DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been examined and are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/23/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The use of the term (e.g., Windows, Mac OS, Unix, Linux, Android, etc.) on page 7, line 19, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 6-12, and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hassan et al “ClaimBuster: The First-ever End-to-end Fact-checking System” was submitted in 10/23/2024 IDS.
Regarding claims 1 and 11, Hassan teaches a method of facilitating provisioning of a verification data, the method comprising; and a system for facilitating provisioning of a verification data, the system comprising [Hassan “ClaimBuster: The First-ever End-to-end Fact-checking System” was submitted in 10/23/2024 IDS, p. 1946, Introduction, ¶3 “Businesses can use ClaimBuster to identify falsehoods in their competitors' and their own reports and press releases. It can also assist professionals such as lawyers in verifying documents.”]:
receiving, using a communication device, a digital content data from at least one of a content provider device associated with a content provider and a user device; [Hassan p. 1946, 2 System Overview ¶2 Claim Monitor component continuously monitors and retrieves texts from a variety of sources (i.e. Broadcast Media, Social Media, Websites, etc.)]
analyzing, using a processing device, the digital content data; [Hassan pp. 1946-1947, 2 System Overview ¶¶6-7 Claim Spotter performs Ad-hoc analysis on received content sentences, tweets, Hansard, etc. based on a classification and scoring model to correlate for scoring and ranking of datasets]
identifying, using the processing device, a claimed data associated with the digital content data based on the analyzing; [Hassan, p. 1946, Introduction, ¶¶3-5 ClaimBuster discovers when factual claims from various sources (i.e. live discourses, social media, news, etc.) are worth checking provided with a priority ranking that assists fact-checkers]
generating, using the processing device, a query based on the claimed data; [Hassan, p. 1946, Introduction, ¶3 use of database query techniques aid in the process of fact-checking of discovered]
retrieving, using a storage device, a reference data, wherein the retrieving of the reference data is based on the query; [Hassan, p. 1945, 1 Introduction, ¶3 use of database query techniques; ¶7 Claim Matchers “Given an important factual claim identified by the claim spotter, the claim matcher searches a fact-check repository and returns those fact-checks matching the claim. The repository was curated from various fact-checking websites.”]
analyzing, using the processing device, the claimed data, and the reference data; [Hassan, p. 1946, Introduction, ¶¶3-4 identified factual claims matched with a curated repository of fact-checks based on database query techniques]
generating, using the processing device, a validity data based on the analyzing, wherein the validity data indicates an authenticity of the claimed data based on the reference data; [Hassan, p.1946 1 Introduction, ¶6 ClaimBuster produces true-or-false verdicts for certain types of factual claims; then sends questions to question-answering systems and compares returned results with aforementioned answers or produces a verdict based on presence/absence of discrepancies between two sets of answers. Examiner recognizes how validity data and authenticity are described in the specification: the method 300 generates validity data which include one of a numerical value, a textual data, and an accuracy rating; and the authenticity includes: factual accuracy of the claimed data (p. 23, lines 6-11).] and
transmitting, using the communication device, the validity data, and an association indicator to at least one of the content provider device and the user device, wherein the association indicator represents association between the validity data and the claimed data. [Hassan, p. 1947, 2 System Overview, ¶8 Fact-check Report synthesizes a report combining the aforementioned evidence and delivers to users through a project website claim spotter scores on claims in a combined report. ClaimBuster also delivers the claim spotter scores through a variety of channels: websites, Twitter account, API, and Slackbots. Examiner interprets that synthesized report as analogous to the association indicator which include a combination of the aforementioned data (i.e. curated repository of fact-checks, true-or-false verdicts]
Regarding claims 2 and 12, Hassan teaches claim 1 as described above.
Hassan teaches wherein the analyzing of the digital content data is based on a first machine learning model, wherein the first machine learning model is trained on a natural language processing algorithm, wherein the first machine learning model is configured to receive the digital content data as input, wherein the first machine learning model is configured recognize a pattern associated with the digital content data based on the training, wherein the first machine learning model is configured to identify the claimed data based on recognizing of the pattern. [Hassan p. 1946, 1 Introduction, ¶3 “ClaimBuster, an end-to-end system that uses machine learning, natural language processing”. p. 1946, 2 System Overview, ¶6 “The model was trained using tens of thousands of sentences from past general election debates that were labeled by human coders.”]
Regarding claims 6 and 16, Hassan teaches claim 1 as described above.
Hassan teaches further comprising: generating, using the processing device, a user interface based on the validity data, wherein the user interface is configured to present the validity data as an annotation corresponding to the claimed data; [Hassan p. 1948, 3 User Interface and Demonstration Plan ¶4 When a user selects a sentence in the transcript panel, the factcheck report panel displays the supporting or debunking evidence for the selected sentence. Specifically, it shows three types of evidence. The leftmost column displays similar fact-checks (along with the verdicts) from the fact-check repository, if any.] and transmitting, using the communication device, the user interface to at least one of a content provider device and the user device. [Hassan p. 1947, 2 System Overview, ¶9 Claim Checker “Simultaneously, it sends the aforementioned questions to Google via HTTP requests and extracts the answers from Google's answer boxes in the HTML responses.”]
Regarding claims 7 and 17, Hassan teaches claim 1 as described above.
Hassan teaches wherein the analyzing of the claimed data and the reference data is based on a second machine learning model, wherein the second machine learning model is trained on a natural language processing algorithm, wherein the second machine learning model is configured to receive the claimed data and the reference data as input, wherein the machine learning model is configured to determine one or more of a similarity data, a dissimilarity data, a context data, and a verdict data, wherein the generation of the validity data is based on the determination of the one or more of the similarity data, the dissimilarity data, the context data, and the verdict data. [Hassan p. 1947, 2 System Overview, ¶8 Claim Matcher: “system has two approaches to measuring the similarity…One is based on similarity of tokens…the other is based on semantic similarity.”]
Regarding claims 8 and 18, Hassan teaches claim 1 as described above.
Hassan teaches wherein the reference data comprises a plurality of reference data, wherein the method further comprises generating, using a processing device, a plurality of results, wherein each of the plurality of results is associated with a factual accuracy of the digital content data based on each of the plurality of reference data, wherein the validity data is generated based on the plurality of initial fact checks [Hassan p. 1946 1 Introduction, ¶6 ClaimBuster reaches true-or-false verdicts for certain types of factual claims…One method is to translate the factual claim into questions and their accompanying answers. It then sends the questions to question-answering systems and compares the returned results with the aforementioned answers. It produces a verdict based on the presence/absence of a discrepancy between these two sets of answers. The other method is to search in a repository for similar or identical claims that have already been fact-checked by professionals and to use the verdicts from the professionals. In the case that ClaimBuster is not able to produce a verdict, it provides processed search results from a general search engine to assist vetting the claim.].
Regarding claims 9 and 19, Hassan teaches claim 1 as described above.
Hassan teaches further comprising communicating, using the communication device, with the user device comprising of a user processing device, a user communication device, and a user device sensor, wherein the user device sensor is configured to generate a sensor data, wherein the user communication device is configured to transmit the digital content data based on the sensor data [Hassan p. 1947, 2 System Overview, ¶1 “Figure 1 depicts its system architecture of ClaimBuster (includes the Claim Monitor, Claim Spotter, Claim Matcher, Claim Checker, Fact-check Reporter, Repository, and Web). The claim monitor interfaces various data sources (social media, broadcasted TV programs, and websites) with ClaimBuster. The claim spotter identifies check-worthy factual claims in verbose text from the data sources. The claim matcher finds existing fact-checks that are closely-related or identical to the discovered claims. In this way, we fully leverage well-researched fact-checks from professional fact-checkers. This is particularly useful, because oftentimes the same false claims are repeated. When a matching fact-check cannot be found, the claim checker queries external knowledge bases and the Web to vet the factual claims. The fact-check reporter compiles the evidence from the claim matcher and the claim checker, and presents fact-check reports to users through various channels, such as the project website, its Twitter account, a Slackbot, and a public APL” Examiner interprets the system architecture of ClaimBuster that monitor interfaces of used to capture and communicate digital content data are well known in the arts of computing devices comprising of transmitters, buses, receivers, and other components to exchange data; as analogous to a user processing device, user communication device and user device sensor.].
Regarding claims 10 and 20, Hassan teaches claim 1 as described above.
Hassan teaches further comprising: generating, using the processing device, a question data based on the claimed data;
transmitting, using the processing device, the question data to a plurality of user devices; receiving, using the communication device, the reference data, wherein the reference data is received in response to the question data [See Hassan p. 1946 1 Introduction, ¶6 ClaimBuster reaches true-or-false verdicts for certain types of factual claims…One method is to translate the factual claim into questions].
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Hassan et al “ClaimBuster: The First-ever End-to-end Fact-checking System” was submitted in 10/23/2024 IDS, in view of Rind et al, hereinafter (“Rind”), US PG Publication 20210326478 A1.
Regarding claims 3 and 13, Hassan teaches claim 1 as described above.
However, Hassan fails to explicitly teach but Rind teaches wherein the reference data is stored in a hierarchical data structure comprising of a plurality of reference data, wherein each of the plurality of reference data comprises an index data configured to allow accessing of a corresponding reference data within the hierarchical data structure. [Rind 20210326478 A1 ¶0034 hierarchical data serialization format: JSON, YAML, or XML document. ¶0036 users may access content from content repository 24. ¶0047 a given pseudonymous identifier may be indexed to a plurality of ciphertexts and a plurality of records in the content repository 24 by the data repository 20, or in some cases, the pseudonymous identifier may be indexed to a most current ciphertext, and that ciphertext may include a pointer to address of a previous ciphertext].
Hassan teaches all the features of claims 1 and 11 not wherein the reference data is stored in a hierarchical data structure comprising of a plurality of reference data, wherein each of the plurality of reference data comprises an index data configured to allow accessing of a corresponding reference data within the hierarchical data structure. Rind teaches a staged information exchange facilitated by content-addressable records indexed to pseudonymous identifiers by a tamper-evident data structure. Because both Hassan and Rind are from the same field of endeavor of protecting data integrity, e.g. using checksums, certificates or signatures, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention was made to allow access the use the hierarchical format for corresponding reference data [Rind ¶¶0034 0036 and 0047].
Regarding claims 4 and 14, Hassan teaches claim 1 as described above.
However, Hassan fails to explicitly teach but Rind teaches wherein the digital content data comprises a visual data, wherein the analyzing of the digital content data is based on an image processing model, wherein the method further comprises generating, using the processing device, a text data based on the analyzing, wherein the identification of the claimed data is further based on the text data. [Rind ¶0081 obtain an image of the user holding an identifying legal document, including: non-pseudo ID]
Hassan teaches all the features of claims 1 and 11 not wherein the digital content data comprises a visual data, wherein the analyzing of the digital content data is based on an image processing model, wherein the method further comprises generating, using the processing device, a text data based on the analyzing, wherein the identification of the claimed data is further based on the text data. Rind teaches a staged information exchange facilitated by content-addressable records indexed to pseudonymous identifiers by a tamper-evident data structure. Because both Hassan and Rind are from the same field of endeavor of protecting data integrity, e.g. using checksums, certificates or signatures, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention was made to allow access the use the hierarchical format for corresponding reference data [Rind ¶¶0034 0036 and 0047].
Claim(s) 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Hassan et al “ClaimBuster: The First-ever End-to-end Fact-checking System” was submitted in 10/23/2024 IDS, in view of Luo et al, hereinafter (“Luo”), “Knowledge Mining and Visualization on NewsWebpages and Large-Scale News Video Database”.
Regarding claims 5 and 15, Hassan teaches claim 1 as described above.
Hassan teaches the digital content [Hassan p. 1946 1 Introduction, ¶]; however, Hassan fails to explicitly teach but Luo teaches wherein the digital content data comprises an audio data, wherein the analyzing of the digital content data is based on an audio processing model [Luo et al p. 452 1. Introduction, ¶1 analyzing international news reports…; p. 455 3. News Browsing and Retrieval via Knowledge Visualization, ¶3 audio of news videos], wherein the method further comprises generating, using the processing device, an audio extracted data based on the analyzing, wherein the identification of the claimed data is further based on the audio extracted data. [Luo Figure 1: The workflow of the framework. p. 453 2. Related Works ¶2¶3 based on above observations new techniques of knowledge extraction where news reports may be in text, video, audio and image formats]
Hassan teaches all the features of claims 1 and 11 not wherein the digital content data comprises an audio data, wherein the analyzing of the digital content data is based on an audio processing model, wherein the method further comprises generating, using the processing device, an audio extracted data based on the analyzing, wherein the identification of the claimed data is further based on the audio extracted data. Luo teaches visualization based interface can directly represent valuable information to the users. The users can browse the content of the news database efficiently and submit queries visually via the visualization interface even they cannot provide appropriate search keywords.. Because both Hassan and Luo manipulate audio and visual aspects of data sources, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention was made to use the visualization techniques of Luo [Luo ¶Abstract].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
McKervey et al 11921873 B1 teaches authenticating data associated with a data intake and query system using a distributed ledger system.
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SAKINAH WHITE-TAYLOR
Primary Examiner
Art Unit 2407
/Sakinah White-Taylor/Primary Examiner, Art Unit 2407