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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/08/2025 has been entered.
Status of Claims
This action is a responsive to the application filed on 12/08/2025.
Claims 1-20 are pending.
Claims 1, 6, and 11 have been amended.
Claims 16-20 have been added.
Response to Arguments
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 101, have been considered but they are not persuasive. The applicant argues that the amended claims “conform with the requirements of 35 U.S.C. 101”, and therefore overcome the 101 rejections. The examiner respectfully disagrees.
The amendments and use in the claim do not operate to overcome the previous 101 abstract idea rejection. The system performing the operations of the independent claims are determined to be recited at a high level of generality and amount to merely uses a computer as a tool to perform an abstract idea; and the new claims are determined to be mental processes. See 35 U.S.C 101 section for full, updated analysis of claim limitations necessitated by applicant amendments.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1, 8, and 15 under 35 U.S.C. 103, have been considered but they are not persuasive. Applicant argues that no reference teaches the amended limitations, since Elisseef’s “changes in status or actions taken by a user or AVA may result in a step of changing indicia associated with record(s) and viewable on the user interface” is not the same as the amended claim limitations. The examiner respectfully disagrees.
Elisseeff has been found to teach the amendments, since Elisseeff teaches in 0092-0093 “In other embodiments, the AVA may be configured to automatically determine the appropriate reporting and analysis to supply to the user in response to an inquiry, instruction or command, including through the use of driver graph logic described in greater detail below…[and] perform historical context analysis and determine whether other users are making similar or related requests”. Which describes that the system determined dynamically-generated data points (as Elisseeff teaches in 0090, the data points are recognized as metrics of the data, such as revenue, income, profit loss.) Furthermore, as seen by Fig. 3A of Elisseeff. the nodes are understood to be the data points that are generated for the user. Further still, paragraphs 0098 and 0120, “Once the system detects anomalies with the performance or state of specific nodes, the Driver Graph is configured to determine the root cause of such anomalies to generate a useful business insight”, and displaying to the user. Elisseeff also teaches in 153 that the analytic information about these anomalies is broadcasted to the user.
See 35 U.S.C 103 section for full mapping of claim limitations necessitated by applicant amendments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1, 6, and 11 and dependent claims 2-5, 7-10, and 12-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1 - 20 are directed to a process. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Independent Claim 1:
Step 2A Prong 1:
a crawl subsystem that operates to generate a data index for use by the system in processing a received data request or query; (Observing data and generating an index for the data based on a received query, as mentioned with such generality, is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
a query subsystem that operates with a semantic data model and the data index to provide semantic analysis of the received data request or query; (Reading a query and providing a result based on the query in the form of a semantic analysis is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
and a data metrics subsystem including a measures generator that assess changes in data values or data metrics and identifies and tracks patterns of use for each of one or more users, including viewing habits or actions that are identified as occurring based on the changes in data values or data metrics; (Observing data and picking out changes/patterns is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
wherein the metric, wherein each dynamically-generated analytic data metric operating as a smart measure is scoped to a dataset and associated with a metadata that indicates a scope of data of interest to a user or group of users; (Correlating metadata with a set of data of remembered access values is practically implementable in the human mind and is understood to be a recitation of a mental process.)
and wherein the system operates in accordance with a defined smart measure to monitor its associated data and broadcast analytic information describing the associated data to subscribed listeners, including one or more detected anomalies, trends, or changes within the data. (Monitoring data for anomalies is practically implementable in the human mind and is understood to be a recitation of a mental process.)
If a claim limitation under its broadest reasonable interpretation, covers performance of the
limitation that can be performed in the human mind and/or using pen and paper as a physical aid, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2:
The judicial exception is not integrated into a practical application.
A system for providing dynamically-generated analytic data metrics or measures, for use with a data analytics environment, comprising:
a computer including one or more processors, that provides access to a data analytics environment including a data analytic system provided thereon, wherein the data analytic system, wherein the analytic system comprises: (The computer and the processors are understood to be reciting generic computer components. See MPEP 2106.05(f). The data analytics environment and system are considered elements of the field-of-use. See MPEP 2106.05(h).)
data metrics subsystem, and the system (The data metrics subsystem is considered elements of the field-of-use. See MPEP 2106.05(h).)
Step 2B:
A system for providing dynamically-generated analytic data metrics or measures, for use with a data analytics environment, comprising:
a computer including one or more processors, that provides access to a data analytics environment including a data analytic system provided thereon, wherein the data analytic system, wherein the analytic system comprises: (The computer and the processors are understood to be reciting generic computer components. See MPEP 2106.05(f). The data analytics environment and system are considered elements of the field-of-use. See MPEP 2106.05(h).)
data metrics subsystem, and the system (The data metrics subsystem is considered elements of the field-of-use. See MPEP 2106.05(h).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, they do not add significantly more (also known as an “inventive concept”) to the exception.
Independent Claim 6:Step 2A Prong 1:
a crawl subsystem that operates to generate a data index for use by the system in processing a received data request or query; (Observing data and generating an index for the data based on a received query, as mentioned with such generality, is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
a query subsystem that operates with a semantic data model and the data index to provide semantic analysis of the received data request or query; (Reading a query and providing a result based on the query in the form of a semantic analysis is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
and a data metrics subsystem including a measures generator that assess changes in data values or data metrics and identifies and tracks patterns of use for each of one or more users, including viewing habits or actions that are identified as occurring based on the changes in data values or data metrics; (Observing data and picking out changes/patterns is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
wherein the (Correlating metadata with a set of data of remembered access values is practically implementable in the human mind and is understood to be a recitation of a mental process.)
and wherein the system operates in accordance with a defined smart measure to monitor its associated data and broadcast analytic information describing the associated data to subscribed listeners, including one or more detected anomalies, trends, or changes within the data. (Monitoring data for anomalies is practically implementable in the human mind and is understood to be a recitation of a mental process.)
If a claim limitation under its broadest reasonable interpretation, covers performance of the
limitation that can be performed in the human mind and/or using pen and paper as a physical aid, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2:
The judicial exception is not integrated into a practical application.
A method for providing dynamically-generated analytic data metrics or measures, for use with a data analytics environment, comprising:
providing, by a computer including one or more processors, that provides access to a data analytics environment including a data analytic system, wherein the analytic system comprises:(The computer and the processors are understood to be reciting generic computer components. See MPEP 2106.05(f). The data analytics environment and system are considered elements of the field-of-use. See MPEP 2106.05(h).)
data metrics subsystem, and the system (The data metrics subsystem is considered elements of the field-of-use. See MPEP 2106.05(h).)
Step 2B:
A method for providing dynamically-generated analytic data metrics or measures, for use with a data analytics environment, comprising:
providing, by a computer including one or more processors, that provides access to a data analytics environment including a data analytic system, wherein the analytic system comprises:(The computer and the processors are understood to be reciting generic computer components. See MPEP 2106.05(f). The data analytics environment and system are considered elements of the field-of-use. See MPEP 2106.05(h).)
data metrics subsystem, and the system (The data metrics subsystem is considered elements of the field-of-use. See MPEP 2106.05(h).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, they do not add significantly more (also known as an “inventive concept”) to the exception.
Independent Claim 11:
Step 2A Prong 1:
a crawl subsystem that operates to generate a data index for use by the system in processing a received data request or query; (Observing data and generating an index for the data based on a received query, as mentioned with such generality, is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
a query subsystem that operates with a semantic data model and the data index to provide semantic analysis of the received data request or query; (Reading a query and providing a result based on the query in the form of a semantic analysis is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
and a data metrics subsystem including a measures generator that assess changes in data values or data metrics and identifies and tracks patterns of use for each of one or more users, including viewing habits or actions that are identified as occurring based on the changes in data values or data metrics; (This step for identifying certain elements of the data as “smart measures” is practically implementable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
wherein the (Correlating metadata with a set of data of remembered access values is practically implementable in the human mind and is understood to be a recitation of a mental process.)
and wherein the system operates in accordance with a defined smart measure to monitor its associated data and broadcast analytic information describing the associated data to subscribed listeners, including one or more detected anomalies, trends, or changes within the data. (Monitoring data for anomalies is practically implementable in the human mind and is understood to be a recitation of a mental process.)
If a claim limitation under its broadest reasonable interpretation, covers performance of the
limitation that can be performed in the human mind and/or using pen and paper as a physical aid, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2:
The judicial exception is not integrated into a practical application.
A non-transitory computer readable storage medium having instructions thereon, which when read and executed by a computer including one or more processors cause the computer to perform a method comprising:
providing, by a computer including one or more processors, that provides access to a data analytics environment including a data analytic system, wherein the analytic system comprises; (The computer, the processors, and the compute readable medium are understood to be reciting generic computer components. See MPEP 2106.05(f). The data analytics environment and system are considered elements of the field-of-use. See MPEP 2106.05(h).)
data metrics subsystem, and the system (The data metrics subsystem is considered elements of the field-of-use. See MPEP 2106.05(h).)
Step 2B:
A non-transitory computer readable storage medium having instructions thereon, which when read and executed by a computer including one or more processors cause the computer to perform a method comprising:
providing, by a computer including one or more processors, that provides access to a data analytics environment including a data analytic system, wherein the analytic system comprises; (The computer, the processors, and the compute readable medium are understood to be reciting generic computer components. See MPEP 2106.05(f). The data analytics environment and system are considered elements of the field-of-use. See MPEP 2106.05(h).)
data metrics subsystem, and the system (The data metrics subsystem is considered elements of the field-of-use. See MPEP 2106.05(h).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, they do not add significantly more (also known as an “inventive concept”) to the exception.
Dependent Claims 2 – 5, 7 – 10, and 12 – 15 for the same reasons given with respect
to claim 1. The dependent claims describe further mental and/or do not include additional active functional limitations/steps:
Claim 2:
Step 2A Prong 1:
The system of claim 1, wherein smart measures are automatically discovered and updated by the system based on the system observing that a particular user or community of users regularly accesses a particular type of a data or particular analytic data metric or preforms actions in response to changes in such data or data metric. (Generically updating data and observing data is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 3:
Step 2A Prong 1:
The system of claim 2, wherein the data analytic system records an indication of updated knowledge/understanding as an updated metadata/rules associated with a smart measure. (This step for storing records tied to the measures is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 4:
Step 2A Prong 1:
The system of claim 3, wherein once a smart measure has been defined and associated with a user, or group of users(This step for defining the smart measure as an element tied to a user is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A Prong 2:
The judicial exception is not integrated into a practical application.
the smart measure is communicated, broadcast, or otherwise provided to the user (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
Step 2B:
the smart measure is communicated, broadcast, or otherwise provided to the user (This step is directed to transmitting information, which is well-understood, routine, and conventional activities as supported under Berkheimer Evidence “Receiving or transmitting data over a network”. See MPEP 2106.05(d)(ll).)
or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (This step is directed to transmitting information, which is well-understood, routine, and conventional activities as supported under Berkheimer Evidence “Receiving or transmitting data over a network”. See MPEP 2106.05(d)(ll).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, they do not add significantly more (also known as an “inventive concept”) to the exception.
Claim 5:
Step 2A Prong 1:
The system of claim 1, wherein the system applies conditional formatting to present the analytic data metric provided by a smart measure in a readily-identifiable format. (This step for updating the visual representation of the smart metric so that its easily readable is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process.)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 7:
Step 2A Prong 1:
The system of claim 6, wherein smart measures are automatically discovered and updated by the system based on the system observing that a particular user or community of users regularly accesses a particular type of a data or particular analytic data metric or preforms actions in response to changes in such data or data metric. (Generically updating data and observing data is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 8:
Step 2A Prong 1:
The system of claim 7, wherein the data analytic system records an indication of updated knowledge/understanding as an updated metadata/rules associated with a smart measure. (This step for storing records tied to the measures is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 9:
Step 2A Prong 1:
The system of claim 8, wherein once a smart measure has been defined and associated with a user, or group of users, (This step for defining the smart measure as an element tied to a user is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A Prong 2:
The judicial exception is not integrated into a practical application.
the smart measure is communicated, broadcast, or otherwise provided to the user (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
Step 2B:
the smart measure is communicated, broadcast, or otherwise provided to the user (This step is directed to transmitting information, which is well-understood, routine, and conventional activities as supported under Berkheimer Evidence “Receiving or transmitting data over a network”. See MPEP 2106.05(d)(ll).)
or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (This step is directed to transmitting information, which is well-understood, routine, and conventional activities as supported under Berkheimer Evidence “Receiving or transmitting data over a network”. See MPEP 2106.05(d)(ll).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, they do not add significantly more (also known as an “inventive concept”) to the exception.
Claim 10:
Step 2A Prong 1:
The system of claim 6, wherein the system applies conditional formatting to present the analytic data metric provided by a smart measure in a readily-identifiable format. (This step for updating the visual representation of the smart metric so that its easily readable is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process.)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 12:
Step 2A Prong 1:
The system of claim 11, wherein smart measures are automatically discovered and updated by the system based on the system observing that a particular user or community of users regularly accesses a particular type of a data or particular analytic data metric or preforms actions in response to changes in such data or data metric. (Generically updating data and observing data is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 13:
Step 2A Prong 1:
The system of claim 12, wherein the data analytic system records an indication of updated knowledge/understanding as an updated metadata/rules associated with a smart measure. (This step for storing records tied to the measures is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 14:
Step 2A Prong 1:
The system of claim 13, wherein once a smart measure has been defined and associated with a user, or group of users, (This step for defining the smart measure as an element tied to a user is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process (i.e., judgment).)
Step 2A Prong 2:
The judicial exception is not integrated into a practical application.
the smart measure is communicated, broadcast, or otherwise provided to the user (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
Step 2B:
the smart measure is communicated, broadcast, or otherwise provided to the user (This step is directed to transmitting information, which is well-understood, routine, and conventional activities as supported under Berkheimer Evidence “Receiving or transmitting data over a network”. See MPEP 2106.05(d)(ll).)
or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (This step is directed to transmitting information, which is well-understood, routine, and conventional activities as supported under Berkheimer Evidence “Receiving or transmitting data over a network”. See MPEP 2106.05(d)(ll).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, they do not add significantly more (also known as an “inventive concept”) to the exception.
Claim 15:
Step 2A Prong 1:
The system of claim 11, wherein the system applies conditional formatting to present the analytic data metric provided by a smart measure in a readily-identifiable format. (This step for updating the visual representation of the smart metric so that its easily readable is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process.)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 16:
Step 2A Prong 1:
The system of claim 1, wherein actions taken by a user in connection with a smart measure or associated dataset are recorded by an action recorder and analyzed to extract activity data and context data, determine patterns, and generate personalization recommendations for users based on the actions of other users. (This step for remembering and analyzing the smart metric to recognize patterns to make semantic connections to personal preferences is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process.)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 17:
Step 2A Prong 1:
The system of claim 1, wherein the data analytic system determines a smart measure for a dataset by collecting metadata that indicates when an analytic metric or measure is trending upwardly or downwardly and meets a threshold that causes the system to associate the trending information with a smart measure. (This step for determining the smart metric from data summaries to recognize patterns and comparing the pattern to a predetermined value is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process.)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 18:
Step 2A Prong 1:
The system of claim 1, wherein the system determines which users may have used similar analytic metrics or measures, and associates the smart measure with an indication as to particular users who may be interested in the smart measure, and records such information in an associated metadata. (This step for determining that the smart metric has been interacted with a number of times by remembering, and storing the determination to memory is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process.)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 19:
Step 2A Prong 1:
The system of claim 1, wherein the system interprets input from a user as text input, associated with a particular dataset or changes therein, and uses natural language processing techniques in determining an appropriate smart measure. (This step for determining the smart metric from remembered textual data processing is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process.)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 20:
Step 2A Prong 1:
The system of claim 1, wherein the system identifies relationships between metric indicators by analyzing user logs to find indicators that are viewed closely in time by that user. (This step for determining a semantic correspondence between determined values based on remembered textual queries within a time period is practically implementable in the human mind with a pen and paper and is understood to be a recitation of a mental process.)
Step 2A, Prong 2: This judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elisseeff (U.S. Patent Application US20190095507A1)
Regarding Claim 1, Elisseeff teaches:
A system for providing dynamically-generated analytic data metrics or measures, for use with a data analytics environment, comprising: (Elisseeff teaches in Fig. 22, data analysis environment which is represented by the analysis page with several metrics (activations, revenues, call volume).)
a computer including one or more processors, that provides access to and a data analytics environment including a data analytic system provided thereon, wherein the data analytic system comprises: (Elisseeff teaches in 0188 “general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods.” Which describes that the methods enclosed are applied by a processor. As described by paragraph 0089, “The present disclosure, in varying embodiments described in this Detailed Description, relates to systems and methods for supplying information to a user through one or more autonomous virtual analysts (“AVA”). In embodiments disclosed herein, an AVA may substitute for or otherwise provide the equivalent functions of a financial or business analyst (reads on analytic system and environment)” and paragraph 0090, the AVA system is used by the system to gather information relating to financial or business fields, which finally as seen by Fig. 28 describes a data analytics environment with the data analytical system (AVA).)
a crawl subsystem that operates to generate a data index for use by the system in processing a received data request or query; (Elisseeff teaches in 0150 – 0151 a recommendation engine that goes through a repository and searches for metrics that fit a desired query. This is understood to be the crawl subsystem, as the system generates “may comprise creation of a new comparison of related data sets, or generation of additional reports and projections” which are data indexes as taught by 152 as they are specific “shape, color, pattern, image” [152] which is tied to each category of the data sets. Overall, the system by use of the recommendation engine of Elisseeff generates data index based on data collection (data crawl) on a specific repository which is used to output data based on a received query.)
a query subsystem that operates with a semantic data model and the data index to provide semantic analysis of the received data request or query; (Elisseeff teaches in 0150 – 0151, which teach a recommendation engine (reads on also the query subsystem as the engine both data crawls and handles queries as taught by paragraph 0150) that operates on a repository with reports (semantic data model) and then after analyzing the set of data in the model, recommends alternative routines or processes based on the received request.)
and a data metrics subsystem including a measures generator that assess changes in data values or data metrics and identifies and tracks patterns of use for each of one or more users, including viewing habits or actions that are identified as occurring based on the changes in data values or data metrics; (Elisseeff teaches paragraph 0139, which teaches: “Changes in status or actions taken by the user or a AVA may result in a step of changing indicia associated with the record(s) and viewable on the user interface, and may further results in the step of generating an updated message to the users involved in the request” which teaches tracking actions by a user on the site based on the data. The measures generator then, is the insight generator model 175 (Fig 1) which generates insights of the data.)
and wherein the system operates in accordance with a defined smart measure to monitor its associated data and broadcast analytic information describing the associated data to subscribed listeners, as data visualizations including one or more detected anomalies, trends, or changes within the data. (Elisseeff teaches in 0120, “Once the system detects anomalies with the performance or state of specific nodes, the Driver Graph is configured to determine the root cause of such anomalies to generate a useful business insight.” Elisseeff also teaches in 153 that the analytic information about these anomalies is broadcasted to the user.)
However, Elisseeff does teach in another embodiment the limitations.
wherein the data metrics subsystem automatically generates analytic data metrics operating as smart measures by observing, based on the system learning that a particular user or community of users regularly accesses a particular data or a particular analytic data metric, or performs actions in response to changes in such data or data metric, wherein each dynamically-generated analytic data metric operating as a smart measure is scoped to a dataset and associated with a metadata that indicates a scope of data of interest to a user or group of users (Elisseeff teaches in 0092-0093 “In other embodiments, the AVA may be configured to automatically determine the appropriate reporting and analysis to supply to the user in response to an inquiry, instruction or command, including through the use of driver graph logic described in greater detail below…[and] perform historical context analysis and determine whether other users are making similar or related requests”. Which describes that the system determined dynamically-generated data points (as Elisseeff teaches in 0090, the data points are recognized as metrics of the data, such as revenue, income, profit loss.) Furthermore, as seen by Fig. 3A of Elisseeff. the nodes are understood to be the data points that are generated for the user).)
Elisseeff is/are analogous art to the present invention, as it/they are in the same field of endeavor, that being creating means of analyzing data analytics environments effectively. It would have been obvious to one of ordinary skill to which said subject matter pertains before the effective filling date of the invention to combine the separate embodiments of Elisseeff as it would lead to the known result of allowing the system to automatically decide what data measures are most applicable to the user.
Regarding Claim 2, Elisseeff teaches all the limitations of Claim 1, and further teaches: The system of claim 1, wherein smart measures are automatically discovered and updated by the system based on the system observing that a particular user or community of users regularly accesses a particular type of a data or particular analytic data metric or preforms actions in response to changes in such data or data metric. (Elisseeff teaches in 0109 “In other embodiments, the AVA is able to recognize the different interrelationships between the one or more nodes and establish rules and/or methodologies without assistance of the user.” Which teaches that as nodes are found, they are automatically updated with connections. Elisseeff further teaches in paragraph 0141 “This method also has the ability to test links and edges between nodes 2050 and determine if current behavior and changes to nodes is expected and explained by the current graph. If any nodes or edges are found invalid, the driver graph may be autonomously updated 2070 until the graph is fully verified. Alternatively, a user may be alerted of the invalid node(s) or edge(s) and take appropriate action” which teaches that the system of smart measures (nodes) is updated over time.)
Regarding Claim 3, Elisseeff teaches all the limitations of Claim 2, and further teaches: The system of claim 2, wherein the data analytic system records an indication of updated knowledge/understanding as an updated metadata/rules associated with a smart measure. (Elisseeff further teaches in paragraph 0109 “As additional nodes 210 are identified, the AVA may assign previously determined relationships between the one or more additional nodes. The relationships may be sophisticated and evolve into dimensional hierarchies 60, which may follow established or ad hoc rules or methodologies.” Which describes the updates to the dimensional hierarchies based on the updated changes.)
Regarding Claim 4, Elisseeff teaches all the limitations of Claim 3, and further teaches: The system of claim 3, wherein once a smart measure has been defined and associated with a user, or group of users, the smart measure is communicated, broadcast, or otherwise provided to the user, or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (Elisseeff teaches in 0139, “Changes in status or actions taken by the user or a AVA may result in a step of changing indicia associated with the record(s) and viewable on the user interface, and may further results in the step of generating an updated message to the users involved in the request.” That the smart measures shown by record(s), when updated, are sent out to the users. Elisseeff teaches in 0094 “In embodiments, the system and each AVA is advantageously configured to receive and send information by, for example, a user's mobile device” which teaches that mobile device is one of the means for accessing the AVA holding the nodes (smart measures) and further teaches in 0112 “In one embodiment, the application 110 is designed to operate on a mobile device or mobile computer and assist a user with managing data and providing organization among the AVA.”)
Regarding Claim 5, Elisseeff teaches all the limitations of Claim 1, and further teaches: The system of claim 1, wherein the system applies conditional formatting to present the analytic data metric provided by a smart measure in a readily-identifiable format. (Elisseeff teaches in Fig. 25 an example of a display that shows the data metrics (Activations for example) which is in a readily-identifiable format and conditionally formatted based on the data metric (the data metrics are shown in different ways based on the type).)
Regarding Claim 6, Elisseeff teaches:
A method for providing dynamically-generated analytic data metrics or measures, for use with a data analytics environment, comprising: (Elisseeff teaches in Fig. 22, the data analysis environment which is represented by the analysis page with several metrics (activations, revenues, call volume).)
providing, by a computer including one or more processors, that provides access to a data analytics environment including a data analytic system, wherein the analytic system comprises: (Elisseeff teaches in 0188 “general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods.” Which describes that the methods enclosed are applied by a processor. As described by paragraph 0089, “The present disclosure, in varying embodiments described in this Detailed Description, relates to systems and methods for supplying information to a user through one or more autonomous virtual analysts (“AVA”). In embodiments disclosed herein, an AVA may substitute for or otherwise provide the equivalent functions of a financial or business analyst” and paragraph 0090, the AVA system is used by the system to gather information relating to financial or business fields, which finally as seen by Fig. 28 describes a data analytics environment with the data analytical system (AVA).)
a crawl subsystem that operates to generate a data index for use by the system in processing a received data request or query; (Elisseeff teaches in 0150 – 0151 a recommendation engine that goes through a repository and searches for metrics that fit a desired query. This is understood to be the crawl subsystem, as the system generates “may comprise creation of a new comparison of related data sets, or generation of additional reports and projections” which are data indexes as taught by 152 as they are specific “shape, color, pattern, image” [152] which is tied to each category of the data sets. Overall, the system by use of the recommendation engine of Elisseeff generates data index based on data collection (data crawl) on a specific repository which is used to output data based on a received query.)
a query subsystem that operates with a semantic data model and the data index to provide semantic analysis of the received data request or query; (Elisseeff teaches in 0150 – 0151, which teach a recommendation engine (reads on also the query subsystem as the engine both data crawls and handles queries as taught by paragraph 0150) that operates on a repository with reports (semantic data model) and then after analyzing the set of data in the model, recommends alternative routines or processes based on the received request.)
and a data metrics subsystem including a measures generator that assess changes in data values or data metrics and identifies and tracks patterns of use for each of one or more users, including viewing habits or actions that are identified as occurring based on the changes in data values or data metrics; (Elisseeff teaches paragraph 0139, which teaches: “Changes in status or actions taken by the user or a AVA may result in a step of changing indicia associated with the record(s) and viewable on the user interface, and may further results in the step of generating an updated message to the users involved in the request” which teaches tracking actions by a user on the site based on the data. The measures generator then, is the insight generator model 175 (Fig 1) which generates insights of the data.)
and wherein the system operates in accordance with a defined smart measure to monitor its associated data and broadcast analytic information describing the associated data to subscribed listeners, including one or more detected anomalies, trends, or changes within the data. (Elisseeff teaches in 0120, “Once the system detects anomalies with the performance or state of specific nodes, the Driver Graph is configured to determine the root cause of such anomalies to generate a useful business insight.” Elisseeff also teaches in 153 that the analytic information about these anomalies is broadcasted to the user.)
Elisseeff does not disclose in the current embodiment:
wherein the data metrics subsystem automatically generates analytic data metrics operating as smart measures by observing, based on the system learning that a particular user or community of users regularly accesses a particular data or a particular analytic data metric, or performs actions in response to changes in such data or data metric, wherein each dynamically-generated analytic data metric operating as a smart measure is scoped to a dataset and associated with a metadata that indicates a scope of data of interest to a user or group of users
However, Elisseeff does teach in another embodiment the limitations.
wherein the data metrics subsystem automatically generates analytic data metrics operating as smart measures by observing, based on the system learning that a particular user or community of users regularly accesses a particular data or a particular analytic data metric, or performs actions in response to changes in such data or data metric, wherein each dynamically-generated analytic data metric operating as a smart measure is scoped to a dataset and associated with a metadata that indicates a scope of data of interest to a user or group of users (Elisseeff teaches in 0092-0093 “In other embodiments, the AVA may be configured to automatically determine the appropriate reporting and analysis to supply to the user in response to an inquiry, instruction or command, including through the use of driver graph logic described in greater detail below…[and] perform historical context analysis and determine whether other users are making similar or related requests”. Which describes that the system determined dynamically-generated data points (as Elisseeff teaches in 0090, the data points are recognized as metrics of the data, such as revenue, income, profit loss.) Furthermore, as seen by Fig. 3A of Elisseeff. the nodes are understood to be the data points that are generated for the user).)
Elisseeff is/are analogous art to the present invention, as it/they are in the same field of endeavor, that being creating means of analyzing data analytics environments effectively. It would have been obvious to one of ordinary skill to which said subject matter pertains before the effective filling date of the invention to combine the separate embodiments of Elisseeff as it would lead to the known result of allowing the system to automatically decide what data measures are most applicable to the user.
Regarding Claim 7, Elisseeff teaches all the limitations of Claim 6, and further teaches: The method of claim 6, wherein smart measures are automatically discovered and updated by the system based on the system observing that a particular user or community of users regularly accesses a particular type of a data or particular analytic data metric or preforms actions in response to changes in such data or data metric. (Elisseeff teaches in 0109 “In other embodiments, the AVA is able to recognize the different interrelationships between the one or more nodes and establish rules and/or methodologies without assistance of the user.” Which teaches that as nodes are found, they are automatically updated with connections. Elisseeff further teaches in paragraph 0141 “This method also has the ability to test links and edges between nodes 2050 and determine if current behavior and changes to nodes is expected and explained by the current graph. If any nodes or edges are found invalid, the driver graph may be autonomously updated 2070 until the graph is fully verified. Alternatively, a user may be alerted of the invalid node(s) or edge(s) and take appropriate action” which teaches that the system of smart measures (nodes) is updated over time.)
Regarding Claim 8, Elisseeff teaches all the limitations of Claim 7, and further teaches: The method of claim 7, wherein the data analytic system records an indication of updated knowledge/understanding as an updated metadata/rules associated with a smart measure. (Elisseeff further teaches in paragraph 0109 “As additional nodes 210 are identified, the AVA may assign previously determined relationships between the one or more additional nodes. The relationships may be sophisticated and evolve into dimensional hierarchies 60, which may follow established or ad hoc rules or methodologies.” Which describes the updates to the dimensional hierarchies based on the updated changes.)
Regarding Claim 9, Elisseeff teaches all the limitations of Claim 8, and further teaches: The method of claim 8, wherein once a smart measure has been defined and associated with a user, or group of users, the smart measure is communicated, broadcast, or otherwise provided to the user, or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (Elisseeff teaches in 0139, “Changes in status or actions taken by the user or a AVA may result in a step of changing indicia associated with the record(s) and viewable on the user interface, and may further results in the step of generating an updated message to the users involved in the request.” That the smart measures shown by record(s), when updated, are sent out to the users. Elisseeff teaches in 0094 “In embodiments, the system and each AVA is advantageously configured to receive and send information by, for example, a user's mobile device” which teaches that mobile device is one of the means for accessing the AVA holding the nodes (smart measures) and further teaches in 0112 “In one embodiment, the application 110 is designed to operate on a mobile device or mobile computer and assist a user with managing data and providing organization among the AVA.”)
Regarding Claim 10, Elisseeff teaches all the limitations of Claim 6, and further teaches: The method of claim 6, wherein the system applies conditional formatting to present the analytic data metric provided by a smart measure in a readily-identifiable format. (Elisseeff teaches in Fig. 25 an example of a display that shows the data metrics (Activations for example) which is in a readily-identifiable format and conditionally formatted based on the data metric (the data metrics are shown in different ways based on the type).)
Regarding Claim 11, Elisseeff teaches:
A non-transitory computer readable storage medium having instructions thereon, which when read and executed by a computer including one or more processors cause the computer to perform a method comprising: (Elisseeff teaches in paragraph 0176 “The application/modules are preferably configured to run on a computer server or similar computational machinery.” And Elisseeff teaches in paragraph 0188 “It should also be appreciated that the methods described above may be performed by hardware components or may be embodied in sequences of executable instructions on machine-readable media, and which cause a machine, such as a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods.”)
providing, by a computer including one or more processors, that provides access to a data analytics environment including a data analytic system, wherein the analytic system comprises: (Elisseeff teaches in 0188 “general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods.” Which describes that the methods enclosed are applied by a processor. As described by paragraph 0089, “The present disclosure, in varying embodiments described in this Detailed Description, relates to systems and methods for supplying information to a user through one or more autonomous virtual analysts (“AVA”). In embodiments disclosed herein, an AVA may substitute for or otherwise provide the equivalent functions of a financial or business analyst” and paragraph 0090, the AVA system is used by the system to gather information relating to financial or business fields, which finally as seen by Fig. 28 describes a data analytics environment with the data analytical system (AVA).)
a crawl subsystem that operates to generate a data index for use by the system in processing a received data request or query; (Elisseeff teaches in 0150 – 0151 a recommendation engine that goes through a repository and searches for metrics that fit a desired query. This is understood to be the crawl subsystem, as the system generates “may comprise creation of a new comparison of related data sets, or generation of additional reports and projections” which are data indexes as taught by 152 as they are specific “shape, color, pattern, image” [152] which is tied to each category of the data sets. Overall, the system by use of the recommendation engine of Elisseeff generates data index based on data collection (data crawl) on a specific repository which is used to output data based on a received query.)
a query subsystem that operates with a semantic data model and the data index to provide semantic analysis of the received data request or query; (Elisseeff teaches in 0150 – 0151, which teach a recommendation engine (reads on also the query subsystem as the engine both data crawls and handles queries as taught by paragraph 0150) that operates on a repository with reports (semantic data model) and then after analyzing the set of data in the model, recommends alternative routines or processes based on the received request.)
and a data metrics subsystem including a measures generator that assess changes in data values or data metrics and identifies and tracks patterns of use for each of one or more users, including viewing habits or actions that are identified as occurring based on the changes in data values or data metrics; (Elisseeff teaches paragraph 0139, which teaches: “Changes in status or actions taken by the user or a AVA may result in a step of changing indicia associated with the record(s) and viewable on the user interface, and may further results in the step of generating an updated message to the users involved in the request” which teaches tracking actions by a user on the site based on the data. The measures generator then, is the insight generator model 175 (Fig 1) which generates insights of the data.)
and wherein the system operates in accordance with a defined smart measure to monitor its associated data and broadcast analytic information describing the associated data to subscribed listeners, including one or more detected anomalies, trends, or changes within the data. (Elisseeff teaches in 0120, “Once the system detects anomalies with the performance or state of specific nodes, the Driver Graph is configured to determine the root cause of such anomalies to generate a useful business insight.” Elisseeff also teaches in 153 that the analytic information about these anomalies is broadcasted to the user.)
Elisseeff does not disclose in the current embodiment:
wherein the data metrics subsystem automatically generates analytic data metrics operating as smart measures by observing, based on the system learning that a particular user or community of users regularly accesses a particular data or a particular analytic data metric, or performs actions in response to changes in such data or data metric, wherein each dynamically-generated analytic data metric operating as a smart measure is scoped to a dataset and associated with a metadata that indicates a scope of data of interest to a user or group of users
However, Elisseeff does teach in another embodiment the limitations.
wherein the data metrics subsystem automatically generates analytic data metrics operating as smart measures by observing, based on the system learning that a particular user or community of users regularly accesses a particular data or a particular analytic data metric, or performs actions in response to changes in such data or data metric, wherein each dynamically-generated analytic data metric operating as a smart measure is scoped to a dataset and associated with a metadata that indicates a scope of data of interest to a user or group of users (Elisseeff teaches in 0092-0093 “In other embodiments, the AVA may be configured to automatically determine the appropriate reporting and analysis to supply to the user in response to an inquiry, instruction or command, including through the use of driver graph logic described in greater detail below…[and] perform historical context analysis and determine whether other users are making similar or related requests”. Which describes that the system determined dynamically-generated data points (as Elisseeff teaches in 0090, the data points are recognized as metrics of the data, such as revenue, income, profit loss.) Furthermore, as seen by Fig. 3A of Elisseeff. the nodes are understood to be the data points that are generated for the user).)
Elisseeff is/are analogous art to the present invention, as it/they are in the same field of endeavor, that being creating means of analyzing data analytics environments effectively. It would have been obvious to one of ordinary skill to which said subject matter pertains before the effective filling date of the invention to combine the separate embodiments of Elisseeff as it would lead to the known result of allowing the system to automatically decide what data measures are most applicable to the user.
Regarding Claim 12, Elisseeff teaches all the limitations of Claim 11, and further teaches: The non-transitory computer readable storage medium of claim 11, wherein smart measures are automatically discovered and updated by the system based on the system observing that a particular user or community of users regularly accesses a particular type of a data or particular analytic data metric or preforms actions in response to changes in such data or data metric. (Elisseeff teaches in 0109 “In other embodiments, the AVA is able to recognize the different interrelationships between the one or more nodes and establish rules and/or methodologies without assistance of the user.” Which teaches that as nodes are found, they are automatically updated with connections. Elisseeff further teaches in paragraph 0141 “This method also has the ability to test links and edges between nodes 2050 and determine if current behavior and changes to nodes is expected and explained by the current graph. If any nodes or edges are found invalid, the driver graph may be autonomously updated 2070 until the graph is fully verified. Alternatively, a user may be alerted of the invalid node(s) or edge(s) and take appropriate action” which teaches that the system of smart measures (nodes) is updated over time.)
Regarding Claim 13, Elisseeff teaches all the limitations of Claim 12, and further teaches: The non-transitory computer readable storage medium of claim 12, wherein the data analytic system records an indication of updated knowledge/understanding as an updated metadata/rules associated with a smart measure. (Elisseeff further teaches in paragraph 0109 “As additional nodes 210 are identified, the AVA may assign previously determined relationships between the one or more additional nodes. The relationships may be sophisticated and evolve into dimensional hierarchies 60, which may follow established or ad hoc rules or methodologies.” Which describes the updates to the dimensional hierarchies based on the updated changes.)
Regarding Claim 14, Elisseeff teaches all the limitations of Claim 13, and further teaches: The non-transitory computer readable storage medium of claim 13, wherein once a smart measure has been defined and associated with a user, or group of users, the smart measure is communicated, broadcast, or otherwise provided to the user, or to other users as contextually appropriate across different/various types of computing environments, including one or more mobile devices, web browsers, on-premise system, or third-party applications. (Elisseeff teaches in 0139, “Changes in status or actions taken by the user or a AVA may result in a step of changing indicia associated with the record(s) and viewable on the user interface, and may further results in the step of generating an updated message to the users involved in the request.” That the smart measures shown by record(s), when updated, are sent out to the users. Elisseeff teaches in 0094 “In embodiments, the system and each AVA is advantageously configured to receive and send information by, for example, a user's mobile device” which teaches that mobile device is one of the means for accessing the AVA holding the nodes (smart measures) and further teaches in 0112 “In one embodiment, the application 110 is designed to operate on a mobile device or mobile computer and assist a user with managing data and providing organization among the AVA.”)
Regarding Claim 15, Elisseeff teaches all the limitations of Claim 11, and further teaches: The non-transitory computer readable storage medium of claim 11, wherein the system applies conditional formatting to present the analytic data metric provided by a smart measure in a readily-identifiable format. (Elisseeff teaches in Fig. 25 an example of a display that shows the data metrics (Activations for example) which is in a readily-identifiable format and conditionally formatted based on the data metric (the data metrics are shown in different ways based on the type).)
Regarding Claim 16, Elisseeff teaches all the limitations of Claim 1, and further teaches: The system of claim 1, wherein actions taken by a user in connection with a smart measure or associated dataset are recorded by an action recorder and analyzed to extract activity data and context data, determine patterns, and generate personalization recommendations for users based on the actions of other users. (Elisseeff teaches in paragraphs 0092-0093 “the AVA may be further configured to perform historical context analysis and determine whether other users are making similar or related requests, thereby reducing processing and analyzing time required to provide the requested information. In embodiments, the AVA may report to a primary user that number of instances in which multiple requests for the same reports or information have occurred within a set time period”, and thus “supply the user with specific reports, graphs, analysis and insights in a predetermined or independent manner”)
Regarding Claim 17, Elisseeff teaches all the limitations of Claim 1, and further teaches: The system of claim 1, wherein the data analytic system determines a smart measure for a dataset by collecting metadata that indicates when an analytic metric or measure is trending upwardly or downwardly and meets a threshold that causes the system to associate the trending information with a smart measure. (Elisseeff teaches in paragraph 0090 “the AVAs provide analysis and business intelligence relating to revenue, income, profit, loss, expenses, historical data, projections, trends”; and paragraphs 0143-0144 further teaches “[c]orrelation may be coupled with the anomaly detection functions described above to spot trends, or to alert the user of upward and downward trends that are difficult to spot with traditional tools. Thus, by transforming the metrics to include specific product purchases, the system may determine which groups of customers are likely to purchase a product”; mapped to hierarchical values)
Regarding Claim 18, Elisseeff teaches all the limitations of Claim 1, and further teaches: The system of claim 1, wherein the system determines which users may have used similar analytic metrics or measures, and associates the smart measure with an indication as to particular users who may be interested in the smart measure, and records such information in an associated metadata. (Elisseeff teaches in 0092-0093 “the AVA may be further configured to perform historical context analysis and determine whether other users are making similar or related requests, thereby reducing processing and analyzing time required to provide the requested information. In embodiments, the AVA may report to a primary user that number of instances in which multiple requests for the same reports or information have occurred within a set time period”. Paragraph 0162 further teaches processing and outputting corresponding “meta-data” by the AVA)
Regarding Claim 19, Elisseeff teaches all the limitations of Claim 1, and further teaches: The system of claim 1, wherein the system interprets input from a user as text input, associated with a particular dataset or changes therein, and uses natural language processing techniques in determining an appropriate smart measure. (Elisseeff teaches in paragraphs 0090 and 0092-0093 “AVA possesses the capability to engage in natural language dialog with one or more users and receive and understand various inquiries, instructions and commands” when querying the data and computing metrics)
Regarding Claim 20, Elisseeff teaches all the limitations of Claim 1, and further teaches: The system of claim 1, wherein the system identifies relationships between metric indicators by analyzing user logs to find indicators that are viewed closely in time by that user. (Elisseeff teaches in 0092-0093 “In other embodiments, the AVA may be configured to automatically determine the appropriate reporting and analysis to supply to the user in response to an inquiry, instruction or command, including through the use of driver graph logic described in greater detail below.” Further, “the AVA may be further configured to perform historical context analysis and determine whether other users are making similar or related requests, thereby reducing processing and analyzing time required to provide the requested information. In embodiments, the AVA may report to a primary user that number of instances in which multiple requests for the same reports or information have occurred within a set time period.”)
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Friedman et al (US Pub 20220165007) teaches utilizing machine learning to recommend semantic searches to a user query for display on a visualization screen.
Conclusion
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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123