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 .
Status of Claims
In response to communications filed on 26 November 2025, claims 1, 3, 5-6, 8, 10, 12-13, 15, 17, and 19-20 are presently pending in the application, of which, claims 1, 8, and 15 are presented in independent form. The Examiner acknowledges amended claims 1, 3, 5-6, 8, 10, 13, 15, 17, and 20 and canceled claims 2, 9, and 16. Claims 4, 7, 11, 14, and 18 were previously cancelled.
Response to Remarks/Arguments
All objections and/or rejections issued in the previous Office Action, mailed 26 August 2025, have been withdrawn, unless otherwise noted in this Office Action.
Applicant’s arguments are directed to amended features and have been incorporated into the rejection below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 5-6, 8, 10, 12-13, 15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable by Steenstra, Danlel, et al (U.S. 2024/0145073 and known hereinafter as Steenstra) (previously presented) in view of Dorrington, Tracy, et al (U.S. 2022/0147555 and known hereinafter as Dorrington)(previously presented).
As per claim 1, Steenstra teaches a computer-implemented method of automatic onboarding of an entity to a computer application on a client computer, the method comprising:
rendering, by the server computer, a user interface for an entity user to enter an entity’s universal resource locator (URL) (e.g. Steenstra, see paragraphs [0052-0055], which discloses a computing device may be configured to access information from at least one data source, where the data source may be a website displayed on a user interface of the computing device.);
receiving, by the server computer, a URL entered via the user interface (e.g. Steenstra, see paragraphs [0052-0055], which discloses a computing device may be configured to access information from at least one data source, where the data source may be a website displayed on a user interface of the computing device.);
scraping, by the server computer, entity data from the URL using a website scraper (e.g. Steenstra, see paragraphs [0052-0059], which discloses performing scraping techniques to receive information from data sources, where the scraping may include data aggregation that involves a machine, including but not limited to crawling across websites, identifying links and changes to websites, data transfer through API’s FTP’s, GUI, direct database connections through parsing and extraction of website pages.);
extracting, by the server computer, entity information using a large language model on the scraped entity data (e.g. Steenstra, see paragraphs [0055-0059, 0075-0079], which discloses extracting through scraping the internet and generating a classification of a technological need based on the scraped information. Additionally, an extraction bot may be configured to copy the crawled data and then parse, search, reformat, etc., the crawled data.); and
auto-populating, by the server computer, the extracted entity information and the second entity information on a second user interface of the computer application (e.g. Steenstra, see paragraphs [0167-0169], which discloses generating automatically, the information from the identified plurality of entities associated with the subset of information.), the second user interface being an update to the first user interface (e.g. Steenstra, see paragraph [00278-0279], which discloses receiving second onboarding information from a plurality of second entities, where the onboarding information may be received through a user input, such as by clicking or typing, or through data provided by sensors, etc, where a pathway is established between a first particular node and the second particular node).;
receiving, by the sever computer and from the second user interface (e.g. Steenstra, see paragraphs [0076-0078], which discloses receiving inputs through user interface and/or via computing device, where the computing device generates a signal based on the user input and transmit the signal to a server. The server may then parse the signal to extract information indicating the user input. See further Figures 4A-4C.), updates to the auto-populated entity information (e.g. Steenstra, see paragraphs [0278-0280], which discloses updating dynamically based on the second onboarding information, where the second onboarding information may involve altered or updated information. Additionally, see paragraph [0078], which discloses a user may input a description which may be used to populate data structure, where a description may be stored exactly as it is input into the field.); and
updating, by the server computer, the auto-populated entity information on the second user interface with the received updates (e.g. Steenstra, see paragraphs [0278-0280], which discloses updating dynamically based on the second onboarding information, where the second onboarding information may involve altered or updated information. See further paragraph [0199], which discloses data source may be populated as the result of research, where the source of the stored data may be a database.).
Steenstra does not explicitly disclose receiving, by the server computer from the client computer, a local file; and extracting, by the server computer, a second entity information from the local file, and augmenting, by the server computer, the entity information and the second entity information with additional information stored in a database of the server, the additional information comprises prior onboarding sessions of other entities and historical information.
Dorrington teaches receiving, by the server computer from the client computer, a local file (e.g. Dorrington, see paragraphs [0051-0055], which discloses receiving unstructured data from one or more unstructured data sources, where the unstructured data sources may reside on a client computing device.);
extracting, by the server computer, a second entity information from the local file (e.g. Dorrington, see paragraphs [0045-0047, 0053-0055], which discloses performing an extract analysis, where the extracted data is from unstructured data source that includes a plurality of entity information.); and
augmenting, by the server computer, the entity information and the second entity information with additional information stored in a database of the server, the additional information comprises prior onboarding sessions of other entities and historical information (e.g. Dorrington, see paragraphs [0055-0056], which discloses storing information scoring or ranking rules, which may be updated dynamically (augmented) based on industry trends indicating attributes of interest to a particular industry, user historical feedback indicating attributes of news items considered to be relevant to the user, user job role, user’s industry, user’s interest, etc., all which are examples of entity information.).
Steenstra is directed to quantifying unmet needs through internet scraping. Dorrington is directed to curating and graphically presenting unstructured data based on analytics. Both are analogous art because they are directed to extracting information and populating it onto another device and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Steenstra with the teachings of Dorrington to include the claimed feature with the motivation extracted information.
As per claim 8, Steenstra teaches a system comprising:
a non-transitory storage medium storing computer program instructions (e.g. Steenstra, see paragraphs [0189-0191], which discloses a non-transitory computer readable medium); and
a processor configured to execute the computer program instructions to cause (e.g. Steenstra, see paragraphs [0189-0191], which discloses a non-transitory computer readable medium which contains instructions that when executed by a processor causes the processor to perform.) operations comprising:
rendering, by the server computer, a user interface for an entity user to enter an entity’s universal resource locator (URL) (e.g. Steenstra, see paragraphs [0052-0055], which discloses a computing device may be configured to access information from at least one data source, where the data source may be a website displayed on a user interface of the computing device.);
receiving, by the server computer, a URL entered via the user interface (e.g. Steenstra, see paragraphs [0052-0055], which discloses a computing device may be configured to access information from at least one data source, where the data source may be a website displayed on a user interface of the computing device.);
scraping, by the server computer, entity data from the URL using a website scraper (e.g. Steenstra, see paragraphs [0052-0059], which discloses performing scraping techniques to receive information from data sources, where the scraping may include data aggregation that involves a machine, including but not limited to crawling across websites, identifying links and changes to websites, data transfer through API’s FTP’s, GUI, direct database connections through parsing and extraction of website pages.);
extracting, by the server computer, entity information using a large language model on the scraped entity data (e.g. Steenstra, see paragraphs [0055-0059, 0075-0079], which discloses extracting through scraping the internet and generating a classification of a technological need based on the scraped information. Additionally, an extraction bot may be configured to copy the crawled data and then parse, search, reformat, etc., the crawled data.); and
auto-populating, by the server computer, the extracted entity information and the second entity information on a second user interface of the computer application (e.g. Steenstra, see paragraphs [0167-0169], which discloses generating automatically, the information from the identified plurality of entities associated with the subset of information.), the second user interface being an update to the first user interface (e.g. Steenstra, see paragraph [00278-0279], which discloses receiving second onboarding information from a plurality of second entities, where the onboarding information may be received through a user input, such as by clicking or typing, or through data provided by sensors, etc, where a pathway is established between a first particular node and the second particular node).;
receiving, by the sever computer and from the second user interface (e.g. Steenstra, see paragraphs [0076-0078], which discloses receiving inputs through user interface and/or via computing device, where the computing device generates a signal based on the user input and transmit the signal to a server. The server may then parse the signal to extract information indicating the user input. See further Figures 4A-4C.), updates to the auto-populated entity information (e.g. Steenstra, see paragraphs [0278-0280], which discloses updating dynamically based on the second onboarding information, where the second onboarding information may involve altered or updated information. Additionally, see paragraph [0078], which discloses a user may input a description which may be used to populate data structure, where a description may be stored exactly as it is input into the field.); and
updating, by the server computer, the auto-populated entity information on the second user interface with the received updates (e.g. Steenstra, see paragraphs [0278-0280], which discloses updating dynamically based on the second onboarding information, where the second onboarding information may involve altered or updated information. See further paragraph [0199], which discloses data source may be populated as the result of research, where the source of the stored data may be a database.).
Steenstra does not explicitly disclose receiving, by the server computer from the client computer, a local file; and extracting, by the server computer, a second entity information from the local file, and augmenting, by the server computer, the entity information and the second entity information with additional information stored in a database of the server, the additional information comprises prior onboarding sessions of other entities and historical information.
Dorrington teaches receiving, by the server computer from the client computer, a local file (e.g. Dorrington, see paragraphs [0051-0055], which discloses receiving unstructured data from one or more unstructured data sources, where the unstructured data sources may reside on a client computing device.);
extracting, by the server computer, a second entity information from the local file (e.g. Dorrington, see paragraphs [0045-0047, 0053-0055], which discloses performing an extract analysis, where the extracted data is from unstructured data source that includes a plurality of entity information.); and
augmenting, by the server computer, the entity information and the second entity information with additional information stored in a database of the server, the additional information comprises prior onboarding sessions of other entities and historical information (e.g. Dorrington, see paragraphs [0055-0056], which discloses storing information scoring or ranking rules, which may be updated dynamically (augmented) based on industry trends indicating attributes of interest to a particular industry, user historical feedback indicating attributes of news items considered to be relevant to the user, user job role, user’s industry, user’s interest, etc., all which are examples of entity information.).
Steenstra is directed to quantifying unmet needs through internet scraping. Dorrington is directed to curating and graphically presenting unstructured data based on analytics. Both are analogous art because they are directed to extracting information and populating it onto another device and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Steenstra with the teachings of Dorrington to include the claimed feature with the motivation extracted information.
As per claim 15, Steenstra teaches a non-transitory storage medium storing computer program instructions that when executed by a processor cause operations:
rendering, by the server computer, a user interface for an entity user to enter an entity’s universal resource locator (URL) (e.g. Steenstra, see paragraphs [0052-0055], which discloses a computing device may be configured to access information from at least one data source, where the data source may be a website displayed on a user interface of the computing device.);
receiving, by the server computer, a URL entered via the user interface (e.g. Steenstra, see paragraphs [0052-0055], which discloses a computing device may be configured to access information from at least one data source, where the data source may be a website displayed on a user interface of the computing device.);
scraping, by the server computer, entity data from the URL using a website scraper (e.g. Steenstra, see paragraphs [0052-0059], which discloses performing scraping techniques to receive information from data sources, where the scraping may include data aggregation that involves a machine, including but not limited to crawling across websites, identifying links and changes to websites, data transfer through API’s FTP’s, GUI, direct database connections through parsing and extraction of website pages.);
extracting, by the server computer, entity information using a large language model on the scraped entity data (e.g. Steenstra, see paragraphs [0055-0059, 0075-0079], which discloses extracting through scraping the internet and generating a classification of a technological need based on the scraped information. Additionally, an extraction bot may be configured to copy the crawled data and then parse, search, reformat, etc., the crawled data.); and
auto-populating, by the server computer, the extracted entity information and the second entity information on a second user interface of the computer application (e.g. Steenstra, see paragraphs [0167-0169], which discloses generating automatically, the information from the identified plurality of entities associated with the subset of information.), the second user interface being an update to the first user interface (e.g. Steenstra, see paragraph [00278-0279], which discloses receiving second onboarding information from a plurality of second entities, where the onboarding information may be received through a user input, such as by clicking or typing, or through data provided by sensors, etc, where a pathway is established between a first particular node and the second particular node).;
receiving, by the sever computer and from the second user interface (e.g. Steenstra, see paragraphs [0076-0078], which discloses receiving inputs through user interface and/or via computing device, where the computing device generates a signal based on the user input and transmit the signal to a server. The server may then parse the signal to extract information indicating the user input. See further Figures 4A-4C.), updates to the auto-populated entity information (e.g. Steenstra, see paragraphs [0278-0280], which discloses updating dynamically based on the second onboarding information, where the second onboarding information may involve altered or updated information. Additionally, see paragraph [0078], which discloses a user may input a description which may be used to populate data structure, where a description may be stored exactly as it is input into the field.); and
updating, by the server computer, the auto-populated entity information on the second user interface with the received updates (e.g. Steenstra, see paragraphs [0278-0280], which discloses updating dynamically based on the second onboarding information, where the second onboarding information may involve altered or updated information. See further paragraph [0199], which discloses data source may be populated as the result of research, where the source of the stored data may be a database.).
Steenstra does not explicitly disclose receiving, by the server computer from the client computer, a local file; and extracting, by the server computer, a second entity information from the local file, and augmenting, by the server computer, the entity information and the second entity information with additional information stored in a database of the server, the additional information comprises prior onboarding sessions of other entities and historical information.
Dorrington teaches receiving, by the server computer from the client computer, a local file (e.g. Dorrington, see paragraphs [0051-0055], which discloses receiving unstructured data from one or more unstructured data sources, where the unstructured data sources may reside on a client computing device.);
extracting, by the server computer, a second entity information from the local file (e.g. Dorrington, see paragraphs [0045-0047, 0053-0055], which discloses performing an extract analysis, where the extracted data is from unstructured data source that includes a plurality of entity information.); and
augmenting, by the server computer, the entity information and the second entity information with additional information stored in a database of the server, the additional information comprises prior onboarding sessions of other entities and historical information (e.g. Dorrington, see paragraphs [0055-0056], which discloses storing information scoring or ranking rules, which may be updated dynamically (augmented) based on industry trends indicating attributes of interest to a particular industry, user historical feedback indicating attributes of news items considered to be relevant to the user, user job role, user’s industry, user’s interest, etc., all which are examples of entity information.).
Steenstra is directed to quantifying unmet needs through internet scraping. Dorrington is directed to curating and graphically presenting unstructured data based on analytics. Both are analogous art because they are directed to extracting information and populating it onto another device and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Steenstra with the teachings of Dorrington to include the claimed feature with the motivation extracted information.
As per claims 3, 10, and 17, the modified teachings of Steenstra and Dorrington teaches the computer-implemented method of claim 1, the system of claim 8, and the non-transitory storage medium of claim 15, respectively, the auto-populating the extracted entity information comprises:
auto-populating business information of the entity on the second user interface of the computer application (e.g. Steenstra, see paragraphs [0167-0169], which discloses generating automatically, the information from the identified plurality of entities associated with the subset of information.).
As per claims 5, 12, and 19, the modified teachings of Steenstra and Dorrington teaches the computer-implemented method of claim 1, the system of claim 8, and the non-transitory storage medium of claim 15, respectively, the using the large language model comprising:
using specific prompts for the large language model to extract the entity information (e.g. Steenstra, see paragraph [0164], which discloses performing semantic analysis using natural language to draw meaning and/or context, where semantic analysis may include related syntactic structures from phrases, clauses, sentences, paragraphs, etc, which are used to identify a subset of the plurality of entities.).
As per claims 6, 13, and 20, the modified teachings of Steenstra and Dorrington teaches the computer-implemented method of claim 1, the system of claim 8, and the non-transitory storage medium of claim 15, respectively, further comprising:
storing the extracted entity information on a data storage (e.g. Steenstra, see paragraphs [0055-0059, 0075-0079], which discloses extracting through scraping the internet and generating a classification of a technological need based on the scraped information. Additionally, an extraction bot may be configured to copy the crawled data and then parse, search, reformat, etc., the crawled data and then stored.); and
auto-populating the second user interface of the computer application using the stored extracted entity information (e.g. Steenstra, see paragraphs [0167-0169], which discloses generating automatically, the information from the identified plurality of entities associated with the subset of information.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 February 24, 2026