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 .
DETAILED ACTION
1. This action is responsive to applicant’s amendment dated 11/30/2025.
2. Claims 1- 20 are pending in the case.
3. Claims 1, 11, and 20 are independent claims.
Applicant’s Response
4. In Applicant’s response dated 11/30/2025, applicant has amended the following:
a) Claim 19
Based on Applicant’s amendments and remarks, the following rejection previously set forth in Office Action dated 9/18/2025 is withdrawn:
35 U.S.C. 112(b) Rejection to claim 19
Examiner Note
A new examiner has been assigned to U.S. Application No. 18420219. Per MPEP 704.01, full faith and credit will be given to the searches and actions of the previous examiner where appropriate.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claim(s) 1-4, 11-18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Miller (U.S. Publication 2014/0025608 A1).
As per independent claim 1, Miller discloses a computer-implemented method (See Miller, Abstract), comprising:
sending, using at least one processor, a plurality of electronic documents to a generative artificial intelligence (AI) model to determine a structure and one or more portions of each electronic document in the plurality of electronic documents (See Miller, paragraphs 0010, 0028, and 0059-0060, describing using a machine learning model to construct at least one machine learned, form selection classifier based on a training set, wherein the training set is a first subset of a set of form usage data. The computer readable medium further contains a set of rule engine instructions that, if executed by the processor, are operable to cause the server system to construct a rules engine, the rules engine comprising form selection rules and resources and logic to apply the form selection rules, wherein the form selection rules include the at least one machine learned, form selection classifier);
identifying, using the at least one processor, a type in a plurality of types for each electronic document in the plurality of electronic documents (See Miller, paragraphs 0084-0086, describing baseline performance for the positive selection rule is the performance of a model which in each case estimates the probability of selection of the given clause or form as being equal to the percentage of positive selections of such form or clause in the training set);
generating, using the at least one processor, using a machine learning model, based on the structure and the one or more portions of each electronic document in the plurality of electronic documents, one or more templates defining a structural arrangement of the one or more portions of the electronic document for each type of electronic document (See Miller, paragraph 0060, describing a Document Generation System 150 that asks expert 107 to input selection rules. Where practical, the domain expert inputs to Rules Engine 180 either a decision table or rule of thumb for the selection of a document or clause based on the values of the attributes. The decision tables and rules of thumb together constitute the initial selection rules upon which Rules Engine 180 selects documents and clauses as customary based on the applicable set of attribute values. When sufficient examples have been collected for training, Learning Agent 170 will construct classifiers, test them against the existing rules and replace outperformed rules with the best performing classifier…);
associating, using the at least one processor, at least one portion in the one or more portions with at least one template in the one or more templates (See Miller, paragraph 0020, describing that when working with insurance forms, a rule set may be assigned to insurance policy clauses and insurance endorsement clauses. The insurance policy clauses, endorsement clauses, and rule sets are stored in a database coupled to a main processor. Each rule set includes at least one rule that must be satisfied in order to include the associated clause in the final insurance document); and
storing, using the at least one processor, the at least one template and the at least one portion associated with the at least one template in a storage location (See Miller, paragraph 0019, describing that the document is saved and may be printed or transmitted electronically).
As per dependent claim 2, Miller discloses the limitations of claim 1 as described above. Miller also discloses receiving a request to generate an electronic document of a first type; retrieving, from the storage location, a first template in the one or more templates and a first portion in the one or more portions associated with the first template, wherein at least one of the first template and the first portion is associated with the first type of electronic document; and generating the electronic document of the first type using the first template and the first portion (See Miller, paragraph 0019).
As per dependent claim 3, Miller discloses the limitations of claim 2 as described above. Miller also discloses inserting, using the first template, the first portion at a first location within the electronic document of the first type (See Miller, paragraph 0019).
As per dependent claim 4, Miller discloses the limitations of claim 1 as described above. Miller also discloses wherein the type of the electronic document includes at least one of the following: an agreement type, a legal document type, a non-legal document type, and any combinations thereof (See Miller, Abstract and paragraph 0006).
As per independent claim 11, Miller discloses a system (See Miller, Figure 1A), comprising:
at least one processor; and at least one non-transitory storage media storing instructions, that when executed by the at least one processor, cause the at least one processor to… (See Miller, paragraph 0028).
Independent claim 11 additionally incorporates substantially similar subject matter as that of independent claim 1 above, and is additionally rejected along the same rationale as used in the rejection of claim 1.
As per dependent claim 12, Miller discloses the limitations of claim 11 as described above. Miller also discloses wherein the at least one processor is configured to store the at least one template and the at least one portion associated with the at least one template in a storage location (See Miller, paragraph 0019).
As per dependent claim 13, Miller discloses the limitations of claim 11 as described above. Miller also discloses wherein the at least one processor is configured to send the plurality of electronic documents to a generative artificial intelligence (AI) model to determine a structure and one or more portions of each electronic document in the plurality of electronic documents; and generate the one or more templates based on the structure and the one or more portions of each electronic document in the plurality of electronic documents (See Miller, paragraph 0060).
As per dependent claim 14, Miller discloses the limitations of claim 11 as described above. Claim 14 additionally incorporates substantially similar subject matter as that of claim 2 above, and is additionally rejected along the same rationale as used in the rejection of claim 2.
As per dependent claim 15, Miller discloses the limitations of claim 14 as described above. Claim 15 additionally incorporates substantially similar subject matter as that of claim 3 above, and is additionally rejected along the same rationale as used in the rejection of claim 3.
As per dependent claim 16, Miller discloses the limitations of claim 11 as described above. Claim 16 additionally incorporates substantially similar subject matter as that of claim 4 above, and is additionally rejected along the same rationale as used in the rejection of claim 4.
As per dependent claim 17, Miller discloses the limitations of claim 11 as described above. Miller also discloses wherein at least one portion in the one or more portions is stored as an object model, wherein the object model includes the one or more labels (See Miller, paragraphs 0010 and 0026).
As per dependent claim 18, Miller discloses the limitations of claim 17 as described above. Miller also discloses wherein the at least one portion in the one or more portions includes one or more labels identifying the at least one portion (See Miller, paragraphs 0010-0011 and 0014).
As per independent claim 20, Miller discloses a computer program product comprising a non-transitory machine-readable medium storing instructions (See Miller, paragraph 0028) that, when executed by at least one programmable processor, cause the at least one programmable processor to:
identify a type in a plurality of types for each electronic document in a plurality of electronic documents (See Miller, paragraphs 0084-0086, describing baseline performance for the positive selection rule is the performance of a model which in each case estimates the probability of selection of the given clause or form as being equal to the percentage of positive selections of such form or clause in the training set);
generate, using a machine learning model, one or more templates defining a structural arrangement of one or more portions of the electronic document for each type of electronic document (See Miller, paragraph 0060, describing a Document Generation System 150 that asks expert 107 to input selection rules. Where practical, the domain expert inputs to Rules Engine 180 either a decision table or rule of thumb for the selection of a document or clause based on the values of the attributes. The decision tables and rules of thumb together constitute the initial selection rules upon which Rules Engine 180 selects documents and clauses as customary based on the applicable set of attribute values. When sufficient examples have been collected for training, Learning Agent 170 will construct classifiers, test them against the existing rules and replace outperformed rules with the best performing classifier…);
associate at least one portion in the one or more portions with at least one template in the one or more templates (See Miller, paragraph 0020, describing that when working with insurance forms, a rule set may be assigned to insurance policy clauses and insurance endorsement clauses. The insurance policy clauses, endorsement clauses, and rule sets are stored in a database coupled to a main processor. Each rule set includes at least one rule that must be satisfied in order to include the associated clause in the final insurance document);
present the at least one template and the at least one portion on a graphical user interface of at least one computing device (See Miller, paragraph 0019, describing that the document is saved and may be printed or transmitted electronically);
receive a request to generate an electronic document of a first type in the plurality of types (See Miller, claim 8, describing receiving transaction data for a legal transaction, wherein the transaction data includes at least one form selection attribute; selecting at least one legal document form from the plurality of legal document forms for the legal transaction, wherein selecting the at least one legal document further comprises applying at least one form selection rule to the transaction data; populating the at least one selected legal document form with data corresponding to the transaction data; and capturing form usage data associated with the selected legal document);
retrieve a first template in the one or more templates and a first portion in the one or more portions associated with the first template, wherein at least one of the first template and the first portion is associated with the first type of electronic document (See Miller, paragraph 0019, describing that a template document is produced using word processing software. The template document is then reviewed and revised by a document developer, and stored in a storage device, such as a disk drive of a computer. An end user then retrieves the template document from the storage device and enters deal specific content in the document. Once the desired content is inserted into specified locations in the document, the document is saved and may be printed or transmitted electronically); and
generate the electronic document of the first type using the first template and the first portion, wherein the first portion is inserted, using the first template, at a first location within the electronic document of the first type (See Miller, paragraphs 0060, 0010-0011, and 0014, describing a Document Generation System 150 that asks expert 107 to input selection rules. Where practical, the domain expert inputs to Rules Engine 180 either a decision table or rule of thumb for the selection of a document or clause based on the values of the attributes. The decision tables and rules of thumb together constitute the initial selection rules upon which Rules Engine 180 selects documents and clauses as customary based on the applicable set of attribute values. When sufficient examples have been collected for training, Learning Agent 170 will construct classifiers, test them against the existing rules and replace outperformed rules with the best performing classifier).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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.
6. Claim(s) 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller (U.S. Publication 2014/0025608 A1), as applied to claim 1 above, and further in view of Wodetzki (U.S. Publication 2022/0398680 A1).
As per dependent claim 5, Miller teaches the limitations of claim 1 as described above. Miller does not teach expressly wherein at least one portion in the one or more portions is stored as an object model, however, Wodetzki teaches this limitation (See Wodetzki, Abstract and paragraph 0008, describing defining and storing an object model containing a structural representation of events and artifacts through which contracts are created, changed and brought to an end in a computer memory). Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to include the content stored as an object model of Wodetzki with the content portions of Miller. The motivation for doing so would have been to evaluate all child contract transaction objects to build a single set of contract data variables and values, as taught by Wodetzki (See Wodetzki, paragraph 0008). Therefore, it would have been obvious to combine Wodetzki with Miller for the benefit of evaluating all child contract transaction objects to build a single set of contract data variables and values to obtain the invention as specified in claim 5.
As per dependent claim 6, Miller and Wodetzki teach the limitations of claim 5 as described above. Miller and Wodetzki also teach wherein the at least one portion in the one or more portions includes one or more labels identifying the at least one portion (See Miller, paragraphs 0010-0011 and 0014).
As per dependent claim 7, Miller and Wodetzki teach the limitations of claim 6 as described above. Miller and Wodetzki also teach wherein the object model includes the one or more labels (See Miller, paragraphs 0010 and 0026).
7. Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller (U.S. Publication 2014/0025608 A1), as applied to claim 1 above, and further in view of Bui (U.S. Publication 2018/0239959 A1).
As per dependent claim 8, Miller teaches the limitations of claim 1 as described above. Miller does not teach expressly receiving at least one feedback from at least one user computing device, however, Bui teaches this limitation (See Bui, paragraph 0187, describing that user roles can review and approve (e.g., sign) the uploaded term sheet, and/or can provide feedback to counsel regarding specifics of the term sheets which the counsel can implement). Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to include the feedback of Bui with the document generation of Miller. The motivation for doing so would have been to enable users to have clear insights into the sorts of actions that are appropriate based on a present state associated with completion of the goal, as taught by Bui (See Bui, paragraph 0187). Therefore, it would have been obvious to combine Bui with Miller for the benefit of enabling users to have clear insights into the sorts of actions that are appropriate based on a present state associated with completion of the goal to obtain the invention as specified in claim 8.
8. Claim(s) 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller (U.S. Publication 2014/0025608 A1), as applied to claims 1 and 11 above, and further in view of Marom (U.S. Publication 2025/0209256 A1).
As per dependent claim 10, Miller teaches the limitations of claim 1 as described above. Miller does not teach expressly wherein the at least one machine learning model includes at least one of the following: a large language model, at least another generative AI model, and any combination thereof, however, Marom teaches this limitation (See Marom, Abstract and paragraph 0006, describing generating a digital document using a large language model). Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to include the large language model to generate a document of Marom with the document generation of Miller. The motivation for doing so would have been to improve efficiency, automate tasks, and enhance user experiences by understanding and generating human language. Therefore, it would have been obvious to combine Marom with Miller for the benefit of improving efficiency, automating tasks, and enhancing user experiences by understanding and generating human language to obtain the invention as specified in claim 10.
As per dependent claim 19, Miller teaches the limitations of claim 11 as described above. Claim 19 additionally incorporates substantially similar subject matter as that of claim 10 above, and is additionally rejected along the same rationale as used in the rejection of claim 10.
Allowable Subject Matter
Claim 9 is 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 filed 11/30/2025 have been fully considered but they are not persuasive.
Prior Art Rejections
1) Applicant argues that Miller does not identify a type of each electronic document for the purposes of generating templates. Instead, it appears to use a title or a type of legal document as a root node for a tree structure of the legal document (Miller, para. [0059]), where type of legal document appears to refer to a specific type of transactions (e.g., real estate) for which specific legal documents are needed. (Miller, para. [0061]). This is different from identification of a type of electronic document (e.g., legal agreement, etc.) and using the identified type when creating templates with associated portions for electronic documents. (see Response; pages 9 and 10)
Examiner respectfully disagrees.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., using the identified type when creating templates) (emphasis added) are not recited in the rejected claim(s).
Examiner notes that the claim language of the independent claims merely recite “identifying a type in a plurality of types for each electronic document in the plurality of electronic documents” and does not include “using the identified type” when creating the templates. In other words, the generated templates for each type of electronic document is not recited to be based on the identified type. Therefore, the claim language fails to support Applicant’s arguments.
Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
2) Applicant argues that Miller fails to generate templates based on specific types of electronic documents and structure and one or more portions of each electronic document in the plurality of electronic documents that have been determined by a generative AI model. To the contrary, Miller uses selection rules that specify whether or not a particular document or clause is included or excluded. The selection rules are determined by its learning agent that predicts which documents or clauses will be needed for a particular transaction. There is no template that Miller creates or uses for its transactions. (see Response; page 10)
Examiner respectfully disagrees.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., generate templates based on specific types of electronic documents) (emphasis added) are not recited in the rejected claim(s).
Examiner notes that the claim language of the independent claims merely recite “identifying a type in a plurality of types for each electronic document in the plurality of electronic documents” and does not include “using the identified type” when creating the templates. In other words, the generated templates for each type of electronic document is not recited to be based on the identified type. Therefore, the claim language fails to support Applicant’s arguments.
Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
3) Applicant argues that Miller does not associate document portions with templates. Instead, Miller uses selection rules, as predicted by its learning agent for a particular transaction, to determine whether a specific document or clause needs to be included. It does not associate any document portions that are determined by a generative AI model from a plurality of documents, where templates define a structural arrangement of such document portions for each type of electronic document. Miller's selection rules appear to simply pick documents/clauses for a particular type of transaction and generate the documents needed for that transaction. (see Response; page 10)
Examiner respectfully disagrees.
Examiner notes that Applicant admits that Miller’s teaches “using a title or a type of legal document as a root node for a tree structure of the legal document “(see Response; page 9; Miller; par. 59) . Examiner submits that one of ordinary skill would consider the optional clauses of the tree structure as taught by Miller to be a structural arrangement of one or more portions of a document (i.e., template). In other words, it is unclear how Applicant’s interpretation of the Miller reference such as creating a legal document with optional clauses based on a tree structure fails to teach or suggest “associating document portions with template defining a structural arrangement”.
For at least the foregoing reasons, Examiner maintains prior art rejections.
4)In effort to overcome applied refence, Examiner encourages Applicant to further amend to include clarifying amendments that require “using the identified determined type” and addressing the difference between the tree structure of Miller and the recited “structural arrangement”. In other words, there is nothing in the claim language that connects the identified type to the structural arrangement of the template (see par. 25 of instant application; generation of templates that may be used to create other electronic documents of a particular type (e.g., sales agreements, non-disclosure agreements, etc.) par. 27; determine a structure and/or portions of documents that may be common to particular types of documents) . It appears further reciting a template defining a structural arrangement based on the identified type, wherein the identified type is based on the structure and portions of the sent documents, would be sufficient to provide a persuasive argument to overcome Miller.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 HENRY ORR whose telephone number is (571)270-1308. The examiner can normally be reached 9AM-5PM EST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Queler can be reached at (571)272-4140. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HENRY ORR/ Primary Examiner, Art Unit 2172
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