Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the amendments filed 02/17/2026. Claims 1, 9-16, and 20 have been amended, claims 5-8 and 18-19 have been cancelled, claims 21-26 have been added. Claims 1-4, 9-17, and 20-26 are currently pending.
Response to Arguments
Claims 5-8 and 18-19 have been cancelled, therefore the rejections of claims 5-8 and 18-19 no longer stand.
In light of Applicant’s amendment, the 112(b) rejection of claims 10 has been withdrawn.
Applicant’s arguments regarding the 101 rejection have been fully considered but they are not persuasive. Applicant argues that the amended claims recite “a practical application through the recited element of “switching from the product to the modified product with respect to the user account” based on receiving acceptance data associated with a provided offer. Examiner respectfully disagrees and notes that this limitation was rejected under 35 U.S.C. 112(a) as new matter lacking support from Applicant’s original disclosure. The 101 rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended where necessary.
Applicant’s arguments regarding the prior art rejection have been fully considered but are moot because of the new ground(s) of rejection. Applicant argues that the Taslakian reference does not teach “determining a consensus value from performing the multiple iterations based on an averaging of respective values. . .”. Examiner respectfully disagrees and notes that Taslakian teaches in at least paragraph [0148] that as part of the convergence analysis, calculating an aggregate can include taking the average.
Examiner also notes that the Shoshan reference has been brought in to teach the amended limitations directed to providing offering data associated with a predicted cost to a user and receiving an acceptance of that offer from a user. The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended where necessary.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 9-17, and 20-26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites the limitation “based on receiving acceptance data that is associated with the user account and that is indicative of accepting the offer, switching, by the system, from the product to the modified product with respect to the user account”. Applicant’s original disclosure does not provide support for receiving acceptance data indicative of accepting an offer to modify a product or provide support for switching the product to a modified product. For purposes of examination, Examiner is interpreting that actionable actions that are related to dynamic pricing of a product and are customized to a particular user can be provided, based on at least paragraphs [0037], [0040]-[0042], and [0054] of Applicant’s specification.
Claims 9 and 16 recite a similar limitation and are rejected for the same reasons. Dependent claims 2-4, 10-15, 17, and 20-26 are also rejected because they fail to correct the deficiencies of the independent claims on which they depend.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 9-17, and 20-26 are rejected under 35 U.S.C. 101. Claims 1-4 are directed to a system, claims 9-15 are directed to a method, and claims 16-17 and 20-26 are directed to a non-transitory computer-readable medium; therefore, claims 1-4, 9-17, and 20-26 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). However, claims 1-4, 9-17, and 20-26 fall within the judicial exception of an abstract idea, specifically the abstract ideas of “Mental Processes” (including observation, evaluation, and opinion) and “Mathematical Concepts (including mathematical calculations and relationships)”.
Claim 1:
Claim 1 is directed to a system; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 1 recites the following abstract ideas:
performing reconstructive self-supervised learning on a group of features of a user account to produce a complete group of features that are specified for the user account (mental step directed to observation, evaluation – a person could reconstruct an observed group of features in their mind to produce a complete group of features specified for an observed user account by mentally self-supervising the learning of the observed group of features);
performing multiple iterations with respective different input values for the reconstructive self-supervised learning, wherein the respective different input values comprise respective different random seed values (mental step directed to observation, evaluation – a person could reconstruct an observed group of features and determine a value for missing data in their mind to produce a complete group of features specified for an observed user account by mentally self-supervising the learning of the observed group of features multiple times with different observed or mentally determined random seed values);
and determining a consensus value from performing the multiple iterations based on an averaging of respective values of respective features of the complete group of features in the respective multiple iterations, wherein the complete group of features comprises the consensus value (mental step directed to evaluation, judgement – a person could determine a consensus value by averaging an observed or mentally determined complete group of features in their mind having mentally iterated a reconstructive self-supervised learning of the features in their mind).
Claim 1 recites the following additional elements:
a system, comprising: a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations (the system, processor, and memory are all interpreted as generic computer components merely used to apply the claimed abstract idea (see MPEP 2106.05(d) and MPEP 2106.05(f));
training an artificial intelligence risk model to produce a trained model (training an artificial intelligence model is interpreted as well-understood, routine, conventional activity in light of US 20220374810 A1 (Mandal et al), paragraph [0093] of which recites “As is well known, self-supervised learning 630 provides additional inputs to models 620 that facilitate the individual models to modify their weights, correlation approaches, etc. to achieve higher accuracies of the outputs, here, the accuracies in outlier predictions” (see MPEP 2106.05(d). Wherein in the model is a “risk model” is interpreted as the field of use or technological environment for which the model is trained (see MPEP 2106.05(h)),
wherein labeled training data for the training comprises respective features of user accounts and products, and corresponding labels of respective support costs applicable to supporting the products (the labeled training data comprising features and labels corresponding to user accounts, products, and support costs is interpreted as the field of use or technological environment in which the mental step of evaluating features is performed (see MPEP 2106.05(h));
and in response to applying an input to the trained model, wherein the input comprises the complete group of features and a product of the products, producing an output that indicates a predicted cost that corresponds to the input (inputting a group of features and a product and outputting a predicted cost are interpreted as transmitting and receiving data over a network (see MPEP 2016.05(d)(II));
based on the first predicted support cost, making offering data available to the user account, wherein the offering data is indicative of an offer to modify the product to a modified product, and wherein the modified product is associated with a second predicted support cost with respect to the user account (making offering data indicative of an offer to modify a product is interpreted as transmitting and receiving data over a network (see MPEP 2016.05(d)(II));
and based on receiving acceptance data that is associated with the user account and that is indicative of acceptance of the offer, switching from the product to the modified product with respect to the user account (Examiner notes that this limitation is interpreted in light of the 112(a) rejection such that actionable actions that are related to dynamic pricing of a product and are customized to a particular user can be provided. Given this interpretation, providing actionable actions related to dynamic pricing to a user is interpreted as transmitting and receiving data over a network (see MPEP 2016.05(d)(II)).
These additional elements do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claim 9:
Claim 9 is directed to a method; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 9 recites the following abstract ideas:
performing. . .reconstructive self-supervised learning on a group of features of a user account to produce a complete group of features that are specified for the user account comprising determining at least one value for the data that is missing (mental step directed to observation, evaluation – a person could reconstruct an observed group of features and determine a value for missing data in their mind to produce a complete group of features specified for an observed user account by mentally self-supervising the learning of the observed group of features);
performing multiple iterations with respective different input values for the reconstructive self-supervised learning, wherein the respective different input values comprise respective different random seed values (mental step directed to observation, evaluation – a person could reconstruct an observed group of features and determine a value for missing data in their mind to produce a complete group of features specified for an observed user account by mentally self-supervising the learning of the observed group of features multiple times with different observed or mentally determined random seed values);
and determining a consensus value from performing the multiple iterations based on an averaging of respective values of respective features of the complete group of features in the respective multiple iterations, wherein the complete group of features comprises the consensus value (mental step directed to evaluation, judgement – a person could determine a consensus value by averaging an observed or mentally determined complete group of features in their mind having mentally iterated a reconstructive self-supervised learning of the features in their mind).
Claim 9 recites the following additional elements:
performing supervised learning, by a system comprising a processor, with respect to an explainable artificial intelligence risk model to produce a trained model (supervised learning of an artificial intelligence model is interpreted as well-understood, routine, conventional activity in light of US 20220374810 A1 (Mandal et al), paragraph [0093] of which recites “As is well known, self-supervised learning 630 provides additional inputs to models 620 that facilitate the individual models to modify their weights, correlation approaches, etc. to achieve higher accuracies of the outputs, here, the accuracies in outlier predictions” (see MPEP 2106.05(d). Wherein in the model is a “risk model” is interpreted as the field of use or technological environment for which the model is trained (see MPEP 2106.05(h)),
wherein labeled training data for the training comprises respective features of user accounts and products, and corresponding labels of respective costs of support for the products (the labeled training data comprising features and labels corresponding to user accounts, products, and support costs is interpreted as the field of use or technological environment in which the mental step of evaluating features is performed (see MPEP 2106.05(h));
and in response to applying an input to the trained model, wherein the input comprises the complete group of features and a product of the products, outputting, by the system using the trained model, an indication of a predicted support cost that corresponds to the input (inputting a group of features and a product and outputting a predicted cost are interpreted as transmitting and receiving data over a network (see MPEP 2016.05(d)(II));
based on the first predicted support cost, making offering data available to the user account, wherein the offering data is indicative of an offer to modify the product to a modified product, and wherein the modified product is associated with a second predicted support cost with respect to the user account (making offering data indicative of an offer to modify a product is interpreted as transmitting and receiving data over a network (see MPEP 2016.05(d)(II));
and based on receiving acceptance data that is associated with the user account and that is indicative of acceptance of the offer, switching from the product to the modified product with respect to the user account (Examiner notes that this limitation is interpreted in light of the 112(a) rejection such that actionable actions that are related to dynamic pricing of a product and are customized to a particular user can be provided. Given this interpretation, providing actionable actions related to dynamic pricing to a user is interpreted as transmitting and receiving data over a network (see MPEP 2016.05(d)(II)).
These additional elements do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claim 16 is a non-transitory computer-readable medium claim and its limitation is included in claim 9. The only difference is that claim 16 requires a non-transitory computer-readable medium, which is interpreted as a generic computer component merely used to apply the claimed abstract ideas (see MPEP 2106.05(f)) and does not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas. Therefore, claim 16 is rejected for the same reasons as claim 9.
The independent claims are not patent eligible.
Dependent claims 2-4, 10-15, 17, and 20-26 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Claim 2 recites wherein the trained model comprises a causal tree model that is configured to differentiate between first features that are immutable to an entity that utilizes the trained model and second features that are mutable to the entity (the trained model comprising a causal tree model is interpreted as a generic computer component merely used to apply an abstract idea, as a person could mentally create a causal tree model to differentiate between immutable and mutable features in their mind (see MPEP 2106.05(f)).
Claim 3 recites wherein the group of features is represented with a graph that comprises nodes and edges (mental step directed to observation, evaluation – a person could represent a group of features with a graph comprising nodes and edges in their mind, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III)).
Claim 4 recites wherein the complete group of features is represented with a graph that comprises nodes and edges (mental step directed to observation, evaluation – a person could represent a complete group of features with a graph comprising nodes and edges in their mind, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III)).
Claim 10 recites wherein the trained model comprises a causal tree model (the trained model comprising a causal tree model is interpreted as a generic computer component merely used to apply an abstract idea, as a person could mentally create a causal tree model in their mind (see MPEP 2106.05(f)).
Claim 11 recites wherein the data that is missing comprises an information technology capability associated with the user account (wherein the missing data comprises information technology capability for a given user account is interpreted as part of the field of use or technological environment in which the mental step of evaluating features is performed (see MPEP 2106.05(h)).
Claim 12 recites wherein the data that is missing comprises an information technology maturity associated with the user account (wherein the missing data comprises information technology maturity for a given user account is interpreted as part of the field of use or technological environment in which the mental step of evaluating features is performed (see MPEP 2106.05(h)).
Claim 13 recites wherein performing the reconstructive self-supervised learning with respect to the group of features to produce the complete group of features comprises: weighting a first mistake in a first feature of the group of features more than a second mistake in a second feature of the group of features (mental step directed to observation, evaluation – a person could decide to weight a first observed mistake in a group of features more than a second observed mistake in their mind).
Claim 14 recites wherein performing the reconstructive self-supervised learning with respect to the group of features to produce the complete group of features comprises: determining not to mask a feature of the group of features based on the feature being determined to be specified for the user accounts (mental step directed to evaluation, judgement – a person could determine not to mask a given feature in their mind having mentally determined that a given feature is specified for a given user account).
Claim 15 recites wherein performing reconstructive self-supervised learning on the group of features to produce the complete group of features comprises: determining to mask a feature of the group of features based on the feature being determined not to be specified for the user accounts (mental step directed to evaluation, judgement – a person could determine to mask a given feature in their mind having mentally determined that a given feature is not specified for a given user account).
Claim 17 recites wherein the trained model enables provision of a user-understandable explanation regarding why the trained model produced the predicted support cost information (this limitation is interpreted as the intended use or necessary outcome of using the trained model to produce predicted support cost information and does not provide additional patentable weight to claim 15 on which it depends (see MPEP 2103)).
Claim 20 recites wherein determining the consensus value comprises: determining the consensus value from multiple candidate complete groups of features (mental step directed to evaluation, judgement – a person could determine a consensus value in their mind from multiple observed or mentally determined candidate complete groups of features).
Claim 21 is a non-transitory computer-readable medium claim and its limitation is included in claim 2. Claim 21 is rejected for the same reasons as claim 2.
Claim 22 is a non-transitory computer-readable medium claim and its limitation is included in claim 3. Claim 22 is rejected for the same reasons as claim 3.
Claim 23 is a non-transitory computer-readable medium claim and its limitation is included in claim 4. Claim 23 is rejected for the same reasons as claim 4.
Claim 24 is a non-transitory computer-readable medium claim and its limitation is included in claim 11. Claim 24 is rejected for the same reasons as claim 11.
Claim 25 is a non-transitory computer-readable medium claim and its limitation is included in claim 12. Claim 25 is rejected for the same reasons as claim 12.
Claim 26 is a non-transitory computer-readable medium claim and its limitation is included in claim 14. Claim 26 is rejected for the same reasons as claim 14.
Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 1-4, 9-17, and 20-26 are rejected under 35 U.S.C. 103 as being unpatentable over Taslakian et al (US 20230025826 A1, herein Taslakian) in view of Spiegel et al* (“Cost-Sensitive Learning for Predictive Maintenance”, herein Spiegel), in further view of Shoshan** (US 20200380573 A1, herein Shoshan).
*a copy of this document was included with the IDS dated 07/10/2025
**this document was included with the IDS dated 04/28/2026
Regarding claim 1, Taslakian teaches a system, comprising: a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations (Taslakian para. [0009] recites “a computing system may include at least one processor, as well as memory and program instructions. The program instructions may be stored in the memory, and upon execution by the at least one processor, cause the computing system to perform operations”)), comprising:
training an artificial intelligence risk model to produce a trained model (Taslakian para. [0148] recites “Training a neural network usually involves providing the neural network with some form of supervisory training data, namely sets of input values and desired, or ground truth, output values. The training process involves applying the input values from such a set to the neural network and producing associated output values”. Taslakian para. [0004] recites “The embodiments herein overcome these and potentially other problems through the use of graph neural networks (GNNs). Particularly, GNNs are used in self-supervised models that represent the attributes of configuration items (nodes) as vectors in a multi-dimensional feature space. To achieve such a representation, various attributes and/or features (i.e., elements in the associated vectors) are hidden or masked while one or more GNNs are trained to be able to use the available features to predict the features that cannot be observed. Since the values of the predicted features take into account the visible attributes of each node as well as those of its neighboring nodes, they are more useful in anomaly detection than just using the attributes in isolation. Then, various anomaly detection algorithms can be applied to the features as predicted” (i.e., training an artificial intelligence model to produce a model that is trained to identify anomalies, or risks, from input data)),
wherein labeled training data for the training comprises respective features of user accounts and products (Taslakian para. [0083] recites “In order to establish this functionality, a user from managed network 300 might first establish an account with public cloud networks 340, and request a set of associated resources. Then, the user may enter the account information into the appropriate modules of remote network management platform 320. These modules may then automatically discover the manageable resources in the account, and also provide reports related to usage, performance, and billing” (i.e., a user product account). Taslakian para. [0131] recites “FIG. 6 provides an example graph of a network map including applications and devices that make up an email service that supports redundancy and high-availability”. Taslakian para. [0142] recites “Additionally, while FIG. 6 is focused on an example email service, similar network graphs may be generated and displayed for other types of services, such as web services, remote access services, automatic backup services, content delivery services, and so on”. Taslakian para. [0163]-[0164] recite “Consider a graph G=(V, E), where V is the set of nodes in the graph and E is the set of edges between pairs of nodes. It is assumed that there is a feature function F(v) = (F(v)1, ... , F(v)f) ϵ Rf for all v ϵ V, where f is the size of the feature space. Here, the features are assumed to be projections of the attributes of the nodes into the feature space” (i.e., the training data is comprised of features which can associated with user information from a user product account));
performing reconstructive self-supervised learning on a group of features of a user account to produce a complete group of features that are specified for the user account (Taslakian para. [0161] recites “To learn the proper embedding function, self-supervised learning techniques are used. At every step of the algorithm, a feature of the node is hidden or masked. The model guesses the missing information. The assumption is that if the model gets good at this guessing, it will learn how to detect "normal" nodes. As a result, any nodes that are not considered to be "normal" will be classified as anomalous” (i.e., using a self-supervised learning method to reconstruct, or produce a complete group of features for user data with missing information));
comprising performing multiple iterations with respective different input values for the reconstructive self-supervised learning, wherein the respective different input values comprise respective different random seed values (performing multiple iterations with respective different random seed values for the reconstructive self-supervised learning (Taslakian para. [0161] recites “To learn the proper embedding function, self-supervised learning techniques are used. At every step of the algorithm, a feature of the node is hidden or masked. The model guesses the missing information. The assumption is that if the model gets good at this guessing, it will learn how to detect "normal" nodes. As a result, any nodes that are not considered to be "normal" will be classified as anomalous”. Taslakian para. [0191] recites “The MaskGNN model takes a graph in which the embeddings have been determined, such as graph 700, and randomly masks various attributes and then uses the remaining attributes to embed the nodes into a feature space trained to represent the masked attributes. This masking and projecting process iterates some number of times, until a three-dimensional embedding is obtained. The iterations may be complete when the embedding stabilizes in the feature space, e.g., when the Euclidean distance between sequential iterations of embeddings is less than a threshold value. Or, the iterations may complete once a threshold number of iterations have been performed (e.g., 100 or 1000), and the loss function has similarly plateaued. But other stopping conditions could be used” (i.e., performing multiple iterations of self-supervised learning with different randomly determined input values)), and
determining a consensus value from performing the multiple iterations based on an averaging of respective values of respective features of the complete group of features in the respective multiple iterations, wherein the complete group of features comprises the consensus value (Taslakian para. [0148] recites “A loss function is used to evaluate the error between the produced output values and the ground truth output values. This loss function may be a sum of differences, mean squared error, or some other metric. In some cases, error values are determined for all of the sets of input values, and the error function involves calculating an aggregate (e.g., an average) of these values”. Taslakian para. [0150] recites “The training process continues applying the training data to the neural network until the weights converge. Convergence occurs when the error is less than a threshold value or the change in the error is sufficiently small between consecutive iterations of training.” Taslakian para. [0170] recites “The AGGREGATE function uses the previous embeddings of the node v and its neighbors u and combines them. Once this combination is computed, the UPDATE function modifies the output of the AGGREGATE function and uses it to update the node embeddings”. Taslakian para. [0191] recites “This masking and projecting process iterates some number of times, until a three-dimensional embedding is obtained. The iterations may be complete when the embedding stabilizes in the feature space, e.g., when the Euclidean distance between sequential iterations of embeddings is less than a threshold value” (i.e., determining a consensus, or convergence value from the complete group of features output by the model based on a calculating average));
and in response to applying an input to the trained model, wherein the input comprises the complete group of features and a product of the products, producing an output [that indicates a predicted cost] that corresponds to the input (Taslakian para. [0153] recites “Once trained, the neural network can be given new input values and produce corresponding output values that reflect what the neural network has learned by way of training. These output values may be predictions or classifications of the input values” (i.e., producing an output from a trained model that corresponds to an input, such as the input features from paragraph [0163]-[0164])).
However, Taslakian does not explicitly teach wherein labeled training data comprises labels of respective support costs applicable to supporting the products, and an output that indicates a predicted cost.
Spiegel teaches wherein labeled training data comprises labels of respective support costs applicable to supporting the products, and an output that indicates a predicted cost (Spiegel section 1 para. 1-2 recite “Predictive maintenance (PdM) uses machine learning models to forecast failures of mechanical or electronic devices that are prone to degradation. Training a PdM system actually corresponds to finding the model parameters that best fit or explain the observed device behavior. Best fit is usually defined by a cost function, which estimates the model performance by comparing actual and predicted condition for all devices”. Spiegel section 1 para. 4 recites “we propose to incorporate all of the individual economic cost factors into the confusion matrix, which produces an application specific cost function that allows us to optimize the PdM strategy, instead of maximizing decoupled performance measures” (i.e., training data for a model can include label data related to product support costs). Spiegel section 3.2 para. 2 recites “Considering a set of devices that are prone to degradation and consequently fail at times, we assume that each incident is associated with a certain cost. Common cost factors include: (i) ticket creation and processing, (ii) service time and effort for repair or replacement of parts, and (iii) down time during which the device is inoperable. Figure 4 presents the approximate costs for our example use case, including the cost for reactive and predictive maintenance as well as the corresponding savings” (i.e., the output of the model is associated with a predicted cost)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by incorporating the cost-related features from Spiegel into the configuration management database graph from Taslakian. Spiegel and Taslakian are both directed to methods of monitoring and modeling device behavior. Taslakian mentions in at least paragraphs [0005] and [0226] that other kinds of data can be used along with its CMDB data, which one of ordinary skill in the art would recognize includes the cost-related data from Spiegel.
However, the combination of Taslakian and Spiegel does not explicitly teach based on the first predicted cost, making offering data available to the user account, wherein the offering data is indicative of an offer to modify the product to a modified product, and wherein the modified product is associated with a second predicted cost with respect to the user account; and based on receiving acceptance data that is associated with the user account and that is indicative of accepting the offer, switching from the product to the modified product with respect to the user account.
Shoshan teaches based on the first predicted cost, making offering data available to the user account, wherein the offering data is indicative of an offer to modify the product to a modified product, and wherein the modified product is associated with a second predicted cost with respect to the user account (Shoshan fig. 2 and para. [0034] recite “At 206, (3.0) a first reply is returned to the user 104 from the CCP 102. The first reply includes a prediction probability and a corresponding price quote for that prediction probability”. Shoshan para. [0041]-[0042] recite “Returning to 206, if the user 104 accepts the price quote, method 200 proceeds to 212. At 212, (6.0) a second reply is returned to the user 104 from the CCP 102. The second reply includes the prediction probability and the detailed prediction made by the dOBP Broker 110”. Shoshan para. [0046] recites “The service provider may choose to add dynamic amount and adjust the insurance premium (i.e., modify the product) based on demand and supply of the market to maximize its profit from the insurance service” (i.e., offering data associated with predicted costs associated with modifying a product is provided to a user));
and based on receiving acceptance data that is associated with the user account and that is indicative of accepting the offer, switching from the product to the modified product with respect to the user account (Examiner notes that this limitation is interpreted in light of the 112(a) rejection such that actionable actions that are related to dynamic pricing of a product and are customized to a particular user can be provided. Given this interpretation, at least para. [0041]-[0042] of Shoshan teaches that a user can accept a provided offer associated with modifying a product)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by applying the price offering determination methods from Shoshan to the configuration management database system from Taslakian (as modified by Spiegel). Shoshan and Taslakian are both directed to methods of monitoring and modeling device behavior. Taslakian mentions in at least paragraphs [0005] and [0226] that other kinds of data can be used along with its CMDB data, which one of ordinary skill in the art would recognize includes the price offering data from Shoshan.
Regarding claim 2, the combination of Taslakian, Spiegel, and Shoshan teaches the system of claim 1, wherein the trained model comprises a causal tree model that is configured to differentiate between first features that are immutable to an entity that utilizes the trained model and second features that are mutable to the entity (Spiegel section 1 para. 2 recites “Training a PdM (i.e., predictive maintenance) system actually corresponds to finding the model parameters that best fit or explain the observed device behavior. Best fit is usually defined by a cost function, which estimates the model performance by comparing actual and predicted condition for all devices”. Spiegel section 2.3 recites “A popular PdM classifier is the random forest model which is also employed for our empirical evaluation (in Section 4). The individual decision trees can model temporal dependencies, since their decision paths (root node to leaf node) assign an order to the features that were extracted from consecutive time periods” (i.e., a tree model used to determine the causes of device behavior). Taslakian para. [0104] recites “In the identification phase, proxy servers 312 may determine specific details about a classified device. An appropriate set of tasks may be placed in task list 502 for proxy servers 312 to carry out. These tasks may result in proxy servers 312 reading information from the particular device, such as basic input/output system (BIOS) information, serial numbers, network interface information, media access control address(es) assigned to these network interface(s), IP address(es) used by the particular device and so on. Taslakian para. [0105] recites “These tasks may result in proxy servers 312 reading additional information from the particular device, such as processor information, memory information, lists of running processes (applications), and so on” (i.e., different features can correspond to immutable features such as a serial number or to mutable features such as the list of running processes at a given time)).
Regarding claim 3, the combination of Taslakian, Spiegel, and Shoshan teaches the system of claim 1, wherein the group of features is represented with a graph that comprises nodes and edges (Taslakian para. [0163]-[0164] recite “Consider a graph G=(V, E), where V is the set of nodes in the graph and E is the set of edges between pairs of nodes. It is assumed that there is a feature function F(v) = (F(v)1, ... , F(v)f) ϵ Rf for all v ϵ V, where f is the size of the feature space. Here, the features are assumed to be projections of the attributes of the nodes into the feature space” (i.e., input data features are represented by a graph comprising nodes and edges)).
Regarding claim 4, the combination of Taslakian, Spiegel, and Shoshan teaches the system of claim 1, wherein the complete group of features is represented with a graph that comprises nodes and edges (Taslakian para. [0163]-[0164] recite “Consider a graph G=(V, E), where V is the set of nodes in the graph and E is the set of edges between pairs of nodes. It is assumed that there is a feature function F(v) = (F(v)1, ... , F(v)f) ϵ Rf for all v ϵ V, where f is the size of the feature space. Here, the features are assumed to be projections of the attributes of the nodes into the feature space” (i.e., a complete set of input data features is represented by a graph comprising nodes and edges)).
Regarding claim 9, Taslakian teaches a method, comprising: performing supervised learning, by a system comprising at least processor, with respect to an explainable artificial intelligence risk model to produce a trained model (Taslakian para. [0023] recites “Example methods, devices, and systems are described herein”. Taslakian para. [0006] recites “The first example embodiment may also involve one or more processors configured to: (i) select a subset of the configuration items that are connected by way of a subset of the pairwise relationships; (ii) form a graph representation in which the subset of the configuration items is represented as nodes and the subset of the pairwise relationships is represented as edges between pairs of the nodes; (iii) train a graph neural network with k layers on the graph representation, (iv) based a kth of the embeddings, determine that a particular node of the nodes is anomalous; and (v) provide an indication that a particular configuration item represented by the particular node is anomalous”. Taslakian para. [0162] recites “although unsupervised learning is used to explain the embodiments, herein, aspects of supervised learning may be incorporated as well. For instance, some attributes may be labeled with an indication of whether they are indicative of normality or an anomaly, and the learning procedures may take these indications into account” (i.e., a system comprising at least a processor for performing a method to use supervised learning to produce a trained artificial intelligence model capable of determining anomalies, or risks, associated with input data)),
wherein labeled training data for the training comprises respective features of user accounts and products (Taslakian para. [0083] recites “In order to establish this functionality, a user from managed network 300 might first establish an account with public cloud networks 340, and request a set of associated resources. Then, the user may enter the account information into the appropriate modules of remote network management platform 320. These modules may then automatically discover the manageable resources in the account, and also provide reports related to usage, performance, and billing” (i.e., a user product account). Taslakian para. [0131] recites “FIG. 6 provides an example graph of a network map including applications and devices that make up an email service that supports redundancy and high-availability”. Taslakian para. [0142] recites “Additionally, while FIG. 6 is focused on an example email service, similar network graphs may be generated and displayed for other types of services, such as web services, remote access services, automatic backup services, content delivery services, and so on”. Taslakian para. [0163]-[0164] recite “Consider a graph G=(V, E), where V is the set of nodes in the graph and E is the set of edges between pairs of nodes. It is assumed that there is a feature function F(v) = (F(v)1, ... , F(v)f) ϵ Rf for all v ϵ V, where f is the size of the feature space. Here, the features are assumed to be projections of the attributes of the nodes into the feature space” (i.e., the training data is comprised of features which can associated with user information from a user product account));
performing, by the system, reconstructive self-supervised learning on a group of features of a user account to produce a complete group of features that are specified for the user account comprising, determining at least one value for the data that is missing (Taslakian para. [0161] recites “To learn the proper embedding function, self-supervised learning techniques are used. At every step of the algorithm, a feature of the node is hidden or masked. The model guesses the missing information. The assumption is that if the model gets good at this guessing, it will learn how to detect "normal" nodes. As a result, any nodes that are not considered to be "normal" will be classified as anomalous” (i.e., using a self-supervised learning method to reconstruct, or produce a complete group of features for user data with missing information));
performing multiple iterations with respective different input values for the reconstructive self-supervised learning, wherein the respective different input values comprise respective different random seed values (Taslakian para. [0161] recites “To learn the proper embedding function, self-supervised learning techniques are used. At every step of the algorithm, a feature of the node is hidden or masked. The model guesses the missing information. The assumption is that if the model gets good at this guessing, it will learn how to detect "normal" nodes. As a result, any nodes that are not considered to be "normal" will be classified as anomalous”. Taslakian para. [0191] recites “The MaskGNN model takes a graph in which the embeddings have been determined, such as graph 700, and randomly masks various attributes and then uses the remaining attributes to embed the nodes into a feature space trained to represent the masked attributes. This masking and projecting process iterates some number of times, until a three-dimensional embedding is obtained. The iterations may be complete when the embedding stabilizes in the feature space, e.g., when the Euclidean distance between sequential iterations of embeddings is less than a threshold value. Or, the iterations may complete once a threshold number of iterations have been performed (e.g., 100 or 1000), and the loss function has similarly plateaued. But other stopping conditions could be used” (i.e., performing multiple iterations of self-supervised learning with different randomly determined input values)), and
determining a consensus value from performing the multiple iterations based on an averaging of respective values of respective features of the complete group of features in the respective multiple iterations, wherein the complete group of features comprises the consensus value (Taslakian para. [0148] recites “A loss function is used to evaluate the error between the produced output values and the ground truth output values. This loss function may be a sum of differences, mean squared error, or some other metric. In some cases, error values are determined for all of the sets of input values, and the error function involves calculating an aggregate (e.g., an average) of these values”. Taslakian para. [0150] recites “The training process continues applying the training data to the neural network until the weights converge. Convergence occurs when the error is less than a threshold value or the change in the error is sufficiently small between consecutive iterations of training.” Taslakian para. [0170] recites “The AGGREGATE function uses the previous embeddings of the node v and its neighbors u and combines them. Once this combination is computed, the UPDATE function modifies the output of the AGGREGATE function and uses it to update the node embeddings”. Taslakian para. [0191] recites “This masking and projecting process iterates some number of times, until a three-dimensional embedding is obtained. The iterations may be complete when the embedding stabilizes in the feature space, e.g., when the Euclidean distance between sequential iterations of embeddings is less than a threshold value” (i.e., determining a consensus, or convergence value from the complete group of features output by the model based on a calculating average));
and in response to applying an input to the trained model, wherein the input comprises the complete group of features and a product of the products, outputting, by the system using the trained model, an indication [of a predicted support cost] that corresponds to the input (Taslakian para. [0153] recites “Once trained, the neural network can be given new input values and produce corresponding output values that reflect what the neural network has learned by way of training. These output values may be predictions or classifications of the input values” (i.e., producing an output from a trained model that corresponds to an input, such as the input features from paragraph [0163]-[0164])).
However, Taslakian does not explicitly teach wherein labeled training data comprises labels of respective support costs applicable to supporting the products, and an output that indicates a predicted cost.
Spiegel teaches wherein labeled training data comprises labels of respective support costs applicable to supporting the products, and an output that indicates a predicted cost (Spiegel section 1 para. 1-2 recite “Predictive maintenance (PdM) uses machine learning models to forecast failures of mechanical or electronic devices that are prone to degradation. Training a PdM system actually corresponds to finding the model parameters that best fit or explain the observed device behavior. Best fit is usually defined by a cost function, which estimates the model performance by comparing actual and predicted condition for all devices”. Spiegel section 1 para. 4 recites “we propose to incorporate all of the individual economic cost factors into the confusion matrix [7, 11], which produces an application specific cost function that allows us to optimize the PdM strategy, instead of maximizing decoupled performance measures” (i.e., training data for a model can include label data related to product support costs). Spiegel section 3.2 para. 2 recites “Considering a set of devices that are prone to degradation and consequently fail at times, we assume that each incident is associated with a certain cost. Common cost factors include: (i) ticket creation and processing, (ii) service time and effort for repair or replacement of parts, and (iii) down time during which the device is inoperable. Figure 4 presents the approximate costs for our example use case, including the cost for reactive and predictive maintenance as well as the corresponding savings” (i.e., the output of the model is associated with a predicted cost)).
See claim 1 for motivation to combine.
However, the combination of Taslakian and Spiegel does not explicitly teach based on the first predicted cost, making offering data available to the user account, wherein the offering data is indicative of an offer to modify the product to a modified product, and wherein the modified product is associated with a second predicted cost with respect to the user account; and based on receiving acceptance data that is associated with the user account and that is indicative of accepting the offer, switching from the product to the modified product with respect to the user account.
Shoshan teaches based on the first predicted cost, making offering data available to the user account, wherein the offering data is indicative of an offer to modify the product to a modified product, and wherein the modified product is associated with a second predicted cost with respect to the user account (Shoshan fig. 2 and para. [0034] recite “At 206, (3.0) a first reply is returned to the user 104 from the CCP 102. The first reply includes a prediction probability and a corresponding price quote for that prediction probability”. Shoshan para. [0041]-[0042] recite “Returning to 206, if the user 104 accepts the price quote, method 200 proceeds to 212. At 212, (6.0) a second reply is returned to the user 104 from the CCP 102. The second reply includes the prediction probability and the detailed prediction made by the dOBP Broker 110”. Shoshan para. [0046] recites “The service provider may choose to add dynamic amount and adjust the insurance premium (i.e., modify the product) based on demand and supply of the market to maximize its profit from the insurance service” (i.e., offering data associated with predicted costs associated with modifying a product is provided to a user));
and based on receiving acceptance data that is associated with the user account and that is indicative of accepting the offer, switching from the product to the modified product with respect to the user account (Examiner notes that this limitation is interpreted in light of the 112(a) rejection such that actionable actions that are related to dynamic pricing of a product and are customized to a particular user can be provided. Given this interpretation, at least para. [0041]-[0042] of Shoshan teaches that a user can accept a provided offer associated with modifying a product)).
See claim 1 for motivation to combine.
Regarding claim 10, the combination of Taslakian, Spiegel, and Shoshan teaches the method of claim 9, wherein the trained model comprises a causal tree model (Spiegel section 1 para. 2 recites “Training a PdM (i.e., predictive maintenance) system actually corresponds to finding the model parameters that best fit or explain the observed device behavior. Best fit is usually defined by a cost function, which estimates the model performance by comparing actual and predicted condition for all devices”. Spiegel section 2.3 recites “A popular PdM classifier is the random forest model which is also employed for our empirical evaluation (in Section 4). The individual decision trees can model temporal dependencies, since their decision paths (root node to leaf node) assign an order to the features that were extracted from consecutive time periods” (i.e., a tree model used to determine the causes of device behavior)).
Regarding claim 11, the combination of Taslakian, Spiegel, and Shoshan teaches the method of claim 9, wherein the data that is missing comprises an information technology capability associated with the user account (Taslakian para. [0161] recites “To learn the proper embedding function, self-supervised learning techniques are used. At every step of the algorithm, a feature of the node is hidden or masked. The model guesses the missing information. The assumption is that if the model gets good at this guessing, it will learn how to detect "normal" nodes. As a result, any nodes that are not considered to be "normal" will be classified as anomalous”. Taslakian para. [0083] recites “In order to establish this functionality, a user from managed network 300 might first establish an account with public cloud networks 340, and request a set of associated resources. Then, the user may enter the account information into the appropriate modules of remote network management platform 320. These modules may then automatically discover the manageable resources in the account, and also provide reports related to usage, performance, and billing”. Taslakian para. [0104] recites “In the identification phase, proxy servers 312 may determine specific details about a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase. For example, if a device was classified as LINUX®, a set of LINUX®-specific probes may be used. Likewise, if a device was classified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probes may be used. These tasks may result in proxy servers 312 reading information from the particular device, such as basic input/output system (BIOS) information, serial numbers, network interface information, media access control address(es) assigned to these network interface(s), IP address(es) used by the particular device and so on. This identification information may be stored as one or more configuration items in CMDB (i.e., configuration management database) 500” (i.e., missing data imputed by the self-supervised learning algorithm can be related to user capability, or performance associated with a given operating system)).
Regarding claim 12, the combination of Taslakian, Spiegel, and Shoshan teaches the method of claim 9, wherein the data that is missing comprises an information technology maturity associated with the user account (Taslakian para. [0161] recites “To learn the proper embedding function, self-supervised learning techniques are used. At every step of the algorithm, a feature of the node is hidden or masked. The model guesses the missing information. The assumption is that if the model gets good at this guessing, it will learn how to detect "normal" nodes. As a result, any nodes that are not considered to be "normal" will be classified as anomalous”. Taslakian para. [0083] recites “In order to establish this functionality, a user from managed network 300 might first establish an account with public cloud networks 340, and request a set of associated resources. Then, the user may enter the account information into the appropriate modules of remote network management platform 320. These modules may then automatically discover the manageable resources in the account, and also provide reports related to usage, performance, and billing”. Taslakian para. [0104] recites “In the identification phase, proxy servers 312 may determine specific details about a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase. For example, if a device was classified as LINUX®, a set of LINUX®-specific probes may be used. Likewise, if a device was classified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probes may be used. These tasks may result in proxy servers 312 reading information from the particular device, such as basic input/output system (BIOS) information, serial numbers, network interface information, media access control address(es) assigned to these network interface(s), IP address(es) used by the particular device and so on. This identification information may be stored as one or more configuration items in CMDB (i.e., configuration management database) 500” (i.e., missing data imputed by the self-supervised learning algorithm can be related to the specific device version, or maturity, associated with a user)).
Regarding claim 13, the combination of Taslakian, Spiegel, and Shoshan teaches the method of claim 9, wherein performing the reconstructive self-supervised learning with respect to the group of features to produce the complete group of features comprises: weighting a first mistake in a first feature of the group of features more than a second mistake in a second feature of the group of features (Taslakian para. [0148]-[0149] recite “error values are determined for all of the sets of input values, and the error function involves calculating an aggregate (e.g., an average) of these values. Once the error is determined, the weights on the connections are updated in an attempt to reduce the error. In simple terms, this update process should reward "good" weights and penalize "bad" weights. Thus, the updating should distribute the "blame" for the error through the neural network in a fashion that results in a lower error for future iterations of the training data” (i.e., the error, or mistake, corresponding with a feature in a group of features can be weighted more than the error associated with a second feature from the group of features)).
Regarding claim 14, the combination of Taslakian, Spiegel, and Shoshan teaches the method of claim 9, wherein performing the reconstructive self-supervised learning with respect to the group of features to produce the complete group of features comprises: determining not to mask a feature of the group of features based on the feature being determined to be specified for the user accounts (Taslakian para. [0176] recites “various techniques may be used to identify which attributes are the most "important" to consider when attempting to detect anomalies. These may include the attributes that contribute the most to the variability in attribute values between nodes, and could be identified by way of principle component analysis (PCA), for example”. Taslakian para. [0182] recites “The HideGNN model takes a graph in which the embeddings have been determined, such as graph 700, and repeatedly hides an attribute and then uses the remaining attributes to embed the nodes into a one-dimensional feature space trained to represent the hidden attribute. FIG. 7B depicts a step of this process for graph 700” (i.e., determining which feature not to mask can be based at least in part on the specification, or importance of the feature)).
Regarding claim 15, the combination of Taslakian, Spiegel, and Shoshan teaches the method of claim 9, wherein performing reconstructive self-supervised learning on the group of features to produce the complete group of features comprises: determining to mask a feature of the group of features based on the feature being determined not to be specified for the user accounts (Taslakian para. [0176] recites “various techniques may be used to identify which attributes are the most "important" to consider when attempting to detect anomalies. These may include the attributes that contribute the most to the variability in attribute values between nodes, and could be identified by way of principle component analysis (PCA), for example”. Taslakian para. [0182] recites “The HideGNN model takes a graph in which the embeddings have been determined, such as graph 700, and repeatedly hides an attribute and then uses the remaining attributes to embed the nodes into a one-dimensional feature space trained to represent the hidden attribute. FIG. 7B depicts a step of this process for graph 700” (i.e., determining which feature to mask can be based at least in part on the specification, or importance of the feature)).
Claim 16 is a non-transitory computer-readable medium claim and its limitation is included in claim 9. The only difference is that claim 16 requires a non-transitory computer-readable medium (Taslakian para. [0008] recites “an article of manufacture may include a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations in accordance with the first and/or second example embodiment”). Therefore, claim 16 is rejected for the same reasons as claim 9.
Regarding claim 17, the combination of Taslakian, Spiegel, and Shoshan teaches the non-transitory computer-readable medium of claim 16, wherein the trained model enables provision of a user-understandable explanation regarding why the trained model produced the predicted support cost information (Examiner’s Note: this limitation is interpreted as the intended use or necessary outcome of using the trained model to produce predicted support cost information and does not provide additional patentable weight to claim 15 on which it depends (see MPEP 2103). However, Examiner notes that at least section 2.3 of Spiegel states that the random forest tree model can be used to provide an easily interpretable explanation of a prediction).
Regarding claim 20, the combination of Taslakian, Spiegel, and Shoshan teaches the non-transitory computer-readable medium of claim 16, wherein determining the consensus value comprises: determining the consensus value from multiple candidate complete groups of features (Taslakian para. [0150] recites “The training process continues applying the training data to the neural network until the weights converge. Convergence occurs when the error is less than a threshold value or the change in the error is sufficiently small between consecutive iterations of training.” Taslakian para. [0170] recites “The AGGREGATE function uses the previous embeddings of the node v and its neighbors u and combines them. Once this combination is computed, the UPDATE function modifies the output of the AGGREGATE function and uses it to update the node embeddings”. Taslakian para. [0191] recites “This masking and projecting process iterates some number of times, until a three-dimensional embedding is obtained. The iterations may be complete when the embedding stabilizes in the feature space, e.g., when the Euclidean distance between sequential iterations of embeddings is less than a threshold value” (i.e., determining a consensus, or convergence value from the multiple complete groups of features output by multiple iterations of the model)).
Claim 21 is a non-transitory computer-readable medium claim and its limitation is included in claim 2. Claim 21 is rejected for the same reasons as claim 2.
Claim 22 is a non-transitory computer-readable medium claim and its limitation is included in claim 3. Claim 22 is rejected for the same reasons as claim 3.
Claim 23 is a non-transitory computer-readable medium claim and its limitation is included in claim 4. Claim 23 is rejected for the same reasons as claim 4.
Claim 24 is a non-transitory computer-readable medium claim and its limitation is included in claim 11. Claim 24 is rejected for the same reasons as claim 11.
Claim 25 is a non-transitory computer-readable medium claim and its limitation is included in claim 12. Claim 25 is rejected for the same reasons as claim 12.
Claim 26 is a non-transitory computer-readable medium claim and its limitation is included in claim 14. Claim 26 is rejected for the same reasons as claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11645575 B2 (Daly et al) teaches a method for recommending actions to explain and improve machine learning predictions, wherein the recommended actions can be linked to product pricing analysis.
US 20230102786 A1 (Garapati et al) teaches a method for determining causal associations between events in an information technology landscape using a causal tree model and supervised machine learning.
US 20220165007 A1 (Friedman et al) teaches a method for utilizing a causal tree model to analyze estimated risks of a given action and explain potential resource costs and efficiency associated with the action.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/L.M.F./ Examiner, Art Unit 2147
/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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