DETAILED ACTION
1. This is a Non-Final Office Action Correspondence in response to U.S. Application No. 17/79092 filed on July 11, 2022.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The Information Disclosure Statement filed on July 11, 2022 was reviewed and accepted by the Examiner.
Claim Rejections - 35 U.S.C. §101
4. 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.
5. Claims 1-34 and 46-56 are rejected under 35 USC 101 as directed to an abstract idea without significantly more.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 1, specifically claim 1 recites "tracking user queries for a plurality of users” in the context of this claim encompasses the user mentally tracking queries associated with a user, “correlating the user queries between two or more users of the plurality of users” in the context of this claim encompasses the user mentally grouping the queries, “determining that the user queries of the two or more users of the plurality of users are correlated” in the context of this claim encompasses the user mentally grouping the queries as being related, “and classifying the user queries of the at least two users as a workflow neighbor, wherein the workflow neighbor defines a set of time series data or features” in the context of this claim encompasses the user mentally classifying the queries as being related with particular data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 1 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 2, specifically claim 2 recites "tracking a user query for an additional user” in the context of this claim encompasses the user mentally tracking queries associated with a user, “determining that the user query is correlated to the workflow neighbor” in the context of this claim encompasses the user mentally grouping the queries, “generating a recommendation to view at least one additional time series data or feature to the additional user based on determining that the user query is correlated to the workflow neighbor, wherein the at least one additional time series data or feature is within the workflow neighbor” in the context of this claim encompasses the user using a pen and paper to generate a recommendation. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 2 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “and displaying the recommendation on a user interface” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “and displaying the recommendation on a user interface”.
For example, “and displaying the recommendation on a user interface”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 3, specifically claim 3 recites " and increasing a correlation score associated with the workflow neighbor when the additional user views at least the one additional time series data or feature” in the context of this claim encompasses the user increasing a score associated with data associated when a user views the data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 3 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “receiving, at the user interface, feedback from the additional user for the recommendation” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “receiving, at the user interface, feedback from the additional user for the recommendation”.
For example, “receiving, at the user interface, feedback from the additional user for the recommendation”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 4, specifically claim 4 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 4 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “obtaining inputs from the plurality of users on a user interface, wherein the inputs comprise requests for one or more time series data element or a feature of the time series data” is seen as MPEP 2106.05(g) iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “obtaining inputs from the plurality of users on a user interface, wherein the inputs comprise requests for one or more time series data element or a feature of the time series data”.
For example, “obtaining inputs from the plurality of users on a user interface, wherein the inputs comprise requests for one or more time series data element or a feature of the time series data”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a computer-implemented method.
With respect to Step 2A Prong one dependent claim, 5, specifically claim 5 recites " wherein tracking the user queries comprises tracking an order of inputs of each user of the plurality of users” in the context of this claim encompasses the user mentally classifying the queries as being related with particular data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 5 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 6, specifically claim 6 recites "and wherein tracking the user queries comprises tracking metadata associated with the time series data or the features of the time series data” in the context of this claim encompasses the user mentally tracking queries associated with a user. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 6 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “wherein the queries comprise time series data or features of time series data” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the queries comprise time series data or features of time series data,”.
For example, “wherein the queries comprise time series data or features of time series data”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 7, specifically claim 7 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 7 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “wherein the metadata comprises at least one of an identification of the type of time series data or features, a type of sensor, a location of a sensor, or a unit of measurement of a sensor” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the metadata comprises at least one of an identification of the type of time series data or features, a type of sensor, a location of a sensor, or a unit of measurement of a sensor”.
For example, “wherein the metadata comprises at least one of an identification of the type of time series data or features, a type of sensor, a location of a sensor, or a unit of measurement of a sensor”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 8, specifically claim 8 recites “wherein correlating the user queries comprises identifying metadata that matches between the user queries of the two or more users” in the context of this claim encompasses the user mentally grouping the queries. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 8 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 9, specifically claim 9 recites “wherein correlating the user queries comprises identifying the same type of data within the user queries of the two or more users, wherein the metadata for the same type of data is different” in the context of this claim encompasses the user mentally grouping the queries. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 9 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 10, specifically claim 10 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 10 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “wherein correlating the user queries comprises scoring the correlation using normalized correlation ratings or Pearson's coefficient” is seen as MPEP 2106.05(f) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein correlating the user queries comprises scoring the correlation using normalized correlation ratings or Pearson's coefficient”.
For example, “wherein correlating the user queries comprises scoring the correlation using normalized correlation ratings or Pearson's coefficient”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (ii).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one independent claim, 11, specifically claim 11 recites "track user queries for a plurality of users” in the context of this claim encompasses the user mentally tracking queries associated with a user, “correlate the user queries between two or more users of the plurality of users” in the context of this claim encompasses the user mentally grouping the queries, “determine that the user queries of the two or more users of the plurality of users are correlated” in the context of this claim encompasses the user mentally grouping the queries as being related, “and classify the user queries of the at least two users as a workflow neighbor, wherein the workflow neighbor defines a set of time series data or features” in the context of this claim encompasses the user mentally classifying the queries as being related with particular data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 11 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 12, specifically claim 12 recites "track a user query for an additional user” in the context of this claim encompasses the user mentally tracking queries associated with a user, “determine that the user query is correlated to the workflow neighbor” in the context of this claim encompasses the user mentally grouping the queries, “generate a recommendation to view at least one additional time series data or feature to the additional user based on determining that the user query is correlated to the workflow neighbor, wherein the at least one additional time series data or feature is within the workflow neighbor” in the context of this claim encompasses the user using a pen and paper to generate a recommendation. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 12 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “and display the recommendation on a user interface” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “and display the recommendation on a user interface”.
For example, “and display the recommendation on a user interface”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 13, specifically claim 13 recites " and increase a correlation score associated with the workflow neighbor when the additional user views at least the one additional time series data or feature” in the context of this claim encompasses the user increasing a score associated with data associated when a user views the data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 13 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “receive, at the user interface, feedback from the additional user for the recommendation” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “receive, at the user interface, feedback from the additional user for the recommendation”.
For example, “receive, at the user interface, feedback from the additional user for the recommendation”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 14, specifically claim 14 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 14 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “obtain inputs from the plurality of users on a user interface, wherein the inputs comprise requests for one or more time series data element or a feature of the time series data” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “obtain inputs from the plurality of users on a user interface, wherein the inputs comprise requests for one or more time series data element or a feature of the time series data”.
For example, “obtain inputs from the plurality of users on a user interface, wherein the inputs comprise requests for one or more time series data element or a feature of the time series data”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 2A Prong one dependent claim, 15, specifically claim 15 recites " track an order of inputs of each user of the plurality of users” in the context of this claim encompasses the user mentally tracking queries associated with a user. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 15 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 16, specifically claim 16 recites "and wherein tracking the user queries comprises tracking metadata associated with the time series data or the features of the time series data” in the context of this claim encompasses the user increasing a score associated with data associated when a user views the data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 16 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “wherein the queries comprise time series data or features of time series data” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the queries comprise time series data or features of time series data”.
For example, “wherein the queries comprise time series data or features of time series data”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 17, specifically claim 17 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 17 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “wherein the metadata comprises at least one of an identification of the type of time series data or features, a type of sensor, a location of a sensor, or a unit of measurement of a sensor” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the metadata comprises at least one of an identification of the type of time series data or features, a type of sensor, a location of a sensor, or a unit of measurement of a sensor”.
For example, “wherein the metadata comprises at least one of an identification of the type of time series data or features, a type of sensor, a location of a sensor, or a unit of measurement of a sensor”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 2A Prong one dependent claim, 18, specifically claim 18 recites " wherein correlating the user queries comprises identifying metadata that matches between the user queries of the two or more users” in the context of this claim encompasses the user mentally grouping queries associated with a user. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 18 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 2A Prong one dependent claim, 19, specifically claim 19 recites "wherein the processor is further configured to: identify the same type of data within the user queries of the two or more users, wherein the metadata for the same type of data is different” in the context of this claim encompasses the user mentally identifying queries associated with a user. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 19 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 20, specifically claim 20 recites “wherein the processor is further configured to: score the correlation using normalized correlation ratings or Pearson's coefficient” in the context of this claim encompasses the scoring values using a Pearson’s coefficient. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 20 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 2A Prong one dependent claim, 21, specifically claim 21 recites, “correlating the plurality of features to determine similarity scores between two or more features of the plurality of features” in the context of this claim encompasses the user mentally grouping the queries, “determining a plurality of features in a data signal” in the context of this claim encompasses the user mentally grouping the queries as being related, “presenting information related to at least a first feature of the plurality of features” in the context of this claim encompasses the user mentally classifying the queries as being related with particular data.
“and determining, using a first machine learning model, information related to at least a second feature, wherein the determination is made using the similarity scores and the feedback in the first machine learning model” in the context of this claim encompasses the user using a score to learn features. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “receiving feedback on the information” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “receiving feedback on the information”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 22, specifically claim 22 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “presenting information related to the at least second feature with the information related to at least the first feature” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “presenting information related to the at least second feature with the information related to at least the first feature”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 23, specifically claim 23 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the feedback comprises a selection of information related to the second feature” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the feedback comprises a selection of information related to the second feature”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 2A Prong one dependent claim, 24, specifically claim 24 recites, “clustering the information related to at least the first feature and the information related to the second feature to form a feature set of information” in the context of this claim encompasses the user mentally grouping the queries, in the context of this claim encompasses the user mentally presenting the queries as being related with particular data.
These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “presenting the feature set when the first feature or the second feature are detected in the data signal” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “presenting the feature set when the first feature or the second feature are detected in the data signal”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 25, specifically claim 25 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the data signal comprises one or more sensor signals from one or more sensors” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the data signal comprises one or more sensor signals from one or more sensors”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 26, specifically claim 26 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the data signal comprises multidimensional data” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the data signal comprises multidimensional data”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 27, specifically claim 27 recites, no new abstract ideas.
These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “presenting the feature set when the first feature or the second feature are detected in the data signal” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “presenting the feature set when the first feature or the second feature are detected in the data signal”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one dependent claim 28, specifically claim 28 recites, no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 28 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “generate an application interface, wherein the application interface displays one or more features” is seen as MPEP 2106.05(g) iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93;
For example, “receive a plurality of selections of the plurality of features, where the selections comprise one or more feedback signals associated with selections of one or more features of the plurality of features” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “train, using at least the plurality of selections, a machine learning model to determine one or more workflows, wherein the one or more workflows defines a set of features of the plurality of features” is seen as MPEP 2106.05(g) iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93;
For example “present at least one of the one or more workflows on the application interface” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “present at least one of the one or more workflows on the application interface”.
For example, “and displaying the recommendation on a user interface”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a computer-implemented method.
With respect to Step 2A Prong one dependent claim, 29, specifically claim 29 recites "wherein the one or more workflows further define an order of presentation of the set of features” in the context of this claim encompasses the user mentally classifying the queries as being related with particular data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 29 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a computer-implemented method.
With respect to Step 2A Prong one dependent claim, 30, specifically claim 30 recites, no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 30 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
“receive a second plurality of selections from the application interface” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
“generate, using a second machine learning model, one or more recommendations for a feature of the plurality of feature, wherein the one or more recommendations are based on the second plurality of selections received through the application interface” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “receive a second plurality of selections from the application interface”, “generate, using a second machine learning model, one or more recommendations for a feature of the plurality of feature, wherein the one or more recommendations are based on the second plurality of selections received through the application interface”.
For example, “receive a second plurality of selections from the application interface” is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
For example, “generate, using a second machine learning model, one or more recommendations for a feature of the plurality of feature, wherein the one or more recommendations are based on the second plurality of selections received through the application interface”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one dependent claim 31, specifically claim 31 recites, “and identify, using the trained second machine learning model, one or more additional features of the plurality of features to be included in the one or more recommendations” in the context of this claim encompasses the user mentally grouping the queries as being related.
Accordingly, the claim recites an abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 31 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “receive a second plurality of selections from the application interface” is seen as MPEP 2106.05(g) iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93;
For example, “train the second machine learning model using the second plurality of selections” is seen as MPEP 2106.05(g) iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93;
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “receive a second plurality of selections from the application interface”.
For example, “receive a second plurality of selections from the application interface”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
For example, “train the second machine learning model using the second plurality of selections” is seen as MPEP 2106.05(g) iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 32, specifically claim 32 recites no new abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 32 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “wherein the second machine learning model uses reinforcement learning with the plurality of selections to identify the one or more additional features to be included in the one or more recommendations” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the second machine learning model uses reinforcement learning with the plurality of selections to identify the one or more additional features to be included in the one or more recommendations”.
For example, “wherein the second machine learning model uses reinforcement learning with the plurality of selections to identify the one or more additional features to be included in the one or more recommendations”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 33, specifically claim 33 recites “determine a similarity score between the plurality of features, wherein the machine learning model is trained using the plurality of selections and the similarity scores” in the context of this claim encompasses the user mentally grouping the queries as being related. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 33 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “identify, using the plurality of features, a plurality of features from a sensor signal” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “identify, using the plurality of features, a plurality of features from a sensor signal”.
For example, “identify, using the plurality of features, a plurality of features from a sensor signal”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 34, specifically claim 34 recites, no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the features are determined based on one or more sensor inputs” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the features are determined based on one or more sensor inputs”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one dependent claim, 35, specifically claim 35 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 35 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “identify, using the first machine learning model, one or more features in the sensor data signal”, “receive a sensor data signal from one or more sensors”, “wherein the insight engine is configured to: “execute a first machine learning model”, “and generate an indication of the one or more features on an application interface”; “a learning engine, wherein the learning engine is configured to: receive a plurality of selections on the application interface”, “train, using at least the plurality of selections, a second machine learning model to determine a one or more sub-features associated with the one or more features, and present the one or more sub-features on the application interface”, is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “identify, using the plurality of features, a plurality of features from a sensor signal”.
For example, “identify, using the plurality of features, a plurality of features from a sensor signal”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 46, specifically claim 46 recites " correlating the plurality of features to determine similarity scores between two or more features of the plurality of features”, in the context of this claim encompasses the user associating the scores based upon similar features. “and determining, using a first machine learning model, information related to at least a second feature, wherein the determination is made using the similarity scores in the first machine learning model” in the context of this claim encompasses the user determining second features based upon scores from a first set of features. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 46 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “determining a plurality of features in a data signal”, is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “presenting information related to at least a first feature of the plurality of features” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “determining a plurality of features in a data signal”, “presenting information related to at least a first feature of the plurality of features”.
For example, “determining a plurality of features in a data signal”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
For example, “presenting information related to at least a first feature of the plurality of features”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 47, specifically claim 47 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 47 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “presenting information related to the at least second feature with the information related to at least the first feature”, is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “presenting information related to the at least second feature with the information related to at least the first feature”
For example, “presenting information related to the at least second feature with the information related to at least the first feature”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 48, specifically claim 48 recites " clustering the information related to at least the first feature and the information related to the second feature to form a feature set of information” in the context of this claim encompasses the user grouping information together based upon the features. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 48 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “and presenting the feature set when the first feature or the second feature are detected in the data signal”, is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “and presenting the feature set when the first feature or the second feature are detected in the data signal”.
For example, “and presenting the feature set when the first feature or the second feature are detected in the data signal”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 49, specifically claim 49 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the data signal comprises one or more sensor signals from one or more sensors” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the data signal comprises one or more sensor signals from one or more sensors”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 50, specifically claim 50 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the data signal comprises multidimensional data” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the data signal comprises multidimensional data”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 51, specifically claim 51 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “presenting or more solutions based on the correlating of the plurality of features” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “presenting or more solutions based on the correlating of the plurality of features”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 52, specifically claim 52 recites “determining, using a first machine learning model, the occurrence of an event based on the plurality of features” in the context of this claim encompasses the identifying features, “identifying the event based on the feedback”, in the context of this claim encompasses the user identifying the features, “labeling a training data set with the identification of the event, wherein the training data set comprises the plurality of features” in the context of this claim encompasses the user using a pen and paper to label the features, “and updating the first machine learning model with the training data set” in the context of this claim encompasses the user using a pen and paper to update the model. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 52 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example "presenting a plurality of features in a data signal on an application interface” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “receiving feedback on the plurality of features presented on the application interface” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “presenting a plurality of features in a data signal on an application interface”, “receiving feedback on the plurality of features presented on the application interface”
For example, “presenting a plurality of features in a data signal on an application interface” is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “receiving feedback on the plurality of features presented on the application interface” is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 53, specifically claim 53 recites “identifying, using the first machine learning model, two or more features of the plurality of features that are related”, in the context of this claim encompasses the user identifying the features. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can using a pen and paper track queries, identify related queries between users, group the queries together and classify the queries based upon the related data. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 53 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 54, specifically claim 54 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the data signal comprises one or more sensor signals from one or more sensors” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the data signal comprises one or more sensor signals from one or more sensors”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 55, specifically claim 55 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the data signal comprises multidimensional data” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the data signal comprises multidimensional data”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 56, specifically claim 56 recites no new abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “presenting or more solutions using the updated first machine learning model.” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “presenting or more solutions using the updated first machine learning model”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
Claim Rejections - 35 USC § 102
5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
6. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
7. Claim(s) 1-9, 11-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Anwar et al. U.S. Patent No. 11,892,996 (herein as ‘Anwar’).
As to claim 1 Anwar teaches a method for capturing user workflows, the method comprising:
tracking user queries for a plurality of users (Col. 110 Lines 45-50 Anwar discloses a user query, Col. 98 Lines 5-9 Anwar discloses queries are a part of datasets. Col, 97 Lines 20-37 Anwar disclose tracking datasets used by the user);
correlating the user queries between two or more users of the plurality of users, determining that the user queries of the two or more users of the plurality of users are correlated (Col. 193 Lines 15-27 Anwar discloses that multiple users can be associated with the same queries);
and classifying the user queries of the at least two users as a workflow neighbor, wherein the workflow neighbor defines a set of time series data or features (Col. 82 Lines 60-68 and Col. 83 Lines 1-5 Anwar discloses the queries are associated with a group of users based upon metadata information and content).
As to claim 2 Anwar teaches teach and every limitation of claim 1.
In addition Anwar teaches further comprising: tracking a user query for an additional user, determining that the user query is correlated to the workflow neighbor (Col. 82 Lines 60-68 Anwar discloses identifying additional users based upon the data referenced by the query);
generating a recommendation to view at least one additional time series data or feature to the additional user based on determining that the user query is correlated to the workflow neighbor (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries);
wherein the at least one additional time series data or feature is within the workflow neighbor (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries);
and displaying the recommendation on a user interface (Col 94 Lines 15-17 Anwar displaying the information to the users).
As to claim 3 Anwar teaches teach and every limitation of claim 2.
In addition Anwar teaches further comprising: receiving, at the user interface, feedback from the additional user for the recommendation; and increasing a correlation score associated with the workflow neighbor when the additional user views at least the one additional time series data or feature (Col. 105 Lines 13-20 Anwar discloses the user can review recommended datasets and the dataset may provide additional results).
As to claim 4 Anwar teaches teach and every limitation of claim 1.
In addition Anwar teaches wherein tracking user queries comprises: obtaining inputs from the plurality of users on a user interface, wherein the inputs comprise requests for one or more time series data element or a feature of the time series data (Col. 211 Lines 5-10 Anwar discloses the query is associated with time search criteria).
As to claim 5 Anwar teaches teach and every limitation of claim 1.
In addition Anwar teaches wherein tracking the user queries comprises tracking an order of inputs of each user of the plurality of users (Col. 44 Lines 25-26 Anwar discloses the records are ordered based upon sequence of ingestion).
As to claim 6 Anwar teaches teach and every limitation of claim 1.
In addition Anwar teaches wherein the queries comprise time series data or features of time series data, and wherein tracking the user queries comprises tracking metadata associated with the time series data or the features of the time series data (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries).
As to claim 7 Anwar teaches teach and every limitation of claim 6.
In addition Anwar teaches wherein the metadata comprises at least one of an identification of the type of time series data or features, a type of sensor, a location of a sensor, or a unit of measurement of a sensor (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries. The features is the frequently used queries).
As to claim 8 Anwar teaches teach and every limitation of claim 6.
In addition Anwar teaches wherein correlating the user queries comprises identifying metadata that matches between the user queries of the two or more users (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the queries from the same tenant).
As to claim 9 Anwar teaches teach and every limitation of claim 6.
In addition Anwar teaches wherein correlating the user queries comprises identifying the same type of data within the user queries of the two or more users, wherein the metadata for the same type of data is different (Col. 97 Lines 31-37 Anwar discloses the system can group users based upon the accessing the same application).
As to claim 11 Anwar teaches a system comprising:
a processor (Col. 29 Lines 3-5 Anwar discloses a processor);
a memory (Col. 37 Lines 39-42 Anwar discloses a memory);
wherein the memory stores a program, that when executed on the processor, configures the processor to:
track user queries for a plurality of users (Col. 110 Lines 45-50 Anwar discloses a user query, Col. 98 Lines 5-9 Anwar discloses queries are a part of datasets. Col, 97 Lines 20-37 Anwar disclose tracking datasets used by the user);
correlate the user queries between two or more users of the plurality of users, determine that the user queries of the two or more users of the plurality of users are correlated (Col. 193 Lines 15-27 Anwar discloses that multiple users can be associated with the same queries);
and classify the user queries of the at least two users as a workflow neighbor, wherein the workflow neighbor defines a set of time series data or features (Col. 82 Lines 60-68 and Col. 83 Lines 1-5 Anwar discloses the queries are associated with a group of users based upon metadata information and content).
As to claim 12 Anwar teaches teach and every limitation of claim 11.
In addition Anwar teaches wherein the processor is further configured to: track a user query for an additional user; determine that the user query is correlated to the workflow neighbor (Col. 82 Lines 60-68 Anwar discloses identifying additional users based upon the data referenced by the query);
generate a recommendation to view at least one additional time series data or feature to the additional user based on determining that the user query is correlated to the workflow neighbor (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries);
wherein the at least one additional time series data or feature is within the workflow neighbor (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries);
and display the recommendation on a user interface (Col 94 Lines 15-17 Anwar displaying the information to the users).
As to claim 13 Anwar teaches teach and every limitation of claim 12.
In addition Anwar teaches wherein the processor is further configured to: receive, at the user interface, feedback from the additional user for the recommendation; and increase a correlation score associated with the workflow neighbor when the additional user views at least the one additional time series data or feature (Col. 105 Lines 13-20 Anwar discloses the user can review recommended datasets and the dataset may provide additional results).
As to claim 14 Anwar teaches teach and every limitation of claim 11.
In addition, Anwar teaches wherein the processor is further configured to: obtain inputs from the plurality of users on a user interface, wherein the inputs comprise requests for one or more time series data element or a feature of the time series data (Col. 211 Lines 5-10 Anwar discloses the query is associated with time search criteria).
As to claim 15 Anwar teaches teach and every limitation of claim 11.
In addition Anwar teaches wherein the processor is further configured to: track an order of inputs of each user of the plurality of users (Col. 44 Lines 25-26 Anwar discloses the records are ordered based upon sequence of ingestion).
As to claim 16 Anwar teaches teach and every limitation of claim 11.
In addition Anwar teaches wherein the queries comprise time series data or features of time series data, and wherein tracking the user queries comprises tracking metadata associated with the time series data or the features of the time series data (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries).
As to claim 17 Anwar teaches teach and every limitation of claim 16.
In addition Anwar teaches wherein the metadata comprises at least one of an identification of the type of time series data or features, a type of sensor, a location of a sensor, or a unit of measurement of a sensor (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries. The features are the frequently used queries).
As to claim 18 Anwar teaches teach and every limitation of claim 16.
In addition Anwar teaches wherein correlating the user queries comprises identifying metadata that matches between the user queries of the two or more users (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the queries from the same tenant).
As to claim 19 Anwar teaches teach and every limitation of claim 16.
In addition Anwar teaches wherein the processor is further configured to: identify the same type of data within the user queries of the two or more users, wherein the metadata for the same type of data is different (Col. 97 Lines 31-37 Anwar discloses the system can group users based upon the accessing the same application).
6. Claim(s) 21-28, 30, 31 and 35-56 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Balasubramanian et al. U.S. Patent Application Publication No. 2019/0205950 (herein as ‘Bala’).
As to claim 21 Bala teaches a method comprising:
determining a plurality of features in a data signal (Par. 0038 Bala discloses identifying attributes in a document);
correlating the plurality of features to determine similarity scores between two or more features of the plurality of features (Par. 0034 Bala discloses associating a score with each of the sub-attributes that are similar to a particular category);
presenting information related to at least a first feature of the plurality of features (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes);
receiving feedback on the information (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
and determining, using a first machine learning model, information related to at least a second feature (Par. 0073 Bala discloses the user profile specifies a first training machine learning model, that specifies a plurality of attribute values for a plurality of principle attributes);
wherein the determination is made using the similarity scores (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute);
and the feedback in the first machine learning model (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes).
As to claim 22 Bala teaches teach and every limitation of claim 21.
In addition, Bala teaches further comprising: presenting information related to the at least second feature with the information related to at least the first feature (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 23 Bala teaches teach and every limitation of claim 21.
In addition, Bala teaches wherein the feedback comprises a selection of information related to the second feature (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes).
As to claim 24 Bala teaches teach and every limitation of claim 21.
In addition, Bala teaches clustering the information related to at least the first feature and the information related to the second feature to form a feature set of information (Par. 0032 Bala discloses the feature vector has number of dimensions that are associated with electronic document);
and presenting the feature set when the first feature or the second feature are detected in the data signal (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 25 Bala teaches teach and every limitation of claim 21.
In addition, Bala teaches wherein the data signal comprises one or more sensor signals from one or more sensors (Par. 0037 Bala discloses input blogs posted are associated with an electronic document).
As to claim 26 Bala teaches teach and every limitation of claim 22.
In addition, Bala teaches wherein the data signal comprises multidimensional data (Par. 0032 Bala discloses the feature vector has number of dimensions that are associated with electronic document);
As to claim 27 Bala teaches teach and every limitation of claim 21.
In addition, Bala teaches further comprising: presenting or more solutions based on the correlating of the plurality of features (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 28 Bala teaches a system comprising:
a processor (Par. 0021 Bala discloses a processor);
a memory, (Par. 0021 Bala discloses a memory); wherein the memory stores a program, that when executed on the processor, configures the processor to:
generate an application interface, wherein the application interface displays one or more features (Par. 0038 Bala discloses identifying attributes in a document);
receive a plurality of selections of the plurality of features, where the selections comprise one or more feedback signals associated with selections of one or more features of the plurality of features (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
train, using at least the plurality of selections, a machine learning model to determine one or more workflows, wherein the one or more workflows defines a set of features of the plurality of features (Par. 0073 Bala discloses the user profile specifies a first training machine learning model, that specifies a plurality of attribute values for a plurality of principle attributes);
present at least one of the one or more workflows on the application interface (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 30 Bala teaches teach and every limitation of claim 28.
In addition, Bala teaches wherein the processor is further configured to: receive a second plurality of selections from the application interface (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
generate, using a second machine learning model, one or more recommendations for a feature of the plurality of feature, wherein the one or more recommendations are based on the second plurality of selections received through the application interface (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 31 Bala teaches teach and every limitation of claim 30.
In addition, Bala teaches wherein the processor is further configured to: receive a second plurality of selections from the application interface (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
train the second machine learning model using the second plurality of selections (Par. 0073 Bala discloses the user profile specifies a first training machine learning model, that specifies a plurality of attribute values for a plurality of principle attributes);
and identify, using the trained second machine learning model, one or more additional features of the plurality of features to be included in the one or more recommendations (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 35 Bala teaches a system comprising:
an insight engine executing on a processor, wherein the insight engine is configured to receive a sensor data signal from one or more sensors (Par. 0038 Bala discloses identifying attributes in a document);
wherein the insight engine is configured to: execute a first machine learning model, identify, using the first machine learning model, one or more features in the sensor data signal, and generate an indication of the one or more features on an application interface (Par. 0073 Bala discloses the user profile specifies a first training machine learning model, that specifies a plurality of attribute values for a plurality of principle attributes);
a learning engine, wherein the learning engine is configured to: receive a plurality of selections on the application interface (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
train, using at least the plurality of selections, a second machine learning model to determine a one or more sub-features associated with the one or more features (Par. 0035 Bala discloses sub attributes are assigned to the features);
and present the one or more sub-features on the application interface (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 36 Bala teaches teach and every limitation of claim 35.
Bala teaches wherein the learning engine is further configured to: determine, using the second machine learning model, one or more workflows, wherein the one or more workflows define a set of features of the plurality of features (Par. 0073 Bala discloses the user profile specifies a first training machine learning model, that specifies a plurality of attribute values for a plurality of principle attributes);
and present at least one of the one or more workflows on the application interface (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 37 Bala teaches teach and every limitation of claim 36.
In addition, Bala teaches wherein the insight engine is further configured to: receive the plurality of selections from the application interface; update the first machine learning model using the plurality of selections (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
and identify, using the updated first machine learning model, a second set of one or more features (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 38 Bala teaches teach and every limitation of claim 36.
In addition, Bala teaches wherein the application interface comprises an interactive interface configured to receive one or more inputs, wherein the one or more inputs comprise at least one of: a selection of an item, a gesture, or a deselection of an item (Par. 0036 Bala discloses the user selecting the value for particular attributes).
As to claim 39 Bala teaches a method comprising: performing, using one or more computing devices:
identifying, using a first machine learning model, one or more features in a data signal (Par. 0038 Bala discloses identifying attributes in a document);
receiving a plurality of selections from an application interface based on presenting the one or more features on the application interface, wherein the plurality of selections provides an indication of an identification of the one or more features (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
identifying, using a second machine learning model, a corresponding feature based on the plurality of selections; identifying, using the one or more features and the corresponding feature (Par. 0073 Bala discloses the user profile specifies a first training machine learning model, that specifies a plurality of attribute values for a plurality of principle attributes);
a solution associated with the one or more features and the corresponding feature (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes);
and presenting the solution on the application interface in association with the one or more features (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 40 Bala teaches teach and every limitation of claim 39.
In addition, Bala teaches wherein the data signal is a sensor data signal provided by one or more sensors (Par. 0037 Bala discloses input blogs posted are associated with an electronic document).
As to claim 41 Bala teaches teach and every limitation of claim 39.
In addition, Bala teaches wherein the features are determined based on one or more sensor inputs (Par. 0037 Bala discloses input blogs posted are associated with an electronic document).
As to claim 42 Bala teaches teach and every limitation of claim 39.
In addition Bala teaches wherein the solution comprises a prediction of a time to an occurrence of an event (Par. 0045 Bala discloses information is presented after a defined period of time).
As to claim 43 Bala teaches a method comprising:
performing, using one or more computing devices:
identifying, using a first machine learning model, one or more features in a data signal (Par. 0038 Bala discloses identifying attributes in a document);
receiving a selection from an application interface based on presenting the one or more features on the application interface, wherein the selection provides an indication of an identification of the one or more features (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
updating, using at least the selection, the first machine learning model (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
and re-identifying, using the first machine learning model, the one or more features in the sensor data signal (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 44 Bala teaches teach and every limitation of claim 43.
In addition, Bala teaches wherein the data signal comprises a sensor data signal from one or more sensors (Par. 0037 Bala discloses input blogs posted are associated with an electronic document).
As to claim 45 Bala teaches teach and every limitation of claim 43.
In addition, Bala teaches wherein the data signal comprises multidimensional data (Par. 0032 Bala discloses the feature vector has number of dimensions that are associated with electronic document).
As to claim 46 Bala teaches determining a plurality of features in a data signal (Par. 0038 Bala discloses identifying attributes in a document);
correlating the plurality of features to determine similarity scores between two or more features of the plurality of features (Par. 0046 Balas discloses the attributes are compared to determine a match);
presenting information related to at least a first feature of the plurality of features (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes);
and determining, using a first machine learning model, information related to at least a second feature (Par. 0073 Bala discloses the user profile specifies a first training machine learning model, that specifies a plurality of attribute values for a plurality of principle attributes);
wherein the determination is made using the similarity scores in the first machine learning model (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute).
As to claim 47 Bala teaches teach and every limitation of claim 46.
In addition, Bala teaches further comprising: presenting information related to the at least second feature with the information related to at least the first feature (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 48 Bala teaches teach and every limitation of claim 46.
In addition, Bala teaches further comprising: clustering the information related to at least the first feature and the information related to the second feature to form a feature set of information (Par. 0032 Bala discloses the feature vector has number of dimensions that are associated with electronic document);
and presenting the feature set when the first feature or the second feature are detected in the data signal (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 49 Bala teaches teach and every limitation of claim 46.
In addition, Bala teaches wherein the data signal comprises one or more sensor signals from one or more sensors (Par. 0037 Bala discloses input blogs posted are associated with an electronic document).
As to claim 50 Bala teaches teach and every limitation of claim 46.
In addition, Bala teaches wherein the data signal comprises multidimensional data (Par. 0032 Bala discloses the feature vector has number of dimensions that are associated with electronic document).
As to claim 51 Bala teaches teach and every limitation of claim 46.
In addition, Bala teaches further comprising: presenting or more solutions based on the correlating of the plurality of features (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 52 Bala teaches a method comprising:
presenting a plurality of features in a data signal on an application interface (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes);
determining, using a first machine learning model, the occurrence of an event based on the plurality of features;
receiving feedback on the plurality of features presented on the application interface (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes);
identifying the event based on the feedback (Par. 0041 Bala discloses periodically being provided with changes to the training model);
labeling a training data set with the identification of the event, wherein the training data set comprises the plurality of features; and updating the first machine learning model with the training data set (Par. 0041-0042 Bala discloses the Machine Learnings Models can be retrained or refined based upon updates, or changes).
As to claim 53 Bala teaches teach and every limitation of claim 52.
In addition, Bala teaches further comprising: identifying, using the first machine learning model, two or more features of the plurality of features that are related (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
As to claim 54 Bala teaches teach and every limitation of claim 52.
In addition, Bala teaches wherein the data signal comprises one or more sensor signals from one or more sensors (Par. 0037 Bala discloses input blogs posted are associated with an electronic document).
As to claim 55 Bala teaches teach and every limitation of claim 52.
In addition, Bala teaches wherein the data signal comprises multidimensional data (Par. 0032 Bala discloses the feature vector has number of dimensions that are associated with electronic document).
As to claim 56 Bala teaches teach and every limitation of claim 52.
In addition, Bala teaches further comprising: presenting or more solutions using the updated first machine learning model (Par. 0035 Bala discloses principal attributes that include several concepts into each attribute. Par. 0037 Bala discloses presenting information related to attributes).
Claim Rejections - 35 USC § 103
7. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
8. 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.
9. Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anwar et al. U.S. Patent No. 11,892,996 (herein as ‘Anwar’) and further in view of Chechik et al. U.S. Patent Application Publication No. 2014/0188894 (herein as ‘Chechik’).
As to claim 10 Anwar teaches teach and every limitation of claim 1.
Anwar does not teach but Chechik teaches wherein correlating the user queries comprises scoring the correlation using normalized correlation ratings or Pearson's coefficient (Par. 0023 Chechik discloses the score for the search queries can be normalized).
Anwar and Chechik are analogous art because they are in the same field of endeavor, normalizing queries. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the user queries of Anwar to include the normalizing queries of Chechik, to allow account for different user preferences (Par. 0023 Chechik).
As to claim 20 Anwar teaches teach and every limitation of claim 21.
Anwar does not teach but Chechik teaches wherein the processor is further configured to: score the correlation using normalized correlation ratings or Pearson's coefficient (Par. 0023 Chechik discloses the score for the search queries can be normalized).
Anwar and Chechik are analogous art because they are in the same field of endeavor, normalizing queries. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the user queries of Anwar to include the normalizing queries of Chechik, to allow account for different user preferences (Par. 0023 Chechik).
10. Claim(s) 29, 32, 33 and 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramanian et al. U.S. Patent Application Publication No. 2019/0205950 (herein as ‘Bala’) and further in view of Anwar et al. U.S. Patent No. 11,892,996 (herein as ‘Anwar’).
As to claim 29 Bala teaches teach and every limitation of claim 28.
Bala does not teach but Anwar teaches wherein the one or more workflows further define an order of presentation of the set of features (Col. 35 Lines 20-26 Anwar discloses assigning criteria to records. Col. 44 Lines 25-26 Anwar discloses the records are ordered and made available based upon sequence of ingestion).
Bala and Anwar are analogous art because they are in the same field of endeavor, normalizing queries. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the user queries of Bala to include the user queries of Anwar, to allow account for different user preferences (Par. 0023 Anwar).
As to claim 32 Bala teaches teach and every limitation of claim 30.
Bala does not teach but Anwar teaches wherein the second machine learning model uses reinforcement learning with the plurality of selections to identify the one or more additional features to be included in the one or more recommendations (Col. 90 Lines 35-43 Anwar discloses the system can recommend queries to the users based on the frequently used queries).
As to claim 33 Bala teaches teach and every limitation of claim 28.
Bala does not teach but Anwar teaches identify, using the plurality of features, a plurality of features from a sensor signal; determine a similarity score between the plurality of features, wherein the machine learning model is trained using the plurality of selections and the similarity scores (Par. 0046 Balas discloses the attributes are compared to determine a match).
As to claim 34 Bala teaches teach and every limitation of claim 28.
Bala teaches wherein the features are determined based on one or more sensor inputs (Par. 0026 Bala discloses the data is input the machine learning model).
Conclusion
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/J.A.M/ December 13, 2025Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159