DETAILED ACTION
Applicant’s Application filed on October 20, 2023 has been reviewed.
Claims 1-29 were cancelled in the Preliminary Amendment.
Claims 30-49 have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Objections
Claims 30, 34, 40-43, 45, 47 and 49 are objected to because of the following informalities:
In claim 30,
at lines 9, “the basic” should be changed to “[[the]]a basic” and
at lines13-14, “the set of parameter values of the measurable parameter of each visualization item included in the visualization object” should be changed to “[[the]]a set of parameter values of [[the]]a measurable parameter of each visualization item included in [[the]]a object”.
In claim 34,
at line 1, “The method of claim 35” should be changed to “The method of claim [[35]]32” and
at line 2, “for each of two or more visualization items (30)” should be changed to “for each of two or more visualization items”.
In claim 40, at line 4, “plurality of visualization objects (26)” should be changed to “plurality of visualization objects”.
In claim 41, at line 4, “objects associated with the user” should be changed to “objects associated with [[the]]a user”.
In claim 42, at line 1, “The method of claim 44” should be changed to “”The method of claim [[44]]41”.
In claim 43, at line 1, “The method of claim 43” should be changed to “”The method of claim [[43]]41”.
In claim 45,
at lines 1, “The method of claim 41” should be changed to “The method of claim [[41]]44”, and
at lines and 4-5, “the non-availability of a sufficient amount” should be changed to “[[the]]a non-availability of a sufficient amount”.
In claim 47, at lines 4-5, “the non-availability of a sufficient set of parameter values” should be changed to “[[the]]a non-availability of a sufficient set of parameter values”.
In claim 49,
at line 3, “each of the visualization objects (26)” should be changed to “each of the visualization objects”,
at lines 12-13 , “the measurable parameter of each visualization item included in the visualization object” should be changed to “[[the]]a measurable parameter of each visualization item included in [[the]]a object”,
at line 15, “the generation” should be changed to “[[the]]a generation”, and
at line 16, “the basic” should be changed to “[[the]]a basic”.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 20, 2023 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 30-31, 40-44 and 49 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dunwoody (US 2018/0285756 A1).
With respect to claim 30, Dunwoody teaches A method for a network analytics tool capable of analyzing a time-varying set of parameter values for each of a plurality of measurable parameters and visually outputting a plurality of visualization objects, each of the visualization objects comprising one or more visualization items, each visualization item representative of one of the plurality of measurable parameters (an analytics system 100 that operates to receive data from data sources, such as servers 104 associated with one or more organizations, via network 102 and process that data to generate analytics and statistical data about a subject of interest which then be output via one or more dashboards to users, para. 0046-0049; and output including a plurality of portions 410-440 for presenting different types of analytics results data or presenting the analytics results data take many different forms, including graphs, charts, numerical value, para. 0086; figs. 1 and 4), the method comprising:
obtaining, for each of the plurality of visualization objects, a value of a first object- related metric (tracking user interactions with the various dashboards that are provided by the user experience systems 120 as well as interactions the users make both before and after interacting with the dashboards so as to generate a usage pattern for the user; the usage pattern is a representation of the types of actions performed by the user with the dashboard, lengths of time interacting with elements of the dashboard, actions taken prior to or subsequent to interacting with the dashboard such as with other user experience systems 120, para. 0050; also see para. 0091 and 0094; figs. 5 and 6); and
causing generation of a sensory-perceptible output indicative of a visualization object ranking established on the basis of the obtained value of the first object-related metric of each of the plurality of visualization objects (dashboard recommendations are then generated based on the correlation of the predicted types of data being sought by the user with the dashboard characteristics of the various dashboards or portions thereof (step 570); the recommendations are then output to the user via a user interface, para. 0092; the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generate confidence scores for these candidate correlations; based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates selected for use in generating modified or new dashboards and/or recommendations to be output, para. 0105; also see para. 0039, 0052,0053 and 0096; figs. 5 and 6),
wherein obtaining a value of the first object-related metric includes:
for each of one or more of the plurality of visualization objects, determining a value of the first object-related metric based at least on the set of parameter values of the measurable parameter of each visualization item included in the visualization object (the dashboard usage tracking and recommendation system 130 provide usage tracking and analysis across multiple users in the same and/or different organizations and provide recommendations to system administrators and/or other dashboard creators as to the usage patterns observed across multiple users with regard to the pre-defined dashboards, analytics executed on user interaction data tracked for each of the pre-defined dashboards to extract information about the way in which the users utilized the dashboards and the actions that they take both before and after interacting with the dashboards indicating the usefulness of the dashboard to the users' needs, para. 0054; also see para. 0050-0052).
With respect to claim 31, Dunwoody teaches The method of claim 30, wherein determining a value of the first object-related metric comprises:
determining, for each of two or more visualization items included in the visualization object, a value of an item-related metric based on the set of parameter values of the visualization item (combining similar user dashboard usage information with user behavior information to generate recommendations for a user, recommendations made for one user logged in the dashboard usage data 355 and/or as part of a user's profile in the user registry 365, and used as a basis for generating recommendations for other users having similar characteristics or similar dashboard usage behavior patterns, para. 0083; analytics are applied to the tracked interaction data to identify one or more usage patterns ; based on the usage patterns, a prediction of the types of data the user is attempting to access is generated, para. 0091);
determining the value of the first object-related metric based on the determined value of the item-related metric of each of the two or more visualization items included in the visualization object (analytics are applied to the tracked interaction data to identify one or more usage patterns; based on the usage patterns, a prediction of the types of data the user is attempting to access is generated, para. 0091; the behavior pattern reasoning logic 712 generate candidate correlations between user dashboard behavior across multiple user types and/or user groups, e.g., a plurality of pairings or user types and/or user groups generated and each pairing evaluated for correlations, i.e. intersection points in the dashboard usage patterns of the different types of users, para. 0137).
With respect to claim 40, Dunwoody teaches The method of claim 30, comprising:
establishing the visualization object ranking on the basis of the value of the first object- related metric and a value of a second object-related metric of each of the plurality of visualization objects (26), the value of the second object-related metric determined based on a user-specific ranking of measurable parameters independently of the parameter values of the measurable parameter of each visualization item included in the visualization object (dashboard recommendations are then generated based on the correlation of the predicted types of data being sought by the user with the dashboard characteristics of the various dashboards or portions thereof (step 570); the recommendations are then output to the user via a user interface, para. 0092; the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generate confidence scores for these candidate correlations; based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates selected for use in generating modified or new dashboards and/or recommendations to be output, para. 0105; also see para. 0039, 0052,0053 and 0096; figs. 5 and 6).
With respect to claim 41, Dunwoody teaches The method of claim 30, comprising:
obtaining, for each of a plurality of users, a value of a user-specific second object- related metric in relation to each of two or more of the plurality of visualization objects associated with the user (tracking user interactions with the various dashboards that are provided by the user experience systems 120 as well as interactions the users make both before and after interacting with the dashboards so as to generate a usage pattern for the user; the usage pattern is a representation of the types of actions performed by the user with the dashboard, lengths of time interacting with elements of the dashboard, actions taken prior to or subsequent to interacting with the dashboard such as with other user experience systems 120, para. 0050; also see para. 0091 and 0094; figs. 5 and 6);
establishing, for each of the plurality of users, a user-specific object ranking on the basis of the value of the first object-related metric and the value of the user- specific second object-related metric of each of the two or more visualization objects associated with the user (the dashboard usage tracking and recommendation system 130 provide usage tracking and analysis across multiple users in the same and/or different organizations and provide recommendations to system administrators and/or other dashboard creators as to the usage patterns observed across multiple users with regard to the pre-defined dashboards, analytics executed on user interaction data tracked for each of the pre-defined dashboards to extract information about the way in which the users utilized the dashboards and the actions that they take both before and after interacting with the dashboards indicating the usefulness of the dashboard to the users' needs, para. 0054; also see para. 0050-0052; the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generate confidence scores for these candidate correlations; based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates may be selected for use in generating modified or new dashboards and/or recommendations to be output, para. 0105); and
causing generation of a sensory-perceptible output indicative of the user-specific object ranking (dashboard recommendations are then generated based on the correlation of the predicted types of data being sought by the user with the dashboard characteristics of the various dashboards or portions thereof (step 570); the recommendations are then output to the user via a user interface, para. 0092; the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generate confidence scores for these candidate correlations; based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates selected for use in generating modified or new dashboards and/or recommendations to be output, para. 0105; also see para. 0039, 0052,0053 and 0096; figs. 5 and 6).
With respect to claim 42, Dunwoody teaches The method of claim 44, wherein establishing the user-specific object ranking includes:
determining, for each of the two or more visualization objects associated with the user, an object ranking value representative of a multiplicative product of the value of the first object-related metric and the value of the user-specific second object- related metric (the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generate confidence scores for these candidate correlations; based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates selected for use in generating modified or new dashboards and/or recommendations to be output, para. 0105; also see para. 0039, 0052,0053 and 0096; figs. 5 and 6).
With respect to claim 43, Dunwoody teaches The method of claim 43, wherein establishing the user-specific visualization object ranking includes at least one of:
obtaining an object usage matrix specifying for each of the plurality of users a usage value in relation to each of the two or more visualization objects associated with the user, the usage value representative of a probability of usage of the visualization object by the user (the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generate confidence scores for these candidate correlations; based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates selected for use in generating modified or new dashboards and/or recommendations to be output, para. 0105; also see para. 0039, 0052,0053 and 0096; figs. 5 and 6; a ranked listing of candidate reasons for a user's dashboard behavior generated based on the confidence scores associated with the candidate reasons; a top ranked candidate reason selected as the probable reason for the user's dashboard behavior, the behavior pattern reasoning logic 712 of the cognitive system 710 determine that the reason the user is accessing certain information from various user experience systems 340, dashboards, para. 0136); or
decomposing the object usage matrix into the product of a job role matrix (P._) and a toolkit matrix (Q._), the job role matrix (Pj specifying for each of the plurality Page 5 of 8 of users a job role value in relation to each of two or more job roles, the job role value representative of a probability of the user being involved in a task related to the job role, the toolkit matrix (Q_) specifying for each of two or more visualization objects a toolkit value in relation to each of the two or more job roles, the toolkit value representative of a probability of the visualization object requiring to be inspected in order to accomplish a task related to the job role; or
determining an object ranking matrix as a product of the job role matrix (P), the toolkit matrix (Q) and a vector (c) including for each of the two or more visualization objects the value of the first object-related metric.
With respect to claim 44, Dunwoody teaches The method of claim 43, wherein obtaining the object usage matrix includes:
logging, for each of the plurality of users, visualization object interactions of the user (recommendations made for one user logged in the dashboard usage data 355 and/or as part of a user's profile in the user registry 365, and used as a basis for generating recommendations for other users having similar characteristics or similar dashboard usage behavior patterns, para. 0083); and
determining the usage value based on a logged number of visualization object interactions (recommendations constructed based on the user's behavior as it relates to their interactions with other user experience systems and, in some cases, the subject matter of the content that the user accessed via these other user experience systems, para. 0081; recommendations made for one user logged in the dashboard usage data 355 and/or as part of a user's profile in the user registry 365, and used as a basis for generating recommendations for other users having similar characteristics or similar dashboard usage behavior patterns, para. 0083).
With respect to claim 49, Dunwoody teaches An apparatus operable in a network analytics tool capable of analyzing a time- varying set of parameter values for each of a plurality of measurable parameters and visually outputting a plurality of visualization objects, each of the visualization objects (26) comprising one or more visualization items, each visualization item representative of one of the plurality of measurable parameters (an analytics system 100 that operates to receive data from data sources, such as servers 104 associated with one or more organizations, via network 102 and process that data to generate analytics and statistical data about a subject of interest which then be output via one or more dashboards to users, para. 0046-0049; and output including a plurality of portions 410-440 for presenting different types of analytics results data or presenting the analytics results data take many different forms, including graphs, charts, numerical value, para. 0086; figs. 1 and 4), the apparatus comprising:
processing circuitry (processor, para. 0145); and
memory storing executable instructions that, when executed by the processing circuitry (storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements, para. 0145), cause the apparatus to:
obtain, for each of the plurality of visualization objects, a value of a first object- related metric (tracking user interactions with the various dashboards that are provided by the user experience systems 120 as well as interactions the users make both before and after interacting with the dashboards so as to generate a usage pattern for the user; the usage pattern is a representation of the types of actions performed by the user with the dashboard, lengths of time interacting with elements of the dashboard, actions taken prior to or subsequent to interacting with the dashboard such as with other user experience systems 120, para. 0050; also see para. 0091 and 0094; figs. 5 and 6) by at least determining, for each of one or more of the plurality of visualization objects, a value of the first object-related metric based at least on the set of parameter values of the measurable parameter of each visualization item included in the visualization object (the dashboard usage tracking and recommendation system 130 provide usage tracking and analysis across multiple users in the same and/or different organizations and provide recommendations to system administrators and/or other dashboard creators as to the usage patterns observed across multiple users with regard to the pre-defined dashboards, analytics executed on user interaction data tracked for each of the pre-defined dashboards to extract information about the way in which the users utilized the dashboards and the actions that they take both before and after interacting with the dashboards indicating the usefulness of the dashboard to the users' needs, para. 0054; also see para. 0050-0052); and
cause the generation of a sensory-perceptible output indicative of a visualization object ranking established on the basis of the obtained value of the first object-related metric of each of the plurality of visualization objects (dashboard recommendations are then generated based on the correlation of the predicted types of data being sought by the user with the dashboard characteristics of the various dashboards or portions thereof (step 570); the recommendations are then output to the user via a user interface, para. 0092; the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generate confidence scores for these candidate correlations; based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates selected for use in generating modified or new dashboards and/or recommendations to be output, para. 0105; also see para. 0039, 0052,0053 and 0096; figs. 5 and 6).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 32-38 are rejected under 35 U.S.C. 103 as being unpatentable over Dunwoody (US 2018/0285756 A1) in view of Khurshid et al. (US 2022/0094614 A1), hereinafter referred to as Khurshid.
With respect to claim 32, Dunwoody teaches The method of claim 30, wherein determining a value of the first object-related metric includes at least one of:
performing an analysis on a group of parameter values comprising the set of parameter values of at least one visualization item included in the visualization object (the behavior pattern reasoning logic 712 generate candidate correlations between user dashboard behavior across multiple user types and/or user groups, e.g., a plurality of pairings or user types and/or user groups generated and each pairing evaluated for correlations, i.e. intersection points in the dashboard usage patterns of the different types of users, para. 0137); or
performing an anomaly analysis separately on the set of parameter values of each of two or more visualization items included in the visualization object.
Dunwoody does not explicitly teach performing an anomaly analysis on a group of parameter values;
However, Khurshid teaches performing an anomaly analysis on a group of parameter values (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133) in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133);
Therefore, based on Dunwoody in view of Khurshid, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Khurshid to the method of Dunwoody in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
With respect to claim 33, Dunwoody in view of Khurshid teaches The method of claim 32 as described above,
Further, Khurshid teaches wherein performing an anomaly analysis includes at least one of:
separating the parameter values of the group into at least one normally behaving subgroup and at least one anomalously behaving subgroup;
determining a value of a normal-anomalous distance metric based on the at least one normally behaving subgroup and the at least one anomalously behaving subgroup (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133) in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
Therefore, based on Dunwoody in view of Khurshid, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Khurshid to the method of Dunwoody in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
With respect to claim 34, Dunwoody in view of Khurshid teaches The method of claim 35 as described above,
Further, Khurshid teaches wherein performing an anomaly analysis includes:
determining, for each of two or more visualization items (30) included in the visualization object, a value of an item-specific normal-anomalous distance metric based on a normally behaving subset and an anomalously behaving subset of the set of parameter values of the respective visualization item (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133); and
deriving a value of an object-specific normal-anomalous distance metric from the two or more values of the item-specific normal-anomalous distance metric (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133) in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
Therefore, based on Dunwoody in view of Khurshid, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Khurshid to the method of Dunwoody in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
With respect to claim 35, Dunwoody teaches The method of claim 34, wherein deriving a value of the object-specific metric includes:
selecting a largest value among the two or more values of the item-specific metric as the value of the object-specific metric (the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generate confidence scores for these candidate correlations; based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates may be selected for use in generating modified or new dashboards and/or recommendations to be output, para. 0105).
Furthermore, Khurshid teaches a value of an item-specific normal-anomalous distance metric and a value of the object-specific normal- anomalous distance metric (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133) in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
Therefore, based on Dunwoody in view of Khurshid, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Khurshid to the method of Dunwoody in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
With respect to claim 36, Dunwoody in view of Khurshid teaches The method of claim 33 as described above,
Furthermore, Khurshid teaches wherein determining a value of the normal-anomalous distance metric includes:
for each of two or more visualization items included in the visualization object, separating the parameter values of the visualization item into a normally behaving subgroup and an anomalously behaving subgroup (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133);
wherein the normal-anomalous distance metric represents a distance between a set of normally behaving representative values and a set of anomalously behaving representative values, the set of normally behaving representative values comprising a representative value of the normally behaving subgroup of each of the two or more visualization items, the set of anomalously behaving representative values comprising a representative value of the anomalously behaving subgroup of each of the two or more visualization items (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133) in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
Therefore, based on Dunwoody in view of Khurshid, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Khurshid to the method of Dunwoody in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
With respect to claim 37, Dunwoody in view of Khurshid teaches The method of claim 36 as described above,
Furthermore, Khurshid teaches wherein the normal-anomalous distance metric represents an inter-quartile distance between the set of normally behaving representative values and the set of anomalously behaving representative values (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133) in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
Therefore, based on Dunwoody in view of Khurshid, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Khurshid to the method of Dunwoody in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
With respect to claim 38, Dunwoody in view of Khurshid teaches The method of claim 36 as described above,
Furthermore, Khurshid teaches wherein the representative value of the normally behaving subgroup is a mean of the normally behaving subgroup and the representative value of the anomalously behaving subgroup is a mean of the anomalously behaving subgroup (presenting visually in a matrix; the result is a matrix; each row of this matrix would correspond to a particular group among the source query; each column is a group among the destination query, para. 0132; the system analyze or cluster the rows and/or columns of the matrix by similarity, and use this to sort the rows or columns which allow the user to visually identify anomalous cells, para. 0133) in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
Therefore, based on Dunwoody in view of Khurshid, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Khurshid to the method of Dunwoody in order to allow to visually identify anomalous data as taught by Khurshid (para. 0133).
Claim 39 is rejected under 35 U.S.C. 103 as being unpatentable over Dunwoody (US 2018/0285756 A1) in view of LaRowe et al. (US 2013/0110871 A1), hereinafter referred to as LaRowe.
With respect to claim 39, Dunwoody teaches The method of claim 30 as described above,
Dunwoody does not explicitly teach wherein obtaining a value of the first object-related metric includes:
for at least one of the plurality of visualization objects, determining an updated value of the first object-related metric based on an updated set of parameter values of the measurable parameter of at least one visualization item included in the visualization object.
However, LaRowe teaches wherein obtaining a value of the first object-related metric includes:
for at least one of the plurality of visualization objects, determining an updated value of the first object-related metric based on an updated set of parameter values of the measurable parameter of at least one visualization item included in the visualization object (when at least one display parameter is changed, the changed display parameters are called an update specification; the data display 351 changes the stored display parameters; thus if these parameters are exported along with the data model and data analysis model, the current data visualization may be reproduced, para. 0136) in order to allow the visualization to be modified via a graphical user interface, thereby enhancing the interactive capabilities of data analysis as taught by LaRowe (para. 0032).
Therefore, based on Dunwoody in view of LaRowe, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of LaRowe to the method of Dunwoody in order to allow the visualization to be modified via a graphical user interface, thereby enhancing the interactive capabilities of data analysis as taught by LaRowe (para. 0032).
Claims 45-48 are rejected under 35 U.S.C. 103 as being unpatentable over Dunwoody (US 2018/0285756 A1) in view of Krasnow et al. (US 2019/0294591 A1), hereinafter referred to as Krasnow.
With respect to claim 45, Dunwoody teaches The method of claim 41 as described above,
Dunwoody does not explicitly teach wherein obtaining a value of the user-specific second object-related metric includes:
for each of one or more of the plurality of visualization objects, assigning a default value to the user-specific second object-related metric based on the non- availability of a sufficient amount of logged visualization object interactions associated with the user.
However, Krasnow teaches wherein obtaining a value of the user-specific second object-related metric includes:
for each of one or more of the plurality of visualization objects, assigning a default value to the user-specific second object-related metric based on the non- availability of a sufficient amount of logged visualization object interactions associated with the user (generating trend data between two manifests, a first manifest occurring at time t.sub.1 and a second manifest occurring at a time t.sub.2 later than t.sub.1. In block 310, the component identifies the paths that have been logged in each of the first and second manifests. In blocks 320-370, the component loops through each path to determine how the values for the logged paths have changed between the two manifests; if the manifests logged the number of accesses of an item corresponding to each significant path at time t.sub.1, then the component would retrieve the number of accesses of the corresponding item at time t.sub.1. If the path does not exist in the first manifest, then the component sets the first value to a default value, such as 0. For example, if the item corresponding to the path was created after time t.sub.1 or was not considered significant at time t.sub.1, its path would not have been logged at time t.sub.1. In block 340, the component retrieves a second value for the currently-selected path from the second manifest. If the path does not exist in the second manifest, then the component sets the second value to a default value, such as −1, para. 0028) in order to provide significant improvements to the use of a computer in monitoring, analyzing, and visualizing trend data as taught by Krasnow (para. 0018).
Therefore, based on Dunwoody in view of Krasnow, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Krasnow to the method of Dunwoody in order to provide significant improvements to the use of a computer in monitoring, analyzing, and visualizing trend data as taught by Krasnow (para. 0018).
With respect to claim 46, Dunwoody in view of Krasnow teaches The method of claim 45 as described above,
Further, Krasnow teaches comprising:
adjusting the assigned default value of the user-specific second object-related metric based on time (generating trend data between two manifests, a first manifest occurring at time t.sub.1 and a second manifest occurring at a time t.sub.2 later than t.sub.1. In block 310, the component identifies the paths that have been logged in each of the first and second manifests. In blocks 320-370, the component loops through each path to determine how the values for the logged paths have changed between the two manifests; if the manifests logged the number of accesses of an item corresponding to each significant path at time t.sub.1, then the component would retrieve the number of accesses of the corresponding item at time t.sub.1. If the path does not exist in the first manifest, then the component sets the first value to a default value, such as 0. For example, if the item corresponding to the path was created after time t.sub.1 or was not considered significant at time t.sub.1, its path would not have been logged at time t.sub.1. In block 340, the component retrieves a second value for the currently-selected path from the second manifest. If the path does not exist in the second manifest, then the component sets the second value to a default value, such as −1, para. 0028) in order to provide significant improvements to the use of a computer in monitoring, analyzing, and visualizing trend data as taught by Krasnow (para. 0018).
Therefore, based on Dunwoody in view of Krasnow, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Krasnow to the method of Dunwoody in order to provide significant improvements to the use of a computer in monitoring, analyzing, and visualizing trend data as taught by Krasnow (para. 0018).
With respect to claim 47, Dunwoody teaches The method of claim 30 as described above,
Dunwoody does not explicitly teach wherein obtaining a value of the first object-related metric includes:
for each of one or more of the plurality of visualization objects, assigning a default value to the first object-related metric based on the non-availability of a sufficient set of parameter values of the measurable parameter of at least one visualization item included in the visualization object.
However, Krasnow teaches wherein obtaining a value of the first object-related metric includes:
for each of one or more of the plurality of visualization objects, assigning a default value to the first object-related metric based on the non-availability of a sufficient set of parameter values of the measurable parameter of at least one visualization item included in the visualization object (generating trend data between two manifests, a first manifest occurring at time t.sub.1 and a second manifest occurring at a time t.sub.2 later than t.sub.1. In block 310, the component identifies the paths that have been logged in each of the first and second manifests. In blocks 320-370, the component loops through each path to determine how the values for the logged paths have changed between the two manifests; if the manifests logged the number of accesses of an item corresponding to each significant path at time t.sub.1, then the component would retrieve the number of accesses of the corresponding item at time t.sub.1. If the path does not exist in the first manifest, then the component sets the first value to a default value, such as 0. For example, if the item corresponding to the path was created after time t.sub.1 or was not considered significant at time t.sub.1, its path would not have been logged at time t.sub.1. In block 340, the component retrieves a second value for the currently-selected path from the second manifest. If the path does not exist in the second manifest, then the component sets the second value to a default value, such as −1, para. 0028) in order to provide significant improvements to the use of a computer in monitoring, analyzing, and visualizing trend data as taught by Krasnow (para. 0018).
Therefore, based on Dunwoody in view of Krasnow, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Krasnow to the method of Dunwoody in order to provide significant improvements to the use of a computer in monitoring, analyzing, and visualizing trend data as taught by Krasnow (para. 0018).
With respect to claim 48, Dunwoody in view of Krasnow teaches The method of claim 47 as described above,
Further, Krasnow teaches comprising:
adjusting the assigned default value of the first object-related metric based on time (generating trend data between two manifests, a first manifest occurring at time t.sub.1 and a second manifest occurring at a time t.sub.2 later than t.sub.1. In block 310, the component identifies the paths that have been logged in each of the first and second manifests. In blocks 320-370, the component loops through each path to determine how the values for the logged paths have changed between the two manifests; if the manifests logged the number of accesses of an item corresponding to each significant path at time t.sub.1, then the component would retrieve the number of accesses of the corresponding item at time t.sub.1. If the path does not exist in the first manifest, then the component sets the first value to a default value, such as 0. For example, if the item corresponding to the path was created after time t.sub.1 or was not considered significant at time t.sub.1, its path would not have been logged at time t.sub.1. In block 340, the component retrieves a second value for the currently-selected path from the second manifest. If the path does not exist in the second manifest, then the component sets the second value to a default value, such as −1, para. 0028) in order to provide significant improvements to the use of a computer in monitoring, analyzing, and visualizing trend data as taught by Krasnow (para. 0018).
Therefore, based on Dunwoody in view of Krasnow, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Krasnow to the method of Dunwoody in order to provide significant improvements to the use of a computer in monitoring, analyzing, and visualizing trend data as taught by Krasnow (para. 0018).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAO NGUYEN whose telephone number is (571)272-2666. The examiner can normally be reached on Monday through Friday from 7:30 A.M. to 4:00 P.M. (EST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joon H. Hwang can be reached on 571-272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.H.N/Examiner, Art Unit 2447
March 1, 2026
/JOON H HWANG/Supervisory Patent Examiner, Art Unit 2447