DETAILED ACTION
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 .
Status of Claims
2. This Office Action is in response to the initial filing of application # 18/964,249 filed on 11/29/2025.
3. Claims 1-20 are currently pending and are considered below.
Information Disclosure Statement
4. The Applicant is respectfully reminded that each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in 37 CFR 1.56.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 1, recites a computer-implemented system for targeting electronic communications, the system comprising: a memory storing instructions; and at least one processor configured to execute the instructions to:
determine a response to electronic communications for each user of a group of users, by:
receiving interaction data indicating interactions with a first set of electronic communications via a first user device, associated with the user, in a first set period of time;
receiving purchase data indicating the user's purchases in the first set period of time;
determining a metric representing the user's response to the first set of electronic communications based on the received interaction data and the received purchase data; and
comparing the metric to a threshold to determine a response associated with the user regarding electronic communications;
generate first instructions for users with a determined positive response to electronic communications to receive electronic communications;
generate second instructions for a test group of users with a determined negative response to not receive electronic communications;
generate third instructions for a control group of users with a determined negative response to receive electronic communications;
send electronic communications by executing the first and third instructions; and
repeatedly re-assign user identifiers associated with the users in the test group to the control group after each increment of a second set period of time, by:
comparing purchasing behavior of each user in the test group to a purchasing behavior of the control group; and
removing user identifiers of users in the test group and assigning them to the control group to receive electronic communications when the comparison indicates the user's purchasing behavior has declined relative to the purchasing behavior of the control group.
The steps of,
determine a response to electronic communications for each user of a group of users, by:
receiving interaction data indicating interactions with a first set of electronic communications via a first user device, associated with the user, in a first set period of time;
receiving purchase data indicating the user's purchases in the first set period of time;
determining a metric representing the user's response to the first set of electronic communications based on the received interaction data and the received purchase data; and
comparing the metric to a threshold to determine a response associated with the user regarding electronic communications;
generate first instructions for users with a determined positive response to electronic communications to receive electronic communications;
generate second instructions for a test group of users with a determined negative response to not receive electronic communications;
generate third instructions for a control group of users with a determined negative response to receive electronic communications;
send electronic communications by executing the first and third instructions; and
repeatedly re-assign user identifiers associated with the users in the test group to the control group after each increment of a second set period of time, by:
comparing purchasing behavior of each user in the test group to a purchasing behavior of the control group; and
removing user identifiers of users in the test group and assigning them to the control group to receive electronic communications when the comparison indicates the user's purchasing behavior has declined relative to the purchasing behavior of the control group,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a method for targeting electronic communications. The above identified method steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to improving timeline of events for product location pairs.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as commercial interaction, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
As for Independent claim 20: Claim 20 recites a computer-implemented system for targeting electronic communications, the system comprising: a memory storing instructions; and at least one processor configured to execute the instructions to:
determine a response to electronic communications for each user of a group of users, by:
receiving click data indicating interactions with a first set of electronic communications via a first user device, associated with the user, in a first set period of time;
receiving data from the user device indicating at least one characteristic of the user;
utilizing a model to correlate a purchasing tendency with the at least one characteristic and the received interaction data, wherein the model comprises at least one of a linear regression model or neural network:
receiving a user purchase amount in the first set period of time; determining a portion of the user purchase amount allocated the first set of electronic communications using the model; and
comparing the portion of user purchase amount allocated to the first set of electronic communications to a threshold to determine a response associated with the user regarding electronic communications;
generate first instructions for users with a determined positive response to electronic communications to receive electronic communications;
generate second instructions for a test group of users with a determined negative response to not receive electronic communications;
generate third instructions for a control group of users with a determined negative response to receive electronic communications;
send electronic communications by executing the first and third instructions; and
repeatedly re-assign user identifiers associated with the users in the test group to the control group after each increment of a second set period of time, by:
comparing purchasing behavior of each user in the test group to a purchasing behavior of the control group; and
removing user identifiers of users in the test group and assigning them to the control group to receive electronic communications when the comparison indicates the user's purchasing behavior has declined relative to the purchasing behavior of the control group.
The steps of
determine a response to electronic communications for each user of a group of users, by:
receiving click data indicating interactions with a first set of electronic communications via a first user device, associated with the user, in a first set period of time;
receiving data from the user device indicating at least one characteristic of the user;
utilizing a model to correlate a purchasing tendency with the at least one characteristic and the received interaction data, wherein the model comprises at least one of a linear regression model or neural network:
receiving a user purchase amount in the first set period of time; determining a portion of the user purchase amount allocated the first set of electronic communications using the model; and
comparing the portion of user purchase amount allocated to the first set of electronic communications to a threshold to determine a response associated with the user regarding electronic communications;
generate first instructions for users with a determined positive response to electronic communications to receive electronic communications;
generate second instructions for a test group of users with a determined negative response to not receive electronic communications;
generate third instructions for a control group of users with a determined negative response to receive electronic communications;
send electronic communications by executing the first and third instructions; and
repeatedly re-assign user identifiers associated with the users in the test group to the control group after each increment of a second set period of time, by:
comparing purchasing behavior of each user in the test group to a purchasing behavior of the control group; and
removing user identifiers of users in the test group and assigning them to the control group to receive electronic communications when the comparison indicates the user's purchasing behavior has declined relative to the purchasing behavior of the control group,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a method for targeting electronic communications. The above identified method steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to improving timeline of events for product location pairs.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as commercial interaction, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor, memory, a first user device, a second user device, a model. The user device is recited at a high level of generality (i.e., as a generic processor performing a generic computer functions of determine a response to electronic communications: receiving click data; receiving data; utilizing a model: receiving a user purchase amount; and comparing the portion of user purchase amount; generate first instructions for users; generate second instructions for a test group of users; generate third instructions for a control group of users; send electronic communications; and repeatedly re-assign user identifiers associated with the users in the test group, comparing purchasing behavior of each user in the test group; and removing user identifiers of users in the test group and assigning them to the control group) such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation utilizing a model to correlate a purchasing tendency with the at least one characteristic and the received interaction data, wherein the model comprises at least one of a linear regression model or neural network, this feature is considered math, and therefore is a part of the abstract idea. Because the model in this claim is used as a tool for improving the abstract idea, rather than improving any technical feature or function, it is not sufficient to integrate the judicial exception into a practical application. Accordingly, 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. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of
a processor, memory, a first user device, a second user device, a model amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); generating a second menu from a first menu and sending the menu to the second location as performed by a generic computer components (Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is already present when the steps are considered separately. That is, a processor, memory, a first user device, a second user device, a model, performing commercial interactions including: determine a response to electronic communications: receiving click data; receiving data; utilizing a model: receiving a user purchase amount; and comparing the portion of user purchase amount; generate first instructions for users; generate second instructions for a test group of users; generate third instructions for a control group of users; send electronic communications; and repeatedly re-assign user identifiers associated with the users in the test group, comparing purchasing behavior of each user in the test group; and removing user identifiers of users in the test group and assigning them to the control group, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, independent claims 1, 11 and 20 are not eligible.
As for dependent claims 2-10 and 12-19, these claims recite limitations that further define the same abstract idea in claims 1 and 11. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
10. Claims 1-4, 6-14 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yeom et al. (U.S. Patent No. 11,151,613) (hereinafter ‘Yeom’) in view of Bhatia et al. (U.S. Patent No. 10,360,568) (hereinafter ‘Bhatia’).
Claims 1 and 11: Yeom discloses a computer-implemented system and computer-implemented method for targeting electronic communications, the system and method comprising:
a memory, (see at least Figure 2B element 254) storing instructions; and
at least one processor (see at least Figure 2B element 252) configured to execute the instructions to:
determine a response to electronic communications for each user of a group of users, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first group (see at least column 8 lines 6-19); by:
receiving interaction data indicating interactions with a first set of electronic communications via a first user device, associated with the user, in a first set period of time, Yeom teaches receiving a number of positive user interactions with the offer after the first initial display interval, and a total number of positive user interactions with the offer (see at least column 2 lines 48-59);
receiving purchase data indicating the user's purchases in the first set period of time, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15);
determining a metric representing the user's response to the first set of electronic communications based on the received interaction data and the received purchase data, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15); and
comparing the metric to a threshold to determine a response associated with the user regarding electronic communications, Yeom teaches the total number of positive user interactions with the offer is greater than an optimization data threshold (see at least column 3 lines 35-38);
generate first instructions for users with a determined positive response to electronic communications to receive electronic communications, Yeom teaches stream the application to a second computing device, and transmit the offer to download the application to the second computing device, after the adjusted initial display interval (see at least the Abstract and column 5 lines 5-9);
generate second instructions for a test group of users with a determined negative response to not receive electronic communications, Yeom teaches if the user does not positively respond to the second item of content or accept the offer, or if the user responds negatively to the second item of content or ignores the second item of content, the device may cease or terminate display of the second item of content, and subsequently redisplay the second item of content after a determined time interval (see at least column 6 line 63 through column 7 line 2);
generate third instructions for a control group of users with a determined negative response to receive electronic communications, Yeom teaches retransmitting the offer to a subset of the plurality of computing devices after a second display interval comprising a second predetermined time between transmitting the offer and retransmitting the offer, by the content delivery provider, responsive to not receiving a positive user interaction with the offer after the first initial display interval (see at least column 2 lines 60-66);
send electronic communications by executing the first and third instructions; Yeom teaches retransmitting the offer to a subset of the plurality of computing devices after a second display interval comprising a second predetermined time between transmitting the offer and retransmitting the offer, by the content delivery provider, responsive to not receiving a positive user interaction with the offer after the first initial display interval (see at least column 2 lines 60-66); and
repeatedly re-assign user identifiers associated with the users in the test group to the control group after each increment of a second set period of time, Yeom teaches generation or presentation of the content may be performed by an application or virtualization engine 264 in communication with a content delivery engine 268 of a server 250. Content delivery engine 268 may receive identifications of acceptance or positive responses from each application or client device 200, and/or from an application delivery engine 278 or virtualization engine 264, along with identifications of a display interval, display time, or iteration of display of the content. Data may be anonymized or not include personal identifiers or identifiers of the client device 200 in many implementations (see at least column 13 lines 13—26 and column 15 lines 3-17).
While Yeom teaches the limitations mentioned above, Yeom does not explicitly teach comparing purchasing behavior of each user in the test group to a purchasing behavior of the control group; and removing user identifiers of users in the test group and assigning them to the control group to receive electronic communications when the comparison indicates the user's purchasing behavior has declined relative to the purchasing behavior of the control group. However, Bhatia teaches the one or more controlled experiments include comparing: (1) offline behavior, relative to the brand, of an experimental group of individuals who have been exposed to some online advertising associated with the brand, with (2) offline behavior, relative to the brand, of a control group of individuals who have been prevented from being exposed to that online advertising associated with the brand. It is to be understood that in some embodiments, while a control group user may be prevented from receiving online advertising associated with the brand, this does not necessarily mean that the control group user will not receive online advertising associated with the brand from any source. For instance, the experiment may be conducted by an entity that makes arrangements for or facilitates online advertising. It is possible that a control group user may be prevented from receiving online advertising associated with the brand, the online advertising in question being from the entity, but the control group user could possibly still be exposed to other online advertising associated with the brand, for example, from another entity or source (see at least column 8 lines 16-47, column 8 lines 27-36 and column 16 lines 51-61). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Yeom to modify to include the teaching of Bhatia in order to utilize both online and offline information.
Claims 2 and 12: Yeom in view of Bhatia disclose the system and method according to claims 1 and 11, and Yeom further teaches wherein the at least one processor is further configured to:
receive from a second user device at least one of the first set period of time, the second set period of time, a proportion of users to be in the test group, a proportion of users to be in the control group, a number of users to be in the test group, a number of users to be in the control group, or the threshold to determine the response to electronic communications, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15).
Claims 3 and 13: Yeom in view of Bhatia disclose the system and method according to claims 1 and 11, and Yeom further teaches wherein the interaction data comprises at least one of: clicks on the first set of electronic communications, swipes on the first set of electronic communications, impressions on the first set of electronic communications, or mouse hovering over the first set of electronic communications, the item of content may be a pop-up or overlay window, a banner, a dialog box, or any other such interface element. The item of content may include a button (to be click) or element for providing a positive response or acceptance. The item of content may also include a second button or element to dismiss or postpone the offer or advertisement for a delay interval. In some implementations, the offer or advertisement may be dismissed and delayed responsive to expiration of a display timer. The item of content may also include a third button or element to exit the application or stop receiving a media stream, in some implementations, exiting the method of FIG. 3. (see at least column 16 lines 21-33) and Yeom further teaches To provide for interaction with a user, implementations of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT ( cathode ray tube), LCD (liquid crystal display), OLED (organic light emitting diode), TFT (thin-film transistor), plasma, other flexible configuration, or any other monitor for displaying information to the user and a keyboard, a pointing device, e.g., a mouse, trackball, etc., or a touch screen, touch pad, etc., by which the user can provide input to the compute (see at least column 20 lines 21-39).
Claims 4 and 14: Yeom in view of Bhatia disclose the system and method according to claims 1 and 11, and Yeom further teaches wherein the metric comprises at least one of:
a number of purchases made by the user following an interaction by the user with the first set of electronic communications, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15).
Claims 6 and 16: Yeom in view of Bhatia disclose the system and method according to claims 1 and 11, and Yeom further teaches wherein the at least one processor is further configured to:
link the interaction data to at least one of an advertising campaign identifier, advertising channel identifier, or user device identifier, wherein the determined metric corresponds to the at least one identifier, Yeom teaches an interactive second item of content such as an offer, advertisement, link, survey, or other such content, displayed one or more repeated times until a user accepts or provides a positive response to the second item of content (see at least column2 lines 7-33).
Claims 7 and 17: Yeom in view of Bhatia disclose the system and method according to claims 1 and 11, and Yeom further teaches wherein the test group includes more users than the control group, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-30).
Claims 8 and 18: Yeom in view of Bhatia disclose the system and method according to claims 1 and 11, and Yeom further teaches wherein the at least one processor is further configured to:
determine at least one characteristic of the users in the test group and control group, comprising at least one of:
a membership status in a company program,
a purchasing status of the user, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15);
a recency, frequency, monetary value (RFM) status of the user,
an age of the user, or
a gender of the user; and
wherein comparing purchasing behavior of each user in the test group to purchasing behavior of the control group comprises:
for each user in the test group, comparing the user to a subset of users in the control group based on a similarity in the at least one characteristic, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15).
Claims 9 and 19: Yeom in view of Bhatia disclose the system and method according to claims 1 and 11, and Yeom further teaches wherein the at least one processor is further configured to:
determine the user's purchasing behavior has declined relative to the purchasing behavior of the control group based on at least one of:
the user's spending being less than an average spending of the control group, the user's spending being less than a lower standard deviation of the control group's spending, the user's number of purchases being less than an average number of purchases of the control group, or the user's number of purchases being less than a lower standard deviation of the control group's number of purchases, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15).
Claim 10: Yeom in view of Bhatia disclose the system according to claim 1, and Yeom further teaches wherein re-assigning user identifiers associated with the users in the test group to the control group comprises at least one of:
saving the user identifiers of the removed users to a list of users to receive electronic communications,
updating, for each user identifier of the removed users, an indicator in a table to indicate the user is to receive electronic communications, or
sending a notice to a device to indicate the users are to receive electronic, Yeom teaches stream the application to a second computing device, and transmit the offer to download the application to the second computing device, after the adjusted initial display interval (see at least the Abstract and column 5 lines 5-9).
11. Claims 5, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yeom et al. (U.S. Patent No. 11,151,613) (hereinafter ‘Yeom’) in view of Bhatia et al. (U.S. Patent No. 10,360,568) (hereinafter ‘Bhatia’) and further in view of Makhijani et al (U.S. Pub. No. 2024/0289645) (hereinafter ‘Makhijani’).
Claims 5 and 15: Yeom in view of Bhatia disclose the system and method according to claims 1 and 11, and Yeom further teaches wherein determining a response to electronic communications for each user of a group of users further comprises:
receiving data from the user device indicating at least one characteristic of the user, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15);
determining the metric representing the user's response to the first set of electronic communications based on an output of the model, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15); and
wherein generating the first instructions for users with the determined positive response, generating the second instructions for the test group of users, and generating the third instructions for the control group of users is based on the comparing the metric to the threshold, Yeom teaches if the user does not positively respond to the second item of content or accept the offer, or if the user responds negatively to the second item of content or ignores the second item of content, the device may cease or terminate display of the second item of content, and subsequently redisplay the second item of content after a determined time interval (see at least column 6 line 63 through column 7 line 2).
Yeom in view of Bhatia teach all the limitations mentioned above but does not explicitly teach utilizing a model to correlate a purchasing tendency with the at least one characteristic and the received interaction data, wherein the model comprises at least one of a linear regression model or neural network. However, Makhijani teaches In regression tasks with machine learning, it is desirable to normalize or transform the target variable for various reasons including training stability and robustness. Thus, if the configuration engine 117 determines that in the variable target prediction task, the distribution of the target is skewed and heavy-tailed which would result in poor performance and optimization being dominated by outliers or instances on the tail of target distribution, then the configuration engine 117 is configured to apply a non-linear transformation function such as a log transform on the target value when training the prediction models, e.g. the first prediction model 154, (see at least paragraphs 0096 and 0113). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Yeom in view of Bhatia to modify to include the teaching of Makhijani for predicting confidence of the prediction of purchasing tendency of Yeom.
Claim 20: Yeom discloses a computer-implemented system for targeting electronic communications, the system comprising:
a memory, (see at least Figure 2B element 254) storing instructions; and
at least one processor (see at least Figure 2B element 252) configured to execute the instructions to:
determine a response to electronic communications for each user of a group of users, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first group (see at least column 8 lines 6-19), by:
receiving click data indicating interactions with a first set of electronic communications via a first user device, associated with the user, in a first set period of time, Yeom teaches receiving a number of positive user interactions with the offer after the first initial display interval, and a total number of positive user interactions with the offer (see at least column 2 lines 48-59);
receiving data from the user device indicating at least one characteristic of the user, Yeom teaches users may respond quickly to offers or determine quickly while using a trial mode of an application or preview of a streamed application that they are interested in purchase/download/unlocking of the content, while others may require significantly more time. Accordingly, given a display of an offer or second item of content at a first time period, a first subset of users ( e.g. the former group) may accept or respond positively to the second item of content, while a second subset of users ( e.g. the latter group) may respond negatively or not respond at all. Upon acceptance of the offer by the first group, the application or first item of content may be downloaded or provided to devices of the first group, and no further offers of said content are necessary to the first (see at least column 8 lines 6-19 and column 9 lines 4-15);
receiving a user purchase amount in the first set period of time, Yeom teaches executing an application on a remote computing device and streaming a display of the application to the user's device may require significant amounts of resources of the remote computing device ( e.g. memory, processing time, electricity, bandwidth, etc.), particularly as the number of users and client devices trying the application increase. To limit the amount of resources used for streaming the application, an offer or advertisement or similar item of content may be periodically displayed by the user's device, requesting the user to choose to install or purchase the application, end the preview or trial of the application, or continue the trial period (see at least column 1 lines 33-44);
determining a portion of the user purchase amount allocated the first set of electronic communications using the model, Yeom teaches executing an application on a remote computing device and streaming a display of the application to the user's device may require significant amounts of resources of the remote computing device ( e.g. memory, processing time, electricity, bandwidth, etc.), particularly as the number of users and client devices trying the application increase. To limit the amount of resources used for streaming the application, an offer or advertisement or similar item of content may be periodically displayed by the user's device, requesting the user to choose to install or purchase the application, end the preview or trial of the application, or continue the trial period (see at least column 1 lines 33-44); and
comparing the portion of user purchase amount allocated to the first set of electronic communications to a threshold to determine a response associated with the user regarding electronic communications, Yeom teaches executing an application on a remote computing device and streaming a display of the application to the user's device may require significant amounts of resources of the remote computing device ( e.g. memory, processing time, electricity, bandwidth, etc.), particularly as the number of users and client devices trying the application increase. To limit the amount of resources used for streaming the application, an offer or advertisement or similar item of content may be periodically displayed by the user's device, requesting the user to choose to install or purchase the application, end the preview or trial of the application, or continue the trial period (see at least column 1 lines 33-44);
generate first instructions for users with a determined positive response to electronic communications to receive electronic communications, Yeom teaches stream the application to a second computing device, and transmit the offer to download the application to the second computing device, after the adjusted initial display interval (see at least the Abstract and column 5 lines 5-9);
generate second instructions for a test group of users with a determined negative response to not receive electronic communications, Yeom teaches if the user does not positively respond to the second item of content or accept the offer, or if the user responds negatively to the second item of content or ignores the second item of content, the device may cease or terminate display of the second item of content, and subsequently redisplay the second item of content after a determined time interval (see at least column 6 line 63 through column 7 line 2);
generate third instructions for a control group of users with a determined negative response to receive electronic communications, Yeom teaches retransmitting the offer to a subset of the plurality of computing devices after a second display interval comprising a second predetermined time between transmitting the offer and retransmitting the offer, by the content delivery provider, responsive to not receiving a positive user interaction with the offer after the first initial display interval (see at least column 2 lines 60-66);
send electronic communications by executing the first and third instructions, Yeom teaches retransmitting the offer to a subset of the plurality of computing devices after a second display interval comprising a second predetermined time between transmitting the offer and retransmitting the offer, by the content delivery provider, responsive to not receiving a positive user interaction with the offer after the first initial display interval (see at least column 2 lines 60-66); and
repeatedly re-assign user identifiers associated with the users in the test group to the control group after each increment of a second set period of time, Yeom teaches generation or presentation of the content may be performed by an application or virtualization engine 264 in communication with a content delivery engine 268 of a server 250. Content delivery engine 268 may receive identifications of acceptance or positive responses from each application or client device 200, and/or from an application delivery engine 278 or virtualization engine 264, along with identifications of a display interval, display time, or iteration of display of the content. Data may be anonymized or not include personal identifiers or identifiers of the client device 200 in many implementations (see at least column 13 lines 13—26 and column 15 lines 3-17).
While Yeom teaches the limitations mentioned above, Yeom does not explicitly teach comparing purchasing behavior of each user in the test group to a purchasing behavior of the control group; and removing user identifiers of users in the test group and assigning them to the control group to receive electronic communications when the comparison indicates the user's purchasing behavior has declined relative to the purchasing behavior of the control group. However, Bhatia teaches the one or more controlled experiments include comparing: (1) offline behavior, relative to the brand, of an experimental group of individuals who have been exposed to some online advertising associated with the brand, with (2) offline behavior, relative to the brand, of a control group of individuals who have been prevented from being exposed to that online advertising associated with the brand. It is to be understood that in some embodiments, while a control group user may be prevented from receiving online advertising associated with the brand, this does not necessarily mean that the control group user will not receive online advertising associated with the brand from any source. For instance, the experiment may be conducted by an entity that makes arrangements for or facilitates online advertising. It is possible that a control group user may be prevented from receiving online advertising associated with the brand, the online advertising in question being from the entity, but the control group user could possibly still be exposed to other online advertising associated with the brand, for example, from another entity or source (see at least column 8 lines 16-47, column 8 lines 27-36 and column 16 lines 51-61). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Yeom to modify to include the teaching of Bhatia in order to utilize both online and offline information.
Yeom in view of Bhatia teach all the limitations mentioned above but does not explicitly teach utilizing a model to correlate a purchasing tendency with the at least one characteristic and the received interaction data, wherein the model comprises at least one of a linear regression model or neural network. However, Makhijani teaches In regression tasks with machine learning, it is desirable to normalize or transform the target variable for various reasons including training stability and robustness. Thus, if the configuration engine 117 determines that in the variable target prediction task, the distribution of the target is skewed and heavy-tailed which would result in poor performance and optimization being dominated by outliers or instances on the tail of target distribution, then the configuration engine 117 is configured to apply a non-linear transformation function such as a log transform on the target value when training the prediction models, e.g. the first prediction model 154, (see at least paragraphs 0096 and 0113). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Yeom in view of Bhatia to modify to include the teaching of Makhijani for predicting confidence of the prediction of purchasing tendency of Yeom.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
13. Kirkham et al. (U.S. Pub. No. 2013/0339901) discloses determining one or more success metrics indicative of an efficiency of the current configuration of graphical representations, generating, using an optimization technique, a modified configuration of graphical representations based on the usage signals and the one or more success metrics, and presenting, in the user interface, at least a portion of the modified configuration of graphical representations (see at least the Abstract).
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 02/02/2026