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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 18, 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 18, 2025 has been entered. The following has occurred: Claims 1, 10, and 16 have been amended; Claims 2 and 11 have been cancelled;
Claims 1, 3-10, and 12-20 are currently pending and have been examined.
Response to Amendment
Claim Objection has been withdrawn in light of the corrected amendment.
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Previous 35 U.S.C. 103 rejection has been withdrawn and new 35 U.S.C. 103 rejection has been added in light of the amendment.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper time wise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 3-10, and 12-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5, 7-11, 13, 15-18, and 20 of copending Application No. 18/310,689, hereinafter “’689.”
This is a provisional nonstatutory double patenting rejection.
The subject matter claimed in the instant application is fully disclosed in the application 448 and is covered by the application since the copending applications are claiming common subject matter, as follows: Both applications claim a computer-implemented method and computer product for prioritizing customer service with use of beacon information from customer mobile device and displaying the information to the employee at the store. See below for specific analysis and comparison between application 15/006,759, hereinafter “’759” and the claimed invention, hereinafter “418”.
Application 18/310,689
Application 18/310,712
Examiner’s Notes
Claim 1: A method comprising:
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:
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.
Claim 1: A method for predicting outcomes of pending court cases comprising:
Claim 10: An apparatus for predicting outcomes of pending court cases, the 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:
training a model using training sets of dockets comprising a sequence of entries, each entry of the sequence of entries comprising a training docket event,
each of the training docket events assembled into a plurality of numbered groups, wherein each of the plurality of numbered groups includes its own training sets of dockets and training sets of dockets of lower numbered groups,
each of the training docket events included in the training sets of dockets is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order;
identifying a plurality of motion/order pairs from dockets of decided court cases based on the model and based on links in one of a motion or an order of the dockets of decided court cases; and
storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases.
The preamble or the goal is functionally the same.
Both applications recite the steps of training a model using training set of sequence of entries; the training docket events are arranged/assembled into plurality of 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;
Both applications recite the training data as linked motion-order pairs.
The only difference or variance between the two applications is 712 claims the storage of the data for the intended purpose of prediction, and 689 claims the final step to be an active step of the intended purpose. There is no patentable distinction between creating a tool for a specific purpose in ‘712 and the act of using that tool for its stated purpose in ‘689.
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, 3-10, and 12-20 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
training a model using training sets of dockets comprising a sequence of entries, each entry of the sequence of entries comprising a training docket event, each of the training docket events assembled into a plurality of numbered groups, wherein each of the plurality of numbered groups includes its own training sets of dockets and training sets of dockets of lower numbered groups, each of the training docket events included in the training sets of dockets is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order;
identifying a plurality of motion/order pairs from dockets of decided court cases based on the model and based on links in one of a motion or an order of the dockets of decided court cases; and
storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases.
The claimed invention is directed to an abstract idea of court case outcome prediction.
The limitations of using training a model; identifying motion/order pairs from a docket of court case based on the model; storing identifier of the motion/order pairs in database; 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 court case outcome, 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).
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: 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 train, identify, store, 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 3, 4, 6-8, 12, 13, 15, 17, 18, and 20 further recite additional descriptive information regarding the motion/order pairs based 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, 9, 14, and 19 further recite additional abstract step of collecting a plurality of motions and orders from the dockets; comparing the identified plurality of motion/order pairs; and predicting the outcome of pending cases. These additional abstract steps do 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.
Therefore, claims 1, 3-10, and 12-20 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, 9, 10, 12, 14-17, 19, 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”).
Claims 1 and 10, Salas discloses a method (para. [0056], methods), an apparatus (Abstract and para. [0002], system) for predicting outcomes of pending court cases (para. [0060] disclosing court cases outcome prediction) 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):
training a model using training sets of dockets 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. The model is trained on historical docket entry data which corresponds to claimed “training docket event”);
identifying a plurality of motion/order pairs from dockets of decided court cases based on the model (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; means for inputting, using the processor, the data into at least one machine sequence learning model to train a sequence tagging classifier; means for applying, using the processor, the sequence tagging classifier to a new docket with entries of each party to determine the outcome that is generated by each party;” 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; ” The docket entry data stored and identified from the docket event database is representative of docket events of decided court cases. The machine sequence learning model and machine sequence learning module are representative of the model. Then in para. [0009], [0082]-[0083], and [0086] disclosing the docket entry includes motion); and
Salas teaches the intended use of predicting outcomes of pending court cases, however, Salas fails to teach:
each of the training docket events assembled into a plurality of numbered groups wherein each of the plurality of numbered groups includes its own training sets of dockets and training sets of dockets of lower numbered groups,
each of the training docket events included in the training sets of dockets is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order;
identifying a plurality of motion/order pairs from dockets of decided court cases based on links in one of a motion or an order of the dockets of decided court cases; and
storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases.
Nonetheless, 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 assembled into a plurality of numbered groups wherein each of the plurality of numbered groups includes its own training sets of dockets and training sets of dockets of lower 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 each of the plurality of numbered groups includes its own training sets of dockets and training sets of dockets of lower 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 sets of dockets is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order;
identifying a plurality of motion/order pairs from dockets of decided court cases based on links in one of a motion or an order of the dockets of decided court cases; and
storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases.
Nonetheless, Vacek is in the similar field of legal services for identifying and linking events in structured court proceedings, which specifically teaches,
each of the training docket events included in the training sets of dockets 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 of a motion and order. In para. [0039] explicitly teaches the use of machine learning-based approach for the training of docket entry with associated motions and orders);
identifying a plurality of motion/order pairs from dockets of decided court cases based on links in one of a motion or an order of the dockets of decided court cases (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.” para. [0019], [0024], [0028], and [0032] teaching the identifying of plurality of motion/order pair based on linking of motion or order of dockets of decided court cases); and
storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases (para. [0019] and [0028] teaching the linkage indicator of identified docket entries of plurality of motion/order pairs in a motion/order chain database is stored in a database. In para. [0037] further describes the indicator to be a word, flag, color code, field, and/or any other means for indicating the affecting order and the affected motion which is representative of identifier).
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 for predicting outcomes of pending court cases with the feature of each of the training docket events included in the training sets of dockets is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order; identifying a plurality of motion/order pairs from dockets of decided court cases based on links in one of a motion or an order of the dockets of decided court cases; and storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases, as taught by Vacek for the benefit and motivation of providing an improved and more efficient system and method in finding motions or motions affected by the order with the use of computer processor that is less time consuming but also error-prone (para. [0003]). Further, the claimed invention is merely a combination of old elements in a similar legal services field of endeavor. In such combination each element merely would have performed the same legal services 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 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):
training a model using training sets of dockets 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. The model is trained on historical docket entry data which corresponds to claimed “training docket event”);
identifying a plurality of motion/order pairs from dockets of decided court cases based on the model (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; means for inputting, using the processor, the data into at least one machine sequence learning model to train a sequence tagging classifier; means for applying, using the processor, the sequence tagging classifier to a new docket with entries of each party to determine the outcome that is generated by each party;” 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; ” The docket entry data stored and identified from the docket event database is representative of docket events of decided court cases. The machine sequence learning model and machine sequence learning module are representative of the model. Then in para. [0009], [0082]-[0083], and [0086] disclosing the docket entry includes motion); and
Salas teaches the intended use of predicting outcomes of pending court cases, however, Salas fails to teach:
each of the training docket events assembled into a plurality of numbered groups wherein each of the plurality of numbered groups includes its own training sets of dockets and training sets of dockets of lower numbered groups,
each of the training docket events included in the training sets of dockets is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order;
identifying a plurality of motion/order pairs from dockets of decided court cases based on links in one of a motion or an order of the dockets of decided court cases; and
storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases.
Nonetheless, 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 assembled into a plurality of numbered groups wherein each of the plurality of numbered groups includes its own training sets of dockets and training sets of dockets of lower 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 each of the plurality of numbered groups includes its own training sets of dockets and training sets of dockets of lower 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 sets of dockets is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order;
identifying a plurality of motion/order pairs from dockets of decided court cases based on links in one of a motion or an order of the dockets of decided court cases; and
storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases.
Nonetheless, Vacek is in the similar field of legal services for identifying and linking events in structured court proceedings, which specifically teaches,
each of the training docket events included in the training sets of dockets 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 of a motion and order. In para. [0039] explicitly teaches the use of machine learning-based approach for the training of docket entry with associated motions and orders);
identifying a plurality of motion/order pairs from dockets of decided court cases based on links in one of a motion or an order of the dockets of decided court cases (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.” para. [0019], [0024], [0028], and [0032] teaching the identifying of plurality of motion/order pair based on linking of motion or order of dockets of decided court cases); and
storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases (para. [0019] and [0028] teaching the linkage indicator of identified docket entries of plurality of motion/order pairs in a motion/order chain database is stored in a database. In para. [0037] further describes the indicator to be a word, flag, color code, field, and/or any other means for indicating the affecting order and the affected motion which is representative of identifier).
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 for predicting outcomes of pending court cases with the feature of each of the training docket events included in the training sets of dockets is selected based on a positive pairing of a motion and an order or a negative pairing of a motion and an order; identifying a plurality of motion/order pairs from dockets of decided court cases based on links in one of a motion or an order of the dockets of decided court cases; and storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases, as taught by Vacek for the benefit and motivation of providing an improved and more efficient system and method in finding motions or motions affected by the order with the use of computer processor that is less time consuming but also error-prone (para. [0003]). Further, the claimed invention is merely a combination of old elements in a similar legal services field of endeavor. In such combination each element merely would have performed the same legal services 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).
Claims 3, 12, and 17, the combination of Salas, Chan, and Vacek make obvious of the method of claim 1, the apparatus of claim 10, and the computer readable medium of claim 16. Salas further discloses,
wherein the identifying the plurality of motion/order pairs is further based on business rules pertaining to text in one of a motion or an order of the dockets of decided court cases (para. [0021] and [0023] disclosing training and identifying of docket entries based on text from the docket entry data of the decided court cases).
However, Salas does not expressly teach business rules pertaining to text.
Nonetheless, Vacek specifically teaches, wherein the identifying the plurality of motion/order pairs is based on business rules pertaining to text in one of a motion or an order of the dockets of decided court cases (para. [0033], [0034] and [0036] teaches the template rules used to identify motion from text label).
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 for predicting outcomes of pending court cases with the feature of storing identifiers identifying each of the identified plurality of motion/order pairs in a motion/order chain database for use in predicting outcomes of pending court cases, as taught by Vacek for the benefit and motivation of providing an improved and more efficient system and method in finding motions or motions affected by the order with the use of computer processor that is less time consuming but also error-prone (para. [0003]). Further, the claimed invention is merely a combination of old elements in a similar legal services field of endeavor. In such combination each element merely would have performed the same legal services 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).
Claims 5, 14, and 19, the combination of Salas, Chan, and Vacek make obvious of the method of claim 1, the apparatus of claim 10, and the computer readable medium of claim 16. Vacek further teaches,
collecting a plurality of motions and orders from the dockets of the decided court cases based on docket key values identifying a document of a docket database as one of a motion or an order (para. [0033] and [0045]).
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 for predicting outcomes of pending court cases with the feature of collecting a plurality of motions and orders from the dockets of the decided court cases based on docket key values identifying a document of a docket database as one of a motion or an order, as taught by Vacek for the benefit and motivation of providing an improved and more efficient system and method in finding motions or motions affected by the order with the use of computer processor that is less time consuming but also error-prone (para. [0003]). Further, the claimed invention is merely a combination of old elements in a similar legal services field of endeavor. In such combination each element merely would have performed the same legal services 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).
Claims 6, 15, and 20, the combination of Salas, Chan, and Vacek make obvious of the method of claim 3, the apparatus of claim 12, and the computer readable medium of claim 16. Salas further discloses,
wherein the identifying the plurality of motion/order pairs is further based on a number in the text of one of the motion or the order of the dockets of decided court cases (Salas: para. [0081], [0082], [0089] disclosing identifying based on number of parties and dates, which are representative of number in the text of one of the motion or the order of the dockets).
Claim 7, the combination of Salas, Chan, and Vacek make obvious of the method of claim 3. Salas further discloses,
wherein the identifying the plurality of motion/order pairs is further based on an entity name in the text of one of the motion or the order of the dockets of decided court cases (Salas: para. [0074], [0082], [0089] disclosing identifying based on name of parties, which are representative of entity name in the text of one of the motion or the order of the dockets).
Claim 9, the combination of Salas, Chan, and Vacek make obvious of the method of claim 1. Salas further discloses,
comparing the identified plurality of motion/order pairs to motion/order pairs of a pending case; and predicting the outcome of the pending case based on the comparing (para. [0060]-[0063], [0073], [0078], [0079] disclosing outcome prediction based on comparing of docket entries of docket with pending cases. In para. [0082]-[0083] disclosing docket entries includes motions).
Claims 4, 8, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Salas et al. (US 20170076001 A1), in view of Chan et al. (US 20220343444 A1), in view of Vacek et al. (US 20190385254 A1), and further in view of Xie et al (US 20210142103 A1).
Claims 4, 13, and 18, the combination of Salas, Chan, and Vacek make obvious of the method of claim 1, the apparatus of claim 10, and the computer readable medium of claim 16. Vacek teaches,
wherein the model is a transformer based natural language inference (NLI) model (Vacek: para. [0039]-[0042] teaching the identifying of motion/order pair based on machine learning model such as recurrent neural network and bi-directional Long Short-Term Memory (Bi-LSTM) network).
However, the combination does not expressly teach, transformer based natural language inference (NLI) model.
Nonetheless, Xie is in similar field of machine learning model to predict user intention based on natural language expressions, which specifically teaches (italic emphasis included), wherein the model is a transformer based natural language inference (NLI) model (para. [0035] teaching techniques of BERT (Bidirectional Encoder Representations from Transformers) may be used to train the NLI model 207 given the input pairs and labels).
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 for predicting outcomes of pending court cases with the use of machine learning model to include the feature of transformer based natural language inference (NLI) model for the machine learning model as taught by Xie, for the motivation of providing an improved and less costly system and method to learn complex user interfaces, for determining meanings of natural language expressions (para. [0004]).
Claim 8, the combination of Salas, Chan, Vacek, and Xie make obvious of the method of claim 4. Salas further discloses,
wherein the identifying the plurality of motion/order pairs is further based on identifying motion/order pairs that do not have hyperlinks or numbers in text identifying one of a related motion or order (Salas: para. [0074], [0082], [0089] disclosing identifying based on name of parties, which does not include hyperlinks in text).
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 identifying case outcome. These steps can be performed manually with pen and paper, falls under category of “mental process” of abstract idea. Therefore, the claims are directed to abstract idea of court case outcome 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.
The amended claims are still directed to the abstract idea of collecting data, analyzing data to find relationships, and storing the results for future purpose of prediction. The amendments which specify the use of deep learning NLI model and the identification of motion/order pairs are merely further limitations of the analyze process of the abstract idea. The additional element of computer components is recited at high-level of generality that amounts to no more than mere instructions to apply the exception (abstract idea). The use of a computer to perform the abstract steps does not itself, confer patent eligibility. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”). Please note, claim 1 does not recite the need of computer components.
Per remarks on pages 9-10, 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, resulting in a more efficient or effective machine learning model. Instead, the claims are directed to using a known type of model to perform the abstract steps of data analysis. The claims are not directed to an improvement in machine learning models or the training thereof, but rather just applying it. That is, the claims are 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 10 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 pages 11-12, the Applicant asserts the amended claim 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 limitations are not well-understood, routine, or conventional then the specific description for how the steps are actually performed is required in the specification, rather than the result-based description. If the Applicant asserts the steps are 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. 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 Applicant has amended claim 1 to recite, “identifying a plurality of motion/order pairs from dockets of decided court cases based on the model and based on links in one of a motion or an order of the dockets of decided court cases.”
Applicant argues that the cited references, including Vacek, do not disclose this limitation. specifically, Applicant argues that “Vacek does not describe using such links to identify motion and order pairs. The motions and orders in Vacek are linked after they are determined to be related. The links described in Vacek are not used to identify motion and order pairs.”
This argument misinterprets the teachings of Vacek and the knowledge of a person of ordinary skilled in the art.
Vacek teaches a system that uses a machine learning algorithm to identify and link motions and orders. Vacek, claims 1 and 8; at least para. [0019], [0024], [0028], and [0032] teaches the identifying of plurality of motion/order pair based on linking of motion or order of dockets of decided court cases. Specifically in para. [0023] and [0036] describes the “public access to court electronic records (PACER) service” is a source for the docket data and conventionally uses machine-readable hyperlinks to connect related docket entries. This is the same process confirmed by Applicant’s own specification in para. [0036].
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.
Bathaee (US 20200012919 A1) is directed to computer implemented methods and systems are disclosed for obtaining predictions of legal events, such as legal and factual arguments presented to courts, juries or other adjudicative or fact-finding bodies, using machine-learning algorithms, wherein (i) unstructured data, such as natural language text from documents, such as pleadings, briefs or corpuses of evidence are converted into tokens, vectors and/or embeddings; (ii) the machine-learning algorithm(s) are provided the converted unstructured data as inputs; and (iii) the machine-learning algorithms provide confidence or probability scores predicting outcomes of legal events, such as legal proceedings or one or more legal or factual issues to be decided by particular adjudicators, tribunals or fact-finding bodies.
Barrow et al. (US 20230033114 A1) is directed systems and methods for natural language processing are described. One or more embodiments of the present disclosure identify a claim from a document, wherein the claim corresponds to a topic, create a graph comprising a plurality of nodes having a plurality of node types and a plurality of edges having a plurality of edge types, wherein one of the nodes represents the claim, and wherein each of the edges represents a relationship between a corresponding pair of the nodes, encode the claim based on the graph using a graph convolutional network (GCN) to obtain an encoded claim, classify the claim by decoding the encoded claim to obtain a stance label that indicates a stance of the claim towards the topic, and transmit information indicating a viewpoint of the document towards the topic based on the stance label.
Montelongo and J. L. Becker, "Tasks performed in the legal domain through Deep Learning: A bibliometric review (1987–2020)," 2020 International Conference on Data Mining Workshops (ICDMW), Sorrento, Italy, 2020, pp. 775-781. Teaching the analyzing of court cases for court decision predictions using deep learning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WENREN CHEN whose telephone number is (571)272-5208. The examiner can normally be reached Monday - Friday 10AM - 6PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan C Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WENREN CHEN/Examiner, Art Unit 3626