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 . 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 (i.e., changing from AIA to pre-AIA ) 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.
DETAILED ACTION
The following FINAL Office Action is in response to communication filed on 5/28/2026.
Priority
The Examiner has noted the Applicants claiming Priority from Provisional Application 63/348,869 filed 06/03/2022.
Status of Claims
Claims 1, 3-10, 13, 22-25, 27-28 are currently pending.
Claims 1, 3-4, 6, 8-10, 13, 22, 28 are currently amended.
Claims 2, 11-12, 14-21, 26 are cancelled.
Claims 1, 3-10, 13, 22-25, 27-28 are currently under examination and have been rejected as follows.
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Response to Amendment
The previously pending rejection under 35 USC 101 will be maintained. The 101 rejection is updated in light of the amendments.
New grounds for rejection under 35 USC 103 are applied. The 103 rejection is updated in view of the amendments.
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Response to Arguments
Regarding Applicant’s remarks pertaining to 35 USC 101:
Step 2A Prong 1:
Applicant argues on page 9 of remarks 5/28/2026:
“The newly added limitations recite training and using a machine learning model ("MLM") in a way that cannot practically be performed by a human using pen and paper, even accounting for the scale and complexity involved….
“The claimed MLM training on a plurality of previously processed docket items tagged with
correct docketing requirements and correct input parameters requires computational processing
involving multi-dimensional pattern extraction, iterative mathematical optimization across thousands of parameters, and statistical model generation. These activities encompass AI in a way that not only cannot practically be performed in the human mind in line with the August 2025 Memorandum but also are likely impossible for any human mind to perform, such that the claims as currently presented do not fall under the grouping of Mental Processes.”
Examiner respectfully disagrees. The claims as amended, despite being implemented via machine learning, still describe or set forth recordkeeping, organizing and annotating docket items based on requirements, identifying values, selecting templates, and cross-checking docket items, fall within concepts performed in the human mind (including an observation, evaluation, judgement, or opinion) under the abstract grouping of Mental Processes (MPEP 2106.04(a)(2) III). Applicant alleges a level of computational complexity and scale rendering the functions impossible to be performed mentally, but these are unclear to Examiner from the claims as amended. Assuming arguendo that the claims are not directed to a mental process, which Examiner does not concede, the claims still recite, describe, or set forth agreements in the form of contracts and legal obligations as they pertain to commercial or legal interactions under the abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II). Accordingly, the claims recite an abstract idea.
Step 2A Prong 2:
Applicant argues on page 10 of remarks 5/28/2026:
“Further, the presently amended claims integrate any alleged abstract idea into a practical
application such that the claims are patent eligible. The present case is directly analogous to the
precedential Ex Parte Desjardins decision, where the Appeals Review Panel found MLM training
claims eligible because "the specification identified improvements as to how the machine learning
model itself operates" and "the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two". The Desjardins specification
described improvements including effective learning while protecting prior knowledge, overcoming "catastrophic forgetting," reduced storage capacity, and reduced system complexity, and the panel held that "such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation". Here, the specification similarly describes improvements to how the IP docketing system operates through learning from historical correctly classified communications ([0043]), enabling pattern-based predictions ([0043]), and a streamlined docketing workflow with a back-up docket integration ([0034], [0041]). The claim reflects these improvements in the limitations covering specific MLM training, architecture built around values derived by templates, and automated docketing and back up docketing integrations.”
Examiner respectfully disagrees. The additional element “machine learning model” language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. Applicant specification ¶ [0043] describes the operation of the machine learning: “Over time, the machine learning model system 150 can learn which PTO IDs to use for which documents, which document in a bundle of documents may be used to characterize the bundle and may provide predicted PTO IDs for the received documents.” Although Applicant alleges “improvements to how the IP docketing system operates through learning from historical correctly classified communications”, neither the claims nor specification appear to describe improvements in the machine learning itself, as asserted in the Ex Parte Desjardins decision.
Thus, the previously pending rejection under 35 USC 101 will be maintained. The 101 rejection is updated in light of the amendments.
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Regarding Applicant’s remarks pertaining to 35 USC 103:
Applicant argues on page 10 of remarks 5/28/2026:
“Although the Office Action alleges that Shek teaches selecting templates based on annotations, the independent claims now recite an MLM to generate a predicted first and second
template to be included in the process of selecting a first template and a second template. None of the references, including Shek, appear to discuss these added elements, and therefore do not support a rejection of the claims.”
Examiner respectfully finds the argument moot on new grounds of rejection. Examiner points to reference Lundberg et al. US 20200117718 A1, hereinafter Lundberg, which discloses the amended claim limitations inter alia at ¶ [0046], [0047]. Citations and additional details are including in the 103 rejection section below.
Accordingly, new grounds for rejection under 35 USC 103 are applied. The 103 rejection is updated in view of the amendments.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-10, 13, 22-25, 27-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 3-8, 22-24 are directed to a method or process which is a statutory category.
Claims 9-10, 13, 25 are directed to a system or machine which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). Specifically, the claims recite, describe or set forth concepts including: “maintaining the primary docketing system”, “…applying a first set of annotations to the docket item”, “analyzing the unstructured text… to generate a first set of annotations and a second set of annotations… including previously processed docket items manually tagged with correct docketing requirements and correct input parameters”, “output a predicted first template and a predicted second template for each docket item based on patterns learned from the training set”, “selecting a first template”, “applying the first template to the unstructured text included in the docket item to derive a primary value associated with the docket item”, “associating the primary value to the docket item”, and “cross-checking the docket item by comparing the primary value and the secondary value to the base value”. Recordkeeping, organizing and annotating docket items based on requirements, identifying values, selecting templates, and cross-checking docket items fall within agreements in the form of contracts and legal obligations as they pertain to commercial or legal interactions under the abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II); and also fall within concepts performed in the human mind (including an observation, evaluation, judgement, or opinion) under the abstract grouping of Mental Processes (MPEP 2106.04(a)(2) III)1.
Examiner also points to MPEP2106.04(a)(2) III C finding that computer aided processes such as: 1. Performing a mental process on a generic computer, 2. Performing a mental process in a computer environment, 3. Using a computer as a tool to perform a mental process can still be considered to recite a mental process.
Accordingly, the claims recite an abstract idea.
Step 2A Prong Two: Independent claims 1, 9 recite the following computer-based additional elements: “docketing system”, “machine learning model”, “computer”, “template”, “back-up docketing tool”, “computer readable medium”, “memory”, “processor”, “governmental database”, and “docket”. These additional elements merely provide an abstract-idea-based-solution implemented with computer hardware and software components which fail to integrate the abstract idea into a practical application. The capabilities of the additional computer-based elements include maintaining docket items and templates, downloading docket items from a government database, applying annotations correlating to docket requirements and input parameters, selecting templates, calculating due dates, and cross-checking docket items. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of collecting, organizing, comparing, modifying, and storing data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. These capabilities can be viewed as not meaningfully different than a business method or algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i).
The additional element “machine learning model” language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea.
Step 2B: The courts have recognized the following computer functions analogous to the instant application as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II):
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012);
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application (Step 2A Prong Two), and for the same reasons also do not provide significantly more (Step 2B) because this type of recitation is equivalent to the words "apply it". Therefore, when reading the above claimed additional computer-based elements in light of the original specification, the Examiner finds that they merely apply said abstract idea. Thus, these additional limitations, considered individually and in combination, amount to mere instructions to implement an abstract idea on a general-purpose computer or use the computer as a tool to perform the above identified abstract idea and therefore do not integrate the judicial exception into a practical application without imposing meaningful limits on practicing the abstract idea.
Dependent claims 3-8, 10, 13, 22-25, 27-28 do not appear to provide any new additional computer-based elements outside the independent claims, let alone for such additional computer-based elements to integrate the abstract idea into practical application (Step 2A prong two) or providing significantly more (Step 2B).
Further, dependent claims 3-8, 10, 13, 22-25, 27-28 merely incorporate the additional elements recited in claims 1, 9 along with further narrowing of the abstract idea of claims 1, 9 along with their execution of the abstract idea. The functions of the additional computer-based elements are narrowed to capabilities such as downloading, calculating, docketing, comparing, cross-checking, identifying, rectifying, selecting, copying, collecting, receiving, creating, and generating various forms of data such as docket items, base dates, due dates, communications, deadlines, documents, filings, portfolios, errors, requirements, annotations, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform an existing process. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, Claims 1, 1, 3-10, 13, 22-25, 27-28 are reasoned to be patent ineligible.
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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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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, 3-10, 13, 22-25, 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over:
Kamarei et al. US 6859806 B1, hereinafter Kamarei in view of
Shek WO 2019197924 A1, hereinafter Shek, in further view of
Quinn, Jr. US 20120036077 A1, hereinafter Quinn, and in further view of
Lundberg et al. US 20200117718 A1, hereinafter Lundberg. As per,
Regarding Claim 1: Kamarei teaches “A method of creating a back-up docket in a primary docketing system, the method comprising:
“maintaining the primary docketing system containing a plurality of docket items and a plurality of templates” (Kamarei col. 4 line 64: FIG. 2 shows a preferred embodiment of the invention having automated docketing and filing modules for interactions between the host server system, a governmental system, and a third-party authorized system. Col. 5 line 6: FIG. 5 shows a flow chart of the preferred embodiment of automated docketing for a client system through interfacing with a governmental entity. Col. 9 line 3: As shown in FIG. 6 in a preferred embodiment, upon registration of the Client System, the Host Server System provides the Client System with a default Client Rules Subset 42. Each default Client Rules Subset preferably has Pattern Data 44 [EN: template] relating to a particular legal field, such as patent prosecution, trademark prosecution or litigation, an Action Prompt 30 associated with the Pattern Data and a Time Calculus 46. Col. 8 line 52: Pattern Data 44 [EN: template] comprises of any one or combination of the following; a case filing date field, an official action mail date field, a case registration date field, a case appearance date field, a notice of allowance mail date field, a Statement of acceptance date field, a publication date field, an entry of judgment date field, and a priority date field. [Also see Kamarei claim 15 describing pattern data fields consistent with templates]);
“automatically downloading a docket item from a governmental database, the docket item including unstructured text, the unstructured text including a base value” (Kamarei col. 10 line 24: As shown in FIG. 5 in one embodiment, a Docket Request Message 28 is forwarded from the Governmental System 24 to the Host Server System 10. The Docket request Message comprises of a Governmental System Identifier 34, Pattern Data 44 and Pattern Data Date for a legal case identified by a case number. Line 37: The Gateway Server 14 then identifies the Pattern Data and the Pattern Data Date [EN: base value] in the Docket Request Message 28 forwarded by the Governmental System. Line 64: Optionally, the Governmental System appends an electronic document, in ASCII, Word Perfect, MSWord, GIF, JPG, PDF, or any other type of format to the Docket Request Message. [Also see Fig. 5 and related text]);
[..]
“selecting a first template from the plurality of templates [..] and the predicted first template, wherein each annotation from the generated first set of annotations correlates to a docketing requirement contained in the docket item; applying the first template to the unstructured text included in the docket item to derive a primary value associated with the docket item” (Kamarei col. 8 line 34: Each Client Rules Subset [EN: containing ‘pattern data’ template] is modifiable [EN: annotatable] by the Client System and therefore customizable to the requirements of that Client System. Optionally, each user or Subordinated user of the Client System 12 can define [EN: select] Client Rules Subset 42 to fit their unique requirements. Col. 8 line 45: As shown in FIG. 3 the Client Rules Subset contains one or more Pattern Data 44, each associated with an Action Prompt 30 and Time Calculus 46. Kamarei col. 8 line 66: A Time Calculus 46 comprises of any one or combination of the following; a formula for computing a date forward from a Pattern Data, a formula for calculating a date backwards from a Pattern Data. Col. 14 line 31: The method of claim 1 wherein the time calculus comprises of any one or combination of the following; a formula for computing a date forward from a base date, a formula for calculating a date backwards from a base date. [Also see Fig. 7: “This action is triggered from the following date:” and selectable option “Other/Base or Mail Date”; and related text]);
“associating the primary value to the docket item and docketing the docket item with the associated primary value in the primary docketing system” (Kamarei col. 10 line 3: …the Host Server System saves the Action Prompt and the Action Prompt Due Date [EN: primary value] in the Case Listing Database 20 for future retrieval by the Client System);
[..]
selecting a second template from the plurality of templates based on the generated second set of annotations and the predicted second template, wherein each annotation from the generated second set of annotations correlates to an input parameter contained in the docket item” (Kamarei col. 12 line 10: If the second Client System is registered with the Host Server System, preferably by authorization of the client, the administrator of the Host Server System, edits [EN: annotates] the legal case data for the transferred case to indicate that the Second Client System 12, with a new attorney, a new address, a new email address, or any other new data [EN: input parameters], has been designated in charge of the legal case in the Host Server System's database, and Stores the Specific legal case under the Second Client System cases in the Case Listing Database 20. Col. 12 line 27: If the second Client System is not registered with the Host Server System, preferably they register with the Host Server System and Specify their preferences for Action Prompts, Action Prompt Due Dates, Time Calculus 46, and Action Prompt Delivery Type 48 in the Client Rules Subset [EN: template]. [Also see Kamarei Col. 10 lines 24-67 regarding download from government database]);
“applying the second template to the [..] text included in the docket item to derive a secondary value associated with the docket item; associating the secondary value to the docket item and docketing the docket item with the associated secondary value in the back-up docket” (Kamarei col. 12 line 8: …the legal case is transferred from the first Client System to a second Client System by instructions of the client. Line 20: …the second Client System 12 receives Action Prompts 30 and Action Prompt Due Dates [EN: secondary value], using Action Prompt Delivery Types 48 specified by the Second Client System. In this manner, legal docketing has been accomplished according to the reminder Schedules and terminology of the Second Client System without manual entry of substantially identical [duplicative] data and without delay. If the second Client System is not registered with the Host Server System, preferably they register with the Host Server System and Specify their preferences for Action Prompts, Action Prompt Due Dates, Time Calculus 46, and Action Prompt Delivery Type 48 in the Client Rules Subset. Once the second Client System is registered, the administrator of the Host Server System transfers the legal case to the second Client System as specified above);
[..].
Although Kamarei teaches retrieving docket items from a government system, calculating due dates, and transferring docketing items to a second system with a second set of annotations, Kamarei does not specifically teach analyzing unstructured text in docket items to predict and output templates and determine primary values based on annotations using machine learning, nor comparing and cross-checking docketing items.
* However *
Shek in analogous art of automated docketing systems teaches or suggests:
“automatically downloading a docket item [..], the docket item including unstructured text, [..] (Shek ¶ [064]: Block 245 depicts the processing server 130 scanning for new incoming correspondence. Upon a new document being detected, the [EN: unstructured] document is processed in Block 250. The resulting output is processed data, which is a structured machine-readable data representation of the original information from the uploaded incoming correspondence);
[..]
“[selecting a first template…] based on the generated first set of annotations” (Shek ¶ [066]: Block 805 depicts the determination [EN: selection] of the document type. A document type is the type of incoming correspondence…. The document type acts as a template in determining the relevant fields that are required to be docketed, and hence converted to machine readable data [EN: first annotated] as depicted in Block 810. An embodiment of block 810 is to take a scanned document, and then use optical recognition to pull the necessary data from the document and put it into a JSON file)
Shek and Kamarei are found as analogous art of automated docketing systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kamarei’s legal docketing system in combination to have included Shek’s teachings regarding analyzing unstructured text in docket items to select templates. The benefit of these additional features would have helped further streamline and automate docketing tasks by overcoming data entry obstacles (Shek ¶ [002]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kamarei in view of Shek (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor of automated docketing systems. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Kamarei in view of Shek above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
* Furthermore *
Lundberg in analogous art of automated docketing systems teaches or suggests:
“analyzing the unstructured text using a machine learning model to generate a first set of annotations and a second set of annotations, the first and second sets of annotations comprising
metadata associated with the docket item, wherein the machine learning model is trained on a training set including previously processed docket items manually tagged with correct docketing requirements and correct input parameters” (Lundberg ¶ [0046]: As discussed below, machine learning model module 230 (e.g., machine learning technique, algorithm or process) analyzes a collection of matters stored in a docketing system and is trained to generate an expected set of activities for a given document. In some cases, machine learning model module 230 is trained or updated as each new [EN: manually tagged] document is uploaded and the corresponding set of activities are generated and verified [EN: correct data] for the matter. In this way, the machine learning model module 230 is always up-to-date with the latest set of information about the expected set of activities that are needed for a given document as rules, laws and practices change. In particular, machine learning model module 230 may extract a set of features [EN: metadata / annotations] from a given document and a set of activities in a matter. For example, the machine learning model module 230 may extract as the features titles of activities and documents, classification or types of the documents, dates of the activities and the documents, status information, and/or deadlines. The machine learning model module 230 may form relationships between when the extracted features to determine the expected activities associated with a given document);
“invoking the machine learning model to output a predicted first template and a predicted
second template for each docket item based on patterns learned from the training set; outputting, using the machine learning model, the predicted first template for the docket item” (Lundberg ¶ [0047]: For example, the machine learning model module 230 may determine [EN: predict] which activities were generated and when as a result of what type [EN: first template] of document that was added to the matter around or close to the timing of the activities (e.g., within a few or less than a specified number of days). In this way the machine learning model module 230 is trained to recognize documents and their corresponding expected activities. After being trained, the machine learning model module 230 is applied to a new document that is added for another matter to determine the type [EN: second template] of document and whether the set of activities generated based on that type of document match the expected set of activities);
[..]
“in response to the docketing of the docket item and the associated primary value in the primary docketing system, outputting, using the machine learning model, the predicted second template for the docket item” (Lundberg ¶ [0047]: For example, the machine learning model module 230 may determine which activities were generated and when as a result of what type [EN: first template] of document that was added to the matter around or close to the timing of the activities (e.g., within a few or less than a specified number of days). In this way the machine learning model module 230 is trained to recognize documents and their corresponding expected activities [EN: associated primary value]. After being trained, the machine learning model module 230 is applied to a new document that is added for another matter to determine the type [EN: predict second template] of document and whether the set of activities generated based on that type of document match the expected set of activities);
Lundberg, Shek and Kamarei are found as analogous art of automated docketing systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kamarei’s legal docketing system in combination to have included Lundberg’s teachings regarding predicting and outputting templates based on primary values using machine learning. The benefit of these additional features would have reduced errors and increased reliability in docketing systems (Lundberg ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kamarei in view of Shek and Lundberg (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor of automated docketing systems. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Kamarei in view of Shek and Lundberg above, the to-be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
* Further still *
Quinn in analogous art of automated docketing systems teaches or suggests:
“cross-checking the docket item by comparing the primary value and the secondary value to the base value” (Quinn ¶ [0048]: When the drafting law firm 16 receives the documents checklist, it is asked to double-check the priority information listed on the document checklist against their own data in law firm database 56 at step 120. This priority information can include the priority filing dates, priority countries and the priority application numbers. The filing manager 12 also calculates and prominently displays a filing deadline date on the document checklist. As set forth above, this filing deadline date is calculated by the database of the filing manager 12 dependent upon the priority information, the country where applications are to be filed and the types of filings requested. [Also see Fig. 4A and related text]).
Quinn, Lundberg, Shek and Kamarei are found as analogous art of automated docketing systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kamarei’s legal docketing system to have included Quinn’s teachings around cross-checking docketing items. The benefit of these additional features would have aided in managing costs while maintaining quality for legal document filings at corporate firms (Quinn ¶ [0005]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kamarei in view of Shek, Lundberg and Quinn (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor of automated docketing systems. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Kamarei in view of Shek, Lundberg and Quinn above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding Claim 9: Kamarei teaches “A system for creating a back-up docket in a primary docketing system the system comprising:
“the primary docketing system configured to: docket and maintain a patent portfolio containing a plurality of docket items and a plurality of templates” (Kamarei col. 4 line 64: FIG. 2 shows a preferred embodiment of the invention having automated docketing and filing modules for interactions between the host server system, a governmental system, and a third-party authorized system. Col. 5 line 6: FIG. 5 shows a flow chart of the preferred embodiment of automated docketing for a client System through interfacing with a governmental entity. Col. 9 line 3: As shown in FIG. 6 in a preferred embodiment, upon registration of the Client System, the Host Server System provides the Client System with a default Client Rules Subset 42 [template]. Each default Client Rules Subset preferably has Pattern Data 44 [EN: docket item] relating to a particular legal field, such as patent prosecution, trademark prosecution or litigation, an Action Prompt 30 associated with the Pattern Data and a Time Calculus 46. Col. 8 line 52: Pattern Data 44 [EN: template] comprises of any one or combination of the following; a case filing date field, an official action mail date field, a case registration date field, a case appearance date field, a notice of allowance mail date field, a Statement of acceptance date field, a publication date field, an entry of judgment date field, and a priority date field. [Also see Kamarei claim 15 describing pattern data fields consistent with templates]);
“automatically download a docket item from a governmental database, the docket item including unstructured text, the unstructured text containing a base value” (Kamarei col. 10 line 24: As shown in FIG. 5 in one embodiment, a Docket Request Message 28 is forwarded from the Governmental System 24 to the Host Server System 10. The Docket request Message comprises of a Governmental System Identifier 34, Pattern Data 44 and Pattern Data Date for a legal case identified by a case number. Line 37: The Gateway Server 14 then identifies the Pattern Data and the Pattern Data Date [EN: base value] in the Docket Request Message 28 forwarded by the Governmental System. Line 64: Optionally, the Governmental System appends an electronic document, in ASCII, Word Perfect, MSWord, GIF, JPG, PDF, or any other type of format to the Docket Request Message. [Also see Fig. 5 and related text]);
[..]
“select a first template from the primary docketing system [..] and the predicted first template, wherein each annotation from the generated first set of annotations correlates to a docketing requirement contained in the docket item; apply the first template to the unstructured text included in the docket item to derive a primary value associated with the docket item;” (Kamarei col. 8 line 34: Each Client Rules Subset [EN: containing ‘pattern data’ template] is modifiable by the Client System and therefore customizable to the requirements of that Client System. Optionally, each user or Subordinated user of the Client System 12 can define [EN: select] Client Rules Subset 42 to fit their unique requirements. Col. 8 line 45: As shown in FIG. 3 the Client Rules Subset contains one or more Pattern Data 44, each associated with an Action Prompt 30 and Time Calculus 46. Kamarei col. 8 line 66: A Time Calculus 46 comprises of any one or combination of the following; a formula for computing a date forward from a Pattern Data, a formula for calculating a date backwards from a Pattern Data. Col. 14 line 31: The method of claim 1 wherein the time calculus comprises of any one or combination of the following; a formula for computing a date forward from a base date, a formula for calculating a date backwards from a base date. [Also see Fig. 7: “This action is triggered from the following date:” and selectable option “Other/Base or Mail Date”; and related text]);
“associate the primary value to the docket item and docketing the docket item with the associated primary value in the primary docketing system” (Kamarei col. 10 line 3: …the Host Server System saves the Action Prompt and the Action Prompt Due Date [EN: primary value] in the Case Listing Database 20 for future retrieval by the Client System);
[..]
“select a second template from the plurality of templates based on the generated second set of annotations and the predicted second template, wherein each annotation from the generated second set of annotations correlates to an input parameter contained in the docket item” (Kamarei col. 12 line 10: If the second Client System is registered with the Host Server System, preferably by authorization of the client, the administrator of the Host Server System, edits [EN: annotates] the legal case data for the transferred case to indicate that the Second Client System 12, with a new attorney, a new address, a new email address, or any other new data [EN: input parameters], has been designated in charge of the legal case in the Host Server System's database, and Stores the Specific legal case under the Second Client System cases in the Case Listing Database 20. Col. 12 line 27: If the second Client System is not registered with the Host Server System, preferably they register with the Host Server System and Specify their preferences for Action Prompts, Action Prompt Due Dates, Time Calculus 46, and Action Prompt Delivery Type 48 in the Client Rules Subset [EN: template]. [Also see Kamarei Col. 10 lines 24-67 regarding download from government database]); “and
[..]
apply the second template to the unstructured text included in the docket item to derive a secondary value associated with the docket item; associate the secondary value to the docket item and docketing the docket item with the associated secondary value in the back-up docket (Kamarei col. 12 line 8: …the legal case is transferred from the first Client System to a second Client System by instructions of the client. Line 20: …the second Client System 12 receives Action Prompts 30 and Action Prompt Due Dates [EN: secondary value], using Action Prompt Delivery Types 48 specified by the Second Client System. In this manner, legal docketing has been accomplished according to the reminder Schedules and terminology of the Second Client System without manual entry of substantially identical [duplicative] data and without delay. If the second Client System is not registered with the Host Server System, preferably they register with the Host Server System and Specify their preferences for Action Prompts, Action Prompt Due Dates, Time Calculus 46, and Action Prompt Delivery Type 48 in the Client Rules Subset. Once the second Client System is registered, the administrator of the Host Server System transfers the legal case to the second Client System as specified above);
[..].
Although Kamarei teaches a patent portfolio docketing system which calculates due dates, selecting priority items to copy, and transferring docketing items to a second system with a second set of annotations, Kamarei does not specifically teach analyzing unstructured text in docket items to predict and output templates and determine primary values based on annotations using machine learning, nor comparing and cross-checking docketing items.
* However *
Shek in analogous art of automated docketing systems teaches or suggests:
“[select a first template from the primary docketing system] based on the generated first set of annotations [..]” (Shek ¶ [066]: Block 805 depicts the determination [EN: selection] of the document type. A document type is the type of incoming correspondence…. The document type acts as a template in determining the relevant fields that are required to be docketed, and hence converted to machine readable data [EN: first annotated] as depicted in Block 810. An embodiment of block 810 is to take a scanned document, and then use optical recognition to pull the necessary data from the document and put it into a JSON file);
“[select a second template from the second docketing system] based on the second set of annotations” (Shek end-¶ [067]: The loop then repeats itself until there are no further actions to perform. [Also see Shek ¶ [066-067]).
Shek and Kamarei are found as analogous art of automated docketing systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kamarei’s legal docketing system in combination to have included Shek’s teachings regarding analyzing unstructured text in docket items to select templates. The benefit of these additional features would have helped further streamline and automate docketing tasks by overcoming data entry obstacles (Shek ¶ [002]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kamarei in view of Shek (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor of automated docketing systems. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Kamarei in view of Shek above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
* Furthermore *
Lundberg in analogous art of automated docketing systems teaches or suggests:
“analyze the unstructured text using a machine learning model to generate a first set of annotations and a second set of annotations, the first and second sets of annotations comprising
metadata associated with the docket item, wherein the machine learning model is trained on a training set including previously processed docket items tagged with correct docketing requirements and correct input parameters” (Lundberg ¶ [0046]: As discussed below, machine learning model module 230 (e.g., machine learning technique, algorithm or process) analyzes a collection of matters stored in a docketing system and is trained to generate an expected set of activities for a given document. In some cases, machine learning model module 230 is trained or updated as each new [EN: manually tagged] document is uploaded and the corresponding set of activities are generated and verified [EN: correct data] for the matter. In this way, the machine learning model module 230 is always up-to-date with the latest set of information about the expected set of activities that are needed for a given document as rules, laws and practices change. In particular, machine learning model module 230 may extract a set of features [EN: metadata / annotations] from a given document and a set of activities in a matter. For example, the machine learning model module 230 may extract as the features titles of activities and documents, classification or types of the documents, dates of the activities and the documents, status information, and/or deadlines. The machine learning model module 230 may form relationships between when the extracted features to determine the expected activities associated with a given document);
“invoke the machine learning model to output a predicted first template and a predicted second template for each docket item based on patterns learned from the training set; output, using the machine learning model, the predicted first template for the docket item” (Lundberg ¶ [0047]: For example, the machine learning model module 230 may determine [EN: predict] which activities were generated and when as a result of what type [EN: first template] of document that was added to the matter around or close to the timing of the activities (e.g., within a few or less than a specified number of days). In this way the machine learning model module 230 is trained to recognize documents and their corresponding expected activities. After being trained, the machine learning model module 230 is applied to a new document that is added for another matter to determine the type [EN: second template] of document and whether the set of activities generated based on that type of document match the expected set of activities);
[..]
“in response to the docketing of with the docket item and the associated primary value in the primary docketing system, output, using the machine learning model, the predicted second template for the docket item” (Lundberg ¶ [0047]: For example, the machine learning model module 230 may determine which activities were generated and when as a result of what type [EN: first template] of document that was added to the matter around or close to the timing of the activities (e.g., within a few or less than a specified number of days). In this way the machine learning model module 230 is trained to recognize documents and their corresponding expected activities [EN: associated primary value]. After being trained, the machine learning model module 230 is applied to a new document that is added for another matter to determine the type [EN: predict second template] of document and whether the set of activities generated based on that type of document match the expected set of activities);
Lundberg, Shek and Kamarei are found as analogous art of automated docketing systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kamarei’s legal docketing system in combination to have included Lundberg’s teachings regarding predicting and outputting templates based on primary values using machine learning. The benefit of these additional features would have reduced errors and increased reliability in docketing systems (Lundberg ¶ [0003]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kamarei in view of Shek and Lundberg (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor of automated docketing systems. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Kamarei in view of Shek and Lundberg above, the to-be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
* Further still *
Quinn in analogous art of automated docketing systems teaches or suggests:
“cross-check the docket item by comparing the primary value and the secondary value to the base value” (Quinn ¶ [0048]: When the drafting law firm 16 receives the documents checklist, it is asked to double-check the priority information listed on the document checklist against their own data in law firm database 56 at step 120. This priority information can include the priority filing dates, priority countries and the priority application numbers. The filing manager 12 also calculates and prominently displays a filing deadline date on the document checklist. As set forth above, this filing deadline date is calculated by the database of the filing manager 12 dependent upon the priority information, the country where applications are to be filed and the types of filings requested. [Also see Fig. 4A and related text]).
Quinn, Lundberg, Shek and Kamarei are found as analogous art of automated docketing systems. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kamarei’s legal docketing system to have included Quinn’s teachings around cross-checking docketing items. The benefit of these additional features would have aided in managing costs while maintaining quality for legal document filings at corporate firms (Quinn ¶ [0005]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kamarei in view of Shek, Lundberg and Quinn (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor of automated docketing systems. In such combination each element would have merely performed same organizational and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Kamarei in view of Shek, Lundberg and Quinn above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding Claim 2: cancelled.
Regarding Claim 3: Kamarei / Shek / Quinn teaches all of the limitations in claim 1 above.
Kamarei further teaches:
“wherein the primary docketing system containing the back-up docket is an internal system” (Kamarei col. 9 line 3: As shown in FIG. 6 in a preferred embodiment, upon registration of the Client System, the Host Server System provides the Client System with a default Client Rules Subset 42. Each default Client Rules Subset preferably has Pattern Data 44 [EN: template] relating to a particular legal field, such as patent prosecution, trademark prosecution or litigation, an Action Prompt 30 associated with the Pattern Data and a Time Calculus 46. Thereafter, as shown in FIG. 7 the Client System [EN: internal system] optionally modifies any one or combination of the Pattern Data, Action Prompt, or the Time Calculus).
Regarding Claim 4: Kamarei / Shek / Quinn teaches all of the limitations in claim 1 above.
Kamarei further teaches:
“wherein the second template is selected from an external system maintained by a third party” (Kamarei col. 9 line 10: Thereafter, as shown in FIG. 7 the Client System optionally modifies any one or combination of the Pattern Data, Action Prompt, or the Time Calculus. The Pattern Data [EN: template], Action Prompt, or the Time Calculus is optionally modifiable by a third-party authorized by the Client System).
Regarding Claim 5: Kamarei / Shek / Quinn teaches all of the limitations in claim 4 above.
Kamarei further teaches:
“wherein the third party comprise a governmental agency, a foreign associate, an outside firm, or a client company” (Kamarei col. 6 line 30: In an embodiment third-parties such as Third-party Authorized Systems 26 or Governmental Systems 24 register unique Identifiers 34 with the Host Server System. Thereafter, whenever the Third-party Authorized System or Governmental System wishes to communicate with the Host Server System, an Identifier is presented to the Host Server System for identification purposes).
Regarding Claim 6: Kamarei / Quinn / Shek teaches all of the limitations in claim 1 above.
Kamarei further teaches “further comprising docketing the docket item with the secondary value in a second docketing system” (Kamarei col. 12 line 23: legal docketing has been accomplished according to the reminder schedules and terminology of the second Client System without manual entry of substantially identical data and without delay).
Regarding Claim 7: Kamarei / Shek / Quinn teaches all of the limitations in claim 1 above.
Kamarei further teaches:
“wherein the docket item comprises one or more of communications, deadlines, documents, or combinations thereof” (Kamarei col. 4 line 28: The host server system receives a docket request message from the client system, identifies the client system using a client system identifier, identifies the customizable client rules subset for the client system, using the pattern data contained in the docket request message selects an action prompt from the customizable client rules subset, computes an action prompt due date for each of the action prompts using the pattern data date and time calculus, and records the client system defined action prompt and computed action prompt due date for the legal case selected by the client system [Also see Fig. 6 and related text]).
Regarding Claim 8: Kamarei / Shek / Quinn teaches all of the limitations in claim 1 above.
Kamarei further teaches:
“The method of claim 1, wherein the docket item comprises a deadline” (Kamarei col. 8 line 52: Pattern Data 44 comprises of any one or combination of the following; … a priority date field [EN: deadline]. [Also see Fig. 7 and related text, and Fig. 8 “Foreign Filing Date” and related text]).
Regarding Claim 10: Kamarei / Shek / Quinn teaches all of the limitations in claim 9 above.
Kamarei further teaches:
“The system of claim 9, wherein the primary docketing system and the second docketing system are maintained by different parties” (Kamarei col. 12 line 12: …the administrator of the Host Server System, edits the legal case data for the transferred case to indicate that the Second Client System 12, with a new attorney, a new address, a new email address, or any other new data, has been designated in charge of the legal case in the Host Server System's database, and Stores the Specific legal case under the Second Client System cases in the Case Listing Database 20).
Regarding Claims 11-12: cancelled.
Regarding Claim 13: Kamarei / Shek / Quinn teaches all of the limitations in claim 9 above.
Although Kamarei teaches an automated legal docketing system, Kamarei does not specifically teach identifying errors based on cross-checking docketed items.
* However *
Quinn in analogous art of automated docketing systems teaches or suggests:
“wherein the primary docketing system is further configured to identify one or more inconsistencies based on the comparing the primary value and the secondary value to the base value” (Quinn mid-¶ [0048]: …The drafting law firm 16 is asked to check this filing deadline date against a filing deadline date calculated by the law firm database 56. If there is a discrepancy in any of the priority information or the filing deadline date, the drafting law firm 16 is asked to communicate the discrepancy to the filing manager 12. Otherwise, the drafting law firm 16 communicates to the filing manager that the information on the document checklist is accurate. [Also see Fig. 4A and related text]).
Rationales to have combined / modified Kamarei / Shek / Quinn are above and reincorporated.
Regarding Claims 14-21: cancelled.
Regarding Claim 22: Kamarei / Shek / Quinn teaches all the limitations of claim 1 above.
Although Kamarei teaches an automated legal docketing system, Kamarei does not specifically teach “further comprising identifying one or more errors in the primary docketing system on cross-checking, and rectifying the one or more errors”.
* However *
Quinn in analogous art of automated docketing systems teaches or suggests:
“further comprising identifying one or more errors in the primary docketing system on cross-checking [..]” (Quinn ¶ [0048]: The drafting law firm 16 is asked to check this filing deadline date against a filing deadline date calculated by the law firm database 56. If there is a discrepancy in any of the priority information or the filing deadline date, the drafting law firm 16 is asked to communicate the discrepancy to the filing manager 12. Otherwise, the drafting law firm 16 communicates to the filing manager that the information on the document checklist is accurate. [Also see Fig. 4A and related text]).
Rationales to have modified / combined Kamarei / Shek / Quinn are above and reincorporated.
* Furthermore *
Shek in analogous art of automated docketing systems teaches or suggests:
“[..] and rectifying the one or more errors” (Shek ¶ [071]: Block 845 depicts the display of the parsed fields of the incoming correspondence data, data from the client server database 305 and their corresponding actions, on web browser 410 on client terminal 120. Any data that is incorrect can be corrected through block 840, which shows the client terminal 120 receiving corrected data, which is subsequently sent back to processing server 130. An example of the source for corrected data, is user input via the client terminal 120. Any data that is corrected at block 840, such as the wrong document type, is sent back to block 805 for reprocessing with additional input to correct the error, as depicted at block 850. If there are no errors detected, the data is prepared to be written back via the tunnel to the client server database 305 as depicted in block 855).
Rationales to have modified / combined Kamarei / Shek / Quinn are above and reincorporated.
Regarding Claim 23: Kamarei / Shek / Quinn teaches all the limitations of claim 22 above.
Kamarei further teaches:
“wherein rectifying the one or more errors comprises selecting at least one priority docket item for copying to the primary docketing system” (Kamarei col. 3 line 62: Under control of a client system, a docket request message is sent to a host server system that is identified by a first uniform resource locator (URL), the host server system having a client case listing database [backup docketing tool] containing legal case information from at least one client systems [first docketing system]. The docket request message further comprises of a pattern data and a pattern data date. Under control of the host server system, it receives the docket request message, identifies the client system using a client system identifier, identifies the customizable client rules subset in a rule module database associated with the identified client system, and using the pattern data contained, in the docket request message selects at least one associated action prompt [priority item] from the customizable client rules subset. Once the action prompt is selected, the host server system computes an action prompt due date for each of the associated action prompts using the pattern data date and a time calculus. The action prompt and the computed action prompt due date are recorded [copied] in the client system case listing database for the legal case selected by the client system).
Regarding Claim 24: Kamarei / Shek / Quinn teaches all the limitations of claim 23 above.
Although Kamarei teaches copying a priority item to the docketing system, Kamarei does not specifically teach including a correct due date.
* However *
Quinn in analogous art of automated docketing systems teaches or suggests: “wherein the at least one priority docket item includes a correct due date determined by cross-checking the docket item” (Quinn ¶ [0048]: The drafting law firm 16 is asked to check this filing deadline date against a filing deadline date calculated by the law firm database 56. If there is a discrepancy in any of the priority information or the filing deadline date, the drafting law firm 16 is asked to communicate the discrepancy to the filing manager 12. Otherwise, the drafting law firm 16 communicates to the filing manager that the information on the document checklist is accurate [correct]. [Also see Fig. 4A and related text]).
Rationales to have combined / modified Kamarei / Shek / Quinn are above and reincorporated.
Regarding Claim 25: Kamarei / Shek / Quinn teaches all the limitations of claim 13 above.
Although Kamarei teaches copying a priority item to the docketing system, Kamarei does not specifically teach including a correct due date.
* However *
However, Quinn in analogous art of automated docketing systems teaches or suggests: “wherein the at least one priority docket item includes a correct due date determined by the cross-check function.” (Quinn ¶ [0048]: The drafting law firm 16 is asked to check this filing deadline date against a filing deadline date calculated by the law firm database 56. If there is a discrepancy in any of the priority information or the filing deadline date, the drafting law firm 16 is asked to communicate the discrepancy to the filing manager 12. Otherwise, the drafting law firm 16 communicates to the filing manager that the information on the document checklist is accurate [correct]. [Also see Fig. 4A and related text])”.
Rationales to have combined / modified Kamarei / Shek / Quinn are above and reincorporated.
Regarding Claim 26: cancelled.
Regarding Claim 27: Kamarei / Shek / Quinn teaches all of the limitations in claim 9 above.
Kamarei further teaches:
“wherein the docket item comprises one or more of communications, deadlines, documents, or combinations thereof” (Kamarei col. 4 line 28: The host server system receives a docket request message from the client system, identifies the client system using a client system identifier, identifies the customizable client rules subset for the client system, using the pattern data contained in the docket request message selects an action prompt from the customizable client rules subset, computes an action prompt due date for each of the action prompts using the pattern data date and time calculus, and records the client system defined action prompt and computed action prompt due date for the legal case selected by the client system [Also see Fig. 6 and related text]).
Regarding Claim 28: Kamarei / Shek / Quinn teaches all of the limitations in claim 9 above.
Kamarei further teaches:
“wherein the second template is selected from an external system maintained by a third party” (Kamarei col. 9 line 10: Thereafter, as shown in FIG. 7 the Client System optionally modifies any one or combination of the Pattern Data, Action Prompt, or the Time Calculus. The Pattern Data, Action Prompt, or the Time Calculus is optionally modifiable by a third-party authorized by the Client System).
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Conclusion
The following art is made of record and considered pertinent to Applicant’s disclosure:
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE ONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REED M. BOND whose telephone number is (571) 270-0585. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/REED M. BOND/Examiner, Art Unit 3624
July 20, 2026
/HAMZEH OBAID/Primary Examiner, Art Unit 3624 July 23, 2026
1 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
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