DETAILED ACTION
This office action is in response to communication filed on 12 May 2025.
Claims 1 – 13 are presented for examination.
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 .
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.
Claims 1 – 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of abstract ideas without significantly more. The independent claims recite acquiring verification data for verifying a relationship between a prediction value output from a predictor that predicts an evaluation of a user for a candidate item and a true value of the evaluation of the user for the candidate item; inputting the verification data to the predictor, and acquiring a high rank item which is a candidate item of which a rank of the output prediction value is relatively high; extracting high rank data corresponding to the high rank item from the verification data; and training a corrector that corrects an input prediction value such that the input prediction value is close to the true value based on the high rank data. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance section 2106 of the MPEP (hereinafter, MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the apparatus, the method, and the recording medium are directed to eligible categories of subject matter. Step 1 is satisfied.
With respect to Step 2A prong 1 of MPEP 2106, it is next noted that the claims recite an abstract idea by reciting concepts of making predictions for items for a user, which falls into the “certain methods of organizing human activity” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106, as this is a subset of marketing activity. The claimed invention also recites an abstract idea that falls within the mental processes grouping, as independent claims describe acquiring data, inputting data, and extracting data. The limitations reciting the abstract idea in independent claims are acquiring verification data for verifying a relationship between a prediction value output from a predictor that predicts an evaluation of a user for a candidate item and a true value of the evaluation of the user for the candidate item; inputting the verification data to the predictor, and acquiring a high rank item which is a candidate item of which a rank of the output prediction value is relatively high; extracting high rank data corresponding to the high rank item from the verification data; and training a corrector that corrects an input prediction value such that the input prediction value is close to the true value based on the high rank data.
With respect to Step 2A Prong Two of the MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to processors, memories, and non-transitory computer-readable tangible recording medium, to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they are directed to the use of generic computing elements to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the MPEP 2106) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: processors, memories, and non-transitory computer-readable tangible recording medium. These elements have been considered, but merely serve to tie the invention to a particular operating environment, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. This does not amount to significantly more than the abstract idea, and it is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of concepts of identifying models and further data analysis, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
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 following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 – 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. P.G. Pub. 2021/0248651 (hereinafter, Chang).
Regarding claim 1, Chang teaches an information processing apparatus comprising: one or more processors; and one or more memories that store a command to be executed by the one or more processors (¶ 57, “a recommendation model training apparatus is provided, including an input/output interface, a processor, and a memory. The processor is configured to control the input/output interface to send and receive information. The memory is configured to store a computer program. The processor is configured to invoke the computer program from the memory and run the computer program, so that the training apparatus performs the method in any one of the first aspect or the implementations of the first aspect.”), wherein the processor is configured to:
acquire verification data for verifying a relationship between a prediction value output from a predictor that predicts an evaluation of a user for a candidate item and a true value of the evaluation of the user for the candidate item (¶ 113, “The data collection device 160 is configured to collect training data. In a recommendation model training method in the embodiments of this application, a recommendation model may be further trained based on the training data.”);
input the verification data to the predictor, and acquire a high rank item which is a candidate item of which a rank of the output prediction value is relatively high (¶ 39, “the attribute information of the target recommendation user and the information about the candidate recommended object are input into the recommendation model, and the probability that the target recommendation user performs an operational action on the candidate recommended object is predicted.”);
extract high rank data corresponding to the high rank item from the verification data (¶ 200, “when a recommendation request is received, candidate recommended objects are ranked based on expected revenues, and a recommended object to be displayed to the user is determined based on a sequence. To be specific, in this case, each recommended object has a different probability of being displayed to the user, and a probability of displaying a recommended object with higher expected revenues to the user is higher.”); and
train a corrector that corrects an input prediction value such that the input prediction value is close to the true value based on the high rank data (¶ 30, “the interpolation model is trained by using a training sample without bias. In addition, the first loss function and the second loss function are introduced into the target training model, and the weights of the first loss function and the second loss function in the target training model can be adjusted by setting different hyperparameters, to further improve recommendation model accuracy. For example, the model parameter of the interpolation model is obtained through training based on the second training sample”).
Regarding claim 2, Chang teaches the information processing apparatus according to claim 1, wherein the processor is configured to:
predict each evaluation of the user for each candidate item of a plurality of candidate items by using the predictor (¶ 91, “The recommendation system inputs the recommendation request and related information into a recommendation model to predict a selection rate of the user for a commodity in the system. Further, commodities are ranked based on predicted selection rates or functions of the selection rates. The recommendation system may use a commodity to be displayed to the user and a commodity display location as a recommendation result for the user based on a ranking result.”);
select a candidate item of which a rank of a prediction value is relatively higher among the plurality of candidate items, as a recommendation item to be recommended to the user (¶ 91, “The user browses the displayed commodity and may perform an operational action, such as browsing behavior or downloading behavior. The operational action of the user may be stored in a user behavior log, and training data may be obtained by preprocessing the user behavior log.”);
correct the prediction value of the recommendation item by using the corrector; and output the recommendation item and the corrected prediction value (¶ 91, “The training data may be used to continuously update a parameter of the recommendation model, to improve a prediction effect of the recommendation model.”).
Regarding claim 3, Chang teaches the information processing apparatus according to claim 1, wherein the predictor predicts the evaluation of the user by using collaborative filtering (¶ 202, “a recommended application in a plurality of candidate recommended applications is randomly displayed to a user corresponding to the recommendation request, and all recommended applications have a same probability of being displayed to the user corresponding to the recommendation request. In this case, an obtained training sample is a training sample without bias.”).
Regarding claim 4, Chang teaches the information processing apparatus according to claim 1, wherein the predictor includes a trained model, and the processor is configured to: divide a plurality of pieces of data to use a part of the data for training of the predictor and to use a remaining part of the data as the verification data (¶ 234, “The first training sample includes attribute information of a first user and information about a first recommended object. The second training sample includes attribute information of a second user, information about a second recommended object, and a sample label of the second training sample, the sample label of the second training sample is used to indicate whether the second user performs an operational action on the second recommended object, and the second training sample is obtained when the second recommended object is randomly displayed to the second user. The third training sample includes attribute information of a third user, information about a third recommended object, and a sample label of the third training sample, and the sample label of the third training sample is used to indicate whether the third user performs an operational action on the third recommended object.”).
Regarding claim 5, Chang teaches the information processing apparatus according to claim 1, wherein the processor is configured to: limit the high rank item to a candidate item for which the evaluation of the user exists in the verification data (¶ 207, “Different thresholds may be set for different application scenarios to select the interpolation model, and the interpolation model may be flexibly adjusted. Only a small quantity of second training samples are required to alleviate impact caused by a bias problem and improve recommendation model accuracy. This avoids a case in which overall system revenues decrease because a large quantity of recommended objects are randomly displayed for large-scale collection of second training samples”).
Regarding claim 6, Chang teaches the information processing apparatus according to claim 2, wherein the processor is configured to: extract the number of high rank items equal to the number of recommendation items (¶ 123, “the recommendation result may be a recommendation sequence of candidate recommended objects that is obtained based on the probability that the target recommendation user selects the candidate recommended object, or the recommendation result may be a target recommended object obtained based on the probability that the target recommendation user selects the candidate recommended object. The target recommended object may be one or more candidate recommended objects with highest probabilities.”).
Regarding claim 7, Chang teaches the information processing apparatus according to claim 1, wherein the corrector includes a parametric model or a nonparametric model in which the prediction value is an explanatory variable and the true value is a response variable (¶ 26, “A model parameter obtained through the target training model is a model parameter of the trained recommendation model”).
Regarding claim 8, Chang teaches the information processing apparatus according to claim 1, wherein the corrector corrects the prediction value according to the rank of the prediction value of the high rank item (¶ , “”).
Regarding claim 9, Chang teaches the information processing apparatus according to claim 7, wherein the corrector includes the rank of the prediction value in the explanatory variable (¶ 200, “candidate recommended objects are ranked based on expected revenues, and a recommended object to be displayed to the user is determined based on a sequence. To be specific, in this case, each recommended object has a different probability of being displayed to the user, and a probability of displaying a recommended object with higher expected revenues to the user is higher.”).
Regarding claim 10, Chang teaches the information processing apparatus according to claim 1, wherein the processor is configured to: correct the prediction value by assigning a relatively higher weight to a prediction value having a relatively higher rank (¶ 107, “if the predicted value of the network is large, the weight vector is adjusted to decrease the predicted value, and adjustment is continuously performed, until the deep neural network can predict the target value that is actually expected or a value that is very close to the target value that is actually expected.”).
Regarding claim 11, Chang teaches the information processing apparatus according to claim 1, wherein the corrector is trained by assigning the relatively higher weight to the prediction value having a relatively higher rank (¶ 107, “if the predicted value of the network is large, the weight vector is adjusted to decrease the predicted value, and adjustment is continuously performed, until the deep neural network can predict the target value that is actually expected or a value that is very close to the target value that is actually expected.”).
Regarding claims 12 and 13, the claims recite substantially similar limitations to claim 1. Therefore, claims 12 and 13 are similarly rejected for the reasons set forth above with respect to claim 1.
Conclusion
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/AMANDA GURSKI/Primary Examiner, Art Unit 3625
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