DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 10, 2025 has been entered.
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The amendment filed on November 10, 2025 has been entered. The following has occurred: Claims 1, 5, 9, 13, 16, and 20 have been amended; Claims 4, 6, 12, 14, and 19 were previously cancelled.
Claims 1-3, 5, 7-11, 13, 15-18, and 20 are pending.
Response to Amendment
Specification Objection has been withdrawn in view of corrected specification provided.
Claim Objections have been withdrawn in view of amended claims.
35 U.S.C. 112(b) rejection has been withdrawn in view of amended claims.
35 U.S.C. 101 rejection has been maintained in light of the amendment.
Previous 35 U.S.C. 103 rejection has been withdrawn and new 35 U.S.C. 103 rejection has been added in light of the amendment.
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 1-3, 5, 7-11, 13, 15-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-8 are directed to a method (i.e., a process), claims 9-15 are directed to an apparatus (i.e., a machine), and 16-20 are directed to a computer product (i.e., an article of manufacture). Thus, the eligibility analysis proceeds to Step 2A.1.
Step 2A. prong one: Does the claim recite an abstract idea, law of nature, or natural phenomenon? (MPEP 2106.04)
While claims 1, 9, and 16, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The abstract idea recited in claims 1, 9, and 16, is
identifying a docket event of a plurality of docket events associated with a docket of a court case, the plurality of docket events stored in a docket event database;
training a conditional likelihood model using a training set comprising a sequence of entries, each entry of the sequence of entries comprising a training docket event, each of the training docket events arranged in one of a plurality of numbered groups which includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order, wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events; and
predicting, using the conditional likelihood model, a first set of case outcomes for the court case based on outcomes of other cases having the same docket event.
The claimed invention is directed to an abstract idea of court case outcome prediction.
The limitations of identifying docket event from a docket of court case and predicting case outcome of the court case, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer elements, the claims recite processes that are all acts that could be performed by the human mind involved human judgements, observations, and evaluations, e.g., mentally or manually, using a pen and paper, without the need of a computer or any other machine. Thus, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III).
Additionally and alternatively, the same claim limitations above recite an abstract idea in the forms of business or law firm providing services to their client for a predictability of legal outcome to develop litigation strategies. That is, human paralegal, clerk, or attorney of a law firm have been responsible for gathering, compiling, and analyzing information concerning courts and related processes to predict the outcome of certain legal filings, before computers were available to support these tasks because legal industry has existed longer than computers. Because the limitations above closely follow the steps standard in commercial or legal interaction, which legal obligations and business relations in forms of predicting/determining likelihood of judge ruling of court case, the claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Additionally, the examiner further finds that the claims involving the prediction of likelihood (i.e., probability calculation) of case outcome, to be directed to “mathematical concept” category of the abstract ideas. Under the broadest reasonable interpretation, the steps of training a conditional likelihood model and predicting, using the conditional likelihood model, a first set of case outcomes for the court case based on outcomes of other cases having the same docket event amounts to forms of performing mathematical calculations, which falls under “Mathematical Concept” of the abstract idea.
Accordingly, the above-mentioned limitations are considered as a single abstract idea, therefore, the claims recite an abstract idea and the analysis proceeds to Step 2A. prong two.
Step 2A. prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? (MPEP 2106.04)
This judicial exception is not integrated into a practical application because the additional elements merely add instructions to apply the abstract idea to a computer.
The additional elements considered include:
Claim 1: No additional element recited.
Claim 9: An apparatus comprising: a processor; and a memory to store computer program instructions, the computer program instructions when executed on the processor cause the processor to perform operations comprising:
Claim 16: A non-transitory computer readable medium storing computer program instructions, which, when executed on a processor, cause the processor to perform operations comprising:
In particular, the claim only recites the additional elements - the use of “apparatus”, “processor”, “memory” and/or “non-transitory computer readable medium” to identify, train, and predict information. The computer in the steps is recited at a high-level of generality (i.e., as generic computer components performing a generic computer function; See Applicant’s Specification at least at paragraphs [0057] and Fig. 11) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
That is, the function of limitations [A]-[C] are steps of adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer.
Accordingly, even in combination, these additional element(s) do not integrate the abstract idea into a practical application because they do not improve a computer or other technology, do not transform a particular article, do not recite more than a general link to a computer, and do not invoke the computer in any meaningful way; the general computer is effectively part of the preamble instruction to “apply” the exception by the computer. Therefore, the claims are directed to an abstract idea and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations of [A]-[C] amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claims as a whole merely describes how to generally “apply” the concept for court case outcome prediction. Thus, viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claims 2, 10, 17 further recite additional abstract step of predicting another set of case outcome, which does not change the abstract idea of the independent claims. The same additional elements in the independent claims, are recited at a high-level of generality (i.e., as a generic computer system performing generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
As for dependent claims 3, 8, 11, and 18 further recite additional descriptive information regarding the docket event information, which does not change the abstract idea of the independent claims. No new additional element has been introduced. The claims are ineligible.
As for dependent claims 5, 7, 13, 15, and 20, further recite additional abstract information regarding to predicting using conditional likelihood model such as Naive Bayes model and further trained with filing of motion or entry of order. The additional descriptive information does not change the abstract idea of the independent claims. The models are used at a high-level of generality for predictable result using a generic computer component previously discussed in the independent claim, see MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible. Therefore, claims are rejected under 35 U.S.C. 101.
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
Claims 1-3, 5, 7-11, 13, 15-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Salas et al. (US 20170076001 A1, hereinafter “Salas”) in view of Chan et al. (US 20220343444 A1, hereinafter “Chan”), and further in view of Vacek et al (US 20190385254 A1, hereinafter, “Vacek”).
Claim 1, Salas discloses a method (para. [0056], methods) comprising:
identifying a docket event of a plurality of docket events associated with a docket of a court case (para. [0067], identify relevant case docket entries), the plurality of docket events stored in a docket event database (abstract: retrieving docket and other data from a plurality of databases. Para. [0010], “accessing, using a computing device having a processor and memory, data of docket entries, for each party the docket entries have reached a certain outcome for an existing docket;” Para. [0014], “a database having stored therein a first set of docket entry data, the first set of docket entry data including a set of docket entries for at least one existing docket and for each party for which the docket entries have reached a certain outcome;” The docket entry data stored in the database is representative of docket events associated with a docket of a court case stored in a docket event database);
training a conditional likelihood model using a training set comprising a sequence of entries, each entry of the sequence of entries comprising a training docket event (Salas, [0010], [0012], [0014], [0020], [0021], [0023], disclosing the training of sequence tagging classifier of docket entries data, using machine learning models. In para. [0062], Salas teaches that the system “extends the Navie Bayes model from text classification to the outcome duration prediction task. A Naïve Bayes model is conditional likelihood model. The model is trained on historical docket entry data which corresponds to claimed “training docket event), and
predicting, using the conditional likelihood model (para. [0059]-[0063] and [0103] Naïve Bayes model which is representative of conditional likelihood model is used for prediction of outcome), a first set of case outcomes for the court case based on outcomes of other cases having the same docket event (para. [0060], “present invention is outcome prediction for a specific entity or party in a case by the Outcome Prediction Engine. For the Outcome Prediction Engine, given a sequence of n docket entries and an open status for all or a subset of the involved parties, a regression algorithm can determine the remaining time to resolution (i.e., an outcome has been reached). The docket data provides information about the time to resolution and the n-grams derived from the docket entries are used for training a regression algorithm that predicts the remaining time at a given time t based on the docket entries created up to that point. The regression algorithms are based on multi-nominal Naïve Bayes and Survival analysis.” Disclosing the predicting a first set of case outcomes for a court case based on outcome derived from docket entries of similar cases. See para. [0087], “a Bayesian Network Model for predicting docket resolution time may be applied…. The model may be generalized to identify practice areas and jurisdictions that have “similar” dockets and aggregate them together, or to introduce additional predictors that account for these other variables. It is assumed that each model represents a homogenous subset of cases coming from the same state and the same practice area” Which disclosing the same (i.e., similar) docket event is used to be used in the model for the prediction of case outcome).
Salas discloses the above-mentioned limitations. Salas discloses a dynamic model where predictions are updated over time, describing a “sequence function (ft)t that represents the evolving belief as to the predicted outcome as more information about a docket is gathered (para. [0092]). That is, Salas teaches the general concept of a model that learns cumulatively over the life of a case.
However, Salas does not expressly disclose structuring the training data for this “evolving belief” model into cumulative groups corresponding to specific, well-defined stages of legal proceeding.
Particularly, Salas does not expressly disclose the claim limitation, each of the training docket events arranged in one of a plurality of numbered groups which includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order, wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events.
Chan is directed to the specific field of using predictive analytics for patent and non-patent documents, including predicting outcomes in PTAB post-grant review proceedings. Chan, specifically teaches the claim limitation, each of the training docket events arranged in one of a plurality of numbered groups which includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups (Chan para. [0370] teaches determining a validity strength rating for four separate phases: the “Pre-Petition Stage,” “Decision Stage,” “Final Stage,” and “Post-Final Stage”. Chan, para. [0381] further teaches that the models are updated after each respective stage to “reflect data and attributes associated with” that stage’s events. Notes, the four separate phases are a plurality of discrete stages corresponding to the claimed “numbered groups.” For example, “Pre-Petition Stage” is group 1, the “Decision Stage” is group 2, and so on. Chan teaches the analytical models are updated as the cases progresses through these stages, meaning the data from earlier stags is necessarily included in the analysis of later stages. This discloses includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of Salas using Navie Bayes model for prediction of case outcome to include the structural training data with stage-based analysis such as “Final Stage” (group 3) would be trained on the data from the “Pre-Petition Stage” (group 1) and the “Decision Stage” (group 2) for the motivation and benefit of ensuring the result to be more refined prediction. Further, the combination would have a high expectation of success, as it involves applying a specific data structuring method from Chan to similar machine learning model from Salas within the same technical field of legal analytics.
Still, the combination of Salas and Chan fail to expressly teach, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order, wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events;
Vacek is in the related systems and methods for analyzing and extracting docket data related to a structured proceeding, which specifically teaches, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order (para. [0004], [0007], and [0031] teaching the systems and methods for linking the affected motions to the affected orders which is representative a positive pairing. In para. [0039] explicitly teaches the use of machine learning-based approach for the training of docket entry with associated motions and orders), wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events (para. [0023] states that “the dockets of docket sources 170 may be received from court systems, and/or may be received from an external database or system, such as the public access to court electronic records (PACER) service.” In Applicant’s specification confirms the common knowledge that PACER system is the primary source for federal court docket data, conventionally uses machine-readable hyperlinks within docket entries to connect related documents, such as an order pointing back to the motion it resolves, at para. [0036] of app. specification, “For example, a motion or an order document retrieved from the PACER database may contain links (e.g., hyperlinks) that point to orders or motions.”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the predictive system and method of Salas/Chan to incorporate the automated data structuring and linking motions and orders of pre-existing, reliable hyperlinks within the PACER data as a primary source for identifying the positive pairings needed for the training set of Vacek for the motivation of efficiently create the high-quality, structured training data needed for the predictive model. This would be an application of a known data pre-processing technique from Vacek to improve a known data analysis system from Salas and Chan with a high expectation of success. Since, the claimed invention is merely a combination of old elements in a legal service predictive model field of endeavor. In such combination each element merely would have performed the same legal service predictive model related function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Vacek, the results of the combination were predictable (See MPEP 2143 A).
Claim 2, the combination of Salas, Chan, and Vacek make obvious of the method of claim 1. Salas further discloses further comprising:
predicting a second set of case outcomes for the court case based on another docket event of the plurality of docket events, the first set of case outcomes, and outcomes of other cases having the same docket event and the same other docket event, wherein the second set of case outcomes is a subset of the first set of case outcomes (para. [0014], “a database having stored therein a first set of docket entry data, the first set of docket entry data including a set of docket entries for at least one existing docket and for each party for which the docket entries have reached a certain outcome; at least one machine sequence learning module adapted to receive the first set of docket entry data and, based on the received first set of docket entry data, train a sequence tagging classifier; executing by the processor the trained sequence tagging classifier against a second set of docket entry data, the second set of docket entry data being associated with a new docket other than the existing docket, the new docket having an associated set of parties, the trained sequence tagging classifier adapted to process docket entries from the second set of docket entry data associated with each party in the set of parties to determine an outcome attribute associated with at least one party from the set of parties;”).
Claim 3, the combination of Salas, Chan, and Vacek make obvious of the method of claim 2. Salas further discloses wherein the docket event and the other docket event comprise one of filing of a motion or entry of an order (para. [0082]-[0083] disclosing the docket entry data includes motion filings, party dismissals, transfer orders, etc.).
Claim 5, the combination of Salas, Chan, and Vacek make obvious of the method of claim 1, Salas further discloses wherein the conditional likelihood model is a Naïve Bayes model (para. [0060]-[0063] and [0103] disclosing the prediction model is Naïve Bayes model).
Claim 7, the combination of Salas, Chan, and Vacek make obvious of the method of claim 1, Salas further discloses wherein each training docket event is one of filing of a motion or entry of an order (para. [0009]-[0010], [0012], [0014], [0018], [0020], [0021], [0023], [0032], disclosing training of docket entries data. In para. [0009], [0082]-[0083], and [0086] disclosing docket entries including motion filings, party dismissals, transfer orders, filing date, closing date, and other miscellaneous information provided to the court, which are representative of motion or entry of an order to the court database).
Claim 8, the combination of Salas, Chan, and Vacek make obvious of the method of claim 1, Salas further discloses wherein the docket events are motion/order pairs (para. [0009], [0082]-[0083], and [0086] disclosing the docket entry includes motion filings, party dismissals, transfer orders, filing date, closing date, and other miscellaneous information provided to the court, which are representative of motion or order pairs).
Claim 9, Salas discloses an apparatus (Abstract and para. [0002], system) comprising:
a processor (para. [0010], [0045], [0051], and figs. 1-2: processor); and
a memory to store computer program instructions, the computer program instructions when executed on the processor cause the processor to perform operations comprising (para. [0045] and Figs. 1-2: Non-transitory memory 122, which takes the exemplary form of one or more electronic, magnetic, or optical data-storage devices, stores non-transitory machine readable and/or executable instruction sets for wholly or partly defining software and related user interfaces for execution of the processor 121):
identifying a docket event of a plurality of docket events associated with a docket of a court case (para. [0067], identify relevant case docket entries), the plurality of docket events stored in a docket event database (abstract: retrieving docket and other data from a plurality of databases. Para. [0010], “accessing, using a computing device having a processor and memory, data of docket entries, for each party the docket entries have reached a certain outcome for an existing docket;” Para. [0014], “a database having stored therein a first set of docket entry data, the first set of docket entry data including a set of docket entries for at least one existing docket and for each party for which the docket entries have reached a certain outcome;” The docket entry data stored in the database is representative of docket events associated with a docket of a court case stored in a docket event database.);
training a conditional likelihood model using a training set comprising a sequence of entries, each entry of the sequence of entries comprising a training docket event (Salas, [0010], [0012], [0014], [0020], [0021], [0023], disclosing the training of sequence tagging classifier of docket entries data, using machine learning models. In para. [0062], Salas teaches that the system “extends the Navie Bayes model from text classification to the outcome duration prediction task. A Naïve Bayes model is conditional likelihood model. The model is trained on historical docket entry data which corresponds to claimed “training docket event), and
predicting, using the conditional likelihood model (para. [0059]-[0063] and [0103] Naïve Bayes model which is representative of conditional likelihood model is used for prediction of outcome), a first set of case outcomes for the court case based on outcomes of other cases having the same docket event (para. [0060], “present invention is outcome prediction for a specific entity or party in a case by the Outcome Prediction Engine. For the Outcome Prediction Engine, given a sequence of n docket entries and an open status for all or a subset of the involved parties, a regression algorithm can determine the remaining time to resolution (i.e., an outcome has been reached). The docket data provides information about the time to resolution and the n-grams derived from the docket entries are used for training a regression algorithm that predicts the remaining time at a given time t based on the docket entries created up to that point. The regression algorithms are based on multi-nominal Naïve Bayes and Survival analysis.” Disclosing the predicting a first set of case outcomes for a court case based on outcome derived from docket entries of similar cases. See para. [0087], “a Bayesian Network Model for predicting docket resolution time may be applied…. The model may be generalized to identify practice areas and jurisdictions that have “similar” dockets and aggregate them together, or to introduce additional predictors that account for these other variables. It is assumed that each model represents a homogenous subset of cases coming from the same state and the same practice area” Which disclosing the same (i.e., similar) docket event is used to be used in the model for the prediction of case outcome).
Salas discloses the above-mentioned limitations. Salas discloses a dynamic model where predictions are updated over time, describing a “sequence function (ft)t that represents the evolving belief as to the predicted outcome as more information about a docket is gathered (para. [0092]). That is, Salas teaches the general concept of a model that learns cumulatively over the life of a case.
However, Salas does not expressly disclose structuring the training data for this “evolving belief” model into cumulative groups corresponding to specific, well-defined stages of legal proceeding.
Particularly, Salas does not expressly disclose the claim limitation, each of the training docket events arranged in one of a plurality of numbered groups which includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order, wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events.
Chan is directed to the specific field of using predictive analytics for patent and non-patent documents, including predicting outcomes in PTAB post-grant review proceedings. Chan, specifically teaches the claim limitation, each of the training docket events arranged in one of a plurality of numbered groups which includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups (Chan para. [0370] teaches determining a validity strength rating for four separate phases: the “Pre-Petition Stage,” “Decision Stage,” “Final Stage,” and “Post-Final Stage”. Chan, para. [0381] further teaches that the models are updated after each respective stage to “reflect data and attributes associated with” that stage’s events. Notes, the four separate phases are a plurality of discrete stages corresponding to the claimed “numbered groups.” For example, “Pre-Petition Stage” is group 1, the “Decision Stage” is group 2, and so on. Chan teaches the analytical models are updated as the cases progresses through these stages, meaning the data from earlier stags is necessarily included in the analysis of later stages. This discloses includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of Salas using Navie Bayes model for prediction of case outcome to include the structural training data with stage-based analysis such as “Final Stage” (group 3) would be trained on the data from the “Pre-Petition Stage” (group 1) and the “Decision Stage” (group 2) for the motivation and benefit of ensuring the result to be more refined prediction. Further, the combination would have a high expectation of success, as it involves applying a specific data structuring method from Chan to similar machine learning model from Salas within the same technical field of legal analytics.
Still, the combination of Salas and Chan fail to expressly teach, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order, wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events;
Vacek is in the related systems and methods for analyzing and extracting docket data related to a structured proceeding, which specifically teaches, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order (para. [0004], [0007], and [0031] teaching the systems and methods for linking the affected motions to the affected orders which is representative a positive pairing. In para. [0039] explicitly teaches the use of machine learning-based approach for the training of docket entry with associated motions and orders), wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events (para. [0023] states that “the dockets of docket sources 170 may be received from court systems, and/or may be received from an external database or system, such as the public access to court electronic records (PACER) service.” In Applicant’s specification confirms the common knowledge that PACER system is the primary source for federal court docket data, conventionally uses machine-readable hyperlinks within docket entries to connect related documents, such as an order pointing back to the motion it resolves, at para. [0036] of app. specification, “For example, a motion or an order document retrieved from the PACER database may contain links (e.g., hyperlinks) that point to orders or motions.”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the predictive system and method of Salas/Chan to incorporate the automated data structuring and linking motions and orders of pre-existing, reliable hyperlinks within the PACER data as a primary source for identifying the positive pairings needed for the training set of Vacek for the motivation of efficiently create the high-quality, structured training data needed for the predictive model. This would be an application of a known data pre-processing technique from Vacek to improve a known data analysis system from Salas and Chan with a high expectation of success. Since, the claimed invention is merely a combination of old elements in a legal service predictive model field of endeavor. In such combination each element merely would have performed the same legal service predictive model related function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Vacek, the results of the combination were predictable (See MPEP 2143 A).
Claim 10, the combination of Salas, Chan, and Vacek make obvious of the apparatus of claim 9, Salas further discloses the operations further comprising:
predicting a second set of case outcomes for the court case based on another docket event of the plurality of docket events, the first set of case outcomes, and outcomes of other cases having the same docket event and the same other docket event, wherein the second set of case outcomes is a subset of the first set of case outcomes (para. [0014], “a database having stored therein a first set of docket entry data, the first set of docket entry data including a set of docket entries for at least one existing docket and for each party for which the docket entries have reached a certain outcome; at least one machine sequence learning module adapted to receive the first set of docket entry data and, based on the received first set of docket entry data, train a sequence tagging classifier; executing by the processor the trained sequence tagging classifier against a second set of docket entry data, the second set of docket entry data being associated with a new docket other than the existing docket, the new docket having an associated set of parties, the trained sequence tagging classifier adapted to process docket entries from the second set of docket entry data associated with each party in the set of parties to determine an outcome attribute associated with at least one party from the set of parties;”).
Claim 11, the combination of Salas, Chan, and Vacek make obvious of the apparatus of claim 10, Salas further discloses wherein the docket event and the other docket event comprise one of filing of a motion or entry of an order (para. [0082]-[0083] disclosing the docket entry data includes motion filings, party dismissals, transfer orders, etc.).
Claim 13, the combination of Salas, Chan, and Vacek make obvious of the apparatus of claim 9, Salas further discloses wherein the conditional likelihood model is a Naïve Bayes model (para. [0060]-[0063] and [0103] disclosing the prediction model is Naïve Bayes model).
Claim 15, the combination of Salas, Chan, and Vacek make obvious of the apparatus of claim 9, Salas further discloses wherein each training docket event is one of filing of a motion or entry of an order (para. [0009]-[0010], [0012], [0014], [0018], [0020], [0021], [0023], [0032], disclosing training of docket entries data. In para. [0009], [0082]-[0083], and [0086] disclosing docket entries including motion filings, party dismissals, transfer orders, filing date, closing date, and other miscellaneous information provided to the court, which are representative of motion or entry of an order to the court database).
Claim 16, Salas discloses a non-transitory computer readable medium storing computer program instructions, which, when executed on a processor, cause the processor to perform operations comprising (para. [0045] and Figs. 1-2: Non-transitory memory 122, which takes the exemplary form of one or more electronic, magnetic, or optical data-storage devices, stores non-transitory machine readable and/or executable instruction sets for wholly or partly defining software and related user interfaces for execution of the processor 121):
identifying a docket event of a plurality of docket events associated with a docket of a court case (para. [0067], identify relevant case docket entries), the plurality of docket events stored in a docket event database (abstract: retrieving docket and other data from a plurality of databases. Para. [0010], “accessing, using a computing device having a processor and memory, data of docket entries, for each party the docket entries have reached a certain outcome for an existing docket;” Para. [0014], “a database having stored therein a first set of docket entry data, the first set of docket entry data including a set of docket entries for at least one existing docket and for each party for which the docket entries have reached a certain outcome;” The docket entry data stored in the database is representative of docket events associated with a docket of a court case stored in a docket event database.); and
training a conditional likelihood model using a training set comprising a sequence of entries, each entry of the sequence of entries comprising a training docket event (Salas, [0010], [0012], [0014], [0020], [0021], [0023], disclosing the training of sequence tagging classifier of docket entries data, using machine learning models. In para. [0062], Salas teaches that the system “extends the Navie Bayes model from text classification to the outcome duration prediction task. A Naïve Bayes model is conditional likelihood model. The model is trained on historical docket entry data which corresponds to claimed “training docket event), and
predicting, using the conditional likelihood model (para. [0059]-[0063] and [0103] Naïve Bayes model which is representative of conditional likelihood model is used for prediction of outcome), a first set of case outcomes for the court case based on outcomes of other cases having the same docket event (para. [0060], “present invention is outcome prediction for a specific entity or party in a case by the Outcome Prediction Engine. For the Outcome Prediction Engine, given a sequence of n docket entries and an open status for all or a subset of the involved parties, a regression algorithm can determine the remaining time to resolution (i.e., an outcome has been reached). The docket data provides information about the time to resolution and the n-grams derived from the docket entries are used for training a regression algorithm that predicts the remaining time at a given time t based on the docket entries created up to that point. The regression algorithms are based on multi-nominal Naïve Bayes and Survival analysis.” Disclosing the predicting a first set of case outcomes for a court case based on outcome derived from docket entries of similar cases. See para. [0087], “a Bayesian Network Model for predicting docket resolution time may be applied…. The model may be generalized to identify practice areas and jurisdictions that have “similar” dockets and aggregate them together, or to introduce additional predictors that account for these other variables. It is assumed that each model represents a homogenous subset of cases coming from the same state and the same practice area” Which disclosing the same (i.e., similar) docket event is used to be used in the model for the prediction of case outcome).
Salas discloses the above-mentioned limitations. Salas discloses a dynamic model where predictions are updated over time, describing a “sequence function (ft)t that represents the evolving belief as to the predicted outcome as more information about a docket is gathered (para. [0092]). That is, Salas teaches the general concept of a model that learns cumulatively over the life of a case.
However, Salas does not expressly disclose structuring the training data for this “evolving belief” model into cumulative groups corresponding to specific, well-defined stages of legal proceeding.
Particularly, Salas does not expressly disclose the claim limitation, each of the training docket events arranged in one of a plurality of numbered groups which includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order, wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events.
Chan is directed to the specific field of using predictive analytics for patent and non-patent documents, including predicting outcomes in PTAB post-grant review proceedings. Chan, specifically teaches the claim limitation, each of the training docket events arranged in one of a plurality of numbered groups which includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups (Chan para. [0370] teaches determining a validity strength rating for four separate phases: the “Pre-Petition Stage,” “Decision Stage,” “Final Stage,” and “Post-Final Stage”. Chan, para. [0381] further teaches that the models are updated after each respective stage to “reflect data and attributes associated with” that stage’s events. Notes, the four separate phases are a plurality of discrete stages corresponding to the claimed “numbered groups.” For example, “Pre-Petition Stage” is group 1, the “Decision Stage” is group 2, and so on. Chan teaches the analytical models are updated as the cases progresses through these stages, meaning the data from earlier stags is necessarily included in the analysis of later stages. This discloses includes its own training docket events and training docket events of lower numbered groups of the plurality of numbered groups).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of Salas using Navie Bayes model for prediction of case outcome to include the structural training data with stage-based analysis such as “Final Stage” (group 3) would be trained on the data from the “Pre-Petition Stage” (group 1) and the “Decision Stage” (group 2) for the motivation and benefit of ensuring the result to be more refined prediction. Further, the combination would have a high expectation of success, as it involves applying a specific data structuring method from Chan to similar machine learning model from Salas within the same technical field of legal analytics.
Still, the combination of Salas and Chan fail to expressly teach, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order, wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events;
Vacek is in the related systems and methods for analyzing and extracting docket data related to a structured proceeding, which specifically teaches, each of the training docket events included in the training set is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order (para. [0004], [0007], and [0031] teaching the systems and methods for linking the affected motions to the affected orders which is representative a positive pairing. In para. [0039] explicitly teaches the use of machine learning-based approach for the training of docket entry with associated motions and orders), wherein each of the training docket events is identified based on a link in a related training docket event pointing to an associated one of the training docket events (para. [0023] states that “the dockets of docket sources 170 may be received from court systems, and/or may be received from an external database or system, such as the public access to court electronic records (PACER) service.” In Applicant’s specification confirms the common knowledge that PACER system is the primary source for federal court docket data, conventionally uses machine-readable hyperlinks within docket entries to connect related documents, such as an order pointing back to the motion it resolves, at para. [0036] of app. specification, “For example, a motion or an order document retrieved from the PACER database may contain links (e.g., hyperlinks) that point to orders or motions.”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the predictive system and method of Salas/Chan to incorporate the automated data structuring and linking motions and orders of pre-existing, reliable hyperlinks within the PACER data as a primary source for identifying the positive pairings needed for the training set of Vacek for the motivation of efficiently create the high-quality, structured training data needed for the predictive model. This would be an application of a known data pre-processing technique from Vacek to improve a known data analysis system from Salas and Chan with a high expectation of success. Since, the claimed invention is merely a combination of old elements in a legal service predictive model field of endeavor. In such combination each element merely would have performed the same legal service predictive model related function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Vacek, the results of the combination were predictable (See MPEP 2143 A).
Claim 17, the combination of Salas, Chan, and Vacek make obvious of the non-transitory computer readable medium of claim 16, Salas further discloses the operations further comprising:
predicting a second set of case outcomes for the court case based on another docket event of the plurality of docket events, the first set of case outcomes, and outcomes of other cases having the same docket event and the same other docket event, wherein the second set of case outcomes is a subset of the first set of case outcomes (para. [0014], “a database having stored therein a first set of docket entry data, the first set of docket entry data including a set of docket entries for at least one existing docket and for each party for which the docket entries have reached a certain outcome; at least one machine sequence learning module adapted to receive the first set of docket entry data and, based on the received first set of docket entry data, train a sequence tagging classifier; executing by the processor the trained sequence tagging classifier against a second set of docket entry data, the second set of docket entry data being associated with a new docket other than the existing docket, the new docket having an associated set of parties, the trained sequence tagging classifier adapted to process docket entries from the second set of docket entry data associated with each party in the set of parties to determine an outcome attribute associated with at least one party from the set of parties;”).
Claim 18, the combination of Salas, Chan, and Vacek make obvious of the non-transitory computer readable medium of claim 17, wherein the docket event and the other docket event comprise one of filing of a motion or entry of an order (para. [0082]-[0083] disclosing the docket entry data includes motion filings, party dismissals, transfer orders, etc.).
Claim 20, the combination of Salas, Chan, and Vacek make obvious of the non-transitory computer readable medium of claim 16, wherein the conditional likelihood model is a Naïve Bayes model (para. [0060]-[0063] and [0103] disclosing the prediction model is Naïve Bayes model).
Response to Remarks
35 U.S.C. 101 Rejections:
The Applicant’s remarks are fully considered, however, the remarks are directed to amended claim limitations, therefore deemed moot.
Although the remarks are deemed moot, the Examiner will still like to address the Applicant's remarks in the sprite of expediting compact prosecution. The claims recite steps that do not required the need of computer to perform the functional steps of the claims, including training a mathematical model such as conditional likelihood model and predicting case outcome. These steps can be performed manually with pen and paper. Therefore, the claims are directed to abstract idea of court case outcome analysis and prediction. The additional elements of computer system are merely applied to the steps of the abstract idea as discussed in MPEP 2106.05(f), which does not integrate the abstract idea into practical application or significantly more.
Per remarks on pages 10-12, the argument is noted. First, claim 1 does not recite the use of computer system nor required of computer components for the method steps. Still, even if the computer system is recited, the sheer scale of the data may make it impractical for a human but the steps of identifying related documents via links, grouping them, and applying a probabilistic model are all analogs of mental processes. The claims are not directed to a specific improvement in computer technology but rather to the use of a computer as a tool to perform an abstract intellectual task more quickly. In numerous court decisions found the use of computer to perform computer process in a convenience (e.g., more efficient, faster, and etc.) has been held not be an “inventive concept” or specific improvement, see MPEP 2106.05(f)(2), “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016).
Per remarks on pages 12-13, the Applicant’s reliance on Ex parte Desjardins is misplaced. The claims in Desjardins were found eligible because they recited a specific technique for training a machine learning model that resulted in an improvement to the functioning of the computer itself. The amended claims do not recite an improvement to the training process itself. Instead, the claim recites a selection of data (entries of docket event) that is fed into a conventional training process. The step does not improve the functioning of the computer. The claim is recited at a high-level of generality for organizing data to perform the function of executing a mathematical algorithm. Examiner notes that the additional elements (recited in claims 9 and 16), which form the basis of this determination, are nothing more than generic computing elements, used in their ordinary capacity, to facilitate the tasks of the abstract idea. Whether viewed alone or in combination, this is not enough to demonstrate integration into practical application and/or add significantly more. See MPEP 2106.05(f)."
Per remarks on page 14, the Applicant asserts the claims are not well-understood, routine, or conventional. The Examiner needs the Applicant to state clearly which limitations are not well-understood, routine, or conventional. As discussed in MPEP 2106.05(d)(I)(2) - “an examiner should determine that an element (or combination of elements) is well-understood, routine, conventional activity only when the examiner can readily conclude, based on their expertise in the art, that the element is widely prevalent or in common use in the relevant industry. The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 ( Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph); see also Lindemann Maschinenfabrik GMBH v. Am. Hoist & Derrick Co., 730 F.2d 1452, 1463, 221 USPQ 481, 489 (Fed. Cir. 1984) ("[T]he specification need not disclose what is well known in the art."); In re Myers, 410 F.2d 420, 424, 161 USPQ 668, 671 (CCPA 1969) ("A specification is directed to those skilled in the art and need not teach or point out in detail that which is well-known in the art."); Exergen Corp., 725 Fed. App’x. 959, 965 (Fed. Cir. 2018)”
If the specific limitation is not well-understood, routine, or conventional then the specific description for how the step is actually performed is required in the specification, rather than the result-based description. If the Applicant asserts the step is not well-known and conventional with mere application of the additional elements of computer components, without description support in the original filed specification, then this raises the question if the Applicant’s invention fails to meet the analysis under 35 U.S.C. 112(a).
For these reasons above, the 101 rejection has been maintained in light of the amendment.
35 U.S.C. 102 and 103 Rejections:
The Examiner asserts that the applicant’s arguments are directed towards amended claim limitations and are, therefore, considered moot. However, the Examiner has responded to the amended amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments. The prior 35 U.S.C. 103 rejection has been withdrawn and new reference, Vacek has been added to teach the amended claim limitations.
Relevant Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The additional cited art, including but not limited to the excerpts below, further establishes the state of the art at the time of Applicant’s invention and shows the following was known:
Rabinowitz et al. (US 20220156862 A1) is directed to a gradient boosted networked computer system permits users to analyze the grantability of potential legal filings associated with a target entity, using specific externally reported data. By analyzing the target filing and judicial prerogatives, embodiments of the invention can assess, present, and predict outcomes and timings of decisions made by a judge before the filings are submitted.
Bertalan et al., “Using attention methods to predict judicial outcomes”, Artificial Intelligence and Law. 30, 18 July 2022, ARXIV ID: 2207.08823. teaching the use of AI and NLP to predict specific judicial characteristics such as judicial outcome.
Conclusion
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/WENREN CHEN/Examiner, Art Unit 3626