DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/3/2025 has been entered.
Claim Objections
Claim 28 is objected to under 37 CFR 1.75(c) as being in improper form because the claim depends from a cancelled claim. Dependent claim 28 depends from cancelled claim 9. See MPEP § 608.01(n). Accordingly, the claim 28 is not been further treated on the merits.
Specification
The amendment filed 7/20/2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows:
Amended paragraph [0004] includes new limitations “The system integrates advanced technical features-dynamic radar charts, real-time data scraping, and automated conflict detection to address longstanding challenges in legal professional evaluation, surpassing conventional platforms. The radar chart's interactive, multi-axis design leverages JavaScript-driven rendering to enable rapid, multidimensional analysis, reducing assessment time from minutes to seconds compared to static tables. Real-time scraping, executed via server-side scripts querying court APIs, ensures data freshness without manual updates, solving latency issues in traditional systems. The conflict detection module's graph-based algorithm, optimized with caching and parallel processing, automates a manual process, improving accuracy and scalability for large datasets. These innovations collectively enhance system functionality. addressing technical problems in data visualization, currency, and ethical compliance with practical, computer- implemented solutions.”
Amended paragraph [0015A] includes new limitations “The intelligent model further selects representative matters by analyzing the number of 'substantive events' associated with each case. 'Substantive events' are defined as significant legal actions or proceedings that substantially impact the case's progress or outcome. Examples include the filing of a complaint, answer, dispositive motion, trial exhibit, appellate brief, or judgment. The model counts the occurrences of these events in the docket entries and selects the cases with the highest count, indicating the most substantial involvement by the legal professional.”
Amended paragraph [00146] includes new limitations “FIG. 74A illustrates the initial phase of the oral-advocacy subscore computation process, including the start of the workflow, data collection. and preprocessing steps for a legal professional designated as "Attorney X." depicting the use of natural language processing (NLP) to parse multimedia transcripts and extract relevant data such as timestamps and speaker lags to confirm Attorney X's participation in tribunal hearings.
FIG. 74B continues the flowchart from FIG. 74A, detailing the outcome classification and subscore calculation stages, including the analysis of hearing outcomes to determine if Attorney X's position prevailed, leading to a classification of win, loss, or neutral, and further illustrating the calculation of the oral-advocacy subscore using a base formula and an optional weighting factor, resulting in a percentage-based metric.
FIG. 74C concludes the flowchart, showing the storage and integration of the computed subscore into the system's user interface (UI), depicting the display of the subscore along with contextual details, such as the total hearings analyzed and the win/loss breakdown, and highlighting interactive features enabling users to access detailed hearing information.”
Newly added paragraph [00235-00239] includes new limitations “FIG. 74A, FIG. 74B, and FIG. 74C illustrates the intelligent model which fully identifies representative matters for each legal professional through a systematic process that ensures the selection of cases reflecting the most significant legal involvement. First, the model searches docket entries and court filings, such as those accessible via state. federal. and administrative agency databases, to identify case numbers and party names associated with the legal professional. This search leverages advanced data extraction techniques to parse and retrieve relevant identifiers from publicly available court records. Second, the model cross- references these case numbers with a comprehensive database of case details, retrieving critical information such as event types (e.g., as illustrated in FIG. 74, [0159]). dispositions (e.g., as detailed in FIG. 74, [0159]), and case durations (e.g., as calculated in FIG. 49, [0135]). This cross-referencing enables the model to compile a complete profile of each case, including its procedural history and outcomes. Third, the model selects the cases with the most substantive involvement by the legal professional, determined by analyzing the number and type of docket events and filings. Substantive involvement is quantified by counting significant legal actions. such as the filing of complaints, answers. dispositive motions. trial exhibits, appellate briefs, or judgments, as defined and illustrated in FIG. 74. By ranking cases based on this count, the model ensures that the selected representative matters are those where the legal professional had the most substantial impact, providing a reliable basis for evaluation within the user interface (e.g., as described in [0097], [0127]).
[00236] Figure 74A illustrates the initial phase of the oral-advocacy subscore computation process, specifically tailored for a legal professional designated as "Attorney X." The process commences with a start block (2002), labeled "Start: Compute Oral-Advocacy Subscore for Attorney X." This block serves as the entry point, initiating the workflow for evaluating Attorney X's oral advocacy performance. An annotation adjacent to this block reads, "Process begins for Attorney X using data from public records and legal databases," (2004) highlighting the data sources and the focus on a specific legal professional.
The process proceeds to a primary operational step of data collection and preprocessing. depicted as a rectangular process block (2005). This block encapsulates a series of sub-processes designed to gather and prepare multimedia data for analysis, as follows:
Collecting Timestamped Tribunal Transcripts and Hearing Outcome Data (2006): The system retrieves multimedia data, including documents, audio files, and video recordings (denoted as "docs/audios/videos"), from public court records and proprietary legal databases. This step ensures a comprehensive dataset covering tribunal hearings where Attorney X participated.
Preprocessing Data (2008): The collected multimedia data undergoes preprocessing to extract relevant information, comprising: Parsing Transcripts Using Natural Language Processing (NLP) The system employs an NLP module, labeled "NLP: Natural Language Processing Module (Processing docs/audios/videos)", to parse the multimedia transcripts. This module identifies segments within documents, audio, or video where Attorney X is actively involved, isolating their spoken or documented contributions.
Extracting Timestamps, Page Numbers, and Speaker Tags (2010): Metadata such as timestamps (for audio/video), page numbers (for documents), and speaker tags are extracted to precisely attribute contributions to Attorney X across all formats, ensuring accurate temporal and contextual mapping.
Linking Hearing Outcomes to Transcripts (2012): The system correlates hearing outcomes (e.g., rulings or decisions) with the corresponding transcripts using unique identifiers like case IDs and docket numbers, establishing a direct linkage between Attorney X's contributions and judicial outcomes. If not, it will search for the next attorney on the list, e.g., Attorney Y (2014).
Confirming Attorney X's Participation (2016): Following preprocessing. the system verifies Attorney X's involvement in each hearing by cross-referencing speaker tags and case metadata, ensuring only relevant data is processed further
This preprocessing step is annotated with "Data sourced from public court records and proprietary legal databases. NLP isolates Attorney X's contributions", underscoring the technical sophistication and reliability of the data sources. The block is connected by an arrow to a transition point, leading to Figure 74B, indicating the continuation of the process (2018). Additionally, a loop is suggested for processing the next attorney (e.g., "Process the next Attorney, e.g., Attorney 'Y' and repeat the loop"). though the focus remains on Attorney X for this specification.
[00237] Figure 74B extends the flowchart from Figure 74A, detailing the stages of outcome classification and subscore calculation for Attorney X's oral advocacy performance (2019). The figure begins with a connection point implicitly transitioning from the data collection phase, proceeding to the analysis of hearing outcomes (2020), represented as a rectangular process block. This block is annotated with "Determines if Attorney X won, lost, or had a neutral outcome in each hearing by processing the following outcome," providing context for the subsequent steps. (2022)
The outcome analysis step involves:
Extracting Judicial Rulings: The system extracts specific outcomes from the hearing data, such as "motion granted" or "motion denied," derived from the processed multimedia transcripts and records.
Determining Attorney X's Advocated Position: The system identifies Attorney X's advocated position by analyzing case filings or transcript content, establishing the stance they presented during the hearing.
Following this analysis, a rectangular block (2023) poses the critical question: "Did Attorney X's position prevail?" This decision point evaluates the alignment between the judicial ruling and Attorney X's advocated position, (2024): resulting in three possible classifications (2026):
Win If the ruling aligns with Attorney X's position, a counter is incremented ("Wins = Wins + 1").
Loss: If the ruling opposes Attorney X's position, a counter is incremented ("Losses = Losses + 1").
Neutral : If the outcome is partial or ambiguous, the case is flagged for review, as indicated by a side box labeled "Flag for Review."
The decision block is supported by "Ruling Data" (2028), denoting the source of outcome information. An annotation further clarifies: "Process: Extract the ruling and compare it to Attorney X's position (from filings or transcripts). Decision: Classify as Win, Loss, or Neutral based on alignment", ensuring a clear understanding of the decision-making process.
The process then advances to the subscore calculation step (2030), depicted as a Calculating the Base Subscore. The subscore is computed using the formula:
Subscore = (Wins/Wins +loses) X 100
Applying an Optional Weighting Factor: A
configurable multiplier (e.g., 1.5x for appellate hearings) may be applied to enhance granularity.
Outputting the Subscore: The result is expressed as a percentage (0-100%).
This formula yields a percentage reflecting Attorney X's success rate in hearings, providing a quantifiable metric of their oral advocacy effectiveness.
Applying an Optional Weighting Factor: A configurable multiplier (e.g., 1.5x for appellate hearings) may be applied to adjust the subscore, enhancing granularity based on hearing complexity or significance. The weighting is adjustable via system configuration, allowing for flexibility in evaluation criteria.
Outputting the Subscore: The final subscore is expressed as a percentage (0-100%), ready for integration into the system.
This calculation step is annotated with "Weighting enhances granularity: e.g., complex hearings contribute more to score" underscoring the system's adaptability and precision. An arrow directs the process to a transition point labeled "S2" 2032, leading to Figure 74C.
[00238] Figure 74C concludes the flowchart, illustrating the final phase of storing and integrating the computed oral-advocacy subscore into the system's user interface. The figure begins with a connection point from Figure 74B, continuing from "S2" (2032), and leads to the storage and display step starting from "S2" (2034), shown as a rectangular process block. This block encapsulates the following sub-processes:
Storing the Subscore (reference number: (2036) The computed subscore is stored in Attorney X's profile within the system's database. ensuring persistent access for future evaluations and comparisons.
Displaying the Subscore (reference number: (2038): The subscore is presented in the user interface (UI), exemplified as "Oral-Advocacy Subscore: 80%," providing a clear, user-friendly metric for evaluation.
Providing Contextual Details (reference number: (2040): Additional metrics, such as "Total hearings analyzed: 10, Wins: 8. Losses: 2," are displayed alongside the subscore, offering transparency and context to support user decision-making.
Enabling Interactive Features (reference number: (2042): The UI includes interactive elements, such as clickable options to view detailed hearing breakdowns, enhancing user engagement and facilitating deeper analysis of Attorney X's performance.
This block is annotated with "UI display includes interactive elements for user transparency, e.g., clickable win/loss breakdown", emphasizing the system's focus on user-centric design and functionality. The process concludes with an end block (2044), labeled "End: Subscore Computed and Displayed," signifying the completion of the computation and integration workflow.”
New paragraph [00257-00262] includes new limitations “[00257] In some embodiments, the intelligent model identifies representative matters for a legal professional by selecting cases with the greatest number of docket events classified as "substantive events." Substantive events include complaints, answers, dispositive motions, trial exhibits, appellate briefs, or judgments, as illustrated in FIG. 74, which depicts a case events table listing these event types. The model searches docket entries and court filings to identify case numbers and party names, cross-references these with a database of case details to retrieve event types, dispositions. and durations, and applies a threshold count of substantive events to determine the legal professional's involvement. as shown in the algorithm of FIG. 67.
[00258] In certain embodiments, the experience score incorporates a seniority level for each legal professional, determined by extracting the bar-admission date from state-bar databases, as shown in FIG. 5, and calculating managerial tenure from firm-website metadata updates. For example, the intelligent model retrieves the date of licensure to compute years of practice and scrapes law firm websites to identify leadership roles, such as partner or managing attorney, to quantify tenure, consistent with the experience data displayed in FIG. 51.
[00259] The graphical representation in some embodiments is a radar chart, as depicted in FIG. 84, with dynamically generated axes comparing total case experience (calculated as total matters multiplied by a jurisdiction complexity factor), trial experience (sum of jury and bench trials), motion practice experience (ratio of dispositive motions granted to total motions), and appellate experience (ratio of affirmances to total appeals). Hovering over an axis triggers the display of the raw score and jurisdictional percentile, enhancing user interaction, as described in FIG. 85 for 2-D chart interactions.
[00260] In further embodiments, the intelligent model identifies conflicts of interest by cross-referencing opposing party identifiers from a user-specified legal matter, disciplinary records, and appearance history, as illustrated in FIG. 34, which shows working relationships between attorneys. The model alerts the user via a modal window listing conflicted attorneys and evidence, such as prior representations of adverse parties, ensuring compliance with ethical standards, as supported by the conflict association process in FIG. 33.
[00261] The experience score may include an oral-advocacy subscore, calculated as the ratio of dispositive hearings won to total dispositive hearings, multiplied by 100, using timestamped tribunal transcripts. This subscore is derived through the intelligent model's algorithmic analysis of court data, as shown in FIG. 67, which processes case events to quantify performance metrics, including hearing outcomes, consistent with the points assignment system in FIG. 65.
[00262] In some embodiments, the intelligent model retrieves peer-reviewed publications and continuing legal education (CLE) engagements from online databases to calculate an authoritativeness score. The score is computed as (h-index x 0.7) + (CLE frequency x 0.3)], where the h-index reflects publication impact and CLE frequency indicates engagement frequency, as inferred from the points-based scoring system in FIG. 65 and certification data in FIG. 58.”
The above limitations are objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, new claim 21 states “providing a user interface populated with data about past case histories of the plurality of legal professionals, the data including docket entries, court filings, and case outcomes extracted from public court records by an intelligent model;” and “analyzing the additional data using the intelligent model to calculate an experience score for each selected legal professional, the experience score based on a plurality of factors including a number of representative matters handled, a recency of the matters, an average duration of the matters, and a ratio of favorable to unfavorable outcomes”; however, the specificaition fails to provide support for the aforementioned limitation in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
New claims 22 states “wherein the user interface includes selectable options to filter the legal professionals by location, practice area, law firm, and client reviews.” Similarly, claim 23 states “searching docket entries and court filings to identify case numbers and party names associated with the legal professional; " cross-referencing the case numbers with a database of case details to retrieve event types, dispositions, and durations for each case; and " selecting the cases with the most substantive involvement by the legal professional based on the number and type of docket events and filings.” Claim 24 states “a number of years in a leadership role at a law firm.” Claim 25 states “graphical representation is a radar chart with axes corresponding to total case experience, trial experience, motion practice experience, and appellate experience.” Claim 26 states “ identifying, by the intelligent model, one or more legal professionals who have a conflict of interest with an opposing party in a legal matter specified by the user; and " alerting the user via the user interface of the potential conflict.” Claim 29 states “based on an effectiveness of the legal professional in arguing before a judge or appellate panel, as determined by the intelligent model based on transcripts of oral arguments.” Claim 30 states “factoring an authoritativeness score based on the articles and speaking engagements into the experience score.” The aforementioned claim present limitation in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Independent claims 31 & 38 and all the claims dependent also contain same language and thus are rejected under the same legal precedent.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 23 & 28-30 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.
Claims 23 & 28-30 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention. For example, claim 23 states “most substantive involvement”, however, the examiner cannot ascertain what is meant by " most substantive” such that the metes and bounds of the claims can clearly evaluated (most substantive is very subjective term). While claim 28 states “quality of arguments”, claim 29 states “effectiveness of the legal professional” and claim 30 states “authoritativeness score”. The above limitation, result in failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Examiner finds that because the claims are indefinite under 35 U.S.C. §112, 2nd paragraph, it is impossible to properly construe claim scope at this time. However, in accordance with MPEP §2173.06 and the USPTO's policy of trying to advance prosecution by providing art rejections even though these claims are indefinite, the claims are given broadest reasonable interpretation and prior art is applied as much as practically possible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-23, 27, 31-33 & 38-39 are rejected under 35 U.S.C. 103 as being unpatentable over Jia et al. (US Patent 11,017,489; referred to hereinafter as Jia).
Claims 21, 31 & 38: Jia disclose a computer-implemented method for evaluating the legal experience of a plurality of legal professionals (abstract), the method comprising providing a user interface populated with data about past case histories of the plurality of legal professionals, the data including docket entries, court filings, and case outcomes extracted from public court records by an intelligent model (cols. 11-12: 35-13), receiving, via the user interface, a selection by a user of one or more of the legal professionals to evaluate (figures 7-8), in response to the user selection, retrieving additional data regarding the past case histories of the selected legal professionals, including event types, dispositions, and downloadable attachments associated with each representative matter (cols. 3-5: 16-29), analyzing the additional data using the intelligent model to calculate an experience score for each selected legal professional, the experience score based on a plurality of factors including a number of representative matters handled, a recency of the matters, an average duration of the matters, and a ratio of favorable to unfavorable outcomes (abstract & cols. 5-9: 30-9), generating, by the intelligent model, a graphical representation of the selected legal professionals and updating the user interface to display the graphical representation and experience scores, allowing the user to objectively compare the legal experience of the selected professionals (cols. 5-9: 30-5). Thus, Jia discloses the claimed invention except for the specific arrangement and/or content of indicia (printed matter) set forth in the claims (i.e. a graphical representation comparing the experience scores of the selected legal professionals across multiple axes corresponding to different categories of legal experience). It would have been obvious to one having ordinary skill in the art at the time the invention was made to include a multi-axes graphical representation comparing experience score, since it would only depend on the intended use of the assembly and the desired information to be displayed. Further, it has been held that when the claimed printed matter is not functionally related to the substrate it will not distinguish the invention from the prior art in terms of patentability. In re Gulack 217 USPQ 401, (CAFC 1983). The fact that the content of the printed matter placed on the substrate may render the device more convenient by providing an individual with a specific type of graphical representation of the data, does not alter the functional relationship. Mere support by the substrate for the printed matter is not the kind of functional relationship necessary for patentability. Thus, there is no novel and unobvious functional relationship between the printed matter disclosed by Jia in figure 8 and the substrate required for patentability.
Claims 22, 32 & 39: Jia teach wherein the user interface includes selectable options to filter the legal professionals by location, practice area, law firm, and client reviews (figures 7-8).
Claims 23 & 33: Jia teach wherein the intelligent model identifies representative matters for each legal professional by, searching docket entries and court filings to identify case numbers and party names associated with the legal professional; cross-referencing the case numbers with a database of case details to retrieve event types, dispositions, and durations for each case; and selecting the cases with the most substantive involvement by the legal professional based on the number and type of docket events and filings (cols. 5-12: 33-12).
Claim 27: Jia teach wherein the court filings include briefs, motions, and pleadings authored by the legal professional (obvious for any court filings to include legal documents).
Allowable Subject Matter
Claims 24-26, 29-30, 34-37 & 40 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUNIT PANDYA whose telephone number is (571)272-2823. The examiner can normally be reached M-F 9:30-6:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat can be reached at 571-270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUNIT PANDYA/Primary Examiner, Art Unit 3715