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 .
DETAILED ACTION
Status of Claims
This communication is a First Office Action on the merits in reply to application number 18/666,411 filed on 05/16/2024.
Claims 1-17 are currently pending and have been examined.
Claim Objection
37 CFR 1.75 states, in part: “(f) If there are several claims, they shall be numbered consecutively.”
Claim 1 is objected to because there are two versions of claim 1 in the listing of claims, the first instance of claim being an independent claim and the second instance of claim 1 being a dependent claim. Applicant is required to cancel the second version of claim 1, which may be presented as new claim 18 in the next response/amendment. Both versions of claim 1 have been examined and addressed in the office action below.
As best understood by the Examiner, dependent claim 1 is intended to depend from independent claim 1, however dependent claims 2-10 are dependent from claim 1, though it is unclear which version of claim 1 is intended as their direct parent claim, and therefore claims 2-10 are subject to a §112(b) rejection below.
Claim Rejections - 35 USC § 112
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 2-10 and 15 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 pre-AIA the applicant regards as the invention.
Claims 2-10 depend from claim 1, which renders their scope indefinite because there are two versions of claim 1, such that the scope of claims 2-10 cannot be reasonably ascertained because it is unclear which version of claim 1 they depend from. For purposes of examination, claims 2-10 will be interpreted as depending from the first version of claim 1 (i.e., independent claim 1). Appropriate correction is required.
Claim 8 recites the limitation of “the different data types,” which lack antecedent basis because this limitation has not been introduced into the claims. Although parent claim 1 refers to real-time data and social media data, it is unclear whether these are considered as different data types, and unclear whether these elements are intended as the different data types referred to in the claim. Appropriate correction is required.
Claim 15 recites the limitation of “the different data modalities,” which lack antecedent basis because this limitation has not been introduced into the claims. Although parent claim 11 refers to real-time data and social media data, it is unclear whether these are considered as different data modalities, and unclear whether these elements are intended as the different data modalities referred to in the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the subject matter eligibility guidance set forth in MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106.03), it is first noted that the claimed method (claims 1-10), and system (claims 11-17) are each directed to a potentially eligible category of subject matter (i.e., process and machine). Accordingly, claims 1-17 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One of the eligibility inquiry (as explained in MPEP 2106.04), it is next noted that the claims recite an abstract idea by setting forth steps that, but for the generic computer implementation, could be implemented as “Mental Processes” (e.g., observation, evaluation, judgment, or opinion). The limitations reciting the abstract idea as set forth in independent claim 1 are identified in bold text below, whereas the additional elements are presented in plain text and are separately evaluated under Step 2A Prong Two and Step 2B:
receiving, via a data integration module, real-time data streams from a plurality of sources, the plurality of sources comprising: high-resolution satellite imagery from a satellite imagery provider; live news broadcasts in a plurality of languages from a global news media streaming service; and real-time social media data from a social media sentiment analysis engine (This step is an additional element considered insignificant extra-solution data gathering activity, which is evaluated below under Step 2A Prong Two and Step 2B);
analyzing, using a machine learning model of an AI analytics engine, the high-resolution satellite imagery to identify and extract relevant geospatial features and generate corresponding textual descriptions (This step describes activity, that, but for the high-level ML and AI analytics engine, could be implemented as mental activity such as by human observation, evaluation, judgment, or opinion, such as by a human merely observing an image and mentally identifying geospatial features and writing them down with pen/paper);
processing, using a NLP model of the AI analytics engine, the live news broadcasts by: performing real-time translation of the live news broadcasts into a target language; summarizing the translated news broadcasts; and extracting key insights from the summarized news broadcasts (These limitations describe activities that, but for the high-level NLP nature of the model and generically recited AI analytics engine, could be implemented as mental activity such as by human observation, evaluation, judgment, or opinion, such as by a human merely observing the broadcast and mentally translating, summarizing, and extracting insight from it based on human judgment, evaluation, or opinion);
determining, using a sentiment analysis model of the AI analytics engine, public sentiment and identifying trending topics based on the real-time social media data (This step describes activity, that, but for the generic implementation by the AI analytics engine, could be implemented as mental activity such as by human observation, evaluation, judgment, or opinion, such as by a human determining public sentiment and identifying the trending topics based on judgment, evaluation, or opinion of the observed data);
integrating, via a data fusion module, the extracted geospatial features and textual descriptions, the key insights from the summarized news broadcasts, and the public sentiment and trending topics into a unified, coherent intelligence report (This step describes activity, that, but for the generic implementation by the module, could be implemented as mental activity such as by human observation, evaluation, judgment, or opinion, such as by a human observation, judgment, evaluation, or opinion to extract the features and textual representation, key insights, and public sentiment, and providing the report with the aid of pen and paper); and
presenting, via a user interface, the intelligence report and allowing user interaction and customization based on specific intelligence requirements (This step describes activity, that but for the generic user interface, could be performed via human mental judgment/evaluation, such as with the aid of pen and paper, and even if computer-implemented, this step is considered insignificant extra-solution data gathering activity, which is addressed below under Step 2A Prong Two and Step 2B).
Independent claim 11 recites similar limitations as those set forth in claim 1 as discussed above, and have therefore been determined to recite the same abstract idea as claim 1.
With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP 2106.04(d)), the judicial exception is not integrated into a practical application. Independent claims 1 and 11 recite the additional elements of machine-learning, data integration module, data fusion module, AI, NLP, analysis engine, analytics engine, and user interface. The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). The machine-learning, AI, and NLP are recited at a high level of generality and are similar to merely adding the words “apply it” to the abstract idea, and fail to add any technical improvement to ML, AI, NLP, a computer, or any technology, or otherwise integrate the abstract idea into a practical application. Next, even if the receiving and presenting steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry (as explained in MPEP 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent claims 1 and 11 recite the additional elements of machine-learning, data integration module, data fusion module, AI, NLP, analysis engine, analytics engine, and user interface. These additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions/software to perform the abstract idea, which merely serves to tie the abstract idea to a particular technological environment (generic computing environment), similar to adding the words “apply it” (or an equivalent). Accordingly, the generic computer implementation merely serves to link the use of the judicial exception to a particular technological environment and therefore does not amount to significantly more than the abstract idea itself. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The use of ML, AI, and NLP have been considered, however these elements are recited generically and, similar to the generic computer/software implementation using the modules and engines, merely serve to link the use of the judicial exception to a particular technological environment and therefore do not amount to significantly more than the abstract idea itself. Nevertheless, Official Notice is taken that machine-learning, AI, and NLP are considered well-understood, routine, and conventional in the art, and when recited at such a high level of generality, are insufficient to add significantly more to the claims.
Even if the receiving and presenting steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is well-understood, routine, and conventional activity and thus insufficient to add significantly more to the claims. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Similarly, the user interface for performing the presenting at most involves a user interface of a generic computer, which is insufficient for eligibility. See, e.g., Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) (“the interactive interface limitation is a generic computer element”). The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-10 and 12-17 recite the same abstract idea(s) as recited in the independent claims, and have been determined to recite further details/steps falling under the “Mental Processes” abstract idea grouping discussed above along with the same generic computing elements recited in the independent claims as discussed above (modules, engine, AI, interface), which is insufficient to integrate the abstract idea into a practical application or add significantly more to the claims for the same reasons as set forth above in the Step 2A Prong Two and Step 2B analysis of claims 1 and 11. The data encryption recited in claims 10/17 has been considered, however this element is recited at a high level of generality and fails to yield any technical improvement to a computer or any technology or otherwise integrate the abstract idea into a practical application. Under Step 2B, Official Notice is taken that data encryption for implementing security or access control is well-understood, routine, and conventional in the art, and when recited at such a high level of generality, is insufficient to add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 of this title, 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, 3-5, 7, 9, 11, 13, are 16 are rejected under 35 U.S.C. §103 as unpatentable over Jolly et al. (US 2021/0256629, hereinafter “Jolly”) in view of Motayed et al. (US 2024/0233367, hereinafter “Motayed”).
Claims 1/11: As per claim 1, Jolly teaches a method for providing integrated global intelligence (par. 2: methods and systems to generate information about news source items appearing in user social media feed content, where the news source items describe news events or topics), the method comprising:
receiving, via a data integration module, real-time data streams from a plurality of sources (pars. 120,125, 131-132, 145, 175, and 237: e.g., facilitate real-time or near-real time news event or topic identification, there can be a preference for including news source items generated from news sources that have already been assigned a rating, that is, the news source is incorporated in a corpus of rated news sources; plurality of news source items in a corpus of news sources with and without generated ratings can then be processed; collection of additional news source items describing that same news event or topic. Such real-time operation can provide the user with greatly enhanced functionality without reducing the speed at which the subject social media platform operates for the user), the plurality of sources comprising:
live news broadcasts in a plurality of languages from a global news media streaming service (pars. 108-109, 120, 125, 131, 175: As an illustrated example, FIGS. 6a, 6b, 6c, and 6d show a timeline in a sequence of screen shots 600, 625, 630, and 635 for timeline view 605 of news event or topic 110—that is, the Yellow Vest Protests of the weekend of Sep. 21, 2019, can be seen to have first appeared as an “orphan news source item” as a report in Globalnews.com; plurality of news source items may be presented in the collection; might also be of interest to understand that the first news source item reported and included in the collection for the Yellow Vest Protests appeared in non-French news sources several hours before news source items appeared in the French news sources, as shown by timeline view 605 in FIG. 6a. It might then be inferred that these non-French-origin sources (Global News 610 and Arab News 615); To facilitate real-time or near-real time news event or topic identification, there can be a preference for including news source items generated from news sources);
and real-time social media data from a social media sentiment analysis engine (pars. 163, 192, and 194: e.g., social media feed can be associated with any currently available social media platform (e.g., Twitter, Facebook, Instagram, Reddit, SnapChat, TikTok, LinkedIn, Twitch, Discord, YouTube, Substack, etc.); social media feeds, here his Twitter account 1505, can be generated on a specific day…may vary over time due to changes in the real time content of a user's social media feed.) Analysis results 1510 of …Twitter account; social media feeds, here his Twitter account 1505, can be generated on a specific day. (As noted, the information for any social media feed may vary over time due to changes in the real time content of a user's social media feed));
analyzing … to identify and extract relevant geospatial features and generate corresponding textual descriptions (pars. 120-129 and Figs. 7a-d: wherein Fig. 7a depicts an image of a globe with different regions and news sources represented, which may be selected and textual descriptions provided and filtered by location, such as the news sources and textual descriptions extracted from those sources in the geospatial regions shown in Figs. 7b-d);
processing, using a NLP model of the AI analytics engine, the live news broadcasts (par. 111, 134, 139, and 235: e.g., processing of news source items can incorporate NLP techniques to identify keyword commonalities between the items, such as are present in the headline, bylines, and body text, for example. NLP results can be incorporated in the metadata of the corpus of news sources and in the subject news source items, as well as from information derived from relationships therebetween; identification of other news source items describing the same news event or topic through one or more of metadata extraction, NLP and assisted prediction machine learning on using previous linkages between news sources and news source items) by:
performing real-time translation of the live news broadcasts into a target language (pars. 49 and 109: e.g., Readily available translation software, such as Google Translate, can suitably be used to normalize content to a particular language (e.g., English) so that categorization, news event or topic identification, clustering, summary generation, etc. can suitably be conducted, as such are described hereinafter; See also, pars. 30 and 94: delivered news source items are also associated with information about bias, skew, and viewpoint that can be assessed by the user in real-time; such information can be automatically presented to her in real-time);
summarizing the translated news broadcasts (pars. 35, 39, and 68: e.g., automatically generated text summary for the first collection, wherein the text summary provides a description of the first news event or first topic of interest; and news source names and assigned ratings for each of the news sources from which the news source items in the first collection are generated); and
extracting key insights from the summarized news broadcasts (pars. 68, 93, 104, and 233: e.g., textual summary can be derived from a news source item by selecting or extracting one or more sentences from the opening paragraph or “the lede” of a first news source item—that is, the “orphan” news source item from which a news event or topic is identified, or a news source items identified based on a user selection of the news event or topic of interest; for example, source 220 (Haaretz) that is indicated as being “left” in 225 with headline 230 extracted from the original Haaretz newsfeed; extraction of information that can aggregate a large number of news source items describing with a news event or topic of interest where such news source items incorporate appropriate ratings can be useful in the generation of training sets that can be applied in such machine learning method);
determining, using a sentiment analysis model of the AI analytics engine, public sentiment and identifying trending topics based on the real-time social media data (pars. 35, 84, 90, 218, 234, and 247: e.g., identify sentiment and/or bias in news source items. Yet further, a machine learning system can be configured to review generated summaries for a collection of news source items for a collection of news source items; identification of the news event or topic as trending on a social media platform or on a news event or topic aggregation platform; machine learning system can be trained to flag a text summary that has been generated if it comprises language that may be indicative of bias, skew, or viewpoint that is of interest to identify in a feed; trained machine learning systems incorporated in a collection for use in a newsfeed, report, dashboard, or information set can generate real time or near real time fact checking of news source items appearing in a user's newsfeed. Such determinations can also be incorporated into ratings generated for the subject news source. The sentiment analysis can be useful for generating the timeline review; generated sentiment information can be provided to the user on a screen, in a report, in a dashboard; change in sentiment or treatment of a news event or topic over time in one or a plurality of news sources can be presented by grouping of the news source(s) in relation to ratings of the subject news sources; assessment of popularity (or as “trending topics”), the news event and topic selection);
integrating, via a data fusion module, the extracted geospatial features and textual descriptions, the key insights from the summarized news broadcasts, and the public sentiment and trending topics into a unified, coherent intelligence report (pars. 39, 66, 84, 107, 120-129, 140, 144, and Figs. 7a-d: e.g., When combined with at least the presentation of a generated rating for at least some of the news sources having news source items in the collection, the present disclosure generates significant benefits over previous news aggregation and curation methodologies; clustering can provide highly useful insights that can be used to generate information for display in user newsfeeds, as printed or displayed reports, as dashboard configurations that allows a large amount of information to be reviewed and analyzed simultaneously; automatically generated text summary for at least some of the news source items in the first collection; globe representation for coverage analysis view 705, and screen shots 710, 715, and 720 on FIGS. 7b, 7c, and 7d; machine learning system can be configured to review generated summaries for a collection of news source items; See also, par. 269: Each block may represent one or a combination of steps or executions in a process. In this regard, each block can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s)); and
presenting, via a user interface, the intelligence report and allowing user interaction and customization based on specific intelligence requirements (pars. 144, 152, 161, and Figs. 7a-d, 8a-e, 9, and 10a-b: e.g., setting up a newsfeed, report, dashboard and/or information set for use with the methodology herein, the user can select categories for news source items. For example, she can select topics, locations, news sources, places, bylines etc. As noted, the news item content associated with such selections can be presented along with other content, as discussed elsewhere herein; news source items describing a news event or topic can be filtered to limit the generated information to only that which aligns with the user's preferences for news event or topics that align with certain topics; coverage of one or a plurality of news events or topics of interest, or lack thereof, a coverage distribution can be configured into a dashboard form. The dashboard can be configurable to identify coverage by one or more of ratings (e.g., “left,” “center,” “right” etc.) for a collection of new sources to allow a user to review a concise characterization of coverage over a number of news sources; configurable to allow coverage distribution to be provided as a function of news event location, news source location, news source owner/publisher, news source identity (e.g., an individual news source by itself), content mediation, and news event or topic, etc.).
Jolly does not explicitly teach:
high-resolution satellite imagery from a satellite imagery provider;
analyzing, using a machine learning model of an AI analytics engine, the high-resolution satellite imagery.
Motayed teaches:
high-resolution satellite imagery from a satellite imagery provider (par. 41-42: will also be deployable with minimum effort to cover the desired spatial scales and with high resolution (less than 1 acre) to map the spatial variation; image data is taken by a satellite in a single image over a very large area, the ability of the embodiments to reduce that image data to a smaller region, i.e., less than one acre, 1-20 or 1-200 acres worth of data, enables both higher speed detection and the ability to use higher resolution data);
analyzing, using a machine learning model of an AI analytics engine, the high-resolution satellite imagery (pars. 41-42 and 45: e.g., Once images are acquired, a separate image processing AI engine, using machine learning, will detect anomalies and provide alerts; AI algorithm model that fuses environmental data collected from the sensors with satellite data to improve the prediction and detection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jolly with Motayed because the references are analogous since they are each directed to features for extracting useful information (intelligence) from different sources of data, which is within Applicant’s field of endeavor of providing global intelligence based on extracted information from multiple sources, and because modifying Jolly to incorporate Motayed’s features for analyzing high resolution satellite imagery, as claimed, would serve the motivation to extract information from sources that include images and videos (Jolly at par. 49) or to enhance the resolution of Jolly’s globe representation to enhance the coverage analysis or details depicted thereon (Jolly at Fig. 7a and par. 120); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 11 is directed to a system having similar limitations as those set forth in claim 1 and discussed above. Jolly, in view of Motayed, teaches a system for performing the limitations discussed above (pars. 2, 262-263, 269, and Fig. 11: methods and systems; computing device; processor; memory; a module, segment, or portion of code), and claim 11 is therefore rejected using the same references and for substantially the same reasons as set forth above.
Claim 1 (second version): Jolly does not teach the limitation of dependent claim 1.
However, Motayed teaches wherein the high-resolution satellite imagery comprises multispectral imagery captured across multiple frequency bands (pars. 24, 26, 31, 42, and 52-53: Satellite image data in multiple spectral bands (e.g., visible, infrared, microwave) will provide critical data; high accuracy and resolution of satellite imagery; ML models are developed to use time series data, and also multispectral imagery; assessment of spectral bands; Operating over a wider spectral range, and at longer wavelengths, SAR provides unique features).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Jolly/Motayed to incorporate Motayed’s multispectral imagery captured across multiple frequency bands, for analyzing high resolution satellite imagery, as claimed, in order provide critical data even under challenging environmental, terrain, and weather conditions (Motayed at par. 24); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 3: Jolly further teaches wherein the live news broadcasts are received from a plurality of global media outlets in different countries and regions (pars. 120-131, 209, and Figs. 7a-d and 8a: describing/ displaying gathering of the real-time news from around the world, including different media outlets in different countries/regions, such as BBC News, Iran Daily, and Radio France – e.g., FIGS. 7b, 7c, and 7d show each of the locations of the news sources in collection 140 (FIG. 1) in a list view. Such information can be useful to allow a user to assess the potential factual accuracy of a news source item as function of where it originates by specific news source location; facilitate real-time or near-real time news event or topic identification, there can be a preference for including news source items generated from news sources that have already been assigned a rating, that is, the news source is incorporated in a corpus of rated news sources; generate information about the news events or topics, persons, countries, subjects, etc.).
Claims 4/13: Jolly further teaches wherein processing the live news broadcasts further comprises: performing named entity recognition to identify key individuals, organizations, and locations mentioned in the news (pars. 33, 35, 38-39, 48, 85, 93, 120, 167, 185, 193, 195, and 242: e.g., information can comprise one or more of: a location or region associated with the first news event or first topic of interest; an origination location or region for a news source having a news source item incorporated in the first collection; an identity of a news source owner, publisher, or author associated with a news source having a news source item incorporated in the first collection; an identity of an author, reporter, or byline for a news source item incorporated in the first collection; a time or date of publication for a news source item incorporated in the first collection; text summary provides a description of the first news event or first topic of interest; and news source names; the identity/name (e.g., logo, image, tradename) of the publisher or source of each news source item in the generated collection; identify the source each of the news source item incorporated in the collection; specific location might be bot-generated can be inferred from certain origin locations for a news source); and linking recognized named entities to corresponding entities in a knowledge graph (par. 226: methodology herein can suitably form linkages between news sources and the topics; collection of topic-source and source-source linkages can be generated into a master ‘knowledge graph’ which can be useful for B2B companies to identify customers, suppliers and business partners. Subsets of the knowledge graph can be sold, temporarily or permanently licensed).
Claim 5: Jolly further teaches wherein the real-time social media data comprises posts, comments, and metadata from a plurality of social media platforms (pars. 50, 111, 126, 128, 134, 139, 142, 145, 163, 165, 166, and 207: e.g., news source item that are used in the processing herein can be derived from metadata; feeds, tags, metadata, etc., can also be analyzed; social media feed on a specific social media platform may comprise a plurality of social media feed content items that comprise a user's social media feed, some of which may or may not be associated with news events or topics as well as other social media feed content that may not be related to news events or topics (e.g., photos, videos, memes, social media posts from persons/companies, advertisements, etc.); items information generation can be by one or more of the following criteria: time decay (i.e., the age of the first news event or first topic), available images or video content; comments; re-posting, liking, commenting, saving etc.).
Claim 7: Jolly further teaches wherein identifying trending topics further comprises: extracting hashtags and keywords from the real-time social media data; determining frequencies and co-occurrences of extracted hashtags and keywords (pars. 111, 143, 159, 178, and 216-217: Social media feed items 1205 and 1210 can comprise any content or subject matter that typically would appear in social media feed 1200, such as content; items can be identified as being associated with a single, or first recognized news event or topic of interest if they share several keywords and/or comprise similar metadata information, such as tags, close times/dates of publication/origination, which is a common methodology of grouping news source items together in prior art news aggregation products. The processing of news source items can incorporate NLP techniques to identify keyword commonalities between the items; items can be identified as being associated with, or as describing, substantially the same news event or topic if each includes the similar keyword commonalities [i.e., co-occurrences] in one or more of the headline/title, byline or text of each news source; frequency, amount, and characteristics of such coverage; changes in the frequency and characteristics of the coverage), content identified as likely being of interest to the user by operation of the subject social media platform's algorithms); and clustering hashtags and keywords into distinct trending topics (pars. 49, 104, 107, 110, 129, and 178: categorization, news event or topic identification, clustering, summary generation, etc. can suitably be conducted; clustering (e.g., grouping articles relating to a specific news event or topic); news event or topic recognition by the computer and the clustering or grouping of a plurality of news source items therewith can be initially conducted automatically by the computer seeding a clustering event against which other news source items having these same or similar words [i.e., keywords]; can comprise any content or subject matter that typically would appear in social media feed 1200, such as content associated with or provided by groups, companies, individuals etc. to which the user has identified an interest in or about (e.g., …hashtags etc.)).
Claims 9/16: Jolly further teaches wherein the intelligence report is generated in response to a user-specified query (pars. 111, 160, 198, 220, and 222: e.g., perform a query that identifies news sources where and where not such topics are included; user queries can be provided in report or dashboard form, as well as being useful in machine learning processes herein), and wherein the user interaction and customization comprises: filtering the presented insights based on user-defined criteria (pars. 7, 144, and 220: e.g., news source items describing a news event or topic can be filtered to limit the generated information to only that which aligns with the user's preferences for news event or topics that align with certain topics; user can generate a query that allows the corpus of news sources to be searched to identify additional news source items describing the user selected event; queries by a single user can provide insights into coverage interests for that user; news selection and delivery algorithms-which can be referred to as “news curation algorithms”); adjusting the granularity and specificity of reported information; and providing user feedback to refine future intelligence gathering and analysis (pars. 7, 23, 129, 144, and 220: e.g., news source items describing a news event or topic can be filtered to limit the generated information to only that which aligns with the user's preferences [wherein the filtering is an adjustment to granularity/specificity] for news event or topics that align with certain topics; user can generate a query that allows the corpus of news sources to be searched to identify additional news source items [i.e., which reflects granularity/specificity] describing the user selected event; users can impart bias to the news selection process; queries by a single user can provide insights into coverage interests for that user; If the user selects the summary from her device display, she can be directed to the individual news source items; selection and delivery algorithms-which can be referred to as “news curation algorithms”).
Claims 2 and 12 are rejected under 35 U.S.C. §103 as unpatentable over Jolly et al. (US 2021/0256629, hereinafter “Jolly”) in view of Motayed et al. (US 2024/0233367, hereinafter “Motayed”), as applied to claims 1 and 11 above, and further in view of Scharf et al. (US 2024/0011917, hereinafter “Scharf”).
Claims 2/12: Jolly, in view of Motayed, teaches wherein analyzing the high-resolution satellite imagery (as discussed above in the rejection of claims 1/11, which is incorporated herein), but does not teach applying an object detection model to identify and classify objects of interest; and determining changes in identified objects over time by comparing satellite imagery from different time periods.
Scharf teaches applying an object detection model to identify and classify objects of interest; and determining changes in identified objects over time by comparing satellite imagery from different time periods (pars. 3, 25, 38, 41, and 50: learning engine is trained to implement a detection model, e.g., to detect objects and/or assess physical damage; trained learning engine (machine) can also be used to detect objects; damage detector 310 is configured to identify objects/features appearing in the image, and determine if any identified object appearing in the image has sustained damage; detection of damage may be assessed in comparison to an earlier baseline image (e.g., images showing contours of identifiable structures in a geographical area at their pre-damage state)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jolly/Motayed with Scharf because the references are analogous since they are each directed to features for extracting useful information (intelligence) from different sources of data, which is within Applicant’s field of endeavor of providing global intelligence based on extracted information from multiple sources, and because modifying Jolly/Motayed to include Scharf’s object detection features, as claimed, would serve the motivation to extract information from sources that include images and videos (Jolly at par. 49) or to analyze and detect changes in conditions in a geographical area (Motayed at par. 8); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 6 and 14 are rejected under 35 U.S.C. §103 as unpatentable over Jolly et al. (US 2021/0256629, hereinafter “Jolly”) in view of Motayed et al. (US 2024/0233367, hereinafter “Motayed”), as applied to claims 1 and 11 above, and further in view of Intrator et al. (US 2024/0242033, hereinafter “Intrator”).
Claim 6: Jolly, in view of Motayed, teaches the limitations of claim 1 as set forth above, but does not teach the limitations of claim 6.
Intrator teaches wherein determining public sentiment further comprises: training a sentiment classification model on a labeled dataset of social media posts; and applying the trained sentiment classification model to the real-time social media data to determine sentiment scores (pars. 7, 20, 26, 38, and 48 e.g., real-time scan and sentiment analysis of social media posts; include ML models 124 trained and/or adjusted … using past training data and ML training operations; training data may be associated with past social media and networking posts that may include images, text, graphics or emojis, animations (e.g., GIFs), and the like, which may have a corresponding sentiment. The training data may be labeled or unlabeled for different supervised or unsupervised ML and NN training algorithms, techniques, and/or systems. SPA system 110 may further provide sentiment scores 126. For example, initially ML models 124 may be trained on social posts data 134, as well as customer histories 116 from database 114. Social posts data 134 may include historical social media posts, images 136, text in a title or post body associated with images 136 and/or comments 138, and other data used to provide a basis for training data used to train ML models; After initial training, ML models 124 may be deployed in a production computing environment to receive inquires and data for features and predict labels or other classifiers from the data (e.g., sentiment scores and/or sentiment analysis, such as positive, neutral, or negative sentiment, from social media posts including images, text, and other data); analyzing a social media post using an ML model to output sentiment scores; model may be trained on data with labels (or without, for certain training operations and algorithms) that is collected and preprocessed from customer and/or public data sets of images and/or social media posts).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jolly/Motayed with Intrator because the references are analogous since they are each directed to features for extracting useful information (intelligence) from different sources of data, which is within Applicant’s field of endeavor of providing global intelligence based on extracted information from multiple sources, and because modifying Jolly/Motayed to include Intrator’s sentiment classification model features, as claimed, would serve the motivation to extract information from sources that include images and videos (Jolly at par. 49) or to increase the efficiency and speed in analyzing the social media posts (Intrator at par. 23: increases speed and efficiency); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 14: Jolly further teaches extract hashtags and keywords from the real-time social media data, determine frequencies and co-occurrences of extracted hashtags and keywords (pars. 111, 143, 159, 178, and 216-217: Social media feed items 1205 and 1210 can comprise any content or subject matter that typically would appear in social media feed 1200, such as content; items can be identified as being associated with a single, or first recognized news event or topic of interest if they share several keywords and/or comprise similar metadata information, such as tags, close times/dates of publication/origination, which is a common methodology of grouping news source items together in prior art news aggregation products. The processing of news source items can incorporate NLP techniques to identify keyword commonalities between the items; items can be identified as being associated with, or as describing, substantially the same news event or topic if each includes the similar keyword commonalities [i.e., co-occurrences] in one or more of the headline/title, byline or text of each news source; frequency, amount, and characteristics of such coverage; changes in the frequency and characteristics of the coverage), content identified as likely being of interest to the user by operation of the subject social media platform's algorithms), and cluster hashtags and keywords into distinct trending topics (pars. 49, 104, 107, 110, 129, and 178: Readily available translation software, such as Google Translate, can suitably be used to normalize content to a particular language (e.g., English) so that categorization, news event or topic identification, clustering, summary generation, etc. can suitably be conducted; clustering (e.g., grouping articles relating to a specific news event or topic); news event or topic recognition by the computer and the clustering or grouping of a plurality of news source items therewith can be initially conducted automatically by the computer seeding a clustering event against which other news source items having these same or similar words [i.e., keywords]; can comprise any content or subject matter that typically would appear in social media feed 1200, such as content associated with or provided by groups, companies, individuals etc. to which the user has identified an interest in or about (e.g., …hashtags etc.)), but does not teach wherein the sentiment analysis model of the AI analytics engine is trained on a labeled dataset of social media posts and configured to: apply the trained sentiment classification model to the real-time social media data to determine sentiment scores.
Intrator teaches wherein the sentiment analysis model of the AI analytics engine is trained on a labeled dataset of social media posts and configured to: apply the trained sentiment classification model to the real-time social media data to determine sentiment scores (pars. 7, 20, 26, 38, and 48 e.g., real-time scan and sentiment analysis of social media posts; include ML models 124 trained and/or adjusted … using past training data and ML training operations; training data may be associated with past social media and networking posts that may include images, text, graphics or emojis, animations (e.g., GIFs), and the like, which may have a corresponding sentiment. The training data may be labeled or unlabeled for different supervised or unsupervised ML and NN training algorithms, techniques, and/or systems. SPA system 110 may further provide sentiment scores 126. For example, initially ML models 124 may be trained on social posts data 134, as well as customer histories 116 from database 114. Social posts data 134 may include historical social media posts, images 136, text in a title or post body associated with images 136 and/or comments 138, and other data used to provide a basis for training data used to train ML models; After initial training, ML models 124 may be deployed in a production computing environment to receive inquires and data for features and predict labels or other classifiers from the data (e.g., sentiment scores and/or sentiment analysis, such as positive, neutral, or negative sentiment, from social media posts including images, text, and other data); analyzing a social media post using an ML model to output sentiment scores; model may be trained on data with labels (or without, for certain training operations and algorithms) that is collected and preprocessed from customer and/or public data sets of images and/or social media posts).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jolly/Motayed with Intrator because the references are analogous since they are each directed to features for extracting useful information (intelligence) from different sources of data, which is within Applicant’s field of endeavor of providing global intelligence based on extracted information from multiple sources, and because modifying Jolly/Motayed to include Intrator’s sentiment classification model features, as claimed, would serve the motivation to extract information from sources that include images and videos (Jolly at par. 49) or to increase the efficiency and speed in analyzing the social media posts (Intrator at par. 23: increases speed and efficiency); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 8 and 15 are rejected under 35 U.S.C. §103 as unpatentable over Jolly et al. (US 2021/0256629, hereinafter “Jolly”) in view of Motayed et al. (US 2024/0233367, hereinafter “Motayed”), as applied to claims 1 and 11 above, and further in view of Tsytsarau et al. (US 2013/0290232, hereinafter “Tsytsarau”).
Claims 8/15: Jolly, in view of Motayed, teaches wherein integrating the extracted geospatial features and textual descriptions, the key insights from the summarized news broadcasts, and the public sentiment and trending topics (as discussed above in the rejection of claims 1/11, which is incorporated herein), and Jolly further teaches that the integrating comprises: spatially and temporally aligning the different data types/modalities; and generating a unified knowledge representation that captures the integrated insights (pars. 104, 110-111, 133, 185, and 233: aggregation and delivery of aggregated news source items to a user in a newsfeed and or use as other forms of information; select elements such as news source account 1415, time 1425 and/or location 1440 to be provided with news source items; news event or topics can be arranged according to set categories typically associated with a news aggregator, such as by object type; Analysis of metadata and any changes in the nature and characteristics of the corpus of news sources in the aggregate can also be conducted to identify similarities and differences; content aggregation platform; extraction of information that can aggregate a large number of news source items describing with a news event or topic of interest), but does not specifically teach identifying correlations and causal relationships between data points.
Tsytsarau teaches identifying correlations and causal relationships between data points (pars. 19, 30, and 50: determination of the time lag between news events and sentiment shifts, level of correlation, and, finally, probability of their causality; provide time series data for correlation layer 30, which, given a proper measure of correlation, may be able to re-align the time series according to causality).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jolly/Motayed with Tsytsarau because the references are analogous since they are each directed to features for extracting useful information (intelligence) from different sources of data, which is within Applicant’s field of endeavor of providing global intelligence based on extracted information from multiple sources, and because modifying Jolly/Motayed to include Tsytsarau’s feature for identifying correlations and causal relationships between data, as claimed, would serve the motivation to evaluated the relationship between sentiment changes and news events (Tsytsarau at par. 9); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 10 and 17 are rejected under 35 U.S.C. §103 as unpatentable over Jolly et al. (US 2021/0256629, hereinafter “Jolly”) in view of Motayed et al. (US 2024/0233367, hereinafter “Motayed”), as applied to claims 1 and 11 above, and further in view of Hockey et al. (US 2019/0318122, hereinafter “Hockey”).
Claims 10/17: Jolly, in view of Motayed, teaches the limitations of claims 1/11 as set forth above, but does not teach the limitations of claims 10/17.
Hockey teaches secure sharing of the generated intelligence reports with authorized parties; implementing access controls and data encryption to prevent unauthorized access; and maintaining detailed audit logs of user interactions and data access (Abstract, pars. 85, 331, 607, and Figs. 1-2: secure permissioning…including secure distribution of aggregated user account data can include generating a financial report based on account information associated with one or more user accounts; receiving a financial report request for the financial report of the user account, wherein the financial report request is identified as being received from a third-party system; sharing the audit token with the first third-party system in response to the financial report request; and providing the first third-party system account access to the financial report through the report token, where the audit report token can be shared with a second third-party system and provided by the second third-party system in order to confirm authorization to the report and integrity of the report; private data may not be distributed to the third-party by the data management platform, but instead may be packaged, encrypted, or otherwise prepared and sent to the subject as a digital copy or a physical copy for routing to the third-party; requests for access and/or provided access can be tracked and reported to users or other parties; requests for access and/or provided access can be tracked and reported to users or other parties).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jolly/Motayed with Hockey because the references are analogous since they are each directed to features for extracting useful information (intelligence) from different sources of data, which is within Applicant’s field of endeavor of providing global intelligence based on extracted information from multiple sources, and because modifying Jolly/Motayed to include Hockey’s report sharing and secure access features, as claimed, would serve the motivation to confirm authorization to the report and integrity of the report (Hockey at par. 262); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
X. X. Zhu et al., "Geoinformation Harvesting From Social Media Data: A community remote sensing approach," in IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 4, pp. 150-180, Dec. 2022: discloses features for geoinformation extraction from social media data.
Z. Han et al., "A Survey on Event Tracking in Social Media Data Streams," in Big Data Mining and Analytics, vol. 7, no. 1, pp. 217-243, March 2024: discloses features for evaluating events and patterns in social networks, such as by using a graph neural network model for extracting meaningful information from large amounts of data, automatically identifying and tracking known topics in news media streams, and enabling a real-time model to detect feature keywords to identify and analyze hot event on social media.
Havas C et al. (2017) “E2mC: improving emergency management service practice through social media and crowdsourcing analysis in near real time,” Sensors, 17(12) - PMC – PubMed: discloses features for collecting and analyzing satellite imagery and user-generated data such as social media or crowdsourced data to provide up-to-date information about an area of interest to facilitate effective disaster management.
Andrews et al. (US 2012/0296845): discloses features for analyzing sentiment using social media sourced data, including providing intelligent analytics (par. 2) by evaluating keywords (par. 82) and by leveraging news media resources and trends (par. 18).
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
02/06/2026