Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendments and remarks filed on June 12, 2026.
Claims 1, 8, and 15 are currently amended.
Claims 4, 11, and 18 have been canceled.
Claims 1-3, 5-10, 12-17, 19, and 20 are currently pending and have been examined.
Applicant’s remarks and arguments are addressed below.
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, 5-10, 12-17, 19, and 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. When considering subject matter eligibility under 35 U.S.C. § 101, there are multiple steps that may need to be assessed. First, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined in step 2A prong 1 whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim is directed toward a judicial exception, it must then be determined in step 2A prong 2 whether the judicial exception is integrated into a practical application. Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in step 2B whether the claim recites “significantly more” than the abstract idea. See “2019 Revised Patent Subject Matter Eligibility Guidance,” 84 Fed. Reg. (4): 50-57 (Jan. 7, 2019).
In the instant case, Claims 1-3 and 5-7 are directed toward a method, i.e., process, Claims 8-10 and 12-14 are directed toward a system, i.e., apparatus, and Claims 15-17, 19, and 20 are directed toward a non-transitory computer readable medium, i.e., article of composition. Thus, each of the claims falls within one of the four statutory categories as required by step 1. Nevertheless, the claims are directed toward the judicial exception of an abstract idea in step 2A prong 1. Independent Claim 1 recites as follows:
Claim 1. A computer-implemented method for processing a fact pattern to identify applicable legal claims, the method comprising:
storing, in a non-transitory computer-readable medium, a structured data collection comprising a plurality of legal claim records, where each legal claim record of the plurality of legal claim records comprises a record identifier, and one or more conduct descriptors corresponding to one or more essential elements;
receiving, from a user via an input device, a first set of information comprising a textual description of a fact pattern;
processing, by one or more processors, the first set of information using a language model to extract a second set of information comprising one or more identified elements present in the first set of information;
generating, by the one or more processors, a filtered subset of the structured data collection by selecting legal claim records having at least one conduct descriptor with at least one essential element corresponding to at least one of the one or more identified elements;
applying, by the one or more processors, the language model to each legal claim record in the filtered subset and the first set of information to generate a classification output, wherein the classification output assigns each legal claim record to one classification selected from a classification set comprising:
a first classification indicating the legal claim is supported by the first set of information,
a second classification indicating the legal claim requires additional information beyond the first set of information, and
a third classification indicating the legal claim is not supported by the first set of information;
generating, by the one or more processors, for each legal claim record assigned to the first classification or the second classification, an output of a relationship between the first set of information and the legal claim record; and
outputting, to a display, a result data structure comprising data associated with legal claim records assigned to the first classification or the second classification and the explanatory output for each such legal claim record, wherein the explanatory output for each legal claim record assigned to the second classification comprises identification of specific additional information required to satisfy requirements of the legal claim record.
The bold language above corresponds to the abstract ideas recited in Claim 1 (whereas the underlined language is language that is addressed in step 2A prong 2 and step 2B). As the bold language above demonstrates, Applicant’s claims are directed toward analyzing a textual fact pattern and determining whether there is sufficient information to determine the presence of one or more legal causes of action. This is a certain method of organizing human activity, specifically one involving legal interactions. See MPEP § 2106.04(a)(2)(II)(B). Because the instant invention is performing the legal analysis of whether there is a legal cause of action for a certain fact pattern, i.e., performing the evaluations that lawyers do, the invention is reciting a certain method of organizing human activities, specifically legal interactions. Additionally, because the invention recites performing a set of observations, evaluations, and judgments, the claims do recite abstract mental processes. See MPEP § 2106.04(a)(2)(II)(B).
Finding the claims to be directed toward an abstract idea, however, is not the end of the inquiry. Rather, the next step is to determine whether the judicial exception is integrated into a practical application (step 2A prong 2). The revised guidance provides exemplary considerations that are indicative that an additional element or combination of elements may have integrated the exception into a practical application: 1) an additional element reflecting an improvement in the functioning of a computer or an improvement to another technology or technical field, 2) an additional element that implements the judicial exception with a particular machine or manufacture that is integral to the claim, 3) an additional element that effects a transformation or reduction of a particular article to a different state or thing, or 4) an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.04(d). Examples where a judicial exception has not been integrated into a practical application include: 1) use of “apply it” or the equivalent, i.e., merely using a computer to implement or perform an abstract idea, 2) an additional element that adds insignificant extra-solution activity to the judicial exception, and 3) an additional element that does no more than generally link the use of the judicial exception to a particular technological environment or field of use. See id.
Applying these considerations to the claims in the instant application, the claims do not integrate the judicial exception into a practical application. The claims fail to recite an improvement of a computer, any improvement to a technology or technical field, any particular machine, any transformation or reduction of a particular article to a different state or thing, or any additional element that uses the judicial exception in a meaningful way. Instead, the claims are merely reciting instructions to implement the abstract idea on a computer (i.e., “by one or more processors;” using a stored and structured data collection with record identifiers), which is insufficient to provide a practical application of the claims and provide subject matter eligibility. See id. Therefore, there is no integration of the abstract idea into a practical application.
If the claims are not integrated into a judicial exception, the Examiner must consider whether there is “significantly more” recited in the claim in step 2B. See MPEP § 2106.05. There is nothing unconventional or inventive in Applicant’s claims for the purpose of analysis under step 2B, e.g., any combination of elements that provide an advance over any technological state of the art. Rather, as noted above, an abstract legal interaction is merely implemented by a general-purpose computer. Other than the limitations that are abstract for the reasons articulated above, Applicant has merely recited a generic computer that facilitates the steps of the invention. Thus, Applicant’s claims merely recite a computer to implement the abstract idea, which fails to provide “significantly more” than the abstract idea.
As the MPEP states, Examiners may consider the following three factors when determining whether the claim recites mere instructions to implement an abstract idea on a computer: 1) whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished; 2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and 3) the particularity or generality of the application of the judicial exception. See MPEP § 2106.05(f). Applying those factors to the instant application: 1) the claims do not recite how the computer performs any of the steps other than just stating that they do it; 2) the claims invoke the computer to perform a process of legal analysis that has been performed without computers and before the ubiquity of computers; and 3) the claims are general and not recited in much particularity because it can apply to any way of performing the legal analysis.
The dependent claims 2, 3, 5-7, 9, 10, 12-14, 16, 17, 19, and 20 are merely reciting further embellishments of the abstract idea and do not amount to anything that is significantly more than the abstract idea itself. Claims 2, 9, and 16 recite how the computer is merely recited as a tool (i.e., what the prompt templates are and how the language model performs the analytical tasks). Likewise, Claims 3, 10, and 17 merely recite performing parallel processing operations, which relates solely to how the computer is used merely as a tool to perform the abstract idea. Claims 5, 12, and 19 recite conduct descriptors and alternative elements for inclusion in the analysis, which is part of the abstract legal interactions. Claims 6 and 13 recite that the filtered results correspond to subsections of statutory provisions, which is part of the abstract legal interactions. Claims 7, 14, and 20 recites that the structured data collection comprises data referencing statutes, regulations, or judicial opinions, which is part of the abstract legal interactions
In other words, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limitations that, in an ordered combination provide “significantly more” or providing any integration into a practical application. Rather, the dependent claims are merely further reciting features that are just as abstract as independent Claims 1, 8, and 15. Therefore, Claims 1-3, 5-10, 12-17, 19, and 20 are directed to non-statutory subject matter and are rejected as ineligible subject matter 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 5-9, 12-16, 19, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Apfelbaum et al. (US 2022/0012749 A1, hereinafter “Apfelbaum”) in view of Forrest et al. (US 2024/0420260 A1, hereinafter “Forrest”).
Claim 1. Apfelbaum teaches: A computer-implemented method for processing a fact pattern to identify applicable legal claims, the method comprising:
storing, in a non-transitory computer-readable medium, a structured data collection comprising a plurality of legal claim records, where each legal claim record of the plurality of legal claim records comprises a record identifier, and one or more conduct descriptors corresponding to one or more essential elements (see, e.g., at least ¶ 46 teaching a computer readable medium of stored instructions that when executed perform the method; see further Figure 2 feature 216 and ¶ 24 teaching databases 216 that includes “records of prior lawsuits, case management records, judicial decisions and orders, etc.;” see further, e.g., ¶s 28-30 and 36 teaching that “strings” and “keywords” are extracted from the stored cases to match with the input of the user; Examiner notes that the language of where each legal claim record of the plurality of legal claim records comprises a record identifier, and one or more conduct descriptors corresponding to one or more essential elements is further addressed below);
receiving, from a user via an input device, a first set of information comprising a textual description of a fact pattern (see, e.g., at least ¶s 21-24 teaching a chatbot or virtual agent, e.g., via a web site or other portal, engaging a user/client to provide a fact pattern using natural language);
processing, by one or more processors, the first set of information using a language model to extract a second set of information comprising one or more identified elements present in the first set of information (see, e.g., ¶s 28 and 31 teaching a natural language processor that parses the input to extract key words or strings/tokens to match with the stored case data to see if there are any matching identified elements);
generating, by the one or more processors, a filtered subset of the structured data collection by selecting legal claim records having at least one conduct descriptor with at least one essential element corresponding to at least one of the one or more identified elements (see, e.g., ¶ 28 teaching that the parsed data can match particular legal claim records with conduct descriptors and at least one element of a cause of action such as an ”accident” that could match with a car accident, a slip and fall, etc.);
applying, by the one or more processors, the language model to each legal claim record in the filtered subset and the first set of information to generate a classification output, wherein the classification output assigns each legal claim record to one classification selected from a classification set comprising (see, e.g., ¶s 28-30 teaching that the parsed data can match particular legal claim records with conduct descriptors and at least one element of a cause of action such as a “car accident” matching with a potential negligence cause of action):
a first classification indicating the legal claim is supported by the first set of information (see, e.g., Figure 1 feature 116 and ¶s 19-20 teaching the determination that a likely cause of action is found based on the prospective client’s fact pattern; see also ¶ 39),
a second classification indicating the legal claim requires additional information beyond the first set of information (see Figure 1 teaching both a loop back from feature 116 to 114 or from 124 to 114 and ¶ 20 teaching the obtaining of additional facts pertaining to the cause of action before determining whether there is sufficient additional facts to determine there is a likely cause of action; see also ¶ 39), and
a third classification indicating the legal claim is not supported by the first set of information (see, e.g., Figure 1 feature 126 and ¶ 20 teaching the disengagement when the “prospective client has exhausted the additional facts without revealing a potential cause of action;” see also ¶ 39);
generating, by the one or more processors, for each legal claim record assigned to the first classification or the second classification, an output of a relationship between the first set of information and the legal claim record (see Figure 1 feature 122 teaching “initiate engagement” if there is a likely cause of action found either with or without the additional facts required; Examiner notes that this limitation is addressed further below); and
outputting, to a display, a result data structure comprising data associated with legal claim records assigned to the first classification or the second classification and the explanatory output for each such legal claim record (see, e.g., ¶s 43 and 45 teaching the presentation of output such as via display 1012; see also Figure 2 teaching display feature 206, which is a personal computer of the client; Examiner notes that this limitation is further addressed below), wherein the explanatory output for each legal claim record assigned to the second classification comprises identification of specific additional information required to satisfy requirements of the legal claim record (see, e.g., ¶ 39 teaching the output of questions “to obtain additional information that may reveal other potential causes of action and/or additional facts pertaining to the initially identified potential cause of action”).
Examiner notes that Apfelbaum teaches the limitations of Claim 1 above. Nevertheless, for the purpose of compact prosecution, to the extent that Apfelbaum fails to expressly disclose in the last two limitations that the output, such as to a display for the user, is an output of a relationship between the first set of information and the legal claim record or a result data structure comprising data associated with legal claim records assigned to the first classification or the second classification and the explanatory output for each such legal claim record, such an output is taught in analogous prior art. Forrest for example, teaches such a feature. Forrest is analogous to the instant application and to Apfelbaum because it also relates to “issue spotting in legal disputes, and more specifically to training and using an Artificial Intelligence (AI) model to identify possible causes of action in a legal dispute” (see Forrest ¶ 1). Forrest teaches a user inputting facts about a dispute and matching that information with potential causes of action that would be applicable to the dispute based on stored legal data (see, e.g., ¶s 18-20). Forrest further teaches that the data are labeled as a “causes of action model” or “CAM” that is a data record having one or more of a name of the cause of action, a relevant jurisdiction, a source of law, a description of the cause of action, and the legal elements of that cause of action (see ¶s 25-34). Forrest further teaches that the applicable CAM is outputted to the user via the user interface (see, e.g., ¶ 46; see also ¶ 59 teaching output device 570 such as a display). Thus, Forrest teaches assigning the legal claim records with various classifications and outputting, including via a display, the CAM record to the user. Examiner further notes that the language in the first limitation of where each legal claim record of the plurality of legal claim records comprises a record identifier, and one or more conduct descriptors corresponding to one or more essential elements is expressly taught by Forrest because, as explained above, each CAM is a record of legal causes of action that include identifiers such as the essential legal elements, jurisdiction, descriptors, legal judgments, jury instructions, etc. (see ¶s 25-34).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of generating for display an output of the legal causes of action matched to the natural language text of the facts of the dispute (as disclosed by Forrest) to the known method and system of using artificial intelligence to match natural language fact patterns to legal causes of action (as disclosed by Apfelbaum). One of ordinary skill in the art would have been motivated to apply the known technique of generating for display an output of the legal causes of action matched to the natural language text of the facts of the dispute because the user could then “read the details associated with each” matching cause of action (see Forrest ¶ 46).
Furthermore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of generating for display an output of the legal causes of action matched to the natural language text of the facts of the dispute (as disclosed by Forrest) to the known method and system of using artificial intelligence to match natural language fact patterns to legal causes of action (as disclosed by Apfelbaum), because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of providing the textual output of the matched data to a known method and system of extracting natural language and matching parsed keywords or strings to legal causes of action, because predictably when similar matching analysis is performed the outputted data are the same). See also MPEP § 2143(I)(D).
Regarding Claims 8 and 15, these claims recite the same limitations as Claim 1 above though they recite different statutory categories. The rejection of Claim 1 above is incorporated herein. Claim 8 recites a system with a system with a memory and one or more processors communicatively coupled to the memory that are configured to perform the steps of Claim 1. Claim 15 recites a non-transitory computer readable storage medium storing instructions that, when executed by one or more processors, causes the processors to perform the method steps of Claim 1. Apfelbaum further teaches that its method can be performed via a computer system (see Figure 4 and ¶s 43-47), including computer readable storage media (see ¶ 46). Thus, with this further teaching, the combination of Apfelbaum and Forrest render obvious Claims 8 and 15. Dependent claims that are likewise coextensive will be treated together for the sake of brevity.
Claims 2, 9, and 16. The combination of Apfelbaum and Forrest teach the limitations of Claims 1, 8, and 15. Apfelbaum further teaches: The computer-implemented method of claim 1, wherein processing the first set of information and applying the language model utilize different prompt templates configured for different analytical tasks (see, e.g., ¶s 28-29 teaching that the first set of information is obtained as natural language and is processed and parsed differently than the language model that is seeking to classify the natural language into classified legal causes of action).
Claims 5, 12, and 19. The combination of Apfelbaum and Forrest teach the limitations of Claims 1, 8, and 15. Apfelbaum further teaches: The computer-implemented method of claim 1, wherein the one or more essential elements of the one or more conduct descriptors for at least one legal claim record comprise alternative elements, wherein presence of at least one alternative element is sufficient for inclusion of the at least one legal claim record in the filtered subset (see, e.g., ¶s 20 and 39 teaching eliciting additional facts, i.e., alternative elements, that may reveal one or more potential causes of action).
Claims 6 and 13. The combination of Apfelbaum and Forrest teach the limitations of Claims 1 and 8. Forrest further teaches: The computer-implemented method of claim 1, wherein the filtered subset comprises legal claim records corresponding to subsections of statutory provisions (see, e.g., ¶ 28 teaching “a source of law (e.g., law, regulation, … etc.)” for the cause of action; see also ¶ 56 teaching the matched/filtered result including “a statutory authority, wherein the at least one legal element of the cause of action is provided by the at least one of a legal precedent or the statutory authority”). The rationale for combining Forrest and Apfelbaum is provided in the rejection of Claim 1 above.
Claims 7, 14, and 20. The combination of Apfelbaum and Forrest teach the limitations of Claims 1, 8, and 15. Apfelbaum further teaches: The computer-implemented method of claim 1, wherein each legal claim record in the structured data collection further comprises citation data referencing at least one of: a statutory provision, a regulatory provision, or a judicial opinion (see at least ¶s 9 and 37 teaching that the stored information can include available verdict information from prior cases; see further ¶s 7, 8, 24, and 36 teaching that “judicial decisions and orders” may be in the stored records). Alternatively, Forrest further teaches the limitation (see Forrest ¶ 56 teaching the matched/filtered result including “a statutory authority, wherein the at least one legal element of the cause of action is provided by the at least one of a legal precedent or the statutory authority;” see further Figure 2 feature 208 including a “source of law” for the cause of action as a CAM record, which as taught in Figure 3 feature 332 and ¶ 52 is an output of the system).
Claims 3, 10, and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Apfelbaum in view of Forrest and further in view of Raphael et al. (US 2021/0263977 A1, hereinafter “Raphael”).
Claims 3, 10, and 17. The combination of Apfelbaum and Forrest teach the limitations of Claims 1, 8, and 15. Apfelbaum further teaches: The computer-implemented method of claim 1, wherein applying the language model to each legal claim record in the filtered subset is performed using parallel processing operations (see, e.g., ¶ 5 teaching that the rules may be evaluated in parallel with one another). Nevertheless, for the purpose of compact prosecution, Examiner notes that to the extent that Apfelbaum fails to expressly disclose that it is the processing operations that are being performed in parallel (i.e., the actual computing functions), such an operation is expressly taught in analogous prior art. Raphael, for example, teaches a computer server 302 that performs similarity/clustering analysis via a module 320 that “may utilize parallel processing [] to decrease processing time to calculate the similarity measures and generate the document clusters” (see Raphael ¶ 58; see also ¶ 59 teaching substantially the same regarding calculating distance metrics). Raphael is similar to Apfelbaum, Forrest, and the instant application because it relates to using semantic, temporal, and spatial document relationships in the context of litigation (see Raphael ¶s 1-2).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of using parallel processing (as disclosed by Raphael) to the known method and system of using artificial intelligence to match and output natural language fact patterns to legal causes of action (as disclosed by Apfelbaum and Forrest). One of ordinary skill in the art would have been motivated to apply the known technique of using parallel processing because it would decrease processing time to calculate the similarity measures and generate the document clusters” (see Raphael ¶ 58; see also ¶ 59 teaching substantially the same regarding calculating distance metrics)
Furthermore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of using parallel processing (as disclosed by Raphael) to the known method and system of using artificial intelligence to match and output natural language fact patterns to legal causes of action (as disclosed by Apfelbaum and Forrest), because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique using parallel processing to a known method and system of extracting natural language and matching parsed keywords or strings to legal causes of action, because predictably the parallel processing performs the same analyses and computations, the parallel nature changing only in providing faster results because of the multiplicative processing nodes). See also MPEP § 2143(I)(D).
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant specifically addresses the following:
Claim Rejections - 35 U.S.C. § 101:
Claims 1-20 were rejected under § 101 as being directed toward the judicial exception of an abstract idea without any integration into a practical application or significantly more. Applicant first argues that the claims do not recite an abstract idea in step 2A prong 1 (see Remarks pages 8-10). One argument is that the invention cannot practically be performed in the human mind (see Remarks page 10: “a human attorney cannot mentally maintain a database of, e.g., hundreds or thousands of, legal claim records formatted with granular, machine-readable conduct descriptors and essential elements…”). This argument is not persuasive because whether or not something can “practically” be performed in the human mind is relevant only to the subset of abstract mental processes, not to claims as in this instant application where the claims are directed toward certain methods of organizing human activities such as legal interactions. While it is true that Applicant’s claims recite certain filters and language model features, such features are analyzed in step 2A prong 2 and step 2B, not in step 2A prong 1. The claims recite the legal interactions of assessing a client’s case for whether a cause of action exists, which is one of the most fundamental legal interactions that exists.
Applicant argues that the claims integrate the judicial exception into a practical application (see Remarks pages 10-12). Specifically, Applicant argues that the level of granularity and the difference in how the RAG processes work is an improvement “over conventional AI and machine learning systems for determining legal claims in fact patterns” (see Remarks page 12). This argument is not persuasive because the AI or machine learning systems themselves are not improved. What appears to be improved is the results of the abstract legal analysis based on a design choice of what type of AI to use for the analysis. Stated another way, picking one of many off-the-shelf machine-learning systems and applying that to the legal interaction of claim spotting is merely using computers as a tool to perform the abstract idea (see MPEP § 2106.05(f)) or, alternatively, applying the legal interaction of claim spotting to the field of use/technological environment of machine learning (see MPEP § 2106.05(h)). As the MPEP notes, merely using a computer as a tool to perform the abstract idea or generally linking the use of a judicial exception to a particular technological environment or field of use are insufficient to integrate a judicial exception into a practical application. See MPEP § 2106.04(d)(I). Thus, Applicant’s arguments have been considered but are not considered persuasive. The rejection is maintained.
Claim Rejections - Prior Art:
Regarding the application of the prior art to the claims, Applicant makes one main argument: that neither Apfelbaum nor Forrest teach that the explanatory output for each legal claim record assigned to the second classification comprises identification of specific additional information required to satisfy requirements of the legal claim record (see Remarks pages 12-15). Originally, this limitation was in Claims 4, 11, and 18, but has been moved up into Claims 1, 8, and 15. Applicant’s arguments are not persuasive because Apfelbaum does, in fact, “identif[y] specific additional information required to satisfy requirements of the legal claim record” when it outputs questions “to obtain additional information that may reveal other potential causes of action and/or additional facts pertaining to the initially identified potential cause of action” (see ¶ 39). Applicant provides and discusses Apfelbaum’s Figure 1 algorithm (see Remarks page 14) and argues that this algorithm, where if there has not been any likely cause of action found (step 116), is merely “engaging in additional conversations (e.g., asking questions) (see id.). Examiner asserts that this step does require the chatbot of Apfelbaum to engage the prospective client in case the specific additional information is required to satisfy the requirements. Likewise, in step 124 of Apfelbaum’s Figure 1, the chatbot determines if “additional facts are available” to keep engaging with the prospective client to potentially determine a different cause of action or better score a previously found cause of action. Examiner asserts that these steps of Apfelbaum do, under a broadest reasonable interpretation of the limitation, identify specific additional information that would be required to make the algorithm of Apfelbaum’s Figure 1 arrive at identification, after certain additional information, the determination that the requirements of the legal claim of record is satisfied. Thus, Applicant’s arguments have been considered but are not considered persuasive. The rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure: Harrington et al., US 2025/0322469 A1; Collier et al., US 2016/0019665 A1.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAN P MINCARELLI whose telephone number is (571)270-5909. The examiner can normally be reached on Monday through Friday, 8:00 AM to 4:30 PM Eastern Time.
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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.
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/JAN P MINCARELLI/Primary Examiner, Art Unit 3626
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