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.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,099,858. The claims of the instant application and the claims of the reference patent are compared in the table below.
Instant Application No. 18/893,467.
Claim 1. A method comprising: utilizing, by at least one processor, in response to an interface rendering request from a user device associated with a user, a trained rendering type machine learning model to predict a rendering type for displaying a user interface rendering to the user, based at least in part on user data associated with the user;
utilizing, by the at least one processor, a trained rendering layout machine learning model to predict a layout of at least one presentation container based at least in part on: the rendering type, and the user data;
wherein the trained rendering layout machine learning model comprises at least one multi-armed bandit problem trained to optimize at least one location within the
interface rendering of the at least one presentation container;
utilizing, by the at least one processor, a trained presentation machine learning model to predict at least one presentation item to display in the at least one presentation container of a rendering template; and causing, by the at least one processor, the user device to render the user interface rendering on a display so as to display the at least one presentation item within the at least one presentation container of the layout.
2. The method of claim 1, wherein the rendering request is associated with at least one software user interface (UI).
3. The method of claim 2, wherein the at least one software UI comprises at least one of:
a webpage rendering, an application rendering, or an operating system (OS) interface.
4. The method of claim 2, wherein the least one software UI comprises an interface component within at least one of: a webpage rendering, an application rendering, or an operating system (OS) interface.
5. The method of claim 1, further comprising: receiving, by the at least one processor, at least one rendering metric indicating at least one measurement of user engagement with the at least one presentation item of the interface rendering; generating, by the at least one processor, exploit training data comprising: the rendering type, the rendering template, the at least one presentation item, and the at least one rendering metric; training, by the at least one processor, according to the exploit training data, rendering type classification parameters of the rendering type machine learning model to refine a rendering type classification layer; training, by the at least one processor, according to the exploit training data, the rendering template classification parameters of the rendering template machine learning model to refine the trained rendering template classification layer; and training, by the at least one processor, according to the exploit training data, the presentation classification parameters of the presentation machine learning model to refine the trained presentation classification layer.
6. The method of claim 1, further comprising: generating, by the at least one processor, a randomly generated interface rendering in response to a prior rendering request; wherein the interface rendering comprises: a randomly selected rendering type from a plurality of candidate rendering types, a randomly selected rendering template from the plurality of candidate rendering templates, and at least one randomly selected presentation item from the plurality of candidate presentation items;
generating, by the at least one processor, the randomly generated interface rendering in
response to the prior rendering request;
wherein the randomly generated interface rendering comprises the at least one
randomly selected presentation item configured to be positioned according to the randomly selected rendering template; and instructing, by the at least one processor, the user device to render the randomly generated interface rendering on the display so as to display the at least one randomly selected presentation item; receiving, by the at least one processor, at least one prior rendering metric indicating at
least one measurement of user engagement with the at least one randomly selected presentation item of the randomly generated interface rendering; generating, by the at least one processor, explore training data comprising:
the randomly selected rendering type, the randomly selected rendering template, the at least one randomly selected presentation item, and the at least one prior rendering metric; training, by the at least one processor, according to the explore training data, rendering type classification parameters of the rendering type machine learning model to obtain a trained
rendering type classification layer; training, by the at least one processor, according to the explore training data, rendering template classification parameters of the rendering template machine learning model to obtain
the trained rendering template classification layer; and training, by the at least one processor, according to the explore training data, presentation classification parameters of the presentation machine learning model to obtain the trained presentation classification layer.
7. The method of claim 1, wherein the trained rendering template machine learning model comprises at least one multi-armed bandit problem trained to optimize at least one location within the interface rendering of the at least one presentation container.
10. The method of claim 1, wherein the user data comprises a transaction history, and the at least one presentation item comprises at least one offer for a financial product associated with a financial entity.
US Patent No. 12,099,858.
1.A method comprising: receiving, by at least one processor, an interface rendering request from a user device associated with a user; determining, by the at least one processor, a user profile associated with the user device, wherein the user profile comprises user data representative of user online behavior of the user;
utilizing, by the at least one processor, a trained rendering type machine learning model to predict a rendering type from a plurality of candidate rendering types for displaying a user interface rendering to the user, based at least in part on the user data of the user profile;
wherein the trained rendering type machine learning model comprises at least one trained rendering type classification layer having trained rendering type classification parameters;
utilizing, by the at least one processor, a trained rendering template machine learning model to predict an interface rendering template from a plurality of candidate interface rendering templates for the user interface rendering based at least in part on: a library of rendering templates, the rendering type, and the user data of the user profile; wherein the trained rendering template machine learning model comprises at least one trained rendering template classification layer having trained rendering template classification parameters; wherein the rendering template comprises at least one presentation container for displaying at least one presentation item; wherein the trained rendering template machine learning model comprises at least one multi-armed bandit problem trained to optimize at least one location within the interface rendering of the at least one presentation container; utilizing, by the at least one processor, a trained presentation machine learning model to predict the at least one presentation item from a plurality of candidate presentation items to display in the at least one presentation container of the rendering template based at least in part on: the rendering type, and the user data of the user profile; wherein the trained presentation machine learning model comprises at least one trained presentation classification layer having trained presentation classification parameters: generating, by the at least one processor, the user interface rendering in response to the rendering request; wherein the user interface rendering comprises the at least one presentation item configured to be positioned within the at least one container of the rendering template; and instructing, by the at least one processor, the user device to render the user interface rendering on a display so as to display the at least one presentation item within the at least one container of the rendering template.
2. The method of claim 1, wherein the rendering request is associated with at least one software user interface (UI).
3. The method of claim 2, wherein the at least one software UI comprises at least one of: a webpage rendering, an application rendering, or an operating system (OS) interface.
4. The method of claim 2, wherein the least one software UI comprises an interface component within at least one of: a webpage rendering, an application rendering, or an operating system (OS) interface.
5. The method of claim 1, further comprising: receiving, by the at least one processor, at least one rendering metric indicating at least one measurement of user engagement with the at least one presentation item of the interface rendering; generating, by the at least one processor, exploit training data comprising: the rendering type, the rendering template, the at least one presentation item, and the at least one rendering metric; training, by the at least one processor, according to the exploit training data, the trained rendering type classification parameters of the rendering type machine learning model to refine the trained rendering type classification layer; training, by the at least one processor, according to the exploit training data, the rendering template classification parameters of the rendering template machine learning model to refine the trained rendering template classification layer; and training, by the at least one processor, according to the exploit training data, the presentation classification parameters of the presentation machine learning model to refine the trained presentation classification layer.
6. The method of claim 1, further comprising: generating, by the at least one processor, a randomly generated interface rendering in response to a prior rendering request; wherein the interface rendering comprises: a randomly selected rendering type from the plurality of candidate rendering types, a randomly selected rendering template from the plurality of candidate rendering templates, and at least one randomly selected presentation item from the plurality of candidate presentation items; generating, by the at least one processor, the randomly generated interface rendering in response to the prior rendering request; wherein the randomly generated interface rendering comprises the at least one randomly selected presentation item configured to be positioned according to the randomly selected rendering template; and instructing, by the at least one processor, the user device to render the randomly generated interface rendering on the display so as to display the at least one randomly selected presentation item; receiving, by the at least one processor, at least one prior rendering metric indicating at least one measurement of user engagement with the at least one randomly selected presentation item of the randomly generated interface rendering; generating, by the at least one processor, explore training data comprising: the randomly selected rendering type, the randomly selected rendering template, the at least one randomly selected presentation item, and the at least one prior rendering metric; training, by the at least one processor, according to the explore training data, rendering type classification parameters of the rendering type machine learning model to obtain the trained rendering type classification layer; training, by the at least one processor, according to the explore training data, rendering template classification parameters of the rendering template machine learning model to obtain the trained rendering template classification layer; and training, by the at least one processor, according to the explore training data, presentation classification parameters of the presentation machine learning model to obtain the trained presentation classification layer.
7. The method of claim 1, further comprising utilizing, by the at least one processor, the at least one multi-armed bandit problem to optimize the location within the interface rendering of the at least one presentation container based at least in part on a context of the rendering request.
9. The method of claim 1, wherein the user data comprises a transaction history, and the at least one presentation item comprises at least one offer for a financial product associated with a financial entity.
Claim 1 of the reference patent recites all of the limitations of claim 1 of the instant application except “causing, by the at least one processor, the user device to render the user interface rendering on a display so as to display the at least one presentation item within the at least one presentation container of the layout...” However, Mishra teaches the user data associated with the rendering request and/or the data and/or metadata associated with the rendering request to generate UX configurations personalized for the user and responsive to a context of the user interactions. In some embodiments, the UX configuration may include, e.g., a rendering type, a rendering template associated with a layout of one or more UI components and/or elements, one or more content or presentation items presented in each UI component/element, a timing of the content/presentation item, an animation, a transition, among other suitable UI structure and/or behavior; Mishra col. 8, lines 12-20.
It would have been obvious to person of ordinary skill in the art before the effective filing date of the claimed invention to the user data associated with the rendering request and/or the data and/or metadata associated with the rendering request to generate UX configurations personalized for the user and responsive to a context of the user interactions. In some embodiments, the UX configuration may include, e.g., a rendering type, a rendering template associated with a layout of one or more UI components and/or elements, one or more content or presentation items presented in each UI component/element, a timing of the content/presentation item, an animation, a transition, among other suitable UI structure and/or behavior, as disclosed in Mishra, within the method of claim 1 of the reference patent, causing, by the at least one processor, the user device to render the user interface rendering on a display so as to display the at least one presentation item within the at least one presentation container of the layout.
Allowable Subject Matter
After a thorough search, and in light of the prior art of record, claims 1 and 11 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
The best prior arts, Bennion et al. (US Patent Application Publication No. 2019/0114661) and Lee et al. (US Patent Application Publication No. 2019/0139085) combination fail to disclose or suggest one or more of the features of the independent claims 1 and 11.
In summary, Bennion discloses a based on the dynamically generated ads are determined and the dynamically generated ads may be determined based on the candidate ad placement positions within the application. In such an implementation, both factors (candidate ad placement positions and candidate template/content items for each candidate ad placement position) may be considered as inputs to the machine learning system and the output may provide a recommendation of both ad placement positions and candidate template/content items for use in those positions to produce the highest potential for user engagement.
Lee teaches a category associated with the entity or a template for creating a representation of the entity; The social context information can indicate activities of a user or a user's connections in the social networking system. Entity attributes can include any attributes associated with entities or representations of entities, operating hours, contact information (e.g., phone number, address, email address, etc.), an objective, a number of connections or followers, a template.
None of the prior arts of record alone or in any reasonable combination, disclose the claimed invention as recites in the independent claim 1 and similarly recited in independent claims 11.
Specifically, the prior art fail to teach a method comprising: utilizing, by at least one processor, in response to an interface rendering request from a user device associated with a user, a trained rendering type machine learning model to predict a rendering type for displaying a user interface rendering to the user, based at least in part on user data associated with the user; utilizing, by the at least one processor, a trained rendering layout machine learning model to predict a layout of at least one presentation container based at least in part on: the rendering type, and the user data; wherein the trained rendering layout machine learning model comprises at least one multi-armed bandit problem trained to optimize at least one location within the
interface rendering of the at least one presentation container; utilizing, by the at least one processor, a trained presentation machine learning model to predict at least one presentation item to display in the at least one presentation container of a rendering template; and causing, by the at least one processor, the user device to render the user interface rendering on a display so as to display the at least one presentation item within the at least one presentation container of the layout.
The recited limitations, in conjunction with all the features of the independent and dependent claims are not taught nor suggested by the prior arts of record.
Claims 2-10 and 12-20 are also allowed as being directly or indirectly dependent of the allowed independent claims.
Accordingly, the scope of the invention has been sufficiently narrowed to bring the claims into compliance with novelty and non-obviousness requirement of the Patent Act.
Conclusion
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/CAO H NGUYEN/Primary Examiner, Art Unit 2171
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