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 . The following is a Non-Final Action. Claims 1-10 are rejected below.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, Claims 1-10 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
Step 1 of the Alice/Mayo analysis is directed to determining whether or not the claims fall within a statutory class. Based on a facial reading of the claim elements, Claims 1-10 fall within a statutory class of process, machine, manufacture, or composition of matter.
With respect to Step 2A Prong One of the framework, the claims recite an abstract idea. Claim 1, 9, and 10 includes limitations reciting functionality that predicts demand by:
Acquire feature information...
Calculate a base demand quantity...by using two or more types of prediction models...
Calculate a predicted demand quantity...
which is an abstract idea reasonably categorized as
Mental processes – as each of the steps above can be performed in the human mind (including an observation, evaluation, judgment, opinion).
Certain methods of organizing human activity – as the limitations describes the fundamental economic practice of product demand forecasting
Similarly, Claims 2-8 further recite operations that can be practically performed in the human mind or methods of organizing human activities and further narrows the abstract idea.
With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claim 1, 9 and 10 includes various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include device, memory, instructions, processor, computer-readable recording medium. When considered in view of the claim as a whole, Examiner submits that the additional elements are not additional elements that integrate the abstract idea into a practical application because, these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f).
As a result, Claim 1, 9 and 10 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-8 do not include any additional elements beyond those recited with respect to claim 1, 9 and 10. As a result, Claims 2-8 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 9 and 10.
With respect to Step 2B of the framework, the claims do not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include device, memory, instructions, processor, computer-readable recording medium. Examiner submits that the additional elements do not amount to significantly more than the abstract idea because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f) and/or recite generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Further, looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements individually. As a result, Claim 1, 9 and 10 do not include additional elements amounting to significantly more than the abstract idea under Step 2B.
As noted above, Claims 2-8 do not include any additional elements beyond those recited with respect to claim 1. As a result, Claims 2-8 do not include additional elements amounting to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claim 1, 9 and 10.
Accordingly, Claims 1-10 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 of this title, 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, 4-5, 7, 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Vakill (2023/0368264) in view of Zhang (2014/0039979)
Regarding Claim 1, Vakill discloses:
A demand prediction device, comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: (0090-0094- processor, computer program, computer readable medium)
acquire feature information indicating a feature of a target product; (0083- receiving the new items, obtaining a set of attributes of the new items)
calculate a recommendation score for the target product for each prediction
model by using two or more types of prediction models outputting a recommendation score for a product and the feature information of the target product; (0084-0085-For each new item, the recommendation system can determine a cosine similarity between the item attributes of the existing item and the set of attributes of the new item. (using feature information). Based on the cosine similarity, the recommendation system can determine a set of existing items 506 that are most similar to the new item 508 (e.g., the existing items with the highest cosine similarities)......The recommendation system can execute a collaborative filtering model to determine a first recommendation score 510 for the new item based on the historical user interactions with the one or more existing items. The first recommendation score can indicate a likelihood of the new item being accepted by users,...The recommendation system can execute an adaptive model 512 to determine a second recommendation score for each new item. The second recommendation score can indicate a likelihood of the new item being accepted by users...)
receive selection of at least one of the two or more types of prediction models and an operation of a parameter affecting output of the recommendation score for the selected prediction model; (Figure 4 (408-414), [0073] The recommendation system can compare the accuracy of the collaborative filtering model and the accuracy of the adaptive model using an accuracy ratio. (selecting models to apply accuracy ratio parameter) The accuracy ratio is between the accuracy of the collaborative filtering model and the accuracy of the adaptive model using an accuracy ratio. The accuracy ratio is compared with a threshold. Based on whether the accuracy ratio satisfies the threshold, the recommendation system can determine whether to use both the collaborative filtering model and the adaptive model in the next iteration or to solely use the adaptive model in the next iteration.
and calculate a recommendation score for the target product by using a prediction model reflecting the operation. (0074-0075 - At step 410, the recommendation system can update the second score for each new item using the trained adaptive model...Because the accuracy of the adaptive model is not higher than the accuracy of the collaborative filtering model, the recommendation system can keep using both models. As discussed above, the adaptive model is retrained based on more training data including the newly collected user interaction data. The recommendation system can update the second recommendation score for each new item using the retrained adaptive model.
Vakill does not explicitly state the recommendation score is a base demand quantity. Zhang discloses this limitation (0032 - FIG. 7 shows hypothetical previous, current, and estimated sales volumes. Such data could be used for price optimization in the executing and adjusting stages. Once the modeling parameters are available, the actual sales volume (based demand quantity) can be reproduced by applying the execution factors to the selling rate, i.e., multiplying the selling rate by the number of shops and the number of flow days. The sales curve can then be used to optimize the purchase plan and improve the inventory management in advance, and optimize the pricing strategy during operation.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Vakill’s recommendation score to include Zhang’s sales volume, helping “optimize a purchase plan, improve inventory management, and optimize a pricing strategy” (0032) and since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claim 2, Vakill discloses: The demand prediction device according to claim 1, wherein the at least one processor is further configured to execute the instructions to:
specify a similar product similar to the target product from a database including feature information of two or more products by using the feature information of the target product, wherein the two or more types of prediction models include a model outputting the demand quantity based on the similar product; and calculate the base demand quantity based on the specified similar product. [0038] In some examples, the server system 112 can store the item profile of each new item, the item attributes of existing items...The server system 112 can also retrieve those data from the data store 114.
0084-0085-... Based on the cosine similarity, the recommendation system can determine a set of existing items 506 that are most similar to the new item 508 (e.g., the existing items with the highest cosine similarities)......The recommendation system can execute a collaborative filtering model to determine a first recommendation score 510 for the new item based on the historical user interactions with the one or more existing items.
Regarding Claim 4, Vakill discloses The demand prediction device according to claim 3, wherein the two or more types of prediction models include an integrated learning model in which two or more types of individual learning models are integrated and a weight is set for each of the individual learning models. [0077] The ratio between the updated first set of new items and the updated second set of new items can determine how much the recommendation system relies on the collaborative filtering model and the adaptive model. As the prediction accuracy of the retrained adaptive model becomes more satisfactory, the recommendation system can use a larger portion of the prediction results of the adaptive model (e.g., the updated second set of new items)......based on the initial ratio, the recommendation system can initially have a 90% chance using the collaborative filtering model and a 10% chance using the adaptive model to make recommendations in each interaction. After retraining the adaptive model, the recommendation system can have an 85% chance using the collaborative filtering model and a 15% chance using the adaptive model to make recommendations in each interaction. (each individual model integrated via proportional ratios (ie. weights))
the individual learning model is a model in which a relationship between the
feature of the product and the demand quantity is trained by machine learning, and
the at least one processor is further configured to execute the instructions to:
calculate the base demand quantity for the target product by using the integrated learning model weighted according to the one similar product and the feature of the target product. (See Vakill, Claim 1 –machine-learning model used to determine recommendation score;
[0051] At step 306, for each new item, the recommendation system can execute a collaborative filtering model to determine a first recommendation score for the new item based on the historical user interactions with the one or more existing items. The first recommendation score can indicate a likelihood of the new item being accepted by users, which is determined by the collaborative filtering model.
0050 - the recommendation system can select the one or more existing items by selecting a predetermine number of existing items, such as top N existing items whose adjusted cosine similarities are the highest, wherein N can be one or any other integer value that is larger than one.
[0073] - The recommendation system can compare the accuracy of the collaborative filtering model and the accuracy of the adaptive model using an accuracy ratio. (the similar product and the feature of the target product affect the weight).....Based on whether the accuracy ratio satisfies the threshold, the recommendation system can determine whether to use both the collaborative filtering model and the adaptive model in the next iteration or to solely use the adaptive model in the next iteration....(each model weighted)
Regarding Claim 5, Vakill discloses The demand prediction device according to claim 4, wherein the at least one processor is further configured to execute the instructions to: receive an operation of changing the weight for each of the individual learning models. [0077] The ratio between the updated first set of new items and the updated second set of new items can determine how much the recommendation system relies on the collaborative filtering model and the adaptive model. As the prediction accuracy of the retrained adaptive model becomes more satisfactory, the recommendation system can use a larger portion of the prediction results of the adaptive model (e.g., the updated second set of new items)......based on the initial ratio, the recommendation system can initially have a 90% chance using the collaborative filtering model and a 10% chance using the adaptive model to make recommendations in each interaction. After retraining the adaptive model, the recommendation system can have an 85% chance using the collaborative filtering model and a 15% chance using the adaptive model to make recommendations in each interaction. (changing the weights)
Regarding Claim 7, Vakill discloses The demand prediction device according to claim 3, wherein the two or more types of prediction models include a model outputting the demand quantity based on an actual sales performance of the product, and (0031-user interaction includes purchases; 0042, 0074-0075 - The recommendation system can add newly collected user interaction data to the training data and retrain the adaptive model. Because the adaptive model is retrained during the iterative process based on more available training data, the prediction accuracy of the retrained adaptive model increases during the iterative process.....At step 412, the recommendation system can update and output the first set of new items and the second set of new items based on the first score and the updated second score.
calculate the base demand quantity of the target product based on an actual sales performance of the selected one similar product. (0031-user interaction includes purchases; 0084-0085-... Based on the cosine similarity, the recommendation system can determine a set of existing items 506 that are most similar to the new item 508 (e.g., the existing items with the highest cosine similarities)......The recommendation system can execute a collaborative filtering model to determine a first recommendation score 510 for the new item based on the historical user interactions with the one or more existing items.)
Claims 9-10 stand rejected based on the same citations and rationale as applied to Claim 1.
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over Vakill (2023/0368264) in view of Zhang (2014/0039979) in view of Feldman (2021/0334828).
Regarding Claim 3, Vakill discloses: The demand prediction device according to claim 2, wherein the at least one processor is further configured to execute the instructions to:
store a list of one or more specified similar products; receive selection of one similar product; and calculate the base demand quantity based on the selected one similar product. (0084-Based on the cosine similarity, the recommendation system can determine a set of existing items 506 that are most similar to the new item 508 (e.g., the existing items with the highest cosine similarities). (list) The recommendation system can further adjust the cosine similarity based on historical user interactions with the set of existing items. Among the set of existing items, the recommendation system can select one or more existing items based on the adjusted cosine similarities. (selection) The recommendation system can execute a collaborative filtering model to determine a first recommendation score 510 for the new item based on the historical user interactions with the one or more existing items.)
Vakill does not explicitly state the list of one or more specified similar products are displayed. Feldman in analogous art discloses this limitation (Figure 4, 0046 – showing different sizes of the same article associated with different purchase quantities) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Vakill’s in view of Zhang’s product list to include Feldman’s display, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Vakill (2023/0368264) in view of Zhang (2014/0039979) in view of Wu (20200177942)
Regarding Claim 6, Vakill discloses The demand prediction device according to claim 4. Vakill does not explicitly state: Chan in analogous art discloses: wherein the at least one processor is further configured to execute the instructions to: receive an operation of changing a mathematical formula indicating the individual learning model.
(0069- The trend is removed from a demand pattern only if it is statistically significant. The test of significance is performed as follows: [0070] MEAN=average(LAG52), [0071] STD=standard deviation(LAG52), [0072] COUNT=count(LAG52), [0073] STAT (Z)=MEAN/(STD/SquareRoot(COUNT)) [0074] If STAT (Z)>1.28 (99%) then the trend is significant (should be removed). This will be controlled by SS, [0075] Otherwise, the trend should not be removed.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate Chan’s changing a formula to Vakill’s in view of Zhang’s individual learning model, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Vakill (2023/0368264) in view of Zhang (2014/0039979) in view of Wu (20200177942)
Regarding Claim 8, Vakill discloses The demand prediction device according to claim 2, wherein the two or more types of prediction models include a model outputting the demand quantity based on actual sales performances of two or more products, 0031-user interaction includes purchases; 0085-The recommendation system can execute a collaborative filtering model to determine a first recommendation score 510 for the new item based on the historical user interactions with the one or more existing items.
and the at least one processor is further configured to execute the instructions to: specify the two or more similar products by calculating similarity between the target product and each of the two or more products, 0084-0085-... Based on the cosine similarity, the recommendation system can determine a set of existing items 506 that are most similar to the new item 508 (e.g., the existing items with the highest cosine similarities)......The recommendation system can execute a collaborative filtering model to determine a first recommendation score 510 for the new item based on the historical user interactions with the one or more existing items.
and calculate the demand quantity for the target product based on the actual sales performances of a predetermined number of similar products having higher similarity. 0031-user interaction includes purchases; 0084-0085-......The recommendation system can execute a collaborative filtering model to determine a first recommendation score 510 for the new item based on the historical user interactions with the one or more existing items.)
Vakill does not explicitly state the sales performance of the two similar products is a weighted average, nor performing a weighted average based on a weight associated with the similarity. Wu directed to content item similarity, discloses this limitation [0119] In an embodiment, future performance of a particular content item is estimated based on actual performance of content items that are considered similar to the particular content item. Example performance metrics include.....number of other types of actions performed (e.g....making a purchase), spending a total resource allocation (e.g., total allocated budget), and spending a resource allocation regularly (e.g., spending a daily budget consistently).... [0122} ....a weighted average of performance values of similar content items is calculated for a particular content item and used as the estimated performance for the particular content item. The performance values of content items that are most similar to the particular content item are weighted higher than the performance values of content items that are less similar to the particular content item.... It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Vakill’s in view of Zhang’s sales performance to include Wu’s weighted average of similar products, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu, can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Scott Ross/
Examiner - Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623