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
The amendment filed 10/17/2025 has been entered. Claims 1-2, 13, 14, 18, and 21-23 are amended. Claims 1-3, 6-14, 16-18, and 20-23 are pending in the application.
Response to Arguments
Claim Rejections - 35 USC § 103
Regarding the newly amended claim 1, Applicant argues that “The combination of at least Therani, Burtsev, Song, and BMS fails to disclose or suggest at least the amended limitations of the independent clams. For example, amended claim 1 recites, inter alia,
deploy the data aggregator across a plurality of clusters of servers to aggregate the indexed real-time streaming data and the historical data in real-time to generate real-time aggregated data based on a push query from the real-time cube requesting the real-time aggregated data; automatically provide the real-time aggregated data to the real-time cube in response to the data aggregator processing the real-time aggregated data in parallel across the plurality of clusters; and wherein the machine learning model generated by the machine learning component automatically outputs one or more new query attribute recommendations and one or more triggers based on the real-time aggregated data; and query real-time aggregated data from the data aggregator based on the one or more new query attribute recommendations without user input. The Office Action relies on Therani for teaching "aggregate the indexed real-time streaming data and the historical data in real-time to generate real-time aggregated" and "provide the real-time aggregated data to the real-time cube based on a query for the real-time aggregated data." (Office Action at page 6.) Therani discloses a system that "generates... ground truth data, based on traffic and navigation data associate with the one or more entities" and "using the Spatial Kriging technique, estimate the entity counts in real time for a first region with no data or partial data related to one or more entities based on a second region with complete data related to one or more entities. The first region and the second region are closely related region." (See Therani at para. [0059].) In addition, Therani discloses "data aggregator 202 obtains one or more data streams associated with one or more entities... a single entity may optionally engage with one or more entity devices." (Id. at para. [0041].) Stated differently, Therani utilizes only the data aggregator 202 (e.g., single entity) when gathering data. In fact, Therani is silent with respect to "deploy the data aggregator across a plurality of clusters of servers to aggregate the indexed real-time streaming data and the historical data in real-time to generate real-time aggregated data based on a push query from the real-time cube requesting the real-time aggregated data" as recited in amended claim 1. In addition, the Office Action acknowledges Therani in view of Burtsev does not disclose "wherein the machine learning model generated by the machine learning component outputs one or more new query recommendations based and one or more triggers based on the real-time aggregated data; and query real-time aggregated data from the data aggregator based on the one or more new query recommendations without user input." (Office Action at page 10.) Instead, the Office Action relies on Song to cure the deficiencies of Therani and Burtsev. Song discloses "hot topic query recommendation logic or function of the candidate query generator 310 may generate a candidate recommendation query." (Song at para. [0055].) Stated differently, Song generates candidate recommendation queries for selection and use by the user. These candidate recommendation queries are disclosed to be selected and used by the user. Instead, amended claim 1 requires "the machine learning component [that] automatically outputs one or more new query attribute recommendations." (emphasis added) Query attribute recommendations are a) output automatically by a machine learning model based on real-time aggregated data and b) define attribute recommendations (e.g., criteria) for a new query. In addition, the cited references in combination with Song are silent with respect to generating "one or more new query attributes" (e.g., for a separate query than the push query recited in claim 1) as currently recited in amended claim 1. Further, BMS is silent with respect to "one or more new query attribute recommendations." In fact, BMS does not teach or suggest the use of, or generating query attributes based on the real-time aggregated data, as currently recited in amended claim 1. For at least the above reasons, claim 1 cannot be rendered obvious in view of Therani, Burtsev, Song, and/or BMS. Accordingly, Applicant requests that the obviousness rejection of independent claim 1 be withdrawn. “.
In response, Examiner relies on a new combination of references.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6-14, 16-18, and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Therani (US 2021/0256407) in view of Burtsev (US 10,515,063 Bl) in view of SONG (US 2019/0251125 Al) in view of BMS (US 2021/0390144 Al ) in further view of Pandey (US 2011/0016123 Al)
Regarding claim 1, Therani discloses:
A system comprising at least one memory storing instructions and at least one processor executing the instructions to perform operations, the system further comprising:
a data aggregator; (Therani, fig. 2, item 202 “Data Aggregator”)
a real-time cube; (Therani ,fig. 2, item 212, “HYPER CUBE ESTIMATOR; [0010], e.g. line 2- a method of automatically estimating spatio-temporal entity counts in real time)
the data aggregator configured to:
receive indexed real-time streaming data from a plurality of clients;
(Therani, [0022], The system comprises (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources, wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier;)
receive historical data from the plurality of clients;
(Therani , [0041], line 43- the data aggregator 202 obtains the census related data (corresponding to “historical data”) from a global census database. In one embodiment, the data aggregator 202 obtains the census related data from third party sources; [0060], line 7- (i) raw census data from each country which is done periodically at intervals of 5 or 10 years; [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week (corresponding to “historical data”); [0062] the dimension factor is determined based on a maximum bump historically in our traffic from the mean traffic;)
deploy the data aggregator to aggregate the indexed real-time streaming data and the historical data in real-time to generate real-time aggregated data; (Therani, [0022] The system comprises (a) a data aggregator [0063] the entity estimation system 106 generates, using a sample generation method, a ground truth data, based on traffic and navigation data associated with the one or more entities for validating the estimated entity count in real time for each or combinations of the one or more spatio temporal dimensions; [0067] for a query "Give the counts of females, who are aged 45+, in Sydney, who go to cafes more than 3 times a week and love sports-in the past 30 days". In one approach, for the above query, the entity estimation system 106 (i) obtains past 30 days HLL data (corresponding to “historical data”)for Sydney metro area that the past 30 days HLL data which includes a number unique entity identifier and timestamp data (corresponding to “the indexed real-time streaming data and the historical data”), (ii) performs an union operation on Sydney metro area count HLLs for each day of the 30 day; [0010] (g) combining, using an entity estimator, the lower bound number and the upper bound number of the entity count for determining an entity count for each or combinations of the one or more spatio temporal dimensions in real time using a machine learning based time series model;)
based on a push query from the real-time cube requesting the real-time aggregated data; (Therani, [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week. In an embodiment, the data-driven machine learning (ML) model runs on a weekly basis to estimate the dimension factors. For various cells of the hypercube computational data structure, this data-driven ML model is run to update the dimension factors to accommodate the changes in real-world human mobility activity and availability of new data sets. The hypercube estimator 212 constantly updates (corresponding to “push request”) the lower-bound number of the entity count for each or combinations of one or more spatio temporal dimensions in accordance with the dimension factors driven by the data-driven ML model. [0058] the hyper cube estimator 212 updates the dimension factors when new data streams arrive (corresponding to “push request”); [0013] the method further includes updating, using Bayesian updating techniques, the dimensional factor when a new data stream arrives for the data aggregator. [0010] (e) determining, using a hyper-cube estimator, a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure;)
and automatically provide the real-time aggregated data to the real-time cube in response to the data aggregator processing the real-time aggregated data
(Therani, fig. 2, items 202, 204 and 212; [0059] the hyper cube estimator 212, using the Spatial Kriging technique, estimate the entity counts in real time for a first region; [0022], e.g. a system for automatically estimating spatio-temporal entity counts in real time and for a future time window… The system comprises (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources, wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier;…( d) a hyper-cube estimator that is configured to determine a lower bound number of an entity count for each or combinations of one or more spatiotemporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure…and (ii) estimate, using the machine learning based time series model, spatio temporal entity count for a future time window in response to a query criterion.; [0046] The hyper-cube computational data structure 204 is spatially partitioned with geo hashes in terms of a key value data structure for each geolocation.)
and a machine learning component configured to generate a machine learning model based on the historical data and provide the machine learning model to the real-time cube, (Therani, [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week (corresponding to “historical data”). In an embodiment, the data-driven machine learning (ML) model runs on a weekly basis to estimate the dimension factors.)
the real-time cube configured to: query real-time aggregated data from the data aggregator (Therani, fig. 2, items 202 and item 212; [0022], e.g. ( d) a hyper-cube estimator that is configured to determine a lower bound number of an entity count for each or combinations of one or more spatiotemporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure…and (ii) estimate, using the machine learning based time series model, spatio temporal entity count for a future time window in response to a query criterion.; [0059] the hyper cube estimator 212, using the Spatial Kriging technique, estimate the entity counts in real time for a first region [0022] ( d) a hyper-cube estimator that is configured to determine a lower bound number of an entity count for each or combinations of one or more spatiotemporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure;)
receive the automatically queried real-time aggregated data from the data aggregator; (Therani, fig. 2, items 202 and item 212; [0059] the hyper cube estimator 212, using the Spatial Kriging technique, estimate the entity counts in real time for a first region [0022], e.g. a system for automatically estimating spatio-temporal entity counts in real time and for a future time window… The system comprises (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources, wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier;… ( d) a hyper-cube estimator that is configured to determine a lower bound number of an entity count for each or combinations of one or more spatiotemporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure;)
extract updated real-time data from the automatically queried real-time aggregated data; (Therani , [0010], line 18 - ( d) updating, in real time, the key value data structure that corresponds to geolocation of the entity, with the unique entity identifier and the timestamp data;… ( e) determining, using a hyper-cube estimator, a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure; [0013] updating, using Bayesian updating techniques, the dimensional factor when a new data stream arrives for the data aggregator. [0022],e.g. a system for automatically estimating spatio-temporal entity counts in real time and for a future time window… The system comprises (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources, wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier…(d) a hyper-cube estimator that is configured to determine a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure;)
and provide the extracted updated real-time data.
(Therani,[0079], FIGS. 5A and 5B are exemplary user interface views of a search page 500A and a result page 500B of the entity estimation system 106 respectively according to an embodiment herein. The search page 500A includes a query defining space 502 that allows a user to enter a query related to spatio temporal entity count. The result page 500B provides the search results based on the query provided by the user; [0013] updating, using Bayesian updating techniques, the dimensional factor when a new data stream arrives for the data aggregator; [0010], line 18 - ( d) updating, in real time, the key value data structure that corresponds to geolocation of the entity, with the unique entity identifier and the timestamp data;… ( e) determining, using a hyper-cube estimator, a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure;)
However Therani does not clearly disclose:
deploy the data aggregator across a plurality of clusters of servers; in parallel across the plurality of clusters; wherein the machine learning model generated by the machine learning component automatically outputs one or more new query attribute recommendations and one or more triggers based on the real-time aggregated data; based on the one or more new query attribute recommendations without user input; extract updated real-time data from the queried real-time aggregated data; and provide the extracted updated real-time data.
However Burtsev discloses:
extract updated real-time data from the queried aggregated data; and provide the extracted updated real-time data. (Burtsev , column 2, line 19- The mediator server then performs a second in-memory retrieval of the real-time value of the specified object using the object ID. If the real-time value has changed, the mediator updates the real-time value, such that the mediator server stores the updated, current, real-time value of the data. The mediator server also transmits the updated value of the object to the client device, such that the client device can update the current value of the specified object. In other words, the mediator server can push the updated value of the specified object to the client device in real-time. The client device, upon receiving the updated value of the object, can update only those portions of a graphical user interface (or other display) which are changed. By receiving only the updated value of the object, the processing and display of the data by the client device is improved; column 9, lines 32- 54)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani with the teaching of Burtsev to improve updates to the data by communicating only changes in the data to client devices, then modifying a user interface based on the data changes received. Doing so solves a communication problem by reducing the total amount of data being transmitted, thereby reducing bandwidth required (and the actual throughput) while retaining real-time data communications, a problem associated with modem Internet-based communications, and particularly in cases where data may change quickly and where numerous data fields may be of interest, (Burtsev, column 1, lines 46-55).
However Therani in view of Burtsev does not clearly disclose:
deploy the data aggregator across a plurality of clusters of servers; in parallel across the plurality of clusters; wherein the machine learning model generated by the machine learning component automatically outputs one or more new query attribute recommendations and one or more triggers based on the real-time aggregated data; based on the one or more new query attribute recommendations without user input;
However SONG discloses:
wherein the machine learning model generated by the machine learning component automatically outputs one or more new query attribute recommendations based on the real-time aggregated data; (SONG, [0055] In operation S504, the hot topic query recommendation logic or function of the candidate query generator 310 may generate a candidate recommendation query (hereinafter, referred to as a hot issue candidate query) having a temporal issue for the search query of the user. The hot issue candidate query may include a hot topic keyword generated by aggregating a keyword most frequently used in documents generated in news, cafe, biogs, etc., and a real-time sudden rising keyword or a popular keyword that is determined based on a keyword currently most frequently used for a search by users using the search service; [0063] The candidate query clustering is to recommend a semantically different query excluding the same or similar query to the user. An example of a clustering algorithm may use a K-mean clustering algorithm (corresponding to “machine learning model”) that divides a number of candidate queries by K clusters.)
query real-time aggregated data from the data aggregator based on the one or more new query attribute recommendations without user input; (SONG, [0055] the hot topic query recommendation logic or function of the candidate query generator 310 may generate a candidate recommendation query (hereinafter, referred to as a hot issue candidate query) having a temporal issue for the search query of the user. The hot issue candidate query may include a hot topic keyword generated by aggregating a keyword most frequently used in documents generated in news, cafe, biogs, etc., and a real-time sudden rising keyword or a popular keyword that is determined based on a keyword currently most frequently used for a search by users using the search service; [0064] the recommendation query provider 340 may select a final query from the cluster of candidate queries and may provide the selected final query to the electronic device (1) 110 as a recommendation query. For example, the recommendation query provider 340 may select at least one representative query from each cluster and may generate the representative query for each cluster as the recommendation query.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani in view of Burtsev with the teaching of SONG to provide a recommendation query and achieve considerable advantages in terms of efficiency, convenience, and cost saving, (SONG, [0032]).
However Therani in view of Burtsev in view of SONG does not clearly disclose:
deploy the data aggregator across a plurality of clusters of servers; in parallel across the plurality of clusters; and one or more triggers based on the real-time aggregated data;
However BMS discloses:
and one or more triggers based on the real-time aggregated data; (BMS, [0069]-[0072], e.g. [0071] the AI/ML engine 140 may include a recommendation engine 216 that has access to the AI-bot query response database 224 and selects appropriate suggested response recommendations from the AI-bot query response database 224 based on query inputs 232 received from the natural language processing unit 144 and/or the speech recognition engine 148. In one embodiment, the query engine 228 may provide query inputs 232 to the recommendation engine 216 in the form of real-time chat data and/or in the form of conversation state information; [0034] After the SME(s) are identified, the AI-bot analyzes past queries and may determine a set of related response recommendations, or suggested responses, and then forwards the query along with suggested responses to the identified SME(s) to help SME(s) respond to the queries quickly; (Note: Examiner interprets that “selects appropriate suggested response recommendations” and “forwards the query along with suggested responses” steps are corresponding to “one or more triggers”);[0035], e.g. When the AI-bot receives the response from SME(s), the AI-bot may present the response back to the participants of the conference meeting (corresponding to “one or more triggers”); [0147] the AI/ML engine 140 and the conferencing server 116 may only send the suggested response to the query when the confidence level value of the suggested response is above, or higher than, the predetermined confidence level value threshold (e.g., greater than "60" in the example above).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani in view of Burtsev in view of SONG with the teaching of BMS to handle the queries quickly, (BMS, [0023]) and also depending on the content of the query, the AI/ML engine may select an SME as a candidate for responding to the query having the highest rating for a particular need and/or select the highest average rating for a number of needs, (BMS, [0109]) and also providing the response to the query may comprise causing the AI-bot to present the response to the query to the plurality of connected conference client devices on behalf of the subject matter expert(s) without requiring the subject matter expert to be included in the conference meeting and allowing queries to be raised and answered automatically and without delays associated with participants attempting to obtain responses during the meeting, (BMS, [0100]).
However Therani in view of Burtsev in view of SONG in view of BMS does not clearly disclose: deploy the data aggregator across a plurality of clusters of servers ; in parallel across the plurality of clusters;
However Pandey discloses:
deploy the data aggregator across a plurality of clusters of servers (Pandey [0047] in a networked Cluster of Host multi-processor machine…implements…a Concurrent Aggregator…a near real time event stream processor comprises a Distributed Cache 123 of computer-readable memory in conjunction with the Concurrent Aggregator 121; [0034] a large number of multi-processing Host Machine computers 103, which are in networked communication with each other in computing clusters. [0042] a first networked cluster … and a second networked cluster …can be located geographically remote; [0058] Each Event Bean is then pushed out to both the Distributed Cache 123, as well as corresponding data to the file; [0064], e.g. wherein aggregated data statistics are calculated for the level of the Partitions, and the aggregated data is pushed out to Distributed Cache ( as new Event Beans) and corresponding data to database files of the Data Warehouse; [0058] through the client, the user may query the Server for the Data Center level aggregated data, and the Server may serve the Data Center level aggregated data in response to such query from the Client.
in parallel across the plurality of clusters; (Pandey [0047] in a networked Cluster of Host multi-processor machine…implements…a Concurrent Aggregator…a near real time event stream processor comprises a Distributed Cache 123 of computer-readable memory in conjunction with the Concurrent Aggregator 121; [0034] a large number of multi-processing Host Machine computers 103, which are in networked communication with each other in computing clusters. [0042] a first networked cluster … and a second networked cluster …can be located geographically remote;)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani in view of Burtsev in view of SONG in view of BMS with the teaching of Pandey to handle real time, or near real time, processing of substantial and increasing volume of event data streams, in a scalable and efficient manner, (Pandey, [0011]) and also to perform aggregations with as much concurrency and parallelism as reason ably possible. Concurrency features of the Concurrent Aggregator may be particularly advantageous for near real time processing of the substantial volume of Event Streams, and may be especially advantageous for aggregation for a very large number of logical nodes, for example ten thousand nodes, (Pandey, [0067])
Regarding claim 2, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 1 as outlined above. Claim 2 further recites: wherein the real-time cube is configured to query the real-time aggregated data from the data aggregator based on a poll request. (Therani, [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically (corresponding to “poll request”) to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week. In an embodiment, the data-driven machine learning (ML) model runs on a weekly basis to estimate the dimension factors. For various cells of the hypercube computational data structure, this data-driven ML model is run to update the dimension factors to accommodate the changes in real-world human mobility activity and availability of new data sets. The hypercube estimator 212 constantly updates the lower-bound number of the entity count for each or combinations of one or more spatio temporal dimensions in accordance with the dimension factors driven by the data-driven ML model.)
Regarding claim 3, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 2 as outlined above. Therani does not clearly disclose: wherein the updated real-time data is a subset of the real-time aggregated data that is modified between a current query and a previous query.
However Burtsev discloses:
wherein the updated real-time data is a subset of the real-time aggregated data that is modified between a current query and a previous query. (Burtsev , column 10, line 55- the queries to determine what subset of the realtime data is being requested and performs a query on the relevant subset of data rather than the entire data set; column 13, line 53- the client device 102 may subscribe to updates for a subset of the list of objects and may send a request for data values of the subset of the list of objects; column 2, line 19- The mediator server then performs a second in-memory retrieval of the real-time value of the specified object using the object ID. If the real-time value has changed, the mediator updates the real-time value, such that the mediator server stores the updated, current, real-time value of the data; column 9, lines 32- 54;)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani with the teaching of Burtsev to improve updates to the data by communicating only changes in the data to client devices, then modifying a user interface based on the data changes received. Doing so solves a communication problem by reducing the total amount of data being transmitted, thereby reducing bandwidth required (and the actual throughput) while retaining real-time data communications, a problem associated with modem Internet-based communications, and particularly in cases where data may change quickly and where numerous data fields may be of interest, (Burtsev, column 1, lines 46-55).
Regarding claim 6, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 1 as outlined above. Claim 6 further recites: wherein the machine learning model generated by the machine learning component outputs one or more trigger thresholds based on the historical data. (Therani, [0014] In yet another embodiment, the machine learning based time series model is configured to train with real time entity count data to receive an input query related to entity count and to output spatio-temporal entity counts in response to the input query; [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week (corresponding to “historical data”). In an embodiment, the data-driven machine learning (ML) model runs on a weekly basis to estimate the dimension factors; [0010] ( e) determining, using a hyper-cube estimator, a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions; (f) determining, using a census-based extrapolator, an upper bound number of the entity count each or combinations of the one or more spatio temporal dimensions by extrapolating recent census data; (g) combining, using an entity estimator, the lower bound number and the upper bound number of the entity count for determining an entity count for each or combinations of the one or more spatio temporal dimensions in real time using a machine learning based time series model; and (h) estimating, using the machine learning based time series model, spatio temporal entity count for a future time window in response to a query criterion; [0058] the hyper cube estimator 212 detects anomalies when specific events happen or the traffic is bursty for example;)
Regarding claim 7, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 6 as outlined above. Therani does not clearly disclose:
wherein the real-time cube generates a trigger warning based on real-time aggregated data meeting the one or more trigger thresholds.
However Burtsev discloses:
wherein the real-time cube generates a trigger warning based on real-time aggregated data meeting the one or more trigger thresholds. (Burtsev, column 9, line 17-The doctor may then access real-time vital signs of patients through the mediator server…line 24- client device 102 can issue an alert or other notification if the updated value exceeds the previous value by a certain threshold. For example, if a patient's heartbeat jumps (or drops) by more than 20% over a previous reading, the client device 102 can issue an alert to notify the nurses and/or the doctor that the patient is at risk. Such alerts can be determined and issued based on historical data, moving averages, patterns identified, etc.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani with the teaching of Burtsev to improve updates to the data by communicating only changes in the data to client devices, then modifying a user interface based on the data changes received. Doing so solves a communication problem by reducing the total amount of data being transmitted, thereby reducing bandwidth required (and the actual throughput) while retaining real-time data communications, a problem associated with modem Internet-based communications, and particularly in cases where data may change quickly and where numerous data fields may be of interest, (Burtsev, column 1, lines 46-55).
Regarding claim 8, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 1 as outlined above. Claim 8 further recites: wherein the real-time cube passively receives the real-time aggregated data from the data aggregator. (Therani, [0058] the hyper cube estimator 212 uses Bayesian techniques to update the dimension factors by (i) receiving the new data streams from the one or more user devices; [0013] the method further includes updating, using Bayesian updating techniques, the dimensional factor when a new data stream arrives for the data aggregator. [0022] The system comprises (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources, wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier; … (c) a geolocation mapper that is configured to identify corresponding geolocation of the entity in a hyper-cube computational data structure that is spatially partitioned with geo hashes in terms of a key value data structure for one or more geolocations by mapping the geolocation of the entity with the key value data structure in the hyper-cube computational data structure)
Regarding claim 9, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 1 as outlined above. Claim 9 further recites: a data broker configured to index real-time streaming data. (Therani, [0010]The method includes the steps of (a) obtaining, in real time, one or more data streams with a unique entity identifier from independently controlled sources (corresponding to “a data broker”), wherein the one or more data streams includes timestamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier;)
Regarding claim 10, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 9 as outlined above. Claim 10 further recites: wherein the data broker provides the indexed real-time streaming data to the data aggregator. (Therani, The system comprises (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources (corresponding to “a data broker”), wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier; )
Regarding claim 11, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 10 as outlined above. Claim 11 further recites: wherein the data broker receives real-time streaming data from a plurality of clients. (Therani, [0022], to obtain, in real time, one or more data streams ;[0058] the hyper cube estimator 212 uses Bayesian techniques to update the dimension factors by (i) receiving the new data streams from the one or more user devices; [0019] In yet another embodiment, the one or more data streams with a unique entity identifier are obtained from one or more entity devices engaged with at least one of (i) a plurality of applications, (ii) a wireless network, or (iii) a mobile network.)
Regarding claim 12, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 10 as outlined above. Claim 12 further recites: wherein the data broker comprises a cluster of data brokers. (Therani , [0042] the entity estimation system 106 may optionally include a clustering device, a disambiguator and a validator. The clustering device receives the one or more data streams and clusters the one or more entity devices; [0041] a single entity may optionally engage with one or more entity devices.)
Regarding claim 13, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 1 as outlined above. Claim 13 further recites: a historical database configured to: receive relational data; receive streaming real-time data from a data processor; and provide the historical data to the data aggregator and the machine learning component. (Therani, [0022], to obtain, in real time, one or more data streams ;[0041] the data aggregator 202 obtains the census related data from a global census database. In one embodiment, the data aggregator 202 obtains the census related data from third party sources; [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week. In an embodiment, the data-driven machine learning (ML) model runs on a weekly basis to estimate the dimension factors.)
Regarding claim 14, Therani discloses: A method for real-time data aggregation and analytics, the method comprising: receiving indexed real-time streaming data from a plurality of sources; (Therani, [0022], The system comprises (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources, wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier;)
receiving historical data from the plurality of sources;
(Therani , [0041], line 43- the data aggregator 202 obtains the census related data (corresponding to “historical data”) from a global census database. In one embodiment, the data aggregator 202 obtains the census related data from third party sources; [0060], line 7- (i) raw census data from each country which is done periodically at intervals of 5 or 10 years; [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week (corresponding to “historical data”); [0062] the dimension factor is determined based on a maximum bump historically in our traffic from the mean traffic;)
for aggregating the indexed real-time streaming data and the historical data in real time to generate real-time aggregated data (Therani, [0022] The system comprises (a) a data aggregator [0063] the entity estimation system 106 generates, using a sample generation method, a ground truth data, based on traffic and navigation data associated with the one or more entities for validating the estimated entity count in real time for each or combinations of the one or more spatio temporal dimensions; [0067] for a query "Give the counts of females, who are aged 45+, in Sydney, who go to cafes more than 3 times a week and love sports-in the past 30 days". In one approach, for the above query, the entity estimation system 106 (i) obtains past 30 days HLL data (corresponding to “historical data”)for Sydney metro area that the past 30 days HLL data which includes a number unique entity identifier and timestamp data (corresponding to “the indexed real-time streaming data and the historical data”), (ii) performs an union operation on Sydney metro area count HLLs for each day of the 30 day; [0010] (g) combining, using an entity estimator, the lower bound number and the upper bound number of the entity count for determining an entity count for each or combinations of the one or more spatio temporal dimensions in real time using a machine learning based time series model;)
based on a push query requesting the real-time aggregated data;
(Therani, [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week. In an embodiment, the data-driven machine learning (ML) model runs on a weekly basis to estimate the dimension factors. For various cells of the hypercube computational data structure, this data-driven ML model is run to update the dimension factors to accommodate the changes in real-world human mobility activity and availability of new data sets. The hypercube estimator 212 constantly updates (corresponding to “push request”) the lower-bound number of the entity count for each or combinations of one or more spatio temporal dimensions in accordance with the dimension factors driven by the data-driven ML model. [0058] the hyper cube estimator 212 updates the dimension factors when new data streams arrive (corresponding to “push request”); [0013] the method further includes updating, using Bayesian updating techniques, the dimensional factor when a new data stream arrives for the data aggregator. [0010] (e) determining, using a hyper-cube estimator, a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure;)
generating a machine learning model based on the historical data and providing the machine learning model to a real-time cube, (Therani, [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week. In an embodiment, the data-driven machine learning (ML) model runs on a weekly basis to estimate the dimension factors.)
automatically providing the real-time aggregated data to the real-time cube in response to processing the real-time aggregated data without user input, (Therani, fig. 2, items 202, 204 and 212; [0059] the hyper cube estimator 212, using the Spatial Kriging technique, estimate the entity counts in real time for a first region; [0022], e.g. a system for automatically estimating spatio-temporal entity counts in real time and for a future time window… The system comprises (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources, wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier;…( d) a hyper-cube estimator that is configured to determine a lower bound number of an entity count for each or combinations of one or more spatiotemporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure…and (ii) estimate, using the machine learning based time series model, spatio temporal entity count for a future time window in response to a query criterion.; [0046] The hyper-cube computational data structure 204 is spatially partitioned with geo hashes in terms of a key value data structure for each geolocation.)
extracting updated real-time data from the automatically queried real-time aggregated data; and providing the extracted updated real-time data for visualization. (Therani , [0010], line 18 - ( d) updating, in real time, the key value data structure that corresponds to geolocation of the entity, with the unique entity identifier and the timestamp data;… ( e) determining, using a hyper-cube estimator, a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure; [0022] (a) a data aggregator that is configured to obtain, in real time, one or more data streams with a unique entity identifier from independently controlled sources, wherein the one or more data streams includes time stamp data and location indexed data that partially characterizes an activity of an entity associated with the unique entity identifier…(d) a hyper-cube estimator that is configured to determine a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions based on the unique entity identifier and the timestamp data updated in the geolocation of the key value data structure; [0079] FIGS. SA and 5B are exemplary user interface views of a search page 500A and a result page 500B of the entity estimation system 106 respectively according to an embodiment herein. The search page 500A includes a query defining space 502 that allows a user to enter a query related to spatio temporal entity count. The result page 500B provides the search results based on the query provided by the user.)
However Therani does not clearly disclose:
deploy a plurality of clusters of servers ;wherein the generated machine learning model automatically outputs one or more new query attribute recommendations and one or more triggers based on the real-time aggregated data; in parallel across the plurality of clusters; the query being based on the one or more new query attribute recommendations; extracting updated real-time data from the automatically queried real-time aggregated data; and providing the extracted updated real-time data for visualization.
However Burtsev discloses:
extracting updated real-time data from the automatically queried real-time aggregated data; and providing the extracted updated real-time data for visualization. (Burtsev , column 2, line 19- The mediator server then performs a second in-memory retrieval of the real-time value of the specified object using the object ID. If the real-time value has changed, the mediator updates the real-time value, such that the mediator server stores the updated, current, real-time value of the data. The mediator server also transmits the updated value of the object to the client device, such that the client device can update the current value of the specified object. In other words, the mediator server can push the updated value of the specified object to the client device in real-time. The client device, upon receiving the updated value of the object, can update only those portions of a graphical user interface (or other display) which are changed. By receiving only the updated value of the object, the processing and display of the data by the client device is improved; column 9, lines 32- 54)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani with the teaching of Burtsev to improve updates to the data by communicating only changes in the data to client devices, then modifying a user interface based on the data changes received. Doing so solves a communication problem by reducing the total amount of data being transmitted, thereby reducing bandwidth required (and the actual throughput) while retaining real-time data communications, a problem associated with modem Internet-based communications, and particularly in cases where data may change quickly and where numerous data fields may be of interest, (Burtsev, column 1, lines 46-55).
However Therani in view of Burtsev does not clearly disclose:
deploy a plurality of clusters of servers ;wherein the generated machine learning model automatically outputs one or more new query attribute recommendations and one or more triggers based on the real-time aggregated data; in parallel across the plurality of clusters; the query being based on the one or more new query attribute recommendations;
However SONG discloses:
wherein the generated machine learning model automatically outputs one or more new query attribute recommendations based on the real-time aggregated data (SONG, [0055] In operation S504, the hot topic query recommendation logic or function of the candidate query generator 310 may generate a candidate recommendation query (hereinafter, referred to as a hot issue candidate query) having a temporal issue for the search query of the user. The hot issue candidate query may include a hot topic keyword generated by aggregating a keyword most frequently used in documents generated in news, cafe, biogs, etc., and a real-time sudden rising keyword or a popular keyword that is determined based on a keyword currently most frequently used for a search by users using the search service; [0063] The candidate query clustering is to recommend a semantically different query excluding the same or similar query to the user. An example of a clustering algorithm may use a K-mean clustering algorithm (corresponding to “machine learning model”) that divides a number of candidate queries by K clusters.)
the query being based on the one or more new query attribute recommendations;
(SONG, [0055] the hot topic query recommendation logic or function of the candidate query generator 310 may generate a candidate recommendation query (hereinafter, referred to as a hot issue candidate query) having a temporal issue for the search query of the user. The hot issue candidate query may include a hot topic keyword generated by aggregating a keyword most frequently used in documents generated in news, cafe, biogs, etc., and a real-time sudden rising keyword or a popular keyword that is determined based on a keyword currently most frequently used for a search by users using the search service; [0064] the recommendation query provider 340 may select a final query from the cluster of candidate queries and may provide the selected final query to the electronic device (1) 110 as a recommendation query. For example, the recommendation query provider 340 may select at least one representative query from each cluster and may generate the representative query for each cluster as the recommendation query.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani in view of Burtsev with the teaching of SONG to provide a recommendation query and achieve considerable advantages in terms of efficiency, convenience, and cost saving, (SONG, [0032]).
However Therani in view of Burtsev in view of SONG does not clearly disclose:
deploy a plurality of clusters of servers; and one or more triggers based on the real-time aggregated data; in parallel across the plurality of clusters;
However BMS discloses:
and one or more triggers based on the real-time aggregated data; (BMS, [0069]-[0072], e.g. [0071] the AI/ML engine 140 may include a recommendation engine 216 that has access to the AI-bot query response database 224 and selects appropriate suggested response recommendations from the AI-bot query response database 224 based on query inputs 232 received from the natural language processing unit 144 and/or the speech recognition engine 148. In one embodiment, the query engine 228 may provide query inputs 232 to the recommendation engine 216 in the form of real-time chat data and/or in the form of conversation state information; [0034] After the SME(s) are identified, the AI-bot analyzes past queries and may determine a set of related response recommendations, or suggested responses, and then forwards the query along with suggested responses to the identified SME(s) to help SME(s) respond to the queries quickly; (Note: Examiner interprets that “selects appropriate suggested response recommendations” and “forwards the query along with suggested responses” steps are corresponding to “one or more triggers”);[0035], e.g. When the AI-bot receives the response from SME(s), the AI-bot may present the response back to the participants of the conference meeting (corresponding to “one or more triggers”); [0147] the AI/ML engine 140 and the conferencing server 116 may only send the suggested response to the query when the confidence level value of the suggested response is above, or higher than, the predetermined confidence level value threshold (e.g., greater than "60" in the example above).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani in view of Burtsev in view of SONG with the teaching of BMS to handle the queries quickly, (BMS, [0023]) and also depending on the content of the query, the AI/ML engine may select an SME as a candidate for responding to the query having the highest rating for a particular need and/or select the highest average rating for a number of needs, (BMS, [0109]) and also providing the response to the query may comprise causing the AI-bot to present the response to the query to the plurality of connected conference client devices on behalf of the subject matter expert(s) without requiring the subject matter expert to be included in the conference meeting and allowing queries to be raised and answered automatically and without delays associated with participants attempting to obtain responses during the meeting, (BMS, [0100]).
However Therani in view of Burtsev in view of SONG in view of BMS does not clearly disclose: deploy a plurality of clusters of servers; in parallel across the plurality of clusters;
However Pandey discloses:
deploy a plurality of clusters of servers; (Pandey, [0047] in a networked Cluster of Host multi-processor machine…implements…a Concurrent Aggregator…a near real time event stream processor comprises a Distributed Cache 123 of computer-readable memory in conjunction with the Concurrent Aggregator 121; [0034] a large number of multi-processing Host Machine computers 103, which are in networked communication with each other in computing clusters. [0042] a first networked cluster … and a second networked cluster …can be located geographically remote; [0058] Each Event Bean is then pushed out to both the Distributed Cache 123, as well as corresponding data to the file; [0064], e.g. wherein aggregated data statistics are calculated for the level of the Partitions, and the aggregated data is pushed out to Distributed Cache ( as new Event Beans) and corresponding data to database files of the Data Warehouse; [0058] through the client, the user may query the Server for the Data Center level aggregated data, and the Server may serve the Data Center level aggregated data in response to such query from the Client.)
in parallel across the plurality of clusters; (Pandey, [0047] in a networked Cluster of Host multi-processor machine…implements…a Concurrent Aggregator…a near real time event stream processor comprises a Distributed Cache 123 of computer-readable memory in conjunction with the Concurrent Aggregator 121; [0034] a large number of multi-processing Host Machine computers 103, which are in networked communication with each other in computing clusters. [0042] a first networked cluster … and a second networked cluster …can be located geographically remote;)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani in view of Burtsev in view of SONG in view of BMS with the teaching of Pandey to handle real time, or near real time, processing of substantial and increasing volume of event data streams, in a scalable and efficient manner, (Pandey, [0011]) and also to perform aggregations with as much concurrency and parallelism as reason ably possible. Concurrency features of the Concurrent Aggregator may be particularly advantageous for near real time processing of the substantial volume of Event Streams, and may be especially advantageous for aggregation for a very large number of logical nodes, for example ten thousand nodes, (Pandey, [0067])
Claim 18 corresponds to claim 14, and is rejected accordingly.
Regarding claim 16, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 14 as outlined above. Claim 16 further recites: wherein the machine learning model provides one or more triggers based on the historical data. (Therani, [0058] the hyper cube estimator 212 detects anomalies when specific events happen or the traffic is bursty for example; [0055] the hypercube estimator 212 embeds a data-driven machine learning (ML) model that runs periodically to estimate dimension factors for each hyper-cube dimension based on the incoming data streams of the past week (corresponding to “historical data”). In an embodiment, the data-driven machine learning (ML) model runs on a weekly basis to estimate the dimension factors; [0014] In yet another embodiment, the machine learning based time series model is configured to train with real time entity count data to receive an input query related to entity count and to output spatio-temporal entity counts in response to the input query; [0010] ( e) determining, using a hyper-cube estimator, a lower bound number of an entity count for each or combinations of one or more spatio temporal dimensions; (f) determining, using a census-based extrapolator, an upper bound number of the entity count each or combinations of the one or more spatio temporal dimensions by extrapolating recent census data; (g) combining, using an entity estimator, the lower bound number and the upper bound number of the entity count for determining an entity count for each or combinations of the one or more spatio temporal dimensions in real time using a machine learning based time series model; and (h) estimating, using the machine learning based time series model, spatio temporal entity count for a future time window in response to a query criterion; [0058] the hyper cube estimator 212 detects anomalies when specific events happen or the traffic is bursty for example;)
Claim 20 corresponds to claim 16, and is rejected accordingly.
Regarding claim 17, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 16 as outlined above. Claim 17 further recites: wherein the one or more triggers are applied by a visualization component (Therani, [0058] the hyper cube estimator 212 detects anomalies when specific events happen or the traffic is bursty for example; [0079], FIGS. 5A and 5B are exemplary user interface views of a search page 500A and a result page 500B of the entity estimation system 106 respectively according to an embodiment herein. The search page 500A includes a query defining space 502 that allows a user to enter a query related to spatio temporal entity count. The result page 500B provides the search results based on the query provided by the user;)
However Therani does not clearly disclose:
wherein the one or more triggers are applied by a visualization component
that displays the extracted updated real-time data.
However Burtsev discloses:
wherein the one or more triggers are applied by a visualization component
that displays the extracted updated real-time data. (Burtsev, column 9, line 17-The doctor may then access real-time vital signs of patients through the mediator server…line 24- client device 102 can issue an alert or other notification if the updated value exceeds the previous value by a certain threshold. For example, if a patient's heartbeat jumps (or drops) by more than 20% over a previous reading, the client device 102 can issue an alert to notify the nurses and/or the doctor that the patient is at risk. Such alerts can be determined and issued based on historical data, moving averages, patterns identified, etc; column 2, line 27- the mediator server can push the updated value of the specified object to the client device in real-time. The client device, upon receiving the updated value of the object, can update only those portions of a graphical user interface ( or other display) which are changed.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani with the teaching of Burtsev to improve updates to the data by communicating only changes in the data to client devices, then modifying a user interface based on the data changes received. Doing so solves a communication problem by reducing the total amount of data being transmitted, thereby reducing bandwidth required (and the actual throughput) while retaining real-time data communications, a problem associated with modem Internet-based communications, and particularly in cases where data may change quickly and where numerous data fields may be of interest, (Burtsev, column 1, lines 46-55).
Regarding claim 21, Therani in view of Burtsev in view of SONG in view of BMS in further view of Pandey discloses all of the features with respect to claim 1as outlined above. Therani in view of Burtsev in view of SONG does not clearly disclose: wherein the one or more query attribute recommendations includes at least one of a query frequency or one or more data items to query.
However BMS discloses:
wherein the one or more query attribute recommendations includes at least one of a query frequency or one or more data items to query. (BMS, [0115] , e.g. The information in the initiator identification field 612 may be used by the AI/ML engine 140 in determining a frequency of queries raised by participants relative to one another, types of queries raised by participants, patterns of queries raised by participants, etc; [0112] The query data structure 600 depicted in FIG. 6 includes a plurality of data fields that allow the AWL engine 140 to analyze a query, determine a suggested response to the query ( e.g., for forwarding or sending to an SME), and/or selecting an SME or a group of SMEs 114 for consultation regarding queries made during a particular conference meeting.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Therani in view of Burtsev in view of SONG with the teaching of BMS to handle the queries quickly, (BMS, [0023]) and also depending on the content of the query, the AI/ML engine may select an SME as a candidate for responding to the query having the highest rating for a particular need and/or select the highest average rating for a number of needs, (BMS, [0109]) and also providing the response to the query may comprise causing the AI-bot to present the response to the query to the plurality of connected conference client devices on behalf of the subject matter expert(s) without requiring the subject matter expert to be included in the conference meeting and allowing queries to be raised and answered automatically and without delays associated with participants attempting to obtain responses during the meeting, (BMS, [0100]).
Claims 22 and 23 correspond to claim 21, and are rejected accordingly.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Faezeh Forouharnejad whose telephone number is (571)270-7416. The examiner can normally be reached on generally Monday through Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shah Sanjiv can be reached on (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/F.F. /
Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166