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 .
Claim Status
Claims 1-16 and 18-21 are pending.
Response to Arguments
Prior Art Rejection: Applicant’s arguments with respect to claims 1-16 and 18-21 have been fully considered and are persuasive. Upon further consideration, and in view of applicant’s amendments, a new grounds of rejection is made in view of newly cited reference Bennett.
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 1-5, 7, 9-16, and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Brooke et al (US 20190347748 A1) hereafter Brooke in view of Langseth et al (US 20070011183 A1) hereafter Langseth in view of Bennett et al (US 20070239689 A1) hereafter Bennett
Regarding claim 1, Brooke teaches a method for identifying overruled in part content based on citationally related content, the method comprising: receiving, at a search field presented at a graphical user interface (GUI), a user input; receiving, by one or more processors and in response to the user input, first case law data from a data source, the first case law data associated with a first case law document (Para 0009, The system may include a data extractor configured to receive case law data from at least one case law document data source); receiving, by the one or more processors, second case law data from the data source, the second case law data associated with a citationally-related case law document that overrules at least a portion of the first case law document (Para 0054, wherein the first case overrules or abrogates the second case); providing, by the one or more processors, a plurality of features extracted from multiple content portions of the citationally-related case law document as input data to a first set of trained machine learning (ML) classifiers to generate probability values associated with the multiple content portions of the citationally-related case law document (Para 0021, The various components of server 110 may cooperatively operate to extract case data from the case law documents from data sources 170 and to apply customized machine learning algorithms); ranking, by the one or more processors, the multiple content portions of the citationally-related case law document based on the associated probability values (Para 0051, ranking of the features extracted by feature generator 121 according to the relevance of the features to the final classification task); selecting, by the one or more processors, a highest ranked content portion of the multiple content portions of the citationally-related case law document as an overruling passage of the citationally-related case law document (Para 0051, main classifier 122 may label an (X, O, A) triple with a label D or N, by determining, based on the features received from feature generator 121, whether there is a high risk, or whether there is a low risk or no risk, that citing case (A) may be impliedly overruled by overruling case (X)); and displaying, by the one or more processors, the overruling passage via a graphical user interface (GUI) based on user selection of the first case law document with respect to the GUI, by; presenting a visual indicator of the overruling passage with an identifier of the first case law document (Para 0058, At optional block 405, a classification indication indicating the classification for the (X, O, A) triple is provided to a user. In aspects, the classification indication may identify the citing third case (A) as potentially not good law); presenting a visual indicator of the overruling passage with the portion of the first case law document (Para 0021, An indication of the risk of impliedly overruled cases may then be provided to a user of system).
Brooke teaches identifying the overruling passage of a portion of the first case law document, as shown above. However, Brooke does not appear to explicitly teach automatically scrolling, in a document pane of the GUI, to the portion of the first case law document.
In analogous art, Langseth teaches automatically scrolling, on the GUI, to the portion of a document (Para 0154, automatically scrolls down to the location of the relevant sentence). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brooke to include the teaching of Langseth. One of ordinary skill in the art would be motivated to implement this modification in order to provide easy reference for the user, as taught by Langseth (Para 0154, highlight it for easy reference by the user),
Brooke in view of Langseth teaches showing an overruling passage, as shown above. However, Brooke in view of Langseth does not appear to explicitly teach and presenting, with the document pane, a pop-up window showing the overruling passage.
In analogous art, Bennett teaches presenting, with the document pane, a pop-up window showing the case law passage (Para 0074, In response to the search initiation, a case law database window 85 is opened to display case law retrieved). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brooke in view of Langseth to include the teaching of Bennett. One of ordinary skill in the art would be motivated to implement this modification in order to search court documents, as taught by Bennett (Para 0003, a method and apparatus for providing context sensitive searching of a current transcript, other case evidence and case law which may be locally or remotely located).
Regarding claim 2, Brooke in view of Langseth in view of Bennett teaches the method of claim 1, wherein the probability values associated with the multiple content portions of the citationally-related case law document indicate a probability that a corresponding content portion is relevant to overruling at least the portion of the first case law document (Para 0025, type of indication that conveys to a user that there is a probability that the case may have been impliedly overruled).
Regarding claim 3, Brooke in view of Langseth in view of Bennett teaches the method of claim 1, wherein the plurality of features includes a set of heuristics-based features extracted from the multiple content portions of the citationally-related case law document and a set of statistics-based features extracted from the multiple content portions of the citationally-related case law document (Para 0030, memory 112 may comprise database 113 for storing analysis data, models, classifiers, rankers, usage metrics, analytics, user preferences, headnotes, headnotes analyses, key numbers, etc., which system 100 may use to provide the features discussed herein).
Regarding claim 4, Brooke in view of Langseth in view of Bennett teaches the method of claim 3, wherein the heuristics-based features include one or more similarity features indicating a similarity between one or more particular headnotes and the multiple content portions of the citationally-related case law document, one or more distance features indicating a positional distance between one or more particular paragraphs and the multiple content portions of the citationally-related case law document, one or more binary features indicating whether the multiple content portions of the citationally-related case law document include particular text, or a combination thereof (Para 0041, measure the similarity between the citation in the (X, O) pair and the citation in the (A, O) pair).
Regarding claim 5, Brooke in view of Langseth in view of Bennett teaches the method of claim 4, wherein the one or more particular headnotes include the particular text, wherein the one or more particular paragraphs include the particular text, and wherein the particular text includes one or more overruling terms or language patterns, a citation to the first case law document, a pincite to the first case law document, or a combination thereof (Para 0042, These bigram-based features may analyze the text of the citation paragraphs within overruling case (X) and citing case (A), and may determine a number of words that are potentially overlapping).
Regarding claim 7, Brooke in view of Langseth in view of Bennett teaches the method of claim 3, wherein the first set of trained ML classifiers comprises an ensemble of a first ML classifier and a second ML classifier, wherein the first ML classifier is configured to receive the heuristics-based features as the input data and to generate a first set of probability values associated with the multiple content portions of the citationally-related case law document, and wherein the second ML classifier is configured to receive the statistics-based features as the input data and to generate a second set of probability values associated with the multiple content portions of the citationally-related case law document (Para 0021, The various components of server 110 may cooperatively operate to extract case data from the case law documents from data sources 170 and to apply customized machine learning algorithms and classifiers to extract features from the case data, and to identify a risk of impliedly overruled cases).
Regarding claim 9, Brooke in view of Langseth in view of Bennett teaches the method of claim 7, wherein generating the probability values associated with the multiple content portions of the citationally-related case law document includes, for each content portion of the multiple content portions of the citationally-related case law document, combining a value from the first set of probability values that is associated with the content portion with a value from the second set of probability values that is associated with the content portion (Para 0041, some features of headnote-related features 302 may aggregate the pairwise similarity values over the two sets of headnote texts).
Claim 10 is the system claim corresponding to method claim 1, and is analyzed and rejected accordingly.
Regarding claim 11, Brooke in view of Langseth in view of Bennett teaches the system of claim 10, wherein the one or more processors are further configured to: select, by the one or more processors, a highest ranked subset of the multiple content portions of the citationally-related case law document (Para 0036, metadata is extracted from the candidate triples); provide, by the one or more processors, a second plurality of features extracted from the highest ranked subset as input data to a second set of trained ML classifiers to generate probability values associated with multiple headnotes of the first case law document (Para 0037, the data extracted by data extractor 120 may be used to train the classifiers); and rank, by the one or more processors, the multiple headnotes of the first case law document based on the associated probability values (Para 0039, the set of training data may include configuration to constrain the various types of cases, such as by date, jurisdiction, headnotes, popularity, etc.).
Regarding claim 12, Brooke in view of Langseth in view of Bennett teaches the system of claim 11, wherein the second plurality of features include one or more similarity features indicating a similarity between the highest ranked subset and the multiple headnotes of the first case law document (Para 0041, These headnote-related features 302 may rely on headnotes and key number (e.g., key numbers associated with headnotes) to measure the similarity between the citation in the (X, O) pair and the citation in the (A, O) pair).
Regarding claim 13, Brooke in view of Langseth in view of Bennett teaches the system of claim 11, wherein the second set of trained ML classifiers comprises a linear ML classifier (Para 0046, the established law classifier 301 may use a logistic regression with L2 regularization classification algorithm, and the output probability for the classifier may be used as established law classifier 301's output feature value for a respective citing case (A)).
Regarding claim 14, Brooke in view of Langseth in view of Bennett teaches the system of claim 11, wherein the one or more processors are further configured to: provide, by the one or more processors, a third plurality of features extracted from multiple content portions of the first case law document and a highest ranked headnote of the multiple headnotes as input data to a third set of trained ML classifiers to generate probability values associated with the multiple content portions of the first case law document (Para 0045, established law classifier 301 may be configured to determine the probability that a citing case (A) in an (X, O, A) triple is citing overruled case (O) as good/established law); rank, by the one or more processors, the multiple content portions of the first case law document based on the associated probability values (Para 0051, Feature selection, e.g., top select features that may be provided to the main classifier, may be performed through ranking of the features extracted by feature generator 121 according to the relevance of the features to the final classification task, and by removing low-ranked features); select, by the one or more processors, a highest ranked content portion of the multiple content portions of the first case law document as an overruled-in-part passage of the first case law document (Para 0057, The ranked features may then be trimmed, e.g., by selecting the N highest-ranked features, or by removing the K lowest-ranked features. The trimmed set of ranked features may then be fed to the classifier); and display, by the one or more processors, the overruled-in-part passage via the GUI (Para 0025, User terminal 160 may be configured to provide a graphical user interface (GUI) via which a user may be provided with information related to the citational relationship between cases, as well as an indication of impliedly overruled cases).
Regarding claim 15, Brooke in view of Langseth in view of Bennett teaches the system of claim 14, wherein displaying the overruled-in-part passage includes highlighting or visually indicating the overruled-in-part passage, automatically scrolling through the first case law document to a beginning of the overruled-in-part passage, or both (Para 0050, the indication of the similar paragraphs may include highlighting the citation paragraphs in the citing case (A), and the corresponding text in the overruled case (O) and the overruling case (X)).
Regarding claim 16, Brooke in view of Langseth in view of Bennett teaches the system of claim 14, wherein the third plurality of features includes one or more similarity features indicating a similarity between the highest ranked headnote and the multiple content portions of the first case law document, one or more distance features indicating a positional distance between one or more paragraphs linked to the highest ranked headnote and the multiple content portions of the first case law document, one or more binary features indicating whether the multiple content portions of the first case law document include holding text or language patterns, or a combination thereof (Para 0043, Established law features 304 may include features that analyze text segments from citing case (A) of an (X, O, A) triple to determine whether there may be an indication in the language proximate the citation of overruled case (O) in citing case (A), which may indicate that citing case (A) considers overruled case (O) to be good/established law).
Regarding claim 18, Brooke in view of Langseth in view of Bennett teaches the system of claim 14, wherein the overruled-in-part passage comprises a sentence, a paragraph, or a footnote of the citationally-related case law document (Para 0042, This may be done in a bigram format, in which a pair of words from a sentence is taken for each iteration of the analysis).
Claim 19 is the device claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 20 is the device claim corresponding to claim 18, and is analyzed and rejected accordingly.
Regarding claim 21, Brooke in view of Langseth in view of Bennett teaches the non-transitory computer-readable storage device of claim 19, wherein: the plurality of sets of trained ML classifiers includes: a first set of ML classifiers including a first feed forward neural network (FNN) classifier with a gradient boosting classifier which generates first probability outputs; a second set of ML classifiers including a linear classifier which uses the first probability outputs from the first set of ML classifiers to generate second probability outputs; and a third set of ML classifiers including a second FNN classifier which uses the first probability outputs of the first set of ML classifiers and the second probability outputs of the second set of ML classifiers to generate third probability outputs; and the ranking of the multiple content portions uses the first probability outputs from the first set of ML classifiers and the third probability outputs from the third set of ML classifiers (Brooke, Para 0021, The various components of server 110 may cooperatively operate to extract case data from the case law documents from data sources 170 and to apply customized machine learning algorithms and classifiers to extract features from the case data, and to identify a risk of impliedly overruled cases).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Brooke in view of Langseth in view of Bennett in view of Joshi et al (US 20200334274 A1) hereafter Joshi
Regarding claim 6, Brooke in view of Langseth in view of Bennett teaches the method of claim 3, as shown above. Brooke in view of Langseth in view of Bennett does not appear to explicitly teach wherein the statistics-based features include term frequency-inverse document frequency (TF-IDF) vectors generated based on the multiple content portions of the citationally-related case law document. In analogous art, Joshi teaches wherein the statistics-based features include term frequency-inverse document frequency (TF-IDF) vectors generated based on the multiple content portions of the citationally-related case law document (Para 0216, the given document is classified by a legal metric using TF-IDF algorithms compared against existing literature). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brooke in view of Langseth in view of Bennett with the teaching of Joshi. One of ordinary skill in the art would be motivated to implement this modification in order to perform data processing, as taught by Joshi (Para 0006, These challenges of storing, sharing, and obtaining commentary data in relation to unstructured data occur in different industries, such as engineering, law, healthcare, insurance, media, and academia, to name a few).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Brooke in view of Langseth in view of Bennett in view of Patodia et al (US 20230135329 A1) hereafter Patodia
Regarding claim 8, Brooke in view of Langseth in view of Bennett teaches the method of claim 7, as shown above. Brooke in view of Langseth in view of Bennett does not appear to explicitly teach wherein the first ML classifier comprises a feed forward neural network (FNN) classifier, and wherein the second ML classifier comprises an extreme gradient boosting (XGBoost) classifier. In analogous art, Patodia teaches wherein the first ML classifier comprises a feed forward neural network (FNN) classifier (Para 0032, one of a Feedforward Neural Network), and wherein the second ML classifier comprises an XGBoost classifier (Para 0082, the machine learning engine employs a XGBoost model). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brooke in view of Langseth in view of Bennett to include the teaching of Patodia. One of ordinary skill in the art would be motivated to implement this modification in order to handle client requests, as taught by Patodia (Abs, a decision service system that handles client requests).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brooks Hale whose telephone number is 571-272-0160. The examiner can normally be reached 9am to 5pm est.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166