CTFR 17/969,238 CTFR 94099 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 in response to the amendment filed on 03/16/2026. Claims 1-20 are pending in the case. This action is Final . Applicant Response 3. In Applicant’s response dated 03/16/2026, Applicant amended Claims 1, 8, 9, 10-11 and 18-20 and argued against all objections and rejections previously set forth in the Office Action dated 10/16/2025. Claim Rejections - 35 USC § 112 07-30-02 AIA 4. The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 5. Claim 1 and 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 and 11: Claim 1 and 11 recites “ applying a recommendation model architecture to select items of the set of items for display to one or more users based on the plurality of user embeddings and the plurality of user embeddings . (emphasis added). This limitation is indefinite because it lacks clarity and appears to be duplicate phrase “plurality of use embeddings”. Based on the specification disclosure and for purpose of examination, Examiner will interpret this limitation as: … plurality of user embeddings and the plurality of item embeddings … “. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 6. 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. 7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea, without significantly more. Step 1 According to the first part of the analysis, in the instant case, claim is directed to a computer implemented method, which is a process and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). Regarding Claim 1 and 11, At step 2A, prong 1, Does the claim recite a judicial exception? Claim 1 further recites the steps of: identifying a training set batch of user-item pairs for a respective user of a set of users with respect to a respective item of a set of items, the user-item pairs including positive user-item pairs labeled with a positive interaction type and negative user-item pairs labeled with a negative interaction type (This step relies data gathering and organizing information falls into the “mental process” grouping of abstract ideas.) , determining, for each user-item pair in the training set batch of user-item pairs, a distance between a user embedding, of a plurality of user embeddings, associated with the respective user and an item embedding, of a plurality of item embeddings, associated with the respective item (This step relies calculating distance between user vector and item vector which falls into the “Mathematical Concepts” grouping of abstract ideas.) , and determining a weight for each user-item pair in the training set batch of user-item pairs based on the distance of the user-item pair and the distance of at least one other user-item pair labeled with the same interaction type (This step involved mathematical calculation and comparison which falls into the “Mathematical Concepts” grouping of abstract ideas.) , training the plurality of user embeddings and the plurality of item embeddings based on the training set batch of user-item pairs and determined weight of each user-item pair (This step relies on generation output based on collected data machine learning model which is mathematical optimization, which falls into the “Mathematical Concepts” grouping of abstract ideas.) , applying a recommendation model architecture to select items of the set of items for display to one or more users based on the plurality of user embeddings and the plurality of user embeddings (This step recites certain methods of organizing human activity/mental process) The claim recites mathematical concept that compute vector distances, computing weights as a function of distance and optimizing the model parameters. Accordingly, the claims recite an abstract idea. Step 2A prong 2 : Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application? Further, the claim does not recite any additional element which could integrate this abstract idea into a practical application, because the additional elements recited of consist of: “… a processor that executes instructions; a non-transitory computer-readable medium having instructions executable by the processor …” (claim1), “non-transitory computer readable medium embodying programmed instructions executed by a processor …”, ( Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f)); “… the user-item pairs including positive user-item pairs labeled with a positive interaction type and negative user-item pairs labeled with a negative interaction type …” (this step merely labeling data and selecting data for optimization does not integrate the abstract idea to practical application) The additional elements are recited at a high level of generality and do not amount to significantly more than the abstract idea (MPEP 2106.05(f)). The claim use a computer to perform a math and does not improve the function of the computer or other technology. Accordingly the claim does not integrate the abstract idea into practical application. Thus the claim is directed towards the abstract idea. Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception? No, as shown above with respect to integration of the abstract idea into a practical application, the additional element of “… a processor that executes instructions; a non-transitory computer-readable medium having instructions executable by the processor …” (claim1), “non-transitory computer readable medium embodying programmed instructions executed by a processor …” ( Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f)); “… the user-item pairs including positive user-item pairs labeled with a positive interaction type and negative user-item pairs labeled with a negative interaction type … ” (This step merely labeling data and selecting data for optimization does not integrate the abstract idea to practical application) The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application or add “significantly more.” Thus, the claims are not patent eligible. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, these independent claims are not patent eligible. The dependent claims respectively recite a judicial exception in limitations of: “wherein the weight for a user-item pair has a component that increases for a positive interaction when the distance for the user-item pair increases.” ( claims 2/12 ), “wherein the weight for a user-item pair has a component that increases for a positive interaction when the distance is relatively high compared to other positive interactions for the same user.”( claims 3/13 ), “wherein the weight for a user-item pair has a component that increases for a positive interaction when the distance is relatively high compared to other positive interactions for different users.” ( claims 4/14 ), “wherein the weight for a user-item pair has a component that increases for a negative interaction when the distance for the user-item pair decreases.” ( claim 5/15 ), “wherein the weight for a user-item pair has a component that increases for a negative interaction when the distance is relatively low compared to other negative interactions for the same user.” ( claims 6/16 ), “wherein the weight for a user-item pair has a component that increases for a negative interaction when the distance is relatively low compared to other negative interactions for different users. ( claims 7/17 ), “wherein the instructions are further executable for selecting the training set batch of user-item pairs from an complete training set, selecting the training set batch , including determining a user-item pair having the negative interaction type that has a minimum distance to a user compared to other user-item pairs having the negative interaction type in the complete training set and selecting user-item pairs having the positive interaction type for the training set that are within a threshold distance of the minimum distance. ”(Claim 8/18), “wherein the instructions are further executable for selecting the training set batch of user-item pairs from a complete training set, selecting the training set batch including determining a user-item pair having the positive interaction type with a maximum distance to a user and selecting user-item pairs having the negative interaction type for the training set that are within a threshold distance of the maximum distance.” ( claim 9/19), and “wherein the instructions are further executable for training the plurality of user embeddings and the plurality of item embeddings includes training parameters of a recommendation model.” ( Claims 10/20). These additional limitations ( in claims 2-10 and 12-20 ) also constitute concepts performed Mathematical concept or mathematical operation groupings of abstract ideas. This judicial exception is not integrated into a practical application. Additional elements “computer readable medium comprising: computer program code ( in claims ( in claims 2-10 and 12-20 ), all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible. Examiner Comments 07-06 AIA 15-10-15 8. 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 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. Claim Rejections - 35 USC § 103 07-20-aia AIA 9. 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 602 claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA 10. Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fang (Pat. No.: US 20190251446 A1, Pub. Date 2019-08-15) in view of Wang (NPL: Title: Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning, Date: 2019-04-19) in further view of WU (US 20220253722 A1, 2022-08-11) Fang teaches a system for training a set of user embeddings and a set of item embeddings for user- item recommendation (see Fang : Fig.2, “the fashion recommendation system employs the training image dataset 202 to generate triplets 206.”), comprising: a processor that executes instructions (see Fang : Fig.11, [0180], “a block diagram of an example computing device 1100 that may be configured to perform one or more of the processes), a non-transitory computer-readable medium having instructions executable by the processor instructions (see Fang : Fig.11, [0180], “a block diagram of an example computing device 1100 that may be configured to perform one or more of the processes”), for: identifying a training set batch of user-item pairs for a respective user of a set of users with respect to a respective item of a set of items (see Fang : Fig.2, [0067], “the fashion recommendation system employs the training image dataset 202 to generate triplets 206. As mentioned above, a triplet includes a user 208, a positive item, and a negative item (i.e. set of user-item pairs) .” … Fig.4, [0104], “the fashion recommendation system generates 406b a personalized ranking model. As mentioned previously, the personalized ranking model often employs Bayesian personalized ranking (BPR), which is an optimization framework that works well with implicit feedback. For instance, the fashion recommendation system employs Bayesian personalized ranking to optimize rankings for a user by considering triplets (u,i,j) or (user, positive item image, negative item image”), the user-item pairs including positive user-item pairs labeled with a positive interaction type and negative user-item pairs labeled with a negative interaction type (see Fang : Fig.2, [0069], “the fashion recommendation system generates triplets 206 for a user 208 by identifying a positive item for the user from the training image dataset 202 and by including the positive image 210 of the positive item in the triplet. Additionally, the fashion recommendation system identifies a negative item in the training image dataset 202 and includes the negative image 212 of the negative item in the triplet. Further, in many embodiments, the fashion recommendation system labels the positive image 210 as positive and the negative image 212 as negative within the triplet.”); See also [0096]- [0097], describing “the fashion recommendation system generates a set of triplets for the user that includes the user (e.g., a user identifier, a positive image, and a negative image) determining, for each user-item pair in the training set batch of user-item pairs, a distance between a user embedding of a plurality of user embeddings, associated with the respective user and an item embedding of a plurality of item embeddings, associated with the respective item (see Fang : Fig.4, [0043], “the Siamese convolutional neural network share the same cost model that compares the output of the networks (e.g., measures scaler loss (distance) based on the distance between a positive output and negative output in vector space) to determine desired latent features.”)… [0160], “the act 920 includes training the Siamese convolutional neural network by determining visual user-item feature preferences based on comparing a positive item feature output from the first convolutional neural network to a negative item feature output from the second convolutional neural network (e.g., using the cost function). In this manner, the act 920 can include determining the visual user-item feature preferences by subtracting, in feature vector space, the negative item feature from the positive item feature.”) training the plurality of user embeddings and the plurality of item embeddings based on the training set batch of user-item pairs (see Fang : Fig.2, [0078], “the fashion recommendation system trains the personalized ranking model 230 using back propagation to improve and optimize the ranking of the personalized ranking model 230. Upon the fashion recommendation system initially training the personalized ranking model 230 for one or more iterations, the personalized ranking model 230 provides the latent user features to the preference predictor 240.”) and determined weight of each user-item pair (see Fang : Fig.4, [0103], “The fashion recommendation system can set the probability of dropout to 0.5 as well as set the weight decay term to 10.sup. −3. Note, the fashion recommendation system can also set the dimension of the last layer to K, as opposed to other CNN-F models that set the dimension of the last layer to 1,000. In this manner, each of the convolutional neural networks in the Siamese convolutional neural network learns a representation (i.e., latent item features) whose dimensions explain the variance in users' fashion preferences.”) As shown above, Fang teaches all the limitations of Claim 1. Fang does not teach the system wherein: determining a weight for each user-item pair in the training set of user-item pairs based on the distance of the user-item pair and the distance of at least one other user-item pair labeled with the same interaction type. However, Wang teaches the system wherein: determining a weight for each user-item pair in the training set batch of user-item pairs based on the distance of the user-item pair and the distance of at least one other user-item pair labeled with the same interaction type (see Wang : Section 3.1, page 5024, PNG media_image1.png 442 420 media_image1.png Greyscale Because both Fang and Wang are in the same/similar field of endeavor of user item recommendation system, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Fang to include the a system that determining a weight for each user-item pair in the training set of user-item pairs based on the distance of the user-item pair and the distance of at least one other user-item pair labeled with the same interaction type as taught by Wang . After modification of Fang , the item/fashion recommendation system that user pairwise training using back propagation can incorporate the weighting framework pair-based loss functions computations teaching of Wang . One would have been motivated to make such a combination in order to provide easy, effective and accurate recommendation model by facilitating and providing a more principled approach for collecting and weighting informative pairs. Fang and Wang does not teach the system wherein: applying a recommendation model architecture to select items of the set of items for display to one or more users based on the plurality of user embeddings and the plurality of user embeddings. However, WU teaches the system wherein: applying a recommendation model architecture to select items of the set of items for display to one or more users based on the plurality of user embeddings and the plurality of item embeddings (see WU Fig.2, [0078], “the training phase of RS 200 is performed until the system parameters (in particular, model embeddings Θ and threshold vector custom-character) have been adaptively learned to optimize a defined objective. When the training phase is complete and the defined objective optimized, a final set Ŷ.sub.UV of relevance scores are generated by relevance score generation module 218 during an inference phase, and this final set of final set Ŷ.sub.UV of relevance scores can be used by a generate ranking lists module 230 to generate a personalized recommendation list x.sub.uv of items that are most relevant for each individual user u. In some examples, the inference phase may be a final iteration of the training phase.”) Because both Fang , Wang and WU are in the same/similar field of endeavor of user item recommendation system, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Fang to include the system that apply a recommendation to select items of the set of items for display to one or more users based on the plurality of user embeddings and the plurality of item embeddings as taught by WU . One would have been motivated to make such a combination in order to provide easy, effective and accurate recommendation model by facilitating and providing a more principled approach for collecting and weighting informative pairs. Regarding Claim 2 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Fang further teaches the system wherein: the weight for a user-item pair has a component that increases for a positive interaction when the distance for the user-item pair increases (see Wang : Fig.3, Section 4.1, Page 5026, “Positive relative similarity. Similarly, the relative similarity also considers the relationship from other positive pairs (with a same anchor). As shown in case-3 of Fig. 2, when these positive samples become closer to the anchor, the relative similarity of current pair is decreased, and thus the pair weight should be reduced accordingly. Triplet loss is computed based on this similarity, as indicated in Eq. 5.”) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Fang to the weight for a user-item pair has a component that increases for a positive interaction when the distance for the user-item pair increase as taught by Wang . One would have been motivated to make such a combination in order to provide easy, effective and accurate recommendation model by facilitating and providing a more principled approach for collecting and weighting informative pairs. Regarding Claim 3 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Wang further teaches the system wherein: the weight for a user-item pair has a component that increases for a positive interaction when the distance is relatively high compared to other positive interactions for the same user (see Wang : Fig.3, Section 4.1, Page 5026, “Positive relative similarity. Similarly, the relative similarity also considers the relationship from other positive pairs (with a same anchor). As shown in case-3 of Fig. 2, when these positive samples become closer to the anchor, the relative similarity of current pair is decreased, and thus the pair weight should be reduced accordingly. Triplet loss is computed based on this similarity, as indicated in Eq. 5.”) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Fang to the weight for a user-item pair has a component that increases for a positive interaction when the distance for the user-item pair increase as taught by Wang . One would have been motivated to make such a combination in order to provide easy, effective and accurate recommendation model by facilitating and providing a more principled approach for collecting and weighting informative pairs. Regarding Claim 4 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Wang further teaches the system wherein: the weight for a user-item pair has a component that increases for a positive interaction when the distance is relatively high compared to other positive interactions for different users (see Wang : Section 5.1, Page 5028, “Similarity-P: As shown in Table 2, by adding a mining step based on Similarity-P, the performances of LiftedStruct ∗ , Binomial and MS weighting are consistently improved by a large margin. For instance, Recall@1 of Binomial is increased by nearly 3%: 71.9% → 74.6%.”) See motivation to combine Fang and Wang in claim 1 above. Regarding Claim 5 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Wang further teaches the system wherein: the weight for a user-item pair has a component that increases for a negative interaction when the distance for the user-item pair user (see Wang : Fig.3, Section 4.1, Page 5026, “N: Negative relative similarity. It is computed by considering the relationship from neighboring negative pairs. As described in case-2 of Fig. 2, the relative similarity of a pair is decreased, even when its self-similarity is unchanged. This is because its neighboring negative samples move closer, which increases the self-similarities of these neighboring pairs, and thus reduce the relative similarity. Lifted structure loss [25] is based on this relative similarity, as shown in Eq. 8.”) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Fang to the weight for a user-item pair has a component that increases for a negative interaction when the distance for the user-item pair user as taught by Wang . One would have been motivated to make such a combination in order to provide easy, effective and accurate recommendation model by facilitating and providing a more principled approach for collecting and weighting informative pairs. Regarding Claim 6 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Wang further teaches the system wherein: the weight for a user-item pair has a component that increases for a negative interaction when the distance is relatively low compared to other negative interactions for the same user (see Wang : Fig.3, Section 5.1, “Similarity-N: Relative similarities are also helpful to measuring the importance of a pair more precisely. With Similarity-N, our MS weighting increases the Recall@1 by 1.3% over Binomial (71.9% → 73.2%). Moreover, with Similarity-N, LiftedStruct ∗ m obtains a significant performance improvement over MS sampling (67% → 72.2%), by considering both Similarity-P and Similarity-N.”), See motivation to combine Fang and Wang in claim 1 above. Regarding Claim 7 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Wang further teaches the system wherein: the weight for a user-item pair has a component that increases for a negative interaction when the distance is relatively low compared to other negative interactions for different users. (see Wang : Fig.2, Section 4.1, Page 5026, “N: Negative relative similarity. It is computed by considering the relationship from neighboring negative pairs. As described in case-2 of Fig. 2, the relative similarity of a pair is decreased, even when its self-similarity is unchanged.”) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Fang to the weight for a user-item pair has a component that increases for a negative interaction when the distance is relatively low compared to other negative interactions for different users as taught by Wang . One would have been motivated to make such a combination in order to provide easy, effective and accurate recommendation model by facilitating and providing a more principled approach for collecting and weighting informative pairs. Regarding Claim 8 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Wang further teaches the system wherein: selecting the training set batch of user-item pairs from a complete training set (see Fang : Fig.2, [0067], “the fashion recommendation system employs the training image dataset 202 to generate triplets 206. As mentioned above, a triplet includes a user 208, a positive item, and a negative item. More particularly, a triplet includes a user 208, a positive image 210 of a positive item, and a negative image 212 of a negative item. Within each triplet, the fashion recommendation system ranks positive items over negative items because an assumption in training is that observed implicit feedback is interpreted as “more preferable” to non-observed feedback.”), selecting the training set batch including determining a user-item pair having the negative interaction type that has a minimum distance to a user compared to other user-item pairs having the negative interaction type in the complete training set and selecting user- item pairs having the positive interaction type for the training set that are within a threshold distance of the minimum distance (see Fang : Fig.2, [0100], “the fashion recommendation system can pair the same positive image with multiple negative images, and vice versa, to generate a large number of triplets for a user. Alternatively, the fashion recommendation system can generate a triplet for some or all of the positive items associated with a user in the training image dataset. In some embodiments, the fashion recommendation system generates at least a minimum number of triplets for each user (e.g., 100, 1000, 10,000) to ensure adequate training of the visually-aware personalized preference ranking network.”); Regarding Claim 9 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Wang further teaches the system wherein: the instructions are further executable for selecting the training set batch of user-item pairs from a complete training set, selecting the training set batch ( see Fang : Fig.2, [0067], “the fashion recommendation system employs the training image dataset 202 to generate triplets 206. As mentioned above, a triplet includes a user 208, a positive item, and a negative item. More particularly, a triplet includes a user 208, a positive image 210 of a positive item, and a negative image 212 of a negative item. Within each triplet, the fashion recommendation system ranks positive items over negative items because an assumption in training is that observed implicit feedback is interpreted as “more preferable” to non-observed feedback.”) , including determining a user-item pair having the positive interaction type that has a maximum distance to a user compared to other user-item pairs having the positive interaction type in the complete training set and selecting user- item pairs having the negative interaction type for the training set batch from the complete training set that are within a threshold distance of the maximum distance ( see Fang : Fig.4, [0121], “the fashion recommendation system can provide one or more of the ranked items to a user. For example, the fashion recommendation system selects a threshold number of top items to present to a user via a client device associated with the user. In another example, the fashion recommendation system provides ranked items to a user that are above a threshold preference prediction score.”) Regarding Claim 10 , As shown above, Fang , Wang and WU and teaches all the limitations of claim 1. Fang further teaches the system wherein: the instructions are further executable for training the plurality of user embeddings and the plurality of item embeddings includes training parameters of a recommendation model (see Fang : Fig.4, [0070], “Each of the item personalization networks determine latent item features for the respective images. Because the item personalization networks employ the same layers along with corresponding weights and parameters, the latent item features produced by the networks are directly comparable to each other. For instance, the outputted feature vectors share the same number of dimensions and visual latent characteristics.”0 Regarding independent Claim 11 , Claim 11 is directed to a method claim and has similar/same claim limitation as claim 1 and is rejected under the same rationale. Regarding Claim 12-20 , Claims 12-20 are directed toa method claim and has similar/same claim limitation as claim 2-10 respectively and are rejected under the same rationale . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. PGPUB NUMBER: INVENTOR-INFORMATION: TITLE / DESCRIPTION US 20220261873 A1 Xu; Da Title : AUTOMATICALLY GENERATING SIMILAR ITEMS USING SPECTRAL FILTERING Description : This disclosure relates generally relates to similar items using spectral filtering. US 20210406761 A1 Yang; Longqi Title : DIFFERENTIABLE USER-ITEM CO-CLUSTERING Description : The ability to infer user preferences and to recommend preferred items to users based on their behavioral history is a growing art with a wide range of applications. Improving recommendation models can enhance user experience as well as generate higher revenues 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Zelalem Shalu/Examiner, Art Unit 2145 /CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145 Application/Control Number: 17/969,238 Page 2 Art Unit: 2145 Application/Control Number: 17/969,238 Page 3 Art Unit: 2145 Application/Control Number: 17/969,238 Page 4 Art Unit: 2145 Application/Control Number: 17/969,238 Page 5 Art Unit: 2145 Application/Control Number: 17/969,238 Page 6 Art Unit: 2145 Application/Control Number: 17/969,238 Page 7 Art Unit: 2145 Application/Control Number: 17/969,238 Page 8 Art Unit: 2145 Application/Control Number: 17/969,238 Page 9 Art Unit: 2145 Application/Control Number: 17/969,238 Page 10 Art Unit: 2145 Application/Control Number: 17/969,238 Page 11 Art Unit: 2145 Application/Control Number: 17/969,238 Page 12 Art Unit: 2145 Application/Control Number: 17/969,238 Page 13 Art Unit: 2145 Application/Control Number: 17/969,238 Page 14 Art Unit: 2145 Application/Control Number: 17/969,238 Page 15 Art Unit: 2145 Application/Control Number: 17/969,238 Page 16 Art Unit: 2145 Application/Control Number: 17/969,238 Page 17 Art Unit: 2145 Application/Control Number: 17/969,238 Page 18 Art Unit: 2145 Application/Control Number: 17/969,238 Page 19 Art Unit: 2145 Application/Control Number: 17/969,238 Page 20 Art Unit: 2145 Application/Control Number: 17/969,238 Page 21 Art Unit: 2145
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