DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-8 and 11-18 are pending.
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 1-8 and 11-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a method (i.e., a process), claim 11 to a system (i.e., a machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1 and 11 is (claim 1 being representative):
receiving attributes regarding a proposed agreement;
analyzing the attributes to classify the proposed agreement according to an agreement type;
receiving a proposed clause input for the proposed agreement;
analyzing the proposed clause input, including:
classifying the proposed clause input according to a clause type;
determining whether the proposed clause input relates to a family of documents; and
determining whether the proposed clause input relates to an amendment to a pre-existing agreement or a new agreement;
generating, using an output, proposed revised clause language to enhance a quality of the proposed revised clause language, including:
identifying a previously utilized legal clause according to the clause type and the agreement type;
reformatting the previously utilized legal clause to incorporate a characteristic associated with the family of documents; and
reformatting the previously utilized legal clause to read as either a proposed revision to a pre-existing clause of the pre-existing agreement or a proposed new clause to the new agreement;
evaluating a potential risk exposure associated with the proposed revision or the proposed new clause based on at least one of a risk management parameter or a regulatory requirement; and
reformatting the proposed revision or the proposed new clause to address the potential risk exposure.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, generating, revising, and incorporating clauses into contracts/agreements. All of the steps above are those that an administrator could accomplish, either mentally or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the sales activity or business relation pertaining to a revised contract/agreement, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [0001]-[0002] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions pertaining to generating a revised contract/agreement, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0001]-[0002] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: training generative transformers on approved legal clauses converted into a standardized format, wherein the training uses a collection of validated legal clauses to enhance an ability to generate appropriate language for proposed revisions, and wherein the generative transformers are tailored to produce the standardized format for improved consistency; generative transformers.
Claim 11 recites the following additional elements: one or more processors; non-transitory computer-readable storage media encoding instructions; train generative transformers on approved legal clauses converted into a standardized format, wherein the training uses a collection of validated legal clauses to enhance an ability to generate appropriate language for proposed revisions, and wherein the generative transformers are tailored to produce the standardized format for improved consistency; generative transformers.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0022] of applicant’s specification as filed, for example.
As seen above, examiner interprets “training generative transformers on approved legal clauses converted into a standardized format, wherein the training uses a collection of validated legal clauses to enhance an ability to generate appropriate language for proposed revisions, and wherein the generative transformers are tailored to produce the standardized format for improved consistency” (claim 1 being representative, similar language found in claim 11), described in [0096] of applicant’s specification as filed, as an additional element. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, “training generative transformers on approved legal clauses converted into a standardized format, wherein the training uses a collection of validated legal clauses to enhance an ability to generate appropriate language for proposed revisions, and wherein the generative transformers are tailored to produce the standardized format for improved consistency” provides nothing more than a results-oriented solution that lacks details of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Claims 2-8 and 12-18 include additional steps and/or information that further narrow the abstract idea, without any further additional elements. This narrowing of the abstract idea does not integrate it into practical application and/or add significantly more.
Accordingly, claims 1-8 and 11-18 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-5, 8, 11-12, 14-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kogut-O'Connell (US 20170061352) in view of Iori (US 10540373) and GoGwilt (US 20240202467).
Claims 1 and 11
Kogut-O'Connell discloses:
(Claim 1) A method for a managing a contract {[0003]}, comprising:
(Claim 11) 11. A computer system for managing a contract {[0003]}, comprising: one or more processors {[0037]}; and non-transitory computer-readable storage media encoding instructions {[0038]} which, when executed by the one or more processors, cause the computer system to:
receiving attributes regarding a proposed agreement {[0019] In step 204, contract analyzer program 104 identifies contract parameters, i.e., attributes, of the received contract. In this embodiment, contract analyzer program 104 identifies contract parameters of the received contract by parsing the contract.};
generating proposed revised clause language {[0011] Computer system 102 includes contract analyzer program 104 and data store 106. Contract analyzer program 104 identifies contract parameters, analyzes received contract parameters, predicts contract terms likely to be accepted by users, and generates suggestions, i.e., proposed revised clause language, based, at least in part, on historic data to computer system 110 via network 108.}, including:
identifying a previously utilized legal clause according to the clause type and the agreement type {[0015] Data store 106 stores historic data of previous contracts and contract parameters. The term “historic data”, as used herein, refers generally to previously analyzed contracts and contract parameters associated with previously analyzed, approved contracts, as well as versions of approved contracts that were rejected. For example, historic data can include internal and external parties, approver profiles negotiator profiles, clause language, terms, products, status and time related metrics, modifications made to previously approved clause language etc. In general, data store 106 can be implemented with any storage medium known in the art.};
reformatting the previously utilized legal clause to incorporate a characteristic associated with the family of documents {[0024] In step 304, contract analyzer program 104 accesses historic data from data store 108 and compares previously approved clauses that match the type of clause to be modified, i.e., characteristic associated with the family of documents. Continuing the example above, contract analyzer program 104 accesses historic data that matches the clause “type” to be modified (e.g., one or more previously approved arbitration clauses) and selects contract clauses that match the clause type to be modified. For example, contract analyzer program 104 can select previously approved arbitration clauses from data store 106, compare the previously approved arbitration clauses to the arbitration clause to be modified, i.e., reformatted, and display the discrepancies or allow users to select previously approved arbitration clauses as alternates that would be likely candidates that would be approved.}; and
reformatting the previously utilized legal clause to read as either a proposed revision to a pre-existing clause of the pre-existing agreement or a proposed new clause to the new agreement {[0026] For example, contract analyzer program 104 can visually display a suggestion, i.e., proposed clause, to change the arbitration clause which currently states that “the decision of the arbiter is binding to both parties” to “the decision of the arbiter is non-binding on both parties”. In other embodiments, contract analyzer program 104 the visual representation of the analysis can be in the form of a new document highlighting potential issues along with the suggested changes. Other embodiments of the present invention can generate a new document (i.e., a new contract) that previews what the contract being modified, i.e., reformatted, would look like with the accepted changes that contract analyzer program 104 generated.};
evaluating a potential risk exposure associated with the proposed revision or the proposed new clause based on at least one of a risk management parameter or a regulatory requirement {[0028] In another example of other relevant information that contract analyzer program 104 can display with the analysis pertains to parties that may or may not be at risk of non-compliance with regulations. For example, contract analyzer program 104 can parse through historic data to display relevant information to a contract parameter pertaining to suppliers along with a description that suppliers may be non-compliant or at risk. Contract analyzer program 104 can, in addition to suggesting contract language to mitigate a supplier's risk, display relevant information such as time taken by a supplier for activation, the status of a contract, and disputed items.}; and
reformatting the proposed revision or the proposed new clause to address the potential risk exposure {[0029] In another embodiment, contract analyzer program 104 can also display modification data, i.e., a proposed revision for reformatting, in addition to the previously generated suggestions based on the historic data. For example, contract analyzer program 104 can parse through historic data to generate a trend line of modification data for a received contract analysis request.}.
Kogut-O'Connell doesn’t explicitly disclose, however, Iori, in a similar field of endeavor directed to a predictive approach to contract management, teaches:
analyzing the attributes to classify the proposed agreement according to an agreement type {Col. 5, lines 9-30: In block 250, the clause is categorized into an identified clause category based on the clause profile. The clause library may maintain any suitable number of different clause categories and sub-categories. The clause categories and sub-categories may be structured in order to group like clauses to enable for efficient searching of the clause library and consideration of similar candidate clauses by a user following a search. Exemplary categories may be defined based on one or more of the following: […] an agreement type (e.g., employment contract, software license, etc.).};
receiving a proposed clause input for the proposed agreement {Col. 6, lines 1-12: In an implementation, the one or more candidate, i.e., proposed, clauses may be presented with a display of the iterations or revisions to the clause. For example, if an adverse party made changes to a clause, the clause library management system may provide the user with the initial version and the revised version for comparison purposes. In implementations, the revised version of the clause may be displayed to the user in a manner enabling the user to efficiently identify the changes that were implemented (e.g., in a red-line format). The revisions to the clause may be associated with the revising party, any comments associated with a revision, etc.};
analyzing the proposed clause input {Col. 4, lines 20-25: In another implementation, the clause may be identified by the system based on review and/or scan of a document (e.g., an agreement uploaded to the clause library management system), i.e., analyzed.}, including:
classifying the proposed clause input according to a clause type {Col. 4, lines 1-15: Referring to FIG. 2, method 200 begins with identifying a clause for adding to a clause library, in block 210. The clause library is configured to include multiple clauses in an indexed, categorized, i.e., classified, and searchable data structure.};
determining whether the proposed clause input relates to a family of documents {Col. 5, lines 9-16: n block 250, the clause is categorized into an identified clause category based on the clause profile. The clause library may maintain any suitable number of different clause categories and sub-categories. The clause categories and sub-categories may be structured in order to group like clauses to enable for efficient searching of the clause library and consideration of similar candidate clauses by a user following a search, i.e., a determining whether the proposed clause input relates to a family of documents.}; and
determining whether the proposed clause input relates to an amendment to a pre-existing agreement or a new agreement {Col. 5, line 65 to col. 6, line 12: In an implementation, the one or more candidate clauses are presented to the user with their corresponding rating. In an implementation, the one or more candidate clauses may be presented with a display of the iterations or revisions, i.e., amendments, to the clause. For example, if an adverse party made changes to a clause, the clause library management system may provide the user with the initial version and the revised version for comparison purposes. In implementations, the revised version of the clause may be displayed to the user in a manner enabling the user to efficiently identify the changes that were implemented (e.g., in a red-line format). The revisions to the clause may be associated with the revising party, any comments associated with a revision, etc.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kogut-O'Connell to include the features of Iori. Given that Kogut-O'Connell is directed to managing a repository of clauses for use in the drafting of legal documents, one of ordinary skill in the art would have been motivated to look to Iori, in order to facilitate identifying one or more candidate clauses that meet criteria established by the user {Col. 2, lines 5-10 of Iori}.
The combination of Kogut-O'Connell and Iori doesn’t explicitly teach, however, GoGwilt, in a similar field of endeavor directed to analyzing contract terms, teaches:
training generative transformers on approved legal clauses converted into a standardized format {[0029] Further, an extractive language model may be pre-trained (e.g., a version of BERT pre-trained using a large corpus of language data) and then further trained to process clauses and/or contractual terms of a contract, i.e., approved legal clauses converted into a standardized format, where the output from BERT may be used when training the one or more generative language models. Also see [0028], which further describes generative transformers: generative language models include GPT-2, GPT-3, GPT-4, or ChatGPT by OpenAI. Also see [0105], which further describes converted into a standardized format: In some instances, a document may be converted into an object-oriented format that includes an object for each document, an object for each clause within a document (e.g., which may include an indication of the classification for the clause, and an indication of where the clause begins and ends within the document), and metadata that indicates additional context for the document and/or clauses (e.g., version number of the document, classification indicators for the clauses, and the like).}, wherein the training uses a collection of validated legal clauses to enhance an ability to generate appropriate language for proposed revisions {[0029] Examples of a suitable extractive language model is a Bidirectional Encoder Representations from Transformers (BERT) or a model based on BERT. The extractive language model may be pre-trained (e.g., a version of BERT pre-trained using a large corpus of language data) or may be specifically trained to process clauses and/or contractual terms of a contract (e.g., a version of BERT trained using a corpus of contract data).}, and wherein the generative transformers are tailored to produce the standardized format for improved consistency {[0110] At step 813, the computing platform may receive second user input based on the initial document and/or workflow rules for the contracting party. The second user input may, for example, modify the initial document and/or workflow rules (e.g., add or remove rules), select between using user-configured language or standardized language, modify the user-configured language or standardized language, and the like. Also see [0113], where second user input represent tuning or tailoring of generative transformer to produce a standardized format, thereby improving efficiency: Configuring the one or more natural language processing models may include configuring one or more generative models and/or one or more extractive language models based on the confidence threshold if it was provided as part of the second user input.};
generating, using an output of the generative transformers, proposed revised clause language to enhance a quality of the proposed revised clause language {[0058] Once the response data 116 is provided to the user, the user may be able to review the one or more suggestions and/or recommendations provided based on the output of the one or more generative language models.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Kogut-O'Connell and Iori to include the features of GoGwilt. Given that Kogut-O'Connell is directed to managing a repository of clauses for use in the drafting of legal documents, one of ordinary skill in the art would have been motivated to look to GoGwilt, in order to facilitate analyzing contractual terms and clauses in a legal document, recommending edits, and/or making changes to a workflow {[0002] of GoGwilt}.
Claims 2 and 12
Iori further teaches: leveraging a private clause library in generating the proposed revised clause language {Col. 2, lines 40-45: The clause library management system 102 maintains and manages a clause library 106 including multiple clauses having associated clause profiles 107.}.
The motivation and rationale to include the additional features of Iori is the same as set forth previously.
Claims 4 and 14
Kogut-O'Connell further discloses: forwarding the proposed revision or the proposed new clause to a human for manual review {[0014] In other embodiments, responsive to reaching or exceeding the number of modifications, contract analyzer program 104 can prompt the user, i.e., forward for manual review, to confirm that the modifications still conform to the broad outline of changes to the standard legal boundaries allowed.}.
Claims 5 and 15
Iori further teaches: identifying a relationship of the proposed agreement to the family of documents {Col. 6, lines 1-12: In an implementation, the one or more candidate clauses may be presented with a display of the iterations or revisions to the clause, i.e., in relationship with family of documents. For example, if an adverse party made changes to a clause, the clause library management system may provide the user with the initial version and the revised version for comparison purposes. In implementations, the revised version of the clause may be displayed to the user in a manner enabling the user to efficiently identify the changes that were implemented (e.g., in a red-line format). The revisions to the clause may be associated with the revising party, any comments associated with a revision, etc.}.
The motivation and rationale to include the additional features of Iori is the same as set forth previously.
Claims 8 and 18
Kogut-O'Connell further discloses: providing a user notification regarding the potential risk exposure associated with the proposed revision or the proposed new clause {[0028] In another example of other relevant information that contract analyzer program 104 can display with the analysis pertains to parties that may or may not be at risk of non-compliance with regulations. For example, contract analyzer program 104 can parse through historic data to display relevant information to a contract parameter pertaining to suppliers along with a description that suppliers may be non-compliant or at risk. Contract analyzer program 104 can, in addition to suggesting contract language to mitigate a supplier's risk, display relevant information such as time taken by a supplier for activation, the status of a contract, and disputed items.).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kogut-O'Connell, Iori, and GoGwilt, further in view of Doran (US 20210125297).
Claims 3 and 13
The combination of Kogut-O'Connell, Iori, and GoGwilt doesn’t explicitly teach, however, Doran, in a similar field of endeavor directed to intelligent contract analysis, teaches: leveraging a publicly available information in generating the proposed revised clause language {[0063] To increase the speed of action response delivery, the domain classification sub-engine may analyze the extracted features of the input data, automatically construct an intelligent query based on the extracted features, fire the intelligent query against an external search engine (e.g., comparing certain contract provisions with a broader set of publicly available contracts).}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Kogut-O'Connell, Iori, and GoGwilt to include the features of Doran. Given that Kogut-O'Connell is directed to managing a repository of clauses for use in the drafting of legal documents, one of ordinary skill in the art would have been motivated to look to Doran, in order to facilitate identifying and returning a consolidated set of responses that match with the queried clauses {[0063] of Doran}.
Claims 6-7 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kogut-O'Connell, Iori, and GoGwilt, further in view of Zhou (US 20030018481).
Claims 6-7 and 16-17
The combination of Kogut-O'Connell, Iori, and GoGwilt doesn’t explicitly teach, however, Zhou, in a similar field of endeavor directed to generating contracts, teaches: tailoring the proposed revision or the proposed new clause to one or more lines of business associated with a financial institution / tailoring the proposed revision or the proposed new clause to one or more financial products or services {[0035] An embodiment of the invention provides users with a flexible tool for defining and subsequently generating different types of documents. For instance embodiments of the invention may be utilized to generate contracts relating to the sale or distribution of a certain product or set of products. The contracts that are ultimately generated can be customized to fit the situation for which the contract is intended. For instance, if a supplier and a distributor meet to negotiate a deal, and both parties come to an agreement that provides for compensation to the distributor's sales team (or to the distributor itself) according to a certain compensation plan, a document that represents the terms of that agreement can be instantaneously generated using embodiments of the invention. Thus, the supplier (e.g., a manufacturer, producer, supply house, or financial institution) may present the distributor or it's representatives with a document that details the negotiated agreement prior to the end of the meeting.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Kogut-O'Connell, Iori, and GoGwilt to include the features of Zhou. Given that Kogut-O'Connell is directed to managing a repository of clauses for use in the drafting of legal documents, one of ordinary skill in the art would have been motivated to look to Zhou, in order to facilitate generating contracts customized to fit the situation for which the contract is intended {[0007] of Zhou}.
Response to Arguments
Applicant’s remarks filed 2/17/26 have been fully considered. Examiner’s response follows, with applicant’s headings used for consistency.
Claims Rejections - 35 USC § 101
Regarding the rejections under 35 USC § 101, applicant offers, after restating the present claim amendments and summarizing MPEP 2106 (brackets indicate portions of response omitted for brevity):
The action erroneously concludes that the claims are directed to an abstract idea. Further, amended claim 1 demonstrates integration of any recited judicial exception into a practical application and significantly more than any abstract idea.
[…]
Amended claim 1 does not recite a judicial exception. The training limitation recites training generative transformers on validated legal clauses in standardized format, which does not recite mathematical calculations, formulas, or equations by name or using mathematical symbols. This is analogous to Example 39 of the subject matter eligibility examples, where the limitation "training the neural network in a first stage using the first training set" was found not to recite a judicial exception because it did not set forth or describe any mathematical relationships, calculations, formulas, or equations. Subject Matter Eligibility Examples: Abstract Ideas at 8-9. The Patent Office recently confirmed that a claim limitation that merely involves or is based on mathematical concepts, but does not set forth or describe mathematical relationships, formulas, or calculations, does not recite a mathematical concept. Memorandum dated August 4, 2025 from Deputy Commissioner Kim titled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" ("August Memorandum") at 2-3.
Furthermore, the claimed process cannot practically be performed in the human mind, as suggested in the action. Action at 3-4. Training generative transformers on a collection of validated legal clauses, tailoring them to produce standardized format for improved consistency, and using their output to generate enhanced quality clause language are not processes that can be practically performed by a human using pen and paper. The August Memorandum instructs that the action should not expand the mental process grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind. August Memorandum at 2.
While well taken, examiner remains unpersuaded. It’s important to note that Example 39 did not recite any abstract idea, which is distinct from applicant’s claim set. Further, examiner did not consider the generative transformers or their training as part of the abstract idea. These additional elements, considered at Step 2A Prong Two and Step 2B, are claimed in a results-oriented manner and lack specificity. MPEP 2106.05(f) is clear that merely appending a computer or machinery, especially when claimed in a results-oriented manner, does not integrate the abstract idea into practical application or add significantly more. This interpretation holds regardless of whether the elements are viewed alone or in combination. Examiner therefore maintains that a proper analysis was performed, one that clearly articulated both the abstract idea and additional element, in line with recent USPTO memoranda.
Applicant continues:
[…]
Amended claim 1 now recites training generative transformers on approved legal clauses converted into a standardized format, wherein the training uses a collection of validated legal clauses to enhance an ability to generate appropriate language for proposed revisions, and wherein the generative transformers are tailored to produce the standardized format for improved consistency. The claim further recites generating the proposed revised clause language using an output of the generative transformers to enhance a quality of the proposed revised clause language. Finally, the claim recites reformatting the proposed revision or the proposed new clause to address the potential risk exposure. These amendments, when considered in combination with the other claim elements, integrate any recited judicial exception into a practical application that improves contract management technology.
The specification discloses that organizations traditionally rely on manual review by legal teams to verify and approve contractual clauses, a process that is time-consuming, resource- intensive, and prone to errors. Specification at [0002], [0014]. The disclosed system addresses these technological challenges by leveraging artificial intelligence and machine learning algorithms to enhance the contract management process, enabling faster review and approval, improving consistency in the review process, and minimizing the occurrence of human errors. Specification at [0015]. The specification further explains that the system embodies a technological advancement in contract management, leveraging artificial intelligence algorithms to efficiently analyze and process large amounts of data at speeds and with a level of accuracy that surpasses human capabilities. Specification at [0019].
Amended claim 1 reflects this technological improvement. The training limitation specifies that generative transformers are trained on approved legal clauses that have been converted into a standardized format, where the training uses a collection of validated legal clauses to enhance the ability to generate appropriate language for proposed revisions, and where the transformers are tailored to produce the standardized format for improved consistency. Specification at [0051], [0096], [0097]. This is not a mere instruction to apply an abstract idea on a computer, but rather a specific implementation that improves the functioning of the contract management system itself by enabling the generation of consistent, high-quality proposed clause language. The tailoring of the generative transformers to produce standardized format addresses the technical problem of inconsistency and error in legal clause generation that plagued manual review processes.
Amended claim 1 further specifies that the generating step uses an output of the generative transformers to enhance a quality of the proposed revised clause language. This limitation integrates the trained transformers into the practical application of generating improved legal clauses. Specification at [0017], [0047]. The claim also recites reformatting the proposed revision or the proposed new clause to address the potential risk exposure, which uses the risk evaluation to provide a technological solution that ensures compliance with regulatory requirements and minimizes organizational exposure to risk. Specification at [0048]-[0050], [0101], [0102].
When these limitations are evaluated in combination with the other claim elements, the claim as a whole is directed to an improvement in contract management technology. The USPTO guidance on subject matter eligibility, particularly the July 2024 Subject Matter Eligibility Examples and the August Memorandum, confirms that claims reflecting technological improvements integrate judicial exceptions into practical applications. The August Memorandum emphasizes that an important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. See August Memorandum at 4.
Amended claim 1 recites a particular solution to the technological problems of time- consuming, error-prone manual contract review by training generative transformers on validated legal clauses in a standardized format and using the output of these transformers to generate and reformat clauses that address risk exposure. This is analogous to Example 48, Claim 3, where a claim was found eligible because it integrated mathematical concepts into a practical application by using a DNN trained on source separation to improve speech-to-text transcription technology. See July 2024 Subject Matter Eligibility Examples at 25-28. Just as Example 48, Claim 3 reflected technical improvements by reciting how the DNN aided in cluster assignments to produce transcripts, the amended claim here reflects technical improvements by reciting how the generative transformers are trained on validated legal clauses in standardized format and used to generate enhanced quality clause language.
Similarly, Example 47, Claim 3 was found eligible because it integrated abstract ideas into a practical application by improving network security technology through detecting source addresses, dropping malicious packets, and blocking future traffic in real time. See July 2024 Subject Matter Eligibility Examples at 10-13. The claim reflected the improvement by reciting specific steps that used the trained ANN to provide security solutions. Likewise, amended claim 1 reflects improvements in contract management technology by reciting specific steps that use the trained generative transformers to generate enhanced quality clause language and reformat clauses to address risk exposure.
The Ex Parte Desjardins precedential decision further supports eligibility here. See Memorandum dated December 5, 2025 from Deputy Commissioner Kim titled "Advance notice of change to the MPEP in light of Ex Parte Desjardins at 2-4. In Desjardins, the specification identified improvements to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks, allowing the system to reduce storage capacity and reduce system complexity. The claims reflected this improvement by including limitations that adjusted values of parameters to optimize performance on new tasks while protecting performance on previous tasks.
Similarly, the instant specification identifies improvements to contract management technology by explaining how the generative transformers are trained on validated legal clauses in standardized format to enhance generation ability and improve consistency. Specification at [0051], [0096]. Amended claim 1 reflects this improvement by reciting the training limitation that tailors the transformers to produce the standardized format and the generating limitation that uses the output to enhance quality.
Accordingly, amended claim 1 integrates any recited judicial exception into a practical application of contract management technology.
Applicant appears to conflate the abstract idea, considered at Step 2A Prong One, with the additional elements, considered at Step 2A Prong Two and Step 2B. Examiner highlighted the following abstract idea at Step 2A Prong One (claim 1 being representative):
receiving attributes regarding a proposed agreement;
analyzing the attributes to classify the proposed agreement according to an agreement type;
receiving a proposed clause input for the proposed agreement;
analyzing the proposed clause input, including:
classifying the proposed clause input according to a clause type;
determining whether the proposed clause input relates to a family of documents; and
determining whether the proposed clause input relates to an amendment to a pre-existing agreement or a new agreement;
generating, using an output, proposed revised clause language to enhance a quality of the proposed revised clause language, including:
identifying a previously utilized legal clause according to the clause type and the agreement type;
reformatting the previously utilized legal clause to incorporate a characteristic associated with the family of documents; and
reformatting the previously utilized legal clause to read as either a proposed revision to a pre-existing clause of the pre-existing agreement or a proposed new clause to the new agreement;
evaluating a potential risk exposure associated with the proposed revision or the proposed new clause based on at least one of a risk management parameter or a regulatory requirement; and
reformatting the proposed revision or the proposed new clause to address the potential risk exposure.
These are all steps an administrator could perform, and, contrary to applicant’s assertions, examiner doesn’t view them as necessarily technical. An individual could analyze an agreement, identify relevant clauses, determine appropriate revisions, and iterate, thereby addressing any potential risks. Indeed, applicant’s own specification, at the outset, identifies the challenges associated with contract analysis, i.e., a non-technical problem, as seen in [0001]:
Large organizations often deal with a multitude of contracts encompassing different products, instruments, and services provided to their customers. These contracts typically contain numerous legal clauses and terms that are negotiated with counterparties. While it is important for these organizations to have a robust system in place to verify and approve these clauses and terms to mitigate potential risks to the organization, ensuring the accuracy and compliance of these clauses and terms poses a significant challenge.
The determination of whether there is a technical improvement is based on the analysis of the additional elements, alone and in combination, performed at Step 2A Prong Two. In this instance, examiner maintains the additional are merely instructions to apply the abstract idea to a computer and/or claimed in a results-oriented manner, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0022] of applicant’s specification as filed, for example. The specifics of the training process are not described in any detail, other than a generic recitation in [0096]. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner.
Given the fact that applicant is merely applying generic computing devices and machinery, claimed in a results-oriented manner, to the tasks of the abstract idea, examiner therefore maintains that the claimed invention does not represent: 1) a technological solution pertaining to “enabling the generation of consistent, high-quality proposed clause language,” where the “tailoring of the generative transformers to produce standardized format addresses the technical problem of inconsistency and error in legal clause generation that plagued manual review processes,”; 2) a “technological solution that ensures compliance with regulatory requirements and minimizes organizational exposure to risk”; 3) a “particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome”; or 4) a “particular solution to the technological problems of time- consuming, error-prone manual contract review.”
Applicant’s reference to Example 47, Claim 3 and the Ex Parte Desjardins decision is appreciated; however, applicant’s claim set is not comparable. Both Example 47, Claim 3 and Ex Parte Desjardins feature technological solutions to technological problems. The same cannot be said about applicant’s claims.
Applicant continues:
[…]
Amended claim 1 further recites additional elements that provide significantly more than any judicial exception. The training limitation specifies a particular implementation: training generative transformers on approved legal clauses converted into a standardized format, using a collection of validated legal clauses to enhance generation ability, and tailoring the transformers to produce the standardized format for improved consistency. This is not a well-understood, routine, conventional activity in the field of contract management.
The specification explains that organizations traditionally rely on time-consuming, error- prone manual review processes. Specification at [0002], [0014]. The specification does not describe the recited training process as conventional, but rather discloses it as part of the technological solution that addresses the problems with manual review. Specification at [0015]- [0020], [0051], [0096]. The use of generative transformers trained on validated legal clauses in a standardized format, tailored to produce that standardized format for improved consistency, represents a specific implementation that is more than generic application of an abstract idea on a computer.
Similarly, the limitation of reformatting the proposed revision or proposed new clause to address the potential risk exposure provides a meaningful limitation that confines the claim to a particular useful application. Specification at [0048]-[0050], [0101]-[0104]. This reformatting addresses specific risk management parameters and regulatory requirements, providing a technological solution to ensure compliance and minimize organizational risk exposure. This is not insignificant post-solution activity, but rather an integral part of the improved contract management process that uses the risk evaluation in a specific manner.
The August Memorandum cautions examiners not to oversimplify claim limitations and expand the application of the "apply it" consideration. See August Memorandum at 4. The memorandum instructs that examiners should consider whether the claim covers a particular solution to a problem or a particular way to achieve a desired outcome. The amended claim here recites a particular solution: training generative transformers on validated legal clauses in standardized format, tailoring them for consistency, and using their output to generate enhanced quality clause language that is then reformatted to address risk exposure. This is a particular way to achieve the desired outcome of improved contract management, not a mere idea of a solution.
Accordingly, the additional elements in amended claim 1 amount to significantly more than any recited judicial exception.
Examiner maintains that a proper analysis was performed, in line with recent USPTO memoranda. For Step 2B, examiner “carried over” his analysis from Step 2A Prong Two, per the guidance set forth in MPEP 2106: In this instance, examiner maintains the additional are merely instructions to apply the abstract idea to a computer and/or claimed in a results-oriented manner, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0022] of applicant’s specification as filed, for example. The specifics of the training process are not described in any detail, other than a generic recitation in [0096]. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner.
For these reasons, examiner maintains that the additional elements, whether viewed alone or in combination, are not significantly more at Step 2B.
Accordingly, the rejections under 35 USC § 101 are maintained.
Claims Rejections - 35 USC § 103
Regarding the rejections under 35 USC § 103, applicant offers:
Amended claim 1 recites training generative transformers on approved legal clauses converted into a standardized format, wherein the training uses a collection of validated legal clauses to enhance an ability to generate appropriate language for proposed revisions, and wherein the generative transformers are tailored to produce the standardized format for improved consistency. The action relies on GoGwilt at [0029] for teaching this limitation. Action at 15-16 (rejection of claims 9-10).
However, GoGwilt [0029] does not teach or suggest this limitation. GoGwilt describes extractive language models, specifically BERT, not generative transformers. GoGwilt at [0029]. GoGwilt expressly distinguishes between extractive language models and generative language models, noting that an extractive language model may extract output data from pre-existing text (e.g., words, sentences, and/or paragraphs extracted from the pre-existing text) while a generative language model may generate output data to appear as human-written language (e.g., words, sentences and/or paragraphs generated by the generative language model). GoGwilt at [0027].
The cited passage in GoGwilt specifically states that an extractive language model may be pre-trained (e.g., a version of BERT pre-trained using a large corpus of language data) and then further trained to process clauses and/or contractual terms of a contract (e.g., the pre-trained version of BERT may be further trained using a corpus of contract data). GoGwilt at [0029]. This passage describes training extractive language models, not generative transformers. The claimed "generative transformers" are fundamentally different from the extractive language models described in GoGwilt. Amended claim 1 requires that the generative transformers be trained on approved legal clauses converted into a standardized format and be tailored to produce the standardized format for improved consistency. GoGwilt teaches no such training of generative transformers.
Amended claim 1 further recites generating, using an output of the generative transformers, proposed revised clause language to enhance a quality of the proposed revised clause language. The action cites paragraph [0029] of GoGwilt for teaching that output from BERT may be used when training the one or more generative language models or that output from BERT may be used as input to the one or more generative language models when analyzing documents. Action at 15. However, the claim does not recite training one or more generative language models or using output as input to generative language models. Rather, the amended claim specifically recites generating proposed revised clause language using an output of the generative transformers.
GoGwilt describes using extractive language models as a preprocessing step to generate training data for generative language models or to enrich input that is sent to generative language models. GoGwilt at [0029]. This is fundamentally different from the claimed step of generating proposed revised clause language using an output of the generative transformers. GoGwilt does not disclose or suggest that the output of generative transformers is used to generate proposed revised clause language. The claimed generating step requires that the output of the generative transformers be used in the generation of the proposed revised clause language, not merely as training data or input enrichment for other models.
Amended claim 1 also recites evaluating a potential risk exposure associated with the proposed revision or the proposed new clause based on at least one of a risk management parameter or a regulatory requirement; and reformatting the proposed revision or the proposed new clause to address the potential risk exposure. The action relies on Kogut-O'Connell at [0028]-[0029] for this limitation. Action at 9-10. Kogut-O'Connell describes displaying relevant information about parties that may or may not be at risk of non-compliance with regulations and suggesting contract language to mitigate a supplier's risk. Kogut-O'Connell at [0028]. Kogut- O'Connell describes displaying modification data in addition to previously generated suggestions. Kogut-O'Connell at [0029].
However, neither passage of Kogut-O'Connell teaches or suggests reformatting the proposed revision or the proposed new clause to address the potential risk exposure as claimed. Kogut-O'Connell describes displaying information and suggesting contract language, but does not teach reformatting a proposed revision or proposed new clause based on the evaluated risk exposure. The claimed reformatting step operates on the proposed revision or proposed new clause itself to modify it in response to the evaluated risk exposure. Kogut-O'Connell merely suggests alternate contract language or displays information; it does not teach reformatting the proposed revision or proposed new clause to address the potential risk exposure.
The proposed combination of art fails to teach or suggest the ordered combination of limitations recited in amended claim 1. The claim requires a specific sequence: (1) training generative transformers on approved legal clauses converted into a standardized format, where the generative transformers are tailored to produce the standardized format for improved consistency; (2) generating proposed revised clause language using an output of the generative transformers to enhance quality; (3) evaluating potential risk exposure; and (4) reformatting the proposed revision or proposed new clause to address the potential risk exposure. The cited art does not teach or suggest this ordered combination.
While well taken, examiner maintains that the combination of features is taught by the cited references. With respect to GoGwilt, examiner asserts that it teaches: training generative transformers on approved legal clauses converted into a standardized format {[0029] Further, an extractive language model may be pre-trained (e.g., a version of BERT pre-trained using a large corpus of language data) and then further trained to process clauses and/or contractual terms of a contract, i.e., approved legal clauses converted into a standardized format, where the output from BERT may be used when training the one or more generative language models. Also see [0028], which further describes generative transformers: generative language models include GPT-2, GPT-3, GPT-4, or ChatGPT by OpenAI, i.e., a generative transformer. Also see [0105], which further describes converted into a standardized format: In some instances, a document may be converted into an object-oriented format that includes an object for each document, an object for each clause within a document (e.g., which may include an indication of the classification for the clause, and an indication of where the clause begins and ends within the document), and metadata that indicates additional context for the document and/or clauses (e.g., version number of the document, classification indicators for the clauses, and the like).}, wherein the training uses a collection of validated legal clauses to enhance an ability to generate appropriate language for proposed revisions {[0029] Examples of a suitable extractive language model is a Bidirectional Encoder Representations from Transformers (BERT) or a model based on BERT. The extractive language model may be pre-trained (e.g., a version of BERT pre-trained using a large corpus of language data) or may be specifically trained to process clauses and/or contractual terms of a contract (e.g., a version of BERT trained using a corpus of contract data).}, and wherein the generative transformers are tailored to produce the standardized format for improved consistency {[0110] At step 813, the computing platform may receive second user input based on the initial document and/or workflow rules for the contracting party. The second user input may, for example, modify the initial document and/or workflow rules (e.g., add or remove rules), select between using user-configured language or standardized language, modify the user-configured language or standardized language, and the like. Also see [0113], where second user input represent tuning or tailoring of generative transformer to produce a standardized format, thereby improving efficiency: Configuring the one or more natural language processing models may include configuring one or more generative models and/or one or more extractive language models based on the confidence threshold if it was provided as part of the second user input.};
generating, using an output of the generative transformers, proposed revised clause language to enhance a quality of the proposed revised clause language {[0058] Once the response data 116 is provided to the user, the user may be able to review the one or more suggestions and/or recommendations provided based on the output of the one or more generative language models.}.
With respect to Kogut-O'Connell, examiner asserts that, in addition to the citations provided previously, it teaches:: reformatting the previously utilized legal clause to read as either a proposed revision to a pre-existing clause of the pre-existing agreement or a proposed new clause to the new agreement {See previous citation to [0024]. Also see [0026]: In other embodiments, contract analyzer program 104 the visual representation of the analysis can be in the form of a new document highlighting potential issues along with the suggested changes. Other embodiments of the present invention can generate a new document (i.e., a new contract) that previews what the contract being modified would look like with the accepted changes that contract analyzer program 104 generated.}.
For these reasons, examiner maintains that the combination of references teaches, contrary to applicant’s assertion: (1) training generative transformers on approved legal clauses converted into a standardized format, where the generative transformers are tailored to produce the standardized format for improved consistency; (2) generating proposed revised clause language using an output of the generative transformers to enhance quality; (3) evaluating potential risk exposure; and (4) reformatting the proposed revision or proposed new clause to address the potential risk exposure.
Accordingly, the rejections under 35 USC § 103 are maintained.
In summary, examiner has responded to all of applicant’s arguments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“LEGAL-BERT: The Muppets straight out of Law School” (NPL attached), which describes in Sections 3 and 4: applications of a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications.
Shah (US 20250307749), which teaches in [0068]: Once clauses, sentences, etc. of the electronic document are extracted, the obligation management engine 150 may be configured identify one or more entities contained in the document portions of the electronic document. Identification of entities may include semantically searching, using a machine learning model, document portions that were extracted from the electronic document to determine content of each document portion. Based on the content, specific entities may then be recognized (e.g., using ML models that may have been trained on historical data defining entities). The entities may be any type of entities, e.g., parties to an agreement (e.g., Company A), a specific term and/or condition of an agreement (e.g., “Term of this agreement shall be 1 year.”), a particular obligation contained in the agreement (e.g., “Goods must be shipped on the first of every month.”), and/or any other type of entities. The engine 150 may then send the entities to a generative artificial intelligence (AI) model to generate one or more rules defining one or more obligations associated with the entities. The generative AI models may be part of the current subject matter system and/or be one or more third party models (e.g., ChatGPT, Bard, DALL-E, Midjourney, DeepMind, etc.). The rules may be determined using content of the document portions and/or entities. Once rules are generated, they may be executed for the purposes of monitoring compliance with obligations by the entities. In some example embodiments, the rules may be executed by an enterprise resource planning system.
Al-Sinan (US 20210304297), which teaches in [0076] In an example, the user can request the system to perform the following procurement: “I would like to procure a maintenance contract for the fire system in North Park Building in Dhahran.” The system can require the user to input all mandatory variables in accordance with the previously described matrix (Table 2—Procurement Request Variable Matrix). After that, the system can search for the key words “fire system” and “maintenance” in the repository. If no similar scopes were found, the system can scrape the Internet and find similar scopes of work to input as training data into AI Text Generator GPT-2 for processing. Finally, the system can create a suitable scope of work along with the relevant terms and conditions. As the system is deployed by more organizations, the database can have a wider range of agreement types and scopes of work that can be utilized across multiple organizations. This feature can be applicable when the notion of an e-marketplace is well established. The final result is that the APS 602 can output a pro forma document 622 based on the requirements of the statement of work 606.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629