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 .
DETAILED ACTION
Applicant has amended claims 1, 7, 13, canceled claims 2, 4, 8, 10, 14, 16, 19-20 and added claims 21-27 in the filed amendment on 6/29/2026.
Claims 1, 3, 5-7, 9, 11-13, 15, 17-18, 21-28 are pending in this office action.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3, 5-7, 9, 11-13, 15, 17-18, 21-28 have been considered but are moot in new ground of rejection.
For 101 rejection:
Applicant argued that claims 1, 7, and 13 have been amended to overcome 101 rejection. Independent claims 1, 7, and 13 as amended, recite a computer-implemented Al/time-series workflow in which first time- series data from a time-series database is analyzed using a trained time-series analysis operator comprising one or more AI models, and a second model parameter is determined by applying a specific algorithm, such as a moving average algorithm, EMA, or SGD, to the first time-series data and the first model parameter. These AI-model parameter updates are not practically performable in the human mind. Furthermore, the claims integrate it into a practical application by improving AI- based time-series analysis using the updated operator for later time-series data to avoid repeated. Accordingly, the claims recite significantly more than any alleged abstract idea and are patent-eligible under §101.
Examiner respectfully disagrees.
Merely applying the mental process (i.e. analyzing the first time series data or determining a second model parameter……) by using a computer or computer environment as a tool (e.g., a trained time-series analysis operator comprising one or more AI models or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm) is not an improvement and does not negate the identified mental process. Rather this appears to be nothing more than mere instructions to apply the abstract idea on a computer or in a computer environment as per MPEP 2106.05(f). More specifically, the amendment essentially recites using a trained time-series analysis operator comprising one or more AI models as a tool or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm as tool to perform the abstract idea. Generic parallel processing is not any improvement to the function of the computer or technology, there is no recitation of any new or improved way or parallelizing operations. Instead it is generic computer implementation, consistent with the Federal Circuit decision for instance in SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1170 (Fed. Cir. 2018) (rejecting the argument that “the inclusion of a ‘parallel processing’ computing architecture in” a claim makes the claim patent eligible when “neither the claims nor the specification call for any parallel processing architectures different from those available in existing systems”).
In addition:
Claims 1, 7, 13 recite abstract idea of
(analyzing, in response to the first analysis mode, the first time series data
updating or update the first time series analysis operator based on the first time series data and the first model parameter by:
determining a second model parameter based on by applying at least one of a
moving average algorithm, an exponential moving average algorithm, or a stochastic
gradient descent (SGD) algorithm to the first time series data and the first model parameter;
determining an updated first time series analysis operator based on the second model parameter;
analyzing or analyze the second time series data using the updated first time series analysis operator) as drafted, is a process or system or medium that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The human mind can perform step of analyzing, updating, determining, determining and analyzing. Accordingly, the claims recite an abstract idea.
b) In analyzing under step 2A Prong Two:
Claims do not recite any additional elements that integrate the judicial exception into a practical application because additional elements of one or more processor; using a first time series analysis operator comprising one or more artificial intelligence models having a first model parameter (in claims 1, 7, 13); a memory configured to store instructions; and one or more processors coupled to the memory, wherein when executed by the one or more processors, the instructions cause the apparatus to (in claim 7); and computer-executable instructions that are stored on a non-transitory computer-readable storage medium and that, when executed by one or more processors, cause an apparatus to (claim 13) that are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component for obtaining that are well understood routine and conventional activities.
The additional limitation of (receiving or receive a first analysis mode selected through a user interface; retrieving or retrieve, from a time-series database, first time-series data; retrieving or retrieve, from the time-series database, second time-series data occurring after the first time-series data) that just indicates ordering of first data and second data and that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
The additional limitation of (wherein the first time series analysis operator is trained based on third time series data that is before the first time-series data) that just indicates operator trained based on data.
In addition:
Merely applying the mental process (i.e. analyzing the first time series data or determining a second model parameter) by using a computer or computer environment as a tool (e.g., a trained time-series analysis operator comprising one or more AI models or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm) is not an improvement and does not negate the identified mental process. Rather this appears to be nothing more than mere instructions to apply the abstract idea on a computer or in a computer environment as per MPEP 2106.05(f). More specifically, the amendment essentially recites using a trained time-series analysis operator comprising one or more AI models as a tool or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm as tool to perform the abstract idea. Generic parallel processing is not any improvement to the function of the computer or technology, there is no recitation of any new or improved way or parallelizing operations. Instead it is generic computer implementation, consistent with the Federal Circuit decision for instance in SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1170 (Fed. Cir. 2018) (rejecting the argument that “the inclusion of a ‘parallel processing’ computing architecture in” a claim makes the claim patent eligible when “neither the claims nor the specification call for any parallel processing architectures different from those available in existing systems”).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
c) In analyzing under step 2B:
Claims do not recite any additional elements that amount to significantly more than the judicial exception because additional elements of one or more processor; using a first time series analysis operator comprising one or more artificial intelligence models having a first model parameter (in claims 1, 7, 13); a memory configured to store instructions; and one or more processors coupled to the memory, wherein when executed by the one or more processors, the instructions cause the apparatus to (in claim 7); and computer-executable instructions that are stored on a non-transitory computer-readable storage medium and that, when executed by one or more processors, cause an apparatus to (claim 13) that are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component for obtaining that are well understood routine and conventional activities.
The additional limitation of (receiving or receive a first analysis mode selected through a user interface; retrieving or retrieve, from a time-series database, first time-series data, retrieving or retrieve, from the time-series database, second time-series data occurring after the first time-series data) that just indicates ordering of first data and second data and that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
In addition:
Merely applying the mental process (i.e. analyzing the first time series data or determining a second model parameter……) by using a computer or computer environment as a tool (e.g., a trained time-series analysis operator comprising one or more AI models or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm) is not an improvement and does not negate the identified mental process. Rather this appears to be nothing more than mere instructions to apply the abstract idea on a computer or in a computer environment as per MPEP 2106.05(f). More specifically, the amendment essentially recites using a trained time-series analysis operator comprising one or more AI models as a tool or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm as tool to perform the abstract idea. Generic parallel processing is not any improvement to the function of the computer or technology, there is no recitation of any new or improved way or parallelizing operations. Instead it is generic computer implementation, consistent with the Federal Circuit decision for instance in SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1170 (Fed. Cir. 2018) (rejecting the argument that “the inclusion of a ‘parallel processing’ computing architecture in” a claim makes the claim patent eligible when “neither the claims nor the specification call for any parallel processing architectures different from those available in existing systems”).
The additional limitation of (wherein the first time series analysis operator is trained based on third time series data that is before the first time-series data) that just indicates operator trained based on data.
Accordingly, these additional elements do not amount to significantly more than the judicial exception. The claims are not patent eligible.
For 103 rejection:
Applicant argued that amended claims overcome 103 rejection.
In response to Applicant’s argument claims are rejected under new ground.
In addition, Hsiao teaches limitations
“receiving a first analysis mode selected through a user interface” as in response to the selection of either user-interface element 1766 or user-interface element 1768 as a first analysis mode, the GUI may further group event stream information shown in the table (figs. 17A-17B, paragraph 254) that indicates the user-interface element 1766 or 1768 as a first analysis mode received and selected via a user interface (figs. 17A-17B, paragraph 254). In particularly: Selection of one user-interface element 1766-1768 may result in the automatic deselection of the other user-interface element. In response to the selection of either user-interface element 1766 or user-interface element 1768, the GUI may further group event stream information shown in the table by the event stream lifecycle represented by the selected user-interface element. For example, the GUI may show only permanent event streams that match the “HTTP” protocol classification in the table of FIG. 17A because user-interface element 1766 and “HTTP” are selected (paragraph 254);
“……first time-series data” as displaying on interface a first time series event data as first time series data e.g., first row in table using a first table that includes columns 1750, 1734-1750 is represented as a first time series analysis operator (fig. 17C, paragraphs 273-274, 281);
“analyzing, in response to the first analysis mode, the first time series data using a first time series analysis operator comprising……having a first model parameter” as grouping as analyzing, in response to the user-interface element 1766 or 1768 as the first analysis mode, first timestamped event as first time series data in first row using a table that includes timestamped events is represented as a first time series analysis operator comprising rows having user-interface element(s) 1720, 1730 (fig. 17B) 1734, or 1750 as a first model parameter (fig. 17D) (paragraphs 254-255, 259-260, 273-274, figs. 17A-17D); or user-interface element 1720 as a first model parameter (fig. 17B, paragraph 257-259);
“updating the first time series analysis operator based on the first time series data and the first model parameter by: ” as updating e.g., including events in the result table as the first time series analysis operator based on the first timestamped event as the first time series data and the user-interface element(s) e.g., 1720 by: (figs. 17C-17D, paragraphs 273-274);
“determining a second model parameter by applying ……to the first time series data and the first model parameter” as displaying as determining a graphic-user element e.g., a column 1734 as a second model parameter by applying a search using user-interface element 1728 to the result table that includes the first timestamped event as the first time series data and the user-interface element(s) 1710 or 1720 as the first model parameter (figs. 17A-17B, paragraphs 272-273);
“determining an updated first time series analysis operator based on the second model parameter” as displaying an updated result table that includes additional timestamped event(s) is represented as an updated first time series analysis operator based on a graphic-user element e.g., a column 1734 as the second model parameter when the column 1734 is selected by a user (figs. 17C-17D, paragraphs 273-274).
For example, selection of the “Group_A” value in column 1734 may cause the GUI to navigate to a screen showing events and the corresponding timestamps of the ephemeral event streams of “Group_A,” graphs of metrics related to the events, and/or other information associated with the events (paragraph 274);
“retrieving, from the time-series database, second time-series data” as retrieving, from data store that stores timestamped events, timestamped events that includes a second timestamped event as second time-series data (paragraphs 69-70, 99-100, fig. 6A);
“……the updated first time series analysis operator” as a table that includes addition event streams e.g., HTTP-80, HTTP-20, is represented as updated first time series analysis operator and the GUI (fig. 17D, paragraph 287).
Esman teaches limitations
“retrieve, from a time-series database, first time-series data” as retrieve, from data store that stores timestamped events, timestamped events that includes a first timestamped event as first time-series data (paragraphs 66, 119-120, fig. 6A);
“retrieving, from the time-series database, second time-series data occurring after the first time-series data” as retrieve, from data store that stores timestamped events, timestamped events that includes second timestamped event as second time-series data having time 1:32:46pm occurring after first timestamped event as first time-series data having time 1:32:45pm (paragraphs 66, 119-120, figs. 6A-6B);
“analyzing the second time series data using……” as filtering as analyzing search results that includes the second timestamped event as the second time series data using filter 1401 (paragraph 137, fig. 14) or viewing each of the returned events that includes the second timestamped event as the second time series data in a different format using fields side bars 606 or 608 (fig. 6A, paragraph 120);
“third time series data that is before the first time-series data” as timestamped event having time 1:32:44 pm is before timestamped event as the first time-series data having time 1:32:45pm (paragraphs 66, 119-120, figs. 6A-6B).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 5-7, 9, 11-13, 15, 17-18, 21-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per Step 1, claim 1 is directed to a method, 7 is directed to an apparatus, and 13 is directed to a computer program product, which are statutory categories of invention per Step 1. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application or are significantly more.
Step 2:
a) In analyzing under step 2A Prong One, Does the claim recite an abstract idea law of nature or natural phenomenon? Yes.
Claims 1, 7, 13 recite abstract idea of
(analyzing or analyze, in response to the first analysis mode, the first time series data;
updating or update the first time series analysis operator based on the first time series data and the first model parameter by:
determining a second model parameter based on by applying at least one of a
moving average algorithm, an exponential moving average algorithm, or a stochastic
gradient descent (SGD) algorithm to the first time series data and the first model parameter;
determining an updated first time series analysis operator based on the second model parameter;
analyzing or analyze the second time series data using the updated first time series analysis operator) as drafted, is a process or system or medium that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The human mind can perform step of analyzing, updating, determining, determining and analyzing. Accordingly, the claims recite an abstract idea.
b) In analyzing under step 2A Prong Two, Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
Claims do not recite any additional elements that integrate the judicial exception into a practical application because additional elements of one or more processor; using a first time series analysis operator comprising one or more artificial intelligence models having a first model parameter (in claims 1, 7, 13); a memory configured to store instructions; and one or more processors coupled to the memory, wherein when executed by the one or more processors, the instructions cause the apparatus to (in claim 7); and computer-executable instructions that are stored on a non-transitory computer-readable storage medium and that, when executed by one or more processors, cause an apparatus to (claim 13) that are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component for obtaining that are well understood routine and conventional activities.
The additional limitation of (receiving or receive a first analysis mode selected through a user interface; retrieving or retrieve, from a time-series database, first time-series data; retrieving or retrieve, from the time-series database, second time-series data occurring after the first time-series data) that just indicates ordering of first data and second data and that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
The additional limitation of (wherein the first time series analysis operator is trained based on third time series data that is before the first time-series data) that just indicates operator trained based on data.
In addition:
Merely applying the mental process (i.e. analyzing the first time series data or determining a second model parameter…) by using a computer or computer environment as a tool (e.g., a trained time-series analysis operator comprising one or more AI models or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm) is not an improvement and does not negate the identified mental process. Rather this appears to be nothing more than mere instructions to apply the abstract idea on a computer or in a computer environment as per MPEP 2106.05(f). More specifically, the amendment essentially recites using a trained time-series analysis operator comprising one or more AI models as a tool or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm as tool to perform the abstract idea. Generic parallel processing is not any improvement to the function of the computer or technology, there is no recitation of any new or improved way or parallelizing operations. Instead it is generic computer implementation, consistent with the Federal Circuit decision for instance in SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1170 (Fed. Cir. 2018) (rejecting the argument that “the inclusion of a ‘parallel processing’ computing architecture in” a claim makes the claim patent eligible when “neither the claims nor the specification call for any parallel processing architectures different from those available in existing systems”).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
c) In analyzing under step 2B, does the claim recite additional elements that amount to significantly more than the judicial exception? NO
Claims do not recite any additional elements that amount to significantly more than the judicial exception because additional elements of one or more processor; using a first time series analysis operator comprising one or more artificial intelligence models having a first model parameter (in claims 1, 7, 13); a memory configured to store instructions; and one or more processors coupled to the memory, wherein when executed by the one or more processors, the instructions cause the apparatus to (in claim 7); and computer-executable instructions that are stored on a non-transitory computer-readable storage medium and that, when executed by one or more processors, cause an apparatus to (claim 13) that are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component for obtaining that are well understood routine and conventional activities.
The additional limitation of (receiving or receive a first analysis mode selected through a user interface; retrieving or retrieve, from a time-series database, first time-series data; retrieving or retrieve, from the time-series database, second time-series data occurring after the first time-series data) that just indicates ordering of first data and second data and that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
In addition:
Merely applying the mental process (i.e. analyzing the first time series data or determining a second model parameter…) by using a computer or computer environment as a tool (e.g., a trained time-series analysis operator comprising one or more AI models or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm) is not an improvement and does not negate the identified mental process. Rather this appears to be nothing more than mere instructions to apply the abstract idea on a computer or in a computer environment as per MPEP 2106.05(f). More specifically, the amendment essentially recites using a trained time-series analysis operator comprising one or more AI models as a tool or at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm as tool to perform the abstract idea. Generic parallel processing is not any improvement to the function of the computer or technology, there is no recitation of any new or improved way or parallelizing operations. Instead it is generic computer implementation, consistent with the Federal Circuit decision for instance in SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1170 (Fed. Cir. 2018) (rejecting the argument that “the inclusion of a ‘parallel processing’ computing architecture in” a claim makes the claim patent eligible when “neither the claims nor the specification call for any parallel processing architectures different from those available in existing systems”).
The additional limitation of (wherein the first time series analysis operator is trained based on third time series data that is before the first time-series data) that just indicates operator trained based on data.
Accordingly, these additional elements do not amount to significantly more than the judicial exception. The claims are not patent eligible.
Dependent claims 3, 5-6, 9, 11-12, 15, 17-18, 21-28 include all the limitations of claims 1, 7, 13. Therefore, claims 3, 5-6, 9, 11-12, 15, 17-18, 21-28 recite the same abstract idea of processing and generating practically being performed in the mind, and the analysis must therefore proceed to Step 2A Prong Two.
In particularly:
Claims 3, 9, 15, recite abstract idea of (storing or store the second model parameter) that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
Claims 5, 11, 17 recite abstract limitation of (obtaining or obtain the third time series data from the time series database) that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
Claims 6, 12, 18 recite limitations (training a second time series analysis operator based on the first time series data; and analyzing, in response to a second analysis mode input, the first time series data using the second time series analysis operator) as drafted, is a process or system or medium that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The human mind can perform step of training and analyzing. Accordingly, the claims recite an abstract idea.
Claims do not recite any additional elements that amount to significantly more than the judicial exception because additional element of user that is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component for obtaining that are well understood routine and conventional activities.
Claim 21 recites limitation of (wherein the one or more artificial intelligence models are encapsulated and deployed on a computing device) that just indicates model encapsulated and deployed.
Claim 22 recites limitation of (wherein analyzing the first time series data comprises determining abnormal data in the first time series data) is a process or system or medium that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The human mind can perform step of analyzing and determining. Accordingly, the claim recites an abstract idea.
Claim 23 recites limitation of (analyzing the first time series data comprises extracting a feature of the time series data) is a process or system or medium that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The human mind can perform step of analyzing. Accordingly, the claim recites an abstract idea.
The additional limitation of (wherein the feature comprises a periodicity or stability of the first time-series data) that just indicates definition of feature.
Claim 24 recites limitation of (wherein analyzing the first time series data comprises predicting time-series data at a next moment or in a next time period based on the first time-series data) is a process or system or medium that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The human mind can perform step of analyzing and predicting. Accordingly, the claim recites an abstract idea.
Claim 25 recites limitation of (deleting the first model parameter after determining the updated first time series analysis operator) that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
Claim 26 recites limitation of (parsing the SQL statement to determine the first analysis mode) is a process or system or medium that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The human mind can perform step of analyzing and predicting. Accordingly, the claim recites an abstract idea.
The additional limitation of (receiving, from a client at a query node of the time-series database, a structured query language (SQL) statement) that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
Claim 27 recites limitation of (retrieving the first time series data comprises obtaining the first time series data from a portal website) that would be insignificant post-solution data outputting, and are insignificant extra solution activities which are well understood routine and conventional activities, see (Presenting offers and gathering statistics, OIP Techs and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec) and See (MPEP 2106.05(g) or 2106.05(d) for Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp).
Claim 28 recites limitation of ( wherein the one or more artificial intelligence models comprise an autoregressive prediction model having an autoregressive parameter as the first model parameter) that just indicates definition of AI models.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Accordingly, these additional elements do not amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 7, 9, 13, 15, 21-23, 27 are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao (US 20220124183) in view of Esman (US 20210191909) and Anand et al (US 20230177024).
As to claim 1, Hsiao teaches a method implemented by one or more processors (paragraph 323), the method comprising:
“receiving a first analysis mode selected through a user interface” as in response to the selection of either user-interface element 1766 or user-interface element 1768 as a first analysis mode, the GUI may further group event stream information shown in the table (figs. 17A-17B, paragraph 254) that indicates the user-interface element 1766 or 1768 as a first analysis mode received and selected via a user interface (figs. 17A-17B, paragraph 254). In particularly: Selection of one user-interface element 1766-1768 may result in the automatic deselection of the other user-interface element. In response to the selection of either user-interface element 1766 or user-interface element 1768, the GUI may further group event stream information shown in the table by the event stream lifecycle represented by the selected user-interface element. For example, the GUI may show only permanent event streams that match the “HTTP” protocol classification in the table of FIG. 17A because user-interface element 1766 and “HTTP” are selected (paragraph 254);
“……first time-series data” as displaying on interface a first time series event data as first time series data e.g., first row in table using a first table that includes columns 1750, 1734-1750 is represented as a first time series analysis operator (fig. 17C, paragraphs 273-274, 281);
“analyzing, in response to the first analysis mode, the first time series data using a first time series analysis operator comprising……having a first model parameter” as grouping as analyzing, in response to the user-interface element 1766 or 1768 as the first analysis mode, first timestamped event as first time series data in first row using a table that includes timestamped events is represented as a first time series analysis operator comprising rows having user-interface element(s) 1720, 1730 (fig. 17B) 1734, or 1750 as a first model parameter (fig. 17D) (paragraphs 254-255, 259-260, 273-274, figs. 17A-17D); or user-interface element 1720 as a first model parameter (fig. 17B, paragraph 257-259);
“updating the first time series analysis operator based on the first time series data and the first model parameter by: ” as updating e.g., including events in the result table as the first time series analysis operator based on the first timestamped event as the first time series data and the user-interface element(s) e.g., 1720 by: (figs. 17C-17D, paragraphs 273-274);
“determining a second model parameter by applying ……to the first time series data and the first model parameter” as displaying as determining a graphic-user element e.g., a column 1734 as a second model parameter by applying a search using user-interface element 1728 to the result table that includes the first timestamped event as the first time series data and the user-interface element(s) 1710 or 1720 as the first model parameter (figs. 17A-17B, paragraphs 272-273);
“determining an updated first time series analysis operator based on the second model parameter” as displaying an updated result table that includes additional timestamped event(s) is represented as an updated first time series analysis operator based on a graphic-user element e.g., a column 1734 as the second model parameter when the column 1734 is selected by a user (figs. 17C-17D, paragraphs 273-274).
For example, selection of the “Group_A” value in column 1734 may cause the GUI to navigate to a screen showing events and the corresponding timestamps of the ephemeral event streams of “Group_A,” graphs of metrics related to the events, and/or other information associated with the events (paragraph 274);
“retrieving, from the time-series database, second time-series data” as retrieving, from data store that stores timestamped events, timestamped events that includes a second timestamped event as second time-series data (paragraphs 69-70, 99-100, fig. 6A);
“……the updated first time series analysis operator” as a table that includes addition event streams e.g., HTTP-80, HTTP-20, is represented as updated first time series analysis operator and the GUI (fig. 17D, paragraph 287).
Hsiao does not explicitly teach limitations
retrieve, from a time-series database;
one or more artificial intelligence models;
wherein the first time series analysis operator is trained based on third time series data that is before the first time-series data;
at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm;
occurring after the first time-series data;
analyzing the second time series data using.
Esman teaches limitations
“retrieve, from a time-series database, first time-series data” as retrieve, from data store that stores timestamped events, timestamped events that includes a first timestamped event as first time-series data (paragraphs 66, 119-120, fig. 6A);
“retrieving, from the time-series database, second time-series data occurring after the first time-series data” as retrieve, from data store that stores timestamped events, timestamped events that includes second timestamped event as second time-series data having time 1:32:46pm occurring after first timestamped event as first time-series data having time 1:32:45pm (paragraphs 66, 119-120, figs. 6A-6B);
“analyzing the second time series data using……” as filtering as analyzing search results that includes the second timestamped event as the second time series data using filter 1401 (paragraph 137, fig. 14) or viewing each of the returned events that includes the second timestamped event as the second time series data in a different format using fields side bars 606 or 608 (fig. 6A, paragraph 120);
“third time series data that is before the first time-series data” as timestamped event having time 1:32:44 pm is before timestamped event as the first time-series data having time 1:32:45pm (paragraphs 66, 119-120, figs. 6A-6B).
Hsiao and Esman disclose a method of analyzing time-series data and updating an operator based on analyzed time-series data. These references are same field with application’s field. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply Esman’s teaching to Hsiao’s system in order to perform searches of different types of data, to improve time-based searching, to allow for events with recent timestamps, which may have a higher likelihood of being accessed, to be stored in a faster memory to facilitate faster retrieval, to produce a reduced set of search results, and further to speed up queries that are performed on a periodic basis.
Anand teaches limitations
“one or more artificial intelligence models” as one artificial intelligence model (paragraphs 14, 214);
“wherein the first time series analysis operator is trained based on third time series data” as analytical model, which is trained using result data of time series as third time series data, is represented as the first time series analysis operator (abstract, paragraphs 214, 268-269)
“at least one of a moving average algorithm, an exponential moving average algorithm, or a stochastic gradient descent (SGD) algorithm” as moving-average (ARMA) model or moving-average (ARMA) model function as moving average algorithm (paragraph 215).
Hsiao and Anand disclose a method of analyzing time-series data and updating an operator based on analyzed time-series data. These references are same field as application’s field. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply Anand’s teaching to Hsiao’s system in order to present to user a visualization of the results data obtained by executing the analytical model results data-query for viewing quickly, to improve the execution speed of the systems, over available data storage and access systems by reducing the resource utilization, and further to permit access to data more quickly by an order of magnitude or greater
As to claims 3, 9, 15, Hsiao, Esman and Anand each limitation
“wherein when executed by the one or more processors, the instructions further cause the apparatus to store or storing the second model parameter” as instructions are executed by the one or more processors, the instructions cause computer system to (Hsiao: fig. 27, paragraph 323-324, col. Left, page 29) save a data model object (Esman: paragraph 131) that includes a second field 703 of data model is represented as a second model parameter: (Esman: fig. 7A, paragraph 134).
As to claims 5, 11,17, Hsiao, Esman and Anand teach limitations
“ obtaining the third time series data from the time series database” as retrieving time stamped data events that includes a third time stamped data event as the third time series data from a time series data store as a time series database (Esmam: paragraphs 66, 119-120, fig. 6A; Hsiao: fig.4) or
“wherein when executed by the one or more processors, the instructions further cause the apparatus to obtain the third time series data from the time series database” as instructions are executed by the one or more processors, the instructions cause computer system to (Hsiao: fig. 27, paragraph 323-324, col. Left, page 29) retrieve time stamped data events that includes a third time stamped data event as the third time series data from a time series data store as a time series database (Esman: paragraphs 66, 119-120, fig. 6A; Hsiao: fig. 4).
As to claim 6, 12, 18, Hsiao, Esman and Anand teach limitations
“wherein when executed by the one or more processors, the instructions further cause the apparatus to:” as instructions are executed by the one or more processors, the instructions cause computer system to (Hsiao: fig. 27, paragraph 323-324, col. Left, page 29) and/or
“train or training a second time series analysis operator based on the first time series data” as training analytical model as a second time series analysis operator, based on result data of time series (Anand: paragraphs 214, 268-269) or time series data, the table (Hsiao: fig. 17C) that includes time series data is represented as a second time series analysis operator (Hsiao: fig. 17C, paragraphs 273, 286-287);
“analyze or analyzing, in response to a second analysis mode input by the user, the first time series data using the second time series analysis operator” as navigating and showing or viewing as analyzing, in response to selection of the Group A value in column 1734 as a first analysis mode input by a user, first time series event data e.g., a row in table as first time series data using a second table that includes a different set of columns 1734-1750 is represented as a first time series analysis operator (Hsiao: fig. 17C, paragraphs 273-274, 281; Esman: fig. 6A).
.Claim 7 has the same limitations as discussed in claim 1; thus claim 7 is rejected under the same reason as discussed in claim 1. In addition, Hsiao teaches an apparatus, comprising “a memory configured to store instructions; and one or more processors coupled to the memory, wherein when executed by the one or more processors, the instructions cause the apparatus to:” as a memory configured to store instructions; and a processor coupled to the memory, wherein when executed by the one or more processors, the instructions cause computer system to (fig. 27, paragraph 323-324, col. Left, page 29).
Claim 13 has the same limitations as discussed in claim 1; thus claim 13 is rejected under the same reason as discussed in claim 1. In addition, Hsiao teaches computer program product comprising computer-executable instructions that are stored on a non-transitory computer-readable storage medium and that, when executed by one or more processors, cause an apparatus to: (fig. 27, paragraph 323-324, col. Left, page 29).
As to claim 21, Hsiao, Esman and Anand teach limitations
“ wherein the one or more artificial intelligence models are encapsulated and deployed on a computing device” as the artificial intelligence model (Ananda: paragraphs 14, 214) is captured and installed on a computing device (Hsiao: paragraphs 125-126; Esman: paragraph 87).
As to claim 22, Hsiao, Esman and Anand teach limitations
“wherein analyzing the first time series data comprises determining abnormal data in the first time series data” as grouping the first timestamped event as the first time series data comprises (Hsiao: figs. 17A-17D, paragraphs 253-255) determining unexpected results as abnormal data in dashboards that includes time series data (Esman: paragraphs 215, 156, fig. 9A) or time series data (Anand: paragraph 219).
As to claim 23, Hsiao, Esman and Anand teach limitation
“wherein analyzing the first time series data comprises extracting a feature of the time series data, and wherein the feature comprises a periodicity or stability of the first time-series data” as grouping the first timestamped event as the first time series data comprises (Hsiao: figs. 17A-17D, paragraphs 253-255) extracting value(s) e.g., time for field(s) of the timestamped event as the first time series data (Esman: paragraph 109), the time as a feature includes value e.g., 4/28/14 at 6:22:16 PM as periodicity or stability of the timestamped event as the first time series (Esman: fig. 6A, paragraphs 191, 119-120).
As to claim 27, Hsiao, Esman and Anand teach limitations
“wherein retrieving the first time series data comprises obtaining the first time series data from” as a portal website” as retrieving the timestamped event as first time series data comprising obtaining the timestamped event from (Hsiao: paragraphs 7, 94-95, a web portal of website (Esman: paragraphs 167 77; Ananda: paragraph 42).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Esman and Anand and further in view of Pelloin (or hereinafter “Pe”) (US 20220398179)
As to claim 24, Hsiao, Esman and Anand teach limitation
“wherein analyzing the first time series data comprises……” as grouping the first timestamped event as the first time series data comprises (Hsiao: figs. 17A-17D, paragraphs 253-255) extracting value(s) e.g., time for field(s) of the timestamped event as the first time series data (Esman: paragraph 109), the time as a feature includes value e.g., 4/28/14 at 6:22:16 PM as periodicity or stability of the timestamped event as the first time series (Esman: fig. 6A, paragraphs 191, 119-120).
Hsiao, Esman and Anand do not explicitly teach limitation
predicting time-series data at a next moment or in a next time period based on the first time-series data.
Pe teaches limitations
“predicting time-series data at a next moment or in a next time period based on the first time-series data” as predicting future event based on real-time data e.g., periodic event (paragraphs 25-26) that occurs at a certain day and/or time over a certain period (time interval) is represented as the first time-series data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply Pe’s teaching to Hsiao’s system in order to reduce the amount of input data searching and viewing data, to allow minimal time imprecision into periodicity seeking by increasing the time granularity and further to allow user to select the right priority for each event for improvements in detection, characterization, and prediction of time lasting events.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Esman and Anand and further in view of Ota et al (US 20230035836)
As to claim 25, Hsiao, Esman and Anand teach limitation
“……after determining the updated first time series analysis operator” as viewing all event streams after displaying the updated table that includes timestamps is represented as the updated first time series analysis operator (Hsiao: paragraph 267, 269).
Hsiao, Esman and Anand do not explicitly teach limitation
deleting the first model parameter
Ota teaches limitation
“deleting the first model parameter” as when the user selects a model desired to delete on the model regeneration screen 2000 and then presses the model delete button 2005 (YES in S71), the model management function 121 deletes all the parameter information of the model desired to delete from the model management table 173 (S72) (paragraph 178).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply Ota’s teaching to Hsiao’s system to make flexibly operate the model for updating data to improve the operation efficiency of the model.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Esman and Anand and further in view of MARQUARDT et al (or hereinafter “Ma”) (US 20140214888).
As to claim 26, Hsiao, Esman and Anand teach limitations
“receiving, from a client at a query node of the time-series database, a structured query language (SQL) statement” as receiving, from a client device, a search request that is expressed in SQL format is represented as a structured query language (SQL) statement at a query processor 404 of (Hsiao: paragraphs 93-94) data store that is stored timestamped events is represented the time-series database (Esman: paragraphs 66, 119-120, fig. 6A);
“……to determine the first analysis mode” as selects the user graphic element 1766o r 1768 as the first analysis model (Hsiao: figs. 17A-17D, paragraphs 254-255, 273-274, 280-281).
Hsiao, Esman and Anand do not explicitly teach limitations
parsing the parsing the SQL statement.
Ma teaches limitation
“parsing the SQL statement ” as parsing the SQL query (paragraphs 43, 150).
Ma further teaches limitations
“parsing the SQL statement to determine the first analysis mode” as parsing the SQL query to partition(s) on an indexer contain event records relevant to the stats query (paragraphs 43, 150).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply Ma’s teaching to Hsiao’s system to enable event records to be identified quickly, to enable fast search and analysis, the results of which may be stored in an index, and further to collect, parse, and store event records to facilitate fast and accurate information retrieval.
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Hsiao in view of Esman and Anand and further in view of Modarresi et al (or hereinafter “Mo”) (US 20170116530).
As to claim 28, Hsiao, Esman and Anand teach limitation
“wherein the one or more artificial intelligence models comprise ……as the first model parameter” as the artificial intelligence model includes parameter as (Anand: paragraphs 214, 222) graphical user element e.g., column 1720 as the first model parameter (Hsiao: figs. 17C-17D, paragraphs 273, 287-288).
Mo teaches limitations
an autoregressive prediction model having an autoregressive parameter
Mo teaches limitations
“the ……comprise an autoregressive prediction model having an autoregressive parameter as the first model parameter” as prediction models includes an autoregressive integrated moving average (ARIMA) model as autoregressive prediction model includes parameter(s) that define a particular model is represented as the first model parameter (paragraph 19).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply Mo’s teaching to Hsiao’s system to generate correctly prediction value in accordance with observed time series data and further to ensure a web page has proper content and/or resources in place to support the anticipated page views.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAM-Y T TRUONG whose telephone number is (571)272-4042. The examiner can normally be reached (571) 272 4042.
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/CAM Y T TRUONG/Primary Examiner, Art Unit 2169
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