DETAILED ACTION
This action is responsive to the response filed on 2/17/2026.
Claims 1-20 remain pending in this application. Claims 1, 8, and 15 have been amended. Claims 1, 8, and 15 are independent claims.
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 .
Specification
The use of the several terms, such as Bluetooth, ZigbBee, etc., which are trade names or a marks used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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.
Claims 1, 2, 8, 9, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rao Karikurve et al., US PGPUB 2022/0335489 Al (hereinafter as Rao) in view of Ranjan et al. US Patent No. 11,381,652 B2 (hereinafter as Ranjan) and Lomada et al., US PGPUB 2020/0107072 A1, (hereinafter as Lomada).
Regarding independent claim 1, Rao teaches a system [see online concierge system 102 shown in figs. 1-2] comprising:
a non-transitory memory [note e.g. in [0069]-[0070] the different memory variations of a computer or device that store the instructions/program];
a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to [note e.g. in [0069]-[0070] the different processor variations of a computer or device that execute the instructions/program]:
receive a request for a user interface, wherein the request includes a user identifier [see [0065] indicating a request from the user; note the user account in [0043]];
obtain a set of features from a database, wherein the set of features are associated with the user identifier in the database [note in [0061] identifying a user from whom a request was received and identifying characteristics of the user; note also the different databases describing the user, such as shopper database 212 and user database 214 in [0031]-[0032]];
generate a user embedding by applying a model to the set of features [note again [0061]; see also [0039] indicating generating a user embedding based on preferences, prior purchases, and any other user characteristics];
obtain a set of potential tasks, wherein the set of potential tasks are associated with an enrollment portion of the user interface; generate a task embedding for each potential task in the set of potential tasks; [note the description in [0039] of the generated item embedding; note in [0057] that the item embedding is associated with user interactions where the user selects the item, which indicates a potential task (of selection) associated with a certain user of the system, i.e. a user with an account or an enrolled user; especially note in [0059] items identified in prior purchases from a specific user or from users having common characteristics]
generate a user-task affinity for each potential task by comparing the user embedding to each task embedding [note in [0039] the generation of probabilities of the user purchasing the items by measuring some sort of similarity, i.e. comparing the embeddings; see also the different similarity measures in [0063]; see also [0054]];
generate a ranked set of tasks by ranking each potential task based on the user-task affinity [note e.g. in [0065] ranking the items based on the probabilities; note also in [0066] ranking a collection of items based on the scores/probabilities of the collection];
generate a set of interface elements related to a predetermined number of highest ranked tasks in the ranked set of tasks [note in [0065]-[0066] generating the interface with items/collections of higher scores/probabilities in higher positions, i.e. in more prominent locations in the interface; note also in [0066] identifying a set of items from the ranking such as those having at least a threshold position in the ranking which indicates a predetermined number of items with the highest ranks available];
generate the user interface including the set of interface elements [note in [0065] the generation of the interface with different elements]; and
transmit the user interface to a device that generated the request for the user interface [again, see the last 4 lines of [0065] indicating transmitting the interface to the device of the requesting user].
Rao does not explicitly teach determining, based on the user identifier, that at least one default task associated with the enrollment portion of the user interface has not been completed and adjusting the ranked set of tasks to include the at least one default task as a highest ranked task or that the highest ranked tasks include the at least one default task.
Rao also does not explicitly teach that the model applied to the set of features to generate a user embedding is an autoencoder.
Ranjan teaches determining, based on a user identifier, that at least one default task associated with an enrollment portion of the user interface has not been completed [note e.g. in col. 6, lines 48-56 the determination of whether a default task such as email address verification related to a user account has been completed and considering it as a potential task if it has not] and adjusting a ranked set of tasks (associated with interface elements) to include the at least one default task as a highest ranked task [note e.g.in col. 13, lines 31-51 the ranking of a set of tasks (associated with interface elements); note e.g. from col. 7, lines 57-64 the assignment of higher priority scores to account-security-related actions/tasks of which email address verification may be an example].
Ranjan further teaches highest ranked tasks that include the at least one default task [again, see col. 13, lines 31-51 and col. 7, lines 57-64 indicated in the previous paragraph].
It would have been obvious to one of ordinary skill in the art having the teachings of Rao and Ranjan, before the effective filing date of the claimed invention, to modify the instructions taught by Rao by explicitly specifying determining, based on the user identifier, that at least one default task associated with the enrollment portion of the user interface has not been completed and adjusting the ranked set of tasks to include the at least one default task as a highest ranked task, as per the teachings of Ranjan and by modifying the generation of a set of interface elements related to a predetermined number of highest ranked tasks in the ranked set of tasks taught by Rao to explicitly specify that the highest ranked tasks include the at least one default task, again, as per the teachings of Ranjan. The motivation for this obvious combination of teachings would be to allow prioritizing actions that are deemed to improve the user experience or the security of the user account, as suggested by Ranjan [again, see e.g. col. 7, lines 57-64 as well as col. 1, lines 15-25].
The previously combined art, still, does not explicitly teach that the model applied to the set of features to generate a user embedding is an autoencoder.
Lomada teaches generating a user embedding by applying an autoencoder to a set of features [see fig. 2, especially 208 and related description].
It would have been obvious to one of ordinary skill in the art having the teachings of Rao and Lomada, before the effective filing date of the claimed invention, to modify the generic user embedding generation taught by Rao by explicitly specifying that generating the user embedding is by applying an autoencoder to the set of features, as per the teachings of Lomada. The motivation for this obvious combination of teachings would be to allow efficiently analyzing heterogenous data created by user trait changes by encoding it into a uniform representation than can more accurately, learn, encode, and reconstruct the data in a way that is data-specific, as suggested by Lomada [see e.g. [0028] and [0040] as well as [0003]-[0008]].
Regarding independent claims 8 and 15, they are analogously rejected.
For claim 8, Rao further teaches a computer-implemented method [see figs/ 5 and 6 and related description] comprising the steps performed by the set of instructions.
For claim 15, Rao further teaches a non-transitory computer-readable storage medium storing instructions which, when executed by one or more processors, cause one or more devices to perform the operations [see e.g. [0069]].
Regarding claims 2, 9, and 16, the rejections of claims 1, 8, and 15 are respectively incorporated.
Rao further teaches that the set of features comprises transactional features, demographic features, enrollment program features, intent features, engagement features, recency, frequency, monetary value (RFM) features, or any combination thereof [note in [0031]-[0032] features including gender, address, stored payment instruments, previous shopping history, and preferences/favorites].
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Ranjan and Lomada (as applied to claims 1, 8, and 15 above, respectively), and further in view of Cao, US PGPUB 2023/0147890 A1 (hereinafter as Cao).
Regarding claims 3, 10, and 17, the rejections of claims 1, 8, and 15 are respectively incorporated.
The previously combined art does not explicitly teach that each task embedding is generated by a word2vec model.
Cao teaches an embedding that is generated by a word2vec model [see e.g. [0006] indicating using word2vec for word embeddings].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Cao, before the effective filing date of the claimed invention, to modify the task embedding generations taught by Rao by explicitly specifying the usage of a word2vec model, as per the teachings of Cao. The motivation for this obvious combination of teachings would be to guarantee that semantically similar items are close to each other in the generated representation which would enable better recommendation generation, as suggested by Cao [see e.g. [0006]-[0007]].
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Ranjan and Lomada (as applied to claims 1, 8, and 15 above, respectively), and further in view of Hicks, US PGPUB 2009/0132459 A1 (hereinafter as Hicks).
Regarding claims 4, 11, and 18, the rejections of claims 1, 8, and 15 are respectively incorporated.
The previously combined art does not explicitly teach that the ranked set of tasks is filtered by a task filter to remove similar, context-appropriate tasks.
Hicks teaches a set of items that are filtered to remove similar, context-appropriate items [see e.g. [0013] indicating filtering to remove similar but non-duplicate items from a list; see also [0016]].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Hicks, before the effective filing date of the claimed invention, to modify the ranked set of tasks taught by Rao by explicitly specifying filtering it by a task filter to remove similar, context-appropriate tasks, as per the teachings of Hicks. The motivation for this obvious combination of teachings would be to increase the diversity and thus the utility of the recommendations presented to the user, as suggested by Hicks [see e.g. [0013]].
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Ranjan and Lomada (as applied to claims 1, 8, and 15 above, respectively), and further in view of Misra et al., US PGPUB 20220397995 A1 (hereinafter as Misra).
Regarding claims 5, 12, and 19, the rejections of claims 1, 8, and 15 are respectively incorporated.
The previously combined art does not explicitly teach that the ranked set of tasks is augmented by a set of basic tasks.
Misra teaches a set of items that are augmented by a set of basic items [see e.g. [0122] indicating having some default set of content items that is rendered in addition to the customized items].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Misra, before the effective filing date of the claimed invention, to modify the ranked set of items to be included in the interface taught by Rao by explicitly specifying augmenting it with a set of basic items, as per the teachings of Misra. The motivation for this obvious combination of teachings would be to enable content providers to have a default set of content items that is always displayed, as in the example suggested by Misra [see e.g. [0122]].
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Ranjan and Lomada (as applied to claims 1, 8, and 15 above, respectively), and further in view of DE BARROS et al., US PGPUB 2023/0410156 Al (hereinafter as De Barros).
Regarding claims 6, 13, and 20, the rejections of claims 1, 8, and 15 are respectively incorporated.
The previously combined art does not explicitly teach: receiving feedback data including at least one event indicator; correlating the at least one event indicator to one of the predetermined number of highest ranked tasks in the ranked set of tasks; and updating a task status element associated with the user identifier based on the correlation between the event indicator and the one of the predetermined number of highest ranked tasks in the ranked set of tasks.
De Barros teaches:
receiving feedback data including at least one event indicator [note e.g. in [0055] receiving an interaction of the user with an item of the shopping feed 122 shown in figs. 1-2 which, according to [0024]-[0025], may include information pertaining to offered products (such as goods, services, etc.)];
correlating the at least one event indicator to one of the items in the predetermined items [again, see [0055] and note the correlation of the interaction with the item]; and
updating a task status element associated with the user identifier based on the correlation between the event indicator and the one of the predetermined items [again, see in [0055] updating a user rewards account based on the interaction with the item].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and De Barros, before the effective filing date of the claimed invention, to apply De Barros’ teaching of receiving feedback data including at least one event indicator, correlating the at least one event indicator to one of the items in the predetermined items, and updating a task status element associated with the user identifier based on the correlation between the event indicator and the one of the predetermined items to one of the predetermined number of highest ranked tasks in the ranked set of tasks taught by Rao. The motivation for this obvious combination of teachings would be to increase user engagement by tailoring the customization to each individual user based on interaction with the platform which also benefits sellers by creating additional marketing strategy opportunities, as suggested by De Barros [see e.g. [0017]].
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Ranjan and Lomada (as applied to claims 1 and 8, above, respectively), and further in view of KHARRAZ TAVAKOL, US PGPUB 2015/0154528 Al (hereinafter as Kharraz).
Regarding claims 7 and 14, the rejections of claims 1 and 8 are respectively incorporated.
The previously combined art does not explicitly teach that the user interface is updated to include a subsequent predetermined number of highest ranked tasks when the predetermined number of highest ranked tasks in the ranked set of tasks is completed.
Kharraz teaches a user interface that is updated to include subsequent highest ranks tasks when an initial number of highest ranked tasks in the ranked set of tasks is completed [see e.g. [0010] indicating updating a set of items on an interface by adding uncompleted tasks in a prioritized scheme].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Kharraz, before the effective filing date of the claimed invention, to apply the teaching of Kharraz of updating an interface by adding a subsequent number of ranked tasks when an initial set of displayed tasks is completed to the interface taught by Rao initially having a predetermined number of highest ranked tasks in the ranked set of task, and to further specify a subsequent predetermined number of highest ranked tasks, as per the initial teaching of Rao. The motivation for this obvious combination of teachings would be to guarantee that users focus on a limited number of items at a time and in a specific ranked order, as in the example suggested by Kharraz [see e.g. [0006]] which would support maintaining the user’s focus in the predetermined order required for offers displayed in the interface of Rao while enabling showing more offers as older ones are interacted with.
Response to Arguments
Applicant’s amendments to the claims in view of the previously presented claim objections have been fully considered and are persuasive. These objections have been accordingly withdrawn.
Applicant’s arguments with respect to the independent claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Examiner notes from the cited art:
Agrawal, US 20180052706 A1, which teaches displaying incomplete tasks associated with the user account [see e.g. [0066]].
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.
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/MARIA S AYAD/Primary Examiner, Art Unit 2172