DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to claims filed 10/22/2025 . Claims 1-5, 7-13, 15-22 are pending.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/22/2025 has been entered.
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) 1-5, 8-13, 16-19, 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Haapoja (US 20210326959 A1) in view of Subramanian (US 20210406978 A1).
For claim 1, Haapoja discloses: a method for dynamically filtering choices, comprising:
identifying a state of a user via at least one of a camera, a heart rate sensor, an eye gaze monitor, and a microphone (0126: detection of stopping and lingering in expressing interest in a product, hence, hesitation in completing purchase, detection including computer vision and audio techniques, hence, camera and microphone);
determining whether the state of the user includes an indecisive behavior (ibid);
identifying a state of an environment of the user (ibid: identifying customer location and associated products);
identifying a set of available choices that are available for the user to select between, from the state of the environment (0128, 0150: based on detecting interest such as based on hesitation, identifying available products (0106, 0108, 0118, 0127, 0156: identifying inventory for use in recommendation) and using rules (see fig.7, 0127) to provide recommendations);
receiving a set of past user decisions relating to the set of past choices (fig.7, 0127: rules include consideration of previous purchases, hence, past user decisions, ),
generating, with a decision making model executed by a processor, a predicted choice from the set of available choices based on the set of past choices and the set of past user decisions in response to determining that the state of the user includes an indecisive behavior (using rules (fig.7, 0127) to generate recommendations in response to and accounting for user interest such as conveyed via hesitation, hence, generating predicted user purchase choices from past choices (fig.7, 0127) and user indecision or interest indicator (fig.7, 0127, 0128: rules conditioned on user having expressed interest, such as fig.7 rule 5));
providing the predicted choice to the user by dynamically updating an interface of an electronic display (0129, 0138-140, 0148: accentuation of product),
wherein the decision making model has been trained based on at least the set of past user decisions to facilitate the decision making model to predict a decision from the set of available choices (0127, 0155).
Haapoja does not disclose: wherein the receiving includes receiving a set of past choices, that had previously been available for the user to select between; wherein the model is trained based on the set of past choices.
Subramanian discloses: wherein the receiving includes receiving a set of past choices, that had previously been available for the user to select between (fig.1, 0029, 0031, 0035 contemplates using historical decision context data including inventory data, i.e., product availability data, to determine consumer behavior and elasticity in generating a customer pricing output, see 0029, 0037); wherein the model is trained based on the set of past choices (fig.1:110, 124; fig.2:120-122, 0031, 0034-36).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of Haapoja by incorporating the past choice data of Subramanian. Both concern the art of consumer product recommendation systems based on machine learning models for user interfaces, and the incorporation would have, according to Subramanian, provide adaptable networks to deal with rapidly-changing decision context data alongside baseline purchase models in order to better present the user with purchase recommendations or selections (0036, 0004).
For claim 2, Haapoja modified by Subramanian discloses the method of claim 1, as described above. Haapoja modified by Subramanian further discloses: wherein the state of the user comprises:
a visual, a biometric, an interaction, an eye gaze, an audio recording, or combinations thereof (0126: detection of stopping and lingering in expressing interest in a product, hence, hesitation in completing purchase, detection including computer vision and audio techniques, hence, camera and microphone).
For claim 3, Haapoja modified by Subramanian discloses the method of claim 1, as described above. Haapoja modified by Subramanian further discloses: wherein determining whether the state of the user includes an indecisive behavior comprises:
identifying a repeated eye gaze on a choice, a repeated interaction with a choice, a lack of interaction with the set of available choices for a threshold period of time, a manual user indication of indecisiveness (Haapoja 0126: manually entering Fred’s browsing behavior), or combinations thereof.
For claim 4, Haapoja modified by Subramanian discloses the method of claim 1, as described above. Haapoja modified by Subramanian further discloses: wherein the state of the environment comprises: a visual (Haapoja 0126: photograph capturing product), an address (ibid: location / address information, see also fig.6 showing location addresses), a current time, or combinations thereof (ibid).
For claim 5, Haapoja modified by Subramanian discloses the method of claim 1, as described above. Haapoja modified by Subramanian further discloses: wherein identifying the set of available choices from the state of the environment comprises:
generating a set of choices by analyzing the visual for choices in response to the state of the environment comprising the visual (Haapoja 0140-142, 0145: determining field of view change, such as via image processing, in order to identify various products within a field of view for possible recommendation; 0147: generating recommendations based on determined products based on field of view, etc);
generating the set of choices by retrieving a list of choices located at the address in response to the state of the environment comprising the address (ibid: a list of products proximate the camera or user location are identified based on the camera address, such as coordinate address); and
generating the set of available choices by filtering the set of choices based on the current time (Haapoja 0127 contemplates the periodic updating of the user profile and recommendation rules or heuristics, hence, the set of choices are filtered via the set of rules or heuristics in real-time based on the current time corresponding to update rule set and corresponding profile in the current time window).
For claim 8, Haapoja modified by Subramanian discloses the method of claim 1, as described above. Haapoja modified by Subramanian further discloses: receiving a selected choice from the user; and
updating the decision making model to incorporate the selected choice for enhancing subsequent generating of predicted choices (Haapoja fig.5:716, 0103, 0157: considering purchase history when making recommendations; Subramanian fig.1:124, 0029: using selection data as training data).
Claims 9-13, 16-19 recite analogous systems and computer media corresponding to the above methods and are hence rejected for the same reasons.
For claim 21, Haapoja modified by Subramanian discloses the method of claim 5, as described above. Haapoja modified by Subramanian further discloses: wherein the visual is captured by a camera imaging the state of the environment (Haapoja 0144-145).
For claim 22, Haapoja modified by Subramanian discloses the method of claim 1, as described above. Haapoja modified by Subramanian further discloses: wherein identifying the set of available choices comprises:
determining GPS location information (0120, 0123: determining GPS location of user);
determining an address corresponding to the GPS location information (fig.6 showing determination of a location, hence, electronic address); and
querying at least one of an external server and an external database for a list of available choices available at an establishment located at the address (fig.6, 0115: shows external database containing product inventory and location, with fig.4 showing remote network communication).
Claim(s) 7, 15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Haapoja (US 20210326959 A1) in view of Subramanian (US 20210406978 A1) in view of Sehgal ("An introduction to selection sort", published 1/10/2018).
For claim 7, Haapoja modified by Subramanian discloses the method of claim 1, as described above. Haapoja modified by Subramanian does not disclose: wherein generating the predicted choice further comprises:
removing the predicted choice from the set of available choices;
repeating the generating and removing steps for a predetermined number of repetitions to generate a ranked list of predicted choices; and
providing for output onto an electronic display the ranked list of predicted choices.
Sehgal discloses: wherein generating the choice further comprises (p.2: application of a sorting algorithm to organizing choices, such as items, selections, etc. in a retail setting, hence, combination with Haapoja yielding application to predicted choices):
removing the choice from the set of available choices (p.2: step 1: a minimum or extreme value is removed from consideration for future steps, hence, combination with Haapoja yielding application to predicted choices);
repeating the generating and removing steps for a predetermined number of repetitions to generate a ranked list of predicted choices (p.2-3: step 1-5: iterating until the list is sorted); and
providing for output onto the electronic display the ranked list of predicted choices (p.2: contemplates outputting ranking of choices for display in a retail setting).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of Haapoja modified by Subramanian by incorporating the ranked sorting technique of Sehgal. Both concern the art of retail presentation, and the incorporation would have, according to Sehgal, provided a simple algorithm for presentation in a retail setting (p.2).
Claim(s) 15 recite analogous systems and computer media corresponding to the above methods and are hence rejected for the same reasons.
For claim 20, Haapoja modified by Subramanian discloses the method of claim 1, as described above. Haapoja modified by Subramanian further discloses: wherein generating the predicted choice comprises:
training the decision making model based on at least the set of past choices and the set of past user decisions to predict a decision from the set of available choices (Haapoja fig.5:716, 0103, 0157: considering purchase history when making recommendations; Subramanian fig.1:124, 0029: using selection data as training data);
receiving a selected choice from the user (Haapoja fig.5:716: making product purchases. Subramanian fig.1:124); and
updating the decision making model to incorporate the selected choice for enhancing subsequent generating of predicted choices (Haapoja fig.5:716, 0127; Subramanian fig.1:124).
Haapoja modified by Subramanian does not disclose: removing the predicted choice from the set of available choices;
repeating the generating and removing steps for a predetermined number of repetitions to generate a ranked list of predicted choices;
providing for output onto the electronic display the ranked list of predicted choices;
Sehgal discloses: removing the choice from the set of available choices (p.2: step 1: a minimum or extreme value is removed from consideration for future steps, hence, combination with Haapoja yielding application to predicted choices);
repeating the generating and removing steps for a predetermined number of repetitions to generate a ranked list of predicted choices (p.2-3: step 1-5: iterating until the list is sorted); and
providing for output onto an electronic display the ranked list of predicted choices (p.2: contemplates outputting ranking of choices for display in a retail setting).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of Haapoja modified by Subramanian by incorporating the ranked sorting technique of Sehgal. Both concern the art of retail presentation, and the incorporation would have, according to Sehgal, provided a simple algorithm for presentation in a retail setting (p.2).
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant argued:
1. The 101 rejections are overcome.
Examiner agrees. In accordance with the arguments presented in the Remarks, the amended claims, when considered as a whole, include user-interface elements that would integrate the abstract idea or mental process of identifying and generating suggestions into a practical application, that of automatically prompting a user to present options to users based on detecting a user emotional state, hence solving technical problems in HMI, such as that of reducing user input, see 0003-4.
2. Regarding the prior art rejections, Albertson is overcome.
Applicant’s arguments have been fully considered but are moot in view of newly presented art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Jung (US 20090113298 A1) discloses responding to user reactions in order to present further options.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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/LIANG LI/
Primary examiner AU 2143