight and, based on the insight, modify the journey (e.g., a journey path, a response, a condition, etc.) in an effort to better obtain a goal (e.g., a marketing goal). For example, journey developer 204 may be used to manipulate or modify the journey);
distributing the electronic communications to the plurality of users across the plurality of paths in accordance with an initial traffic allocation (Paragraph 0011, Workflows can be created to model different paths consumers may progress. In addition to visualizing different paths consumers may progress, performance of consumers traversing such paths can be valuable. For example, monitoring performance of consumers' flow through a workflow can provide information that can be used to better tailor the workflow to the users; Paragraph 0013, for a particular journey path, an audience segment or set of audience segments that describe the population of audience members in that journey path can be identified and presented to a user (e.g., marketer). An indication of audience segments provides insight to the user such that the user can modify a workflow to be better suited to the audience members partaking in the journey. Further, suggestions can be provided to suggest or recommend journey modifications that may facilitate an enhanced journey in terms of achieving a particular objective (e.g., marketing objective). Such suggestions may be directed to a channel for use in communicating content to audience members (consumers), a time for communicating content, a type of content, a sub-segment of audience members to target, and/or the like; Examiner interprets the “audience segment selected for each path” as the “initial traffic allocation”);
monitoring, by a machine learning model during execution of the communication process flow, each of the plurality of paths by evaluating result data for each respective path derived from the data stored within the data platform against the goal (Paragraph 0013, As such, embodiments of the present disclosure are directed to facilitating generation of insights and/or suggestions related to a journey, or journey workflow. In particular, and at a high level, implementations described herein enable journey performance to be monitored (e.g., in real time) and used to identify journey insights, such as audience segments traversing journey paths of a journey; Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation. For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal);
adjusting, by the machine learning model during execution of the communication process flow, a traffic allocation of a subset of the plurality of users to one or more paths of the plurality of paths based at least in part on the result data indicating that the one or more paths satisfy the goal at a higher frequency than a remaining one or more paths of the plurality of paths (Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation; Paragraph 0016, In addition to providing journey insights, such as audience segments, embodiments described herein also provide interventions suggestions. An intervention suggestion generally refer to a suggestion for an intervention that can be applied to a journey to target or engage an audience segment, or portion thereof, better. For example, an intervention may be applied to target an underperforming audience segment. By way of example only, an intervention may include modifying a time at which a response is provided, a channel through which a response is provided, an audience segment to which an intervention is directed (e.g., create a new journey path or segment for a subgroup of an audience segment), content associated with a response, or the like; Examiner interprets “modifying an audience segment, a time, and/or a channel” as “adjusting a traffic allocation”);
and distributing a subset of the electronic communications to the subset of the plurality of users in accordance with the one or more paths (Paragraph 0016, In addition to providing journey insights, such as audience segments, embodiments described herein also provide interventions suggestions. An intervention suggestion generally refer to a suggestion for an intervention that can be applied to a journey to target or engage an audience segment, or portion thereof, better. For example, an intervention may be applied to target an underperforming audience segment. By way of example only, an intervention may include modifying a time at which a response is provided, a channel through which a response is provided, an audience segment to which an intervention is directed (e.g., create a new journey path or segment for a subgroup of an audience segment), content associated with a response, or the like; Paragraph 0046, Responses within a journey workflow enable engagement with audience members. In embodiments, a response includes message or content delivery, but may additionally or alternatively include other methods of engagement with an audience member(s). Responses may be provided to audience members, for example, in the form of push notification, emails, or text messages. As can be appreciated, responses may be generated and/or communicated through the journey manager 202 or other component. For example, a marketing campaign service (e.g., Adobe® Campaign) or other accessible service (e.g., via an API) can provide a response associated with a journey to provide real-time, one-to-one experiences for different audience members; Paragraph 0047, Content may be any content and is not intended to be limited herein. Examples of content may include discounts, target items, promotions, information, and/or the like; Paragraph 0070, Additionally or alternatively to a user initiating an intervention based on analysis of a journey insight, journey suggestion provider 210 may operate to provide an intervention suggestion(s). In some cases, the intervention suggestion can be presented to a user, for example, for selection or confirmation to implement the suggested intervention. In other cases, an intervention suggestion may be automatically implemented. In some implementations in which an automated implementation of an intervention suggestion occurs, the intervention may be presented to the user such that the user views the automatic implementation; Paragraph 0078, By way of example only, assume an audience segment identified as underperforming in connection with a response in a journey includes audience members within an age range of 15-25 years. Such an audience segment can be used in association with various interventions to identify corresponding probabilities that audience members respond to the various proposed interventions. In this regard, upon generating and/or selecting an audience member model, various proposed interventions can be analyzed in light of each audience member within the age range of 15-25 years. For example, a probability of engagement of an audience member between 15-25 years with an email sent at 4:00 PM can be compared to engagement with an SMS sent at 10:00 PM. An intervention (e.g., channel, time, and content combination) resulting in a highest probability score across a majority of audience members between the ages of 15 and 25 years can be presented as an intervention suggestion. As described, a user may manually input the intervention by updating or modifying the journey. In other cases, the intervention may be applied automatically or based on a user selection to implement the intervention; In this case, the marketing campaign is automatically provided to an audience segment that improves the journey performance and/or achieves a specified goal).
Regarding claim 11 (Currently Amended), Singhai et al. discloses an apparatus for data processing, comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to (Abstract, Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained; Paragraph 0037, data store 212 stores information or data received or generated via the various components of journey manager 202 and provides the various components with access to that information or data, as needed; Paragraph 0084, With reference now to FIGS. 7-9, FIGS. 7-9 provide method flows related to facilitating journey management, in accordance with embodiments of the present technology. Each block of method 700, 800, and 900 comprises a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media):
receive an indication of a creation of a communication process flow comprising a plurality of actions that control electronic communications between an entity and a plurality of users, the communication process flow comprising a plurality of paths for the plurality of actions (Paragraph 0040, To generate a journey, a user, such as a marketer, may select or input events, controls, and/or responses. The events, controls, and/or responses may be presented via a graphical user interface in a workflow diagram to illustrate one or more workflow paths. Generally, the journey manager 202 enables a user (e.g., a marketer) to use events, controls (e.g., end, condition, timer), and/or responses (e.g., email, SMS) to design workflows for personalized engagements with audience members (e.g., customers));
receive a first user input that indicates a goal for the plurality of paths of the communication process flow, the goal being based at least in part on data stored within a data platform that is separate from the communication process flow (Paragraph 0039, In embodiments, journeys may be related to a marketing workflow associated with a campaign. In this regard, a journey is developed or created in an effort to achieve a particular marketing goal. A marketing goal may be any goal related to marketing and is not intended to be limited herein. By way of example only, a marketing goal may be a financial goal (e.g., maximize spend), a consumer interaction goal (e.g., click-through rate, conversion rate), or the like; Paragraph 0068, In some cases, upon identifying and presenting a journey insight in the form of an audience segment(s) associated with a journey path(s), a user (e.g., marketer) can select or implement an intervention based on the journey insight. In this regard, a marketer may view the journey insight and, based on the insight, modify the journey (e.g., a journey path, a response, a condition, etc.) in an effort to better obtain a goal (e.g., a marketing goal). For example, journey developer 204 may be used to manipulate or modify the journey);
distribute the electronic communications to the plurality of users across the plurality of paths in accordance with an initial traffic allocation (Paragraph 0011, Workflows can be created to model different paths consumers may progress. In addition to visualizing different paths consumers may progress, performance of consumers traversing such paths can be valuable. For example, monitoring performance of consumers' flow through a workflow can provide information that can be used to better tailor the workflow to the users; Paragraph 0013, for a particular journey path, an audience segment or set of audience segments that describe the population of audience members in that journey path can be identified and presented to a user (e.g., marketer). An indication of audience segments provides insight to the user such that the user can modify a workflow to be better suited to the audience members partaking in the journey. Further, suggestions can be provided to suggest or recommend journey modifications that may facilitate an enhanced journey in terms of achieving a particular objective (e.g., marketing objective). Such suggestions may be directed to a channel for use in communicating content to audience members (consumers), a time for communicating content, a type of content, a sub-segment of audience members to target, and/or the like; Examiner interprets the “audience segment selected for each path” as the “initial traffic allocation”);
monitor, by a machine learning model during execution of the communication process flow, each of the plurality of paths by evaluating result data for each respective path derived from the data stored within the data platform against the goal (Paragraph 0013, As such, embodiments of the present disclosure are directed to facilitating generation of insights and/or suggestions related to a journey, or journey workflow. In particular, and at a high level, implementations described herein enable journey performance to be monitored (e.g., in real time) and used to identify journey insights, such as audience segments traversing journey paths of a journey; Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation. For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal);
adjust, by the machine learning model during execution of the communication process flow, a traffic allocation of a subset of the plurality of users to one or more paths of the plurality of paths based at least in part on the result data indicating that the one or more paths satisfy the goal at a higher frequency than a remaining one or more paths of the plurality of paths (Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation; Paragraph 0016, In addition to providing journey insights, such as audience segments, embodiments described herein also provide interventions suggestions. An intervention suggestion generally refer to a suggestion for an intervention that can be applied to a journey to target or engage an audience segment, or portion thereof, better. For example, an intervention may be applied to target an underperforming audience segment. By way of example only, an intervention may include modifying a time at which a response is provided, a channel through which a response is provided, an audience segment to which an intervention is directed (e.g., create a new journey path or segment for a subgroup of an audience segment), content associated with a response, or the like; Examiner interprets “modifying an audience segment, a time, and/or a channel” as “adjusting a traffic allocation”);
and distribute a subset of the electronic communications to the subset of the plurality of users in accordance with the one or more paths (Paragraph 0016, In addition to providing journey insights, such as audience segments, embodiments described herein also provide interventions suggestions. An intervention suggestion generally refer to a suggestion for an intervention that can be applied to a journey to target or engage an audience segment, or portion thereof, better. For example, an intervention may be applied to target an underperforming audience segment. By way of example only, an intervention may include modifying a time at which a response is provided, a channel through which a response is provided, an audience segment to which an intervention is directed (e.g., create a new journey path or segment for a subgroup of an audience segment), content associated with a response, or the like; Paragraph 0046, Responses within a journey workflow enable engagement with audience members. In embodiments, a response includes message or content delivery, but may additionally or alternatively include other methods of engagement with an audience member(s). Responses may be provided to audience members, for example, in the form of push notification, emails, or text messages. As can be appreciated, responses may be generated and/or communicated through the journey manager 202 or other component. For example, a marketing campaign service (e.g., Adobe® Campaign) or other accessible service (e.g., via an API) can provide a response associated with a journey to provide real-time, one-to-one experiences for different audience members; Paragraph 0047, Content may be any content and is not intended to be limited herein. Examples of content may include discounts, target items, promotions, information, and/or the like; Paragraph 0070, Additionally or alternatively to a user initiating an intervention based on analysis of a journey insight, journey suggestion provider 210 may operate to provide an intervention suggestion(s). In some cases, the intervention suggestion can be presented to a user, for example, for selection or confirmation to implement the suggested intervention. In other cases, an intervention suggestion may be automatically implemented. In some implementations in which an automated implementation of an intervention suggestion occurs, the intervention may be presented to the user such that the user views the automatic implementation; Paragraph 0078, By way of example only, assume an audience segment identified as underperforming in connection with a response in a journey includes audience members within an age range of 15-25 years. Such an audience segment can be used in association with various interventions to identify corresponding probabilities that audience members respond to the various proposed interventions. In this regard, upon generating and/or selecting an audience member model, various proposed interventions can be analyzed in light of each audience member within the age range of 15-25 years. For example, a probability of engagement of an audience member between 15-25 years with an email sent at 4:00 PM can be compared to engagement with an SMS sent at 10:00 PM. An intervention (e.g., channel, time, and content combination) resulting in a highest probability score across a majority of audience members between the ages of 15 and 25 years can be presented as an intervention suggestion. As described, a user may manually input the intervention by updating or modifying the journey. In other cases, the intervention may be applied automatically or based on a user selection to implement the intervention; In this case, the marketing campaign is automatically provided to an audience segment that improves the journey performance and/or achieves a specified goal).
Regarding claim 16 (Currently Amended), Singhai et al. discloses a non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to (Abstract, Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained; Paragraph 0037, data store 212 stores information or data received or generated via the various components of journey manager 202 and provides the various components with access to that information or data, as needed; Paragraph 0084, With reference now to FIGS. 7-9, FIGS. 7-9 provide method flows related to facilitating journey management, in accordance with embodiments of the present technology. Each block of method 700, 800, and 900 comprises a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media):
receive an indication of a creation of a communication process flow comprising a plurality of actions that control electronic communications between an entity and a plurality of users, the communication process flow comprising a plurality of paths for the plurality of actions (Paragraph 0040, To generate a journey, a user, such as a marketer, may select or input events, controls, and/or responses. The events, controls, and/or responses may be presented via a graphical user interface in a workflow diagram to illustrate one or more workflow paths. Generally, the journey manager 202 enables a user (e.g., a marketer) to use events, controls (e.g., end, condition, timer), and/or responses (e.g., email, SMS) to design workflows for personalized engagements with audience members (e.g., customers));
receive a first user input that indicates a goal for the plurality of paths of the communication process flow, the goal being based at least in part on data stored within a data platform that is separate from the communication process flow (Paragraph 0039, In embodiments, journeys may be related to a marketing workflow associated with a campaign. In this regard, a journey is developed or created in an effort to achieve a particular marketing goal. A marketing goal may be any goal related to marketing and is not intended to be limited herein. By way of example only, a marketing goal may be a financial goal (e.g., maximize spend), a consumer interaction goal (e.g., click-through rate, conversion rate), or the like; Paragraph 0068, In some cases, upon identifying and presenting a journey insight in the form of an audience segment(s) associated with a journey path(s), a user (e.g., marketer) can select or implement an intervention based on the journey insight. In this regard, a marketer may view the journey insight and, based on the insight, modify the journey (e.g., a journey path, a response, a condition, etc.) in an effort to better obtain a goal (e.g., a marketing goal). For example, journey developer 204 may be used to manipulate or modify the journey);
distribute the electronic communications to the plurality of users across the plurality of paths in accordance with an initial traffic allocation (Paragraph 0011, Workflows can be created to model different paths consumers may progress. In addition to visualizing different paths consumers may progress, performance of consumers traversing such paths can be valuable. For example, monitoring performance of consumers' flow through a workflow can provide information that can be used to better tailor the workflow to the users; Paragraph 0013, for a particular journey path, an audience segment or set of audience segments that describe the population of audience members in that journey path can be identified and presented to a user (e.g., marketer). An indication of audience segments provides insight to the user such that the user can modify a workflow to be better suited to the audience members partaking in the journey. Further, suggestions can be provided to suggest or recommend journey modifications that may facilitate an enhanced journey in terms of achieving a particular objective (e.g., marketing objective). Such suggestions may be directed to a channel for use in communicating content to audience members (consumers), a time for communicating content, a type of content, a sub-segment of audience members to target, and/or the like; Examiner interprets the “audience segment selected for each path” as the “initial traffic allocation”);
monitor, by a machine learning model during execution of the communication process flow, each of the plurality of paths by evaluating result data for each respective path derived from the data stored within the data platform against the goal (Paragraph 0013, As such, embodiments of the present disclosure are directed to facilitating generation of insights and/or suggestions related to a journey, or journey workflow. In particular, and at a high level, implementations described herein enable journey performance to be monitored (e.g., in real time) and used to identify journey insights, such as audience segments traversing journey paths of a journey; Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation. For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal);
adjust, by the machine learning model during execution of the communication process flow, a traffic allocation of a subset of the plurality of users to one or more paths of the plurality of paths based at least in part on the result data indicating that the one or more paths satisfy the goal at a higher frequency than a remaining one or more paths of the plurality of paths (Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation; Paragraph 0016, In addition to providing journey insights, such as audience segments, embodiments described herein also provide interventions suggestions. An intervention suggestion generally refer to a suggestion for an intervention that can be applied to a journey to target or engage an audience segment, or portion thereof, better. For example, an intervention may be applied to target an underperforming audience segment. By way of example only, an intervention may include modifying a time at which a response is provided, a channel through which a response is provided, an audience segment to which an intervention is directed (e.g., create a new journey path or segment for a subgroup of an audience segment), content associated with a response, or the like; Examiner interprets “modifying an audience segment, a time, and/or a channel” as “adjusting a traffic allocation”);
and distribute a subset of the electronic communications to the subset of the plurality of users in accordance with the one or more paths (Paragraph 0016, In addition to providing journey insights, such as audience segments, embodiments described herein also provide interventions suggestions. An intervention suggestion generally refer to a suggestion for an intervention that can be applied to a journey to target or engage an audience segment, or portion thereof, better. For example, an intervention may be applied to target an underperforming audience segment. By way of example only, an intervention may include modifying a time at which a response is provided, a channel through which a response is provided, an audience segment to which an intervention is directed (e.g., create a new journey path or segment for a subgroup of an audience segment), content associated with a response, or the like; Paragraph 0046, Responses within a journey workflow enable engagement with audience members. In embodiments, a response includes message or content delivery, but may additionally or alternatively include other methods of engagement with an audience member(s). Responses may be provided to audience members, for example, in the form of push notification, emails, or text messages. As can be appreciated, responses may be generated and/or communicated through the journey manager 202 or other component. For example, a marketing campaign service (e.g., Adobe® Campaign) or other accessible service (e.g., via an API) can provide a response associated with a journey to provide real-time, one-to-one experiences for different audience members; Paragraph 0047, Content may be any content and is not intended to be limited herein. Examples of content may include discounts, target items, promotions, information, and/or the like; Paragraph 0070, Additionally or alternatively to a user initiating an intervention based on analysis of a journey insight, journey suggestion provider 210 may operate to provide an intervention suggestion(s). In some cases, the intervention suggestion can be presented to a user, for example, for selection or confirmation to implement the suggested intervention. In other cases, an intervention suggestion may be automatically implemented. In some implementations in which an automated implementation of an intervention suggestion occurs, the intervention may be presented to the user such that the user views the automatic implementation; Paragraph 0078, By way of example only, assume an audience segment identified as underperforming in connection with a response in a journey includes audience members within an age range of 15-25 years. Such an audience segment can be used in association with various interventions to identify corresponding probabilities that audience members respond to the various proposed interventions. In this regard, upon generating and/or selecting an audience member model, various proposed interventions can be analyzed in light of each audience member within the age range of 15-25 years. For example, a probability of engagement of an audience member between 15-25 years with an email sent at 4:00 PM can be compared to engagement with an SMS sent at 10:00 PM. An intervention (e.g., channel, time, and content combination) resulting in a highest probability score across a majority of audience members between the ages of 15 and 25 years can be presented as an intervention suggestion. As described, a user may manually input the intervention by updating or modifying the journey. In other cases, the intervention may be applied automatically or based on a user selection to implement the intervention; In this case, the marketing campaign is automatically provided to an audience segment that improves the journey performance and/or achieves a specified goal).
Regarding claims 2, 12, and 17 (Currently Amended), which are dependent of claims 1, 11, and 16, Singhai et al. discloses all the limitations in claims 1, 11, and 16. Singhai et al. further discloses receiving an indication that the result data of the one or more paths has a higher likelihood of satisfying the goal relative to the remaining one or more paths of the plurality of paths of the communication process flow, wherein adjusting the traffic allocation of the subset of the plurality of users is based at least in part on receiving the indication (Paragraph 0056, As such, the journey insights provider 208 can generate or train a machine learning model, also referred to herein as a segmentation machine learning model or segmentation model. At a high level, such a segmentation machine learning model may be used to which determine audience segments pass through through which journey paths. A segmentation machine learning model can classify or predict which journey path (e.g., journey path 1, journey path 2, journey path 3) an audience member is likely to traverse. A segmentation machine learning model may be a decision tree, a random tree, or any other classification model. A classification model generally refers to a machine learning model used to identify to which of a set of classes or categories (e.g., journey path) a new observation belongs; Paragraph 0058, Using the input, the machine learning model is trained to output predicted journey paths for audience members. As such, the machine learning model may predict which journey path an audience member is likely to traverse. By way of example, assume a segmentation model uses two profile attributes, geo-state and gender, as input and predicts between two classes, namely journey path 1 and journey path 2. In training the segmentation model, the model learns patterns that distinguish users across journey paths. As such, the segmentation machine learning model can predict or classify which path a user is likely to go through, such as journey path 1 or journey path 2; Paragraph 0068, In some cases, upon identifying and presenting a journey insight in the form of an audience segment(s) associated with a journey path(s), a user (e.g., marketer) can select or implement an intervention based on the journey insight. In this regard, a marketer may view the journey insight and, based on the insight, modify the journey (e.g., a journey path, a response, a condition, etc.) in an effort to better obtain a goal (e.g., a marketing goal). For example, journey developer 204 may be used to manipulate or modify the journey).
Regarding claims 3, 13 and 18 (Original), which are dependent of claims 1, 11, and 16, Singhai et al. discloses all the limitations in claims 1, 11, and 16. Singhai et al. further discloses wherein receiving the first user input that indicates the goal for the plurality of paths comprises: receiving one or more first user inputs that indicate two or more goals for the plurality of paths of the communication process flow, the two or more goals comprising the goal (Paragraph 0015, As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation. For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal; Paragraph 0039, In embodiments, journeys may be related to a marketing workflow associated with a campaign. In this regard, a journey is developed or created in an effort to achieve a particular marketing goal. A marketing goal may be any goal related to marketing and is not intended to be limited herein. By way of example only, a marketing goal may be a financial goal (e.g., maximize spend), a consumer interaction goal (e.g., click-through rate, conversion rate), or the like; Paragraph 0068, In some cases, upon identifying and presenting a journey insight in the form of an audience segment(s) associated with a journey path(s), a user (e.g., marketer) can select or implement an intervention based on the journey insight. In this regard, a marketer may view the journey insight and, based on the insight, modify the journey (e.g., a journey path, a response, a condition, etc.) in an effort to better obtain a goal (e.g., a marketing goal). For example, journey developer 204 may be used to manipulate or modify the journey; In this case, Examiner notes that the two or more goals are at least a financial goal and a consumer interaction goal).
Regarding claims 5, 14, and 19 (Currently Amended), which are dependent of claims 1, 11, and 16, Singhai et al. discloses all the limitations in claims 1, 11, and 16. Singhai et al. further discloses transmitting, to the machine learning model, a first indication of the goal and that the result data of the one or more paths satisfies the goal; and receiving, from the machine learning model, a second indication of a second goal for the plurality of paths of the communication process flow that is different from the goal based at least in part on transmitting the first indication to the machine learning model, the second goal being based at least in part on the data stored within the data platform, wherein traffic allocation of the subset of the plurality of users to the one or more paths of the plurality of paths id adjusted based at least in part on the result data of the one or more paths satisfying the goal for the plurality of paths, the second goal for the plurality of paths, or both (Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation; Paragraph 0016, In addition to providing journey insights, such as audience segments, embodiments described herein also provide interventions suggestions. An intervention suggestion generally refer to a suggestion for an intervention that can be applied to a journey to target or engage an audience segment, or portion thereof, better. For example, an intervention may be applied to target an underperforming audience segment. By way of example only, an intervention may include modifying a time at which a response is provided, a channel through which a response is provided, an audience segment to which an intervention is directed (e.g., create a new journey path or segment for a subgroup of an audience segment), content associated with a response, or the like; Examiner interprets “modifying an audience segment, a time, and/or a channel” as “adjusting a traffic allocation”).
Regarding claim 6 (Currently Amended), which is dependent of claim 1, Singhai et al. discloses all the limitations in claim 1. Singhai et al. further discloses wherein adjusting the traffic allocation of the subset of the plurality of users to the one or more paths comprises: adjusting the traffic allocation based at least in part on data within the data platform for one or more prior users subject to the communication process flow satisfying one or more conditions of the goal (Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation. For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal; Paragraph 0016, In addition to providing journey insights, such as audience segments, embodiments described herein also provide interventions suggestions. An intervention suggestion generally refer to a suggestion for an intervention that can be applied to a journey to target or engage an audience segment, or portion thereof, better. For example, an intervention may be applied to target an underperforming audience segment. By way of example only, an intervention may include modifying a time at which a response is provided, a channel through which a response is provided, an audience segment to which an intervention is directed (e.g., create a new journey path or segment for a subgroup of an audience segment), content associated with a response, or the like; Paragraph 0070, Additionally or alternatively to a user initiating an intervention based on analysis of a journey insight, journey suggestion provider 210 may operate to provide an intervention suggestion(s). In some cases, the intervention suggestion can be presented to a user, for example, for selection or confirmation to implement the suggested intervention. In other cases, an intervention suggestion may be automatically implemented. In some implementations in which an automated implementation of an intervention suggestion occurs, the intervention may be presented to the user such that the user views the automatic implementation; Examiner interprets “modifying an audience segment, a time, and/or a channel” as “adjusting a traffic allocation”).
Regarding claim 7 (Currently Amended), which is dependent of claim 6, Singhai et al. discloses all the limitations in claim 6. Singhai et al. further discloses wherein the goal is specified as a Boolean expression and operands of the Boolean expression comprise data stored within the data platform that is separate from the communication process flow, wherein the result data satisfies the Boolean expression (Paragraph 0015, Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation. For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal; Paragraph 0055, One implementation for identifying audience segments includes using model interpretation to identify or extract patterns or rules (e.g., if-then rules) in audience segments traversing through a particular journey, or portion thereof (e.g., journey path or segment). In particular, model interpretation can identify patterns or rules that generally describe the behavior of a machine learning model(s) used for classification (e.g., journey paths); Paragraph 0036, As shown in FIG. 2, journey manager 202 can include a journey developer 204, a journey monitor 206, a journey insights provider 208, a journey suggestion provider 210, and a data store 212. The foregoing components of journey manager 202 can be implemented, for example, in operating environment 100 of FIG. 1. In particular, those components may be integrated into any suitable combination of user devices 102a and 102b through 102n, audience member devices 106a and 106b through 106n, and/or server(s) 108).
Regarding claim 8 (Currently Amended), which is dependent of claim 1, Singhai et al. discloses all the limitations in claim 1. Singhai et al. further discloses displaying, via a first user interface, a set of analytics corresponding to result data associated with the result of the one or more paths satisfying the goal (Paragraph 0014, In operation, a user, such as a marketer, may generate or create a journey workflow via a user interface. As audience members (e.g., consumers) proceed through the journey workflow, such movement can be tracked or monitored to recognize performance of the various journey paths in a journey. For example, a number or percent of audience members that traverse the various journey paths may be monitored and recorded. Such a performance indicator may also be presented in association with the journey workflow for easy visualization by a user. In some embodiments, to obtain or view journey insights associated with a particular journey workflow, a user may select a performance indicator associated with the workflow (e.g., percent of audience members that traverse the corresponding journey path); Paragraph 0015, For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal).
Regarding claim 9 (Original), which is dependent of claim 1, Singhai et al. discloses all the limitations in claim 1. Singhai et al. further discloses wherein receiving the first user input that indicates the goal for the plurality of paths comprises: receiving, via a first user interface, an indication of a one or more data records of a plurality of data records stored within the data platform, an indication of one or more data record fields of the one or more data records, an indication of one or more goal conditions that are based at least in part on one or more values of the one or more data record fields, or any combination thereof (Paragraph 0014, In operation, a user, such as a marketer, may generate or create a journey workflow via a user interface. As audience members (e.g., consumers) proceed through the journey workflow, such movement can be tracked or monitored to recognize performance of the various journey paths in a journey. For example, a number or percent of audience members that traverse the various journey paths may be monitored and recorded. Such a performance indicator may also be presented in association with the journey workflow for easy visualization by a user. In some embodiments, to obtain or view journey insights associated with a particular journey workflow, a user may select a performance indicator associated with the workflow (e.g., percent of audience members that traverse the corresponding journey path); Paragraph 0015, For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal; Paragraph 0039, In embodiments, journeys may be related to a marketing workflow associated with a campaign. In this regard, a journey is developed or created in an effort to achieve a particular marketing goal. A marketing goal may be any goal related to marketing and is not intended to be limited herein. By way of example only, a marketing goal may be a financial goal (e.g., maximize spend), a consumer interaction goal (e.g., click-through rate, conversion rate), or the like; It can be noted that the claim language is written in alternative form. The limitation taught by Singhai et al. is based on “an indication of one or more goal conditions that are based at least in part on one or more values of the one or more data record fields").
Regarding claims 10, 15, and 20 (Currently Amended), which are dependent of claims 1, 11, and 16, Singhai et al. discloses all the limitations in claims 1, 11, and 16. Singhai et al. further discloses wherein the creation of the communication process flow comprises: receiving, prior to receiving the first user input, one or more second user inputs that indicate at least two first action variations of a first action of the plurality of actions and at least two second action variations of a second action of the plurality of actions, the communication process flow comprising the plurality of paths based at least in part on receiving the one or more second user inputs, wherein the plurality of paths are for a plurality of combinations of the at least two first action variations and the at least two second action variations (Paragraph 0014, In operation, a user, such as a marketer, may generate or create a journey workflow via a user interface. As audience members (e.g., consumers) proceed through the journey workflow, such movement can be tracked or monitored to recognize performance of the various journey paths in a journey. For example, a number or percent of audience members that traverse the various journey paths may be monitored and recorded. Such a performance indicator may also be presented in association with the journey workflow for easy visualization by a user. In some embodiments, to obtain or view journey insights associated with a particular journey workflow, a user may select a performance indicator associated with the workflow (e.g., percent of audience members that traverse the corresponding journey path; Paragraph 0015, As described herein, journey insights can be provided in the form of audience segment identification. In this regard, an identification of audience segments that proceed through a journey path can be provided to a user. To identify audience segments associated with a journey path, machine learning can be used to automatically learn patterns that distinguish audience segments across journey paths. By performing machine learning model interpretation on such a machine learned model, patterns indicating audience segments that describe audience members in journey paths can be extracted and provided to a user. As such, a user may utilize such audience segments to target the audience members in a different manner (e.g., to improve journey performance). Advantageously, such a method enables goal-driven audience segmentation. Objective driven, or goal oriented, segmentation, facilitates insights for a given objective. Stated differently, this approach derives audience segments predictive of a given class label (e.g., journey path). A goal can be used to guide the segmentation. For instance, when a marketer selects a particular path of a journey, the goal can include whether an audience member(s) entered the selected journey path. As another example, if a marketer wants to identify audience segments that generate high revenue, a goal may be whether an audience member(s) has spent more than a particular amount of money on a particular website visit. Utilizing a goal and goal-driven segmentation leads to audience segments that optimize for the goal; Paragraph 0040, To generate a journey, a user, such as a marketer, may select or input events, controls, and/or responses. The events, controls, and/or responses may be presented via a graphical user interface in a workflow diagram to illustrate one or more workflow paths. Generally, the journey manager 202 enables a user (e.g., a marketer) to use events, controls (e.g., end, condition, timer), and/or responses (e.g., email, SMS) to design workflows for personalized engagements with audience members (e.g., customers); Paragraph 0079, In this case, the user added condition 408 which includes different journey paths for different types of audience members. For example, the condition 408 separates electronic lovers to journey path 410, fashion aficionados to journey path 412, and gym equipment to journey path 414. A corresponding response is included for each of the journey paths. For example, response 416 is provided for audience members that are identified as electronic lovers, response 418 is provided for audience members that are identified as fashion aficionados, and response 420 is provided for audience members that are identified as associated with gym equipment; Paragraph 0083, In addition to viewing the journey insights, an intervention suggestion(s) may be provided. As shown in FIG. 5, an “add new path” intervention suggestion 504 is presented to enable a user to modify the journey to target audience members more effectively. Assume a user selects the interventions suggestion 504. In such a case, the new path 602 related to “discount lovers” can be added to the journey, as illustrated in FIG. 6. As such, a user may target discount lovers with a flat 10% discount that is provided via email. Although this example provides a new audience segment, or journey path, as an intervention suggestion for enhancing the journey, other interventions may be identified via methods described herein. For example, content to communicate, a channel for communicating, a time to communicate, and/or the like, may be identified and provided as an intervention suggestion for improving the propensity for interacting with different marketing actions; Examiner notes that Singhai et al. evaluates performance of multiple path variations such as a path using different channels, different content, and/or different time).
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, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Singhai et al. (US 2021/0406935 A1), in view of Vest (US 20070271145 A1).
Regarding claim 4 (Original), which is dependent of claim 3, Singhai et al. discloses all the limitations in claim 3. Although Singhai et al. discloses receiving one or more first user inputs that indicate two or more goals for the plurality of paths of the communication process flow (Paragraph 0039, a financial goal and a consumer interaction goal), Singhai et al. does not specifically disclose wherein each goal of the two or more goals is assigned respective weight.
However, Vest discloses wherein receiving the one or more first user inputs comprises: receiving respective weight indications for the two or more goals, wherein each goal of the two or more goals is assigned respective weight (Paragraph 0049, The advertiser can also establish goal weighting for a given creative. For example, the goals may comprise: where a creative is shown; what demographic it is shown to; how often the creative is shown; the desired number of impressions; frequency caps; spending caps; day parting; or how much the advertiser is willing to spend to show the creative. The advertiser can also establish a weighting value for each of these goals).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for routing at least a subset of the plurality of users via one or more paths of the plurality of paths based at least in part on a result of the one or more paths satisfying the goal for the plurality of paths of the communication process flow of the invention of Singhai et al. to further provide incorporate wherein each goal of the two or more goals is assigned respective weight of the invention of Vest because doing so would allow the advertiser to establish a weighting value for each of these goals (see Vest, Paragraph 0049). Further, the claimed invention is merely a combination of old elements, and in combination each element 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
Applicant's arguments filed on 06/26/2026 (related to the 103 Rejection) have been fully considered but are moot in view of new grounds of rejection. Applicant's amendments necessitated the new ground(s) of rejection presented in this Office action. Rejection based on a newly cited reference(s) follows.
Applicant's arguments filed on 06/26/2026 (related to the 101 Rejection) have been fully considered but they are not persuasive.
Applicant states, on pages 10-15, that amended independent claims 1, 11, and 16 recite a specific technical process in which a machine learning model monitors a plurality of paths during execution of a communication process flow by evaluating path-specific result data derived from a separate data platform and dynamically adjusts a traffic allocation of users to one or more paths based on the result data. Thus, the claims recite technical mechanisms that govern the runtime operation of a computing system - not a marketing activity, sales practice, or other commercial interaction between people. Therefore, for at least these reasons, independent claims 1, 11, and 16 do not recite a commercial or legal interaction, nor do independent claims 1, 11, and 16 recite any other abstract idea, law of nature, or natural phenomenon.
Regarding amended independent claim 1, the features of "distributing the electronic communications to the plurality of users across the plurality of paths in accordance with an initial traffic allocation," "monitoring, by a machine learning model during execution of the communication process flow, each of the plurality of paths by evaluating result data for each respective path derived from the data stored within the data platform against the goal," and "adjusting, by the machine learning model during execution of the communication process flow, a traffic allocation of a subset of the plurality of users to one or more paths of the plurality of paths based at least in part on the result data indicating that the one or more paths satisfy the goal at a higher frequency than a remaining one or more paths of the plurality of paths" improve the functioning of an electronic communication distribution system.
This specific technical mechanism recited in amended independent claim 1 yields concrete technical improvements to the electronic communication distribution system itself that address the challenges outlined in the Specification. First, the recited mechanism improves the accuracy of path-performance determinations by grounding those determinations in external-platform result data reflecting real-world outcomes, rather than in shallow in-flow engagement proxies. See Specification, ¶¶ [0029]-[0030]. Second, the recited mechanism improves the efficient utilization of communication resources by enabling an electronic communication distribution system to dynamically reallocate users away from lower-performing paths and toward higher-performing paths during execution, rather than continuing to consume communication resources on paths that are not satisfying the goal. See id., ¶¶ [0055]-[0056],
[0059]. Third, the recited mechanism improves the reliability of the electronic communication distribution system as a whole, because the machine learning model performs the monitoring-and-reallocation loop during execution based on objective, external-platform result data. See id. ¶ [0097]. These technical improvements are not incidental or aspirational. Rather, they are the direct consequence of the specific technical mechanism recited in amended independent claim 1 and represent concrete technical improvements provided by the features of the claim.
Therefore, for at least the reason that amended independent claim 1 includes features that improve the functioning of an electronic communication distribution system, any alleged judicial exception included in amended independent claim 1 is integrated into a practical application, and satisfies the analysis of Step 2A, Prong Two. Amended claims 11 and 16 also satisfy the analysis of Step 2A, Prong Two for at least similar reasons.
Examiner respectfully disagrees with Applicant. Step 2A, Prong One: These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “commercial or legal interactions.” In this case, monitoring a marketing campaign to analyze multiple variations of the campaign is a marketing activity (see at least Paragraph 0028 of the specification). If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Claim 1 includes additional elements: data processing; data platform; and a machine learning model. The data processing is merely used to: receive an indication of a creation of a communication process flow including a set of multiple actions that control electronic communications between an entity and a set of multiple users, the communication process flow including a set of multiple paths for the set of multiple actions; and receive a first user input that indicates a goal for the set of multiple paths of the communication process flow (Paragraph 0079). The data platform is merely used to store a goal (Paragraph 0079). The machine learning is merely used to analyze the performance of a set of paths of the communication process flow that include different combinations of actions and action variations to route a subset of users via one or more paths based on a result of the one or more paths satisfying a goal. Therefore, a subset of the electronic communications may be distributed to the subset of users in accordance with the one or more paths (Paragraphs 0016 & & 0029-0030). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “data processing,” “data platform,” and “machine learning model” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. In this case, the machine learning model includes inputs (e.g., goals) and outputs (e.g., traffic allocation of a subset of the plurality of users to one or more paths of the plurality of paths based at least in part on the result data indicating that the one or more paths satisfy the goal at a higher frequency than a remaining one or more paths of the plurality of paths). Although the specification states that the machine learning model may be trained using successful paths (Paragraph 0029), the claim and specification do not include any specific details about how the trained machine learning operates. Thus, the analysis/learning step of the model is a black box, which is merely claiming the idea of a solution or outcome (MPEP 2106.05(a)). Also, claim does not impose any limits on how the data is adjusted. The limitations of “receiving data” and “distributing a subset of the electronic communications” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: As discussed in Step 2A, Prong Two above, the recitation of a data processing/platform and a machine learning model amounts to no more than mere instructions to apply the exception using a generic computer component. Also, the steps of “monitoring result data” and “adjusting a traffic allocation” are considered a conventional computer function of “receiving and transmitting over a network” and “performing repetitive calculations” (MPEP 2106.05d).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or 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. The claim is ineligible.
Lastly, Examiner notes that claim 1 is not analogous to Ex Parte Desjardins. In Ex Parte Desjardins et al., the board determined that the additional elements reflect '[a]n improvement in the functioning of a computer, or an improvement to other technology or technical field,' as discussed in MPEP §§ 2106.04(d)(l) and 2106.05(a)." Req. 7. In particular, the Appellant identifies certain limitations of independent claim 1 and asserts that "the claimed subject matter provides technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training," citing paragraph 21 of the Specification for support. Id. at 7-9; see also id. at 8 ("This training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems.").
Rather, claim 1 is analogous to USPTO Example 47 claim 2. In this case, Applicant’s specification states that the machine learning is merely used to adjust a traffic allocation of a subset of the plurality of users to one or more paths of the plurality of paths based at least in part on the result data indicating that the one or more paths satisfy the goal at a higher frequency than a remaining one or more paths of the plurality of paths (Paragraph 0029). However, Applicant’s specification is silent as to how the claimed subject matter provides technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.
Independent claims 11 and 16 recite similar features and therefore are rejected for the same reasons as independent claim 1. Claims 2-10, 12-15, and 17-20 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1, 11, and 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Doumar (US 2022/0366448 A1) – discloses 6112 analytic module may analyze the media stream data, interaction details, and test plan data to statistically determine which test advertisement has performed better in regard to the test goal. The statistical data may be sent to or retrieved by alteration module 5804 as well as test plan data, element data, and 3.sup.rd party data, and then alteration module may suggest an advertisement element alteration based on the received/retrieved data 6114. Multi-channel dynamic advertisement system 5800 or business personnel may then create a new advertisement variant which applies the suggested element alteration 6116. This new ad variant may be used in the next round of advertisement variant testing 6118 wherein it can be compared and tested against one of the initial two ads which performed better. This process then proceeds back to step 6102 which allows for iterative testing of advertisement variants until a test goal has been achieved and/or a business personnel ends the test (see at least Paragraph 0247).
Joseph et al. (US 2023/0196393 A1) – discloses to: detect the optimal time of the engagement with the plurality of users in the plurality of journeys using the one or more machine learning algorithms in real-time; and recommend the optimal content for the engagement with the plurality of users throughout the plurality of journeys using the one or more machine learning algorithms in real-time (see at least Paragraphs 0018-0022).
Radhika Sundaram et al. (WO 2016013972 A1) – discloses a trend analysis report allows users to compare trends across campaigns. The trend analysis report may be generated by the one or more servers 2 by comparing the campaign data obtained from the campaign recipients 20 against a trend retrieved from the one or more databases 16. The campaign to compare a trend with may be selected from, for example, the same campaign with a different time duration, a 'golden' campaign or any other accessible campaign from campaign list. This may be followed by a selection of attributes, channel and time duration for the trend comparison. The trend analysis report may provide one or more of a comparison at a channel level, a conversion rate for each channel, a channel performance and return on investment (ROI) charts. The user analytics report may include an overview and a detailed user report. In one embodiment, the user analytics report may include an overall user activities component indicating user reach, engagement, conversion and virility quotient. The overall user activities component may also show users purchase path, behavior and top channels by performance (see at least Pages 11-12).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia H Munson can be reached at (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.P./Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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