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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/6/2026 has been entered.
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 partially overlapping time intervals over a time period range based on the indications of time, each partially overlapping time interval partially overlapping another partially overlapping time interval; 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, each of the two nodes being 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 nodes are in adjacent time interval and 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 and a distance function, each data point in the data set being associated with an indication of time, the distance function to be utilized on the data points of the data set” 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 2, 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 Volovich, Yaroslav et al(PGPUB Document No. 20150089522), hereafter, referred to as “Volovich”, 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 and a distance function, 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)”), the 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 partially overlapping time intervals over a time period range based on the indications of time, each partially overlapping time interval partially overlapping another partially overlapping time interval; 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, each of the two nodes being 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 Volovich teaches determining partially overlapping time intervals over a time period range based on the indications of time, each partially overlapping time interval partially overlapping another partially overlapping time interval; identifying subsets of data in each overlapping time interval based, at least in part, on the indications of time(Volovich, para 0005 discloses identifying data record/subset of data within a determined partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”);
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 Volovich 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 obtain the count of qualified data records utilizing statistically projected aggregated count of qualified records (Volovich, abstract).
But Zheng and Volovich 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, each of the two nodes being 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, each of the two nodes being 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 Volovich 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, Volovich 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, Volovich and Carlsson teach all the limitations of claim 1 and Volovich further teaches wherein determining overlapping intervals over a time period range is determined based (Volovich, para 0005 discloses identifying partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”)
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, Volovich 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 (Volovich, para 0005 discloses identifying partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”)
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.”)
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, Volovich 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 (Volovich, para 0005 discloses identifying partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”)
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, Volovich and Carlsson 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 10 (Previously Presented), Zheng, Volovich and Carlsson 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 and a distance function, 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)”), the 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 partially overlapping time intervals over a time period range based on the indications of time, each partially overlapping time interval partially overlapping another partially overlapping time interval; 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, each of the two nodes being 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 Volovich teaches determining partially overlapping time intervals over a time period range based on the indications of time, each partially overlapping time interval partially overlapping another partially overlapping time interval; identifying subsets of data in each overlapping time interval based, at least in part, on the indications of time(Volovich, para 0005 discloses identifying data record/subset of data within a determined partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”);
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 Volovich 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 obtain the count of qualified data records utilizing statistically projected aggregated count of qualified records (Volovich, abstract).
But Zheng and Volovich 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, each of the two nodes being 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, each of the two nodes being 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 Volovich 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, Volovich 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, Volovich and Carlsson teach all the limitations of claim 11 and Volovich further teaches wherein determining overlapping intervals over a time period range is determined based (Volovich, para 0005 discloses identifying partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”)
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, Volovich and Carlsson teach all the limitations of claim 11 and Volovich further teaches wherein determining overlapping intervals over a time period range is determined based on(Volovich, para 0005 discloses identifying partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”)
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, Volovich and Carlsson teach all the limitations of claim 11 and Volovich further teaches wherein determining overlapping intervals over a time period range is determined based on (Volovich, para 0005 discloses identifying partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”)
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, Volovich and Carlsson 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 20 (Previously Presented), Zheng, Volovich and Carlsson 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 and a distance function, 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 partially overlapping time intervals over a time period range based on the indications of time, each partially overlapping time interval partially overlapping another partially overlapping time interval; 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, each of the two nodes being 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 Volovich teaches determine partially overlapping time intervals over a time period range based on the indications of time, each partially overlapping time interval partially overlapping another partially overlapping time interval; identify subsets of data in each overlapping time interval based, at least in part, on the indications of time (Volovich, para 0005 discloses identifying data record/subset of data within a determined partially overlapping time period range or internal “identifying a number of candidate event records stored in the memory of the computer; and determining, among the identified candidate event records, a count of qualified event records that satisfy the one or more predefined criteria, wherein each qualified event record has a predefined time interval that at least partially overlaps the predefined time period associated with the query”);
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 Volovich 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 obtain the count of qualified data records utilizing statistically projected aggregated count of qualified records (Volovich, abstract).
But Zheng and Volovich 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, each of the two nodes being 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, each of the two nodes being 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 Volovich 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 7, 9, 17 and 19 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 Volovich, Yaroslav et al(PGPUB Document No. 20150089522), hereafter, referred to as “Volovich”, in view of Carlsson, Gunnar et al (PGPUB Document No. US 20100313157), hereafter, referred to as “Carlsson”, in further view of Hartmann, Melanie (WIPO Publication No. WO2013087711), hereafter, referred to as “Hartmann”.
Regarding claim 7(Previously Presented), Zheng, Volovich and Carlsson 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. ”).
But Zheng, Volovich and Carlsson don’t explicitly teach determining a Jaccard score of the shared data point membership between the two nodes of the plurality of nodes.
However, in the same field of endeavor of data analysis 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. ”).
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 determining Jaccard score of Hartmann into time based data analyzed by edit distance of Zheng, Volovich and Carlsson 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).
Regarding claim 9 (Previously Presented), of Zheng, Volovich and Carlsson teach all the limitations of claim 1 but don’t explicitly teach wherein the time period range is shorter than the range of time indicated by the indications of time .
However, in the same field of endeavor of data analysis Hartmann 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. ”).
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 determining Jaccard score of Hartmann into time based data analyzed by edit distance of Zheng, Volovich and Carlsson 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).
Regarding claim 17(Previously Presented), Zheng, Volovich and Carlsson 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. ”).
But Zheng, Volovich and Carlsson don’t explicitly teach determining a Jaccard score of the shared data point membership between the two nodes of the plurality of nodes.
However, in the same field of endeavor of data analysis 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. ”).
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 determining Jaccard score of Hartmann into time based data analyzed by edit distance of Zheng, Volovich and Carlsson 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).
Regarding claim 19 (Previously Presented), Zheng, Volovich and Carlsson teach all the limitations of claim 11 but don’t explicitly teach wherein the time period range is shorter than a range of time indicated by the indications of time.
However, in the same field of endeavor of data analysis Hartmann 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 ”).
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 determining Jaccard score of Hartmann into time based data analyzed by edit distance of Zheng, Volovich and Carlsson 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).
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 Volovich, Yaroslav et al(PGPUB Document No. 20150089522), hereafter, referred to as “Volovich”, in further 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, Volovich 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, Volovich and Carlsson 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, Volovich 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, Volovich and Carlsson 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. 35 U.S.C §101
The crux of the applicant argument regarding 101 abstract idea rejection presented on page 9 paragraph 2~page 10 paragraph 3 of remarks is ”The claims are not directed to a judicial exception and cannot be practically performed in the human mind”.
Applicant’s above mentioned arguments have been fully considered but the examiner respectfully disagrees as recited claim limitations such as “determining partially overlapping time intervals over a time period range based on the indications of time, each partially overlapping time interval partially overlapping another partially overlapping time interval; 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, each of the two nodes being 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” as drafted, are processes that under broadest reasonable interpretation, cover performance of the limitations in the human mind. The claim also recited “processor” for performing the method but, that is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using generic computer component.
Applicant’s arguments regarding additional elements such as receiving and displaying analyzed under Step 2A, Prong 2 resented in paragraph 3 of page 10 through paragraph 3 of page 11 has been fully considered but are not found persuasive.
Argued recited additional elements are insignificant extra-solution activity of mere data receiving/gathering is “obtaining information” as identified in MPEP 2106.05 (g), and further displaying data is insignificant post solution activity of outputting data. Their collective functions merely provide conventional computer implementation but not an improvement to computer or other technology.
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.
II. 35 U.S.C §103
Applicant’s arguments regarding teaching of prior art Hartmann presented on page 13 paragraph 4~ page 14 paragraph have been fully considered but are moot as new prior art teaches those argued limitations.
The applicant further in paragraph 3 of page 14 through paragraph 1 of page 15 not found persuasive as Carlsson in para 0099 and para 0112 disclose defining overlap and further identifying each intervals and their respective overlapping, where Carlsson in Fig. 8 and para 0104 further disclose applying distance function to every received data.
Regarding prior art Zheng, applicant’s arguments presented in paragraph 2 of page 15 through paragraph 1 of page 16 not found persuasive as combination of Zheng, Volovich and Carlsson teaches the argued limitations, details are presented under “Claim Rejections - 35 USC § 103” of this office action.
Conclusion
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.
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/ABDULLAH A DAUD/Examiner, Art Unit 2164 /AMY NG/Supervisory Patent Examiner, Art Unit 2164
