DETAILED ACTION
Priority
This action is in response to the U.S. filing dated 24 September 2024 which claims a foreign priority date of 02 October 2023. Claims 1-17 are pending and have been considered below.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 24 September 2024 has been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 2 and 10 objected to because of the following informalities: claims 2 and 10 recite “generate reason for pointing out”, examiner suggests, “generate a reason for pointing out”. Appropriate correction is required.
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.
Independent claims 1, 9 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 9 and 17 are directed to the abstract idea of creating, extracting, and reviewing the content of a document, as explained in detail below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1, 9 and 17 recite, in part, “generate a tentative document on a basis of the document preparation command and the retrieved related document … extract a check item to be reviewed by a document reviewer in the draft document.” These steps describe the concept of creating, extracting, and reviewing the content of a document which, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim precludes the step from practically being performed in the mind. For example, but for the “processor” language, “generate, extract and review” in the context of these claims encompasses a user manually identifying portions of a document. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As such, the description in claims 1, 9 and 17 of creating, extracting, and reviewing the content of a document is an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element – using a processor to perform the steps. The processor in the steps is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, these claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claims recite the additional limitations of “processor,” and “retrieve related documents.” Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities (e.g. see references cited) amount to no more than implementing the abstract idea with a computerized system. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Thus, taken alone or in combination, the additional elements do not amount to significantly more than the above-identified judicial exception.
Further, the additional elements found in dependent claims 2-4, 6-8, 10-12 and 14-16, considered both individually and as an ordered combination along their dependency trees with their respective independent claims, also do not appear to be drawn to a practical application or amount to significantly more than the abstract idea. Therefore, dependent claims 2-4, 6-8, 10-12 and 14-16 are similarly rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Jin (US 2024/0289536 A1) in view of Law et al. (US 2025/0086386 A1).
As for independent claim 1, Jin teaches the device comprising:
a least one memory configured to store instructions; and at least one processor configured to execute instructions to: [(e.g. see Jin paragraph 0050) ”a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described”].
retrieve a related document corresponding to text included in a document preparation command input by an author [(e.g. see Jin paragraph 0039) ”A user 410, Paula Smith of XYZ Inc., is logged into the document management system 110, and provides input 420 that initiates a workflow for generating a licensing agreement (e.g., the electronic document 120) … The input 420 may be natural language text, as is shown in FIG. 4, or, in other embodiments, a selection of a workflow from a plurality of options”].
generate a tentative document on a basis of the document preparation command and the retrieved related document [(e.g. see Jin paragraph 0039 and Fig. 4) ”The input 420 may be natural language text, as is shown in FIG. 4, or, in other embodiments, a selection of a workflow from a plurality of options. The document management system 110 generates suggestions for values (e.g., the predicted values 380, as output by the machine learning model 300) based on past historical workflows 425. In this example, the document management system 110 analyzed past historical workflows 425 that generated agreements sent to ABC Properties for execution. The interface 400 includes a first set of fields and corresponding predicted values 430. For example, the document management system 110 may determine with near certainty (e.g., likelihood above a predefined high threshold (e.g., 95%+likely)) that the field 435, “Party 1,” corresponds to the value 440 “XYZ Inc.” The user 410, however, may edit, change, and/or override any of the first set of fields and corresponding predicted values 430. The interface 400 also includes a second set of fields and a number of corresponding predicted values 450. For the field 455, “Choice of Law,” the interface 400 displays the values 460 of “California” and “Washington,” which the user 410 can select from”].
and on a basis of a content of a draft document generated from the tentative document by the author [(e.g. see Jin paragraph 0040 and Fig. 5) ”FIG. 5 illustrates an interface 400 of the document management system for viewing the generated electronic document 120, in accordance with an example embodiment. After the user 410 confirms the predicted values 450, the document management system 110 executes the workflow to generate the electronic document 120. The generated document incorporates the first set of values 430, as well as the second set of values 450 that have been confirmed by the user 410, into the generated electronic document 120. In some embodiments, the document management system 110 performs the remainder of any workflow operations on the generated electronic document 120”].
Jin does not specifically teach extract a check item to be reviewed by a document reviewer in the draft document. However, in the same field of invention, Law teaches:
extract a check item to be reviewed by a document reviewer in the draft document [(e.g. see Law paragraphs 0034, 0045, 0054) ”The pre-screener systems may dramatically reduce the amount of time that compliance associates spend reviewing documents by identifying non-compliant portions … supply the submitted document to a compliance user interface 320, which communicates with a compliance team device 204 to allow compliance review of the submitted document by a compliance team member. For example, the compliance user interface 320 may display the submitted document to the compliance team device 204, may transmit the submitted document to compliance team device 204, may include one or more tools to allow compliance associate to provide feedback or edits to the submitted document, may include options for approving a submitted document, etc. … compliance team associates may provide rules via the compliance team device 204 that indicate certain phrases that should not be used according to the compliance rules, for certain words that should be weighted higher during the automated review process of the submitted documents, etc.”].
Therefore, considering the teachings of Jin and Law, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add extract a check item to be reviewed by a document reviewer in the draft document, as taught by Law, to the teachings of Jin because it allows the compliance associates to focus more on the portions that require the most significant human review (e.g. see Law paragraph 0043).
As for dependent claim 2, Jin and Law teach the device as described in claim 1, but Jin does not specifically teach the following limitation. However, Law teaches:
wherein the at least one processor is configured to execute the instructions to, with respect to an indicated part pointed out in the draft document by the document reviewer according to the extracted check item, generate reason for pointing out on a basis of a content of the indicated part [(e.g. see Law paragraphs 0036, 0040) ”the pre-screener system 210 may provide feedback to the submitter at the creator device 202, so the submitter can see why the document was not compliant to assist the submitter in future submissions … provide feedback, based on historical review comments from compliance associates, based on edits from compliance associates, etc. The feedback may be associated with a specific business unit that submitted the document, the type of document, etc. In various implementations, different words in a document may be assigned weights automatically”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 3, Jin and Law teach the device as described in claim 2, but Jin does not specifically teach the following limitation. However, Law teaches:
wherein the at least one processor is configured to execute the instructions to generate a correction proposal to the draft document on a basis of the reason for pointing out [(e.g. see Law paragraphs 0036, 0055) ”the heuristic module 326 may provide automated feedback that can be relayed to the submitter with suggested changes, suggested disclaimers, etc., based on rules of the heuristic module 326 and the text of the submitted document … the pre-screener system 210 may provide feedback to the submitter at the creator device 202, so the submitter can see why the document was not compliant to assist the submitter in future submissions. In various implementations, the pre-screener system 210 may provide suggested changes, suggested text, etc., to the submitter at the creator device 202, in order to increase the likelihood of a revised document being compliant”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 4, Jin and Law teach the device as described in claim 1 and Jin further teaches:
wherein the at least one processor is configured to execute the instructions to: retrieve the related document that is a document used in a past case, on a basis of the text [(e.g. see Jin paragraph 0032) ”The machine learning model 300 learns to draw conclusions from relationships between the data in the training set 310, specifically between the fields 320 and values 330 and the historical workflow 315 that generated each historical electronic document 125. The machine learning model 300 may learn that historical workflows 315 output documents with a first set of consistent fields 320 and values 330, while a second set of fields 320 and values 330 may vary. For example, the machine learning model 300 may learn that one particular user generates real estate agreements (e.g., from a historical workflow 315) and that user's email address and phone number always comprise the contact information in the real estate agreements. Similarly, the machine learning model 300 may learn that all real estate agreements generated by the user are governed by the laws of California. In contrast, the machine learning model 300 may learn that the property addresses and closing dates in these generated real estate agreements vary”].
and generate the tentative document on a bases of a content of the retrieved related document and the document preparation command [(e.g. see Jin paragraph 0039 and Fig. 4) ”The input 420 may be natural language text, as is shown in FIG. 4, or, in other embodiments, a selection of a workflow from a plurality of options. The document management system 110 generates suggestions for values (e.g., the predicted values 380, as output by the machine learning model 300) based on past historical workflows 425. In this example, the document management system 110 analyzed past historical workflows 425 that generated agreements sent to ABC Properties for execution. The interface 400 includes a first set of fields and corresponding predicted values 430. For example, the document management system 110 may determine with near certainty (e.g., likelihood above a predefined high threshold (e.g., 95%+likely)) that the field 435, “Party 1,” corresponds to the value 440 “XYZ Inc.” The user 410, however, may edit, change, and/or override any of the first set of fields and corresponding predicted values 430. The interface 400 also includes a second set of fields and a number of corresponding predicted values 450. For the field 455, “Choice of Law,” the interface 400 displays the values 460 of “California” and “Washington,” which the user 410 can select from”].
As for dependent claim 5, Jin and Law teach the device as described in claim 1 and Jin further teaches:
wherein the at least one processor is configured to execute the instructions to: retrieve the related document that is a document used in a past case, on a basis of the text [(e.g. see Jin paragraph 0032) ” The machine learning model 300 learns to draw conclusions from relationships between the data in the training set 310, specifically between the fields 320 and values 330 and the historical workflow 315 that generated each historical electronic document 125. The machine learning model 300 may learn that historical workflows 315 output documents with a first set of consistent fields 320 and values 330, while a second set of fields 320 and values 330 may vary. For example, the machine learning model 300 may learn that one particular user generates real estate agreements (e.g., from a historical workflow 315) and that user's email address and phone number always comprise the contact information in the real estate agreements. Similarly, the machine learning model 300 may learn that all real estate agreements generated by the user are governed by the laws of California. In contrast, the machine learning model 300 may learn that the property addresses and closing dates in these generated real estate agreements vary”].
Jin does not specifically teach the following limitation. However, Law teaches:
and extract the check item in the draft document on a basis of additional information related to correction of the related document included in the retrieved related document and a content of the draft document [(e.g. see Law paragraphs 0039, 0040) ”The machine learning model(s) in the pre-screener system 210 may be trained by looking at various versions of historical documents that have already been labeled as non-compliant or compliant. For example, stored documents that have already gone through the process of compliance review previously may have a first version identified as non-compliant and a last version identified as compliant. This historical data can be used to train the machine learning models by submitting the labeled first and last versions of documents to the machine learning model, or even submitting intermediate versions of the document for training (which may also likely be identified as non-compliant) … provide feedback, based on historical review comments from compliance associates, based on edits from compliance associates, etc. The feedback may be associated with a specific business unit that submitted the document, the type of document, etc. In various implementations, different words in a document may be assigned weights”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 6, Jin and Law teach the device as described in claim 2 and Jin further teaches:
wherein the at least one processor is configured to execute the instructions to: retrieve the related document that is a document used in a past case, on a basis of the text [(e.g. see Jin paragraph 0032) ” The machine learning model 300 learns to draw conclusions from relationships between the data in the training set 310, specifically between the fields 320 and values 330 and the historical workflow 315 that generated each historical electronic document 125. The machine learning model 300 may learn that historical workflows 315 output documents with a first set of consistent fields 320 and values 330, while a second set of fields 320 and values 330 may vary. For example, the machine learning model 300 may learn that one particular user generates real estate agreements (e.g., from a historical workflow 315) and that user's email address and phone number always comprise the contact information in the real estate agreements. Similarly, the machine learning model 300 may learn that all real estate agreements generated by the user are governed by the laws of California. In contrast, the machine learning model 300 may learn that the property addresses and closing dates in these generated real estate agreements vary”].
Jin does not specifically teach the following limitation. However, Law teaches:
and generate the reason for pointing out on a basis of additional information related to correction of the related document included in the retrieved related document and a content of the indicated part [(e.g. see Law paragraphs 0039, 0040) ”The machine learning model(s) in the pre-screener system 210 may be trained by looking at various versions of historical documents that have already been labeled as non-compliant or compliant. For example, stored documents that have already gone through the process of compliance review previously may have a first version identified as non-compliant and a last version identified as compliant. This historical data can be used to train the machine learning models by submitting the labeled first and last versions of documents to the machine learning model, or even submitting intermediate versions of the document for training (which may also likely be identified as non-compliant) … provide feedback, based on historical review comments from compliance associates, based on edits from compliance associates, etc. The feedback may be associated with a specific business unit that submitted the document, the type of document, etc. In various implementations, different words in a document may be assigned weights”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 7, Jin and Law teach the device as described in claim 1 and Jin further teaches:
wherein the at least one processor is configured to execute the instructions to output, to the author, information related to the retrieved related document in association with the tentative document [(e.g. see Jin paragraph 0039 and Fig. 4) ”The document management system 110 generates suggestions for values (e.g., the predicted values 380, as output by the machine learning model 300) based on past historical workflows 425. In this example, the document management system 110 analyzed past historical workflows 425 that generated agreements sent to ABC Properties for execution. The interface 400 includes a first set of fields and corresponding predicted values 430”].
As for dependent claim 8, Jin and Law teach the device as described in claim 1 and Jin further teaches:
wherein the at least one processor is configured to execute the instructions to calculate reliability of the tentative document on a basis of a content of the retrieved related document and a content of the tentative document, and outputs the reliability to the author in association with the tentative document [(e.g. see Jin paragraph 0039) ”The interface 400 may also include representations of confidence scores associated with each of the predicted values 450”].
As for independent claim 9, Jin and Law teach a method. Claim 9 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1.
As for dependent claim 10, Jin and Law teach the method as described in claim 9; further, claim 10 discloses substantially the same limitations as claim 2. Therefore, it is rejected with the same rational as claim 2.
As for dependent claim 11, Jin and Law teach the method as described in claim 10; further, claim 11 discloses substantially the same limitations as claim 3. Therefore, it is rejected with the same rational as claim 3.
As for dependent claim 12, Jin and Law teach the method as described in claim 9; further, claim 12 discloses substantially the same limitations as claim 4. Therefore, it is rejected with the same rational as claim 4.
As for dependent claim 13, Jin and Law teach the method as described in claim 9; further, claim 13 discloses substantially the same limitations as claim 5. Therefore, it is rejected with the same rational as claim 5.
As for dependent claim 14, Jin and Law teach the method as described in claim 10; further, claim 14 discloses substantially the same limitations as claim 6. Therefore, it is rejected with the same rational as claim 6.
As for dependent claim 15, Jin and Law teach the method as described in claim 9; further, claim 15 discloses substantially the same limitations as claim 7. Therefore, it is rejected with the same rational as claim 7.
As for dependent claim 16, Jin and Law teach the method as described in claim 9; further, claim 16 discloses substantially the same limitations as claim 8. Therefore, it is rejected with the same rational as claim 8.
As for independent claim 17, Jin and Law teach a non-transitory computer-readable medium. Claim 17 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPub 2024/0303247 A1 issued to Sokolov et al. on 12 September 2024. The subject matter disclosed therein is pertinent to that of claims 1-17 (e.g. suggestions and feedback for a draft document).
U.S. PGPub 2024/0061995 A1 issued to Tveit et al. on 22 February 2024. The subject matter disclosed therein is pertinent to that of claims 1-17 (e.g. determining the document readiness score of a draft document).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER J FIBBI whose telephone number is (571)-270-3358. The examiner can normally be reached Monday - Thursday (8am-6pm).
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, William Bashore can be reached at (571)-272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTOPHER J FIBBI/Primary Examiner, Art Unit 2174
Prosecution Insights