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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9, 11, 19 and 20 of U.S. Patent No. 12,067,039. Although the claims at issue are not identical, they are not patentably distinct from each other because they are obvious variants as the independent claims of the instant application are broader than the independent claims of Patent No. 12,067,039 as shown in the comparison table below. It is noted that the instant application claims recite “prompts” and the claims of Patent No. 12,067,039 recite “a query” and “queries” which are interchangeable variants of the invention. Additionally, the claims of the instant application and the claims of Patent No. 12,067,039 interchangeably disclose various portions of a user interface for performing actions and presenting options/outputs.
Instant Application Claims
Patent No. 12,067,039 Claims
A system configured for providing user interfaces for configuration of a flow for extracting information from a set of documents via one or more machine learning models, wherein the set of documents is associated with one or more document classifications, the system comprising: one or more hardware processors configured by machine-readable instructions to: effectuate a presentation of a user interface, the user interface being configured to obtain entry of user input from a user to:(i) select and/or modify one or more extraction fields from a set of extraction fields for an individual document, wherein the individual document is included in the set of documents, and(ii) navigate between one or more portions of the user interface, wherein the one or more portions include a first portion configured to select and/or modify a set of extraction fields for a particular document classification, wherein the particular document classification is associated with the individual document, wherein individual extraction fields correspond to individual prompts to the one or more machine learning models; present the individual document in the user interface; and present the set of extraction fields in the user interface, wherein the individual extraction fields present individual replies obtained from the one or more machine learning models in reply to the individual prompts.
Claim 2. The system of claim 1, wherein the individual document is presented in an individual portion of the one or more portions of the user interface.
Claim 3. The system of claim 1, wherein the set of extraction fields are presented in the first portion of the user interface.
Claim 4. The system of claim 1, wherein the set of extraction fields are presented in an individual portion of the one or more portions of the u ser interface.
Claim 5. The system of claim 1, wherein the one or more portions include a second portion configured to select and/or modify a particular document classification for the individual document.
Claim 6. The system of claim 1, wherein the one or more machine learning models include a large language model that includes a neural network using over a billion parameters and/or weights.
Claim 7. The system of claim 1, wherein the user interface is further configured to select and/or modify the one or more document classifications associated with the set of documents, and wherein the one or more portions of the user interface include a particular portion configured to select and/or modify individual ones of the one or more document classifications.
Claim 1. A system configured for providing user interfaces for configuration of a flow for extracting information from documents via a large language model, wherein the large language model has been trained on at least a million documents, wherein the large language model includes a neural network using over a billion parameters and/or weights, the system comprising: one or more hardware processors configured by machine-readable instructions to: effectuate a presentation of a user interface, the user interface being configured to obtain entry of user input from a user to: (i) select a set of exemplary documents to be provided as input to configure the flow for extracting information from a corpus of electronic documents, (ii) select and/or modify one or more document classifications from a set of document classifications for the set of exemplary documents, (iii) select and/or modify one or more extraction fields from a set of extraction fields for an individual document classification from the set of document classifications, and (iv) navigate between a set of different portions of the user interface, wherein the set of different portions includes: (a) a first portion configured to select, by the user, an individual flow from a set of flows for information extraction, (b) a second portion configured to select and/or modify, by the user, individual document classifications from the set of document classifications, wherein individual documents from the set of exemplary documents are classified into individual ones of the set of document classifications, (c) a third portion configured to select and/or modify, by the user, a particular document classification for a particular individual document, wherein the particular individual document has been classified into the particular document classification, and (d) the fourth portion configured to select and/or modify the set of extraction fields for the particular document classification, wherein individual extraction fields correspond to individual queries that are provided as prompts to the large language model, using the particular individual document as context; responsive to selection of the individual flow, present the set of document classifications in the second portion; subsequent to selection of the particular document classification in the second portion, present the particular individual document in the third portion; and subsequent to the selection of the particular document classification in the second portion, present the set of extraction fields in the fourth portion, wherein the individual extraction fields present individual replies obtained from the large language model in reply to the individual queries.
Claim 8. The system of claim 1, wherein the one or more document classifications include document classifications determined by a trained machine learning model for document classification.
Claim 2. The system of claim 1, wherein the set of document classifications include document classifications determined by a trained machine-learning model for document classification.
Claim 9. The system of claim 1, wherein the set of extraction fields include extraction fields determined by a trained machine learning model for extraction field determination.
Claim 3. The system of claim 1, wherein the set of extraction fields include extraction fields determined by a trained machine-learning model for extraction field determination.
Claim 10. The system of claim 7, wherein modification of the one or more document classifications includes merging multiple document classifications into a single document classification.
Claim 4. The system of claim 1, wherein modification of the set of document classifications includes merging multiple document classifications into a single document classification.
Claim 11. The system of claim 1, wherein the user interface is configured to present multiple documents in the particular document classification.
Claim 5. The system of claim 1, wherein subsequent to selection of the particular document classification in the second portion, the third portion of the user interface is configured to present multiple documents in the particular document classification, wherein the multiple documents are arranged vertically.
Claim 12. The system of claim 1, wherein the user interface is configured to present at least one document for individual ones of the one or more document classifications.
Claim 6. The system of claim 1, wherein subsequent to selection of the particular document classification in the second portion, the third portion of the user interface is configured to present at least one document for individual ones of the set of document classifications.
Claim 13. The system of claim 6, wherein a presentation of an individual extraction field includes one or more of (1) an individual reply that has been provided by the large language model in reply to an individual prompt, (2) a representation of the individual prompt, and/or (3) a graphic user interface element that allows the user to modify the individual prompt.
Claim 7. The system of claim 1, wherein a presentation of an individual extraction field includes (1) an individual reply that has been provided by the large language model in reply to an individual query, wherein the individual query has been provided as a prompt to the large language model, (2) a representation of the individual query, and (3) a graphic user interface element that allows the user to modify the individual query.
Claim 14. The system of claim 1, wherein the user interface is further configured to present additional extraction fields that are available to be added to the set of extraction fields.
Claim 8. The system of claim 1, wherein the fourth portion is further configured to present additional extraction fields that are available to be added to the set of extraction fields.
Claim 15. The system of claim 6, wherein the large language model is based on or derived from Generative Pre-trained Transformer 3 (GPT3).
Claim 9. The system of claim 1, wherein the large language model is based on or derived from Generative Pre-trained Transformer 3 (GPT3) or a successor of Generative Pre-trained Transformer 3 (GPT3).
Claim 17. A method of providing user interfaces for configuration of a flow for extracting information from a set of documents via one or more machine learning models, wherein the set of documents is associated with one or more document classifications, the method comprising: effectuating a presentation of a user interface, wherein the user interface obtains entry of user input from a user to (i) select and/or modify one or more extraction fields from a set of extraction fields for an individual document, wherein the individual document is included in the set of documents, and(ii) navigate between one or more portions of the user interface, wherein the one or more portions include a first portion to select and/or modify a set of extraction fields for a particular document classification, wherein the particular document classification is associated with the individual document, wherein individual extraction fields correspond to individual prompts to the one or more machine learning models; presenting the individual document in the user interface; and presenting the set of extraction fields in the user interface, wherein the individual extraction fields present individual replies obtained from the one or more machine learning models in reply to the individual prompts.
Claim 18. The method of claim 17, the individual document is presented in an individual portion of the one or more portions of the user interface.
Claim 11. A method of providing user interfaces for configuration of a flow for extracting information from documents via a large language model, wherein the large language model has been trained on at least a million documents, wherein the large language model includes a neural network using over a billion parameters and/or weights, the method comprising: effectuating a presentation of a user interface, wherein the user interface obtains entry of user input from a user to (i) select a set of exemplary documents to be provided as input to configure the flow for extracting information from a corpus of electronic documents, (ii) select and/or modify one or more document classifications from a set of document classifications for the set of exemplary documents, (iii) select and/or modify one or more extraction fields from a set of extraction fields for an individual document classification from the set of document classifications, and (iv) navigate between a set of different portions of the user interface, wherein the set of different portions includes (a) a first portion to select, by the user, an individual flow from a set of flows for information extraction, (b) a second portion to select and/or modify, by the user, individual document classifications from the set of document classifications, wherein individual documents from the set of exemplary documents are classified into individual ones of the set of document classifications, (c) a third portion to select and/or modify, by the user, a particular document classification for a particular individual document, wherein the particular individual document has been classified into the particular document classification, and (d) the fourth portion to select and/or modify the set of extraction fields for the particular document classification, wherein individual extraction fields correspond to individual queries that are provided as prompts to the large language model, using the particular individual document as context; responsive to selection of the individual flow, presenting the set of document classifications in the second portion; subsequent to selection of the particular document classification in the second portion, presenting the particular individual document in the third portion; and subsequent to the selection of the particular document classification in the second portion, presenting the set of extraction fields in the fourth portion, wherein the individual extraction fields present individual replies obtained from the large language model in reply to the individual queries.
Claim 19. The method of claim 17, wherein the one or more machine learning models include a large language model based on or derived from Generative Pre-trained Transformer 3 (GPT3).
Claim 19. The method of claim 11, wherein the large language model is based on or derived from Generative Pre-trained Transformer 3 (GPT3) or a successor of Generative Pre-trained Transformer 3 (GPT3).
Claim 20. The method of claim 17, wherein at least two different portions of the user interface are presented to the user at the same time.
Claim 20. The method of claim 11, wherein at least some parts of the first portion, the second portion, the third portion, and the fourth portion are presented to the user at the same time.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-14, 19 and 20 of U.S. Patent No. 12,380,140. Although the claims at issue are not identical, they are not patentably distinct from each other because they are obvious variants as the independent claims of the instant application are broader than the independent claims of Patent No. 12,380,140 as shown in the comparison table below. It is noted that the claims of the instant application and the claims of Patent No. 12,380,140 interchangeably disclose various portions of a user interface for performing actions and presenting options/outputs.
Instant Application Claims
Patent No. 12,380,140 Claims
Claim 1. A system configured for providing user interfaces for configuration of a flow for extracting information from a set of documents via one or more machine learning models, wherein the set of documents is associated with one or more document classifications, the system comprising: one or more hardware processors configured by machine-readable instructions to: effectuate a presentation of a user interface, the user interface being configured to obtain entry of user input from a user to:(i) select and/or modify one or more extraction fields from a set of extraction fields for an individual document, wherein the individual document is included in the set of documents, and(ii) navigate between one or more portions of the user interface, wherein the one or more portions include a first portion configured to select and/or modify a set of extraction fields for a particular document classification, wherein the particular document classification is associated with the individual document, wherein individual extraction fields correspond to individual prompts to the one or more machine learning models; present the individual document in the user interface; and present the set of extraction fields in the user interface, wherein the individual extraction fields present individual replies obtained from the one or more machine learning models in reply to the individual prompts.
Claim 2. The system of claim 1, wherein the individual document is presented in an individual portion of the one or more portions of the user interface.
Claim 3. The system of claim 1, wherein the set of extraction fields are presented in the first portion of the user interface.
Claim 4. The system of claim 1, wherein the set of extraction fields are presented in an individual portion of the one or more portions of the u ser interface.
Claim 5. The system of claim 1, wherein the one or more portions include a second portion configured to select and/or modify a particular document classification for the individual document.
Claim 1. A system configured for providing user interfaces for configuration of a flow for extracting information from documents via one or more machine learning models, the system comprising: one or more hardware processors configured by machine-readable instructions to: effectuate a presentation of a user interface, the user interface being configured to obtain entry of user input from a user to: (i) select a set of exemplary documents to be provided as input to configure the flow for extracting information from a corpus of electronic documents, wherein the set of exemplary documents is associated with one or more document classifications, (ii) select and/or modify one or more extraction fields from a set of extraction fields for an individual document, and (iii) navigate between a set of different portions of the user interface, wherein the set of different portions includes: (a) a first portion configured to select and/or modify, by the user, a particular document classification for a particular individual document, wherein the particular individual document has been classified into the particular document classification, and (b) a second portion configured to select and/or modify the set of extraction fields for the particular document classification, wherein individual extraction fields correspond to individual prompts to the one or more machine learning models; present the particular individual document in the first portion; and present the set of extraction fields in the second portion, wherein the individual extraction fields present individual replies obtained from the one or more machine learning models in reply to the individual prompts.
Claim 6. The system of claim 1, wherein the one or more machine learning models include a large language model that includes a neural network using over a billion parameters and/or weights.
Claim 2. The system of claim 1, wherein the one or more machine learning models include a large language model that includes a neural network using over a billion parameters and/or weights.
Claim 7. The system of claim 1, wherein the user interface is further configured to select and/or modify the one or more document classifications associated with the set of documents, and wherein the one or more portions of the user interface include a particular portion configured to select and/or modify individual ones of the one or more document classifications.
Claim 3. The system of claim 1, wherein the user interface is further configured to select and/or modify the one or more document classifications for the set of exemplary documents, and wherein the set of different portions of the user interface includes a third portion configured to select and/or modify individual document classifications.
Claim 8. The system of claim 1, wherein the one or more document classifications include document classifications determined by a trained machine learning model for document classification.
Claim 5. The system of claim 1, wherein the set of document classifications include document classifications determined by a trained machine-learning model for document classification.
Claim 9. The system of claim 1, wherein the set of extraction fields include extraction fields determined by a trained machine learning model for extraction field determination
.
Claim 6. The system of claim 1, wherein the set of extraction fields include extraction fields determined by a trained machine-learning model for extraction field determination.
Claim 10. The system of claim 7, wherein modification of the one or more document classifications includes merging multiple document classifications into a single document classification.
Claim 7. The system of claim 3, wherein modification of the one or more document classifications includes merging multiple document classifications into a single document classification.
Claim 11. The system of claim 1, wherein the user interface is configured to present multiple documents in the particular document classification.
Claim 8. The system of claim 1, wherein the first portion of the user interface is configured to present multiple documents in the particular document classification.
Claim 12. The system of claim 1, wherein the user interface is configured to present at least one document for individual ones of the one or more document classifications.
Claim 9. The system of claim 1, wherein the first portion of the user interface is configured to present at least one document for individual ones of a set of document classifications.
Claim 13. The system of claim 6, wherein a presentation of an individual extraction field includes one or more of (1) an individual reply that has been provided by the large language model in reply to an individual prompt, (2) a representation of the individual prompt, and/or (3) a graphic user interface element that allows the user to modify the individual prompt.
Claim 10. The system of claim 2, wherein a presentation of an individual extraction field includes (1) an individual reply that has been provided by the large language model in reply to an individual prompt, (2) a representation of the individual prompt, and (3) a graphic user interface element that allows the user to modify the individual prompt.
Claim 14. The system of claim 1, wherein the user interface is further configured to present additional extraction fields that are available to be added to the set of extraction fields.
Claim 11. The system of claim 1, wherein the second portion is further configured to present additional extraction fields that are available to be added to the set of extraction fields.
Claim 15. The system of claim 6, wherein the large language model is based on or derived from Generative Pre-trained Transformer 3 (GPT3).
Claim 12. The system of claim 2, wherein the large language model is based on or derived from Generative Pre-trained Transformer 3 (GPT3) or a successor of Generative Pre-trained Transformer 3 (GPT3).
Claim 16. The system of claim 5, wherein at least two different portions of the user interface are presented to the user at the same time.
Claim 13. The system of claim 1, wherein at least some parts of the first portion and the second portion are presented to the user at the same time.
Claim 17. A method of providing user interfaces for configuration of a flow for extracting information from a set of documents via one or more machine learning models, wherein the set of documents is associated with one or more document classifications, the method comprising: effectuating a presentation of a user interface, wherein the user interface obtains entry of user input from a user to (i) select and/or modify one or more extraction fields from a set of extraction fields for an individual document, wherein the individual document is included in the set of documents, and(ii) navigate between one or more portions of the user interface, wherein the one or more portions include a first portion to select and/or modify a set of extraction fields for a particular document classification, wherein the particular document classification is associated with the individual document, wherein individual extraction fields correspond to individual prompts to the one or more machine learning models; presenting the individual document in the user interface; and presenting the set of extraction fields in the user interface, wherein the individual extraction fields present individual replies obtained from the one or more machine learning models in reply to the individual prompts.
Claim 18. The method of claim 17, the individual document is presented in an individual portion of the one or more portions of the user interface.
Claim 14. A method of providing user interfaces for configuration of a flow for extracting information from documents via one or more machine learning models, the method comprising: effectuating a presentation of a user interface, wherein the user interface obtains entry of user input from a user to (i) select a set of exemplary documents to be provided as input to configure the flow for extracting information from a corpus of electronic documents, wherein the set of exemplary documents is associated with one or more document classifications, (ii) select and/or modify one or more extraction fields from a set of extraction fields for an individual document, and (iii) navigate between a set of different portions of the user interface, wherein the set of different portions includes: (a) a first portion to select and/or modify, by the user, a particular document classification for a particular individual document, wherein the particular individual document has been classified into the particular document classification, and (b) a second portion to select and/or modify the set of extraction fields for the particular document classification, wherein individual extraction fields correspond to individual prompts to the one or more machine learning models; presenting the particular individual document in the first portion; and presenting the set of extraction fields in the second portion, wherein the individual extraction fields present individual replies obtained from the one or more machine learning models in reply to the individual prompts
Claim 19. The method of claim 17, wherein the one or more machine learning models include a large language model based on or derived from Generative Pre-trained Transformer 3 (GPT3).
Claim 19. The method of claim 15, wherein the large language model is based on or derived from Generative Pre-trained Transformer 3 (GPT3).
Claim 20. The method of claim 17, wherein at least two different portions of the user interface are presented to the user at the same time.
Claim 20. The method of claim 14, wherein at least some parts of the first portion and the second portion are presented to the user at the same time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Crabtree (US 2024/0211473): automated analysis of legal documents within and across different fields; an extraction processor identifies and extracts knowledge from data contained in documents and transforms it into a common data form; an analysis processor develops local and global knowledge graphs containing the key entities, relationships and concepts encoded in the text
Espinas (US 11,494,551): automatic population of form fields in a document using a machine learning model;
Meier (US 2020/0302166): computer-implemented text and character recognition and extraction utilizing spatial factors between portions of computer-readable text and contextual relationships between the portions of the computer-readable text;
Morton (US 2022/0035864): tracking of the development of a business integration model using visual elements, viewable as visual elements on a graphical user interface, as they are manipulated during a customized application development process by a user and, using data related to the visual elements, defining a user profile of the user;
Li (WO 2018142266 A1): receiving a document at a classification and extraction engine (CEE), the CEE comprising a CEE processor in communication with a memory, the memory having stored thereon a first machine learning model executable by the CEE processor, the first machine learning model configured to accept a first input and in response generate a first predicted output; generating at the CEE a prediction of one or more of document type and field values for the document, the predictions generated using the first machine learning model wherein the first input comprises one or more computer-readable tokens corresponding to the document and the first predicted output comprises the prediction of one or more of the document type and the field values for the document; sending the prediction from the CEE to a graphical user interface (GUI); receiving at the CEE from the GUI feedback on the prediction to form a reviewed prediction; at the CEE adding the reviewed prediction to a training dataset; selecting at the CEE a second machine learning model configured to accept a second input and generate a second predicted output, the second machine learning model having a maximum prediction accuracy corresponding to the training dataset that is larger than a corresponding maximum prediction accuracy of the first machine learning model corresponding to the training dataset; and forming an updated CEE by adding the second machine learning model to the CEE such that the second input comprises at least the first predicted output and the second predicted output comprises one or more of the document type and the field values.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIEDRA M MCQUITERY whose telephone number is (571)272-9607. The examiner can normally be reached Monday - Thursday, 8 am - 6 pm (C.S.T.).
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, Sanjiv Shah can be reached on (571)272-4098. 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.
/Diedra McQuitery/Primary Examiner, Art Unit 2166
Prosecution Insights