DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office action is in response to Applicant's amendment filed on 10/4/2024.
Claim 1-21 are pending. Claim 1, 11 and 21 are amended. Claim 1-21 are rejected.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 11 is directed to statutory category process. The claim recites “determining overlapping time intervals over a time period range based on the indications of time; identifying subsets of data in each overlapping time interval based, at least in part, on the indications of time; for each of the overlapping time intervals: applying the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time, and constructing a node for each group to create a plurality of nodes within the particular interval of time; determining if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold; the two nodes being connected by a line based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold”. The processes of utilizing mathematical distance function or equation is simple enough to perform in human mind and may be considered mathematical concepts or mental process. Further, determining time overlapping intervals of time, identifying data in overlapping time interval, identifying groups withing interval of time, constructing nodes for groups and determining if two adjacent nodes are connected by comparing scores to a threshold value involve observation, judgement and evaluation. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “receiving a data set, each data point in the data set being associated with an indication of time” recites insignificant extra-solution activity of user specific mere data receiving/gathering is “obtaining information” as identified in MPEP 2106.05 (g). The claim further recites “displaying at least two nodes of the plurality of nodes with an indication of a passage of time” recites insignificant extra solution activity of data output . Viewing the additional limitations together and the claim individually as a whole, nothing provides integration into a practical application. Therefore, claim 11 is directed to an abstract idea.
At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of gathering user data is mere data gathering and, is well-understood, routine or conventional activities[( buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (computer receives and sends information over a network).]. Further, Presenting data is well-understood, routine or conventional based on OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed. Cir. 2015) (presenting offers and gathering statistics). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claims individually as a whole does not change this conclusion and the claim is ineligible. Accordingly, claim 11 is not patent eligible.
Claim 1 differs from claim 11 in that it recites a non-transitory computer readable medium including a sequence of instructions which when executed perform the method of claim 11. For reasons discussed above, the claimed process is directed to mental steps. Use of a non-transitory medium to store instructions which when executed perform the method of claim 11 constitutes use of a component of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 1 is not patent eligible.
Claim 21 differs from claim 11 in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 21 is a system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 21 is not patent eligible.
Dependent claim 12, 16, 18 and 19 are directed to the same abstract idea as the independent claim from which they depend and further recite limitations “indications of time being time stamps”, “distance function is received separately and at a different time than when receiving the data set”, “wherein the time period range is received from a user” and “time period range is shorter than a range of time indicated by the indications of time” recite insignificant extra-solution activity of mere data gathering from generic computing devices is “obtaining information” as identified in MPEP 2106.05 (g). Accordingly, this additional elements do not integrate the abstract idea into a practical application because they don’t impose any meaningful limits on practicing the abstract idea. This does not provide integration into a practical application. Therefore, claim 12, 16, 18 and 19 are directed to an abstract idea.
At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of gathering users’ data from generic computing devices and through user inputs is mere data gathering and, is well-understood, routine or conventional activities. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Accordingly, claim 12, 16, 18 and 19 are not patent eligible.
Claim 1, 6, 8 and 9 differs from claim 12, 16 18 and 19 respectively in that it recites a non-transitory computer readable medium including a sequence of instructions which when executed perform the method of claim 12, 16 18 and 19 respectively. For reasons discussed above, the claimed process is directed to mental steps. Use of a non-transitory medium to store instructions which when executed perform the method of claim 12, 16 18 and 19 constitutes use of a component of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 1, 6, 8 and 9 are not patent eligible.
Claim 13-15, 17 and 20 are directed to statutory category process. The claims recite “determining overlapping intervals over a time period range is determined based on a number of overlapping intervals and a resolution value received from a user”, “determining overlapping intervals over a time period range is determined based on a number of overlapping intervals and a resolution value, the number of overlapping intervals being determined based on the received data set”, “determining overlapping intervals over a time period range is determined based on a number of overlapping intervals and a resolution value, the number of overlapping intervals being indicated within the received data set”, “scoring shared data point membership between the two nodes of the plurality of nodes comprises determining a Jaccard score of the shared data point membership between the two nodes of the plurality of nodes” and “filtering the data set based on one or more features and wherein each node is constructed based on the data set after filtering”. The processes of utilizing mathematical function such as Jaccard scoring between two datasets is simple enough to perform in human mind and may be conspired mathematical concepts or mental process. Further, determining overlapping intervals of time by users provided values or indicated in the data set, filtering data based on some features and constructing nodes with filtered data involve observation, judgement and evaluation. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “intervals and a resolution value received from a user” and “the number of overlapping intervals being indicated within the received data set” recites insignificant extra-solution activity of user specific mere data receiving/gathering is “obtaining information” as identified in MPEP 2106.05 (g). Viewing the additional limitations together and the claims individually as a whole, nothing provides integration into a practical application. Therefore, claim 13-15, 17 and 20 are directed to an abstract idea.
At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of gathering user data from payment devices is mere data gathering and, is well-understood, routine or conventional activities[( buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (computer receives and sends information over a network).]. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claims individually as a whole does not change this conclusion and the claim is ineligible. Accordingly, claim 13-15, 17 and 20 are not patent eligible.
Claim 3-5, 7 and 10 are differ from claim 13-15, 17 and 20 respectively in that it recites a non-transitory computer readable medium including a sequence of instructions which when executed perform the method of claim 13-15, 17 and 20 respectively. For reasons discussed above, the claimed process is directed to mental steps. Use of a non-transitory medium to store instructions which when executed perform the method of claim 13-15, 17 and 20 constitutes use of a component of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 3-5, 7 and 10 are not patent eligible.
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.
Claim 1-7, 9-17 and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng, Yu et al (PGPUB Document No. 20150117713), hereafter referred as to “Zheng”, in view of Hartmann, Melanie (WIPO Publication No. WO2013087711), hereafter, referred to as “Hartmann”, in further view of Carlsson, Gunnar et al (PGPUB Document No. US 20100313157), hereafter, referred to as “Carlsson”.
Regarding claim 1 (Currently Amended), Zheng teaches A non-transitory computer readable medium including executable instructions, the instructions being executable by a processor to perform a method, the method comprising(Zheng, para 0171 discloses processor, storage media to store instructions “The above-described functions and components can be comprised of instructions that are stored on a storage medium (e.g., a computer readable storage medium). The instructions can be retrieved and executed by a processor.”):
receiving a data set, each data point in the data set being associated with an indication of time (Zheng, para 0042 discloses receiving data associated with time “The geographical area from the trajectory data 104 represents roads and streets where the service vehicles 102 travelled transporting passenger(s). For example, GPS sensors record timestamps, coordinates of locations, and status of occupancy of each service vehicle 102 for a GPS point. The GPS point may contain a timestamp of a date with a time in a.m. or p.m. (d)”), and a distance function to be utilized on the data points of the data set(Zheng, para 0056 disclose application of distance algorithm on received data set “the outlier application 110 calculates a score of distort for each link 702 by first calculating an Euclidean distance of a difference between each feature (i.e., #Obj) of two different time frames pertaining to a same link”);
But does not explicitly teach determining overlapping time intervals over a time period range based on the indications of time; identifying subsets of data in each overlapping time interval based, at least in part, on the indications of time; for each of the overlapping time intervals: applying the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time, and constructing a node for each group to create a plurality of nodes within the particular interval of time;
determining if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold; and displaying at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold.
However, in the same field of endeavor of data analysis Hartmann teaches determining overlapping time intervals over a time period range based on the indications of time(Hartmann, para 0038 discloses separating/identifying overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern”; para 0045 further discloses a time indication such as a period over which data was gather “a time dependent approach, the measurement data for example can be gathered from 24 hour time intervals or any other appropriate time interval”); identifying subsets of data in each overlapping time interval based, at least in part, on the indications of time(Hartmann, para 0043-para 0045 discloses identifying data for any interval (overlapping within an interval) based on time “For a time dependent approach, the measurement data for example can be gathered from 24 hour time intervals or any other appropriate time interval. A more fine- or coarse grained consideration, e.g. considering 1 hour or 1 minute, intervals may also be useful in some use cases. …”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying overlapping data of Hartmann into time based data analyzed by edit distance of Zheng to produce an expected result of identification of particular dataset for analysis. The modification would be obvious because one of ordinary skill in the art would be motivated to apply various similarity algorithms for feature extraction more appropriately using different algorithm for different pattern (Hartmann, para 0010).
But Zheng and Hartmann don’t explicitly teach for each of the overlapping time intervals: applying the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time, and constructing a node for each group to create a plurality of nodes within the particular interval of time;
determining if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold; and displaying at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold.
However, in the same field of endeavor of data analysis Carlsson teaches for each of the overlapping time intervals(Carlsson, para 0099 and para0112 disclose defining overlap and further discloses identifying each (number of ) intervals and overlap “the cover of the reference space R may be controlled by the number of intervals and the overlap identified in the resolution”; para 0112 further discloses overlapping time interval is being considered “The greater the overlap, the more times that clusters in S(d) may intersect clusters in S(e)—this means that more “relationships” between points may appear”): applying the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time(Carlsson, Fig. 8 and para 0104 disclose applying distance function to every received data (“ data S”) “data S be specified by a formula, an algorithm, or by a distance matrix which specifies explicitly every pairwise distance”; where para 0112 further discloses overlapping time interval is being considered “The greater the overlap, the more times that clusters in S(d) may intersect clusters in S(e)—this means that more “relationships” between points may appear”), and constructing a node for each group to create a plurality of nodes within the particular interval of time(Carlsson, Fig. 8 and para 0112 and 0116 discloses nodes are being identified and created for visualization “In step 812, the visualization engine 222 identifies nodes which are associated with a subset of the partition elements of all of the S(d) for generating an interactive visualization” ); determining if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold(Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data points “the visualization engine 222 joins clusters to identify edges (e.g., connecting lines between nodes). Once the nodes are constructed, the intersections (e.g., edges) may be computed “all at once,……. There may be an edge between two node_id's if they both belong to the same node_id_set( )value, and the number of points in the intersection is precisely the number of different node_id sets in which that pair is seen. ”; para 0161 discloses using scoring to find similarity and where highest scoring can be the threshold ““similarity” of data in the selected nodes and the differentiating characteristics. There can be many ways of scoring the data fields. The explain information window 1002 (i.e., the scoring window in FIG. 10) is shown along with the selected nodes. The highest scoring fields may distinguish variables with respect to the rest of the data”); and displaying at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line (Carlsson, para 0083 discloses displaying nodes & Fig. 9 further discloses lines connecting nodes “The visualization may show a collection of nodes corresponding to each of the partial clusters in the analysis output and edges connecting them as specified by the output. The interactive visualization is further discussed in FIGS. 9-11”; where element 1002 (day0, day 1 etc.) of Fig.10 discloses passage of time for each node (day0, day 1 etc.)) based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold(Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data pints “the visualization engine 222 joins clusters to identify edges (e.g., connecting lines between nodes). Once the nodes are constructed, the intersections (e.g., edges) may be computed “all at once,……. There may be an edge between two node_id's if they both belong to the same node_id_set( )value, and the number of points in the intersection is precisely the number of different node_id sets in which that pair is seen ”; para 0161 discloses using scoring to find similarity and where highest scoring can be the threshold ““similarity” of data in the selected nodes and the differentiating characteristics. There can be many ways of scoring the data fields. The explain information window 1002 (i.e., the scoring window in FIG. 10) is shown along with the selected nodes. The highest scoring fields may distinguish variables with respect to the rest of the data.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using edit distance for finding similarity of data and grouping data into nodes of Carlsson into the time based data with featured analyzed by edit distance of Zheng and Hartmann to produce an expected result of grouping data into nodes based on time. The modification would be obvious because one of ordinary skill in the art would be motivated to implement an option to quickly modify data to discover new relationships using exploratory data analysis system (Carlsson, para 0007).
Regarding claim 2 (Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 1 and Zheng further teaches the indications of time being time stamps (Zheng, para 0042 discloses receiving data with timestamp “ For example, GPS sensors record timestamps, coordinates of locations, ”).
Regarding claim 3 (Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 1 and Hartmann further teaches wherein determining overlapping intervals over a time period range is determined based (Hartmann, para 0038 discloses overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern”)
Carlsson teaches on a number of overlapping intervals and a resolution value received from a user (Carlsson, element 706 of Fig. 7 discloses that overlapping interval can be chosen by user from the visualization tool; para 0037 further discloses that resolution can be chosen by user “The analysis server 108 is a digital device that may be configured to analyze data. In various embodiments, the analysis server may perform many functions to interpret, examine, analyze, and display data and/or relationships within data. In some embodiments, the analysis server 108performs, at least in part, topological analysis of large datasets applying metrics, filters, and resolution parameters chosen by the user.”).
Regarding claim 4 (Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 1 and Hartmann further teaches wherein determining overlapping intervals over a time period range is determined based on (Hartmann, para 0038 discloses overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern”)
Carlsson teaches a number of overlapping intervals and a resolution value, the number of overlapping intervals being determined based on the received data set (Carlsson, element 706 of Fig. 7 discloses that overlapping interval determination from the visualization tool; para 0037 further discloses that resolution can be chosen by user “The analysis server 108 is a digital device that may be configured to analyze data. In various embodiments, the analysis server may perform many functions to interpret, examine, analyze, and display data and/or relationships within data. In some embodiments, the analysis server 108performs, at least in part, topological analysis of large datasets applying metrics, filters, and resolution parameters chosen by the user.”).
Regarding claim 5 (Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 1 and Hartmann further teaches wherein determining overlapping intervals over a time period range is determined based on (Hartmann, para 0038 discloses overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern”)
Carlsson teaches a number of overlapping intervals and a resolution value, the number of overlapping intervals being indicated within the received data set (Carlsson, element 706 of Fig. 7 discloses that overlapping interval is indicated by time in the dataset and which is later determined from the visualization tool; para 0037 further discloses that resolution can be chosen by user “The analysis server 108 is a digital device that may be configured to analyze data. In various embodiments, the analysis server may perform many functions to interpret, examine, analyze, and display data and/or relationships within data. In some embodiments, the analysis server 108performs, at least in part, topological analysis of large datasets applying metrics, filters, and resolution parameters chosen by the user.”).
Regarding claim 6 (Previously Presented), Zheng, Carlsson and Hartmann teach all the limitations of claim 1 and Carlsson further teaches wherein the distance function is received separately and at a different time than when receiving the data set(Carlsson, para 0092 discloses that distance function/metric can be defined by user and then can be applied “In some embodiments, the user may define a metric. The user defined metric may then be used with the analysis”; this implies having user defied distance function available separately before or after receiving dataset).
Regarding claim 7(Previously Presented), Zheng, Carlsson and Hartmann teach all the limitations of claim 1 and Carlsson further Carlsson wherein scoring shared data point membership between the two nodes of the plurality of nodes comprises (Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data pints “the visualization engine 222 joins clusters to identify edges (e.g., connecting lines between nodes). Once the nodes are constructed, the intersections (e.g., edges) may be computed “all at once,……. There may be an edge between two node_id's if they both belong to the same node_id_set( )value, and the number of points in the intersection is precisely the number of different node_id sets in which that pair is seen. ”).
Hartmann teaches determining a Jaccard score of the shared data point membership between the two nodes of the plurality of nodes (Hartmann, para 0050 discloses finding distance/score by Jaccard-Needham for similarity analysis for any data “ For example, if the data category of the measurement data is binary data then a similarity algorithm may be selected from the group of Hamming Distance, Fuzzy Hamming Distance, Anderberg Distance, Kulzinsky Distance, Jaccard-Needham Distance or any other similarity algorithm which is appropriate to assess the distance of binary measurement data series. ”).
Regarding claim 9 (New), Zheng, Carlsson and Hartmann teach all the limitations of claim 1 and Hartmann further teaches wherein the time period range is shorter than a range of time indicated by the indications of time (Hartmann, para 0042 discloses receiving time period range/window; any shorter duration can fall within the indicated range “For example, for pattern based measurement data, one can define a time window and calculate the average, max/min/deviation, etc. values for this time window and slide it over the signal. This approach is called sliding window…. feature vectors can be calculated for targeted timeslots in case of time dependent data or extracted with the sliding window approach in case of pattern-based data. ”).
Regarding claim 10 (Previously Presented), Zheng, Carlsson and Hartmann teach all the limitations of claim 1 and Carlsson further teaches the method further comprising filtering the data set based on one or more features and wherein each node is constructed based on the data set after filtering (Carlsson, Fig. 7 discloses setting filter for datasets and Fig. 9-10 discloses constructing and displaying nodes accordingly).
Regarding claim 11 (Currently Amended), Zheng teaches A method comprising: receiving a data set, each data point in the data set being associated with an indication of time(Zheng, para 0042 discloses receiving data associated with time “The geographical area from the trajectory data 104 represents roads and streets where the service vehicles 102 travelled transporting passenger(s). For example, GPS sensors record timestamps, coordinates of locations, and status of occupancy of each service vehicle 102 for a GPS point. The GPS point may contain a timestamp of a date with a time in a.m. or p.m. (d)”), and a distance function to be utilized on the data points of the data set(Zheng, para 0056 disclose application of distance algorithm on received data set “the outlier application 110 calculates a score of distort for each link 702 by first calculating an Euclidean distance of a difference between each feature (i.e., #Obj) of two different time frames pertaining to a same link”);
But does not explicitly teach determining overlapping time intervals over a time period range based on the indications of time; identifying subsets of data in each overlapping time interval based, at least in part, on the indications of time;
for each of the overlapping time intervals: applying the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time, and constructing a node for each group to create a plurality of nodes within the particular interval of time;
determining if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold; and displaying at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold.
However, in the same field of endeavor of data analysis Hartmann teaches determining overlapping time intervals over a time period range based on the indications of time(Hartmann, para 0038 discloses separating/identifying overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern” para 0045 further discloses a time indication such as a period over which data was gather “a time dependent approach, the measurement data for example can be gathered from 24 hour time intervals or any other appropriate time interval”); identifying subsets of data in each overlapping time interval based, at least in part, on the indications of time(Hartmann, para 0043-para 0045 discloses identifying data for any interval (overlapping within an interval) based on time “For a time dependent approach, the measurement data for example can be gathered from 24 hour time intervals or any other appropriate time interval. A more fine- or coarse grained consideration, e.g. considering 1 hour or 1 minute, intervals may also be useful in some use cases. …”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying overlapping data of Hartmann into time based data analyzed by edit distance of Zheng to produce an expected result of identification of particular dataset for analysis. The modification would be obvious because one of ordinary skill in the art would be motivated to apply various similarity algorithms for feature extraction more appropriately using different algorithm for different pattern (Hartmann, para 0010).
But Zheng and Hartmann don’t explicitly for each of the overlapping time intervals: applying the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time, and constructing a node for each group to create a plurality of nodes within the particular interval of time;
determining if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold; and displaying at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold.
However, in the same field of endeavor of data analysis Carlsson teaches for each of the overlapping time intervals Carlsson, para 0099 and para0112 disclose defining overlap and further discloses identifying each (number of ) intervals and overlap “the cover of the reference space R may be controlled by the number of intervals and the overlap identified in the resolution” ”; para 0112 further discloses overlapping time interval is being considered “The greater the overlap, the more times that clusters in S(d) may intersect clusters in S(e)—this means that more “relationships” between points may appear”): applying the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time(Carlsson, Fig. 8 and para 0104 disclose applying distance function to every received data (“ data S”) “data S be specified by a formula, an algorithm, or by a distance matrix which specifies explicitly every pairwise distance” ”; para 0112 further discloses overlapping time interval is being considered “The greater the overlap, the more times that clusters in S(d) may intersect clusters in S(e)—this means that more “relationships” between points may appear”), and constructing a node for each group to create a plurality of nodes within the particular interval of time(Carlsson, Fig. 8 and para 0112 and 0116 discloses nodes are being identified and created for visualization “In step 812, the visualization engine 222 identifies nodes which are associated with a subset of the partition elements of all of the S(d) for generating an interactive visualization” );
determining if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold(Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data points “the visualization engine 222 joins clusters to identify edges (e.g., connecting lines between nodes). Once the nodes are constructed, the intersections (e.g., edges) may be computed “all at once,……. There may be an edge between two node_id's if they both belong to the same node_id_set( )value, and the number of points in the intersection is precisely the number of different node_id sets in which that pair is seen. ”; para 0161 discloses using scoring to find similarity and where highest scoring can be the threshold ““similarity” of data in the selected nodes and the differentiating characteristics. There can be many ways of scoring the data fields. The explain information window 1002 (i.e., the scoring window in FIG. 10) is shown along with the selected nodes. The highest scoring fields may distinguish variables with respect to the rest of the data”); and displaying at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line (Carlsson, para 0083 discloses displaying nodes & Fig. 9 further discloses lines connecting nodes “The visualization may show a collection of nodes corresponding to each of the partial clusters in the analysis output and edges connecting them as specified by the output. The interactive visualization is further discussed in FIGS. 9-11”; where element 1002 (day0, day 1 etc.) of Fig.10 discloses passage of time for each node (day 0, day 1 etc.)) based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold(Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data pints “the visualization engine 222 joins clusters to identify edges (e.g., connecting lines between nodes). Once the nodes are constructed, the intersections (e.g., edges) may be computed “all at once,……. There may be an edge between two node_id's if they both belong to the same node_id_set( )value, and the number of points in the intersection is precisely the number of different node_id sets in which that pair is seen ”; para 0161 discloses using scoring to find similarity and where highest scoring can be the threshold ““similarity” of data in the selected nodes and the differentiating characteristics. There can be many ways of scoring the data fields. The explain information window 1002 (i.e., the scoring window in FIG. 10) is shown along with the selected nodes. The highest scoring fields may distinguish variables with respect to the rest of the data.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using edit distance for finding similarity of data and grouping data into nodes of Carlsson into the time based data with featured analyzed by edit distance of Zheng and Hartmann to produce an expected result of grouping data into nodes based on time. The modification would be obvious because one of ordinary skill in the art would be motivated to implement an option to quickly modify data to discover new relationships using exploratory data analysis system (Carlsson, para 0007).
Regarding claim 12 (Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 11 and Zheng further teaches the indications of time being time stamps (Zheng, para 0042 discloses receiving data with timestamp “For example, GPS sensors record timestamps, coordinates of locations”).
Regarding claim 13 (Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 11 and Hartmann further teaches wherein determining overlapping intervals over a time period range is determined based (Hartmann, para 0038 discloses overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern”)
Carlsson teaches on a number of overlapping intervals and a resolution value received from a user (Carlsson, element 706 of Fig. 7 discloses that overlapping interval can be chosen by user from the visualization tool; para 0037 further discloses that resolution can be chosen by user “The analysis server 108 is a digital device that may be configured to analyze data. In various embodiments, the analysis server may perform many functions to interpret, examine, analyze, and display data and/or relationships within data. In some embodiments, the analysis server 108performs, at least in part, topological analysis of large datasets applying metrics, filters, and resolution parameters chosen by the user.”).
Regarding claim 14 (Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 11 and Hartmann further teaches wherein determining overlapping intervals over a time period range is determined based on (Hartmann, para 0038 discloses overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern”)
Carlsson teaches a number of overlapping intervals and a resolution value, the number of overlapping intervals being determined based on the received data set (Carlsson, element 706 of Fig. 7 discloses that overlapping interval determination from the visualization tool; para 0037 further discloses that resolution can be chosen by user “The analysis server 108 is a digital device that may be configured to analyze data. In various embodiments, the analysis server may perform many functions to interpret, examine, analyze, and display data and/or relationships within data. In some embodiments, the analysis server 108performs, at least in part, topological analysis of large datasets applying metrics, filters, and resolution parameters chosen by the user.”).
Regarding claim 15 (Previously Presented), Zheng, Carlsson and Hartmann teach all the limitations of claim 11 and Hartmann further teaches wherein determining overlapping intervals over a time period range is determined based on (Hartmann, para 0038 discloses overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern”)
Carlsson teaches a number of overlapping intervals and a resolution value, the number of overlapping intervals being indicated within the received data set (Carlsson, element 706 of Fig. 7 discloses that overlapping interval is indicated by time in the dataset and which is later determined from the visualization tool; para 0037 further discloses that resolution can be chosen by user “The analysis server 108 is a digital device that may be configured to analyze data. In various embodiments, the analysis server may perform many functions to interpret, examine, analyze, and display data and/or relationships within data. In some embodiments, the analysis server 108performs, at least in part, topological analysis of large datasets applying metrics, filters, and resolution parameters chosen by the user”).
Regarding claim 16 (Previously Presented), Zheng, Carlsson and Hartmann teach all the limitations of claim 11 and Carlsson further teaches wherein the distance function is received separately and at a different time than when receiving the data set(Carlsson, para 0092 discloses that distance function/metric can be defined by user and then can be applied “In some embodiments, the user may define a metric. The user defined metric may then be used with the analysis”; this implies having user defied distance function available separately before or after receiving dataset).
Regarding claim 17(Previously Presented), Zheng, Carlsson and Hartmann teach all the limitations of claim 11 and Carlsson further teaches wherein scoring shared data point membership between the two nodes of the plurality of nodes comprises (Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data pints “the visualization engine 222 joins clusters to identify edges (e.g., connecting lines between nodes). Once the nodes are constructed, the intersections (e.g., edges) may be computed “all at once,……. There may be an edge between two node_id's if they both belong to the same node_id_set( )value, and the number of points in the intersection is precisely the number of different node_id sets in which that pair is seen. ”).
Hartmann teaches determining a Jaccard score of the shared data point membership between the two nodes of the plurality of nodes (Hartmann, para 0050 discloses finding distance/score by Jaccard-Needham for similarity analysis for any data “ For example, if the data category of the measurement data is binary data then a similarity algorithm may be selected from the group of Hamming Distance, Fuzzy Hamming Distance, Anderberg Distance, Kulzinsky Distance, Jaccard-Needham Distance or any other similarity algorithm which is appropriate to assess the distance of binary measurement data series. ”).
Regarding claim 19 (Previously Presented), Zheng, Carlsson and Hartmann teach all the limitations of claim 11 and Hartmann further teaches wherein the time period range is shorter than a range of time indicated by the indications of time (Hartmann, para 0042 discloses receiving time period range/window; any shorter duration can fall within the indicated range “For example, for pattern based measurement data, one can define a time window and calculate the average, max/min/deviation, etc. values for this time window and slide it over the signal. This approach is called sliding window…. feature vectors can be calculated for targeted timeslots in case of time dependent data or extracted with the sliding window approach in case of pattern-based data. ”).
Regarding claim 20 (Previously Presented), Zheng, Carlsson and Hartmann teach all the limitations of claim 11 and Carlsson further teaches the method further comprising filtering the data set based on one or more features and wherein each node is constructed based on the data set after filtering (Carlsson, Fig. 7 discloses setting filter for datasets and Fig. 9-10 discloses constructing and displaying nodes accordingly).
Regarding claim 21 (Currently Amened), Zheng teaches A system comprising: a processor; and a memory including instructions to configure the processor to(Zheng, para 0171 discloses a system with processor, storage media to store executable instructions “The above-described functions and components can be comprised of instructions that are stored on a storage medium (e.g., a computer readable storage medium). The instructions can be retrieved and executed by a processor”): receive a data set, each data point in the data set being associated with an indication of time(Zheng, para 0042 discloses receiving data associated with time “The geographical area from the trajectory data 104 represents roads and streets where the service vehicles 102 travelled transporting passenger(s). For example, GPS sensors record timestamps, coordinates of locations, and status of occupancy of each service vehicle 102 for a GPS point. The GPS point may contain a timestamp of a date with a time in a.m. or p.m. (d)”), and a distance function to be utilized on the data points of the data set(Zheng, para 0056 disclose application of distance algorithm on received data set “the outlier application 110 calculates a score of distort for each link 702 by first calculating an Euclidean distance of a difference between each feature (i.e., #Obj) of two different time frames pertaining to a same link”);
But does not explicitly teach determine overlapping time intervals over a time period range based on the indications of time; identify subsets of data in each overlapping time interval based, at least in part, on the indications of time;
for each of the overlapping time intervals: apply the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time, and construct a node for each group to create a plurality of nodes within the particular interval of time;
determine if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold; and display at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold.
However, in the same field of endeavor of data analysis Hartmann teaches determine overlapping time intervals over a time period range based on the indications of time(Hartmann, para 0038 discloses separating/identifying overlapping time interval of data “In the alternative embodiment it is advantageous when the two test patterns originate from measurement data which are taken during at least overlapping time intervals and the two sensor nodes are located in close neighborhood. The classifier component can then determine a potential relationship or dependency between the first sensor node S1 and the second sensor node S2 by a similarity measure comparison of the first test pattern”; para 0045 further discloses a time indication such as a period over which data was gather “a time dependent approach, the measurement data for example can be gathered from 24 hour time intervals or any other appropriate time interval”); identify subsets of data in each overlapping time interval based, at least in part, on the indications of time(Hartmann, para 0043-para 0045 discloses identifying data for any interval (overlapping within an interval) based on time “For a time dependent approach, the measurement data for example can be gathered from 24 hour time intervals or any other appropriate time interval. A more fine- or coarse grained consideration, e.g. considering 1 hour or 1 minute, intervals may also be useful in some use cases. …”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying overlapping data of Hartmann into time based data analyzed by edit distance of Zheng to produce an expected result of identification of particular dataset for analysis. The modification would be obvious because one of ordinary skill in the art would be motivated to apply various similarity algorithms for feature extraction more appropriately using different algorithm for different pattern (Hartmann, para 0010).
But Zheng and Hartmann don’t explicitly teach for each of the overlapping time intervals: apply the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time, and construct a node for each group to create a plurality of nodes within the particular interval of time;
determine if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold; and display at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold.
However, in the same field of endeavor of data analysis Carlsson teaches for each of the overlapping time intervals(Carlsson, para 0099 and para0112 disclose defining overlap and further discloses identifying each (number of ) intervals and overlap “the cover of the reference space R may be controlled by the number of intervals and the overlap identified in the resolution” ”; para 0112 further discloses overlapping time interval is being considered “The greater the overlap, the more times that clusters in S(d) may intersect clusters in S(e)—this means that more “relationships” between points may appear”): apply the distance function to each subset of data within a particular interval of time of the overlapping time intervals to identify groups within the particular interval of time(Carlsson, Fig. 8 and para 0104 disclose applying distance function to every received data (“ data S”) “data S be specified by a formula, an algorithm, or by a distance matrix which specifies explicitly every pairwise distance”), and construct a node for each group to create a plurality of nodes within the particular interval of time(Carlsson, Fig. 8 and para 0112 and 0116 discloses nodes are being identified and created for visualization “In step 812, the visualization engine 222 identifies nodes which are associated with a subset of the partition elements of all of the S(d) for generating an interactive visualization” );
determine if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data points “the visualization engine 222 joins clusters to identify edges (e.g., connecting lines between nodes). Once the nodes are constructed, the intersections (e.g., edges) may be computed “all at once,……. There may be an edge between two node_id's if they both belong to the same node_id_set( )value, and the number of points in the intersection is precisely the number of different node_id sets in which that pair is seen. ”; para 0161 discloses using scoring to find similarity and where highest scoring can be the threshold ““similarity” of data in the selected nodes and the differentiating characteristics. There can be many ways of scoring the data fields. The explain information window 1002 (i.e., the scoring window in FIG. 10) is shown along with the selected nodes. The highest scoring fields may distinguish variables with respect to the rest of the data”); and display at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line (Carlsson, para 0083 discloses displaying nodes & Fig. 9 further discloses lines connecting nodes “The visualization may show a collection of nodes corresponding to each of the partial clusters in the analysis output and edges connecting them as specified by the output. The interactive visualization is further discussed in FIGS. 9-11”; where element 1002 (day0, day 1 etc.) of Fig.10 discloses indication of passage of time for each node (day0, day 1 etc.)) based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold(Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data pints “the visualization engine 222 joins clusters to identify edges (e.g., connecting lines between nodes). Once the nodes are constructed, the intersections (e.g., edges) may be computed “all at once,……. There may be an edge between two node_id's if they both belong to the same node_id_set( )value, and the number of points in the intersection is precisely the number of different node_id sets in which that pair is seen ”; para 0161 discloses using scoring to find similarity and where highest scoring can be the threshold ““similarity” of data in the selected nodes and the differentiating characteristics. There can be many ways of scoring the data fields. The explain information window 1002 (i.e., the scoring window in FIG. 10) is shown along with the selected nodes. The highest scoring fields may distinguish variables with respect to the rest of the data.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using edit distance for finding similarity of data and grouping data into nodes of Carlsson into the time based data with featured analyzed by edit distance of Zheng and Hartmann to produce an expected result of grouping data into nodes based on time. The modification would be obvious because one of ordinary skill in the art would be motivated to implement an option to quickly modify data to discover new relationships using exploratory data analysis system (Carlsson, para 0007).
Claim 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng, Yu et al (PGPUB Document No. 20150117713), hereafter referred as to “Zheng”, in view of Hartmann, Melanie (WIPO Publication No. WO2013087711), hereafter, referred to as “Hartmann”, in view of Carlsson, Gunnar et al (PGPUB Document No. US 20100313157), hereafter, referred to as “Carlsson”, in further view of Qiu, Zhihong et al (PGPUB Document No. 20150324448), hereafter, referred to as “Qiu”
Regarding claim 8 (Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 1 but don’t explicitly teach wherein the time period range is received from a user.
However, in the same field of endeavor of data analysis Qiu teaches wherein the time period range is received from a user (Qiu, para 0037 discloses receiving a time range from user “The information recommendation time range may further be determined according to a received time range input by the user”)
Therefore, it would have been obvious at to one of ordinary skill in the art before effective filling date of the claimed invention to incorporate the feature of providing time range to view by users of Qiu into time based data with featured analyzed by edit distance of Zheng, Carlsson and Hartmann to produce an expected result of viewing dataset analysis for a particular time. The modification would be obvious because one of ordinary skill in the art would be motivated to improve a content recommendation system by providing recent and personalized contents to users (Qiu, para 004-006).
Regarding claim 18(Previously Presented), Zheng, Hartmann and Carlsson teach all the limitations of claim 11 but don’t explicitly teach wherein the time period range is received from a user.
However, in the same field of endeavor of data analysis Qiu teaches wherein the time period range is received from a user (Qiu, para 0037 discloses receiving a time range from user “The information recommendation time range may further be determined according to a received time range input by the user”)
Therefore, it would have been obvious at to one of ordinary skill in the art before effective filling date of the claimed invention to incorporate the feature of providing time range to view by users of Qiu into time based data with featured analyzed by edit distance of Zheng, Carlsson and Hartmann to produce an expected result of viewing dataset analysis for a particular time. The modification would be obvious because one of ordinary skill in the art would be motivated to improve a content recommendation system by providing recent and personalized contents to users (Qiu, para 004-006).
Response to Arguments
I. Non-statutory double patenting rejection
In light of Terminal Disclaimer filed (and accordingly approved) on 6/28/2023, the non-statutory double patenting rejection considering to claim 1 has been skipped.
II. 35 U.S.C §101
The crux of the applicant argument regarding 101 abstract idea rejection presented on page 7~9 of remarks is” Applicant submits that the claims are not directed to an abstract idea, but rather to an improvement in computer functionality and data analysis technology”.
The applicant further elaborated the improvement as following “the claimed topological data analysis approach provides advantages over conventional techniques. One of the advantages of TDA is that it may rely on many fewer assumptions than standard linear or algebraic models, for example. Further, the methodology provides new ways of visualizing and compressing data sets, which facilitate understanding and monitoring data. For example, the methodology may enable study of interrelationships among data sets over time and/or multiscale/multiresolution study of data sets (see paragraph [0037] of published application).
The combination of performing topological data analysis on time-based data subsets and providing an interactive visualization represents a specific technical solution that improves upon conventional data analysis techniques. The interactive visualization aspect allows users to explore relationships in the data in ways not possible with traditional methods (the traditional methods themselves representing a limitation of technology that the current claims overcome)”.
Applicant’s above mentioned arguments have been fully considered but the examiner respectfully disagrees for following reasons;
Firstly, even though the applicant mentioned about use of fewer assumptions in TDA than linear algebraic model that attributes to new ways for visualization and compression of data but there is no specific mention about how that impacted the technological improvement.
Secondly, the recited visualization improvement which is mere data presentation and, under Step 2A Prong II and Step 2B which found as well-understood routine and conventional extra solution activity.
Therefore, the examiner maintain the abstract idea rejection to claim 1, 11 and 21. No other arguments are presented other than discussion above for claims dependent of independent claim 1 and 11.
Thus, the examiner maintain the abstract idea rejection to claim 1~21.
II. 35 U.S.C §103
Regarding amended independent claim 1, 11 and 21 the applicant on page 11 paragraph 1 stated that “….neither Carlsson nor Hartmann appear to teach or disclose “determining overlapping time intervals over a time period range based on the indications of time;” “identifying subsets of data in each overlapping time interval based, at least in part, on the indications of time”.
Applicant’s above mentioned arguments have been fully considered but the examiner respectfully disagrees as Hartmann in para 0038 discloses identifying overlapping time interval of data and further on paragraph 0045 Hartmann detailed out how a time indication such as a period and, over which data was gather. For the second part of the argument regarding identifying subsets of data in each overlapping time interval, Hartmann in paragraph 0043 and 0045 discloses identifying data for any interval (overlapping within an interval) based on time.
The applicant further on 12 paragraph 3 argued that “Applicant respectfully submits that the Zheng does not appear to teach or suggest for each overlapping time interval, “applying the distance function to each subset of data within a particular interval of time of the overlapping intervals to identify groups within the particular interval of time,” much less “constructing a node for each group (identified based on distance within the particular interval of time) to create a plurality of nodes within the particular interval of time” as required by the claim as amended”.
Applicant’s above mentioned arguments have been fully considered but the examiner respectfully disagrees as Carlsson, para 0099 and para0112 disclose defining overlap and identifying each (number of ) intervals and overlap as following “the cover of the reference space R may be controlled by the number of intervals and the overlap identified in the resolution”. Carlsson further on Fig. 8 and para 0104 disclose applying distance function to every received data (“ data S”) as following “data S be specified by a formula, an algorithm, or by a distance matrix which specifies explicitly every pairwise distance”. Carlsson further in Fig. 8 and para 0112 and 0116 discloses nodes are being identified and created for visualization.
The crux of the applicant argument regarding presented on page 12 paragraph 4 through paragraph 1 page 14 is “Since the prior art does not teach or suggest constructing nodes based on groups that are identified based on distance within the time interval, the prior art does not teach or suggest utilizing such nodes. For example, the prior art does not appear to teach or suggest “determining if two nodes of the plurality of nodes in adjacent time intervals are connected by scoring shared data point membership between the two nodes of the plurality of nodes and comparing a score of the shared data point membership to a threshold” or “displaying at least two nodes of the plurality of nodes with an indication of a passage of time, the two nodes being connected by a line based on the comparison of the score of the shared data point membership between the two nodes of the plurality of nodes to the threshold” as required by the claim”.
Applicant’s above mentioned arguments are fully considered but not found persuasive for following reasons;
Firstly, Carlsson, para 0118-0119 discloses finding adjacent nodes based to commonality of data between two data points and para 0161 further discloses using scoring to find similarity, where highest scoring similarity can be the threshold value.
Secondly, Carlsson further in para 0083 and Fig. 9 disclose displaying nodes with connecting lines among them; where identification of nodes is being performed based on commonality of data between two data points.
Therefore, the examiner maintains the rejection to independent claim 1, 11 and 21.
Regarding claims dependent on independent claim 1, 11 and 21 no additional arguments were presented other than discussed above.
Thus, the examiner maintains the rejection to claim 1-21.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH A DAUD whose telephone number is (469)295-9283. The examiner can normally be reached M~F: 9:30 am~6:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached at 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ABDULLAH A DAUD/Examiner, Art Unit 2164
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
Prosecution Insights