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 .
DETAILED ACTION
This office action is in response to the claims filed on 12/08/2022.
Claims 1-9 and 13-23 are presented for examination.
Information Disclosure Statement
3. The information disclosure statements (IDS) filed 11/26/2024; 10/28/2025; 12/22/2025;07/24/2026 are in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statement is being considered by the examiner. However, the IDS filed on 02/28/2023 is not considered by examiner since the patent number 10110667 was cited with incorrected inventor’s name.
Priority
The following claimed benefit is acknowledged: the instant application, filed 12/08/2022 claims priority from PCT application PCT/US2022/023046, filed 04/01/2022.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 13-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a method (claims 1-9), system (claims 13-21) and non-transitory computer readable storage medium (claims 22-23). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract idea of “Mental Processes/Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”.
The claim 1 recites:
Step 2A: prong 1 analysis:
-“selecting, from a plurality of digital components and based at least in part on the set of predicted user attributes, a given digital component for display at the client device” this is a mental process, the human can select/chose which the digital component to be displayed at the client device based on the set of predicted user attributes, as the human can select to display the particular component that the user subscribed, (observation/Evaluation).
“ converting the contextual information into input data comprising input feature values for a transfer machine learning model trained to output predictions of user attributes of users based on feature values for features representing display environments” this is a mental process, a human can convert information into an input data format
“predict user attributes of non-subscribing users viewing electronic resources to which the non- subscribing users are not subscribed” this is a mental process, the human can predict the attribute of the non subscribing user, for example, the human can predict non subscriber user is not subscribe the particular item of the purchase, (observation/Evaluation).
a) Step 2A: Prong 2 analysis:
-“ receiving, from a client device of a user, a digital component request comprising at least input contextual information for a display environment in which a selected digital component will be displayed”, , These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering . As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
“for a transfer machine learning model trained to output predictions of user attributes of users based on feature values for features representing display environments”, “training data for subscriber users obtained from a data pipeline associated with electronic resources to which the subscriber users are subscribed and (ii) adapted to”, “wherein the training data comprises first feature values for features representing training contextual information for display environments in which digital components were displayed to the subscriber users, second feature values for online activity of the subscriber users, and a label representing a user attribute profile for each of the subscriber users”, “providing, as an input to the transfer machine learning model, the input data;”, “wherein the transfer machine learning model is (i) trained using training data”, “receiving, as an output of the transfer machine learning model, data indicating a set of predicted user attributes of the user”, “and sending the given digital component to the client device of the user.” these limitations recite the machine learning model (MPEP 2106.05(f)(2) in order to "do" mental process of prediction. "Use a computer or other machinery as a tool to perform a mental process".
b) Step 2B analysis:
-“ receiving, from a client device of a user, a digital component request comprising at least input contextual information for a display environment in which a selected digital component will be displayed”, , These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
“for a transfer machine learning model trained to output predictions of user attributes of users based on feature values for features representing display environments”, “training data for subscriber users obtained from a data pipeline associated with electronic resources to which the subscriber users are subscribed and (ii) adapted to”, “wherein the training data comprises first feature values for features representing training contextual information for display environments in which digital components were displayed to the subscriber users, second feature values for online activity of the subscriber users, and a label representing a user attribute profile for each of the subscriber users”, “providing, as an input to the transfer machine learning model, the input data;”, “wherein the transfer machine learning model is (i) trained using training data”, “receiving, as an output of the transfer machine learning model, data indicating a set of predicted user attributes of the user”, “and sending the given digital component to the client device of the user.” these limitations recite the machine learning model (MPEP 2106.05(f)(2) in order to "do" mental process of prediction. "Use a computer or other machinery as a tool to perform a mental process".
The claim 2 recites:
Step 2A: prong 1 analysis:
a) Step 2A: Prong 2 analysis:
-“ wherein the electronic resources to which the subscriber users are subscribed comprise content platforms that display content to the subscribing users.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“ wherein the electronic resources to which the subscriber users are subscribed comprise content platforms that display content to the subscribing users.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 3 recites:
Step 2A: prong 1 analysis:
a) Step 2A: Prong 2 analysis:
-“wherein the training contextual information for display environments in which digital components were displayed to the subscriber users comprises client device attributes of subscribing users, the client device attributes of each individual client device comprising at least one of (i) information indicative of one or more of an operating system of the individual client device, or (ii) a type of browser of the individual client device.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“wherein the training contextual information for display environments in which digital components were displayed to the subscriber users comprises client device attributes of subscribing users, the client device attributes of each individual client device comprising at least one of (i) information indicative of one or more of an operating system of the individual client device, or (ii) a type of browser of the individual client device.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 4 recites:
Step 2A: prong 1 analysis:
a) Step 2A: Prong 2 analysis:
-“ wherein the training contextual information for display environments in which digital components were displayed to the subscriber users comprises, for each user visit to the electronic resources to which the subscriber users are subscribed, at least one of (i) information indicative of an electronic resource address of the electronic resource, (ii) a category of the electronic resource, (iii) a time at which the user visit occurred, (iv) a geographic location of a client device used to visit the electronic resource, or (v) a type of data traffic for the user visit.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“ wherein the training contextual information for display environments in which digital components were displayed to the subscriber users comprises, for each user visit to the electronic resources to which the subscriber users are subscribed, at least one of (i) information indicative of an electronic resource address of the electronic resource, (ii) a category of the electronic resource, (iii) a time at which the user visit occurred, (iv) a geographic location of a client device used to visit the electronic resource, or (v) a type of data traffic for the user visit.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 5 recites:
Step 2A: prong 1 analysis:
a) Step 2A: Prong 2 analysis:
-“the second feature values for online activity of the subscriber users comprise feature values for features indicative of digital components with which the subscriber users interacted during the user visits, including feature values indicative of a category for each digital component.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“the second feature values for online activity of the subscriber users comprise feature values for features indicative of digital components with which the subscriber users interacted during the user visits, including feature values indicative of a category for each digital component.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 6 recites:
Step 2A: prong 1 analysis:
a) Step 2A: Prong 2 analysis:
-“wherein the second feature values for online activity of the subscriber users comprise feature values for features indicative of one or more of (i) selecting a user selectable element, (ii) providing a search query, or (iii) viewing a particular page.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
“wherein the second feature values for online activity of the subscriber users comprise feature values for features indicative of one or more of (i) selecting a user selectable element, (ii) providing a search query, or (iii) viewing a particular page.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 7 recites:
Step 2A: prong 1 analysis:
a) Step 2A: Prong 2 analysis:
-“ generating the transfer machine learning model based on the first feature values and the second feature values.” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)(3)).
b) Step 2B analysis:
-“ generating the transfer machine learning model based on the first feature values and the second feature values.” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the
The claim 8 recites:
Step 2A: prong 1 analysis:
a) Step 2A: Prong 2 analysis:
-“ wherein generating the transfer machine learning model comprises training a neural network with an objective function.” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)(3)).
b) Step 2B analysis:
-“ wherein generating the transfer machine learning model comprises training a neural network with an objective function.” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)(3)).
The claim 9 recites:
Step 2A: prong 1 analysis:
-“wherein selecting the given digital component comprises selecting the given digital component based at least on the predicted likelihood for each of the plurality of digital components.” This is a mental process; the human can select the component to be displayed based on the likelihood of each of the digital component (observation/Evaluation).
a) Step 2A: Prong 2 analysis:
-“ providing the set of predicted user attributes of the user as input to a second machine learning model trained to predict user engagement with digital components based on user attributes” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data storing. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
-“and receiving, as an output of the second machine learning model and for each digital component in the plurality of digital components, output data indicating a predicted likelihood that the user will interact with the digital component” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data storing. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
b) Step 2B analysis:
-“ providing the set of predicted user attributes of the user as input to a second machine learning model trained to predict user engagement with digital components based on user attributes” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
Further, the specification of the instant application teaches that training a neural network on training data and executing that neural network are well-understood, routine, and conventional actions ([0002], Machine learning models receive an input and generate an output, e.g., a predicted output, based on the received input. Some machine learning models are parametric models and generate the output based on the received input and on values of the parameters of the model.”
-“and receiving, as an output of the second machine learning model and for each digital component in the plurality of digital components, output data indicating a predicted likelihood that the user will interact with the digital component” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data output. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data output to a judicial exception do not amount to significantly more than the judicial exception itself .
The claims 13-21 are rejection for the same reason as the claims 1-9, since these claims recite the same limitations.
The claims 22-23 are rejection for the same reason as the claims1-2, since these claims recite the same limitations.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 13-18, 22, 23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rezaeian et al. (Pub. No. US 20200125586 – hereinafter, Rezaeian).
Regarding to claim 1, Rezaeian teaches a computer-implemented method, comprising: receiving, from a client device of a user, a digital component request comprising at least input contextual information for a display environment in which a selected digital component will be displayed (Rezaeian, [Par.0033], “The system 100 may include one or more users at one or more user stations 103 that use the system 100 to operate and interact with the log analytics system 101. The user station 103 comprises any type of computing station that may be used to operate or interface with the log analytics system 101 in the system 100. Examples of such user stations include, for example, workstations, personal computers, mobile devices, or remote computing terminals. The user station comprises a display device, such as a display monitor, for displaying a user interface to users at the user station” and [Par.0079], “FIG. 4 shows an example network environment 400 that enables an interface associated a cloud network to display suggested tasks for users. In some implementations, network environment 400 may include user device 480, contextual user data generator 440, learner system 450, performance tasks 460, and intelligent UI (user interface) generator 470. While user device 480 is shown as a mobile device (e.g., a smartphone), it will be appreciated that user device 480 can be any computing device that is operated by a user. User device 480 can access an interface. For example, the interface may display various links and/or context relevant to the users”)
converting the contextual information into input data comprising input feature values for a transfer machine learning model trained to output predictions of user attributes of users based on feature values for features representing display environments [Par.0006], “In some implementations, the machine-learning model may be generated using one or more machine-learning techniques, such as a multi-armed bandit or a contextual multi-armed bandit in a reinforcement learning approach. The machine-learning model may represent a model of some or all of the users of an entity and their interactions with various applications (e.g., which tasks those users have previously completed). When a particular user accesses the intelligent UI, a learner system may generate a user vector for that particular user. In some implementations, the user vector may be a vector representation of various information about the user. For example, the user vector may include the user's access level, current location, whether the user is working remotely, previous interactions with applications, previous tasks completed using the applications, frequency of completing certain tasks, and other suitable information. The user vector may be fed into the machine-learning model to predict which tasks the user will need to complete (e.g., on a given day). The machine-learning model can output a prediction of one or more tasks that the user will likely need to complete. The machine-learning model prediction is based, at least in part, on the user vector, the set of suggestable tasks, and/or the tasks completed by users with similar attributes (e.g., users in the same location).”)
wherein the transfer machine learning model is (i) trained using training data for subscriber users obtained from a data pipeline associated with electronic resources to which the subscriber users are subscribed and (ii) adapted to predict user attributes of non-subscribing users viewing electronic resources to which the non- subscribing users are not subscribed, (Rezaeian [Par.0006-0010], “[0006], In some implementations, the machine-learning model may be generated using one or more machine-learning techniques, such as a multi-armed bandit or a contextual multi-armed bandit in a reinforcement learning approach. The machine-learning model may represent a model of some or all of the users of an entity and their interactions with various applications (e.g., which tasks those users have previously completed). When a particular user accesses the intelligent UI, a learner system may generate a user vector for that particular user. In some implementations, the user vector may be a vector representation of various information about the user. For example, the user vector may include the user's access level, current location, whether the user is working remotely, previous interactions with applications, previous tasks completed using the applications, frequency of completing certain tasks, and other suitable information. The user vector may be fed into the machine-learning model to predict which tasks the user will need to complete (e.g., on a given day). The machine-learning model can output a prediction of one or more tasks that the user will likely need to complete. The machine-learning model prediction is based, at least in part, on the user vector, the set of suggestable tasks, and/or the tasks completed by users with similar attributes (e.g., users in the same location)…[0010] ] In some implementations, the trained machine-learning model can provide contextual information in real time to enable tasks to be suggested on a version of the intelligent UI presented to the user. For example, even if a user is new (e.g., there is limited data about previous interactions by the user), the trained machine-learning model can be used to predict tasks on the version of the intelligent UI presented to the new user. The trained machine-learning model can cluster all users into one or more clusters (as described above). For example, one of the clusters may be other new users (e.g., in the past). By evaluating the contextual user data associated with other new users (e.g., which tasks those other new users completed or performed when they started), the intelligent UI can nonetheless accurately predict tasks for the new user to complete. Advantageously, the trained machine-learning model provides a technical solution to the cold start problem, in that even though there may not be much data from the interactions of the new user and various applications (e.g., “thin data”), the trained machine-learning model can nonetheless predict the tasks that the new user will likely need to perform on a particular day. For users who have performed a large amount of tasks, the trained machine-learning model can be used to evaluate the previous tasks performed by the user and predict which task(s) the user will likely need to complete or perform in a particular day.”)
wherein the training data comprises first feature values for features representing training contextual information for display environments in which digital components were displayed to the subscriber users, second feature values for online activity of the subscriber users (Rezaeian, [Par.0068], “] In some implementations, the contextual learner may be contextual multi-armed bandit learner that is trained using a user profile to obtain the context of a user. The user profile can include, for example, the user's organization, the user's role, the user's position, and/or other suitable information. Further, the contextual learner can be trained on all available tasks that can be suggested to users. The full set of tasks can be gathered by harvesting interaction data associated with each of a plurality of applications. For example, interaction data can include clicks by users using an application and/or other interactions performed by users within an application. A click by a user within an application may cause a task to be performed within that application. The click and other data characterizing the click (e.g., which task was clicked on and performed by the user) can be recorded as a log record for each user across a network associated with an entity. The log records can be recorded for all applications. Over time, the log records can be harvested and evaluated to analyze all of the tasks and/or actions that were completed by clicks or other interaction, such as a tap or swipe. Additionally, the log records can also be evaluated for a specific user to identify the most frequent tasks that the user will or is likely to perform. The contextual learner can create a user context (e.g., a user vector) based, at least in part, on data representing the previous tasks performed by the user, and evaluate the user context in light of the set of tasks to predict which tasks the user is likely to select at a given time or day.” Examiner’s note, the training data/log record data include the task that the user likely to perform at the particular time.
and a label representing a user attribute profile for each of the subscriber users (Rezaeian, [Par.0012], “; inputting the user vector into the contextual model; in response to inputting the user vector into the contextual model, determining a subset of the set of tasks for presenting on the interface (e.g., the suggested tasks to be presented on the intelligent UI), the subset of tasks being determined as an output of the contextual model, and the subset of tasks corresponding one or more tasks predicted to be selected by the particular user device (e.g., the subset of tasks can be the suggested tasks to be presented on the intelligent UI); and presenting the subset of tasks at the interface displayed on the particular user device (e.g., the tasks can be presented on the intelligent UI in any manner, including, but not limited to, being represented as selectable links that, when selected, cause the corresponding actions to be performed by the corresponding application). Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.”)
providing, as an input to the transfer machine learning model, the input data; receiving, as an output of the transfer machine learning model, data indicating a set of predicted user attributes of the user (Rezaeian, [Par.0010], “In some implementations, the trained machine-learning model can provide contextual information in real time to enable tasks to be suggested on a version of the intelligent UI presented to the user. For example, even if a user is new (e.g., there is limited data about previous interactions by the user), the trained machine-learning model can be used to predict tasks on the version of the intelligent UI presented to the new user. The trained machine-learning model can cluster all users into one or more clusters (as described above). For example, one of the clusters may be other new users (e.g., in the past). By evaluating the contextual user data associated with other new users (e.g., which tasks those other new users completed or performed when they started), the intelligent UI can nonetheless accurately predict tasks for the new user to complete. Advantageously, the trained machine-learning model provides a technical solution to the cold start problem, in that even though there may not be much data from the interactions of the new user and various applications (e.g., “thin data”), the trained machine-learning model can nonetheless predict the tasks that the new user will likely need to perform on a particular day. For users who have performed a large amount of tasks, the trained machine-learning model can be used to evaluate the previous tasks performed by the user and predict which task(s) the user will likely need to complete or perform in a particular day.” ;
selecting, from a plurality of digital components and based at least in part on the set of predicted user attributes, a given digital component for display at the client device; and sending the given digital component to the client device of the user. (Rezaeian, [Par.0006], [Par.0065-0066], “In some implementations, the machine-learning model may be generated using one or more machine-learning techniques, such as a multi-armed bandit or a contextual multi-armed bandit in a reinforcement learning approach. The machine-learning model may represent a model of some or all of the users of an entity and their interactions with various applications (e.g., which tasks those users have previously completed). When a particular user accesses the intelligent UI, a learner system may generate a user vector for that particular user. In some implementations, the user vector may be a vector representation of various information about the user. For example, the user vector may include the user's access level, current location, whether the user is working remotely, previous interactions with applications, previous tasks completed using the applications, frequency of completing certain tasks, and other suitable information. The user vector may be fed into the machine-learning model to predict which tasks the user will need to complete (e.g., on a given day). The machine-learning model can output a prediction of one or more tasks that the user will likely need to complete. The machine-learning model prediction is based, at least in part, on the user vector, the set of suggestable tasks, and/or the tasks completed by users with similar attributes (e.g., users in the same location).” Examiner’s note, the selected subset of the tasks are displayed to particular user device.)
Regarding the claim 2, Rezaeian teaches the method of claim 1, wherein the electronic resources to which the subscriber users are subscribed comprise content platforms that display content to the subscribing users ([Par.0124-0126], “In some embodiments, the services provided by cloud infrastructure system 802 may include one or more services provided under Software as a Service (SaaS) category, Platform as a Service (PaaS) category, Infrastructure as a Service (IaaS) category, or other categories of services including hybrid services. A customer, via a subscription order, may order one or more services provided by cloud infrastructure system 1002. Cloud infrastructure system 1002 then performs processing to provide the services in the customer's subscription order.”).
Regarding claim 3, Rezaeian teaches the method of claim 1, wherein the training contextual information for display environments in which digital components were displayed to the subscriber users comprises client device attributes of subscribing users (Rezaeian, [Par.0012], “detecting one or more attributes associated with the particular user device, each attribute of the one or more attributes characterizing a profile corresponding to the particular user device”),
the client device attributes of each individual client device comprising at least one of (i) information indicative of one or more of an operating system of the individual client device, or (ii) a type of browser of the individual client device (Rezaeian, [Par.0098], “For example, a log record may capture an interaction, in which user A selects task A while using application A. Each log record of the plurality of log records may represent one or more attributes of an interaction between a user device and an application (e.g., timestamp of interaction, identifier of task selected for performance, application identifier, user identifier, and other suitable attributes). The application may facilitate performance of one or more tasks, and the interaction may be associated with a task previously selected by a user using the application.”).
Regarding claim 4, Rezaeian teaches the method of any preceding claim 1, wherein the training contextual information for display environments in which digital components were displayed to the subscriber users comprises, for each user visit to the electronic resources to which the subscriber users are subscribed , at least one of (i) information indicative of an electronic resource address of the electronic resource, (ii) a category of the electronic resource, (iii) a time at which the user visit occurred, (iv) a geographic location of a client device used to visit the electronic resource, or (v) a type of data traffic for the user visit (Rezaeian [Par.0011-0012], “[Par.00011], “In certain embodiments, a system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. One general aspect includes a computer-implemented method, including: collecting a plurality of log records stored at one or more servers, each log record of the plurality of log records representing one or more attributes of an interaction between a user device and an application, the application facilitating performance of one or more tasks, and the interaction being associated with a task previously selected by a user using the application;” and [Par.0060], “In the “normalize” stage 314, the identified fields are normalized. For example, a “time” field may be represented in any number of different ways in different logs. This time field can be normalized into a single recognizable format (e.g., UTC format).” Examiner’s note, the contextual data include the time that the user interact with the particular application.).
Regarding claim 5, Rezaeian teaches the method of any preceding claim 1, wherein the second feature values for online activity of the subscriber users comprise feature values for features indicative of digital components with which the subscriber users interacted during the user visits, including feature values indicative of a category for each digital component (Rezaeian [Par.00011], “… One general aspect includes a computer-implemented method, including: collecting a plurality of log records stored at one or more servers, each log record of the plurality of log records representing one or more attributes of an interaction between a user device and an application, the application facilitating performance of one or more tasks, and the interaction being associated with a task previously selected by a user using the application;” and [Par.0098], “For example, a log record may capture an interaction, in which user A selects task A while using application A. Each log record of the plurality of log records may represent one or more attributes of an interaction between a user device and an application (e.g., timestamp of interaction, identifier of task selected for performance, application identifier, user identifier, and other suitable attributes). The application may facilitate performance of one or more tasks, and the interaction may be associated with a task previously selected by a user using the application.” Examiner’s note, the attributes representing the particular application that the user selected to the perform the particular task during visited, that is corresponding to the second attributes.).
Regarding claim 6, Rezaeian teaches the method of any preceding claim 1, wherein the second feature values for online activity of the subscriber users comprise feature values for features indicative of one or more of (i) selecting a user selectable element, (ii) providing a search query, or (iii) viewing a particular page (Rezaeian, [Par.0017], “In some implementations, the contextual model can be continuously updated based on signals received from the homepages or dashboards. For example, when a user requests access to the homepage, a user vector is generated for the user, inputted into the contextual model, and one or more suggested tasks and/or explored tasks are presented to the user. However, when the user selects a particular suggested task presented on the homepage, the homepage can generate a signal that is transmitted to the contextual model as a feedback. For example, clicking a suggested task (e.g., presented as a link) may be a reward that is fed back to the contextual model with reinforcement learning. Upon receiving the feedback signal indicating that a particular task was selected, the contextual model is updated for that particular user to indicate a bias towards selection of that particular task. As a result, that particular task may be presented more often during future instances of presenting suggested tasks on the homepage. Similarly, when a task is not selected (e.g., no feedback signal is received), the contextual model can bias towards not selecting those tasks (e.g., not presenting those tasks as suggestions).”
Regarding claims 13-18 are rejected for the same reason as the claims 1-6, since these claims recite the same limitation.
Regarding claims 22-23 are rejected for the same reason as the claims 1-2, since these claims recite the same limitation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 7, 8, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rezaeian et al. (Pub. No. US 20200125586 – hereinafter, Rezaeian) in view of Shaver et al. (Pub. No. US 20230253067-hereinafter, Shaver).
Regarding claims 7, Rezaeian teaches the method of the claim 1 but it does not teach further comprising generating the transfer machine learning model based on the first feature values and the second feature values On the other hand, Shaver teaches further comprising generating the transfer machine learning model based on the first feature values and the second feature values (Shaver, [Par.0028, 0058-0059], “[0028], The one or more first trained models 106 can undergo transfer learning at 108 based on second protein sequence data 110. The transfer learning that is performed at 108 can modify the one or more first trained models 106 based on the amino acid sequences included in the second protein sequence data 110. The transfer learning that is performed at 108 can produce one or more second trained models 112 that are modified versions of the one or more first trained models 106.” And “[0058], The light chain generating component 304 can implement one or more first models to produce light chain sequences 312 based on the first input data 308. The one or more first models can include one or more functions having one or more variables, one or more parameters, one or more weights, or one or more combinations thereof…[0059]. The one or more second models can include one or more additional functions having one or more variables, one or more parameters, one or more weights, or one or more combinations thereof.”).
Rezaeian and Shaver are analogous in arts because they have the same field of endeavor of generating the machine learning model.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the generating of the machine learning model, as taught by Rezaeian, to include the generating the transfer machine learning model based on the first feature values and the second feature values, as taught by Shaver. The modification would have been obvious because one of the ordinary skills in art would be motivated to improve to minimize the (Shaver, [Par. 0029], “In various implementations, the one or more first trained models 106 can be trained to produce the one or more second trained models 112 in a manner that is similar to the training of the generative machine learning architecture 102 that produced the one or more first trained models 106. In one or more examples, components of the one or more first trained models 106 may be modified to minimize at least one loss function.”).
Regarding claims 8, Rezaeian teaches the method of the claim 7, but it does not teach the wherein generating the transfer machine learning model comprises training a neural network with an objective function.
On the other hand, Shaver teaches wherein generating the transfer machine learning model comprises training a neural network with an objective function (Shaver, [par.0077], “In various implementations, the one or more first trained generating components 330 can be further trained using the second antibody sequence data 334 as part of the transfer learning at 332 to produce the one or more second trained generating components 338 in a manner that is similar to the training of the generative adversarial network 302 that produced the one or more first trained generating components 330. In one or more examples, components of the one or more modified generative adversarial network architectures 336 can be trained to minimize at least one loss function. Additionally, the training process used in the transfer learning at 332 to produce the one or more second trained generating components 338 can be complete after one or more modified functions implemented by the one or more modified generative adversarial network architectures 336 converge.”
Rezaeian and Shaver are analogous in arts because they have the same field of endeavor of generating the machine learning model.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the generating of the machine learning model, as taught by Rezaeian, to include the generating the transfer machine learning model comprises training a neural network with an objective function, as taught by Shaver. The modification would have been obvious because one of the ordinary skills in art would be motivated to improve to minimize the (Shaver, [Par. 0029], “In various implementations, the one or more first trained models 106 can be trained to produce the one or more second trained models 112 in a manner that is similar to the training of the generative machine learning architecture 102 that produced the one or more first trained models 106. In one or more examples, components of the one or more first trained models 106 may be modified to minimize at least one loss function.”).
Regarding claim 19-20 is rejected for the same reason as the claims 7-8, since these claims recite the same limitation.
Claims 9, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Rezaeian et al. (Pub. No. US 20200125586 – hereinafter, Rezaeian) in view of Yates et al. (Pub. No. US 20180253651 -hereinafter, Yates).
Regarding claim 9, Rezaeian teaches the method the claim 1, output data indicating a predicted likelihood that the user will interact with the digital component (Rezaeian, [Par.0006], “In some implementations, the machine-learning model may be generated using one or more machine-learning techniques, such as a multi-armed bandit or a contextual multi-armed bandit in a reinforcement learning approach. The machine-learning model may represent a model of some or all of the users of an entity and their interactions with various applications (e.g., which tasks those users have previously completed). When a particular user accesses the intelligent UI, a learner system may generate a user vector for that particular user. In some implementations, the user vector may be a vector representation of various information about the user. For example, the user vector may include the user's access level, current location, whether the user is working remotely, previous interactions with applications, previous tasks completed using the applications, frequency of completing certain tasks, and other suitable information. The user vector may be fed into the machine-learning model to predict which tasks the user will need to complete (e.g., on a given day). The machine-learning model can output a prediction of one or more tasks that the user will likely need to complete. The machine-learning model prediction is based, at least in part, on the user vector, the set of suggestable tasks, and/or the tasks completed by users with similar attributes (e.g., users in the same location).” And [Par.0011], “The computer-implemented method also includes determining a set of tasks performable using one or more applications, each task of the set of tasks including one or more actions performable using an application of the one or more applications;” Examiner’s note, the machine learning to predict the task that the user likely to perform, wherein, the task includes the one or more action is performed in the particular application, therefore, predicting the particular task likely performed by the user that corresponding to the predicting the likely that the user will interact with the digital component. )
wherein selecting the given digital component comprises selecting the given digital component based at least on the predicted likelihood for each of the plurality of digital components (Rezaeian, [Par.0065-0066], “User experience is becoming increasingly important in cloud or locally hosted applications that perform functionality targeted to specific domains. Browsers enable users to access and navigate cloud or locally hosted applications. A technical challenge arises in filtering through the vast number of tasks available to users through these applications. Certain embodiments of the present disclosure provide an interface (e.g., that may or may not be part of any particular application) that provides links to suggested tasks the user is likely to need on a particular time or day. The suggested tasks are provided to filter through the noise of the large volume of tasks available in applications. The suggested tasks may be determined based on a contextual model that analyzes the user's history of interactions with various applications and/or other contextual data. [0066] In some implementations, when a user accesses an interface, the interface presents the most relevant tasks that the user will likely perform. A contextual learner can learn the tasks that the user is likely to perform based, at least in part, on the previous tasks that the user has performed. The suggested tasks on the homepage solve the burden of navigating large menus within applications by anticipating the tasks that the user will need to perform using artificial intelligence and/or machine learning techniques.”)
However, Rezaeian does not teach providing the set of predicted user attributes of the user as input to a second machine learning model trained to predict user engagement with digital components based on user attributes; and receiving, as an output of the second machine learning model and for each digital component in the plurality of digital components,
On the other hand, Yates teaches providing the set of predicted user attributes of the user as input to a second machine learning model trained to predict user engagement with digital components based on user attributes (Yates, [Par.0007], “The online system selects content items for presentation to users of the online system based on predicted outcomes generated using this prediction improvement data. When the prediction improvement data includes additional features, the online system may re-train the outcome prediction model with the additional feature data as additional input data for the outcome prediction model, and use the model to generate predicted outcomes for pairs of content items and users, selecting content items for presentation to users in the content presentation opportunities based on the new predictions. Once selected, the online system transmits the selected content items to users for presentation.”).;
and receiving, as an output of the second machine learning model and for each digital component in the plurality of digital components, (Yates, [Par.0090-0091], “The online system 140 selects 420 content items for presentation to users of the online system in content presentation opportunities based on predicted outcomes generated using the received prediction improvement data. [0091] When the prediction improvement data includes additional features, the online system 140 may re-train the outcome prediction model with the additional feature data as additional input data for the outcome prediction model. The online system may also generate predicted outcomes for pairs of content items and users in content presentation opportunities based on the re-trained outcome prediction model, and select content items for presentation to users in the content presentation opportunities based on the predicted outcomes generated for the respective pairs of content items and users.”)
Rezaeian and Yates are analogous in arts because they have the same field of endeavor of generating the machine learning model.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the output data indicating a predicted likelihood that the user will interact with the digital component, wherein selecting the given digital component comprises selecting the given digital component based at least on the predicted likelihood for each of the plurality of digital components, as taught by Rezaeian, to include the providing the set of predicted user attributes of the user as input to a second machine learning model trained to predict user engagement with digital components based on user attributes; and receiving, as an output of the second machine learning model and for each digital component in the plurality of digital components, as taught by Yates. The modification would have been obvious because one of the ordinary skills in art would be motivated to improve the prediction outcome (Yates, [Par. 0004], “Embodiments of the disclosure include an online system that is capable of receiving data from a third party system to improve the accuracy of the prediction of outcomes in content distribution programs.”).
Regarding claim 21 is rejected for the same reason as the claim 9, since these claims recite the same limitation.
Conclusion
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/E.T./Examiner, Art Unit 2128
/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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