DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is responsive to remarks filed 04/13/2026. Claims 1, 2, 4, 14, 15, 19, and 20 are amended. No claim has been cancelled, and there are no new claims.
Claims 1–20 are pending for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/05/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
Response to Arguments
In reference to 35 USC § 101
Applicant’s arguments, filed on 04/13/2026, with respect to the § 101 rejections have been fully considered, and along with the amendments, are persuasive.
Examiner notes that while the claims recite several limitations that are abstract ideas (mental concepts), the claims as a whole are not directed to an abstract idea. Applicant has amended the claims, which recite a specific collection of hardware to accomplish the steps of the limitations (“receive a model-agnostic qiuery statement including (i) a specified machine learning model, (ii) input data, and (iii) one or more parameters, wherein the machine learning model is selected from among a plurality of different models, wherein the input data includes a table comprising a plurality of data items, wherein each data item of the table is associated with a plurality of feature values: and wherein each parameter of the one or more parameters specifies a respective parameter of a model explainability function applied to the machine learning model”) are not abstract ideas, respectively (see MPEP 2106.04(a)(1)). Thus, these limitations must be considered additional elements to the abstract idea. Examiner notes that these additional element integrates the abstract idea into a practical application because the entire claim amounts to a detailed recitation of how a specific set of hardware processes information to achieve the claimed methods of model explainability function applied to machine learning models (as opposed to a broad recitation of broads steps performed at a high level of generality), and the specific method of steps recited in the additional element amounts to an improvement to the functioning of a computer/field, as set forth by MPEP 2106.05(a)), which states “the claim must include the components or steps of the invention that provide the improvement described in the specification.” Pursuant to this requirement set forth by the MPEP, Examiner points out that the Specification states in at least [0003–0006]: “Through the availability of the model explanation data, the platform-driven models can operate in less of a ‘black-box’ manner, without sacrificing user accessibility or depth in user-facing features available on the platform. Furthermore, the platform is scalable. According to aspects of the disclosure, the platform can implement processing shards maintaining local servers for the duration of time needed to execute received query statements. The local servers can process incoming data according to a variety of different specified model explainability functions, which can be user-selected or automatically provided based on the type of machine learning model received as input. The platform can serve query responses in a distributed and parallel manner, even when the selected data is made up of many table rows potentially having millions of feature values” Thus, the additional elements reflects the improvement set forth and explains what the resulting improvement is.
In reference to 35 USC § 103
Applicant’s arguments filed on 04/13/2026, with respect to the newly amended limitations have been fully considered but are not persuasive.
Furthermore, the arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Thus, examiner maintains the § 103 rejections.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, recites: “a model-agnostic qiuery statement” which should be written as “a model-agnostic query statement”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1–6 and 8–20 are rejected under 35 U.S.C. 103 as being unpatentable over Tomsett et al., (US 20200402658 A1), hereinafter “Tomsett”, in view of Szeto et al., (US 20180018590 A1), hereinafter “Szeto”.
Regarding claim 1, Tomsett teaches:
a system comprising (Tomsett ¶0004, 0021: “One or more embodiments are directed to computer processing systems, computer-implemented methods, apparatus and/or computer program products that facilitate efficiently, effectively, and automatically (e.g., without direct human involvement) choosing explanations to present to a machine learning (ML) system user”):
one or more memory devices, and one or more processors configured to (Tomsett ¶0004: “According to an aspect of the present invention there is provided a system, comprising: a memory that stores computer executable components; and a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory, wherein the computer executable components comprise: an explanation selection component that accesses a plurality of explanation generation components to generate different types of explanations of a machine learning output”):
receive a model-agnostic qiuery statement (Tomsett ¶¶0031–0033, 0060, 0092: “receives input data 108”)
including (i) a specified machine learning model (Tomsett ¶¶0031: “The machine learning system 100 can comprise one or more pre-trained machine learning models (not shown) and a user interface (not shown). The machine learning models can be fixed or can be updated continually”),
(ii) input data, and (iii) one or more parameters selected using one or more query statements, wherein the machine learning model is selected from among a plurality of different models (Tomsett ¶0087: “In one example, the system 100 can be a neural network (e.g., an artificial neural network, a machine learning neural network, etc.) associated with interconnected deep learning that provides an estimated functional model from a set of unknown inputs. In another example, the system 100 can be associated with a Bayesian network that provides a graphical model that represents relationships between a set of variables (e.g., a set of random variables). In yet another example, the system 100 can be associated with a hidden Markov model that models data over a continuous time interval and/or outputs a probability distribution. However, the system 100 can alternatively be associated with a different machine learning system”),
wherein the input data includes a table comprising a plurality of data items (Tomsett ¶¶0059–0065, 0087: “An example is given below of a possible instantiation of the user description data and the explanation generator metadata for two explanation generators. In the example, the user description contains the user emotional state already inferred from the biological and behavioral measurements, so these measurements are not mentioned directly. It also contains several “user preferences” set both by the entity and the system administrator”) and,
wherein each parameter of the one or more parameters specifies a respective parameter of a model explainability function applied to the machine learning model (Tomsett Table 3, ¶0060: “The explanation metadata contains information for different explanation generator types: the Local Interpretable Model-Agnostic Explanations (LIME) method, and an explanation-by-example method (which selects 1-9 examples from the training data that are the most similar examples to the user data, and returns these to show the entity). In this example instantiation, the metadata indicates the type of explanation, its characteristics, how the explanation could be displayed to the entity, and how big a cognitive effort is required by the entity to understand the explanation”)
process the input data through a machine learning model to generate a model output (Tomsett Fig. 2, ¶0030–0041, 0045: “The computer-implemented method can actuate 208 the one or more selected generators to provide the explanation of the machine learning system output of the generator(s). Alternatively, if the generator metadata is based on the already generated explanation, the explanation is retrieved. The one or more explanations are provided to the entity via a user interface for evaluation of the machine learning system output with the explanation”; and
generate, using at least the model output and the model explainability function structured according to the one or more parameters, a plurality of feature attributions for the input data (Tomsett ¶0086: “In some embodiments, the system 100 can employ learning (e.g., machine learning) to determine features, classifications and/or patterns associated with data provided to the explanation generation components 102.sub.1-N”); and
wherein each respective feature attribution is a score indicating how much of an impact the corresponding feature value has on the model output of the machine learning model (Tomsett Table 3 “feature_importance”, ¶0102: “The explanation selection component 112 can repeatedly form groups within the explanation generation components 102.sub.1-N until a defined criterion associated with the input data 108 and/or the explanation generation components 102.sub.1-N is satisfied. By way of example, but not limitation, the defined criterion can be a number of groups formed by the explanation selection component 112 reaching a defined value or an error value (e.g., a training error value, etc.) associated with the input data 108 reaching a defined value. Accordingly, collaborative groups of explanations can be dynamically synchronized by the explanation selection component 112 for parallel learning during a deep learning process”).
Tomsett does not appear to explicitly teach:
wherein each data item of the table is associated with a plurality of feature values; and
wherein each feature attribution is associated with a respective corresponding feature value of the plurality of feature values.
However, Szeto teaches:
wherein each data item of the table is associated with a plurality of feature values (Szeto ¶¶0063–0064: “Once a description of the metadata (reflecting the private data) is available, model instructions can then be configured to make reference to the private data, thereby providing instructions regarding selection of inputs to the machine learning systems. In cases in which a query by a researcher is ongoing and continuously updated, e.g., at periodic intervals, the system can be configured to recognize the metadata, determine whether key parameters are present, and then cause generation and transmission of model instructions corresponding to the query set up by the researcher. In other cases, for novel queries, a researcher may generate the model instructions in a manual or semi-automated manner. For new queries, the system may be configured to provide recommendations regarding types of data to analyze in order to generate model instructions for such new queries … Metadata from each private data server can be provided to the global model server. The metadata returns the attribute space (and not raw or private data)”—[(emphasis added) wherein metadata is used to configure instructions that makes reference to, based on the attribute space (i.e., attributes associated with feature values), the private data]); and
wherein each feature attribution is associated with a respective corresponding feature value of the plurality of feature values (Szeto Fig. 2, ¶¶0069, 0073: “An active model represents a model that is dynamic and can be updated based on various circumstances. In some embodiments, the trained actual model 240 is updated in real-time, on a daily, weekly, bimonthly, monthly, quarterly, or annual basis. As new information is made available (e.g., to update model instructions 230, shifts in time, new or corrected private data 222, etc.), an active model will be further updated. In such cases, the active model carries metadata that describes the state of the model with respect to its updates. The metadata can include attributes describing one or more of the following: a version number, date updated, amount of new data used for the update, shifts in model parameters, convergence requirements, or other information. Such information provides for managing large collections of models over time, where each active model can be treated as a distinct manageable object” and “In the example shown, modeling engine 226 analyzes the training data set used to create trained actual model 240 in order to generate an understanding of the nature of the training data set as represented by private data distributions 250. Thus, modeling engine 226 is further configurable to generate a plurality of private data distributions 250 that represent the local private data in aggregate used as a training set to create trained actual model 240. In some embodiments, modeling engine 226 can automatically execute many different algorithms (e.g., regressions, clustering, etc.) on the training data sets in an attempt to discover, possibly in an unsupervised manner, relationships within the data that can be represented via private data distributions 250. Private data distributions 250 describe the overall nature of the private data training set”—[wherein each metadata parameter (i.e., corresponding feature value) is managed to describe the state of the model (i.e., associated feature attribution) based on the input data]).
The methods of Tomsett, the teachings of Szeto, and the instant application are analogous art because they pertain to using machine learning models to generate data for explaining model features.
It would be obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the methods of Tomsett with the teachings of Szeto to provide for feature attributions that are scores representing the impact each parameter has on the output of the model. One would be motivated to do so to improve queries for specific characteristics of input data, increasing model efficiency (Szeto ¶0063: “In cases in which a query by a researcher is ongoing and continuously updated, e.g., at periodic intervals, the system can be configured to recognize the metadata, determine whether key parameters are present, and then cause generation and transmission of model instructions corresponding to the query set up by the researcher. In other cases, for novel queries, a researcher may generate the model instructions in a manual or semi-automated manner. For new queries, the system may be configured to provide recommendations regarding types of data to analyze in order to generate model instructions for such new queries”).
Regarding claim 2, Tomsett in view of Szeto teaches all the limitations of claim 1.
Szeto teaches:
wherein a feature attribution for a respective feature value of the input data corresponds to a value measuring the degree of importance the respective feature value has in generating the model output (Szeto Fig. 4, ¶0095: “Similarity score 490 can be calculated through various techniques and according to the goals of a researcher as outlined in the corresponding model instructions. In some embodiments, similarity score 490 can be calculated based on the differences among the model parameters (e.g., parameters differences 480) … In cases where the parameters can have widely different definitions, the values can be normalized or weighted so that each difference contributes equally or according to their importance”—[(emphasis added)]).
The same motivation used to combine Tomsett with Szeto in claim 1 is equally applicable to claim 2.
Regarding claim 3, Tomsett in view of Szeto teaches all the limitations of claim 1.
Tomsett teaches:
wherein the one or more processors are part of a network of distributed devices (Tomsett ¶0146: “The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network”), and
wherein in generating the feature attributions, the one or more processors are further configured to: launch a local server on a distributed device of the network (Tomsett Table 3 “Black_Box”, “Local”, ¶0060: “The explanation metadata contains information for different explanation generator types: the Local Interpretable Model-Agnostic Explanations (LIME) method, and an explanation-by-example method (which selects 1-9 examples from the training data that are the most similar examples to the user data, and returns these to show the entity). In this example instantiation, the metadata indicates the type of explanation, its characteristics, how the explanation could be displayed to the entity, and how big a cognitive effort is required by the entity to understand the explanation”—[(emphasis added)]); and
generate the feature attributions using the local server (Tomsett ¶0061: “In the example instantiation, the explanation selection method uses a set of rules and a constraint satisfaction solver to select a suitable subset of explanation generators to choose. The constraints could be soft or hard; for example, an entity with restricted privileges should have a hard constraint that they should be unable to see the training data, but a user preference for a particular type of explanation method is a soft constraint, as it could be overruled given other constraints”).
Regarding claim 4, Tomsett in view of Szeto teaches all the limitations of claim 3.
Tomsett teaches:
wherein in generating the feature attributions using the local server, the one or more processors are further configured to: process respective portions of the input data using the model explainability function to generate the feature attributions (Tomsett Figs. 1–2, ¶0037–0039: “The explanation selection component 112 can be integral to a machine learning system 100 or provided as a remote system for providing output explanation of the machine learning system 100. A machine learning system output is generated 201 in response to receiving an entity input to be modeled by the machine learning system”).
Regarding claim 5, Tomsett in view of Szeto teaches all the limitations of claim 3.
Szeto teaches:
wherein in processing the input data through the machine learning model, the one or more processors initialize a first process (Szeto Fig. 2, ¶0068: “Modeling engine 226 creates trained actual model 240 as a function of the results set representing at least some of private data 222. This is achieved by modeling engine 226 training the desired implementation of machine learning algorithm 295 on the private data 222 results set. In view that the desired machine learning algorithm 295 could include a wide variety of possible algorithms, model instructions 230 can include instructions that define the condition under which training occurs. For example, the conditions could include a number of iterations or epochs to execute on the training data, learning rates, convergence requirements, time limits for training, initial conditions, sensitivity, specificity or other types of conditions that are required or optional. Convergence requirements can include first order derivatives such as “rates of change”, second order derivatives such as “acceleration”, or higher order time derivatives or even higher order derivatives of other dimensions in the attribute space of the data, etc.”—[(emphasis added)]); and
wherein the one or more processors are further configured to launch a sub-process from the first process to launch the local server (Szeto Fig. 2, ¶0071: “When actual model parameters 245 are packaged and transmitted to remote non-private computing devices or to peer private data servers, the remote non-private or peer computing devices can accurately reconstruct trained actual model 240 via instantiating a new instance of trained actual model 240 from the parameters locally at the remote computing device without requiring access to private data 222”—[(emphasis added) wherein the first process (i.e., trained actual model) packages and transmits (i.e., launches a sub-process) to the remote server to instantiate a new instance of the model (i.e., launch the local server)]) and
generate the feature attributions (Szeto Fig. 2, ¶0071: “When actual model parameters 245 are packaged and transmitted to remote non-private computing devices or to peer private data servers, the remote non-private or peer computing devices can accurately reconstruct trained actual model 240 via instantiating a new instance of trained actual model 240 from the parameters locally at the remote computing device without requiring access to private data 222”—[(emphasis added)]).
The same motivation used to combine Tomsett with Szeto in claim 1 is equally applicable to claim 5.
Regarding claim 6, Tomsett in view Szeto teaches all the limitations of claim 5.
Tomsett teaches:
wherein the model agnostic query statement is a first query statement (Tomsett Figs. 1–3, ¶0092–0093: “In an aspect, the input data 108 can be stored in a database to which the explanation generation components 1021-N and/or the user interface 350 are communicatively coupled via the answer generation component 106. Therefore, in some embodiments, the explanation generation components 1021-N can receive the input data 108 from a remote location via the user interface 350.”—[(emphasis added)]) and
wherein the one or more processors are further configured to: receive one or more second model-agnostic query statements (Tomsett Figs. 1–3, ¶0098: “In an example, the explanation generation component 102.sub.1 can generate output data based on the first portion of the input data 108 (e.g., during the first act for the deep learning process), the explanation generation component 102.sub.2 can generate output data based on the second portion of the input data 108”);
determine, from the one or more second model-agnostic query statements, that the one or more second model-agnostic query statements comprise one or more second parameters for generating second feature attributions (Tomsett Figs. 1–3, ¶0098, 0106: “During a first act for a deep learning process, the explanation generation components can generate respective output data based on processing the respective portions of the input data 108 received by the answer generation component 106. After performing the first act for the deep learning process, one or more of the (or, in some embodiments, each of the) explanation generation components 1021-N can store respective output data in one or more respective memories operatively coupled to the explanation generation components 1021-N. In an example, the explanation generation component 1021 can generate output data based on the first portion of the input data 108 (e.g., during the first act for the deep learning process), the explanation generation component 1022 can generate output data based on the second portion of the input data 108 (e.g., during the first act for the deep learning process), etc. As such, in some embodiments, the explanation generation components 101-N can store output data generated in response to the input data 108 rather than storage of the output data at a centralized entity (for example, at the explanation selection component 112)” and “A subsequent act for the deep learning process can be a subsequent processing act that is performed by explanation generation components 1021-N after a first process act associated with the input data 108. For example, explanation generation component 1021 can generate other output data based on the first portion of the input data 108 and/or data received from at least one other explanation generation component (e.g., during a subsequent act for the deep learning process), explanation generation component 1022 can generate other output data based on the second portion of the input data 108 and/or data received from at least one other explanation generation component (e.g., during the subsequent act for the deep learning process), etc. The explanation generation component 1021 can generate updated output data (e.g., new output data) based on the input data 108 (e.g., a first portion of the input data 108) and/or the other output data generated by the explanation generation component 1022.”—[(emphasis added)]); and
launch the sub-process from the first process to launch the local server and generate the second feature attributions in response to the determination that the one or more second model-agnostic query statements comprise the one or more second parameters for generating the second feature attributions (Tomsett Figs. 1–3, ¶0107–0108: “In response to selection of a second group by the explanation selection component 112, the explanation generation component 1021 and the explanation generation component 1023 can exchange data. For example, the explanation generation component 1021 can transmit the updated output data to the explanation generation component 1023. Furthermore, the explanation generation component 1023 can transmit the data (e.g., communication data) to explanation generation component 1021. The explanation generation component 1021 can then perform further processing based on the other data generated by the explanation generation component 1023”—[(emphasis added)]).
Szeto teaches:
the feature attributions are first feature attributions (Szeto ¶0062–0064: “Modeling engine 226 receives model instructions to create a trained actual model 240 from at least some local private data 222 and according to an implementation of machine learning algorithm 295 ... In cases in which a query by a researcher is ongoing and continuously updated, e.g., at periodic intervals, the system can be configured to recognize the metadata, determine whether key parameters are present, and then cause generation and transmission of model instructions corresponding to the query set up by the researcher … the metadata returns the attribute space (and not raw or private data). Based on this information, the researcher generating the machine learning task(s) can configure model instructions for a particular private data server to analyze a particular set of private data”—[(emphasis added) wherein the model engine is configured to generate model instructions for a particular server based on the attribute (i.e., first attributes corresponding to one or more first feature values e.g., private data server)]).
The same motivation used to combine Tomsett with Szeto in claim 1 is equally applicable to claim 6.
Regarding claim 8, Tomsett in view of Szeto teaches all the limitations of claim 1.
Tomsett teaches:
wherein the input data is training data or validation data used to train the machine learning model (Tomsett Figs. 1–3, ¶0099: “The explanation selection component 112 can select an explanation from the explanation generation components 1021-N, in one example, based on a training process (e.g., a deep learning process, a mini-batch training process, etc.) associated with the input data 108”).
Regarding claim 9, Tomsett in view of Szeto teaches all the limitations of claim 1.
Tomsett teaches:
wherein the one or more processors are further configured to train the machine learning model (Tomsett ¶0004: “According to an aspect of the present invention there is provided a system, comprising: a memory that stores computer executable components; and a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory, wherein the computer executable components comprise: an explanation selection component that accesses a plurality of explanation generation components to generate different types of explanations of a machine learning output”), and
wherein the model-agnostic query statements select data for processing through the trained machine learning model to generate one or more model predictions (Tomsett ¶0099: “For example, the explanation selection component 112 can determine an explanation from the explanation generation components 1021-N based on a rate (e.g., a processing speed) in which training data is processed by explanation generation components 1021-N”).
Regarding claim 10, Tomsett in view of Szeto teaches all the limitations of claim 1.
Tomsett teaches:
wherein the feature attributions are first feature attributions (Tomsett Figs. 1–3, ¶0096: “In yet another example, the explanation generation components 1021-N can collectively determine features, classifications and/or patterns associated with the input data 108. In yet another example, the explanation generation components 1021-N can collectively perform a set of processing acts and/or a deep learning process associated with the input data 108”—[(emphasis added)])); and
wherein the one or more processors are further configured to: generate second feature attributions for training data used to train the machine learning model (Tomsett Figs. 1–3, ¶0104–0109: “The defined criterion associated with the third group can correspond to the defined criterion associated with the first group and/or the second group. Alternatively, the defined criterion associated with the third group can be different than (e.g., distinct from) the defined criterion associated with the first group and/or the second group. The defined criterion can be associated with the input data 108, the updated output data generated by the explanation generation component 1021, the explanation generation component 1021 and/or the other explanation generation component from the explanation generation components 1021-N”);
generate global feature attributions for the trained model (Tomsett Table 3 “universal”, Figs. 1–3, ¶0060–0061, 0067: “In the example instantiation, the explanation selection method uses a set of rules and a constraint satisfaction solver to select a suitable subset of explanation generators to choose. The constraints could be soft or hard; for example, an entity with restricted privileges should have a hard constraint that they should be unable to see the training data, but a user preference for a particular type of explanation method is a soft constraint, as it could be overruled given other constraints” and “The explanation selection component 112 can include an explanation generator accessing component 410 that accesses a plurality of explanation generators (as shown in FIGS. 3A and 3B) that generates different types of explanation of a machine learning output. The explanation generators can provide different types of explanations including, as examples only: text-based explanations; visual explanations; in-depth explanations; summary explanations; analogy explanations; and counterfactual explanations”—[(emphasis added)),
wherein in generating the global feature attributions the one or more processors are configured to aggregate the second feature attributions (Tomsett Figs. 1–3, ¶0123: “Further yet, one or more components and/or sub-components can be combined into a single component providing aggregate functionality. The components can also interact with one or more other components not specifically described herein for the sake of brevity, but known by those of skill in the art”—[(emphasis added)]); and
store, in the one or more memory devices, the global feature attribution (Tomsett Figs. 1–3, ¶0097: “Output data generated by the explanation generation components 102.sub.1-N can be stored locally at the explanation generation components 102.sub.1-N (e.g., output data generated by the explanation generation components 102.sub.1-N can be stored in at least one memory associated with or comprised within the explanation generation components 102.sub.1-N).”).
Regarding claim 11, Tomsett in view of Szeto teaches all the limitations of claim 10.
Tomsett teaches:
wherein in generating the first feature attributions, the one or more processors are configured to receive at least a portion of the stored global feature attributions (Tomsett Figs. 1–3, ¶0098: “During a first act for a deep learning process, the explanation generation components can generate respective output data based on processing the respective portions of the input data 108 received by the answer generation component 106”).
Regarding claim 12, Tomsett in view of Szeto teaches all the limitations of claim 1.
Tomsett teaches:
wherein the one or more processors are further configured to output the feature attributions for display on a display device coupled to the one or more processors (Tomsett Figs. 1–3, ¶0058, 0101: “The device can connect to a smartphone wirelessly using Bluetooth” and “In a non-limiting example, the explanation selection component 112 can assign the explanation generation component 102.sub.1 and the explanation generation component 102.sub.3 to the same group in response to an explanation that the explanation generation component 102.sub.1 and the explanation generation component 102.sub.3 are implemented on a common computing device (e.g., a common processor or a common set of processors). The one or more defined criteria (which can be the same as or different from one another) employed by the explanation selection component 112 to form groups of explanation generation components 102.sub.1-N can vary during the deep learning process associated with the explanation generation components 102.sub.1-N”).
Regarding claim 13, Tomsett in view of Szeto teaches all the limitations of claim 1.
Szeto teaches:
wherein the one or more query statements are one or more Structured Query Language (SQL) statements (Szeto ¶0067: “For example, the query could include a SQL query properly formatted from the requirements in model instructions 230 to access or retrieve the attributes or tables stored in private data 222”).
The same motivation used to combine Tomsett with Szeto in claim 1 is equally applicable to claim 13.
Regarding claim 14, Tomsett teaches:
a computer-implemented method comprising (Tomsett ¶0142: “The present invention can be a system, a method, an apparatus and/or a computer program product at any possible technical detail level of integration”).
With respect to the remaining limitations in claim 14, although varying in scope, the remaining limitations of claim 14 are substantially the same as the limitations of claim 1, respectively. Thus, claim 14 is rejected using the same reasoning and analysis as claim 1 above.
Regarding claims 15–18, although varying in scope, the limitations of claims 15–18 are substantially the same as the limitations of claims 2 and 9–11, respectively. Thus, claims 15–18 are rejected using the same reasoning and analysis as claims 2 and 9–11 above, respectively.
Regarding claim 19, Tomsett teaches:
one or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising (Tomsett ¶0142: “The present invention can be a system, a method, an apparatus and/or a computer program product at any possible technical detail level of integration”).
With respect to the remaining limitations in claim 19, although varying in scope, the remaining limitations of claim 19 are substantially the same as the limitations of claim 1, respectively. Thus, claim 19 is rejected using the same reasoning and analysis as claim 1 above.
Regarding claim 20, although varying in scope, the limitations of claim 20 are substantially the same as the limitations of claim 2. Thus, claim 20 is rejected using the same reasoning and analysis as claim 2 above.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Tomsett in view of Szeto, and further in view of Januschowski et al., (US-11120361-B1), hereinafter “Januschowski”.
Regarding claim 7, Tomsett in view of Szeto teaches all the limitations of claim 1.
Tomsett in view of Szeto does not appear to explicitly teach:
wherein the input data comprises one or more inputs, each input corresponding to a row of a database stored on the one or more memory devices selected using the one or more query statements.
However, Januschowski teaches:
wherein the input data comprises one or more inputs, each input corresponding to a row of a database stored on the one or more memory devices selected using the one or more query statements (Januschowski Col. 8, line 63 – Col. 9 line 5: “The data integration components 140 of the scalable pipeline layer 135 may comprise, for example, multi-data-source tools 148 to combine data from a variety of data sources, raw data pre-processing tools 146, data-cleansing tools 142 as well as tools for performing distributed joins among observation data records as needed in the depicted embodiment. Such joins may be used, for example, to construct distributed de-normalized tables in which each row comprises all the available data for a given item of an inventory or catalog”—[(emphasis added)]).
The methods of Tomsett, the teachings of Januschowski, and the instant application are analogous art because they pertain to using machine learning to tailor data based on provided queries and databases.
It would be obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the methods of Tomsett with the teachings of Januschowski to provide for data that corresponds to database rows used to store data for analysis. One would be motivated to do so to tailor models to make predictions for specific characteristics of input data (Januschowski Col. 7, line 59 – Col. 8, line 13: “In some embodiments, respective subsets of an input data set may be used to train different learning algorithms to be used for predicting time series values at the machine learning service. For example, some models may be tailored towards specific groups of items that share characteristics such as seasonality, or towards groups of items within a particular price range. In at least one embodiment, the machine learning service may allow clients to provide training specifications which include routing directives for selecting respective subsets of the input observations and associated feature metadata for training different learning algorithms. In some embodiments, the machine learning service may allow clients to provide prediction aggregation specifications indicating how samples of results obtained from multiple trained learning algorithms should be combined to generate output probabilistic forecasts for various items”).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS SHINE whose telephone number is (571)272-2512. The examiner can normally be reached M-F, 11a-7p ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached on (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.B.S./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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