DETAILED ACTION
Claims 1-20 are presented for examination.
Claims 1-7, 10-16 and 19-20 have been amended.
This office action is in response to the RCE submitted on 09-July-2026.
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 .
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
Claim Objections
The word ‘event’ appears to be mistakenly omitted from the last limitation of claim 1, “the plurality of simulated spatio-temporal significant being interactive with a user”. Appropriate correction is requested.
Response to Arguments – 35 USC 103
Applicant’s arguments with respect to the 103 rejections have been considered, but are moot in view of the new ground(s) of rejection provided below.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Noueihed et al. (Knowledge-based virtual outdoor weather event simulator using unity 3D (Prior Art of Record)) in view of Alsaedi (Event Identification in Social Media using Classification-Clustering Framework (Prior Art of Record)) and further in view of Shafter et al. (An Assessment of Areal Coverage of Severe Weather Parameters for Severe Weather Outbreak Diagnosis)
Regarding Claim 1, Noueihed teaches receiving, by a computing device, a plurality of environmental data associated with a geographic area (Pg. 10, Paragraph 1, "if a snow event is taking place in a certain geographic area, temperature, humidity, wind, and dust levels will be sensed by one or multiple sensors placed in one or multiple geographic locations, allowing to trace of the snow’s fluctuating parameters over the concerned area. These measurements are individually collected at the sensors’ specific locations and are then aggregated in the data repository to allow weather event analysis").
retrieving, by the computing device, one or more data feeds including a plurality of real-time events (Pg. 2, Introduction, "We also utilize the WeatherStack API [58] to capture real-time weather measurements and conditions from the geographic area").
generating, by the computing device, a knowledge graph for the geographic area comprising a plurality of simulated spatio-temporal significant events based on (Pg. 2, Introduction, "we design and implement an integrated knowledge graph (KG) by creating two constituent KGs: (i) Weather KG describing weather measurements (e.g., wind, temperature, humidity) and weather events (e.g. storm, fire, and tornado), by extending an existing representation from [10]; and (ii) Simulator KG describing the simulator’s components and properties. We also utilize an adaptation of the Semantic Sensor Network (SSN) KG [53] to represent virtual and physical weather sensors and connect all three KGs to form an integrated structure serving as the knowledge backbone of the simulator").
create the plurality of simulated spatio-temporal significant events (Pg. 15, “Our integrated VOWES KG is shown in Fig. 12. It connects the Weather KG and the Simulator KG through the SSN/SOSA KG to form the knowledge backbone of our simulation environment. We utilize weather measurements and virtual weather measurements as the physical and virtual sensors’ features of interest, respectively, whereby any added sensor can measure any weather property (e.g., temperature, humidity, wind, dust). In other words, the virtual weather measurements sub-graph (Fig. 8) serves as the central connector between the Weather KG (Fig. 6) and the Simulator KG (Fig. 11). The resulting KG then connects with the SSN/SOSA KG (through five main relationship instances: (i) Unity3D—subclassOf—Platform, (i) WeatherWeatherValue—subclassOf—Result, EstimatingWeatherValueOfCollision-Area—subclassOf—Procedure, (iv) VirtualSensor—isAvatarOf—Sensor, and (v) VirtualMulti-sensor—isAvatarOf—Multi-sensor) in order to produce the integrated VOWES KG.”)
the plurality of simulated spatio-temporal significant being interactive with a user (Pg. 28, “This is applied on all user-interfaces in the whole simulator, starting from testing the capability of generating more than one project simultaneously (through the main page), to the ability to select a country/city and viewing it in a 3D environment, as well as scrolling and zooming in and out of the map with high resolution and details. We also evaluate and test the ability to add weather measurements and events in the same simulation project, and we test the functionality of the designed buttons by pressing each button more than 50 times consecutively. In addition, we make sure that all the weather measurements are movable around the map, by relocating every one of them more than once.”)
Noueihed however is not relied upon for:
employing a machine learning model to correlate the plurality of real-time events and a subset of the plurality of environmental data into a bounded training dataset derived from the plurality of simulated spatio-temporal significant events
wherein the plurality of simulated spatio-temporal significant events comprises a cluster of geolocated textual data at a particular time comprising a weight and a similarity score that exceeds a similarity threshold
wherein the correlating comprises iteratively synthesizing the plurality of real-time events and the subset of the plurality of environmental data
prioritized via the weight based on a detected level of impact to the geographic area,
create the plurality of simulated spatio-temporal significant events
Alsaedi teaches employing a machine learning model to correlate the plurality of real-time events and a subset of the plurality of environmental data into a bounded training dataset derived from the plurality of simulated spatio-temporal significant events (Pg. 27-28 discloses the data collection including live stream tweets, tagged with location. Pg. 37-38 discusses the ML used for classification and the supervised learning algorithms. Pg. 42-44 detail the classification of events vs non events for labeling and construction of the training data. Pg. 71-73 discloses the various textual features used in the analysis including weather, and region. Pg. 73-75 discusses the clustering features including temporal and special features. Pg. 57-58 discusses the correlation algorithm based on the identified features. Pg. 49-50 teaches the bounded dataset of 5,000 tweets and its classification. See Pg. 81 Table 4.1 for the weather events classified. Also see Shafer 817)
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wherein the plurality of simulated spatio-temporal significant events comprises a cluster of geolocated textual data at a particular time comprising a weight and a similarity score that exceeds a similarity threshold ((Pg. 23, "They use the spatial and temporal information from tweets to detect new events and extract the meta information by a number of text mining techniques (e.g., geo-location names, temporal phrase, and keywords) for event interpretation" Pg. 24, "they employed topic modelling using the LDA [20] model to further classify the informative tweets into 10 clusters" and pg. 43, "we introduce the threshold (D)… If D < 0, then the tweet is classified as an event. Otherwise, the tweet is classified as a non-event and discarded" and Pg. 116, "We address this problem by automatically selecting most representative messages that best represent the event, which was identified using an online clustering technique that groups together topically similar Twitter message" pg. 67, "Figure 4.1 illustrates the F-measure scores for different thresholds where the best performing threshold t =0.45 seems to be reasonable because it allows some similarity between posts but does not allow them to be nearly identical" and Pg. 38, “Each tweet is represented as a single document and the TF-IDF weights of textual terms are used as features to train the classifier”)
wherein the correlating comprises iteratively synthesizing the plurality of real-time events and the subset of the plurality of environmental data (Pg. 65-67 discusses the iterative nature of the algorithm used to calculate the similarity between the cluster. “Note that the clustering algorithm considers each tweet in turn, and determines the suitable cluster assignment based on the tweet’s similarity to any existing clusters. If there is no cluster whose centroid similarity function E(Di;Cj) is greater than _ , we increment m by one and create a new cluster Cm for Di. Otherwise, Di is assigned to a cluster Cj with maximum E(Di;Cj) . Therefore, these steps are repeated for all tweets (documents) in a timeframe.)
Noueihed, and Alsaedi are analogous art because they are from the same field of endeavor in analysis and prediction of spatio-temporal events. Noueihed focuses on weather simulation and prediction but appears to lack ML incorporation, similarity clustering, thresholding and social media analysis. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Noueihed and Alsaedi to arrive at incorporating ML, social media and crowdsourcing data with similarity clustering techniques in the spatio temporal events analysis to benefit from “these highly interactive systems [where] the general public are able to post real-time reactions to “real world" events - thereby acting as social sensors of terrestrial activity.” (Alsaedi, Page IV, Abstract) Additionally, Noueihed plans to incorporate ML into their software, Pg. 33, “In the long run, we plan to investigate different machine learning models [16, 36] and evolutionary developmental techniques [2, 43], to perform weather measurement forecasting and event prediction [21, 34], as well as event-based KG evolution [30]. Forecasting, prediction, and evolution functionalities will be added as dedicated plug-and-play software-as-a-service layers on top of the VOWES simulation environment, allowing for model transparency and extensibility.”
Shafer teaches prioritized via the weight based on a detected level of impact to the geographic area (Pg. 815, Each case is ranked based on the magnitude of one of the outbreak ranking index scores developed by SD11. To remain consistent with previous research (i.e., S10a), the N15 index described in SD11 is selected for this study. The N15 index weights events with multiple significant tornadoes (i.e., $F2) highest, with moderately high weights given to significant nontornadic reports [i.e., wind speeds $33.4 m s21 (65 kt) and hail size $5 cm].”)
Noueihed, Alsaedi and Shafter are analogous art because they are from the same field of endeavor in analysis and prediction of spatio-temporal events. Noueihed focuses on weather simulation and prediction but appears to lack ML incorporation, similarity clustering, thresholding and social media analysis. Shafter teaches the known method of weighing and ranking based on impact to the geographic area into its ML model. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to combine Noueihed, Alsaedi, and Shafer to augment Alsaedi’s clustering techniques with ranking and weighing based on the impact to geography in order to produce more realistic results with the highest significance first. Note MPEP 2143- (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Regarding Claim 2, Noueihed in view of Alsaedi and further in view of Shafer teaches the method of claim 1. Noueihed further teaches the knowledge graph is configured to support generation of the simulated weather event associated with the geographic area for a period of time in the future (Pg. 20-21, "VOWES will allow the user to easily fast-forward (or fast-backward) in time to visualize the weather environment and its events in the future (or in the past), according to the available temporal data in its database. The predicted events and their measurements will simply plug into VOWES and benefit from all its knowledge representation and visualization functionalities").
wherein the knowledge graph comprises one or more inferences derived from the bounded training dataset (Pg. 33, Para 2, “we plan to investigate different machine learning models… as well as event-based KG evolution [30]. Forecasting, prediction, and evolution functionalities will be added as dedicated plug-and-play software-as-a-service layers on top of the VOWES simulation environment.” Forecasting and prediction are inferences. VOWES incorporates them into the KC (knowledge graph)).
Regarding Claim 3, Noueihed in view of Alsaedi and further in view of Shafer teaches the method of claim 1. Alsaedi further teaches extracting, via the computing device, the plurality of real-time events based on metadata indicating the plurality of real-time events pertain to the geographic area (Pg. 23, "They use the spatial and temporal information from tweets to detect new events and extract the meta information by a number of text mining techniques (e.g., geo-location names, temporal phrase, and keywords) for event interpretation").
clustering, via the computing device, the plurality of real-time events based on the similarity threshold; wherein the metadata is derived from at least one piece of social media content (Pg. 24, "they employed topic modelling using the LDA [20] model to further classify the informative tweets into 10 clusters" and pg. 43, "we introduce the threshold (D)… If D < 0, then the tweet is classified as an event. Otherwise, the tweet is classified as a non-event and discarded" and Pg. 116, "We address this problem by automatically selecting most representative messages that best represent the event, which was identified using an online clustering technique that groups together topically similar Twitter message").
Regarding Claim 4, Noueihed in view of Alsaedi and further in view of Shafer teaches the method of claim 1. Alsaedi further teaches assigning, via the computing device, the similarity score and at least the weight to each event of the plurality of events (pg. 67, "Figure 4.1 illustrates the F-measure scores for different thresholds where the best performing threshold t =0.45 seems to be reasonable because it allows some similarity between posts but does not allow them to be nearly identical" and Pg. 38, “Each tweet is represented as a single document and the TF-IDF weights of textual terms are used as features to train the classifier”).
removing, via the computing device, events of the plurality of events that fail to exceed the similarity threshold (pg. 43, "we introduce the threshold (D)…If D < 0, then the tweet is classified as an event. Otherwise, the tweet is classified as a non-event and discarded").
Regarding Claim 5, Noueihed in view of Alsaedi and further in view of Shafer teaches the method of claim 1. Alsaedi further teaches receiving, via the computing device, at least one event descriptor associated with an event of the plurality of events from the user; and filtering, via the computing device, the plurality of events based on the at least one event descriptor (Pg. 79, "The Hashtag ratio is computed as the ratio of tweets containing hashtag (#) over the total number of tweets in the Timeframe" and Pg. 115 "Regarding the textual features, we show that the Dictionary-based model, Retweet ratio and Hashtag ratio are the most discriminative features, suggesting that references to present time and references to descriptive terms (e.g. live, breaking, etc.) are good discriminators. The retweet ratio suggests that other Twitter users pick up on event commentaries and propagate them more often through the network than non-event tweets. Linking content features, such as Hashtags and URLs, are also very predictive of disruptive events and made more discoverable via a self-defined topic discriminator in the form of a Hashtag." The filtering is done according to the hashtag ratio).
Regarding Claim 6, Noueihed in view of Alsaedi and further in view of Shafer teaches the method of claim 1. Noueihed further teaches generating, via the computing device, a machine learned model based on training data sets including the plurality of environmental data and the plurality of events; wherein the machine learned model is configured to generate an output designed to be utilized by the computing device to generate the knowledge graph (Pg. 33, "we plan to investigate different machine learning models [16, 36] and evolutionary developmental techniques [2, 43], to perform weather measurement forecasting and event prediction [21, 34], as well as event-based KG evolution [30]" EN: Tocornal generates the ML model based on the training data from environmental data. Please see [0069, 0071 and 0124])).
Regarding Claim 7, Noueihed in view of Alsaedi and further in view of Shafer teaches the method of claim 1. Noueihed further teaches accessing, via the computing device, a plurality of spatio-temporal data based on the association; and generating, via the computing device, the knowledge graph wherein the knowledge graph includes the plurality of spatio-temporal data including a plurality of geospatial data associated with the geographic area (Pg. 20-21, "VOWES will allow the user to easily fast-forward (or fast-backward) in time to visualize the weather environment and its events in the future (or in the past), according to the available temporal data in its database. The predicted measurements will simply plug into VOWES and benefit from all its knowledge representation and visualization functionalities").
Regarding Claim 8, Noueihed in view of Alsaedi and further in view of Shafer teaches the method of claim 1. Noueihed further teaches the plurality of environmental data includes at least one of historical climate data (Pg. 17, "while storing a 14-day historical record of the weather information. The historical record is useful to allow weather forecasting through the simulator").
topographic/urban data (Pg. 7, "the authors develop a Unity 3D virtual environment to study the properties and potential prospects of using solar energy on buildings in a highly populated urban area. They mimic building structured using dedicated 3D visualizations, and mimic solar energy measurements based on values and calculations accumulated from a real-world urban area in the city of Istanbul")
forecasting weather projections (pg. 19-20, 4.4 Environment Data Storage, "The data from every simulation project are saved in the database, with its timestamp under the user’s account, and can be utilized by the user to save, exit, reload, refresh and query the simulation project. The data are also essential to allow the development of data monitoring, mining, and extrapolation functionalities, including project versioning, temporal querying, measurement forecasting, and event prediction").
control variables (Pg. 9, 3.1.2 Weather measurements sub-graph, "Our weather measurements sub-graph is shown in Fig. 4. It consists of four main weather measurement concepts considered in our present simulator: temperature, wind speed, humidity, and dust. Other weather measurements can be easily added following the user’s needs." Control variables are defined in the instant application's [0027] as: "a plurality of control variables associated with weather including but not limited to precipitation level, atmospheric temperature, atmospheric pressure, wind speed, humidity, wind direction, applicable coordinates, and/or any other applicable data variable designed for the analysis of forces of nature in water bodies and land known to those of ordinary skill in the art.").
Alsaedi further teaches crowdsourcing data (Pg. 116, 5.1 Introduction, "this chapter focuses on the problem of selecting Twitter content from event clusters").
Regarding Claim 9, Noueihed in view of Alsaedi and further in view of Shafer teaches the method of claim 1. Alsaedi further teaches the one or more data feeds originate at least from one of social media content and internet-based content (Pg 116, 5.1 Introduction, "the Twitter API allows users to see only the most recent posts on a topic, in chronological order; it does not present posts in order on the basis of relevance" The section describes the usage of the Twitter API/Feed and using it to categorize/cluster information of significance).
Regarding Claim 10, Noueihed teaches one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media, the program instructions, when executed by the computing device, cause the computing device to perform a method comprising (Pg. 30, “Tests were conducted on a network version of the tool made available through the university’s computer labs, where every computer lab consists of an HP ProLiant ML350 Generation 5 (G5) Dual-Core Intel XeonTM 5000 processor with 2.66 GHz processing speed and 16 GB of RAM”).
The remaining limitations are similar to claim 1 and are rejected under the same rationale.
Claims 11-18 are medium claims reciting limitations similar to claims 2-5 and 6-9 respectively and are rejected under the same rationale.
Regarding Claim 19, Noueihed teaches one or more processors, one or more computer-readable memories, and program instructions stored on at least one of the one or more computer-readable memories for execution by at least one of the one or more processors to cause the computer system to (Pg. 30, “Tests were conducted on a network version of the tool made available through the university’s computer labs, where every computer lab consists of an HP ProLiant ML350 Generation 5 (G5) Dual-Core Intel XeonTM 5000 processor with 2.66 GHz processing speed and 16 GB of RAM”).
The remaining limitations are similar to claim 1 and are rejected under the same rationale.
Claim 20 is a system claim reciting limitations similar to claim 7 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kodra et al. (US20170176640A1) Discloses: providing multivariate climate change forecasting from climate model datasets, simulated historical and future climate model data, as well as observed datasets.
Tagoe et al. (US-12410013-B1) Discloses: using ML and weights to represent the impact of weather related events.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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