DETAILED ACTION
Claims 1-20 are presented for examination.
Claims 1, 3-4, 6-7, 10, 12-13, 15-16, and 19-20 have been amended.
This office action is in response to the RCE submitted on 18-Mar-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.
Response to Arguments – 35 USC 101
On pgs. 12-15 of the Applicant/Arguments Remarks (hereinafter ‘Remarks’), Applicant argues the amended claims have overcome the rejection under 35 USC 101. The arguments are persuasive and the rejection for claims 1-18 has been withdrawn.
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.
For the new limitations added regarding Noueihed teaching simulated spatio-temporal events interactive with a user, please see the updated mapping below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
More specifically Claims 1, 2, 10, 11 and 19 recite an optimized knowledge graph.
The written description doesn’t provide adequate details for the determining what constitutes an optimized knowledge graph, nor how it is derived. In paragraph [0036], the specification recites: “allowing filtering and fine-tuning of the datasets that not only supports rendering of the optimized knowledge graphs via knowledge graph module 260, but also more efficient and accurate weather simulations of geographic location 130 based on the optimized knowledge graphs.” However, the optimal setting is not defined. Additionally, the optimization algorithm is not defined either. A POSITA would not be able to understand which optimization is required to produce the desired optimized graph and consequently achieve the intended results of the inventors.
Claims 2-9 are dependent on claim 1. Claims 11-18 are dependent on claim 10. Claim 20 is dependent on Claim 19. Consequently Claims 2-9, 11-18, and 20 are rejected for the same reason as their parent claims above.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 2, 6, 7, 10, 11 and 19 recite an optimized knowledge graph. The specification refers to the optimized knowledge graph yet fails to define what is considered optimized (Specification [0036]). Optimized is a relative term and is rejected for its lack of clarity.
Claims 2-9 are dependent on claim 1. Claims 11-18 are dependent on claim 10. Claim 20 is dependent on Claim 19. Consequently Claims 2-9, 11-18, and 20 are rejected for the same reason as their parent claims above.
Claim 10 is further rejected for reciting a computing device on line 7 where ‘a computing device’ is already defined on line 1. It’s unclear if the applicant is requiring a second computing device or is referring to the initial computing device. Appropriate correction is required.
Claims 11-18 are dependent on claim 10 and are rejected for dependency accordingly.
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, 2, 6-7, 10-11, 15-16, and 19-20 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 Tocornal et al (US20200356839A1)
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").
the plurality of simulated spatio-temporal significant events are 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 correlating comprises iteratively synthesizing the plurality of real-time events and the subset of the plurality of environmental data to create the plurality of simulated spatio-temporal significant events
Tocornal 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 ([0069] “More specifically, the CLIMATEAI climate forecasting system employs a deep learning network that is capable of extracting spatial-temporal features as well as functional dependencies and correlations among different GCM simulation datasets to predict future climate conditions. Typically in supervised learning, a predictor model such as a neural network is first trained using a first set of labeled training data to determine an optimal set of internal parameters. The capability of the predictor model is then validated on a second validation dataset and tuned accordingly. A third test dataset is then used to evaluate the predictive or forecast skill of the model.” [0110] “Training data 625 is a documented dataset containing multiple instances of system inputs (e.g., input climate variables) and correct outcomes (e.g., forecasting results of output climate variables). It trains the ML model to optimize the performance for a specific target task, such as forecasting a specific target output climate variable at a specific target lead-time. In diagram 600, training data 625 may also include subsets for validating and testing the ML model. For an NN-based ML model, the quality of the output may depend on (a) NN architecture design and hyperparameter configurations, (b) NN coefficient or parameter optimization, and (c) quality of the training data set. These components may be refined and optimized using various methods. For example, training data 625 may be expanded via a climate data augmentation process.”)
wherein the correlating comprises iteratively synthesizing the plurality of real-time events and the subset of the plurality of environmental data to create the plurality of simulated spatio-temporal significant events ([0146] “Further fine-tuning of the NN may occur at step 940, based on observational historical data 935 including reanalysis data 938. Recall that climate reanalysis combines and assimilates observational historical data with physical dynamical models to “fill in gaps” and provide a physically coherent and consistent, synthesized estimate of the climate in the past, while keeping the historical record uninfluenced by artificial factors. The availability of reanalysis data is limited by that of the observational historical data, and typical reanalysis datasets span over 40 to 80 years. To maximize the effectiveness of reanalysis data 938 in step 940, in some embodiments, some NN layers may be frozen during tuning, with only a selected subset of NN layers further updated. For example, in CRNN 500 shown in FIG. 5A, the first five convolutional layers and the RNN may be frozen, while the last convolutional layer is fine-tuned on 80 years of reanalysis data.” [0071] “Such selection and combination of GCM datasets are performed in successive stages, possibly with multiple iterations or passes, to minimize computation overheads without significantly compromising accuracy of the end result. The second novel feature of the CLIMATEAI system is its ability to pre-process the multi-model data ensemble to reduce or remove data heterogeneity, and to augment the data ensemble further, reinforcing the underlying hidden functional dependencies among different simulated climate datasets.” Also see [0069 and 0076])
Noueihed and Tocornal 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 training methodologies. 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 Tocornal to arrive at incorporating robust ML methodologies in simulation and prediction of spatio-temporal events with expected results. “More recently, advanced machine learning algorithms developed in other research fields and application areas have been suggested for climate analysis and other Earth System Science applications. Such data-driven approaches may attempt to learn spatial-temporal features from existing observational historical climate data, yet are generally constrained by the short observational record of climate data, which often have high spatial resolutions but short temporal durations. For example, the modern global instrumental record of surface air temperatures and ocean surface” (Tocornal, [0007])
Regarding Claim 2, Noueihed in view of Tocornal teaches the method of claim 1. Noueihed further teaches the optimized 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 optimized 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 6, Noueihed in view of Tocornal 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 optimized 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 Tocornal 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 optimized knowledge graph wherein the optimized 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 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 and 15-16 are medium claims reciting limitations similar to claims 2, and 6-7 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.
Claims 3-5, 8-9, 12-14 and 17-18 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 Tocornal et al (US20200356839A1) and further in view of Alsaedi (Event Identification in Social Media using Classification-Clustering Framework (Prior Art of Record)).
Regarding Claim 3, Noueihed in view of Tocornal 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 a 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").
Noueihed, Tocornal 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 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, Tocornal and Alsaedi to arrive at incorporating 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)
Regarding Claim 4, Noueihed in view of Tocornal teaches the method of claim 1. Alsaedi further teaches assigning, via the computing device, a similarity score and at least a 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 a 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").
For motivation to combine see claim 3.
Regarding Claim 5, Noueihed in view of Tocornal 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 a user; and filtering, via the computing device, the plurality of events based on the 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).
For motivation to combine see claim 3.
Regarding Claim 8, Noueihed in view of Tocornal 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").
For motivation to combine please see claim 3.
Regarding Claim 9, Noueihed in view of Tocornal 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).
For motivation to combine see claim 3.
Claims 12-14 and 17-18 are medium claims reciting limitations similar to claims 3-5 and 8-9 respectively and are 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.
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/A.E.D./Examiner, Art Unit 2199
/QAMRUN NAHAR/Primary Examiner, Art Unit 2199
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