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 4/22/2026. Claims 1-5, 7-13, 15-22 are pending.
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) 1-2, 4-5, 8-10, 12-13, 16-17, 19, 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Haapoja (US 20210326959 A1) in view of Takahashi (US 20160148120 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 made by the user 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)); and
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 the set of past user decisions made by the user to facilitate the decision making mode 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 presented to the user to select between, and a set of past user decisions made by the user relating to the set of past choices;
wherein the training includes: training based on at least the set of past choices presented to the user, the set of past user decisions being made by the user with respect to those choices.
Takahashi discloses: wherein the receiving includes: receiving a set of past choices that had previously been presented to the user to select between, and a set of past user decisions made by the user relating to the set of past choices;
wherein the training includes: training based on at least the set of past choices presented to the user (0027-29, fig.5:0054-55: history of user alternatives and selections for learning), the set of past user decisions being made by the user with respect to those choices (ibid).
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 training technique of Takahashi. Both concern the art of consumer product recommendation systems based on machine learning models, and the incorporation would have, according to Takahashi, allow calculation and prediction of consumer choice models efficiently (0003).
For claim 2, Haapoja modified by Takahashi discloses the method of claim 1, as described above. Haapoja 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 4, Haapoja modified by Takahashi discloses the method of claim 1, as described above. Haapoja 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 Takahashi discloses the method of claim 1, as described above. Haapoja 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 Takahashi 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).
Claims 9-10, 12-13, 16-17, 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 Takahashi discloses the method of claim 5, as described above. Haapoja 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 Takahashi discloses the method of claim 1, as described above. Haapoja 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) 3, 11, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Haapoja (US 20210326959 A1) in view of Takahashi (US 20160148120 A1) in view of Recker (US 20200388177 A1).
For claim 3, Haapoja modified by Takahashi discloses the method of claim 1, as described above. Haapoja modified by Takahashi does not disclose: wherein determining whether the state of the user includes an indecisive behavior comprises:
detecting repeated shifts in the user’s eye gaze between at least two different available choices within a defined time interval.
Recker discloses: wherein determining whether the state of the user includes an indecisive behavior comprises:
detecting repeated shifts in the user’s eye gaze between at least two different available choices within a defined time interval (fig.16A-B, 0093-95: during the time interval of performance of a particular selection task, hesitation is detected by a user’s gaze from A towards choice F and then back to choice A, hence, repeated shifts from A to F).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Haapoja modified by Takahashi by incorporating the hesitation detection technique of Recker. Both concern the art of gaze-based user interfaces, and the incorporation would have, according to Recker, allowed more comprehensive feedback in user interactions including a hesitation or confidence metric, providing graded feedback regarding hesitation (0006, 0008, 0094).
Claims 11, 18 recite analogous systems and computer media corresponding to the above methods and are hence rejected for the same reasons.
Claim(s) 7, 15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Haapoja (US 20210326959 A1) in view of Takahashi (US 20160148120 A1) in view of Sehgal ("An introduction to selection sort", published 1/10/2018).
For claim 7, Haapoja modified by Takahashi discloses the method of claim 1, as described above. Haapoja modified by Takahashi 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 Takahashi 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 Takahashi discloses the method of claim 1, as described above. Haapoja 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); and
updating the decision making model to incorporate the selected choice for enhancing subsequent generating of predicted choices (Haapoja fig.5:716, 0127).
Haapoja modified by Takahashi 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 Takahashi 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 cited art does not disclose new amendments directed to past user available choices, particularly as Subramanian discloses anonymized data.
Applicant’s arguments are moot in view of newly cited art.
2. The cited art does not disclose detecting repeated eye shifts.
Applicant’s arguments are moot in view of newly cited art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lee (US 20220319536 A1) discloses gaze-based selection with hesitation detection.
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).
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 extension fee 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 date of this final action.
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/LIANG LI/
Primary examiner AU 2143
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