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 .
Response to Arguments
Applicant’s arguments filed 12/30/2025, with respect to claims 1-25 have been fully considered but are moot in view new ground(s) of rejection.
Claim Rejections - 35 USC § 112
The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection to claims 1, 8 and 15 are withdrawn in light of the Applicant’s amendments.
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.
Claim(s) 1, 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bean (PGPUB Document No. US 2024/0320867) in view of Lin et al. (PGPUB Document No. US 2017/0004383).
Regarding claim 1, Bean teaches a system for guiding generation of items, the system comprises:
A computing device comprising one or more processors; a neural network; a transceiver;
And at least one memory in communication with the computing device, the neural network, and the transceiver and storing computer program code that, when executed by the computing device, is configured to cause the system to:
Output a first set of items having a first attribute (image 670 output in response to the initial user input that comprise of a set of objects/items such as the car and road (Bean: 0070-0071, FIG.6, step 802 of FIG.8));
Receive from a user a first user input of a selection of a selected item in the first set of items (selecting alternate words 650 and 660 to the initial selections associated with the items above of shown in FIG.6 (Bean: 0075-0076));
Store the first user input to the neural network (using neural networks for the text-to-image generator (Bean: 0034-35), wherein the Examiner submits that said process requires storing the user input (text prompt));
Generate, using the neural network (neural network for the text-to-image generator (Bean: 0034-35)), a second set of items comprising a first modified first attribute, wherein the first modified first attribute is based, at least in part, on the user preference for the first attribute (“After receiving one or more subsequent user inputs and selections updating and/or modifying the initial text prompt and/or the subsequent text prompt, the user interface 610 may submit a new image generation request to the image generation system” (Bean: 0078). The selected alternate word(s) correspond to the “user preference”);
Output the second set of items (the resulting image from the updated prompts (text input));
Receive from the user a second user input of a selection of a selected item in the second set of items (the process above may be repeated any number of times (Bean: 0094). Repeating the steps above results in further modifying the text prompt, wherein the modified text prompt corresponds to the second user input);
Store the second user input to the neural network (the Examiner submits data such as user input/feedback is stored to be utilized when utilizing neural networks for the text-to-image generator (Bean: 0034));
Determine, based on at least the second user input, an updated user preference for the first attribute (the modified text prompt in response to the user selection correspond to the updated user preference);
Generate, using the neural network, a third set of items comprising a second modified first attribute, wherein the second modified first attribute is based, at least in part, on the updated user preference for the first attribute (the resulting image from the updated prompts above, wherein the Examiner submits data such as user input/feedback is stored to be utilized when utilizing neural networks for the text-to-image generator (Bean: 0034));
However, Bean does not expressly teach but Lin teaches
Determine, based on the first user input, a user preference for the first attribute, the user preference free of any textual articulation for the first attribute (Lin teaches the concept of iteratively providing feedback by selecting images to better achieve desirable results (Lin: 0056)).
The updated user preference free of any textual articulation for the first attribute (applying the teachings of Bean results in the user input/feedback also being images based).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of an ordinary skill in the art to modify the teachings of Bean such as to enable user selection as taught by Lin, because this enables an added variety of methods of receiving user input.
Claim(s) 8 are corresponding method claim(s) of claim(s) 1. The limitations of claim(s) 8 are substantially similar to the limitations of claim(s) 1. Therefore, it has been analyzed and rejected substantially similar to claim(s) 8.
Claim(s) 15 are corresponding method claim(s) of claim(s) 1. The limitations of claim(s) 15 are substantially similar to the limitations of claim(s) 1. Therefore, it has been analyzed and rejected substantially similar to claim(s) 15. Note, Bean teaches a non-transitory computer readable medium (memory (Bean: 0104)), one or more processors (processors (Bean: 0098)) as presently claimed.
Claim(s) 2, 9 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bean as applied to the claim(s) above, and further in view of Fang et al. (PGPUB Document No. US 2019/0251612).
Regarding claim 2, Bean does not expressly teach but Fang teaches the system of claim 1, wherein the neural network is further configured to map similar user preferences related to the first modified first attribute and a second modified second attribute closely together within a structured latent space to improve the system prediction and generation of at least one additional item (“machine learning may operate by building models from example inputs (e.g., training), such as latent code, to make data-driven predictions or decisions…. Machine learning can include neural networks” (Fang: 0041)).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of an ordinary skill in the art to modify the teachings of Bean such as to utilize the machine learning process of Fang, because this effectively facilitates the text to image generation process.
Claims 9 and 16 are similar in scope to claim 2.
Allowable Subject Matter
Clams 21-24 are allowed. Claims 3-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David H Chu whose telephone number is (571)272-8079. The examiner can normally be reached M-F: 9:30 - 1:30pm, 3:30-8:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel F Hajnik can be reached at (571) 272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID H CHU/Primary Examiner, Art Unit 2616