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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2 and 11-12 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Baeuml et al. (US PGPUB No. 2024/0311575; Date Filed: Mar. 17, 2023, Pub. Date: Sep. 19, 2024)
Regarding independent claim 1,
Baeuml discloses a computer-implemented method for generating a large language model (LLM)-based user interface for a user at a user device, comprising: providing, a memory comprising a database, the database comprising at least one historical content version and at least one criteria prompt; See FIG. 1 & Paragraph [0031], (Disclosing a system for dialog management of a large language model (LLM) utilized in generating natural language output during an ongoing dialog. NL-based output system 120 may interact with various databases including dialog(s) database 130A, ongoing dialog context(s) database 140A, etc. Note [0065] wherein the system can obtain a corresponding dialog context from one or more databases such as the dialog context(s) database 140A illustrated in FIG. 1, i.e. a computer-implemented method for generating a large language model (LLM)-based user interface for a user at a user device (e.g. Note [0053] wherein NL-based output generated by the LLM is rendered at a client device or may be audibly rendered via speakers of a client device), comprising: providing, a memory comprising a database, the database comprising at least one historical content version (e.g. ongoing dialog context(s) database 140A) and at least one criteria prompt (e.g. dialog(s) 130A database);
automatically transmitting, at a network device, a content collection request; See FIG. 3 & Paragraph [0049], (FIG. 3 illustrates method 300 comprising step 352 of receiving an NL-based input associated with a client device, i.e. automatically transmitting, at a network device, a content collection request;)
receiving, at the network device based on the content collection request, a content collection response comprising an updated content version; See FIG. 3 & Paragraph [0051], (Method 300 comprises step 354 of generating an NL-based output responsive to the NL-based input received at step 352, i.e. receiving, at the network device based on the content collection request, a content collection response comprising an updated content version (e.g. at step 354, the LLM engine generates a response based on a user input).)
generating, at a processor in communication with the memory and the network device, an LLM request comprising the at least one historical content version, the updated content version, and the at least one criteria prompt; See FIG. 3 & Paragraphs [0049] & [0060], (Method 300 comprises step 352 of receiving an NL-based input associated with a client device. At step 360, the system may determine whether to modify dialog context followed by step 362 of modifying a corresponding dialog content for a given subsequent turn of an ongoing dialog to generate a corresponding modified dialog context, i.e. generating, at a processor in communication with the memory and the network device, an LLM request comprising the at least one historical content version (e.g. a current dialog context) , the updated content version (e.g. the subsequent dialog context) , and the at least one criteria prompt (e.g. the NL-based user input associated with a client device);
transmitting, from the network device to an LLM system, the LLM request; See Paragraph [0058], (The system may generate a corresponding modified dialog context by using the LLM, i.e. transmitting, from the network device to an LLM system, the LLM request;)
receiving, at the network device from the LLM system, an LLM response; See Paragraph [0034], (LLM engine 161 is used to generate an LLM output that NL-based output engine 162 may process in generating an ML-based output 204 responsive to a NL-based input 201 for rendering to a user, i.e. receiving, at the network device from the LLM system, an LLM response;)
and generating, at the processor, a user interface for content analysis based on the LLM response. See FIG. 3 & Paragraphs [0055] & [0059], (Method 300 comprises step 356 of rending an NL-based output at a client device in addition to step 366 comprises causing the subsequent NL-based output to be rendered at a client device, i.e. generating, at the processor, a user interface for content analysis based on the LLM response.)
Regarding dependent claim 2,
As discussed above with claim 1, Baeuml discloses all of the limitations.
Baeuml further discloses the step of transmitting, using the network device, the user interface to the user device. See Paragraph [0026], (Client device 110 comprises a rendering engine 112 configured to provide content in the form of the NL-based output for audible and/or visual presentation to a user of the client device 110. Note FIG. 1 wherein client device 110 communicates with the NL-based output system 120 via network 199, i.e. transmitting, using the network device, the user interface to the user device (e.g. NL-based output from the system 120 is delivered to the client device 110 via network 199).)
Regarding independent claim 11,
The claim is analogous to the subject matter of independent claim 1 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 12,
The claim is analogous to the subject matter of dependent claim 2 directed to a computer system and is rejected under similar rationale.
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.
Claim(s) 3-5 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baeuml et al. (US PGPUB No. 2024/0311575; Pub. Date: Sep. 19, 2024) in view of Mahedy et al. (US GPPUB No. 2025/0139350; Pub. Date: May 1, 2025).
Regarding dependent claim 3,
As discussed above with claim 2, Baeuml discloses all of the limitations.
Baeuml does not disclose the step wherein the transmitting of the content collection request and the receiving of the content collection response are performed using a web scraper.
Mahedy discloses the step wherein the transmitting of the content collection request and the receiving of the content collection response are performed using a web scraper. See Paragraph [0022], (Disclosing a system for mechanisms for dynamically generating at least one page using one or more screenshots. The system comprises a page generation engine 130 for generating a plurality of entities based on a pre-defined correlation of one or more fragments to an entity. Each entity may be generated via searching techniques including web-scraping for entities associated with one or more fragments.) See Paragraph [0049], (Page generation module 130 may build page 650 by updating UI elements presented on a user interface of a client device, i.e. wherein the transmitting of the content collection request and the receiving of the content collection response are performed using a web scraper (e.g. page generation engine 130 uses web-scraping to generate pages).)
Baeuml and Mahedy are analogous art because they are in the same field of endeavor, content generation via large language models. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Baeuml to include the method of collecting web data using web-scraping techniques as disclosed by Mahedy. Paragraph [0014] of Mahedy discloses that the system provides at least the following advantages: providing users with the ability to interact with the content of generated screenshots without needing to access a separate application. The method provides users with a personalized application experience based on the generated screenshots that match and/or exceed the functionality of the original application within the screenshot.
Regarding dependent claim 4,
As discussed above with claim 3, Baeuml-Mahedy discloses all of the limitations.
Mahedy further discloses the step wherein the web scraper receives text-based data and image-based data in the content collection response. See Paragraphs [0021]-[0022], (Page generation engine 130 may extract content from a screenshot in order to generate fragments wherein content may include text, images, URLs, etc., i.e. wherein the web scraper receives text-based data and image-based data in the content collection response.)
Regarding dependent claim 5,
As discussed above with claim 4, Baeuml-Mahedy discloses all of the limitations.
Mahedy further discloses the step wherein the web scraper receives text-based data and image-based data in the content collection response. See Paragraphs [0021]-[0022], (Page generation engine 130 may extract content from a screenshot in order to generate fragments wherein content may include text, images, URLs, etc., i.e. wherein the web scraper receives text-based data and image-based data in the content collection response.)
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 3 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 14,
The claim is analogous to the subject matter of dependent claim 4 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 15,
The claim is analogous to the subject matter of dependent claim 5 directed to a computer system and is rejected under similar rationale.
Claim(s) 6-8 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baeuml in view of Mahedy as applied to claim 5 above, and further in view of Jindal et al. (US PGPUB No. 2023/0360271; Pub. Date: Nov. 9, 2023).
Regarding dependent claim 6,
As discussed above with claim 5, Baeuml-Mahedy discloses all of the limitations.
Mahedy further discloses the step of storing, in the database, the screenshot, the text-based data, and the image-based data as a new historical content version, See Paragraph [0030], (Page generation engine 10 may deploy a page to data store 110. Note [0016] data store 110 may include a media cache that stores copies of screenshots received from client devices.) See Paragraph [0043], (Fragment information may be enriched by enrichment module 430 to generate an enriched item which may be stored as an entity in data store 110. Note Paragraphs [0021]-[0022], (Page generation engine 130 may extract content from a screenshot in order to generate fragments wherein content may include text, images, URLs, etc., i.e. storing, in the database, the screenshot (e.g. screenshots are stored in data store 110), the text-based data, and the image-based data as a new historical content version (e.g. enriched fragments comprising web-page content are stored in data store 110).)
Baeuml-Mahedy does not disclose the step wherein the new historical content version indexed with a timestamp.
Jindal discloses the step wherein the new historical content version indexed with a timestamp. See Paragraph [0098], (Disclosing a system for detecting changes to a point of interest between a selected version and a previous version of a digital image. Image modification tracking system 102 may receive a payload of a neural network output including a digital image version, timestamp data, an identifier, thumbnail data, etc.)
Baeuml, Mahedy and Jindal are analogous art because they are in the same field of endeavor, content generation via large language models. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Baeuml-Mahedy to include the method of detecting differences between images as disclosed by Jindal. Paragraph [0032] of Jindal discloses that the image modification tracking system may improve the efficiency of conventional image editing systems by tracking the changes to a selected point of interest within a digital image by detecting a point of interest and determining modifications to the point of interest between versions of the digital image.
Regarding dependent claim 7,
As discussed above with claim 6, Baeuml-Mahedy-Jindal discloses all of the limitations.
Jindal further discloses the step wherein a first content version of the at least one historical content version is associated with a first timestamp, and the updated content version is associated with a second timestamp. See Paragraph [0035], (Image modification tracking system provides relevant details for changes to a point of interest including timestamps, i.e. wherein a first content version of the at least one historical content version is associated with a first timestamp, and the updated content version is associated with a second timestamp (e.g. both the previous and current versions of the image are timestamped).)
Regarding dependent claim 8,
As discussed above with claim 7, Baeuml-Mahedy-Jindal discloses all of the limitations.
Jindal further discloses the step of comparing, at the processor, the first content version and the updated content version to determine a content difference; See FIG. 1 & Paragraphs [0045] & [0048], (FIG. 1 illustrates an image capturing system comprising an image modification tracking system. The image capturing system utilizes a detection machine learning model which includes detection neural networks and detection artificial intelligence models.) See FIG. 4 & Paragraph [0087], (FIG. 4 illustrates interactions between client devices and the image modification tracking system 102 including step 424 of determining image modifications to a point of interest based on an image metadata file, i.e. comparing, at the processor, the first content version and the updated content version to determine a content difference;)
and wherein the generating at the processor, the LLM request comprises the content difference. See Paragraphs [0148]-[0149], (The detection machine learning model may generate bounds corresponding to an object as the point of interest. The process may then determine image modifications to the point o interest, i.e. wherein the generating at the processor, the LLM request comprises the content difference (e.g. image modification tracking system 102 uses ML models to determine modifications).)
The examiner notes that while Jindal discloses the use of artificial intelligence tools, Jindal does not explicitly use the term "LLM", however Baeuml is relied upon to disclose the use of an LLM to generate content for display on a user interface.
Regarding dependent claim 16,
The claim is analogous to the subject matter of dependent claim 6 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 17,
The claim is analogous to the subject matter of dependent claim 7 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 18,
The claim is analogous to the subject matter of dependent claim 8 directed to a computer system and is rejected under similar rationale.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baeuml in view of Mahedy and Jindal as applied to claim 8 above, and further in view of Gupta et al. (US PGPUB No. 2017/0091673; Pub. Date: Mar. 30, 2017).
Regarding dependent claim 9,
As discussed above with claim 8, Baeuml-Mahedy-Jindal discloses all of the limitations.
Baeuml-Mahedy-Jindal does not disclose the step of wherein the user interface comprises a scorecard user interface, the scorecard user interface comprising at least one scorecard metric for each of the at least one criteria prompt, the scorecard user interface updated automatically based on the received updated content version.
Gupta discloses the step of wherein the user interface comprises a scorecard user interface, the scorecard user interface comprising at least one scorecard metric for each of the at least one criteria prompt, See Paragraph [0095], (Disclosing a system for exporting a training model for performing a prediction. The system comprises monitoring module 360 configured to maintain a live updatable scoreboard for portable models deployed for scoring predictions of a prediction server 108, i.e. wherein the user interface comprises a scorecard user interface, the scorecard user interface comprising at least one scorecard metric for each of the at least one criteria prompt (e.g. the updatable scoreboard corresponds to a deployed portable model. Note [0036] wherein a portable model may be generated by a user and is configured to process data and provide predictions prescribed by the portable model, i.e. the portable model performs a desired operation).)
the scorecard user interface updated automatically based on the received updated content version. See Paragraph [0095], (Monitoring module 360 maintains a live updatable scoreboard for portable models deployed for scoring predictions of a prediction server 108.) See Paragraph [0077], (Model training module 270 may export an updated model to prediction server 108, i.e. the scorecard user interface updated automatically based on the received updated content version (e.g. the updatable scoreboard would be updated to display an updated portable model).)
Baeuml, Mahedy, Jindal and Gupta are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Baeuml-Mahedy-Jindal to include the method of updating a live scoreboard associated with a plurality of machine learning models as disclosed by Gupta. Paragraph [0096] of Gupta discloses that the live scoreboard allows users to observe the performance of a plurality of models in order to determine whether a particular model outperforms a champion portable model, which represents an optimization in model training and execution.
Regarding dependent claim 19,
The claim is analogous to the subject matter of dependent claim 9 directed to a computer system and is rejected under similar rationale.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baeuml in view of Mahedy, Jindal and Gupta as applied to claim 8 above, and further in view of URIARTE (US PGPUB No. 2024/0169286; Pub. Date: May 23, 2024).
Regarding dependent claim 10,
As discussed above with claim 9, Baeuml-Mahedy-Jindal discloses all of the limitations.
Baeuml-Mahedy-Jindal does not disclose the step wherein the user interface comprises a checklist user interface, the checklist user interface comprising at least one checklist metric for each of the at least one criteria prompt, the checklist user interface updated automatically based on the received updated content version.
URIARTE discloses the step wherein the user interface comprises a checklist user interface, the checklist user interface comprising at least one checklist metric for each of the at least one criteria prompt, the checklist user interface updated automatically based on the received updated content version. See FIG. 3A & Paragraph [0057], (Disclosing a video markup system including presenting a checklist item and receiving an evaluation video documenting an element of a worksite corresponding to the checklist item. FIG. 3A illustrates a method of presenting a checklist item. At step 312, the system may update a status of a checklist item. Note [0065] wherein updates to the status of a checklist item may be performed automatically by an LLM, i.e. wherein the user interface comprises a checklist user interface.), i.e. the checklist user interface comprising at least one checklist metric for each of the at least one criteria prompt (e.g. checklist items comprise tasks to be performed), the checklist user interface updated automatically based on the received updated content version (an LLM is used to update a checklist item upon receipt of updated information associated with a checklist item).)
Baeuml, Mahedy, Jindal, Gupta and URIARTE are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Baeuml-Mahedy-Jindal-Gupta to include the method of automatically updating a checklist of tasks using a large language model as disclosed by URIARTE. Paragraph [0065] of URIARTE discloses the system applying a large language model for performing automatic updates to a checklist element based on LLM outputs that indicate the manner in which the checklist should be updated. This allows the system to automatically detect and react to changes in task status without requiring user intervention.
Regarding dependent claim 20,
The claim is analogous to the subject matter of dependent claim 10 directed to a computer system and is rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159 /AMRESH SINGH/Primary Examiner, Art Unit 2159