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 .
Priority
Applicant claims the benefit of prior-filed a U.S. National Stage Application filed under 35 U.S.C. §371, International Patent Application No. PCT/US2022/034936, filed on 106/24/2022,, which is acknowledged.
Drawings
The drawings were received on 12/09/2022. These drawings are acceptable.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on the following date(s): 01/30/2025 and 05/01/2023 have been considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 7, the limitation “a modified set of data wherein the modified set of data comprises subtracting the output of the first machine learning model and the output of the second machine learning model from the combined set of data” that renders the claim indefinite because it is unclear how to subtract data variable or data set from the claimed “the combined set of data” that does not include the data to be subtracted. The combined set of data is claimed to included claimed “a combined set of data wherein the combined set of data comprises the first set of index data and the second set of index data” and where the output of the model is claimed “wherein the first machine learning model is configured to output a prediction that describes whether a particular user received treatment or not… a second machine learning model trained on the combined set of data and configured to output a prediction that describes user session data”. The relationship between the claimed indexed data and the claimed output data sets are not clear/unknown. How can data be removed from a dataset that does not appear to contain the data to be removed/subtracted? It is unclear how one of ordinary skill in the art ascertain the intended scope of the claimed limitation.
Examiner interprets any training dataset combination as within the scope of the claimed invention.
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.
Claims 1, 3, 5-6, 8-10, 15, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Price et al. (US 11907997, hereinafter ‘Price’) in view of Moyerman et al. (US 20230419396, hereinafter ‘Moyer’).
Regarding independent claim 1, Price teaches a computer-implemented method, comprising: providing, by a computing system comprising one or more processors, (in 5:46-59: As seen in FIG. 1, computing device 101 may include a processor 111, RAM 113, ROM 115, network interface 117, input/output interfaces 119 (e.g., keyboard, mouse, display, printer, etc.), and memory 121. Processor 111 may include one or more computer processing units (CPUs), graphical processing units (GPUs), and/or other processing units such as a processor adapted to perform computations associated with machine learning. I/O 119 may include a variety of interface units and drives for reading, writing, displaying, and/or printing data or files. I/O 119 may be coupled with a display such as display 120. Memory 121 may store software for configuring computing device 101 into a special purpose computing device in order to perform one or more of the various functions discussed herein… 6:15-26: One or more aspects discussed herein may be embodied in computer-usable or readable data and/or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices as described herein [a computer-implemented method, comprising: providing, by a computing system comprising one or more processors]. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. The modules may be written in a source code programming language that is subsequently compiled for execution, or may be written in a scripting language such as (but not limited to) HTML or XML…)
a network resource to a first plurality of users; (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users [a network resource to a first plurality of users;], a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset… Examiner notes the users are in a networked service provider for accessing network resource as purchases, social media network context/messages etc… as in 4:42-47: Aspects described herein remedy this problem by, as will be explained in greater detail below, using a machine learning model to analyze the first user's purchase intentions, their social network, and the purchasing history of their social network to provide a recommendation of a connection between the first user and the second user… 5:30-34: Computing device 101 may, in some embodiments, operate in a standalone environment. In others, computing device 101 may operate in a networked environment. As shown in FIG. 1, computing devices 101, 105, 107, and 109 may be interconnected via a network 103, such as the Internet…)
obtaining, by the computing system, a first set of index data associated with the first plurality of users, the first set of index data describing first user session data for the first plurality of users subsequent to receipt of the network resource; (2:31-60: … The computing device may receive purchase history data indicating one or more purchases [the first set of index data describing first user session data for the first plurality of users subsequent to receipt of the network resource], of one or more assets associated with the type of asset, made by the second plurality of users [obtaining, by the computing system, a first set of index data associated with the first plurality of users, the first set of index data describing first user session data for the first plurality of users subsequent to receipt of the network resource as purchases of the user subsequent to receipt of network resources provided for purchases the provided assets]. The computing device may provide, to the trained machine learning model, input data comprising the purchase intention data, the social networking data, and the purchase history data. The computing device may receive, as output from the trained machine learning model and based on the input data, an indication of at least one second user of the second plurality of users. The at least one second user may be associated with a purchase of a second asset associated with the type of asset. The computing device may generate, for the at least one second user, a notification prompting the second user to contact the first user regarding the intention of the first user to acquire the type of asset. The computing device may cause the notification to be transmitted to the second user; Examiner notes that claimed the first set of index data are the purchase history data)
obtaining, by the computing system, a second set of index data associated with a second plurality of users, the second set of index data describing second user session data for the second plurality of users in the absence of the network resource; (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users [obtaining, by the computing system, a second set of index data associated with a second plurality of users, the second set of index data describing second user session data for the second plurality of users in the absence of the network resource], a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset [the second set of index data describing second user session data for the second plurality of users in the absence of the network resource as users intention to purchase as absence of the network resources for making purchase by the first plurality of users]. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset...; Examiner notes that claimed the second set of index data are the purchase intention data associated with the first plurality of users)
training, by the computing system, one or more machine learning models based on the first set of index data and the second set of index data; (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users, a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset. The computing device may receive social networking data that comprises a plurality of associations between a second plurality of users. The computing device may receive purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users. The computing device may provide, to the trained machine learning model, input data comprising the purchase intention data, the social networking data, and the purchase history data [training, by the computing system, one or more machine learning models based on the first set of index data and the second set of index data]. The computing device may receive, as output from the trained machine learning model and based on the input data, an indication of at least one second user of the second plurality of users. The at least one second user may be associated with a purchase of a second asset associated with the type of asset. The computing device may generate, for the at least one second user, a notification prompting the second user to contact the first user regarding the intention of the first user to acquire the type of asset. The computing device may cause the notification to be transmitted to the second user.)
generating, by the computing system using the one or more machine learning models, a first probability and a second probability for each of a third plurality of users based on feature data associated with such user, wherein the first probability is a respective probability of user session data subsequent to receipt of the network resource and the second probability is a respective probability of the user session data in absence of the network resource; (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model [by the computing system using the one or more machine learning models] may be used to select a connection [generating, by the computing system using the one or more machine learning models, a first probability and a second probability for each of a third plurality of users based on feature data associated with such user, as the selected user set of user by the model based on the respective probability of the relationship] between a first user (e.g., a potential purchaser of an asset) [the second probability is a respective probability of the user session data in absence of the network resource] and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past) [wherein the first probability is a respective probability of user session data subsequent to receipt of the network resource]. To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis [for each of a third plurality of users based on feature data associated with such user,], the trained machine learning model may output an indication of at least one second user of the plurality of different users […a third plurality of users based on feature data associated with such user,]. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.)
generating, by the computing system, an incremental label for each of the third plurality of users, wherein the respective incremental label for each of the third plurality of users is descriptive of a difference in the first probability and the second probability for such user. (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis, the trained machine learning model may output an indication [generating, by the computing system, an incremental label for each of the third plurality of users] of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) [wherein the respective incremental label for each of the third plurality of users is descriptive of a difference in the first probability and the second probability for such user] to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.)
Price teaches the use of a trained machine learning model for outputting probability outcomes for connecting users based on the predicted differences of user that intent to purchase an item vs predicted connection of users who have pervious made purchases, as noted above.
Price does not expressly use the term probability.
Moyer uses the term probability, in [0027] FIG. 2 is a is a diagram illustrating components of an example machine learning system 118, according to example embodiments. The machine learning system 118 is configured to train the conversion probability model. During runtime, the machine learning system 118 also monitors user account behavior associated with a user account of a potential buyer and uses the conversion probability model to determine a probability for transaction conversion based on the user account behavior [the second probability is a respective probability of the user session data in absence of the network resource]. The machine learning system 118 can then trigger a counterfactual analysis and automatically implement a change associated with an item to increase the probability. In some cases, the machine learning system 118 determines a probability for each next action that the user of the user account may perform [wherein the first probability is a respective probability of user session data subsequent to receipt of the network resource and the second probability is a respective probability of the user session data in absence of the network resource] (e.g., add an item to cart, complete a checkout process) selects one of the next actions (e.g., a highest probability next action) [wherein the first probability is a respective probability of user session data subsequent to receipt of the network resource] to conduct counterfactual analysis with.
Moyer and Price are analogous art because both involve developing information retrieval and processing techniques using machine learning models and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing information processing systems and methods that dynamically personalizes elements of an online session based on counterfactual machine-learning analysis, as disclosed by Moyer with the method of developing information retrieval and modeling techniques to automate the selection of content item objects for display in the graphical environment, as disclosed by Price.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Moyer and Price, as noted above. Doing so allows for using counterfactual machine-learning analysis to improve a probability of transaction conversion, (Moyer, Abstract).
Regarding claim 3, the rejection of claim 1 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 1, wherein the one or more machine learning models comprise: a first machine learning model trained on the first set of index data (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users, a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset. The computing device may receive social networking data that comprises a plurality of associations between a second plurality of users. The computing device may receive purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users. The computing device may provide, to the trained machine learning model, input data comprising the purchase intention data, the social networking data, and the purchase history data [wherein the one or more machine learning models comprise: a first machine learning model trained on the first set of index data]. The computing device may receive, as output from the trained machine learning model and based on the input data, an indication of at least one second user of the second plurality of users...)
and configured to output a first prediction that describes a probability of user session data subsequent to receipt of the network resource; (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection between a first user (e.g., a potential purchaser of an asset) [and configured to output a first prediction that describes a probability of user session data subsequent to receipt of the network resource] and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users [and configured to output a first prediction that describes a probability of user session data subsequent to receipt of the network resource]. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.)
a second machine learning model trained on the second set of index data and configured to output a second prediction that describes a probability of user session data in the absence of the network resource; (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model [a second machine learning model trained on the second set of index data] may be used to select a connection [a second machine learning model trained on the second set of index data and configured to output a second prediction that describes a probability of user session data in the absence of the network resource as the selected user set of user by the model based on the respective probability of the relationship and selected user] between a first user (e.g., a potential purchaser of an asset) [and configured to output a second prediction that describes a probability of user session data in the absence of the network resource] and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like. And in 10:13-21: The machine learning device 306 may be a computing device configured to provide one or more machine learning models [a second machine learning model trained on the second set of index data]. The machine learning model may be implemented by the machine learning software 127 and/or the deep neural network 200. A machine learning model may be trained using training data to become a trained machine learning model. The training data may indicate connections between a first plurality of users. The first plurality of users may be a training set of users, such as a large plurality of users that need not necessarily have any interest in purchasing an asset….)
and wherein generating, by the computing system, the incremental label comprises determining a difference between the first prediction and the second prediction. (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) [and wherein generating, by the computing system, the incremental label comprises determining a difference between the first prediction and the second prediction] to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.)
Moyer uses the term probability, in [0027] FIG. 2 is a is a diagram illustrating components of an example machine learning system 118, according to example embodiments. The machine learning system 118 is configured to train the conversion probability model. During runtime, the machine learning system 118 also monitors user account behavior associated with a user account of a potential buyer and uses the conversion probability model to determine a probability for transaction conversion based on the user account behavior [configured to output a second prediction that describes a probability of user session data in the absence of the network resource]. The machine learning system 118 can then trigger a counterfactual analysis and automatically implement a change associated with an item to increase the probability. In some cases, the machine learning system 118 determines a probability for each next action that the user of the user account may perform (e.g., add an item to cart, complete a checkout process) selects one of the next actions (e.g., a highest probability next action) to conduct counterfactual analysis with.
Additionally, Moyer teaches the use of one or more machine learning models, including claimed second model, in [0053] In operation 306, one or more conversion probability models are trained by the training module 210 [a second machine learning model trained on the second set of index data and configured to output a second prediction that describes a probability of user session data…]. In example cases, the extracted features from operation 304 are provided to the training module 210. The machine learning can occur using, for example, linear regression, logistic regression, a decision tree, an artificial neural network, k-nearest neighbors, and/or k-means. The training of the conversion probability model may include calculating probabilities for transaction conversions based on different combinations of extracted features [a second machine learning model trained on the second set of index data and configured to output a second prediction that describes a probability of user session data…].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Moyer and Price for the same reasons disclosed above in claim 1.
Regarding claim 5, the rejection of claim 1 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 1, wherein the one or more machine learning models comprise: a single machine learning model trained on a combined set of data wherein the combined set of data includes the first set of index data and the second set of index data (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users, a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset. The computing device may receive social networking data that comprises a plurality of associations between a second plurality of users. The computing device may receive purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users. The computing device may provide, to the trained machine learning model, input data comprising the purchase intention data, the social networking data, and the purchase history data [wherein the one or more machine learning models comprise: a single machine learning model trained on a combined set of data wherein the combined set of data includes the first set of index data and the second set of index data]. The computing device may receive, as output from the trained machine learning model and based on the input data, an indication of at least one second user of the second plurality of users. The at least one second user may be associated with a purchase of a second asset associated with the type of asset. The computing device may generate, for the at least one second user, a notification prompting the second user to contact the first user regarding the intention of the first user to acquire the type of asset. The computing device may cause the notification to be transmitted to the second user.)
and wherein the single machine learning model is configured to output a first probability wherein the first probability is directed to a respective probability of user session data subsequent to receipt of the network resource and a second probability wherein the second probability is directed to a respective probability of user session data in absence of the network resource for each of a third plurality of users. (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model [wherein the single machine learning model is configured to output a first probability …] may be used to select a connection between a first user (e.g., a potential purchaser of an asset). and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past) [wherein the single machine learning model is configured to output a first probability wherein the first probability is directed to a respective probability of user session data subsequent to receipt of the network resource]. To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis [for each of a third plurality of users], the trained machine learning model may output an indication of at least one second user of the plurality of different users [and a second probability wherein the second probability is directed to a respective probability of user session data in absence of the network resource for each of a third plurality of users]. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.)
Regarding claim 6, the rejection of claim 1 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 1, comprising: generating, by the computing system, a third set of index data wherein the third set of index data comprises applying a first treatment value to the first set of index data and a second treatment value to the second set of index data; (in 4:5-25: … trained machine learning model may be used to select a connection between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase) [applying a first treatment value to the first set of index data as received intention input data ], social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past) [second treatment value to the second set of index data as asset purchase history values]. Based on this analysis [and training, by the computing system, the one or more machine learning models based on the third set of index data], the trained machine learning model may output [generating, by the computing system, a third set of index data wherein the third set of index data as generated plurality of different users for outputting notification and providing feedback on asset] an indication of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset [comprising: generating, by the computing system, a third set of index data wherein the third set of index data comprises applying a first treatment value to the first set of index data and a second treatment value to the second set of index data], such as pros/cons, issues with the purchasing process, or the like.)
and training, by the computing system, the one or more machine learning models based on the third set of index data. (in 11:28-42: The machine learning model may be trained using the same plurality of users that are indicated in the social networking data (e.g., stored in step 403) [training, by the computing system, the one or more machine learning models based on the third set of index data]. For example, at least a portion of the first plurality of users (e.g., used to train the machine learning model) may be the same as the second plurality of users (e.g., indicated by the social networking data, as discussed below). In this manner, the machine learning model may be trained on a particular plurality of users, such as users in a particular community, in a particular geographic location, or the like. For example, a machine learning model may be trained based on users in a particular town, in a particular demographic (e.g., thirtysomethings living in a city), or the like. As such, multiple trained machine learning models may be maintained, each for a different group of users.)
Additionally, Moyer teaches that machine learning model analysis for producing modeling outcomes as re-training operations, in [0016] Thus, the present disclosure provides technical solutions that dynamically personalizes elements of an online session that increases proclivity of a user to complete a transaction. Using known attributes of the user, their behavior as they traverse the shopping process, and item attributes, the system can dynamically change an element associated with the item (e.g., decrease price or shipping cost, provide an incentive or promotion) to increase a conversion probability. Thus, the technical solutions predict, using a machine-trained conversion probability model, whether a transaction conversion will likely occur, trigger a counterfactual analysis if the conversion probability is low, and change one or more elements associated with the item to increase the probability for transaction conversion. The technical solutions use machine-learning to train the conversional probability model that, at runtime, determines the probability for transaction conversion. New completed transactions can be used to retrain/refine the conversion probability model [training, by the computing system, the one or more machine learning models based on the third set of index data]. Thus, the probability can be continuously modeled, learned, and changed (e.g., on a daily or weekly basis).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Moyer and Price for the same reasons disclosed above in claim 1.
Regarding claim 8, the rejection of claim 1 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 1, comprising: determining, by the computing system, a content value associated with the network resource, wherein the content value is a parameter quantifying a similarity in content between first and second network resources; wherein the first set of index data and the second set of index data is obtained in response to the first network resource; (2:31-60: … The computing device may receive purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users. The computing device may provide, to the trained machine learning model [comprising: determining, by the computing system, a content value associated with the network resource, wherein the content value is a parameter quantifying a similarity in content between first and second network resources a learned parameter values of the one or more training models], input data [the combined set of data comprises the first set of index data and the second set of index data] comprising the purchase intention data [the second set of index data for modeling quantifying a similarity in content between first and second network resources; wherein the first set of index data and the second set of index data is obtained in response to the first network resource], the social networking data, and the purchase history data [the first set of index data for modeling quantifying a similarity in content between first and second network resources; wherein the first set of index data and the second set of index data is obtained in response to the first network resource]. The computing device may receive, as output from the trained machine learning model and based on the input data, an indication of at least one second user of the second plurality of users. The at least one second user may be associated with a purchase of a second asset associated with the type of asset. The computing device may generate, for the at least one second user, a notification prompting the second user to contact the first user regarding the intention of the first user to acquire the type of asset. The computing device may cause the notification to be transmitted to the second user.)
wherein the respective incremental label for each of the third plurality of users is descriptive of a predicted change in user session data effected by providing the second network resource to the user; and wherein generating the respective incremental label for each of the third plurality of users is based at least in part on a combination of feature data associated with the user and the content value. (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection [wherein the respective incremental label for each of the third plurality of users is descriptive of a predicted change in user session data effected by providing the second network resource to the user as the selected user set of user by the model based on the respective probability of the relationship and selected user] between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model [wherein generating the respective incremental label for each of the third plurality of users is based at least in part on a combination of feature data associated with the user and the content value as learned model parameters combined with the claimed features as input data to provide a model output] may receive input data [feature data associated with the user] that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users... And in 10:13-21: The machine learning device 306 may be a computing device configured to provide one or more machine learning models. The machine learning model may be implemented by the machine learning software 127 and/or the deep neural network 200. A machine learning model may be trained using training data to become a trained machine learning model. The training data may indicate connections between a first plurality of users. The first plurality of users may be a training set of users, such as a large plurality of users that need not necessarily have any interest in purchasing an asset….)
Regarding claim 9, the rejection of claim 1 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 1, comprising: generating, by the computing system, a graphical illustration based at least in part on the incremental label, the first set of index data, and the second set of index data; and surfacing, by the computing system, the graphical illustration to a user. (As depicted in Fig. 6A and 6B and in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past)... Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) [generating, by the computing system, a graphical illustration based at least in part on the incremental label, the first set of index data, and the second set of index data; and surfacing, by the computing system, the graphical illustration to a user] to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.
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Regarding claim 10, the rejection of claim 1 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 1, wherein the incremental label is a first incremental label, the method comprising: generating, by the computing system using the one or more machine learning models, a second respective incremental label for each of the third plurality of users based on the feature data associated with the user. (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection [the incremental label is a first incremental label, the method comprising: generating, by the computing system using the one or more machine learning models] between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past)... Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like… 7:22-29: FIG. 3 depicts a system which may be used to transmit notifications [including a second respective incremental label] to prompt users to share their knowledge about assets [using the one or more machine learning models, a second respective incremental label for each of the third plurality of users based on the feature data associated with the use]. One or more user devices 301 are shown as connected to a network 103. The network 103 may be the same or similar as the network 103 of FIG. 1. The network 103 also connects an analysis device 302, a purchase intention database 303, a social networking database 304, a purchase history database 305, and a machine learning device 306…
Regarding claim 15, the claim limitations are similar to those in claim 1 and are rejected under the same rationale.
Regarding independent claim 17, Price teaches one or more computer-readable media that store instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising: (in 5:46-59: As seen in FIG. 1, computing device 101 may include a processor 111, RAM 113, ROM 115, network interface 117, input/output interfaces 119 (e.g., keyboard, mouse, display, printer, etc.), and memory 121. Processor 111 may include one or more computer processing units (CPUs), graphical processing units (GPUs), and/or other processing units such as a processor adapted to perform computations associated with machine learning. I/O 119 may include a variety of interface units and drives for reading, writing, displaying, and/or printing data or files. I/O 119 may be coupled with a display such as display 120. Memory 121 may store software for configuring computing device 101 into a special purpose computing device in order to perform one or more of the various functions discussed herein… 6:15-26: One or more aspects discussed herein may be embodied in computer-usable or readable data and/or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices as described herein [one or more computer-readable media that store instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising]. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. The modules may be written in a source code programming language that is subsequently compiled for execution, or may be written in a scripting language such as (but not limited to) HTML or XML…)
obtaining, feature data associated with a candidate user; (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data [obtaining, feature data associated with a candidate user] indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users, a machine learning model to select one or more of the first plurality of users.… as in 4:42-47: Aspects described herein remedy this problem by, as will be explained in greater detail below, using a machine learning model to analyze the first user's purchase intentions, their social network, and the purchasing history of their social network [obtaining, feature data associated with a candidate user] to provide a recommendation of a connection between the first user and the second user… 5:30-34: Computing device 101 may, in some embodiments, operate in a standalone environment. In others, computing device 101 may operate in a networked environment. As shown in FIG. 1, computing devices 101, 105, 107, and 109 may be interconnected via a network 103, such as the Internet…)
and determining, whether to provide a network resource to the candidate user based on the feature data associated with a candidate user, wherein said determining comprises: accessing, one or more machine learning models that have been trained using a first set of index data and a second set of index data, wherein the first set of index data is associated with a first plurality of users and describes first user session data for the first plurality of users subsequent to receipt of a network resource; (2:31-60: … The computing device may receive purchase history data indicating one or more purchases [a first set of index data…, wherein the first set of index data is associated with a first plurality of users and describes first user session data for the first plurality of users subsequent to receipt of a network resource], of one or more assets associated with the type of asset, made by the second plurality of users [a first set of index data…, wherein the first set of index data is associated with a first plurality of users and describes first user session data for the first plurality of users subsequent to receipt of a network resource as purchases of the user subsequent to receipt of network resources provided for purchases the provided assets]. The computing device may provide, to the trained machine learning model, input data comprising the purchase intention data, the social networking data, and the purchase history data. The computing device may receive, as output from the trained machine learning model [determining, whether to provide a network resource to the candidate user based on the feature data associated with a candidate user, wherein said determining comprises: accessing, one or more machine learning models that have been trained using a first set of index data and a second set of index data] and based on the input data [trained using a first set of index data and a second set of index data], an indication of at least one second user of the second plurality of users. The at least one second user may be associated with a purchase of a second asset associated with the type of asset. The computing device may generate, for the at least one second user, a notification prompting the second user to contact the first user regarding the intention of the first user to acquire the type of asset. The computing device may cause the notification to be transmitted to the second user; Examiner notes that claimed the first set of index data are the purchase history data)
wherein the second set of index data is associated with a second plurality of users and describes second user session data for the second plurality of users in the absence of the network resource; (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users, a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset [wherein the second set of index data is associated with a second plurality of users and describes second user session data for the second plurality of users in the absence of the network resource as users intention to purchase as absence of the network resources for making purchase by the first plurality of users]. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset...; Examiner notes that claimed the second set of index data are the purchase intention data associated with the first plurality of users)
training, by the computing system, one or more machine learning models based on the first set of index data and the second set of index data; (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users, a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset. The computing device may receive social networking data that comprises a plurality of associations between a second plurality of users. The computing device may receive purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users. The computing device may provide, to the trained machine learning model, input data comprising the purchase intention data, the social networking data, and the purchase history data [training, by the computing system, one or more machine learning models based on the first set of index data and the second set of index data]. The computing device may receive, as output from the trained machine learning model and based on the input data, an indication of at least one second user of the second plurality of users. The at least one second user may be associated with a purchase of a second asset associated with the type of asset. The computing device may generate, for the at least one second user, a notification prompting the second user to contact the first user regarding the intention of the first user to acquire the type of asset. The computing device may cause the notification to be transmitted to the second user.)
generating, using the one or more machine learning models, a first probability and a second probability for each of a third plurality of users based on feature data associated with such user, wherein the first probability is a respective probability of user session data subsequent to receipt of the network resource and the second probability is a respective probability of the user session data in absence of the network resource; (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection [generating, using the one or more machine learning models, a first probability and a second probability for each of a third plurality of users based on feature data associated with such user, as the selected user set of user by the model based on the respective probability of the relationship] between a first user (e.g., a potential purchaser of an asset) [the second probability is a respective probability of the user session data in absence of the network resource] and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past) [wherein the first probability is a respective probability of user session data subsequent to receipt of the network resource ]. To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis [for each of a third plurality of users based on feature data associated with such user], the trained machine learning model may output an indication of at least one second user of the plurality of different users […a third plurality of users based on feature data associated with such user]. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.)
and generating, an incremental label for each of the third plurality of users, wherein the respective incremental label for each of the third plurality of users is descriptive of a difference in the first probability and the second probability for such user. (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis, the trained machine learning model may output an indication [generating, an incremental label for each of the third plurality of users,…] of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) [wherein the respective incremental label for each of the third plurality of users is descriptive of a difference in the first probability and the second probability for such user] to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.)
Price teaches the use of a trained machine learning model for outputting probability outcomes for connecting users based on the predicted differences of user that intent to purchase an item vs predicted connection of users who have pervious made purchases, as noted above.
Price does not expressly use the term probability.
Moyer uses the term probability, in [0027] FIG. 2 is a is a diagram illustrating components of an example machine learning system 118, according to example embodiments. The machine learning system 118 is configured to train the conversion probability model. During runtime, the machine learning system 118 also monitors user account behavior associated with a user account of a potential buyer and uses the conversion probability model to determine a probability for transaction conversion based on the user account behavior [wherein the second set of index data is associated with a second plurality of users and describes second user session data for the second plurality of users in the absence of the network resource]. The machine learning system 118 can then trigger a counterfactual analysis and automatically implement a change associated with an item to increase the probability. In some cases, the machine learning system 118 determines a probability for each next action that the user of the user account may perform (e.g., add an item to cart, complete a checkout process) selects one of the next actions (e.g., a highest probability next action) [wherein the first probability is a respective probability of user session data subsequent to receipt of the network resource] to conduct counterfactual analysis with.
Moyer and Price are analogous art because both involve developing information retrieval and processing techniques using machine learning models and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing information processing systems and methods that dynamically personalizes elements of an online session based on counterfactual machine-learning analysis, as disclosed by Moyer with the method of developing information retrieval and modeling techniques to automate the selection of content item objects for display in the graphical environment, as disclosed by Price.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Moyer and Price, as noted above. Doing so allows for using counterfactual machine-learning analysis to improve a probability of transaction conversion, (Moyer, Abstract).
Regarding claim 19, the rejection of claim 17 is incorporated and the claim limitations are similar to those in claim 3 and are rejected under the same rationale.
Claims 2, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Price et al. (US 11907997, hereinafter ‘Price’) in view of Moyerman et al. (US 20230419396, hereinafter ‘Moyer’) in further view of Rajasekaran et al. (US 20230245174, hereinafter ‘Raj’).
Regarding claim 2, the rejection of claim 1 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 1, comprising: (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past)... Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users [comprising: selecting the most relevant/ranking users to send output indication as claimed incremental label]. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like.)
determining, by the computing system, whether to provide the network resource to each of the third plurality of users based at least in part on(in 14:53-67: The second user might be rewarded by the first user. For instance, the first user might be prompted to provide a tip to the second user if they provide information about the asset [determining, by the computing system, whether to provide the network resource to each of the third plurality of users based at least in part onthe selected users for receiving the resource prompt as provided claimed resource to provide information about the asset]. In this manner, the second user might be incentivized to aid the first user. For example, as part of generating the notification in step 407, the first user might be prompted to provide an indication of a tip (e.g., ten dollars). The notification generated in step 407 might indicate the amount of the tip (e.g., “Will you provide User A feedback about your SUV? They have offered a ten-dollar reward for your thoughts!”). Then, if the second user accepts the notification (and, e.g., instantiates a chat session with the first user) [and providing, by the computing system, the network resource to each of the third plurality of users for which a determination was made to provide the network resource], the second user might be provided the tip. For example, the tip might be added to a bank account of the second user. )
While, Price and Moyer teaches the managing of network resources to users regarding an asset in a social network environment as noted above.
Price and Moyer do not expressly use the term ranking as part of the machine learning process.
Raj teaches the use of the term ranking as part of the machine learning process, in [0004] The embodiments described herein are directed to automatically determining and providing digital, platform-specific, user personas for a user navigating various retailer platforms or applications, for example, on a website. The user personas may be used to further determine item recommendations for a user to be presented on the retailer's platform or application. The embodiments may allow a person, such as a customer, to be presented with user personas or item recommendations determined based on the user personas that may be more likely to interest the customer on a specific platform. For example, the embodiments may allow the person to view recommendations or personas that differ based on the platform (e.g., email, homepage, advertisements, etc.) being used and that the person may be more willing to purchase or interact with on the corresponding platform. In some examples, the embodiments may provide scoring processes that score and/or rank potential user personas [ranking, by the computing system, the third plurality of users based at least in part on the incremental label; determining, by the computing system, whether to provide the network resource to each of the third plurality of users based at least in part on the ranking] for a specific platform based on historical user data, catalog data, and co-purchase data specific to the platform and also across platforms in an efficient and accurate manner to increase user interaction and relevancy of the recommendations within the corresponding platform…
Raj, Moyer and Price are analogous art because both involve developing information retrieval and processing techniques using machine learning models and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing information processing techniques for determining and providing personalized digital recommendations based on predicted user personas within a specific platform, as disclosed by Raj with the method of developing information retrieval and modeling techniques to automate the selection of content item objects for display in the graphical environment, as collectively disclosed by Price and Moyer.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Raj, Moyer and Price, as noted above. Doing so allow for implementing a more robust and personalized generation of assets and campaigns targeted towards that customer within that platform that streamline the customer's experience with the retailer's platform, (Raj 0003).
Regarding claim 16, the rejection of claim 15 is incorporated and the claim limitations are similar to those in claim 2 and are rejected under the same rationale.
Regarding claim 18, the rejection of claim 17 is incorporated and the claim limitations are similar to those in claim 2 and are rejected under the same rationale.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Price et al. (US 11907997, hereinafter ‘Price’) in view of Moyerman et al. (US 20230419396, hereinafter ‘Moyer’) in further view of Li et al. (US 20200226489, hereinafter ‘Li’).
Regarding claim 7, the rejection of claim 1 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 1, wherein the one or more machine learning models comprise: a first machine learning model trained on a combined set of data wherein the combined set of data comprises the first set of index data and the second set of index data and wherein the first machine learning model is configured to output a prediction that describes whether a particular user received treatment or not; (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users, a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset. The computing device may receive social networking data that comprises a plurality of associations between a second plurality of users. The computing device may receive purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users. The computing device may provide, to the trained machine learning model [wherein the one or more machine learning models comprise: a first machine learning model trained on a combined set of data wherein the combined set of data comprises the first set of index data and the second set of index data], input data [the combined set of data comprises the first set of index data and the second set of index data] comprising the purchase intention data [the second set of index data], the social networking data, and the purchase history data [the first set of index data]. The computing device may receive, as output [and wherein the first machine learning model is configured to output a prediction that describes whether a particular user received treatment or not from the trained machine learning model and based on the input data, an indication of at least one second user of the second plurality of users [wherein the first machine learning model is configured to output a prediction that describes whether a particular user received treatment or not as receiving a treatment indication for engaging to purchase as associated type of asset or not]. The at least one second user may be associated with a purchase of a second asset associated with the type of asset. The computing device may generate, for the at least one second user, a notification prompting the second user to contact the first user regarding the intention of the first user to acquire the type of asset. The computing device may cause the notification to be transmitted to the second user.)
a second machine learning model trained on the combined set of data and configured to output a prediction that describes user session data; (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model [a second machine learning model trained on the combined set of data…] may be used to select a connection [and configured to output a prediction that describes user session data as the selected user set of user by the model based on the respective probability of the relationship and selected user] between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users... And in 10:13-21: The machine learning device 306 may be a computing device configured to provide one or more machine learning models [a second machine learning model trained on the combined set of data…]. The machine learning model may be implemented by the machine learning software 127 and/or the deep neural network 200. A machine learning model may be trained using training data to become a trained machine learning model. The training data may indicate connections between a first plurality of users. The first plurality of users may be a training set of users, such as a large plurality of users that need not necessarily have any interest in purchasing an asset….)
a modified set of data wherein the modified set of data comprises subtracting the output of the first machine learning model and the output of the second machine learning model from the combined set of data; and a third machine learning model trained on the modified set of data configured to output the incremental label. in 10:13-21: The machine learning device 306 may be a computing device configured to provide one or more machine learning models [a third machine learning model trained on the modified set of data configured to output the incremental label]. The machine learning model may be implemented by the machine learning software 127 and/or the deep neural network 200. A machine learning model may be trained using training data to become a trained machine learning model. The training data may indicate connections between a first plurality of users. The first plurality of users may be a training set of users, such as a large plurality of users that need not necessarily have any interest in purchasing an asset [a modified set of data wherein the modified set of data comprises subtracting the output of the first machine learning model and the output of the second machine learning model from the combined set of data by removing data od users having an intention or associated with a purchase of an asset]….)
While, Price and Moyer teaches the managing of network resources to users regarding an asset in a social network environment as noted above.
Additionally, Frank teaches managing network resources by managing and removing bias, in [0005] Certain embodiments involve generating de-biased training data [a modified set of data wherein the modified set of data comprises subtracting] for fairness-aware predictive models […the output of the first machine learning model and the output of the second machine learning model from the combined set of data], and, in some cases, facilitating online resource access by users using the fairness-aware predictive models… Based on the latent features, the de-biasing server can train a second machine learning model to generate de-biased training data for the first machine learning model. Training the second machine learning model can include applying a loss function that includes a loss term associated with an individual bias of the de-biased training data and another loss term associated with a group bias of the de-biased training data. The de-biased training data are then utilized to train the first machine learning model and to update an access flag for a user by applying the first machine learning model to attributes associated with the user. Based on the updated access flag for the user, a user device associated with the user can be provided with access to the online environment… [0020] Because this fair access problem is specific to online resources, embodiments described herein utilize automated models that are uniquely suited for online resource access. For instance, a computing system automatically applies various rules (e.g. various relationship between the bias attribute, non-bias attributes and prediction outputs) to the training data to obtain bias corrected training data [a modified set of data wherein the modified set of data comprises subtracting the output of the first machine learning model and the output of the second machine learning model from the combined set of data]. The computing system further uses these bias corrected training data to automatically establish new rules (e.g., relationship between user attributes and the predicted outcome on the access permission obtained based on de-biased training data) that are fair and accurately predict the users' activities with respect to the online resources…
Li, Moyer and Price are analogous art because both involve developing information retrieval and processing techniques using machine learning models and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing information processing techniques for generating bias-corrected training data, as disclosed by Li with the method of developing information retrieval and modeling techniques to automate the selection of content item objects for display in the graphical environment, as collectively disclosed by Price and Moyer.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Li, Moyer and Price, as noted above. Doing so allow for implementing access-facilitation computing system that control distribution of interactive contents to user computing devices based on the access flags of the users and allow user computing devices to navigate the online environment based on the interactive contents., (Li, 0018).
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Price et al. (US 11907997, hereinafter ‘Price’) in view of Moyerman et al. (US 20230419396, hereinafter ‘Moyer’) in further view of Priness et al. (US 20160350812, hereinafter ‘Prin’).
Regarding claim 11, the rejection of claim 10 is incorporated and Price in combination with Moyer teaches the computer-implemented method of claim 10, comprising: generating, by the computing system, (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model may be used to select a connection between a first user (e.g., a potential purchaser of an asset) and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past)... Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) [generating, by the computing system, ] to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like… 7:22-29: FIG. 3 depicts a system which may be used to transmit notifications [including a generating, by the computing system, ] to prompt users to share their knowledge about assets. One or more user devices 301 are shown as connected to a network 103. The network 103 may be the same or similar as the network 103 of FIG. 1. The network 103 also connects an analysis device 302, a purchase intention database 303, a social networking database 304, a purchase history database 305, and a machine learning device 306…)
Price and Moyer does not expressly teach the use of a score in generating model outputs/notifications.
Prin uses the use of a score in generating model outputs/notifications, in [0103] The notification logic may be generated based on the determined schedule, urgency, relevance, and/or confidence score associated with the information item, and/or other user data, such as current user information or contextual information... In one embodiment, notification logic includes priority information such that where more than one information items are pending, notifications can be prioritized based on relevance or urgency. In this way, notifications provided to the user can be managed (such as by presentation component 218 or another application or service) so that a user isn't overwhelmed by information. For example, in one embodiment, the relevance or urgency of information items may be used for ranking or otherwise prioritizing pending or potential notifications [generating, by the computing system, ] that correspond to those information items. Information indicating the determined priority or ranking [generating, by the computing system, ] may be included in the notification logic. Moreover, in an embodiment, based on the notification logic, pending or potential notifications may be ranked or scored relative to other pending or potential notifications based on the relevance or urgency of their corresponding information items. For example, in an embodiment, the score may comprise a weighted rank of each notification, which may be scored on the same scale and used for assigning a priority [generating, by the computing system, ]…
Prin, Moyer and Price are analogous art because both involve developing information retrieval and processing techniques using machine learning models and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing information processing techniques for providing information from venues that are of interest to a user, as disclosed by Prin with the method of developing information retrieval and modeling techniques to automate the selection of content item objects for display in the graphical environment, as collectively disclosed by Price and Moyer.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Prin, Moyer and Price, as noted above. Doing so allow for implementing a personal assistant application to automatically determine venues and corresponding information items to provide service content tailored to venues of interest to a user, (Prin, 0004-0005).
Regarding claim 12, the rejection of claim 11 is incorporated.
Price in combination with Moyer further teaches the computer-implemented method of claim 11, … providing, by the computing system, the network resource to each of the third plurality of users … (in 14:53-67: The second user might be rewarded by the first user. For instance, the first user might be prompted to provide a tip to the second user if they provide information about the asset [providing, by the computing system, the network resource to each of the third plurality of users … the selected users for receiving the resource prompt as provided claimed resource to provide information about the asset]. In this manner, the second user might be incentivized to aid the first user. For example, as part of generating the notification in step 407, the first user might be prompted to provide an indication of a tip (e.g., ten dollars). The notification generated in step 407 might indicate the amount of the tip (e.g., “Will you provide User A feedback about your SUV? They have offered a ten-dollar reward for your thoughts!”). Then, if the second user accepts the notification (and, e.g., instantiates a chat session with the first user) [providing, by the computing system, the network resource to each of the third plurality of users …], the second user might be provided the tip. For example, the tip might be added to a bank account of the second user. )
Prin further teaches the computer-implemented method of claim 11, further comprising: determining, by the computing system, one of the first incremental label and the second incremental label based at least in part on the comparison score; ranking, by the computing system, the third plurality of users based at least in part on the determined incremental label; (in [0103] The notification logic may be generated based on the determined schedule, urgency, relevance, and/or confidence score associated with the information item, and/or other user data, such as current user information or contextual information... In one embodiment, notification logic includes priority information such that where more than one information items are pending, notifications can be prioritized based on relevance or urgency. In this way, notifications provided to the user can be managed (such as by presentation component 218 or another application or service) so that a user isn't overwhelmed by information. For example, in one embodiment, the relevance or urgency of information items may be used for ranking or otherwise prioritizing pending or potential notifications [determining, by the computing system, one of the first incremental label and the second incremental label based at least in part on the comparison score; ranking, by the computing system, the third plurality of users based at least in part on the determined incremental label] that correspond to those information items. Information indicating the determined priority or ranking [determining, by the computing system, one of the first incremental label and the second incremental label based at least in part on the comparison score; ranking, by the computing system, the third plurality of users based at least in part on the determined incremental label] may be included in the notification logic. Moreover, in an embodiment, based on the notification logic, pending or potential notifications may be ranked or scored relative to other pending or potential notifications based on the relevance or urgency of their corresponding information items. For example, in an embodiment, the score may comprise a weighted rank of each notification, which may be scored on the same scale and used for assigning a priority [determining, by the computing system, one of the first incremental label and the second incremental label based at least in part on the comparison score; ranking, by the computing system, the third plurality of users based at least in part on the determined incremental label]…)
and providing, by the computing system, the network resource to each of the third plurality of users based on the ranking. (in [0084] Some embodiments of relevance analyzer 288 may determine a relevance score or value for a venue and/or an information item from a venue information source… Further, in some embodiments, where multiple information items from different venues are relevant to a user, the relevance score (and in some cases an urgency score, described below) may be used to prioritize which information items should be provided to a user. For example, potential notifications to the user may be ranked based on relevance to the user [and providing, by the computing system, the network resource to each of the third plurality of users based on the ranking] (and/or urgency), as indicated by the relevance score. In this way, only the most relevant notifications are provided and the user [and providing, by the computing system, the network resource to each of the third plurality of users based on the ranking] is not overwhelmed with less relevant (or less urgent) notifications…)
Regarding claim 13, the rejection of claim 11 is incorporated and Price in combination with Moyer and Prin teaches the computer-implemented method of claim 11, further comprising: generating, by the computing system, a combined incremental label based at least in part on the first incremental label and the second incremental label; (7:22-29: FIG. 3 depicts a system which may be used to transmit notifications [including generating, by the computing system, a combined incremental label based at least in part on the first incremental label and the second incremental label] to prompt users to share their knowledge about assets. One or more user devices 301 are shown as connected to a network 103. The network 103 may be the same or similar as the network 103 of FIG. 1. The network 103 also connects an analysis device 302, a purchase intention database 303, a social networking database 304, a purchase history database 305, and a machine learning device 306…)
Prin further teaches: ranking, by the computing system, the third plurality of users based at least in part on the combined incremental label; and providing, by the computing system, the network resource to each of the third plurality of users based on the ranking. ((in [0084] Some embodiments of relevance analyzer 288 may determine a relevance score or value for a venue and/or an information item from a venue information source… Further, in some embodiments, where multiple information items from different venues are relevant to a user [ranking, by the computing system, the third plurality of users based at least in part on the combined incremental label; and providing, by the computing system, the network resource to each of the third plurality of users based on the ranking], the relevance score (and in some cases an urgency score, described below) may be used to prioritize which information items should be provided to a user. For example, potential notifications to the user may be ranked based on relevance to the user (and/or urgency), as indicated by the relevance score. In this way, only the most relevant notifications [ranking, by the computing system, the third plurality of users based at least in part on the combined incremental label; and providing, by the computing system, the network resource to each of the third plurality of users based on the ranking] are provided and the user is not overwhelmed with less relevant (or less urgent) notifications…)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Prin, Moyer and Price for the same reasons disclosed above.
Regarding claim 14, the rejection of claim 13 is incorporated and Prin further teaches the computer-implemented method of claim 13, wherein the first incremental label and the second incremental label are weighted differently in the combined incremental label. (in [0103] The notification logic may be generated based on the determined schedule, urgency, relevance, and/or confidence score associated with the information item, and/or other user data, such as current user information or contextual information. In one embodiment, notification logic is generated for each information item. In another embodiment, the same or similar notification logic is included in the notification content corresponding to information items of a certain category or class (such as information items corresponding to the same venue types). In one embodiment, notification logic includes priority information such that where more than one information items are pending, notifications can be prioritized based on relevance or urgency [wherein the first incremental label and the second incremental label are weighted differently in the combined incremental label]. In this way, notifications provided to the user can be managed (such as by presentation component 218 or another application or service) so that a user isn't overwhelmed by information. For example, in one embodiment, the relevance or urgency of information items may be used for ranking or otherwise prioritizing pending or potential notifications that correspond to those information items. Information indicating the determined priority or ranking may be included in the notification logic. Moreover, in an embodiment, based on the notification logic, pending or potential notifications may be ranked or scored relative to other pending or potential notifications based on the relevance or urgency of their corresponding information items [weighted differently in the combined incremental label relative to other potential notification in a class or plurality of combined notifications].]. For example, in an embodiment, the score may comprise a weighted rank of each notification, which may be scored on the same scale and used for assigning a priority. In some embodiments, the pending or potential notifications may be logically organized in queue based on a weighted ranking or score..)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Prin, Moyer and Price for the same reasons disclosed above.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Price et al. (US 11907997, hereinafter ‘Price’) in view of Moyerman et al. (US 20230419396, hereinafter ‘Moyer’) in further view of Comar et al. (US 10776847, hereinafter ‘Comar’).
Regarding claim 20, the rejection of claim 19 is incorporated and Price in combination with Moyer further teaches the one or more computer-readable media of claim 19, the operations comprising: generating a modified output of the first machine learning model, wherein the modified output comprises fitting the output of the first machine learning model, (2:31-60: More particularly, some aspects described herein may provide for a computing device that may train, using training data indicating connections between a first plurality of users, characteristics of the first plurality of users, and a plurality of assets purchased by the first plurality of users, a machine learning model to select one or more of the first plurality of users. The computing device may receive purchase intention data that indicates an intention of a first user to acquire a type of asset. That purchase intention data may indicate characteristics of the first user and preferences of the first user corresponding to the type of asset. The computing device may receive social networking data that comprises a plurality of associations between a second plurality of users. The computing device may receive purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users. The computing device may provide, to the trained machine learning model, input data comprising the purchase intention data, the social networking data, and the purchase history data. The computing device may receive, as output [the operations comprising: generating a modified output of the first machine learning model, wherein the modified output comprises fitting the output of the first machine learning model as the output from the trained model based on input as claimed modified output based on input data into the trained model] from the trained machine learning model and based on the input data, an indication of at least one second user of the second plurality of users...)
generating a modified output of the second machine learning model, wherein the modified output comprises fitting the output of the second machine learning model, (in 10:13-21: The machine learning device 306 may be a computing device configured to provide one or more machine learning models [generating a modified output of the second machine learning model, wherein the modified output comprises fitting the output of the second machine learning model, for generating an output from a trained set of models noted in the previous limitation ]. The machine learning model may be implemented by the machine learning software 127 and/or the deep neural network 200. A machine learning model may be trained using training data to become a trained machine learning model. The training data may indicate connections between a first plurality of users. The first plurality of users may be a training set of users, such as a large plurality of users that need not necessarily have any interest in purchasing an asset….)
generating (in 4:5-25: By way of introduction, aspects discussed herein may relate to connecting users in a manner which allows for the exchange of information about purchases. A trained machine learning model [generating ] may be used to select a connection between a first user (e.g., a potential purchaser of an asset) [generating ] and at least one second user (e.g., that has purchased the same asset, or something like the asset, in the past). To perform this task, the trained machine learning model may receive input data that includes purchase intention data (e.g., reflecting what the first user would potentially like to purchase), social networking data (e.g., reflecting connections between the first user and a plurality of different users), and purchase history data (e.g., reflecting what the plurality of different users have purchased in the past). Based on this analysis, the trained machine learning model may output an indication of at least one second user of the plurality of different users. That at least one second user may be prompted (e.g., with a notification) to reach out to the first user. In this manner, the at least one second user may be able to provide the user feedback about the asset, such as pros/cons, issues with the purchasing process, or the like. And in 10:13-21: The machine learning device 306 may be a computing device configured to provide one or more machine learning models [generating ]. The machine learning model may be implemented by the machine learning software 127 and/or the deep neural network 200. A machine learning model may be trained using training data to become a trained machine learning model. The training data may indicate connections between a first plurality of users. The first plurality of users may be a training set of users, such as a large plurality of users that need not necessarily have any interest in purchasing an asset….)
Price teaches the use of machine learning models to model the selection of notification/prompts to help incentivize user to engage an asset/purchasing network resource.
Price does not expressly teach the use of a score in generating modeled outcomes.
Moyer teaches the use of a score in generating modeled outcomes as claimed generating a propensity score based on the, in [0032] The training module 210 trains the conversion probability model using, for example, neural networks or classical machine learning. The training data used for training may include the item attributes, seller account attributes, and/or user account attributes (collectively referred to as “features”) for the past transactions in any combination. The training of the conversion probability model may include training for probabilities (e.g., thresholds and/or ranges) [generating a propensity score based on the as including in the probability scores for determining a user’s intentions of engaging with a network resource] of whether a user will complete a transaction (e.g., that a next action performed by the user is or will lead to checkout completion). The machine training can occur using, for example, linear regression, logistic regression, a decision tree, an artificial neural network, k-nearest neighbors, and/or k-means. And in [0068] In operation 508, the analysis module 314 applies the extracted attributes (e.g., user account and item attributes) and user account behavior to the conversion probability model. In some embodiments, the analysis looks at what the user did the last predetermined number of operations and predicts what the probability is that the next operation is completing a checkout or will lead to completing the checkout. In some embodiment, the analysis looks at what the user did the last predetermined number of operations and predicts probabilities for what the next operation(s) may be (e.g., checkout out, adding an item to the cart) [generating a propensity score based on the as including in the probability scores for determining a user’s intentions of engaging with a network resource].
Additionally, Moyer teaches the use of one or more machine learning models, including claimed second model, in [0053] In operation 306, one or more conversion probability models are trained by the training module 210 [generating a propensity score based on ]. In example cases, the extracted features from operation 304 are provided to the training module 210. The machine learning can occur using, for example, linear regression, logistic regression, a decision tree, an artificial neural network, k-nearest neighbors, and/or k-means. The training of the conversion probability model may include calculating probabilities for transaction conversions based on different combinations of extracted features [a second machine learning model trained on the second set of index data and configured to output a second prediction that describes a probability of user session data…].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Moyer and Price for the same reasons disclosed above in claim 1.
One of ordinary skill in the art would interpret that machine learning algorithms are used to fit modeling parameters to learn data for producing model outputs/outcomes, as noted above.
Prin and Moyer do not expressly teach the use of data processing techniques including removing biases for producing a modified learning model and modified output as claimed generating a modified output of the first/second machine learning model, wherein the modified output comprises fitting the output of the first/second machine learning model … wherein fitting the output comprises removing bias due to few data points; …
Comar teaches the use of data processing techniques including removing biases for producing a modified learning model and modified output as claimed generating a modified output of the first/second machine learning model, wherein the modified output comprises fitting the output of the first/second machine learning model … wherein fitting the output comprises removing bias due to few data points; … (in 3:5-39: Further, a bias such as user behavior or “intent” can impact the relevance of an instance of content to a user. A common query might be submitted by two users, or the same user at different times, but there might be different purposes or intents behind each submission… Approaches presented herein attempt to remove, or at least minimize, the effect of at least some of these biases [generating a modified output of the first/second machine learning model, wherein the modified output comprises fitting the output of the first/second machine learning model … wherein fitting the output comprises removing bias due to few data points] in selecting content to be presented to a user. In at least some embodiments, it can be desirable to rank or select content based on normalized performance values where the bias has been reduced or removed. If sufficient data is available for a particular instance of content, whereby performance can adequately be determined at the relevant positions for the relevant intents, then the effects of those biases for that particular content can be removed from the performance value…; And in 7:23-50: For content, such as pages or descriptions of items offered for consumption, where it may be desirable to prioritize by performance, it can be desirable to train a bias model as discussed herein. As mentioned, this can include training for various types of bias, such as position and intent bias. In this example the content provider environment 306 will at least include a bias model training component or service that includes intent logic 320 for determining intent and training a bias model using the determined intent data. A data store 322 can store the relevant action and position data, which can be analyzed by the intent logic 320 that can use the data to train, test, and/or refine the model over time [generating a modified output of the first/second machine learning model, wherein the modified output comprises fitting the output of the first/second machine learning model … as refined models for generating a modified output ]. When a request for content is received, the content manager 310 can communicate with the search engine 316, or other such system or service, to attempt to determine normalized ranking scores [wherein fitting the output comprises removing bias due to few data points to determine normalized scores as modeled outputs] for the various relevant offers that can be used by the content manager to select and/or position the content to be displayed…)
Comar, Moyer and Price are analogous art because both involve developing information retrieval and processing techniques using machine learning models and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing information processing techniques for selecting content to be presented to a user, as disclosed by Comar with the method of developing information retrieval and modeling techniques to automate the selection of content item objects for display in the graphical environment, as collectively disclosed by Price and Moyer.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Comar, Moyer and Price, as noted above. Doing so to remove, or at least minimize, the effect of at least some biases in selecting content to be presented to a user, (Comar, 3:29-39).
Allowable Subject Matter
Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 4 has been fully considered by the examiner and no prior art could be found to maintain a rejection under 35 USC 102 and/or 35 USC 103. The following is an examiner's statement of reasons for allowance: Claims are considered allowable since when reading the claims in light of the specification, as per MPEP 2111.01, none of the references of record alone or in combination disclose or suggest the limitations found within claim 6 as a whole as recited by the claim limitations. The noted limitations were deemed allowable over the cited prior art:
Claim 4: “…generating a modified output of the first machine learning model, wherein the modified output comprises fitting the output of the first machine learning model, and wherein fitting the output comprises removing bias due to few data points; generating a modified output of the second machine learning model, wherein the modified output comprises fitting the output of the second machine learning model, and wherein fitting the output comprises removing bias due to few data points; leveraging a third machine learning model wherein the third machine learning model inputs the modified output of the first machine learning model; leveraging a fourth machine learning model wherein the fourth machine learning model inputs the modified output of the second machine learning model; generating a propensity score based on the outputs of the third and fourth machine learning models; and wherein generating, by the computing system, the incremental label comprises combining the outputs of the third and fourth machine learning model based on the propensity score.”
The closest prior art are noted above, in addition to:
Chai et al. (US 202303158, hereinafter ‘Chai’): Chai teaches in [0006] In this manner, present embodiments provide technology to improve machine learning systems by removing or reducing biases associated with some features by performing a loss adjustment operation that utilizes a modified or customized loss function during the training of the machine learning model. Additionally and advantageously, embodiments of these technologies can remove biases in machine learning applications…
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Frank et al. (US 20160300252): teaches in [0075] In some embodiments, biases may be represented by values (referred to herein as “bias values”), which quantify the influence of factors of an event on the affective response (e.g., a measurement corresponding to the event). For example, bias values may be random variables (e.g., the bias values may be represented via parameters of distributions). In another example, bias values may be scalar values or multidimensional values such as vectors. As typically used herein, a bias value quantifies the effect a certain factor of an event has on the affective response of the user. In some embodiments, the bias values may be determined from models generated using training data comprising samples describing factors of events and labels derived from measurements of affective response corresponding to the events… [0091] The bias model learner 710 is configured to utilize the samples 708 to generate bias model 712. Depending on the type of approach to modeling biases that is utilized, the bias model learner 710 may utilize the samples 708 in different ways, and/or the bias model 712 may comprise different values…
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/OLUWATOSIN ALABI/Primary Examiner, Art Unit 2129
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