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 .
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1. Claims presented examination: 1-20
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 05/29/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-20 are directed to a system, non-transitory computer readable medium, and the method which belongs to a statutory class.
Step 2A, Prong One:
Claims recite “generate a set of weights for at least one offer associated with a network application” the mathematical calculation. “Apply a trained optimized feature value model to determine a feature adjustment for the base feature value based at least in part on the feature reduction goal, wherein the feature adjustment and the feature reduction goal are different” is a mental step of applying the mental step which is a continuation of the mental step.
Step 2A, Prong Two:
Claims recite the processor and memory including instruction to perform the method. These are generic computer components and programs which use to perform abstract ideas.
“Generate a set of weights for at least one offer associated with a network application” is a calculation algorithm.
“Obtain a feature reduction goal for a first feature” the process of retrieving information.
“Receive a base feature value of the first feature for the at least one offer” is computer process to obtain a value using the receiving value.
“Apply the feature adjustment to the base feature value to generate an optimized feature value” is a computer process to beast value
“Transmit the offer including the optimized feature value to at least one user device” is the computer process to provide data to user.
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As to claims 2 and 10, the limitation:
“The at least one offer is a first offer of a plurality of offers, and wherein an average of an offer-specific feature adjustment of each of the plurality of offers is equal to the feature reduction goal” is further defined what an offer is and the generalized concept of mathematical calculation.
As to claims 3, 11 and 18, the limitation:
“The plurality of offers are grouped into a plurality of sets, and wherein the offer-specific feature adjustment of each offer includes a set adjustment applied to each offer in a corresponding one of the plurality of sets” is only further defined what plurality of offers are and the offer-specific feature adjustment and insignificantly to amount significantly more.
As to claims 4, 12 and 19, the limitation:
“The plurality of sets comprise a plurality of time slots” is further defined what a set are and insignificantly to amount significantly more.
Claims 5, 13 and 20, the limitation:
“The trained optimized feature value model comprises a first layer configured to obtain a plurality of parameters and a second layer configured to generate the feature adjustment based at least in part on the plurality of parameters” is computer rule-based algorithm which is use to learn to produce the result and this is generic computer routine.
Claims 6 and 14, the limitation:
“The feature adjustment comprises a multiplier value” is a mathematical algorithm.
Claims 7 and 15, the limitation:
“The network application comprises a last mile delivery system, and wherein the optimized feature value comprises a base trip value” is the computer algorithm which provide service and that what computer algorithm perform to provide service.
Claims 8 and 16, the limitation:
“The set of weights are generated by applying a logistic regression process” is mathematical algorithm.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claim(s) 1-7 and 9-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang (Patent No. US 11,443,335 B2).
As to claim 1, Tang discloses a system, comprising:
a non-transitory memory (a main memory) (col. 13, lines 55-56);
a processor (executed by processor) (col. 13, lines 58-59) communicatively coupled to the non-transitory memory (a main memory) (col. 13, lines 55-56), wherein the processor (a main memory) (col. 13, lines 55-56) is configured to read a set of instructions (instructions) (col. 13, lines 58-59) to:
generate a set of weights for at least one offer associated with a network application (ride-haling platform) (col. 4, lines 27);
obtain a feature reduction goal for a first feature (1) number of created order, 2) a number of accepted orders at each price increment…) (col. 7, lines 45-52)
receive a base feature value of the first feature for the at least one offer (previous pricing action) (col. 3, lines 10-11);
apply a trained optimized feature value model (train RL model) (col. 2, lines 29-30) to determine a feature adjustment (previous recurrent state) (col. 3, lines 6-7) the base feature value (previous pricing action) (col. 3, lines 10-11) based at least in part on the feature reduction goal (1) number of created order, 2) a number of accepted orders at each price increment…) (col. 7, lines 45-52),
apply the feature adjustment to the base feature value to generate an optimized feature value (the operations may further include generating a price for at least one current trip request on the ride-hailing platform based on the updated set of pricing candidates) (col. 1, lines 38-41); and
transmit the offer including the optimized feature value to at least one user device (at block 432, the price may be to at least one messenger or driver of the ride-hailing platform) (col 13, lines 45-46).
Tang does not explicitly disclose wherein the feature adjustment and the feature reduction goal are different. Tang discloses the feature adjustment (previous recurrent state) (col. 3, lines 6-7) and feature reduction goad (previous pricing action) (col. 3, lines 10-11) is different. Since applicant does not describe or define what feature adjustment is and what feature reduction goal is. One can assume previous recurrent state is feature adjustment and previous pricing action is reduction goal and they are both different. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date to include previous recurrent state is feature adjustment and previous pricing action is reduction goal and they are both different would provide better price for both or either rider or driver.
As to claim 2, Tang discloses the system of claim 1, wherein the at least one offer is a first offer of a plurality of offers, and wherein an average of an offer-specific feature adjustment of each of the plurality of offers is equal to the feature reduction goal (updating targeting price based …) (col. 4, lines 17-23).
As to claim 3, Tang discloses the system of claim 2, wherein the plurality of offers are grouped into a plurality of sets (candidate prices) (lines 35-37), and wherein the offer-specific feature adjustment of each offer includes a set adjustment applied to each offer in a corresponding one of the plurality of sets (updating targeting price based …) (col. 4, lines 17-23).
As to claim 4, Tang discloses the system of claim 1, wherein the plurality of sets comprise a plurality of time slots (sequential time steps) (col. 7, lines 8-21).
As to claim 5, Tang discloses the system of claim 1, wherein the trained optimized feature value model comprises a first layer configured to obtain a plurality of parameters and a second layer configured to generate the feature adjustment based at least in part on the plurality of parameters (training objective may be optimized using reparameterization of the variational bound such that stochastic gradient descent may be applied) (col. 10, lines 62-65).
As to claim 6, Tang discloses the system of claim 5, wherein the feature adjustment comprises a multiplier value (pricing multipliers for multiplying with a base price of a trip request) (col. 6, lines 59-67).
As to claim 7, Tang discloses the system of claim 1, wherein the network application comprises a last mile delivery system, and wherein the optimized feature value comprises a base trip value (a model-based deep reinforcement learning system may optimized the transit efficient through dynamic pricing by explicitly learning and planning for spatial-temporal effects) (col. 4, lines 36-49).
Claim 9 is rejected under the same reason as to claim 1, Tang discloses a computer-implemented method (method) (col. 1, line 30).
Claim 10 is rejected under the same reason as to claim 2.
Claim 11 is rejected under the same reason as to claim 3.
Claim 13 is rejected under the same reason as to claim 4.
Claim 14 is rejected under the same reason as to claim 5.
Clam 15 is rejected under the same reason as to claim 6.
Claim 16 is rejected under the same reason as to claim 7.
Clam 17 is rejected under the same reason as to claim 17, Tang discloses a non-transitory computer readable medium (a main memory) (col. 13, lines 55-56) having instructions stored thereon (instructions) (col. 13, lines 58-59), wherein the instructions, when executed by at least one processor (executed by processor) (col. 13, lines 58-59), cause at least one device to perform operations (method) (col. 1, line 30).
Claim 18 is rejected under the same reason as to claim 2.
Claim 19 is rejected under the same reason as to claim 3.
Claim 20 is rejected under the same reason as to claim 4.
5. Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang (Patent No. US 11,443,335 B2) in view of Travaglini et al. (Pub. No. US 2026/0259821 A1).
As to claim 8, Tang discloses the system of claim 1 excepting for wherein the set of weights are generated by applying a logistic regression process. However, Travaglini discloses wherein the set of weights are generated by applying a logistic regression process (the intelligent routing and assignment engine may determine that the price range associated with the repair maintenance job has changed and may modify the weight of the price node (e.g., the coefficient of the data input used by the regression model) and may sequentially adjust the weight…) (paragraph 0118). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Tang to include the set of weights generated by applying a logistic regression process as disclosed by Travaglini in order to calculate weights.
Conclusion
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
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