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
This office action is in response to the restriction election filed on 12/22/2025.
Applicant has elected claims 41-48 without traverse for further prosecution.
Claims 61-72 were added.
Claims 41-48 and 61-72 are pending and have been examined.
Election/Restrictions
Newly submitted claims 69-72 directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: Newly added claim 69 does not include various features of the other independent claims including at least the limitations regarding monitoring of a web session and claims 41 and 61 do not include at least the limitations regarding the predictive algorithm.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 69-72 (claim 72 is a typo and meant to depend from claim 69, otherwise it would merely be a repeat of claim 68 – discussed with attorney Simon on 1/6/2025) are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 41-60 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 41-60 are directed to methods. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
Claim 41:
obtaining historical user activity data of a user by:
monitor a web session, wherein the user activity data monitored includes one or more of a URL of a webpage visited, content of the webpage, a search query submitted by the user, or a user interaction of the user; and
detect, via the monitoring, occurrence of one or more predetermined trigger conditions, and transmit data indicative of the occurrence of the one or more predetermined trigger conditions to a data store, the data being associated with the web session;
determining, based on the data associated with the web session, that the occurrence of the one or more predetermined trigger conditions is indicative of a transaction intention of the user with regard to one or more categories of products;
obtaining offer information for obtaining at least one product associated with the one or more categories from one or more vendors;
determining, based on the historical user activity data, a likelihood of the user to engage with the offer information;
in response to the likelihood being above a predetermined threshold, generating a user-specific message that includes at least a portion of the offer information, and transmitting the user-specific message to the user.
Claim 61:
obtaining historical user activity data of a user, by:
monitor a web session of the user;
detect, via the monitoring, occurrence of one or more predetermined trigger conditions; and
in response to detection of the occurrence of the one or more trigger conditions, transmitting data indicative of the occurrence of the one or more predetermined trigger conditions to the data storage, the data being associated with the web session;
determining, based on the data associated with the web session, that the occurrence of the one or more predetermined trigger conditions is indicative of a transaction intention of the user with regard to one or more categories of products;
obtaining offer information for obtaining at least one product associated with the one or more categories from one or more vendors;
determining, based on the historical user activity data, a likelihood of the user to engage with the offer information;
in response to the likelihood being above a predetermined threshold, generating a user-specific electronic message that includes at least a portion of the offer information, and transmitting the user-specific electronic message to the user device.
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
Claim 42, 62:
wherein determining the likelihood includes applying a predictive algorithm to the historical user activity data.
Claims 43, 63:
wherein the predictive algorithm includes applying a trained model that has been trained, based on training historical user activity data of other users and training historical engagement data of the other users, to learn associations between the training historical user activity data and the training historical engagement data, such that the trained model is configured to generate a prediction of the likelihood.
Claims 44, 64:
wherein the historical user activity data includes data regarding one or more of a rewards program or a membership that one or more of the user or the at least one vendor is enrolled in; and
the offer information is adjusted based on the enrollment.
Claims 45, 65:
wherein the user-specific message includes at least one selectable option operable to navigate to a selected offer.
Claim 46, 66:
wherein the likelihood of engagement is based on one or more of a value to the user for offers in the offer information, a timing of the user-specific message relative to other communications to the user, or at least one historical response of the user to one or more previous communication that included an offer.
Claims 47, 67:
wherein the likelihood of engagement is determined based on a portion of the historical user activity data associated with one or more of a particular product associated with the one or more products or a category of products associated with the one or more products.
Claims 48, 68:
wherein the transmitting of the user-specific message is only performed in response to the likelihood being above the predetermined threshold, such that no message is sent to the user if the likelihood is not above the predetermined threshold.
The claims provide a manner of monitoring a user to obtain historical user activity data. When it is detected that the data includes a trigger condition, it is determined whether the trigger is indicative of an intention of the user to complete a transaction with regard to a category of products. Vendors provide offers associated with the categories and once the likelihood of responding to an offer is above a threshold the offer is provided to the user. Thus, when considered individually and as an ordered combination, the claims embody certain methods of organizing human activity. Specifically, such activity is in the form of commercial interactions (in the form of advertising, marketing or sales activities or behaviors).
Step 2A prong 2: This judicial exception is not integrated into a practical application. The claims recite the following additional elements: first electronic application of a user device (claim 41, 45, 61, 65); user device (claim 41, 61); electronic message (41, 45, 46, 48, 61, 65, 66, 67); trained machine learning model (claim 43, 63); link operable to navigate the electronic application to a selected offer (claim 45, 65); second electronic application operating on the user device (claim 41, 61); database (41, 61); website ( claim 65);
Examiner’s Comment: Though the claims are said to be a “computer-implemented method” it is not clear which if any steps are actually performed by a computer as the steps are not positively recited as being performed by any computing device. Thus, the inferred use of a computer is not considered an additional element.
The user device is recited at a high level of generality and amount to merely applying the abstract idea using a generic computing device. Nothing in the claims improves upon computers, computer technology, or a technical field (See MPEP 2106.05(f)).
The electronic messages merely provide a general link to a particularly technological environment (i.e. on a computer). The messages are merely provided electronically as opposed to any other medium. Nothing in the claims improves upon electronic communication technology or a technical field (See MPEP 2106.05(h)). Similarly, the use of a website as the medium for which the products are being viewed as opposed to any other medium does not improve website technology or a technical field and merely provides the technological environment in which the abstract idea takes place (See MPEP 2106.05(h)).
The first electronic application, second electronic application, database, link to the selected offer, and trained machine learning model are merely recited at a high level of generality and amount to generic computer implementation. Nothing in the claims improves upon computer application technology, link technology, machine learning, databases or a technical field. Thus, the high-level usage of electronic applications, links, databases, and machine learning does not go beyond the “apply it” level of implementation (See MPEP 2106.05(f)).
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea or provide a general link to a particular technological environment or field of use (i.e. on a computer).
As a result, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 41-43, 45-48, 61-63, and 65-68 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al (US 2019/0102698) in view of Everingham (US 2012/0179545)
As per claim 41:
Roberts teaches:
A computer-implemented method of providing offer information to a user that is likely to engage, the computer-implemented method comprising: (paragraph [0005], [0006])
obtaining historical user activity data of a user on a first electronic application operating on a user device, by: causing a second electronic application also operating on the user device to monitor a web session of the first electronic application, wherein user activity data monitored by the second electronic application includes one or more of a URL of a webpage visited by the first electronic application, content of the webpage, a search query submitted by the user to the first electronic application, or a user interaction of the user with the first electronic application; (paragraph [0026] As one example, a trajectory for a user can be initialized upon detecting that profile data corresponding to the user includes information for at least a predefined set of fields. The profile data can be collected using one or more web servers over one or more sessions associated with the user and/or retrieved from a remote data source. In some instances, a user device automatically detects at least some of the profile data and communicates it to the web server(s) (e.g., via automatically populated header information in a communication that identifies, for example, a unique device identifier, MAC address, browser type, browser version, operating system type, operating system version, device type, language to which the device is set, etc.). In some instances, a communication includes data that represents user input (e.g., text entered into a web form, link selections, page navigation, etc.), which can then be logged as profile data. [0065] The machine-learning model (configured with the first parameters) can use profile data associated with the trajectory to determine which communication channel to user. The profile data can include client-collected profile data (e.g., using metadata, cookies and/or inputs associated with previous HTML requests from a user device associated with the trajectory). The profile data may further include other profile data requested and received from a remote user-profile data store, which may collect and manage profile data from multiple web hosts, clients, etc. [0067] Thus, second branching node 415 is connected to a first notification content node 430a that represents content that identifies a product most recently viewed by the user at the web site, a second notification content node 430b that represents content that identifies four of the products most viewed (across users) at the web site over the last week, and a third notification content node 430c that represents content that includes an identification of a discounts. The second decision can be made using the machine-learning model configured based upon one or more second parameters. Thus, in some (but not all) instances, a general type of machine-learning model used at various branching nodes to make decisions can be the same, though particular configurations (e.g., indicating weights to be assigned to various user attributes, which user attributes are to be considered at all and/or target outcomes) can differ.)
obtaining offer information for obtaining at least one product associated with the one or more categories from one or more vendors; (paragraph [0048] Dynamic content generator 147 can identify a type of communication (e.g., email, SMS message, pop-up browser window or pushed app alert) to be transmitted, which can inform (for example) which of web server 135, email server 140 and/or app server 145 is to transmit the communication. The identification can be made explicitly (e.g., based on a machine-learning result, parameter, and/or machine-learning-model configuration) or implicitly (e.g., due to a selected content object being of a particular type). [0049] Identifying the content object can include selecting from amongst a set of existing content objects or generating a new content object. The content object can include (for example) a webpage, an object within a webpage, an image, a text message, an email, an object within an email and/or text. In some instances, a result of executing a configured machine-learning model on profile data identifies a particular content object. In some instances, a result identifies a characteristic of content (e.g., having a particular metadata category) and/or identifies a particular technique for selecting content. For example, a result may indicate that a “tools” item is to be featured in a content object and/or that a communication is to include four content objects that correspond to four different (though unspecified) categories. In such instances, dynamic content generator 147 can (for example) select from amongst a set of potential content objects using a selection technique that is (for example) indicated via a result of the machine-learning implement, via a parameter, and/or via a predefined setting. For example, a selection technique may indicate that a selection technique is to include a pseudo-random selection technique, a technique to identify a most recently added object, a technique to identify a highest-conversion object within a set of potential content objects (e.g., having one or more attributes as indicated in a machine-learning result).)
determining, based on the historical user activity, a likelihood of the user to engage with the offer information; (paragraph [0045] Identifying a next node and/or communications specification(s) can include running a machine learning model (associated with a current branching node) using particular profile data and one or more learned parameters. A result can indicate (for example) which of various content-presentation characteristics is associated with a high (e.g., above-threshold) or highest probability of leading to a particular target outcome (e.g., target conversion). In some instances, the analysis includes identifying one or more content-presentation characteristics associated with a highest probability of leading to a particular conversion target outcome. [0053] As one example, a client system may offer online purchases of food delivery. It may be detected that a particular user had looked at a menu for a given restaurant at 2 pm. The client system may retrieve a set of user attributes from a profile data for the user from its client-managed user data. Client-specific learned data may indicate that there is a 76% chance that the user will make a purchase from the restaurant if an email including a discount code is sent in the evening to the user (e.g., as compared to a lower probability associated with other types of communication and other times). In response to determining that the 76% chance is above a 65% threshold for sending a discount threshold, email server 140 transmits an email to the user device.)
in response to the likelihood being above a predetermined threshold, generating a user-specific electronic message that includes at least a portion of the offer information, and transmitting the user-specific electronic message to the user device. (paragraph [0048] Dynamic content generator 147 can identify a type of communication (e.g., email, SMS message, pop-up browser window or pushed app alert) to be transmitted, which can inform (for example) which of web server 135, email server 140 and/or app server 145 is to transmit the communication. [0053] As one example, a client system may offer online purchases of food delivery. It may be detected that a particular user had looked at a menu for a given restaurant at 2 pm. The client system may retrieve a set of user attributes from a profile data for the user from its client-managed user data. Client-specific learned data may indicate that there is a 76% chance that the user will make a purchase from the restaurant if an email including a discount code is sent in the evening to the user (e.g., as compared to a lower probability associated with other types of communication and other times). In response to determining that the 76% chance is above a 65% threshold for sending a discount threshold, email server 140 transmits an email to the user device. The user opens the email the next day at 10 am. The code is executed to request the restaurant and discount from the client system. The client system has since received updated public learned correlation data. The client system inputs the time, the user's location (as she is now at work) and prior purchase information to a decision tree built based on the learned data. It is determined that the discount is to be 10% (e.g., to maintain a threshold likelihood of conversion) and the restaurant is to be a deli near the user's work (e.g., to maximize a likelihood of conversion), whereas—had the user opened the email the night before, different user attributes and learned data would have resulted in a 15% discount (e.g., to maintain the threshold likelihood) from an Indian restaurant near the user's home (e.g., to maximize the likelihood). The email includes a link to order from the deli. When the user clicks on the link, the web server determines what content is to be presented—specifically, which food items are to be recommended. The recommendations are based on even more recently updated public learned correlation data, which indicate that salads and sandwiches should be recommended over soup and entrees, as the former options have been recently popular (predicted to be popular due to the warmer weather).)
Roberts does not expressly teach causing the second electronic application to detect, via the monitoring, occurrence of one or more predetermined trigger conditions, and transmit data indicative of the occurrence of the one or more predetermined trigger conditions to a database, the data being associated with the web session or determining, based on the data associated with the web session, that the occurrence of the one or more predetermined trigger conditions is indicative of a transaction intention of the user with regard to one or more categories of products.
Everingham teaches:
causing the second electronic application to detect, via the monitoring, occurrence of one or more predetermined trigger conditions, and transmit data indicative of the occurrence of the one or more predetermined trigger conditions to a database, the data being associated with the web session; ([0035] Memory 112 also contains a decision engine program 140. In one embodiment, decision engine program 140 is used to identify a creative relevant to the search query. For example, decision engine program 140 may identify whether the search query includes words indicative of a purchase intent. Such purchase intent can then be used to identify and provide a relevant creative that may direct the user to a merchant's website. In one embodiment, decision engine program 140 analyzes the search query to identify one or more nouns in the search query. Each identified noun is then considered a product tag. The decision engine program 140 can then search database 125 or database 130 for creatives indexed with a matching product tag (matches need not be exact matches). Creatives (in the form of images, links, or other documents) with matching product tags can then be returned to the user for display on client device 102. [0036] Additional descriptive words in the search query may be used to add specificity to the creative selection. For example, if a user submits an image search query for a "black purse," search engine program 120 identifies a plurality of documents (e.g., webpages, web images, etc.) that are responsive to the search "black purse." Decision engine program 140 identifies the term "purse" as a product tag indicative of a purchase intent. Decision engine program then searches product database 125, 130 for creatives matching product tag "purse." The term "black" may be used as a subject tag to add specificity to the creative selection. After decision engine program 140 identifies one or more creatives relevant to the search "black purse" (e.g., a creative advertising a black purse), server 110 can then return to the user an interface to view the plurality of documents responsive to the search "black purse," as well as one or more creatives advertising black purses.)
determining, based on the data associated with the web session, that the occurrence of the one or more predetermined trigger conditions is indicative of a transaction intention of the user with regard to one or more categories of products; ([0035] Memory 112 also contains a decision engine program 140. In one embodiment, decision engine program 140 is used to identify a creative relevant to the search query. For example, decision engine program 140 may identify whether the search query includes words indicative of a purchase intent. Such purchase intent can then be used to identify and provide a relevant creative that may direct the user to a merchant's website. In one embodiment, decision engine program 140 analyzes the search query to identify one or more nouns in the search query. Each identified noun is then considered a product tag. The decision engine program 140 can then search database 125 or database 130 for creatives indexed with a matching product tag (matches need not be exact matches). Creatives (in the form of images, links, or other documents) with matching product tags can then be returned to the user for display on client device 102. [0036] Additional descriptive words in the search query may be used to add specificity to the creative selection. For example, if a user submits an image search query for a "black purse," search engine program 120 identifies a plurality of documents (e.g., webpages, web images, etc.) that are responsive to the search "black purse." Decision engine program 140 identifies the term "purse" as a product tag indicative of a purchase intent. Decision engine program then searches product database 125, 130 for creatives matching product tag "purse." The term "black" may be used as a subject tag to add specificity to the creative selection. After decision engine program 140 identifies one or more creatives relevant to the search "black purse" (e.g., a creative advertising a black purse), server 110 can then return to the user an interface to view the plurality of documents responsive to the search "black purse," as well as one or more creatives advertising black purses.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include causing the second electronic application to detect, via the monitoring, occurrence of one or more predetermined trigger conditions, and transmit data indicative of the occurrence of the one or more predetermined trigger conditions to a database, the data being associated with the web session or determining, based on the data associated with the web session, that the occurrence of the one or more predetermined trigger conditions is indicative of a transaction intention of the user with regard to one or more categories of products as taught by Everingham in order to increase revenues through advertising to a customer who has indicated an interest in making a purchase.
As per claim 61:
Roberts teaches:
A computer-implemented method of providing offer information to a user that is likely to engage, the computer-implemented method comprising: (paragraph [0005], [0006])
obtaining, at a database, historical user activity data of a user on a first electronic application operating on a user device, by: causing a second electronic application also operating on the user device to monitor a web session of the first electronic application;(paragraph [0026] As one example, a trajectory for a user can be initialized upon detecting that profile data corresponding to the user includes information for at least a predefined set of fields. The profile data can be collected using one or more web servers over one or more sessions associated with the user and/or retrieved from a remote data source. In some instances, a user device automatically detects at least some of the profile data and communicates it to the web server(s) (e.g., via automatically populated header information in a communication that identifies, for example, a unique device identifier, MAC address, browser type, browser version, operating system type, operating system version, device type, language to which the device is set, etc.). In some instances, a communication includes data that represents user input (e.g., text entered into a web form, link selections, page navigation, etc.), which can then be logged as profile data. [0043] Client system 110 can transmit at least part of the user data from client-managed user data store 150 to machine learning data platform 105, which can store it in secure client-availed user data store 120. [0065] The machine-learning model (configured with the first parameters) can use profile data associated with the trajectory to determine which communication channel to user. The profile data can include client-collected profile data (e.g., using metadata, cookies and/or inputs associated with previous HTML requests from a user device associated with the trajectory). The profile data may further include other profile data requested and received from a remote user-profile data store, which may collect and manage profile data from multiple web hosts, clients, etc. [0067] Thus, second branching node 415 is connected to a first notification content node 430a that represents content that identifies a product most recently viewed by the user at the web site, a second notification content node 430b that represents content that identifies four of the products most viewed (across users) at the web site over the last week, and a third notification content node 430c that represents content that includes an identification of a discounts. The second decision can be made using the machine-learning model configured based upon one or more second parameters. Thus, in some (but not all) instances, a general type of machine-learning model used at various branching nodes to make decisions can be the same, though particular configurations (e.g., indicating weights to be assigned to various user attributes, which user attributes are to be considered at all and/or target outcomes) can differ.)
obtaining offer information for obtaining at least one product associated with the one or more categories from one or more vendors; (paragraph [0048] Dynamic content generator 147 can identify a type of communication (e.g., email, SMS message, pop-up browser window or pushed app alert) to be transmitted, which can inform (for example) which of web server 135, email server 140 and/or app server 145 is to transmit the communication. The identification can be made explicitly (e.g., based on a machine-learning result, parameter, and/or machine-learning-model configuration) or implicitly (e.g., due to a selected content object being of a particular type). [0049] Identifying the content object can include selecting from amongst a set of existing content objects or generating a new content object. The content object can include (for example) a webpage, an object within a webpage, an image, a text message, an email, an object within an email and/or text. In some instances, a result of executing a configured machine-learning model on profile data identifies a particular content object. In some instances, a result identifies a characteristic of content (e.g., having a particular metadata category) and/or identifies a particular technique for selecting content. For example, a result may indicate that a “tools” item is to be featured in a content object and/or that a communication is to include four content objects that correspond to four different (though unspecified) categories. In such instances, dynamic content generator 147 can (for example) select from amongst a set of potential content objects using a selection technique that is (for example) indicated via a result of the machine-learning implement, via a parameter, and/or via a predefined setting. For example, a selection technique may indicate that a selection technique is to include a pseudo-random selection technique, a technique to identify a most recently added object, a technique to identify a highest-conversion object within a set of potential content objects (e.g., having one or more attributes as indicated in a machine-learning result).)
determining, based on the historical user activity data, a likelihood of the user to engage with the offer information; (paragraph [0045] Identifying a next node and/or communications specification(s) can include running a machine learning model (associated with a current branching node) using particular profile data and one or more learned parameters. A result can indicate (for example) which of various content-presentation characteristics is associated with a high (e.g., above-threshold) or highest probability of leading to a particular target outcome (e.g., target conversion). In some instances, the analysis includes identifying one or more content-presentation characteristics associated with a highest probability of leading to a particular conversion target outcome. [0053] As one example, a client system may offer online purchases of food delivery. It may be detected that a particular user had looked at a menu for a given restaurant at 2 pm. The client system may retrieve a set of user attributes from a profile data for the user from its client-managed user data. Client-specific learned data may indicate that there is a 76% chance that the user will make a purchase from the restaurant if an email including a discount code is sent in the evening to the user (e.g., as compared to a lower probability associated with other types of communication and other times). In response to determining that the 76% chance is above a 65% threshold for sending a discount threshold, email server 140 transmits an email to the user device.)
in response to the likelihood being above a predetermined threshold, generating a user-specific electronic message that includes at least a portion of the offer information, and transmitting the user-specific electronic message to the user device. (paragraph [0048] Dynamic content generator 147 can identify a type of communication (e.g., email, SMS message, pop-up browser window or pushed app alert) to be transmitted, which can inform (for example) which of web server 135, email server 140 and/or app server 145 is to transmit the communication. [0053] As one example, a client system may offer online purchases of food delivery. It may be detected that a particular user had looked at a menu for a given restaurant at 2 pm. The client system may retrieve a set of user attributes from a profile data for the user from its client-managed user data. Client-specific learned data may indicate that there is a 76% chance that the user will make a purchase from the restaurant if an email including a discount code is sent in the evening to the user (e.g., as compared to a lower probability associated with other types of communication and other times). In response to determining that the 76% chance is above a 65% threshold for sending a discount threshold, email server 140 transmits an email to the user device. The user opens the email the next day at 10 am. The code is executed to request the restaurant and discount from the client system. The client system has since received updated public learned correlation data. The client system inputs the time, the user's location (as she is now at work) and prior purchase information to a decision tree built based on the learned data. It is determined that the discount is to be 10% (e.g., to maintain a threshold likelihood of conversion) and the restaurant is to be a deli near the user's work (e.g., to maximize a likelihood of conversion), whereas—had the user opened the email the night before, different user attributes and learned data would have resulted in a 15% discount (e.g., to maintain the threshold likelihood) from an Indian restaurant near the user's home (e.g., to maximize the likelihood). The email includes a link to order from the deli. When the user clicks on the link, the web server determines what content is to be presented—specifically, which food items are to be recommended. The recommendations are based on even more recently updated public learned correlation data, which indicate that salads and sandwiches should be recommended over soup and entrees, as the former options have been recently popular (predicted to be popular due to the warmer weather).)
Roberts does not expressly teach causing the second electronic application to detect, via the monitoring, occurrence of one or more predetermined trigger conditions; and in response to detection of the occurrence of the one or more trigger conditions, transmitting, via the first electronic application, data indicative of the occurrence of the one or more predetermined trigger conditions to the database, the data being associated with the web session and determining, at the database and based on the data associated with the web session, that the occurrence of the one or more predetermined trigger conditions is indicative of a transaction intention of the user with regard to one or more categories of products.
Everingham teaches:
causing the second electronic application to detect, via the monitoring, occurrence of one or more predetermined trigger conditions; and in response to detection of the occurrence of the one or more trigger conditions, transmitting, via the first electronic application, data indicative of the occurrence of the one or more predetermined trigger conditions to the database, the data being associated with the web session; ([0035] Memory 112 also contains a decision engine program 140. In one embodiment, decision engine program 140 is used to identify a creative relevant to the search query. For example, decision engine program 140 may identify whether the search query includes words indicative of a purchase intent. Such purchase intent can then be used to identify and provide a relevant creative that may direct the user to a merchant's website. In one embodiment, decision engine program 140 analyzes the search query to identify one or more nouns in the search query. Each identified noun is then considered a product tag. The decision engine program 140 can then search database 125 or database 130 for creatives indexed with a matching product tag (matches need not be exact matches). Creatives (in the form of images, links, or other documents) with matching product tags can then be returned to the user for display on client device 102. [0036] Additional descriptive words in the search query may be used to add specificity to the creative selection. For example, if a user submits an image search query for a "black purse," search engine program 120 identifies a plurality of documents (e.g., webpages, web images, etc.) that are responsive to the search "black purse." Decision engine program 140 identifies the term "purse" as a product tag indicative of a purchase intent. Decision engine program then searches product database 125, 130 for creatives matching product tag "purse." The term "black" may be used as a subject tag to add specificity to the creative selection. After decision engine program 140 identifies one or more creatives relevant to the search "black purse" (e.g., a creative advertising a black purse), server 110 can then return to the user an interface to view the plurality of documents responsive to the search "black purse," as well as one or more creatives advertising black purses.)
determining, at the database and based on the data associated with the web session, that the occurrence of the one or more predetermined trigger conditions is indicative of a transaction intention of the user with regard to one or more categories of products; ([0035] Memory 112 also contains a decision engine program 140. In one embodiment, decision engine program 140 is used to identify a creative relevant to the search query. For example, decision engine program 140 may identify whether the search query includes words indicative of a purchase intent. Such purchase intent can then be used to identify and provide a relevant creative that may direct the user to a merchant's website. In one embodiment, decision engine program 140 analyzes the search query to identify one or more nouns in the search query. Each identified noun is then considered a product tag. The decision engine program 140 can then search database 125 or database 130 for creatives indexed with a matching product tag (matches need not be exact matches). Creatives (in the form of images, links, or other documents) with matching product tags can then be returned to the user for display on client device 102. [0036] Additional descriptive words in the search query may be used to add specificity to the creative selection. For example, if a user submits an image search query for a "black purse," search engine program 120 identifies a plurality of documents (e.g., webpages, web images, etc.) that are responsive to the search "black purse." Decision engine program 140 identifies the term "purse" as a product tag indicative of a purchase intent. Decision engine program then searches product database 125, 130 for creatives matching product tag "purse." The term "black" may be used as a subject tag to add specificity to the creative selection. After decision engine program 140 identifies one or more creatives relevant to the search "black purse" (e.g., a creative advertising a black purse), server 110 can then return to the user an interface to view the plurality of documents responsive to the search "black purse," as well as one or more creatives advertising black purses.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include causing the second electronic application to detect, via the monitoring, occurrence of one or more predetermined trigger conditions; and in response to detection of the occurrence of the one or more trigger conditions, transmitting, via the first electronic application, data indicative of the occurrence of the one or more predetermined trigger conditions to the database, the data being associated with the web session and determining, at the database and based on the data associated with the web session, that the occurrence of the one or more predetermined trigger conditions is indicative of a transaction intention of the user with regard to one or more categories of products. as taught by Everingham in order to increase revenues through advertising to a customer who has indicated an interest in making a purchase.
Roberts in view of Everingham teaches the limitations of claims 41 and 61. As per claims 42 and 62:
Roberts further teaches:
wherein determining the likelihood includes applying a predictive algorithm to the historical user activity data. (paragraph [0045] Identifying a next node and/or communications specification(s) can include running a machine learning model (associated with a current branching node) using particular profile data and one or more learned parameters. A result can indicate (for example) which of various content-presentation characteristics is associated with a high (e.g., above-threshold) or highest probability of leading to a particular target outcome (e.g., target conversion). In some instances, the analysis includes identifying one or more content-presentation characteristics associated with a highest probability of leading to a particular conversion target outcome. [0053] As one example, a client system may offer online purchases of food delivery. It may be detected that a particular user had looked at a menu for a given restaurant at 2 pm. The client system may retrieve a set of user attributes from a profile data for the user from its client-managed user data. Client-specific learned data may indicate that there is a 76% chance that the user will make a purchase from the restaurant if an email including a discount code is sent in the evening to the user (e.g., as compared to a lower probability associated with other types of communication and other times). In response to determining that the 76% chance is above a 65% threshold for sending a discount threshold, email server 140 transmits an email to the user device.)
Roberts in view of Everingham teaches the limitations of claims 42 and 62. As per claims 43 and 63:
Roberts further teaches:
wherein the predictive algorithm includes applying a trained machine-learning model that has been trained, based on training historical user activity data of other users and training historical engagement data of the other users, to learn associations between the training historical user activity data and the training historical engagement data, such that the trained machine-learning model is configured to generate a prediction of the likelihood. (paragraph [0040] Training a machine-learning technique (to identify one or more parameters) can include identifying how a set of observed inputs (e.g., content of a marketing email, content of a promotion, and/or the configuration of a web site) relates to a set of corresponding outputs (e.g., an outcome, such as the presence or absence of certain conversion event, for a corresponding marketing email, a corresponding promotion, and/or a corresponding web site configuration). These observed observations can be used to identify modeled relationships and/or trends, with a goal of predicting candidate factual information (e.g., a predicted next input to be received or a predicted output based on certain inputs) that has not yet occurred based on factual information leading up to the candidate factual information. Each prediction can carry a confidence or probability, and chains of predictions have a combined confidence or probability. [0041] Thus, machine learning model configurator 123 can identify model parameters for particular client systems 110 based on (for example) target outcomes, client-specific profile data and/or machine-learning techniques. Client-specific learned data can be selectively shared with a client system having provided the underlying client-availed profile data. [0045] Identifying a next node and/or communications specification(s) can include running a machine learning model (associated with a current branching node) using particular profile data and one or more learned parameters. A result can indicate (for example) which of various content-presentation characteristics is associated with a high (e.g., above-threshold) or highest probability of leading to a particular target outcome (e.g., target conversion). In some instances, the analysis includes identifying one or more content-presentation characteristics associated with a highest probability of leading to a particular conversion target outcome. See also [0053], [0092], [0094], [0106])
Roberts in view of Everingham teaches the limitations of claims 41 and 61. As per claims 45 and 65:
Roberts further teaches:
wherein the user-specific electronic message includes at least one selectable link operable to navigate the first electronic application to a selected offer. (paragraph [0052] As an additional or alternative example, the communication may contain one or more references or links to pages that, when opened (e.g., in a web browser), render content for display. The pages targeted by the links may include some content that was determined, by the machine learning engine, before or at the time the communication was generated. [0053] The client system inputs the time, the user's location (as she is now at work) and prior purchase information to a decision tree built based on the learned data. It is determined that the discount is to be 10% (e.g., to maintain a threshold likelihood of conversion) and the restaurant is to be a deli near the user's work (e.g., to maximize a likelihood of conversion), whereas—had the user opened the email the night before, different user attributes and learned data would have resulted in a 15% discount (e.g., to maintain the threshold likelihood) from an Indian restaurant near the user's home (e.g., to maximize the likelihood). The email includes a link to order from the deli. When the user clicks on the link, the web server determines what content is to be presented—specifically, which food items are to be recommended.)
Roberts in view of Everingham teaches the limitations of claims 41 and 61. As per claims 46 and 66:
Roberts further teaches:
wherein the likelihood of engagement is based on one or more of a value to the user for offers in the offer information, a timing of the user-specific electronic message relative to other communications to the user, or at least one historical response of the user to one or more previous communication that included an offer. (paragraph [0053] As one example, a client system may offer online purchases of food delivery. It may be detected that a particular user had looked at a menu for a given restaurant at 2 pm. The client system may retrieve a set of user attributes from a profile data for the user from its client-managed user data. Client-specific learned data may indicate that there is a 76% chance that the user will make a purchase from the restaurant if an email including a discount code is sent in the evening to the user (e.g., as compared to a lower probability associated with other types of communication and other times). In response to determining that the 76% chance is above a 65% threshold for sending a discount threshold, email server 140 transmits an email to the user device. The user opens the email the next day at 10 am. The code is executed to request the restaurant and discount from the client system. The client system has since received updated public learned correlation data. The client system inputs the time, the user's location (as she is now at work) and prior purchase information to a decision tree built based on the learned data. It is determined that the discount is to be 10% (e.g., to maintain a threshold likelihood of conversion) and the restaurant is to be a deli near the user's work (e.g., to maximize a likelihood of conversion), whereas—had the user opened the email the night before, different user attributes and learned data would have resulted in a 15% discount (e.g., to maintain the threshold likelihood) from an Indian restaurant near the user's home (e.g., to maximize the likelihood). The email includes a link to order from the deli. When the user clicks on the link, the web server determines what content is to be presented—specifically, which food items are to be recommended. The recommendations are based on even more recently updated public learned correlation data, which indicate that salads and sandwiches should be recommended over soup and entrees, as the former options have been recently popular (predicted to be popular due to the warmer weather).)
Roberts in view of Everingham teaches the limitations of claims 41 and 61. As per claims 47 and 67:
Roberts teaches:
wherein the likelihood of engagement is determined based on a portion of the historical user activity data associated with one or more of a particular product associated with the one or more products or a category of products associated with the one or more products. (paragraph [0026] As one example, a trajectory for a user can be initialized upon detecting that profile data corresponding to the user includes information for at least a predefined set of fields. The profile data can be collected using one or more web servers over one or more sessions associated with the user and/or retrieved from a remote data source. In some instances, a user device automatically detects at least some of the profile data and communicates it to the web server(s) (e.g., via automatically populated header information in a communication that identifies, for example, a unique device identifier, MAC address, browser type, browser version, operating system type, operating system version, device type, language to which the device is set, etc.). In some instances, a communication includes data that represents user input (e.g., text entered into a web form, link selections, page navigation, etc.), which can then be logged as profile data. [0065] The machine-learning model (configured with the first parameters) can use profile data associated with the trajectory to determine which communication channel to user. The profile data can include client-collected profile data (e.g., using metadata, cookies and/or inputs associated with previous HTML requests from a user device associated with the trajectory). The profile data may further include other profile data requested and received from a remote user-profile data store, which may collect and manage profile data from multiple web hosts, clients, etc. [0067] Thus, second branching node 415 is connected to a first notification content node 430a that represents content that identifies a product most recently viewed by the user at the web site, a second notification content node 430b that represents content that identifies four of the products most viewed (across users) at the web site over the last week, and a third notification content node 430c that represents content that includes an identification of a discounts. The second decision can be made using the machine-learning model configured based upon one or more second parameters. Thus, in some (but not all) instances, a general type of machine-learning model used at various branching nodes to make decisions can be the same, though particular configurations (e.g., indicating weights to be assigned to various user attributes, which user attributes are to be considered at all and/or target outcomes) can differ.)
Roberts in view of Everingham teaches the limitations of claims 41 and 61. As per claims 48 and 68:
Roberts teaches:
wherein the transmitting of the user-specific electronic message is only performed in response to the likelihood being above the predetermined threshold, such that no message is sent to the user if the likelihood is not above the predetermined threshold. (paragraph [0053] As one example, a client system may offer online purchases of food delivery. It may be detected that a particular user had looked at a menu for a given restaurant at 2 pm. The client system may retrieve a set of user attributes from a profile data for the user from its client-managed user data. Client-specific learned data may indicate that there is a 76% chance that the user will make a purchase from the restaurant if an email including a discount code is sent in the evening to the user (e.g., as compared to a lower probability associated with other types of communication and other times). In response to determining that the 76% chance is above a 65% threshold for sending a discount threshold, email server 140 transmits an email to the user device)
Claim(s) 44 and 64 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al (US 2019/0102698) in view of in view of Everingham (US 2012/0179545) in view of Kim et al (US 2008/0262925)
Roberts in view of Everingham teaches the limitations of claims 41 and 61. As per claims 44 and 64:
Roberts does not expressly teach wherein the historical information includes data regarding one or more of a rewards program or a membership that one or more of the user or the at least one vendor is enrolled in; and the offer information is adjusted based on the enrollment.
Kim eaches;
wherein the historical user activity data includes data regarding one or more of a rewards program or a membership that one or more of the user or the at least one vendor is enrolled in; and the offer information is adjusted based on the enrollment. (paragraph [0264] For example, the rules may include time-based criteria (e.g., time period in which the incentives offered are valid), user-specific criteria (e.g., incentive only available to members of a specific loyalty program), and terms of the incentive/discount. Other business rules related to the incentive are stored in the rules database to be applied in processing the transaction data. For example, the conditions may include, but are not limited to, the type of incentive, the amount of incentive, to whom the incentive applies, the time frame for the offer, and any other conditions of the offer. Moreover, the same merchant may target a specific type of user by customizing the amount of the incentive or terms of the offer down to the individual level. In order words, an offer by the same merchant may be different between users based on the users' profiles. In this way, the merchants can customize the offer as generally or as detailed as the merchant desires. The rules management module 4510b automatically applies the rules to the user's PQT to determine the level of discount/incentive based on these rules.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the historical information includes data regarding one or more of a rewards program or a membership that one or more of the user or the at least one vendor is enrolled in; and the offer information is adjusted based on the enrollment as taught by Kim with the incentivized rewards of Roberts in view of Everinghame in order to customize the offer as generally or as detailed as the merchant desires (paragraph [0264]).
Response to Arguments
With regard to double patenting, the examiner withdraws such rejection based on the amended claim language.
The examiner has considered but does not find persuasive applicant’s arguments regarding rejections under 35 USC 101. With regard to the monitoring, the examiner respectfully disagrees. The monitoring is recited at high level and amounts to “apply it” with a computer. There are no meaningful details provided with regard to how such monitoring is accomplished. As a result, such rejections have been maintained.
Argument’s regarding prior art are moot in light of new ground of rejections which have been necessitated by amendment.
Conclusion
The following references are considered relevant though not currently relied upon:
Hawkins et al (US 2016/0055499) – Generally teaches provided offers to users based on exceeding threshold probability of converting on the offer.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621B
/CHRISTOPHER STROUD/Primary Examiner, Art Unit 3621