DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding independent claims 1 and 11, the claims recites the limitation, “user desires to optimize”, there is antecedent basis for this limitation. Therefore, it renders the claims indefinite.
Regarding dependent claim 18, the claim recites limitation, “the test delay”, there is antecedent basis for this limitation. Therefore, it render the claim indefinite.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 1 recites:
“a user-configuration module programmed and operable to receive a plurality of types of electronic messages, a set of recipients, and optimization parameters, wherein the optimization parameters comprise: a time delay for testing, a contextual variable corresponding to the recipients, and a target behavior of the recipient that the user desires to optimize.”
“a testing module programmed and operable to: select a sample of recipients from the set of recipients.”
“a build-model module programmed and operable to build a recipient assignment model based on the computed metric.”
“a manage module programmed and operable to: administer data transfer between the user-configuration, testing, and build model modules.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 5, 7, 9, 11, 14, 15, 18, 19, and 20 are rejected under U.S.C. 103 as being unpatentable over Clark et al. (US 20210241325), in view of Wala et al. (US 20180219808), in further view of Tselikis et al. (US 20210034595).
Regarding claim 1, Clark discloses, a system comprising at least one server for optimizing assignment of electronic message recipients, the at least one server comprising:
a user-configuration module programmed and operable to receive a plurality of types of electronic messages, a set of recipients, and optimization parameters (Par. 0091, The messaging campaign engine 350 may then provide the generated messages to the communications facility 129, which in turn transmits the messages to the intended recipient(s) over the defined distribution channel(s), Par. 0099-0100, a set of proposed campaign parameters is received, the campaign generator 354 may receive identification of a set of intended recipients from the default parameters 352, if no user-submitted intended recipients was provided. The set of intended recipients may be identified by recipient identifier (e.g., customer profile ID), recipient contact information (e.g., email address, phone number), demographic group (e.g., certain characteristics such as age, gender, geographical location, etc.), among other possibilities), wherein the optimization parameters comprise:
a contextual variable corresponding to the recipients (Par. 0099-0100The set of intended recipients may be identified by recipient identifier (e.g., customer profile ID), recipient contact information (e.g., email address, phone number), demographic group (e.g., certain characteristics such as age, gender, geographical location, etc, i.e. demographic variable corresponding to the recipients), and
a target behavior of the recipient that the user desires to optimize (Par. 0085, A messaging campaign may be defined by a set of campaign parameters, such as: conditional terms (e.g., conditional on an intended recipient clicking on a link), i.e. target behavior of the recipient clicking on a link that merchant (i.e. user) desires to optimize);
a testing module programmed and operable to:
select a sample of recipients from the set of recipients, thereby grouping the recipients into selected recipients and unselected recipients (Par. 0143-0144, a subgroup of recipient(s) may be identified from the first group. The subgroup may be identified from the first group based on, for example, the online activity of certain members of the first group, the subgroup may be considered a second recipient group separate from and non-overlapping with the first group. Specific recipients may be reassigned from the first group to the second group, to enable more accurate analysis of future online activity. It may be useful for the identified subgroup to be separated from and non-overlapping with the original group, i.e. creating non-overlapping subgroups from set of intended recipients, selecting non-overlapping subgroups = grouping recipients in to selected recipients for first one subgroup while not selecting some recipients for that subgroup);
send electronic messages from the plurality of types of electronic messages to the selected recipients (Par. 0123, fig. 7, At 722, the messaging campaign is conducted (e.g. campaign messages are sent to the intended recipients) using the campaign parameters);
detect, after the time delay for testing, for the target behavior of the selected recipients (Par. 1038, The online activity analyzed at step 904 may have a temporal alignment with the first phase of the messaging campaign. By “temporal alignment”, it is meant that the online activity occurs during, or shortly after (e.g., within a predetermined time interval of, i.e. after the time delay) the first phase of the campaign, i.e. detect online activity of recipients of first phase of messaging shortly after or after the predetermined time delay of the first phase of the campaign, Par. 0147, analysis of online activity may indicate that one or more intended recipients (or a subgroup) has made an online purchase (whether at the online store associated with the campaign or at a different store) of the offering that was marketed by the first phase of the campaign. Accordingly, the second phase of the campaign may exclude those recipients (or subgroup) or may suppress messaging so that the recipients (or subgroup) are not messaged again for an offering they have already purchased, i.e. detect behavior of the selected recipients in first phase of the campaign); and
compute a metric corresponding to the target behavior of the selected recipients based on the detect step (Par. 0094, In some examples, the engagement metrics 358 may be stored in association with information identifying an individual recipient or a generalized recipient group (e.g., demographic group). Engagement metrics 358 may be updated over the life of a messaging campaign (e.g., as recipients are messaged over multiple phases of the campaign). In some examples, engagement metrics 358 may be updated over multiple messaging campaigns (e.g., if a merchant conducts multiple messaging campaigns over time). Par. 0147, analysis of online activity may indicate that one or more intended recipients (or a subgroup) has made an online purchase (whether at the online store associated with the campaign or at a different store) of the offering that was marketed by the first phase of the campaign, i.e. generating and updating a metric corresponding to selected recipients in each phase of delivering messages to target recipients);
a build-model module programmed and operable to build a recipient assignment model based on the computed metric, wherein the recipient assignment model is operable to predict the likelihood a recipient shall perform the target behavior after receiving each of the plurality of types of electronic messages and to determine an optimal type of electronic message for each recipient (Par. 0139-0144 using predefined rules and in addition to or instead of, use machine learning system that outputs (i.e. build) model to determine second set of parameters, The machine-learning system can identify more sophisticated behavior patterns, based on historical messaging campaigns (e.g., as stored in defined campaign parameters 356) and historical engagement scores (e.g., stored in engagement metrics 358), a subgroup of recipient(s) may be identified from the first group. The subgroup may be identified from the first group based on, for example, the online activity of certain members of the first group. For example, if certain members of the first group have viewed the website of the online store associated with the campaign, those members may be identified as a subgroup for which a different message should be communicated in the second phase than the message that is communicated to other members (who did not view the website) of the first group, the subgroup may be considered a second recipient group separate from and non-overlapping with the first group. Specific recipients may be reassigned from the first group to the second group, to enable more accurate analysis of future online activity. Par. 0147-0148, the second set of parameters may include changing the messaging schedule and/or excluding a particular subgroup so as to suppress/delay further messaging in the second phase of the campaign. For example, analysis of online activity may indicate that one or more intended recipients (or a subgroup) has made an online purchase (whether at the online store associated with the campaign or at a different store) of the offering that was marketed by the first phase of the campaign. Accordingly, the second phase of the campaign may exclude those recipients (or subgroup) or may suppress messaging so that the recipients (or subgroup) are not messaged again for an offering they have already purchase, the second set of parameters may include changing the message content. This may include adding/changing/removing an incentive from the message content, changing the format (e.g., visual, audio, textual, etc.) of the message content, or changing the offering promoted in the message content, among other possibilities, i.e. assigning recipients to subgroup based on analysis of the online activities (i.e. based on stored behavior in model), optimizing the type of message to each recipients based on their behavior, such as for example if analysis of online activity shows that an intended recipient (or subgroup of intended recipients) mostly views online reviews having a video component, then the message content in the second phase may be changed to include a video. In another example, if analysis of online activity shows that an intended recipient (or subgroup) has made an online purchase of a first offering promoted in the first phase of the campaign, then the message content in the second phase may be changed to promote a second offering that is related to the first offering. Par. 0093, discloses the messaging campaign engine 350 to determine (e.g., using a predictive algorithm or using a machine-learning system) which campaign parameters are more likely to result in positive engagement from intended recipients. For example, a response likelihood matrix or other statistics-based analytics may be generated to learn typical recipient responses); and
a manage module programmed and operable to:
administer data transfer between the user-configuration, testing, and build-model modules (Par. 0080, fig. 6, The campaign generator 354 enables automatic generation of recommended parameters for a messaging campaign, The messaging campaign engine 350 stores messaging campaign-related data, such as default campaign parameters 352, defined campaign parameters 356 and engagement metrics 358 (i.e. model generating module), Par. 0082, A proposed messaging campaign may be submitted by, for example, selecting from available campaign templates that may be populated using the default campaign parameters 352 (i.e. submitted user configuration such as intended recipient), par. 0089, the campaign generator 354 (i.e. creates campaign in phases using sub groups of recipients, i.e. test the user campaign in phases to determine user response and later modify message and recipient groups based on recipient’s behavior in initial phases) may generate recommended parameters for a messaging campaign, such as recommended distribution channel(s) for certain groups (or subgroups) of intended recipients. i.e. campaign engine components (i.e. software sub modules) contains such as campaign generator 354, campaign parameters 352, defined campaign parameters 356 and engagement metrics 358, that communicates with each other in order to carry out campaign);
Clark does not disclose, optimization parameters comprise: a time delay for testing,
run the recipient assignment model on the group of unselected recipients to determine an optimal type of electronic message for each of the unselected recipients;
send the optimal type of electronic message to each of the unselected recipients or instruct another to send the optimal type of electronic message to each of the unselected recipients.
Wala discloses, optimization parameters comprise: a time delay for testing (Par. 0036, The metric selection section 206 can also include an option 224 for selecting a time period over which the evaluation should occur. For example, in FIG. 2, a time period of four hours has been selected using via the option 224. Thus, interactions with test messages (e.g., clicks) that occur within four hours of sending the test messages will be used by the message management application 104 for evaluating the effectiveness of test message versions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Clark, by teachings of optimization parameters comprise a time delay for testing, as taught by Wala, for accurately select the time delay in terms of duration or period over which the measurement of effectiveness of the messages that users actually interacted with, as disclosed in Wala, par. 0017.
Clark in view of Wala does not discloses, run the recipient assignment model on the group of unselected recipients to determine an optimal type of electronic message for each of the unselected recipients;
send the optimal type of electronic message to each of the unselected recipients or instruct another to send the optimal type of electronic message to each of the unselected recipients.
Tselikis discloses, run the recipient assignment model on the group of unselected recipients to determine an optimal type of electronic message for each of the unselected recipients (Par. 0035, the multichannel message may be initially sent to a testing subset of the intended recipients (e.g., 5% of the intended recipients) across all the supported messaging communication channels to obtain engagement data for the message with respect to the different messaging communication channels. Thereafter, the per channel engagement data may be input or otherwise provided to the channel selection algorithm(s) as the engagement data associated with the message to adjust the messaging communication channel utilization. The message may then be sent to a subset of intended recipients using each of the supported channels. If the engagement data indicates that the email engagement is relatively low compared to the push notification messaging channel, subsequent iterations (i.e. sending to unselected intended recipient that were not selected in first phase) of the multichannel message distribution process 500 may prioritize the push notification messaging channel over the email messaging channel for the remaining subset of recipients for which push notification communications are available, i.e. initially sending message to subset of intended recipient, that means selecting group of recipient from total intended recipient while remaining unselected recipient will be targeted later on by determining optimal electronic message such as push message type instead of email format);
send the optimal type of electronic message to each of the unselected recipients or instruct another to send the optimal type of electronic message to each of the unselected recipients (Par. 0035, The message may then be sent to a subset of intended recipients using each of the supported channels. If the engagement data indicates that the email engagement is relatively low compared to the push notification messaging channel, subsequent iterations (i.e. sending to unselected intended recipient that were not selected in first phase) of the multichannel message distribution process 500 may prioritize the push notification messaging channel over the email messaging channel for the remaining subset of recipients for which push notification communications are available, i.e. initially sending message to subset of intended recipient, that means selecting group of recipient from total intended recipient while remaining unselected recipient will be targeted later on by determining optimal electronic message such as push message type instead of email format).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Clark in view of Wala, by teachings of run the recipient assignment model on the group of unselected recipients to determine an optimal type of electronic message for each of the unselected recipients and send the optimal type of electronic message to each of the unselected recipients or instruct another to send the optimal type of electronic message to each of the unselected recipients, as taught by Tselikis, to provide systems and methods that facilitate communicating to multiple intended recipients in an improved manner, as disclosed in Tselikis, par. 0004.
Regarding claim 2, The system of claim 1,
Clark in view of Wala in further view of Tselikis further discloses, wherein the user-configuration module is further operable to receive the sample size for testing (Wala par. 0040, fig. 2, The message management application 104 can display the recommended segment size 232 to guide a user in selecting options for a test transmission. For example, as a user selects more message attributes to be varied, more variations in a given message attribute, or both, the actual segment size will decrease due to the pool of test recipients being further sub-divided).
Regarding claim 4, The system of claim 1,
Clark further discloses, wherein the contextual variables include age, gender, geographical location, number of purchases, or average purchase amount (Par. 0100, the campaign generator 354 may receive identification of a set of intended recipients from the default parameters 352, if no user-submitted intended recipients was provided. The set of intended recipients may be identified by recipient identifier (e.g., customer profile ID), recipient contact information (e.g., email address, phone number), demographic group (e.g., certain characteristics such as age, gender, geographical location, etc.),).
Regarding claim 5, The system of claim 1,
Clark further discloses, wherein the at least one server is further programmed and operable to compute default parameters for increasing the likelihood of determining the optimal type of electronic message for each recipient (Par. 0093, engagement metrics 358 may be stored in association with information about certain parameters of the messaging campaign. Such detailed information may enable the messaging campaign engine 350 to determine (e.g., using a predictive algorithm or using a machine-learning system) which campaign parameters are more likely to result in positive engagement from intended recipients. For example, a response likelihood matrix or other statistics-based analytics may be generated to learn typical recipient responses, based on multiple historical campaigns).
Regarding claim 7, The system of claim 1,
Clark further discloses, wherein the at least one server is further programmed and operable to compute at least one insight rule, indicating a characteristic of the recipients that contributes to the recipient's affinity towards a type of electronic message (Par. 0147-0148, optimizing the type of message to each recipients based on their behavior, such as for example if analysis of online activity shows that an intended recipient (or subgroup of intended recipients) mostly views online reviews having a video component, then the message content in the second phase may be changed to include a video. In another example, if analysis of online activity shows that an intended recipient (or subgroup) has made an online purchase of a first offering promoted in the first phase of the campaign, then the message content in the second phase may be changed to promote a second offering that is related to the first offering. i.e. making rule to use certain type of message to send that indicates the recipient has affinity for message that includes video component so in future phase include the video in message).
Regarding claim 9, The system of claim 1,
Clark further discloses, wherein the at least one server is further programmed and operable to compute: the likelihood that a recipient shall perform a target behavior for a targeted message is greater than the likelihood that a recipient will perform a target behavior for a general message (Par. 0148, if analysis of online activity shows that an intended recipient (or subgroup of intended recipients) mostly views online reviews having a video component, then the message content in the second phase may be changed to include a video, i.e. determine that recipient will perform positive behavior when targeted message includes video, compared to when not have video in message (i.e. general message)).
Regarding claim 11, Clark in view of Wala in further view of Tselikis meets the claim limitations as set forth in claim 1.
Regarding claim 14, Clark meets the claim limitations as set forth in claim 4.
Regarding claim 15, Clark meets the claim limitations as set forth in claim 7.
Regarding claim 18, The method of claim 11,
Clark in view of Wala in further view of Tselikis further discloses, further comprising providing guidance if the test delay or number of recipients in the set is not sufficient for obtaining a personalization model with performance higher than a non-personalized message (Wala Par. 0068, the effectiveness of a test message version can be determined more accurately using the time period or range of time periods. The message testing module 106 can compare the identified time period to a time period included in the selection received at block 710. If the time period included in the selection is less than the time period or range of time periods identified as preferable, the message testing module 106 can determine that the time period will adversely impact the test transmission. Par. 0070-0071, generating interface that includes an alert identifying adverse impact, i.e. providing guidance to vender if the test requires more time or will be delayed).
Regarding claim 19, The system of claim 11,
Clark further discloses, wherein the types of electronic messages differ based on at least one of the following: text size, images, illustrations, format, graphics, and send times (Par. 0085, a schedule of sending out messages (i.e. different send times), content of message such as to include coupon or not include coupon, i.e. different illustrations of message).
Regarding claim 20, The method of claim 11,
Clark in view of Wala in further view of Tselikis further discloses, further comprising sending to each of the unselected recipients an optimal type of electronic message based on running step (Tselikis Par. 0035-0036, he multichannel message may be initially sent to a testing subset of the intended recipients (e.g., 5% of the intended recipients) across all the supported messaging communication channels to obtain engagement data for the message with respect to the different messaging communication channels, message may then be sent to a subset of intended recipients (i.e. dividing unselected remaining recipients into groups) using each of the supported channels equally (e.g., by using all supported channels for each recipient, i.e. according to the different messaging (i.e. channel) is assigned), the multichannel message data structure to send personalized versions of a message (i.e. optimal message) to any number of different recipients using the respective messaging communication channel for which each respective recipient is most responsive or engaged, or for which each respective recipient prefers to receive such messages, i.e. steps are performed to send optimal message to remaining (i.e. unselected in initial phase) recipients).
Claims 3 and 13 are rejected under U.S.C. 103 as being unpatentable over Clark et al. (US 20210241325), in view of Wala et al. (US 20180219808), in further view of Tselikis et al. (US 20210034595), in further view of Eidelman et al. (US 20230124697).
Regarding claim 3, The system of claim 1,
Clark in view of Wala in further view of Tselikis does not disclose, wherein the metrics comprise at least one selected from the following:
total number of clicks on a link in the message, average dwell time for a message, and message open rates.
Eidelman discloses, wherein the metrics comprise at least one selected from the following:
total number of clicks on a link in the message, average dwell time for a message, and message open rates (Par. 0144, Using machine learning models including correlations based on past results of prior messages, the system can optimize for a desired result metric. Par. 0192, models can determine, for example: the open rate of campaign messages, the action rate of messages, and the ultimate success of the campaign).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Clark in view of Wala in further view of , by teachings of metrics comprise at least one selected from the following: total number of clicks on a link in the message, average dwell time for a message, and message open rates, as taught by Eidelman, to determine aggregate performance of an advocacy campaign with respect to multiple or all messages and/or recipients by calculating percent of targeted recipients who opened the message, as disclosed in Eidelman, par. 0038-0041.
Regarding claim 13, Clark in view of Wala in further view of Tselikis in further view of Eidelman meets the claim limitations as set forth in claim 3.
Claims 6 and 12 are rejected under U.S.C. 103 as being unpatentable over Clark et al. (US 20210241325), in view of Wala et al. (US 20180219808), in further view of Tselikis et al. (US 20210034595), in further view of Chittilappilly et al. (US 20160210657).
Regarding claim 6, The system of claim 5,
Clark in view of Wala in further view of Tselikis does not disclose, wherein the computing comprises use of a lookup table populated with statistics from the recipients or other recipients, a simulation model operable to predict behaviors of the recipients, or an insight rule based on previous historical data or tests from the recipients or other recipients.
Chittilappilly discloses, wherein the computing comprises use of a lookup table populated with statistics from the recipients or other recipients, a simulation model operable to predict behaviors of the recipients, or an insight rule based on previous historical data or tests from the recipients or other recipients (Par. 0059, the ad server 116 can deliver advertising stimuli to the audience 150 through certain media channels according to one or more marketing campaigns (see message 122). Par. 0060, the user interaction sequence for the subject user might be applied to the touchpoint response predictive model to simulate a set of predicted responses used to generate a user propensity score for the subject user. Such a user propensity score can indicate the subject user's propensity to convert given the subject user's touchpoint experiences up to that moment in time and/or certain further stimuli that might be presented to the subject user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Clark, by teachings of use of a lookup table populated with statistics from the recipients or other recipients, a simulation model operable to predict behaviors of the recipients, or an insight rule based on previous historical data or tests from the recipients or other recipients, as taught by Chittilappilly, to apply the user interaction data corresponding to predictive model to generate a set of predicted responses using simulation technique, as disclosed in Chittilappilly, par. 0053.
Regarding claim 12, Clark in view of Wala in further view of Tselikis in further view of Chittilappilly meets the claim limitations as set forth in combined claim 5 and 6.
Claim 10 is rejected under U.S.C. 103 as being unpatentable over Clark et al. (US 20210241325), in view of Wala et al. (US 20180219808), in further view of Tselikis et al. (US 20210034595), in further view of Bangad et al. (US 20240362676).
Regarding claim 10, The system of claim 1,
Clark in view of Wala in further view of Tselikis does not disclose, wherein the recipient assignment model is a decision tree-based algorithm, optionally, uplift random forest model.
Bangad discloses, wherein the recipient assignment model is a decision tree-based algorithm, optionally, uplift random forest model (Par. 0058, identifying similar offers (e.g., offer similarity data 524), analyzing a likelihood of redemption of a given offer by a customer or customer group (e.g., redemption probability data 520), or generating customer-offer lists of ranked customers or customer groups by propensity to redeem a given offer (e.g., customer list data 522). In example implementations, the one or more predictive models 510 may be implemented as different types of models depending on the function performed, such as neural networks, decision trees, and the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Clark, by teachings of use of a lookup table populated with statistics from the recipients or other recipients, a simulation model operable to predict behaviors of the recipients, or an insight rule based on previous historical data or tests from the recipients or other recipients, as taught by Bangad, it is well known to use the simple easy to understand decision tree based model, because they are intuitive, mirrors how human makes decisions.
Claim 16 is rejected under U.S.C. 103 as being unpatentable over Clark et al. (US 20210241325), in view of Wala et al. (US 20180219808), in further view of Tselikis et al. (US 20210034595), in further view of Bastide et al. (US 20180060299).
Regarding claim 16. The method of claim 11,
Clark further discloses, comprising computing (a) the likelihood that a recipient shall perform a target behavior for an optimized message is greater than the likelihood that a recipient will perform a target behavior for a general message (Par. 0148, if analysis of online activity shows that an intended recipient (or subgroup of intended recipients) mostly views online reviews having a video component, then the message content in the second phase may be changed to include a video, i.e. determine that recipient will perform positive behavior when targeted message includes video, compared to when not have video in message (i.e. general message)).
Clark in view of Wala in further view of Tselikis does not disclose, the likelihood that a recipient shall perform a target behavior for a targeted message is within a predetermined confidence interval.
Bastide discloses, the likelihood that a recipient shall perform a target behavior for a targeted message is within a predetermined confidence interval (Par. 0054, the electronic messaging service 115 can determine the prediction of how long it will take the user 165 to respond to the electronic message 170 based on processing respective periods of time it took the user 165 to respond to other electronic messages that were assigned to the same category of the electronic message 170).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Clark, by teachings of computing the likelihood that a recipient shall perform a target behavior for a targeted message is within a predetermined confidence interval, as taught by Bastide, to predict how long it will take the user to respond to the electronic message, as disclosed in Bastide, par. 0053-0054.
Claim 17 is rejected under U.S.C. 103 as being unpatentable over Clark et al. (US 20210241325), in view of Wala et al. (US 20180219808), in further view of Tselikis et al. (US 20210034595), in further view of Kras et al. (US 20180309764).
Regarding claim 17. The method of claim 11,
Clark further discloses, further comprising dividing the unselected recipients into groups according to which message the unselected recipients were assigned, and saving the groups, and any rules used to determine the groups (Par. 0035-0036, he multichannel message may be initially sent to a testing subset of the intended recipients (e.g., 5% of the intended recipients) across all the supported messaging communication channels to obtain engagement data for the message with respect to the different messaging communication channels, message may then be sent to a subset of intended recipients (i.e. dividing unselected remaining recipients into groups) using each of the supported channels equally (e.g., by using all supported channels for each recipient, i.e. according to the different messaging (i.e. channel) is assigned), the multichannel message data structure to send personalized versions of a message to any number of different recipients using the respective messaging communication channel for which each respective recipient is most responsive or engaged, or for which each respective recipient prefers to receive such messages, ).
Clark in view of Wala in further view of Tselikis does not disclose, saving the groups, and any rules used to determine the groups.
Kras discloses, saving the groups, and any rules used to determine the groups (Par. 107, he system 200 saves a name for the smart group and criteria selected for the smart group using the smart group criteria selector 274. The name of the smart group and the criteria for the smart group may be stored in smart groups storage 272).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Clark, by teachings of saving the groups, and any rules used to determine the groups, as taught by Kras, to keep recipient group information ready beforehand to be used for campaign as needed, as disclosed in Kras, par. 0107.
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for objected the claim to be allowed:
The examiner has found that the prior arts of records does not appear to teach or suggest or render obvious the claimed limitations in combination with the specific added limitations as recited in dependent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKSHAY DOSHI whose telephone number is (571)272-2736. The examiner can normally be reached M-F 9:30 AM to 6:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN W MILLER can be reached at (571)272-7353. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.D./Examiner, Art Unit 2422
/JOHN W MILLER/Supervisory Patent Examiner, Art Unit 2422
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