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 .
Priority
Acknowledgment is made of applicant’s claim for Domestic Benefit to Provisional Application # 63/625508 originally filed on 01/26/2024.
Status of Claims
Claims 1-15 were restricted on 05/12/2026. Applicant’s election of Claims 1-14 with traverse was entered on 06/12/2026. Herein this Non-Final Office Action, Claims 1-14 are rejected.
Response to Arguments
Applicant’s arguments filed 06/12/2026, with respect to Traversing Restriction, have been fully considered and are not persuasive.
Regarding Section I.A on Page 7, Applicant argues “The grouping is internally inconsistent because elected claims 11 and 12 already recite the same phase-specific billing-estimation and litigation-budgeting functionality that the Office identifies as the basis for separating claim 15. Claim 11 recites correlating docket events and time narratives to generate phase-specific billing estimates, and claim 12 recites interactive tools for generating litigation budgets, including predictive cost breakdowns by litigation phase, comparisons of historical averages, and scenario modeling for alternative fee arrangements. Claim 15 likewise recites ingestion of docket entries, billing records, and time narratives, phase detection, cost and duration prediction, visualization, real-time updating, and budgetadjustment/reporting tools. Thus, claim 15 is not in a separate, non-overlapping search field from the elected claims. Any search for claim 15 would necessarily cover the same docket/billing integration, phase-detection, billing-estimation, and budgeting-visualization art already required for examination of elected claims 11 and 12.” (Emphasis added). Examiner does not agree.
Examiner responds that the scope of Claims 11-12 includes “The system of claim 1, wherein [(dependent limitations)].” First, the dependent limitations of Claims 11-12 are not the “same” as Claim 15. Claims 11-12 broadly relate to billing, budgeting, and docket events, whereas Claim 15 provides specific modules and functionality not required in Claims 11-12. Second, even if a search for Claim 15 would necessarily cover the further dependent limitations of Claims 11-12, it would not cover Claim 11-12 in its entirety, i.e. including depended upon Claim 1.
Regarding Section I.B on Page 7, Applicant argues “The asserted classification distinction does not justify restriction here because the actual claim language substantially overlaps. Elected claims 11 and 12 already require searching subject matter recited in claim 15. In addition, the subject matter of Group II (claim 15) operates upon the same integrated dataset recited in the independent claims of Group I, namely the integration of law-firm billing data with public court docket data and related legal data sources. Any prior-art search directed to claim 15' s cost forecasting will, of necessity, traverse the same body of references concerning integrated legal-data infrastructure, billing data ingestion, docket data ingestion, and the synchronization and normalization mechanisms recited in Group I. The classification difference between G06Q10/06 and G06Q30/02 does not reflect a genuine divide in search fields under the facts of this application, and the Examiner has not identified non-overlapping search resources or distinct search strategies that would be required. MPEP § 806.05(d), MPEP § 808.02.” (Emphasis added). Examiner does not agree.
Examiner responds, as discussed above, that the scope of Claims 11-12 includes “The system of claim 1, wherein [(dependent limitations)].” First, the dependent limitations of Claims 11-12 are not the “same” as Claim 15. Claims 11-12 broadly relate to billing, budgeting, and docket events, whereas Claim 15 provides specific modules and functionality not required in Claims 11-12. Second, even if a search for Claim 15 would necessarily cover the further dependent limitations of Claims 11-12, it would not cover Claim 11-12 in its entirety, i.e. including depended upon Claim 1.
Examiner responds that the “a data ingestion module configured to collect docket entries, billing records, and time narratives from disparate legal sources” of Claim 15 and “a data receiving module configured to integrate data from multiple legal sources including public records and law firm databases” of Claim 1 are not the “same” dataset, and could be embodied by two mutually exclusive systems.
Regarding Section I.C on Page 8, Applicant argues “The Office Action asserts that the subcombinations have separate utility, with Group II having "separate utility such as predicting cost and timeline of a litigation for budgeting purposes." Respectfully, the asserted utility of Group II is not separate from Group I. Claim 15's forecasting of litigation cost depends upon the multi-source integrated billing and docket data that is the express subject of the Group I claims. The same integrated data layer and phasespecific billing-estimation functionality are already recited in elected claims 11 and 12, such that the asserted separate utility does not establish a materially separate, non-overlapping field of search. The two groups are not merely "usable together" in the abstract sense contemplated by MPEP § 806.05( d); rather, Group II derives its recited utility from the structures of Group I. Where one subcombination supplies the data structures and integration operations upon which the other subcombination depends for its recited function, the separate-utility requirement is not satisfied, and restriction is not proper.” (Emphasis added). Examiner does not agree.
Examiner responds that, as discussed above, (1) Claims 11-12 recite a more broad limitation than Claim 15, (2) Claims 11-12 include the scope of Claim 1, which would not be covered by a search of Claim 15, and (3) the datasets used in Claim 1 and Claim 15 are different. Concisely, Claims 1 and 15 operate on different data and provide different functionality.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/27/2025 was filed before the mailing of the first office action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
The drawings are not labeled as figures.
The specification does not reference the drawing figures.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
(Claim 1) “A system for AI-enhanced legal data integration and management, the system comprising:
a data receiving module configured to integrate data from multiple legal sources including public records and law firm databases;
a data processing module equipped with an artificial intelligence model for normalizing and categorizing legal data into standardized formats;
a security module implementing field-level role and access controls for data security and privacy;
a synchronization module for updating and replicating legal data across various platforms in real-time;
a user interface module providing functionalities for manual review, data correction, and system interaction;
a database for storing and managing the integrated and processed legal data;
a training mechanism for the Al model using historical legal data and ongoing data updates;
a citation analysis module for analyzing and contextualizing legal citations within the integrated data;
a reporting module for generating audit trails and compliance reports; and
a communication module for interfacing with external legal data sources and systems.” (Emphasis added).
(Claim 2) “A system for AI-enhanced legal data integration and management, the system comprising:
a data receiving module configured to integrate data from multiple legal sources including public records and law firm databases;
a data processing module equipped with an artificial intelligence model for normalizing and categorizing legal data into standardized formats;
a security module implementing field-level role and access controls for data security and privacy;
a synchronization module for updating and replicating legal data across various platforms in real-time;
a user interface module providing functionalities for manual review, data correction, and system interaction;
a database for storing and managing the integrated and processed legal data;
a training mechanism for the Al model using historical legal data and ongoing data updates; a citation analysis module for analyzing and contextualizing legal citations within the integrated data;
a reporting module for generating audit trails and compliance reports; and
a communication module for interfacing with external legal data sources and systems.” (Emphasis added).
(Claim 3) “The system of claim 1, wherein the data receiving module is further configured to interface with and aggregate data from diverse public legal information sources including, but not limited to, Lexis, Westlaw, Bloomberg, vLex, Unicourt, Docket Alarm, CourtListener.” (Emphasis added).
(Claim 4) “The system of claim 1, wherein the data processing module utilizes a Generative Pre-trained Transformer (GPT) model tailored for legal data analysis and summarization.” (Emphasis added).
(Claim 5) “The system of claim 1, wherein the security module includes implementing ethical firewalls within the law firm to prevent conflicts of interest in data access.” (Emphasis added).
(Claim 6) “The system of claim 1, wherein the synchronization module includes a real-time updating mechanism to reflect the most current legal activities in the database.” (Emphasis added).
(Claim 7) “The system of claim 1, wherein the user interface module includes customizable alert settings for legal deadlines and court dates based on the Federal Rules of Civil Procedure (FRCP) and Civil Practice Law and Rules (CPLR).” (Emphasis added).
(Claim 8) “The system of claim 1, wherein the citation analysis module is equipped with Al and machine learning technologies for format recognition and normalization of various legal citations.” (Emphasis added).
(Claim 9) “The system of claim 1, wherein the reporting module's audit trails include time-stamped entries for detailed historical record keeping and regulatory compliance.” (Emphasis added).
(Claim 10) “The system of claim 1, wherein the communication module includes an API gateway for secure data exchanges between the law firm's internal systems and external legal data sources.” (Emphasis added).
(Claim 11) “The system of claim 1, wherein the data processing module further applies machine learning models to correlate docket events and time narratives, generating phase-specific billing estimates and updating those estimates.” (Emphasis added).
(Claim 12) “The system of claim 1, wherein the user interface module provides interactive tools for generating litigation budgets, including predictive cost breakdowns by litigation phase, comparisons of historical averages, and scenario modeling for alternative fee arrangements.” (Emphasis added).
(Claim 13) “The system of claim 1, further comprising an entity resolution module within the data processing module, configured to reconcile conflicting legal party data by applying AI-based record matching and normalization techniques across multiple data sources.” (Emphasis added).
(Claim 14) “The system of claim 1, wherein the synchronization module is further configured to enable bi-directional communication with external platforms, ensuring real-time updates to legal matter metadata and associated billing metrics.” (Emphasis added).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Regarding Claim 3, the claim recites “Lexis, Westlaw, Bloomberg, vLex, Unicourt, Docket Alarm, CourtListener.” The use of these trademarks or trade names complies with 35 U.S.C. 112(b) per MPEP 608.01(v)I and 2173.05(u).
Regarding Claim 7, the claim recites “. . . the Federal Rules of Civil Procedure (FRCP) and Civil Practice Law and Rules (CPLR).” Although this limitation lacks explicit antecedent basis within the claim, the limitation is definite under 35 U.S.C. 112(b) per MPEP 2173.05(e) (“Obviously, however, the failure to provide explicit antecedent basis for terms does not always render a claim indefinite. If the scope of a claim would be reasonably ascertainable by those skilled in the art, then the claim is not indefinite.”). Because the FRCP and CPLR are unambiguous standards known in the art, reference to them does yield a rejection under 35 U.S.C. 112(b).
Claim Objections
Applicant is advised that should Claim 1 be found allowable, Claim 2 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “A system for AI-enhanced legal data integration and management, the system comprising: a data receiving module configured to integrate data from multiple legal sources including public records and law firm databases; a data processing module equipped with an artificial intelligence model for normalizing and categorizing legal data into standardized formats; . . . a database for storing and managing the integrated and processed legal data; . . . a citation analysis module for analyzing and contextualizing legal citations within the integrated data; . . .” (Emphasis added). There is insufficient antecedent basis for the limitation of “the integrated and processed legal data.” It is unclear as to whether this limitation is referencing “the integrated data” and “the processed legal data,” or this limitation is limiting the “processed legal data” to the “integrated data.” Therefore, Claim 1 is rejected as indefinite under 35 U.S.C. 112(b). For Examination purposes herein, this limitation is interpreted as stating “a database for storing and managing the integrated data and the processed legal data.”
Claims 2-14 are rejected via dependency on Claim 1 or for reciting similar limitation to that of Claim 1 rejected above.
Claim 3 recites “The system of claim 1, wherein the data receiving module is further configured to interface with and aggregate data from diverse public legal information sources . . .” (emphasis added) and depended upon Claim 1 recites “. . . a data receiving module configured to integrate data from multiple legal sources including public records and law firm databases . . .” (emphasis added). The term “diverse” in Claim 3 is a relative term which renders the claim indefinite. The term “diverse” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Additionally, as depended upon Claim 1 already provides that the “data receiving module” interacts with “multiple” sources, the limitation of “diverse” is intended to provide the term-of-degree limitation, i.e. a limitation beyond the plurality of “multiple.” Therefore, Claim 3 is rejected as indefinite under 35 U.S.C. 112(b). For Examination purposes herein, this limitation is interpreted as stating “wherein the data receiving module is further configured to interface with and aggregate data from public legal information sources . . .”
Claim 3 recites “wherein the data receiving module is further configured to interface with and aggregate data from diverse public legal information sources including, but not limited to, Lexis, Westlaw, Bloomberg, vLex, Unicourt, Docket Alarm, [(i.e. no “and” or “or” in the list)] CourtListener.” The scope of the claim is indefinite as it is unclear as to what the “public legal information sources” are limited to. Therefore, Claim 3 is rejected as indefinite under 35 U.S.C. 112(b). For Examination purposes herein, the list is interpreted as non-patentable-weight examples of sources, i.e. “. . . public legal information sources, for example, without limitation, could include the likes of: Lexis, Westlaw, Bloomberg, vLex, Unicourt, Docket Alarm, or CourtListener.”
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-14 recite a system (i.e. a machine or manufacture). Therefore, Claims 1-14 all fall within the one of the four statutory categories of invention of 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 1 recites the abstract idea of:
“. . . integrate data from multiple legal sources including public records and law firm [records];
. . . normalizing and categorizing legal data into standardized formats;
. . . implementing field-level role and access controls for data security and privacy;
. . . for updating and replicating legal data across various platforms in real-time;
. . . providing functionalities for manual review, data correction, and system interaction;
. . . storing and managing the integrated and processed legal data;
. . . the . . . model using historical legal data and ongoing data updates;
. . . analyzing and contextualizing legal citations within the integrated data;
. . . generating audit trails and compliance reports; and
. . . interfacing with external legal data sources and systems.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) integrating data from multiple legal sources, (2) normalizing and categorizing legal data into standardized formats, (3) implementing role level access control, (4) updating and replicating legal data, (5) providing functionalities of review, data correction, and system interaction, (6) storing, managing, processing, and integrating legal data, (7) using historical legal data and ongoing data updates, (8) analyzing and contextualizing legal citations within integrated data, (9) generating audits and compliance reports, (10) interfacing with external legal data sources and systems, all of which are:
commercial or legal interactions (i.e. receiving, integrating, normalizing, categorizing, updating, replicating, reviewing, correcting, analyzing, managing, and contextualizing legal data) and managing personal behavior by following rules and interacting between people (i.e. normalizing and categorizing legal data and controlling access to data based on a user’s role), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II and
observation (i.e. receiving and storing legal data) and evaluation (i.e. integrating data, generating reports, and contextualizing legal citations), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III.
The mere the recitation of generic computer components (i.e., the “system,” “module[s],” “database[s],” and “mechanism”) implementing the identified abstract idea does not take the claim out of the certain methods of organizing human activity and mental processes groupings. MPEP 2106.04(d). If a claim limitation, under its broadest reasonable interpretation, covers “commercial or legal interactions,” “managing personal behavior or relationships or interactions between people,” “observation,” and “evaluation,” but for the recitation of generic computer components, then it falls in the certain methods of organizing human activity and mental processes groupings of abstract ideas. MPEP 2106.04. Therefore, Claim 1 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) a system, comprising:
(ii) law firm databases;
(iii) a data processing module equipped with an artificial intelligence model,
(iv) a security module,
(v) a synchronization module,
(vi) a user interface module,
(vii) a database,
(viii) a training mechanism,
(ix) a citation analysis module,
(x) a reporting module, and
(xi) a communication module.
The additional elements of (i) a system, comprising: (ii) law firm databases; (iii) a data processing module equipped with an artificial intelligence model, (iv) a security module, (v) a synchronization module, (vi) a user interface module, (vii) a database, (viii) a training mechanism, (ix) a citation analysis module, (x) a reporting module, and(xi) a communication module (The specification provides no limitations as to the configuration of the system. Therefore, a person of ordinary skill in the art would understand that the system can implemented using any general-purpose computer.), are recited at a high-level of generality, such that, when viewed as whole/ordered combination, they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) a system, comprising: (ii) law firm databases; (iii) a data processing module equipped with an artificial intelligence model, (iv) a security module, (v) a synchronization module, (vi) a user interface module, (vii) a database, (viii) a training mechanism, (ix) a citation analysis module, (x) a reporting module, and(xi) a communication module, when viewed as whole/ordered combination (The specification provides no limitations as to the configuration of the system. Therefore, a person of ordinary skill in the art would understand that the system can implemented using any general-purpose computer.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements (i) a system, comprising: (ii) law firm databases; (iii) a data processing module equipped with an artificial intelligence model, (iv) a security module, (v) a synchronization module, (vi) a user interface module, (vii) a database, (viii) a training mechanism, (ix) a citation analysis module, (x) a reporting module, and(xi) a communication module, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Claim 2 recite elements and limitations that are substantially similar to Claim 1. Therefore, Claim 2 is rejected under 35 U.S.C. 101 just as Claim 1 is rejected under 35 U.S.C. 101 as discussed above.
Dependent Claims 3-14 recite the abstract idea of:
“. . . configured to interface with and aggregate data from diverse public legal information sources including, but not limited to, Lexis, Westlaw, Bloomberg, vLex, Unicourt, Docket Alarm, CourtListener.” (Claim 3).
“. . . utilizes a Generative Pre- trained Transformer (GPT) model tailored for legal data analysis and summarization.” (Claim 4).
“. . . implementing ethical firewalls within the law firm to prevent conflicts of interest in data access.” (Claim 5).
“. . . to reflect the most current legal activities . . .” (Claim 6).
“. . . customizable alert settings for legal deadlines and court dates based on the Federal Rules of Civil Procedure (FRCP) and Civil Practice Law and Rules (CPLR).” (Claim 7).
“. . . format recognition and normalization of various legal citations.” (Claim 8).
“. . . wherein the . . . audit trails include time-stamped entries for detailed historical record keeping and regulatory compliance.” (Claim 9).
“. . . for secure data exchanges between the law firm's internal systems and external legal data sources.” (Claim 10).
“. . . to correlate docket events and time narratives, generating phase-specific billing estimates and updating those estimates.” (Claim 11).
“. . . provides interactive tools for generating litigation budgets, including predictive cost breakdowns by litigation phase, comparisons of historical averages, and scenario modeling for alternative fee arrangements.” (Claim 12).
“. . . to reconcile conflicting legal party data by applying AI-based record matching and normalization techniques across multiple data sources.” (Claim 13).
“. . . to enable bi-directional communication with external platforms, ensuring real-time updates to legal matter metadata and associated billing metrics.” (Claim 14).
Dependent Claims 3-14, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 3-14 fail to establish claims that are not directed to an abstract idea because the further limitations (1) interface with certain sources, (2) utilize a model tailored for legal data analysis and summarization (Examiner notes that the limitation is to merely utilize the model, and therefore the creation of the model using a computer is not within the scope of the limitation.), (3) implement ethical firewalls to prevent conflicts (Examiner notes that an “ethical firewall” is not referencing a IT network firewall at a port, but more broadly relates to a policy that prevents access to data.), (4) reflecting most current legal activities, (5) customizable alert settings for deadlines and dates based on certain information, (6) format recognition and normalization of various legal citations, (7) audit trails include time-stamped entries, (8) secure data exchange between systems and sources, (9)correlating docket events and time narratives and generating and updating phase -specific billing estimates, (10) providing interactive tools for budgeting activities, (11) reconciling conflicting legal part data by matching and normalizing data from multiple sources, and (12) bi-directional communication ensuring real-time updates to certain business data.
The further elements of Claims 3-14 (i.e. “data receiving module” of Claim 3, “data processing module” of Claims 4 and 11, “security module” of Claim 5, “synchronization module” in Claims 6 and 14, “real-time updating mechanism” in Claim 6, “database” in Claim 6, “user interface modules” in Claims 7 and 12, “citation analysis module” in Claim 8, “AI and machine learning technologies” in Claim 8, “reporting module” in Claim 9, “communication module” in Claim 10, “API gateway” in Claim 10, “machine learning models” in Claim 11, “entity resolution module within the data processing module” in Claim 13, and “AI-based record matching and normalization techniques” in Claim 13.) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer components similar to the generic computer components of Claim 1 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) just as in Claim 1. The organization of the further limitations of Claims 3-14 fail to integrate an abstract idea into a practical application just as discussed above for Claim 1. Additionally, performing the abstract idea of Claim 1 as recited in each of the further limitations of Claims 3-14, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 1. Therefore, Claims 3-14 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 3-14 fail to establish that the claims provide an inventive concept, just as in Claim 1. Therefore, Claims 3-14 fails the Subject Matter Eligibility Test and are consequently 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 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, 6, 8-10, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over US-20040019496-A1 (“Angle”) in view of US-20240184974-A1 (“Burris”).
Regarding Claim 1, Angle teaches “A system for AI-enhanced legal data integration and management” (Fig. 1-3 and 5 and Paragraphs 26-27 show “Legal Information System 142.” Paragraph 22 shows the use of “program modules” and “computer systems.” See also Paragraphs 50-52 discussing the program.), “the system comprising:”
“a data receiving module configured to integrate data from multiple legal sources including public records and law firm databases” (Fig. 2 and Paragraph 29 shows “Data 205, including documents, are input to the Legal Information System 142 via one or more Data Input Systems 116 [(i.e. “data receiving module”)].” Fig. 3 and Paragraph 34 shows “Data Input Systems 116 may include some users/administrators 260, e.g., where data is keyed in, but also may include automated data entry systems such as optical scanners, data or files received over Wide Area Network 130, e.g., from a court, other governmental agency [(i.e. public records)], or from automatically generated or collected data from other information systems. Legal Information Systems 142 stores data and files in data/file store 145/143.” Fig. 4 and Paragraphs 39-41 shows database tables that are linked together, i.e. “integrated.” At least “Participants Table 410,” “Legal matter table 430,” and “Financial Table 450,” teaches data received from “law firm databases,” i.e. the “other systems” of Paragraph 34.”);
“a data processing module . . . for normalizing and categorizing legal data into standardized formats” (Fig. 2 and Paragraph 29 shows “Data 205, including documents, are input to the Legal Information System 142 via one or more Data Input Systems 116. Legal Information System 142 [(i.e. data processing module)] either stores data 205 as received processes according to business rules, therefore converting it to information 220 or stores data 205 and information 220 in data storage 145 and files in file storage 143.” (Emphasis added). Paragraph 41 shows “Modification of the shown tables [of Fig. 4] as well as additional tables, their domains, keys, and links to other tables, and associated queries and reports, and appropriate normalization of each, useful in implementing the databases used in the invention, given the disclosure herein, could be implemented by data base designers of ordinary skill in the art.” (Emphasis added). Further, the population of the tables of the database of Fig. 4 teaches “normalizing and categorizing legal data into standardized formats.”);
“a security module implementing field-level role and access controls for data security and privacy” (Paragraph 35 shows “A user may log on using a typical personal computer system or workstation system. Conventional or other types of security and/or user-access rights management are typically included.” Fig. 7 and Paragraph 36 shows “A table 710 is maintained defining user-access rights for categories 715 for all user groups 725. For example, system administrators have full access rights (as shown by “F” 735), but court staff have view only rights. Each user would be assigned a matter/group [(i.e. field-level role)], an access category and have the rights [(i.e. access controls for data security and privacy)] associated with that category. A change in the master access-category table would thus propagate to all users having an assigned category.”);
“a synchronization module for updating and replicating legal data across various platforms in real-time” (Paragraphs 22-23 shows “[0022] . . . The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. [0023] The invention generally relates to a law practice information management system. Basic components of an information management system include: all of the software which will create, update, and manage the system, storage devices, processing devices, input/output devices, and network devices and software.” (Emphasis added). Fig. 1 and Paragraphs 26-27 shows “[0026] FIG. 1 is a schematic block system diagram of one embodiment of the invention. Each entity involved in the method, in one embodiment, is depicted. The various components and participants using the system are interconnected via Wide Area Network 130. In-house counsel, outside counsel, and others are exemplary users of the system and are depicted as Hosts on LAN 1, block 115, Hosts on LAN 2, block 120, and hosts on LAN 3, block 125, respectively. Legal Information System 142 is connected to Wide Area Network 130, to eMail System 136, and to Reports 138. Legal Information System 142 includes law practice information management system 135, database management system 140, database storage 145, and file server 143. Reports 138 and alarms 150 via eMail system 136 issue from legal information system 142. [0027] The relationships between these entities are provided in FIG. 2. Wide Area Network 130 is optionally the Internet or other public or private networks or combinations thereof. The communication of all entities through a common Wide Area Network 130 is illustrative only, and the invention includes embodiments where some entities communicate through one network, other entities through a different network, and various permutations thereof. That is, the Legal Information Management System 142, as well as any general-purpose computers utilized by users, e.g., Hosts 115, 120, and 125, and other entities (collectively, the “nodes”) preferably transmit digitally encoded data and other information between one another.” (Emphasis added). See also Paragraph 32 showing a computer program for transmitting data on each computer. Paragraph 34 shows “[The steps of Fig. 6, including automatic data entry,] are only exemplary steps and the invention contemplates other related steps including, but not limited to: creating, deleting, and updating data, alarms, automated reports, and interactive user-access.” (Emphasis added). Paragraph 13 shows “In another embodiment of the invention, it includes a method for law practice information management including entering into a single web-enabled database all case information selected from the group consisting of parties, counsel, properties' subject matter, and mixtures thereof; distributing documents in the database over the Internet to entities selected from the group consisting of counsel, experts, advisors, and mixtures thereof; entering into the database discovery documents selected from the group consisting of discovery requests, discovery responses, produced documents, and mixtures thereof; . . .” (Emphasis added). Paragraph 5 shows “It is an object of this invention: . . . (iii) to eliminate duplicative information, thereby reducing or eliminating data update or delete errors; and (iv) to permit data returned from a search request to be instantly available for analysis and reuse on other related legal matters.” See also Paragraph 46 discussing functionality of computer storage. Because Angle shows a distributed computer system with a plurality of nodes that is updated with automatic communication with other systems such that the data is available “instantly” and errors in data updating are reduced or eliminated, Angle teaches this limitation.);
“a user interface module providing functionalities for manual review, data correction, and system interaction” (Paragraph 37 shows “[T]he system may be coupled via the bus to a display device [(i.e. user interface module)], such as a cathode ray tube, for displaying information to a computer user [(i.e. manual review)]. The computer system further includes a keyboard and a cursor control, such as a mouse [(i.e. providing functionalities for system interaction)]. Any other access devices for accessing a network are intended to be included in the invention. Such devices may include properly equipped and configured cellular phones and personal digital assistants.” Fig. 7 and Paragraphs 35-36 shows that users can have “full access” rights or “view only” rights. Thus, Angle teaches that the functionalities of “data correction,” i.e. modifying data, is provided to user with “full access” rights. See also Paragraphs 23 and 34 showing creating, deleting and updating data.);
“a database for storing and managing the integrated and processed legal data” (Fig. 1 and Paragraph 26 shows “Legal Information System 142 includes . . . database management system 140, database storage 145, and file server 143.” Fig. 1-2 and Paragraphs 29-30 shows “[0029] . . . Data 205, including documents, are input to the Legal Information System 142 via one or more Data Input Systems 116. Legal Information System 142 either stores data 205 as received processes according to business rules, therefore converting it to information 220 or stores data 205 and information 220 in data storage 145 and files in file storage 143. [0030] Data storage 145 is any conventional hardware and/or software for storing data of the database in known architectures such as relational databases. File storage 143 is any conventional file storage hardware and software such as file server hardware and software. . .” See also Paragraphs 23 and 32 discussing the creation and management of stored data.); . . .
“a reporting module for generating audit trails and compliance reports” (Paragraph 30 shows “Both file storage 143 and data storage 145 are configured for providing reports and files 227 to one or more users/administrators 260. Such reports typically are in response to queries posed by users/administrators 260 but are not so limited. That is, business rules for automatic generation or delivery of reports or files may be included, e.g., regularly scheduled status reports. Legal Information System 142 optionally is configured to provide alarms 150 to the users/administrators 260. Alarms may be standard system alarms or based on customized rules created by users/administrators 260, e.g., notification of an imminent docket date deadline.” Paragraph 7 shows “It is further an object of this invention to allow corporate legal departments to standardize data creation, preservation, analysis and reporting of information used in handling legal claims and matters. The invention is unique, for example, in that corporate knowledge management is maintained after the end of each legal matter.” Because Angle shows that reports can be automatically generated at regularly scheduled intervals that can be accessed after the end of the legal matter, Angle teaches “audit trails.” Because Angle shows that a notification can be automatically sent based on an imminent deadline Angle teaches “compliance reports,” i.e. reporting on compliance with the deadline. See also Fig. 6 and Paragraph 34 showing automated reports and Fig. 4 and Paragraphs 39-41 showing reports generated based on tables stored in the database.); and
“a communication module for interfacing with external legal data sources and systems” (Fig. 1-2 and Paragraphs 26-28 shows that “Legal Information System 142” (i.e. communication module) communicates with other computer systems via “Wide Area Network 130” or other types of network. Fig. 2-3 and Paragraph 34 shows “Data Input Systems 116” can “automatically [collect] data from other information systems.” See also Fig. 5 and Paragraphs 32-33 showing computer network.).
Angle does not explicitly teach, but Burris teaches:
“a data processing module equipped with an artificial intelligence model for normalizing and categorizing legal data into standardized formats” (Generally, Fig. 1A, 4-6, and 8a, as discussed in Paragraphs 49, 60-62, 72, 82-86, shows steps of method 100 performed by an AI assistant, including: collect the law and create the legal text (Step 40), code the law (Step 50), publish and disseminate the data generated (Step 60), and track and update the law (Step 70). Paragraph 88 shows “An AI Assist feature is configured to search for new instances of the text and generate its proposal of the features observed in a dataset. A simple and automatically learning AI assistant uses at least AI, ML, and reinforcement learning (RL) patterns to automatically identify texts within the scope of a dataset (e.g., new versions of a law), automatically generate features of the text, automatically update underlying training models, propose texts and features to a SME, and, as authorized, update datasets as new versions of the target text are created. In the legal example, the AI Assist may be configured to add additional jurisdictions to a dataset being constructed or to update a completed dataset as new laws are passed.” Paragraph 90 shows “In addition to the model prediction, a template of features is presented after a text is identified for inclusion in the dataset. This template, (e.g., a fair housing law) can be as simple as an array of named arrays, for example [key, value] pairs, constituting features or sub features of interest, i.e. [{“key”: “race,” values: [“ethnicity,” “skin color”] } ]. Much like the previous step, this information is displayed to the SME, and then a sub-model predicts the feature selection while the SME determines the ground truth. If the SME discovers additional features, the SME may then add them (e.g., if jurisdictions begin to add a distinct new feature to a law that the AI Assist has already “learned”). Like the previous step, when the prediction by the model reaches a threshold of accuracy, the model notifies the interface that meaningful prediction is possible, and then begins to identify the features within the text as well.” The “AI assistant” of Paragraphs 88 and 90 teaches “a data processing module equipped with an artificial intelligence model.” The “template” of Paragraph 90 teaches “standardized formats.” Fig. 7 and Paragraphs 91-93 showing legal text being sorted into topics, i.e. “normalizing and categorizing legal data.” See also Paragraphs 13 and 94 showing that deep learning includes normalization, Paragraph 145 showing that any type of source data can be used (requiring normalization), and Paragraph 68 showing creation of MonQcle database.);
“a training mechanism for the Al model using historical legal data and ongoing data updates” (Paragraph 88 shows “An AI Assist feature is configured to search for new instances of the text and generate its proposal of the features observed in a dataset. A simple and automatically learning AI assistant uses at least AI, ML, and reinforcement learning (RL) patterns to automatically identify texts within the scope of a dataset (e.g., new versions of a law), automatically generate features of the text, automatically update underlying training models, propose texts and features to a SME, and, as authorized, update datasets as new versions of the target text are created. In the legal example, the AI Assist may be configured to add additional jurisdictions to a dataset being constructed or to update a completed dataset as new laws are passed.” Paragraph 91 shows “The AI Assist tool 90 is illustrated in FIG. 7 , with two general dialog boxes labeled with the numbers “1” and “2.” The AI Assist tool 90 functions to speed up both the AI learning process and the work of the SMEs, with each subsequent jurisdiction, citation, and feature marking becoming more and more accurate as the model retrains, eventually becoming a confirmation process without needing adjustment. As illustrated in FIG. 7 , and for purposes of example, the AI Assist tool 90 is applied to a legal text.” Paragraph 94 shows “The AI model may be retrained when one or more of the following events occurs: the SME (or machine) selects a citation 711 in the topic assistant as correct; the SME adjusts a citation 711; the SME adds a feature 714; the SME checks and marks a feature 714 not automatically identified, the SME unchecks a feature (e.g. 710) that was incorrectly identified, or the SME adds a feature mutation (e.g. 709). From this process, the AI model may automatically adjust internal deep neural connections without input or human assistance, to: (i) identify any relevant citations for a topic or subtopic (or tertiary or further subtopic) within any text; (ii) mark the text, even across paragraphs, parts of other paragraphs, and so on; and (iii) identify any features within that text, such as the protected populations for fair housing, or the number of days required for an eviction notice, etc.” Thus, Burris shows that the model is retrained as new legal information is updated, teaching “a training mechanism for the Al model using historical legal data and ongoing data updates.” See also Paragraphs 99-109 showing further detail regarding the training and scope of the AI-based system.); and
“a citation analysis module for analyzing and contextualizing legal citations within the integrated data” (Fig. 7 and Paragraphs 90-94 shows that for a given “topic 704,” multiple segments of legal text with “citation 711” and “context [706]” are determined. Therefore, Burris teaches that each legal citation is analyzed and contextualized by the system. See also, Paragraph 94 showing “From this process, the AI model may automatically adjust internal deep neural connections without input or human assistance, to: (i) identify any relevant citations for a topic or subtopic (or tertiary or further subtopic) within any text; (ii) mark the text, even across paragraphs, parts of other paragraphs, and so on; and (iii) identify any features within that text, such as the protected populations for fair housing, or the number of days required for an eviction notice, etc.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Burris with Angle because Burris teaches that its preferable to perform legal research using AI with a high degree of reliability to limit human involvement and better analyze laws over time (Paragraphs 6, 12, 16, 47, and 49). Thus, combining Burris with Angle furthers the interest taught in Burris, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Angle teaches “A system for AI-enhanced legal data integration and management” (Fig. 1-3 and 5 and Paragraphs 26-27 show “Legal Information System 142.” Paragraph 22 shows the use of “program modules” and “computer systems.” See also Paragraphs 50-52 discussing the program.), “the system comprising:”
“a data receiving module configured to integrate data from multiple legal sources including public records and law firm databases” (Fig. 2 and Paragraph 29 shows “Data 205, including documents, are input to the Legal Information System 142 via one or more Data Input Systems 116 [(i.e. “data receiving module”)].” Fig. 3 and Paragraph 34 shows “Data Input Systems 116 may include some users/administrators 260, e.g., where data is keyed in, but also may include automated data entry systems such as optical scanners, data or files received over Wide Area Network 130, e.g., from a court, other governmental agency [(i.e. public records)], or from automatically generated or collected data from other information systems. Legal Information Systems 142 stores data and files in data/file store 145/143.” Fig. 4 and Paragraphs 39-41 shows database tables that are linked together, i.e. “integrated.” At least “Participants Table 410,” “Legal matter table 430,” and “Financial Table 450,” teaches data received from “law firm databases,” i.e. the “other systems” of Paragraph 34.”);
“a data processing module . . . for normalizing and categorizing legal data into standardized formats” (Fig. 2 and Paragraph 29 shows “Data 205, including documents, are input to the Legal Information System 142 via one or more Data Input Systems 116. Legal Information System 142 [(i.e. data processing module)] either stores data 205 as received processes according to business rules, therefore converting it to information 220 or stores data 205 and information 220 in data storage 145 and files in file storage 143.” (Emphasis added). Paragraph 41 shows “Modification of the shown tables [of Fig. 4] as well as additional tables, their domains, keys, and links to other tables, and associated queries and reports, and appropriate normalization of each, useful in implementing the databases used in the invention, given the disclosure herein, could be implemented by data base designers of ordinary skill in the art.” (Emphasis added). Further, the population of the tables of the database of Fig. 4 teaches “normalizing and categorizing legal data into standardized formats.”);
“a security module implementing field-level role and access controls for data security and privacy” (Paragraph 35 shows “A user may log on using a typical personal computer system or workstation system. Conventional or other types of security and/or user-access rights management are typically included.” Fig. 7 and Paragraph 36 shows “A table 710 is maintained defining user-access rights for categories 715 for all user groups 725. For example, system administrators have full access rights (as shown by “F” 735), but court staff have view only rights. Each user would be assigned a matter/group [(i.e. field-level role)], an access category and have the rights [(i.e. access controls for data security and privacy)] associated with that category. A change in the master access-category table would thus propagate to all users having an assigned category.”);
“a synchronization module for updating and replicating legal data across various platforms in real-time” (Paragraphs 22-23 shows “[0022] . . . The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. [0023] The invention generally relates to a law practice information management system. Basic components of an information management system include: all of the software which will create, update, and manage the system, storage devices, processing devices, input/output devices, and network devices and software.” (Emphasis added). Fig. 1 and Paragraphs 26-27 shows “[0026] FIG. 1 is a schematic block system diagram of one embodiment of the invention. Each entity involved in the method, in one embodiment, is depicted. The various components and participants using the system are interconnected via Wide Area Network 130. In-house counsel, outside counsel, and others are exemplary users of the system and are depicted as Hosts on LAN 1, block 115, Hosts on LAN 2, block 120, and hosts on LAN 3, block 125, respectively. Legal Information System 142 is connected to Wide Area Network 130, to eMail System 136, and to Reports 138. Legal Information System 142 includes law practice information management system 135, database management system 140, database storage 145, and file server 143. Reports 138 and alarms 150 via eMail system 136 issue from legal information system 142. [0027] The relationships between these entities are provided in FIG. 2. Wide Area Network 130 is optionally the Internet or other public or private networks or combinations thereof. The communication of all entities through a common Wide Area Network 130 is illustrative only, and the invention includes embodiments where some entities communicate through one network, other entities through a different network, and various permutations thereof. That is, the Legal Information Management System 142, as well as any general-purpose computers utilized by users, e.g., Hosts 115, 120, and 125, and other entities (collectively, the “nodes”) preferably transmit digitally encoded data and other information between one another.” (Emphasis added). See also Paragraph 32 showing a computer program for transmitting data on each computer. Paragraph 34 shows “[The steps of Fig. 6, including automatic data entry,] are only exemplary steps and the invention contemplates other related steps including, but not limited to: creating, deleting, and updating data, alarms, automated reports, and interactive user-access.” (Emphasis added). Paragraph 13 shows “In another embodiment of the invention, it includes a method for law practice information management including entering into a single web-enabled database all case information selected from the group consisting of parties, counsel, properties' subject matter, and mixtures thereof; distributing documents in the database over the Internet to entities selected from the group consisting of counsel, experts, advisors, and mixtures thereof; entering into the database discovery documents selected from the group consisting of discovery requests, discovery responses, produced documents, and mixtures thereof; . . .” (Emphasis added). Paragraph 5 shows “It is an object of this invention: . . . (iii) to eliminate duplicative information, thereby reducing or eliminating data update or delete errors; and (iv) to permit data returned from a search request to be instantly available for analysis and reuse on other related legal matters.” See also Paragraph 46 discussing functionality of computer storage. Because Angle shows a distributed computer system with a plurality of nodes that is updated with automatic communication with other systems such that the data is available “instantly” and errors in data updating are reduced or eliminated, Angle teaches this limitation.);
“a user interface module providing functionalities for manual review, data correction, and system interaction” (Paragraph 37 shows “[T]he system may be coupled via the bus to a display device [(i.e. user interface module)], such as a cathode ray tube, for displaying information to a computer user [(i.e. manual review)]. The computer system further includes a keyboard and a cursor control, such as a mouse [(i.e. providing functionalities for system interaction)]. Any other access devices for accessing a network are intended to be included in the invention. Such devices may include properly equipped and configured cellular phones and personal digital assistants.” Fig. 7 and Paragraphs 35-36 shows that users can have “full access” rights or “view only” rights. Thus, Angle teaches that the functionalities of “data correction,” i.e. modifying data, is provided to user with “full access” rights. See also Paragraphs 23 and 34 showing creating, deleting and updating data.);
“a database for storing and managing the integrated and processed legal data” (Fig. 1 and Paragraph 26 shows “Legal Information System 142 includes . . . database management system 140, database storage 145, and file server 143.” Fig. 1-2 and Paragraphs 29-30 shows “[0029] . . . Data 205, including documents, are input to the Legal Information System 142 via one or more Data Input Systems 116. Legal Information System 142 either stores data 205 as received processes according to business rules, therefore converting it to information 220 or stores data 205 and information 220 in data storage 145 and files in file storage 143. [0030] Data storage 145 is any conventional hardware and/or software for storing data of the database in known architectures such as relational databases. File storage 143 is any conventional file storage hardware and software such as file server hardware and software. . .” See also Paragraphs 23 and 32 discussing the creation and management of stored data.); . . .
“a reporting module for generating audit trails and compliance reports” (Paragraph 30 shows “Both file storage 143 and data storage 145 are configured for providing reports and files 227 to one or more users/administrators 260. Such reports typically are in response to queries posed by users/administrators 260 but are not so limited. That is, business rules for automatic generation or delivery of reports or files may be included, e.g., regularly scheduled status reports. Legal Information System 142 optionally is configured to provide alarms 150 to the users/administrators 260. Alarms may be standard system alarms or based on customized rules created by users/administrators 260, e.g., notification of an imminent docket date deadline.” Paragraph 7 shows “It is further an object of this invention to allow corporate legal departments to standardize data creation, preservation, analysis and reporting of information used in handling legal claims and matters. The invention is unique, for example, in that corporate knowledge management is maintained after the end of each legal matter.” Because Angle shows that reports can be automatically generated at regularly scheduled intervals that can be accessed after the end of the legal matter, Angle teaches “audit trails.” Because Angle shows that a notification can be automatically sent based on an imminent deadline Angle teaches “compliance reports,” i.e. reporting on compliance with the deadline. See also Fig. 6 and Paragraph 34 showing automated reports and Fig. 4 and Paragraphs 39-41 showing reports generated based on tables stored in the database.); and
“a communication module for interfacing with external legal data sources and systems” (Fig. 1-2 and Paragraphs 26-28 shows that “Legal Information System 142” (i.e. communication module) communicates with other computer systems via “Wide Area Network 130” or other types of network. Fig. 2-3 and Paragraph 34 shows “Data Input Systems 116” can “automatically [collect] data from other information systems.” See also Fig. 5 and Paragraphs 32-33 showing computer network.).
Angle does not explicitly teach, but Burris teaches:
“a data processing module equipped with an artificial intelligence model for normalizing and categorizing legal data into standardized formats” (Generally, Fig. 1A, 4-6, and 8a, as discussed in Paragraphs 49, 60-62, 72, 82-86, shows steps of method 100 performed by an AI assistant, including: collect the law and create the legal text (Step 40), code the law (Step 50), publish and disseminate the data generated (Step 60), and track and update the law (Step 70). Paragraph 88 shows “An AI Assist feature is configured to search for new instances of the text and generate its proposal of the features observed in a dataset. A simple and automatically learning AI assistant uses at least AI, ML, and reinforcement learning (RL) patterns to automatically identify texts within the scope of a dataset (e.g., new versions of a law), automatically generate features of the text, automatically update underlying training models, propose texts and features to a SME, and, as authorized, update datasets as new versions of the target text are created. In the legal example, the AI Assist may be configured to add additional jurisdictions to a dataset being constructed or to update a completed dataset as new laws are passed.” Paragraph 90 shows “In addition to the model prediction, a template of features is presented after a text is identified for inclusion in the dataset. This template, (e.g., a fair housing law) can be as simple as an array of named arrays, for example [key, value] pairs, constituting features or sub features of interest, i.e. [{“key”: “race,” values: [“ethnicity,” “skin color”] } ]. Much like the previous step, this information is displayed to the SME, and then a sub-model predicts the feature selection while the SME determines the ground truth. If the SME discovers additional features, the SME may then add them (e.g., if jurisdictions begin to add a distinct new feature to a law that the AI Assist has already “learned”). Like the previous step, when the prediction by the model reaches a threshold of accuracy, the model notifies the interface that meaningful prediction is possible, and then begins to identify the features within the text as well.” The “AI assistant” of Paragraphs 88 and 90 teaches “a data processing module equipped with an artificial intelligence model.” The “template” of Paragraph 90 teaches “standardized formats.” Fig. 7 and Paragraphs 91-93 showing legal text being sorted into topics, i.e. “normalizing and categorizing legal data.” See also Paragraphs 13 and 94 showing that deep learning includes normalization, Paragraph 145 showing that any type of source data can be used (requiring normalization), and Paragraph 68 showing creation of MonQcle database.);
“a training mechanism for the Al model using historical legal data and ongoing data updates” (Paragraph 88 shows “An AI Assist feature is configured to search for new instances of the text and generate its proposal of the features observed in a dataset. A simple and automatically learning AI assistant uses at least AI, ML, and reinforcement learning (RL) patterns to automatically identify texts within the scope of a dataset (e.g., new versions of a law), automatically generate features of the text, automatically update underlying training models, propose texts and features to a SME, and, as authorized, update datasets as new versions of the target text are created. In the legal example, the AI Assist may be configured to add additional jurisdictions to a dataset being constructed or to update a completed dataset as new laws are passed.” Paragraph 91 shows “The AI Assist tool 90 is illustrated in FIG. 7, with two general dialog boxes labeled with the numbers “1” and “2.” The AI Assist tool 90 functions to speed up both the AI learning process and the work of the SMEs, with each subsequent jurisdiction, citation, and feature marking becoming more and more accurate as the model retrains, eventually becoming a confirmation process without needing adjustment. As illustrated in FIG. 7, and for purposes of example, the AI Assist tool 90 is applied to a legal text.” Paragraph 94 shows “The AI model may be retrained when one or more of the following events occurs: the SME (or machine) selects a citation 711 in the topic assistant as correct; the SME adjusts a citation 711; the SME adds a feature 714; the SME checks and marks a feature 714 not automatically identified, the SME unchecks a feature (e.g. 710) that was incorrectly identified, or the SME adds a feature mutation (e.g. 709). From this process, the AI model may automatically adjust internal deep neural connections without input or human assistance, to: (i) identify any relevant citations for a topic or subtopic (or tertiary or further subtopic) within any text; (ii) mark the text, even across paragraphs, parts of other paragraphs, and so on; and (iii) identify any features within that text, such as the protected populations for fair housing, or the number of days required for an eviction notice, etc.” Thus, Burris shows that the model is retrained as new legal information is updated, teaching “a training mechanism for the Al model using historical legal data and ongoing data updates.” See also Paragraphs 99-109 showing further detail regarding the training and scope of the AI-based system.); and
“a citation analysis module for analyzing and contextualizing legal citations within the integrated data” (Fig. 7 and Paragraphs 90-94 shows that for a given “topic 704,” multiple segments of legal text with “citation 711” and “context [706]” are determined. Therefore, Burris teaches that each legal citation is analyzed and contextualized by the system. See also, Paragraph 94 showing “From this process, the AI model may automatically adjust internal deep neural connections without input or human assistance, to: (i) identify any relevant citations for a topic or subtopic (or tertiary or further subtopic) within any text; (ii) mark the text, even across paragraphs, parts of other paragraphs, and so on; and (iii) identify any features within that text, such as the protected populations for fair housing, or the number of days required for an eviction notice, etc.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Burris with Angle because Burris teaches that its preferable to perform legal research using AI with a high degree of reliability to limit human involvement and better analyze laws over time (Paragraphs 6, 12, 16, 47, and 49). Thus, combining Burris with Angle furthers the interest taught in Burris, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 6, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle further teaches “wherein the synchronization module includes a real-time updating mechanism to reflect the most current legal activities in the database” (Paragraph 5 shows “It is an object of this invention: (i) to reduce the time to search, retrieve, sort, and organize data needed in legal practices to minutes rather than months; (ii) to have any information requested be returned in a more complete form than results from known methods and systems; (iii) to eliminate duplicative information, thereby reducing or eliminating data update or delete errors; and (iv) to permit data returned from a search request to be instantly available for analysis and reuse on other related legal matters.” (Emphasis added). Because Angle shows that the data is “instantly available,” Angle teaches a “real-time updating mechanism.” See also Paragraph 34 showing automated data entry from court or governmental information systems and Paragraphs 10-13 further discussing use of the system in a law practice.).
Regarding Claim 8, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle does not explicitly teach, but Burris further teaches “wherein the citation analysis module is equipped with Al and machine learning technologies for format recognition and normalization of various legal citations” (Paragraph 71 shows use of “software for natural language process (NLP)” including “a web-scraping or data-scraping tool” to create the database. Paragraph 49 shows “The steps of the method 100 illustrated in FIG. 1A do not distinguish between whether the steps are performed by a human being or a machine. Preferably, and over time, machines will perform more and more of the steps of the method 100. A tabular listing of the steps of method 100 is shown in FIG. 8A. A machine (the “AI Assistant” or “AI Assist”) can enter the method at step 40, as illustrated in FIG. 8A, of even earlier (e.g., at step 20) in the method 100.” Thus, Burris shows that step 40 can be performed by the AI assistant (i.e. “citation analysis module is equipped with Al and machine learning technologies”). Fig. 4 and Paragraphs 60-62 show the steps of collecting legal data and creating the legal text including a “master sheet.” Paragraph 62 shows “The “master sheet” created in substep 43 records citations of laws that are within the scope of the project, with one master sheet per jurisdiction. For each law, the master sheet may include the citation and title, the statutory history, and the effective dates. The laws in the master sheet may be organized hierarchically by jurisdiction (e.g., federal, state, local), by type of law (e.g., statute, regulation, ordinance), and by chapter and citation number (e.g., 12.55.135 comes before 12.55.150).” Thus, the creation of the master sheet in Burris teaches “format recognition and normalization of various legal citations.” See also Paragraphs 86-87 showing use of the “master sheet,” and Paragraph 68 discussing creation of the database.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Burris with Angle because Burris teaches that its preferable to perform legal research using AI with a high degree of reliability to limit human involvement and better analyze laws over time (Paragraphs 6, 12, 16, 47, and 49). Thus, combining Burris with Angle furthers the interest taught in Burris, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 9, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle does not explicitly teach, but Burris further teaches “wherein the reporting module's audit trails include time-stamped entries for detailed historical record keeping and regulatory compliance” (Paragraph 22 shows “In one embodiment, the corpus of texts comprises statutes and judgments. In one embodiment, the method further comprises the step of tagging one or more texts in the corpus of texts with a time stamp.” (Emphasis added). Fig. 8a and Paragraph 72 shows “In the fifth step 50 of the method 100, the law is coded. The goal of the fifth step 50 is to read, observe, and record the law, rather than to read and interpret the law. The legal text collected in the database is used to answer the questions developed in the third step 30 of the method 100. Coding is done both for legal assessments (cross-sectional), in which the law is coded once for each jurisdiction, capturing a snapshot of the law at one point in time, and for policy surveillance (longitudinal), in which multiple iterations of the law are coded for each jurisdiction, representing different points in time. Longitudinal coding shows the evolution of the law over time; researchers code a new record of the law for each amendment made to the law.” (Emphasis added). Paragraph 99 shows “More specifically, as longitudinal research is conducted during the step 50 of coding the law, human processes (I) create a training set of laws of n jurisdictions from the earliest included date; code the n jurisdictions; verify the returns from the AI Assist functions (III); and verify the coding done by the AI Assist functions (III). The MonQcle software (II) contains the corpus of law for the included time and jurisdictions; creates records; allows the human processes (I) and the AI Assist functions (III) to compare versions of legal text within jurisdictions to identify changes; and provides the user interface for the human processes (I) to assign and verify research and coding. Finally, the AI Assist functions (III) learn to retrieve each temporal iteration of law for each jurisdiction from the corpus; learn the coding scheme; and compare earlier law and propose coding for each retrieved iteration.” (Emphasis added). Paragraphs 66-69 providing further detail regarding time usage in the database. Specifically, Paragraph 69 shows “
The first interface of MonQcle allows SMEs to . . . (iii) view the text for a particular time and place (e.g., the Nevada Fair Housing Law in effect from Jan. 1, 2019 to Jan. 1, 2021) [(i.e. “audit trail”)] . . .” Thus, Burris teaches “time-stamped entries for detailed historical record keeping and regulatory compliance.” See also Paragraph 146 showing “compliance tool.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Burris with Angle because Burris teaches that its preferable to perform legal research using AI with a high degree of reliability to limit human involvement and better analyze laws over time (Paragraphs 6, 12, 16, 47, and 49). Thus, combining Burris with Angle furthers the interest taught in Burris, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 10, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle further teaches “wherein the communication module includes [a function] for secure data exchanges between the law firm's internal systems and external legal data sources” (Fig. 3 shows that “legal information system 142” (i.e. synchronization module) has input from and output to (i.e. data exchange) with “Data Input Systems 116” and “Users/Administrators 260.” Fig. 3 and 5 and Paragraph 33 shows “FIG. 3 is a schematic block context diagram of one embodiment of the invention. Entities interacting with Legal Information System 142 include Data Input Systems 116, Users/Administrators 260, and Data/File Storage 145/143. Other entities not shown are contemplated within the scope of the invention. Users/Administrators 260 includes attorneys, administrative and other legal staff, courts [(i.e. external legal data source)], party principles [(i.e. external legal data source)], consultants [(i.e. external legal data source)], witnesses [(i.e. external legal data source)], and other interested parties [(i.e. external legal data source)]. FIG. 5 depicts in one embodiment exemplary users interacting with Legal Information System 142. These include, but are not limited to, Document Center 505, Law Firm Attorneys 510, Trial Support Staff 515, Parties 520, Paralegals 525, System Administrators 530, In-house Attorneys 535, and Others 540.” Additionally, Fig. 1 and Paragraphs 26-27 shows “Legal Information System 142” “interconnected” with “In-house counsel, outside counsel, and others are exemplary users of the system and are depicted as Hosts on LAN 1, block 115, Hosts on LAN 2, block 120, and hosts on LAN 3, block 125,” teaching “external legal sources.” Paragraph 35 shows “A user may log on using a typical personal computer system or workstation system. Conventional or other types of security and/or user-access rights management are typically included.” Fig. 7 and Paragraphs 36-37 shows user-specific “user-access rights” stored in “table 710,” including full access, i.e. “F,” read-only, i.e. “V,” or no access, i.e. blank, that are implemented by a user logging on to the system. Therefore, Angle teaches that the “data exchange between the law firm's internal systems and external legal data sources” (e.g. legal staff and court staff in Fig. 7) is “secure” because on certain users only have certain levels of access rights to certain information. Although Paragraph 38 shows communication links between entities, Angle does not explicitly teach the use of on API Gateway to implement the taught secure data exchange.)
Angle does not explicitly teach, but Burris further teaches “wherein the communication module includes an API gateway for secure data exchanges . . .” (Paragraphs 121-24 shows that the “AI-based system” enables users with unique user profiles to interact with entities via “an application programing interface (API)” (i.e. “the communication module includes an API gateway”). Paragraph 124 shows “[T]he AI-based system may include [an] authorization/privacy server, . . . The AI-based system may also include suitable components such as network interfaces [and] security mechanisms, . . . [T]he AI-based system may include one or more user-profile stores for storing user profiles. . . A web server may be used to link the AI-based system to one or more client systems 130 or one or more third-party systems 170 via the network 110. . . An API-request server may allow the third-party system 170 to access information from the AI-based system by calling one or more APIs. . . Authorization servers may be used to enforce one or more privacy settings of the users of the AI-based system. A privacy setting of a user determines how particular information associated with a user can be shared.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Burris with Angle because Angle teaches conventional or other types of security and access management is used to implement user access controls based on a user’s login and entity with communication over a network (Paragraphs 35-38) and Burris teaches an API gateway can be used to implement access controls over a network (Paragraphs 123-24.). Thus, combining Burris with Angle furthers the interest taught in Angle, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 14, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle further teaches “wherein the synchronization module is further configured to enable bi-directional communication with external platforms, ensuring real-time updates to legal matter metadata and associated billing metrics” (Fig. 3 shows that “legal information system 142” (i.e. synchronization module) has input from and output to (i.e. bi-directional communication) with “Data Input Systems 116” and “Users/Administrators 260.” Fig. 3 and 5 and Paragraph 33 shows “FIG. 3 is a schematic block context diagram of one embodiment of the invention. Entities interacting with Legal Information System 142 include Data Input Systems 116, Users/Administrators 260, and Data/File Storage 145/143. Other entities not shown are contemplated within the scope of the invention. Users/Administrators 260 includes attorneys, administrative and other legal staff, courts, party principles, consultants, witnesses, and other interested parties. FIG. 5 depicts in one embodiment exemplary users interacting with Legal Information System 142. These include, but are not limited to, Document Center 505, Law Firm Attorneys 510, Trial Support Staff 515, Parties 520, Paralegals 525, System Administrators 530, In-house Attorneys 535, and Others 540 [(i.e. external platforms)].” Additionally, Fig. 1 and Paragraphs 26-27 shows “Legal Information System 142” “interconnected” with “In-house counsel, outside counsel, and others are exemplary users of the system and are depicted as Hosts on LAN 1, block 115, Hosts on LAN 2, block 120, and hosts on LAN 3, block 125,” teaching “external platforms.” Paragraph 5 shows “It is an object of this invention: (i) to reduce the time to search, retrieve, sort, and organize data needed in legal practices to minutes rather than months; (ii) to have any information requested be returned in a more complete form than results from known methods and systems; (iii) to eliminate duplicative information, thereby reducing or eliminating data update or delete errors; and (iv) to permit data returned from a search request to be instantly available [(i.e. real-time synchronization)] for analysis and reuse on other related legal matters.” (Emphasis added). Paragraph 17 shows “FIG. 4 depicts a Conceptual Data Model in one embodiment of the invention, simplified view of tables, attributes, and relationships for implementing the database aspects of the invention.” Fig. 7 and Paragraphs 39-41 shows multiple tables linked together including (1) at least “Financial Table 450” containing “Budget Amount” and “Expenditure Amount,” teaching “associated billing metrics” and (2) at least “legal matters 430” and “matter property 432” containing keys linked to other related data, teaching “legal matter metadata.”).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over US-20040019496-A1 (“Angle”) in view of US-20240184974-A1 (“Burris”), “A Guide to Fee-Based U.S. Legal Research Databases” (“Rumsey” August 2018, https://www.nyulawglobal.org/globalex/US_Fee-Based_Legal_Databases1.html), and “Free Case Law and Legal Databases” (“WNFRHC” 12/01/2021, https://web.archive.org/web/20211201084504/https://nebraskaancestors.org/free-case-law-and-legal-databases/).
Regarding Claim 3, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle further teaches “wherein the data receiving module is further configured to interface with and aggregate data from diverse public legal information sources including, but not limited to, [legal information systems]” (Fig. 3 and Paragraph 34 shows “Data Input Systems 116 [(i.e. data receiving module)] may include some users/administrators 260, e.g., where data is keyed in, but also may include automated data entry systems such as optical scanners, data or files received over Wide Area Network 130, e.g., from a court, other governmental agency, or from automatically generated or collected data from other information systems [(i.e. diverse public legal information sources)].”).
Examiner notes that Burris Paragraph 53 shows “The sources that encompass the texts within the scope of the topic may in various embodiments include published and unpublished federal and state court decisions; current and historical statutes; current and historical regulations; ordinances from municipalities such as cities and counties; federal and state dockets and case records; law reviews; and more. These sources make available a large amount of digital legal information.”
Angle and Burris do not explicitly teach, but Rumsey teaches “[legal information systems]” includes “Lexis, Westlaw, Bloomberg, vLex” (Pages 1-2 shows that major legal databases include “Lexis Advance,” “Westlaw,” “Bloomberg Law,” and “vlex.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rumsey with Angle and Burris because Angle teaches receiving legal data from a variety of legal information systems and Rumsey teaches specific examples of useful legal information systems (Pages 1-2.). Thus, combining Rumsey with Angle and Burris furthers the interest taught in Angle, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Angle, Burris, and Rumsey do not explicitly teach, but WNFRHC teaches “[legal information systems]” includes “Unicourt, Docket Alarm, CourtListener” (Pages 1-2 shows that “CourtListener,” “Fastcase Docket Alarm,” and “UniCourt” are useful legal databases.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine WNFRHC with Angle, Burris, and Rumsey because Angle teaches receiving legal data from a variety of legal information systems and WNFRHC teaches specific examples of useful legal information systems (Pages 1-2.). Thus, combining WNFRHC with Angle, Burris, and Rumsey furthers the interest taught in Angle, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over US-20040019496-A1 (“Angle”) in view of US-20240184974-A1 (“Burris”) and US-20230419110-A1 (“Ramezani”).
Regarding Claim 4, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle does not explicitly teach, but Burris teaches “wherein the data processing module utilizes a [natural language] model tailored for legal data analysis and summarization” (Paragraph 71 shows “software for natural language process (NLP)” is used in “coding.” Paragraph 17 defines “coding” “as the process of assigning a code to something for classification or feature identification.” See also Paragraph 15 discussing natural language processing. Fig. 8a and Paragraphs 72 shows that “coding” includes a legal assessment identifying jurisdiction and a point in time. Paragraphs 54 and 140 show summarizing legal data. See also Fig. 8A and Paragraphs 83-85 further discussing “Step 60 Publish and disseminate the data generated.” Although Burris teaches using natural language processing to generate legal data, Burris does not explicitly teach the use of a “Generative Pre- trained Transformer (GPT) model.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Burris with Angle because Burris teaches that its preferable to perform legal research using AI with a high degree of reliability to limit human involvement and better analyze laws over time (Paragraphs 6, 12, 16, 47, and 49). Thus, combining Burris with Angle furthers the interest taught in Burris, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Angle and Burris does not explicitly teach, but Ramezani teaches “wherein the data processing module utilizes a Generative Pre- trained Transformer (GPT) model tailored for legal data analysis and summarization” (Paragraphs 101-05 shows “summarization generator 802” (i.e. data processing module utilizes a Generative Pre- trained Transformer (GPT) model) used to analyze and summarize legal text. Paragraph 103 shows “Various natural language processing models such as T5, BART, BERT, GPT-2, XLNet, and BigBird-PEGASUS provide functions that may be configured to perform abstractive text summarization.” (Emphasis added). See also Paragraph 64 further discussing “generative pretrained transformer.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ramezani with Angle and Burris because Ramezani teaches that pre-trained models may provide inferences without further training (Paragraph 64). Thus, Ramezani with Angle and Burris furthers the interest taught in Ramezani, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over US-20040019496-A1 (“Angle”) in view of US-20240184974-A1 (“Burris”) and US-20220147898-A1 (“Gaurav”).
Regarding Claim 5, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle and Burris does not explicitly teach, but Gaurav teaches “wherein the security module includes implementing ethical firewalls within the law firm to prevent conflicts of interest in data access” (Paragraph 49 shows “In some implementations, the system 300 determines, through an access authorization module 316, whether a user submitting a message is authorized to access information requested by the message. The access authorization module 316 may apply one or more lists of authorized users or user classes stored in the data storage area 308, such as by accessing a Microsoft Azure Active Directory. When the system 300 determines that a user is not authorized to access a data source identified by the source identification and data retrieval module 322, the display component 314 causes to be displayed at the display device 306 a message that the user is denied access to the identified data source. In some implementations, the access authorization module 316 [(i.e. security module)] applies one or more rules associated with screening users from data associated with one or more clients or matters (e.g., to enforce conflict of interest rules).” (Emphasis added). Paragraph 80 shows “FIG. 5C illustrates another example interaction between a user and the system. In this example interaction, the system determines that a user is not authorized to access information from an identified data source. The user submits a message 524 at the interface asking for ‘Timekeepers worked on matter 020346.1105?’ After determining an intent for the message 524, extracting entities, and identifying a data source to retrieve information, the system can determine that the user associated with the received message is not authorized to access the identified data source. The system then displays, at the interface, a response 526 stating ‘You do not have access.’ To determine whether a user is authorized to access an identified data source, the system stores or accesses one or more lists of users and corresponding levels of access. These lists may be structured in various ways using, for example, tables and/or databases. The levels of access may be based on different user classes. For example, partners in the law firm may have generally unlimited access to all data sources. Non-partner attorneys, associates, or law firm staff may have only limited access, such as access to data sources that store information only about the individual user [(i.e. within the law firm)]. In these and other implementations, one or more lists may additionally or alternatively relate to user restrictions and/or rules associated with screening of users (e.g., based on conflict of interest or other ethical rules, which differ among states and thus affect personnel in an interstate law firm differently depending upon the state in which they practice), which may limit or prohibit a user's access to data related to one or more clients and/or one or more client matters.” (Emphasis added).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gaurav with Angle and Burris because Gaurav teaches that user access controls to information can be implemented to prevent conflicts of interest (Paragraphs 49 and 80). Thus, Gaurav with Angle and Burris furthers the interest taught in Gaurav, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over US-20040019496-A1 (“Angle”) in view of US-20240184974-A1 (“Burris”) and “New York Practice: Responding to the Complaint” (“Evangelista” 2015, https://www.marshalldennehey.com/thought-leadership/new-york-practice-responding-to-the-complaint).
Regarding Claim 7, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle further teaches “wherein the user interface module includes customizable alert settings for legal deadlines and court dates based on [rules]” (Paragraph 30 shows “Alarms may be standard system alarms or based on customized rules created by users/administrators 260, e.g., notification of an imminent docket date deadline.”).
Angle and Burris does not explicitly teach, but Evangelista teaches “legal deadlines and court dates based on [rules]” includes “legal deadlines and court dates based on the Federal Rules of Civil Procedure (FRCP) and Civil Practice Law and Rules (CPLR)” (Pages 1-2 show that the “CLPR” provides deadlines of a certain number of days, e.g. 20, 30, or 120, to file in state court. Page 4 shows that the “FRCP” provides deadlines, e.g. 21 days, in federal court.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Evangelista with Angle and Burris because Evangelista teaches that FRCP and CPLR determine the deadlines in litigation (Pages 1-2). Thus, Evangelista with Angle and Burris furthers the interest taught in Evangelista, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over US-20040019496-A1 (“Angle”) in view of US-20240184974-A1 (“Burris”) and US-20200082482-A1 (“Tan”).
Regarding Claim 11, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle further teaches “wherein the data processing module further applies . . . models to correlate docket events and time narratives, generating phase-specific billing estimates and updating those estimates” (Paragraphs 39-41 show “[0039] FIG. 4 depicts in one embodiment of the invention, a conceptual data model for implementing databases described herein. . . [0040] Legal matter table 430 is the central table where the law management information system is used in legal dispute information management. . . [0041] . . . Each contains a key attribute and is linked to one or more other table via this key attribute and/or via containing a foreign key of another table. FIG. 4 is only one exemplary data model. Modification of the shown tables as well as additional tables, their domains, keys, and links to other tables, and associated queries and reports, and appropriate normalization of each, useful in implementing the databases used in the invention, given the disclosure herein, could be implemented by data base designers of ordinary skill in the art.” Thus, Fig. 4 teaches that the “Events 440” table is correlated with “Narrative 435” table. Fig. 4 shows that “financials 450” includes “Case Key,” “Report Year” (i.e. “phase”), “Budget Amount” (i.e. “billing estimates”), and “Expenditure Amount.” Thus, “financials 450” table teaches a generated “phase-specific billing estimates.” Paragraphs 10, 23, and 35 show that the data is updated, i.e. “updating those estimates.” See also Paragraphs 11-13 show functionality of database including “scheduling events” and to “summarize and analyze cost information.” However, Angle does not explicitly teach the use of “machine learning” in its data management.).
Angle and Burris do not explicitly teach, but Tan teaches “wherein the data processing module further applies machine learning models to correlate docket events and time narratives, generating phase-specific billing estimates and updating those estimates” (Fig. 1 and Paragraph 36 shows “a dynamic machine-learning-based pre-legal-filing self-service system 104.” Paragraph 49 shows “the dynamic machine-learning-based pre-legal-filing self-service includes a plurality of services including a legal cost estimation, a settlement plan, a legal option presentation, a legal referral auction, a legal practitioner matching, and a supplementary support service offer, and so on.” Paragraph 50 shows the training of the machine learning model and the use of it to estimate cost and time of legal representation. Paragraphs 77 and 83 shows that the estimate is updated during the legal representation.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tan with Angle and Burris because Tan teaches that machine learning can improve methods of estimating legal costs (Paragraphs 4 and 69). Thus, Tan with Angle and Burris furthers the interest taught in Tan, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim 12 are rejected under 35 U.S.C. 103 as being unpatentable over US-20040019496-A1 (“Angle”) in view of US-20240184974-A1 (“Burris”) and US-20200380612-A1 (“Derry”).
Regarding Claim 12, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle and Burris do not explicitly teach, but Derry teaches “wherein the user interface module provides interactive tools for generating litigation budgets, including predictive cost breakdowns by litigation phase, comparisons of historical averages, and scenario modeling for alternative fee arrangements” (Paragraphs 20-28 show “legal task value management system” that enables clients and counsel to develop a budget and execute legal services. Fig. 25 and Paragraphs 135-37 show the user interfacing with the system. For Example, Paragraph 136 shows “The cloud based system 1000 further can be utilized to include real time analysis module, a real time budget and cost to complete module, and an exceptions module. It will be further appreciated that the cloud based system 1000 can be used with a law firm for managing the cost of legal services including the steps of corporate counsel generating a working budget [(i.e. the user interface module provides interactive tools for generating litigation budgets)], the budget being submitted to the law firm through the cloud 1006, the budget being considered by the law firm or outside counsel 1004, the outside counsel 1004 then performing the budgeted legal service, and then the legal firm electronically requesting payment or possible exceptions when the task is outside of the agreed upon budget.” Paragraphs 105, 117-19, and 124-25 show use of tools to create reports and interact with the system. Paragraphs 9-13 shows that the fee agreement of an attorney or law firm can be “fixed fee,” “contingent fee,” “hourly fee,” or “blended rate.” Fig. 9 and Paragraph 38 shows “FIG. 9 illustrates a screen output for the alternative fee arrangements possible,” teaching “scenario modeling for alternative fee arrangements.” Fig. 12B and Paragraph 42 shows “FIG. 12B is a continued screen printout from the legal task value management system illustrating a budget based on phases and litigation worksheet prepared for submission to outside counsel.” Fig. 14 and Paragraph 44 shows “FIG. 14 illustrates a screen output for the present invention from outside counsel's perspective and depicting a current case budget status by phases of the matter.” Fig. 21-24 and Paragraphs 51-54 show different statistical reports including comparison of (1) original negotiated budget with amount invoiced and (2) different counsel’s billing for a specific phase or process in a legal matter. Thus, at least Fig. 12B, 14, and 21-24 teaches “predictive cost breakdowns by litigation phase.” Fig. 9 and Paragraph 100 shows that a “default values 114” for a certain fee arrangement for a proposed matter is based on a historical average. Thus, Derry teaches “comparisons of historical averages.” Additionally, Fig. 22-23 and Paragraphs 127-28 show a comparison against an average cost.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Derry with Angle and Burris because Derry teaches the difficulties of budgeting for legal expenses and that the use of a system that displays real time information can improve the accuracy of budgeting for a lawsuit or other legal matter (Paragraphs 20-28). Thus, Derry with Angle and Burris furthers the interest taught in Derry, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over US-20040019496-A1 (“Angle”) in view of US-20240184974-A1 (“Burris”) and US-20180144067-A1 (“Chatelain”).
Regarding Claim 13, Angle and Burris teach “The system of claim 1,” as discussed above.
Angle further teaches “an entity resolution module within the data processing module, configured to reconcile conflicting legal party data . . .” (Fig. 4 and Paragraphs 39-41 shows the database includes “Participants Table 410” linked to other information, such as name, phone number, address, and email (i.e. “legal party data”). Paragraph 7 shows “It is further an object of this invention to allow corporate legal departments to standardize data creation, preservation, analysis and reporting of information used in handling legal claims and matters. The invention is unique, for example, in that corporate knowledge management is maintained after the end of each legal matter. By using this invention and the data processed therein in many different ways and in multiple legal matters, redundant, inconsistent or incomplete [(i.e. conflicting)] results are minimized [(i.e. reconciled)].” (Emphasis added). Therefore, the system of Angle teaches “an entity resolution module within the data processing module, configured to reconcile conflicting legal party data.”).
Angle and Burris do not explicitly teach, but Chatelain teaches “reconcile conflicting . . . data” includes “reconcile conflicting . . . data by applying AI-based record matching and normalization techniques across multiple data sources” (Paragraph 89 shows “Within the universal metadata repository, AI/ML may be employed to resolve data conflicts and verify data integrity. The architecture may use the dynamic analysis to automatically repeat the process, resulting in measuring and grading on live data.” (Emphasis added). Paragraph 52 shows “the architecture 110 may use the back end layer 706 to perform computer implemented conflict resolution during distinct phases of quality and reconciliation, and actionable management.” Paragraph 56 shows “The back end layer 706 may also include a metadata conflict resolution circuitry 724 and a metadata schema enforcement circuitry 726. The metadata conflict resolution circuitry 724 may perform metadata object matching and conflict resolution among data from different data sources. Accordingly, the metadata conflict resolution circuitry 724 may resolve any duplicated information by identification and deletion of repeated metadata within the universal metadata repository once the metadata from the different data sources has been normalized and duplication can be recognized. Thus, the metadata conflict resolution circuitry 724 may “clean” the data received in the universal metadata repository.” (Emphasis added). See also Paragraph 60 showing functionality of The metadata analytics circuitry 728.” Fig. 11 and Paragraph 81 shows “FIG. 11 is an operational flow diagram illustrating example operation of the architecture. Referring to FIGS. 7, 8 and 11, the universal metadata selection circuitry 720 may identify and select data sources 818 to be sources of metadata and provide transaction flow data 816 (1102). The metadata ingestion circuitry 722 may ingest the metadata information (1104) and reconcile the data by normalizing the information (1106). The metadata conflict resolution circuitry 724 may review the normalized information for conflicting information (1108) using, for example, machine learning and artificial intelligence (Al). The metadata conflict resolution circuitry 724 may also catalog all of the data sources from which metadata is obtained (1110). User input regarding identification of additional data sources to add to the catalog may be received (1111), and the operation may return to selecting metadata sources (1102). In addition, the metadata conflict resolution circuitry 724 may map the transactions to the data sources (1112) and map the transactions to the data destinations (1114) as part of creating information for the lineage group structure. The catalog of data sources and the source and destination mapping may be stored in the universal metadata repository.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chatelain with Angle and Burris because Chatelain teaches that AI can affectively reconcile inconsistent data from multiple sources to improve data quality (Paragraph 3, 16, 24-25, and 46-47). Thus, Chatelain with Angle and Burris furthers the interest taught in Chatelain, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is as follows:
“Ethical Firewall” (“Sustainability Directory” 07/11/2025, https://product.sustainability-directory.com/term/ethical-firewall/) defines an ethical firewall as a “self-regulated cognitive system” (emphasis added).
“Legal Definitions – ethical wall” (“LSD” June 2026, https://definitions.lsd.law/ethical-wall) shows that an ethical wall is a common legal practice that prevents conflicts of interest.
“Law Office Management 101 for Paralegals” (“Hatch” 08/27/2019 https://www.dummies.com/article/business-careers-money/careers/legal-careers/law-office-management-101-for-paralegals-263640/) shows the importance of including filing deadlines on a law firm’s calendars.
“Civ Pro Quick Tip: Keeping Track of Deadlines” (“UWorld” 08/07/2020 https://legal.uworld.com/blog/bar-review/civ-pro-quick-tip-keeping-track-of-deadlines/) shows the deadlines provided by the federal rules of civil procedure.
US-20090254572-A1 (“Redlich”) shows use of a firewall to ensure security and includes conflicts of interest as a security need.
US-20210109958-A1 (“Behtash”) shows cleaning a case file, converting to to xml/html format, and adding footnotes and labels.
US-20050149343-A1 (“Rhoads”) shows legal services system that includes firewall.
US-11361151-B1 (“Guberman”) shows integration of a plurality of legal sources.
US-12169516-B1 (“Xie”) shows a system and method for extracting citations from documents and constructing enriched citation databases.
US-20110093792-A1 (“Frayman”) shows a system for identifying, assessing and address conflicts of interest.
US-20060129593-A1 (“Slovak”) shows use of profiles and formats in automatically collected case data from websites.
US-20140114962-A1 (“Rosenburg”) shows automatically analyzing and tagging legal data to be displayed in an organized graphic display.
US-20240289559-A1 (“Gajek”) shows a text generation modeling system for legal analysis.
US-20080033929-A1 (“Al-Kofahi”) shows consolidation of multiple legal sources to be presented in a summary.
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/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628
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