DETAILED ACTION
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 .
Status of Claims
Applicant's “Amendment” filed on 7/17/2026 has been considered.
Rejection to Claims 1, 4-13, 15-16, 20 under 35 USC 101 have not been overcome.
Claims 1, 4, 6, 15-16, 20 are amended.
Claims 2-3, 14, 17-19, 21-22 are cancelled.
Claims 1, 4-13, 15-16, 20 are currently pending and have been examined.
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, 4-13, 15-16, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test for Products and Processes (see MPEP § 2106 Subsection III), it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 15 as representative, the claim recites limitations that fall within the certain methods of organizing human activity grouping of abstract ideas, including:
An electronic device, comprising: a memory; and
a processor coupled to the memory having stored therein instructions that, when executed by the processor, cause the electronic device to:
generate a recommended item by a recommendation model;
calculate a similarity between a plurality of explanatory items and the recommended item,
wherein the calculating of the similarity between the plurality of explanatory items and the recommended item comprises: characterizing the similarity between the plurality of explanatory items and the recommended item by a counterfactual proximity;
obtain a predetermined number of explanatory items from the plurality of explanatory items, as a recommended explanation of the recommended item, wherein a similarity between the predetermined number of explanatory items and the recommended item is greater than a similarity between other explanatory items and the recommended item; and
output identification information of the predetermined number of explanatory items;
wherein the method further comprises:
before calculating the similarity between the plurality of explanatory items and the recommended item, removing at least a portion of the plurality of explanatory items from a training set, and training the recommendation model by remaining items in the training set;
calculating a loss function value of the recommendation model during each training process; and
determining a recommendation model with a smallest loss function value as a trained recommendation model.
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations as emphasized, are a process that, under its broadest reasonable interpretation, covers a commercial interaction. That is, other than reciting that a user interface is generated from the list and products are displayed on the user interface, nothing in the claim element precludes the step from practically being performed by people. For example, “generate, calculate, characterizing, obtain, output, removing, calculating, and determining” in the context of this claim encompasses advertising, and marketing or sales activities.
If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
The claim recites additional elements beyond the judicial exception(s), including:
An electronic device, comprising: a memory; and
a processor coupled to the memory having stored therein instructions that, when executed by the processor, cause the electronic device to:
generate a recommended item by a recommendation model;
calculate a similarity between a plurality of explanatory items and the recommended item;
wherein the calculating of the similarity between the plurality of explanatory items and the recommended item comprises: characterizing the similarity between the plurality of explanatory items and the recommended item by a counterfactual proximity;
obtain a predetermined number of explanatory items from the plurality of explanatory items, as a recommended explanation of the recommended item, wherein a similarity between the predetermined number of explanatory items and the recommended item is greater than a similarity between other explanatory items and the recommended item; and
output identification information of the predetermined number of explanatory items;
wherein the method further comprises:
before calculating the similarity between the plurality of explanatory items and the recommended item, removing at least a portion of the plurality of explanatory items from a training set, and training the recommendation model by remaining items in the training set;
calculating a loss function value of the recommendation model during each training process; and
determining a recommendation model with a smallest loss function value as a trained recommendation model.
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 15 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea.) Specifically, the additional element of a memory, a processor, instructions, and a recommendation model, is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of connecting to a platform on a network) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements to no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). For example, stating that the recommended item is generated by a recommendation model, only generally links the commercial interactions and management of relationships or interactions between people to a computer environment. Additionally, no details are recited regarding steps taken by the recommendation model in order to determine a recommendation model with a smallest lost function value. The claims recite only the idea of training a recommendation model and determining a recommendation model. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of system claim 15 taken individually or as a whole, the additional elements of claim 15 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claim 15 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 1 is a method reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claim 16 is a computer readable storage medium reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 4-13 and 20 are dependencies of claims 1 and 15. The dependent claims do not add “significantly more” to the abstract idea. They recite additional functions that describe the abstract idea and only generally link the abstract idea to a particular technological environment, including:
The concepts claimed in dependent claims 4-7 and 10-12 recite various mathematical functions which fall into the abstract grouping of mathematical concepts.
Accordingly, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
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, 4-13, 15-16, 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20160078520 A1 to NICE in view of CN 113609388 A to WU (as provided on IDS dated 9/16/2024).
Regarding Claim 1, NICE discloses a method for obtaining a recommended explanation, comprising:
generating a recommended item by a recommendation model; ([0035] In step 530, user-to-item recommendations may be generated using the user model U)
calculating a similarity between a plurality of explanatory items and the recommended item; ([0003] recommendations that are near to the vector can be provided to the users along with explanations (e.g., a recommendation is given because of an item's proximity to a particular feature).)
obtaining a predetermined number of explanatory items from the plurality of explanatory items, as a recommended explanation of the recommended item, and ([0037] in step 545 explanations are generated using the content-based item model V)
outputting identification information of the predetermined number of explanatory items. ([0037] recommendations and explanations may be sent to the multimedia console and surfaced through its UI to the user in respective steps 550 and 555.)
But does not explicitly disclose wherein a similarity between the predetermined number of explanatory items and the recommended item is greater than a similarity between other explanatory items and the recommended item; wherein the calculating of the similarity between the plurality of explanatory items and the recommended item comprises: characterizing the similarity between the plurality of explanatory items and the recommended item by a counterfactual proximity; wherein the method further comprises: before calculating the similarity between the plurality of explanatory items and the recommended item, removing at least a portion of the plurality of explanatory items from a training set, and training the recommendation model by remaining items in the training set; calculating a loss function value of the recommendation model during each training process; and determining a recommendation model with a smallest loss function value as a trained recommendation model.
WU, on the other hand, teaches wherein a similarity between the predetermined number of explanatory items and the recommended item is greater than a similarity between other explanatory items and the recommended item. ([p.3] Among them is the total number of items in the item set; using the K-nearest neighbor algorithm, the vector dot product is used as the similarity function to calculate the similarity between the user feature representation x and the representation of each item, and the highest similarity to the user feature representation x is obtained The K items of are returned to the user to be recommended as K recommendations.)
wherein the calculating of the similarity between the plurality of explanatory items and the recommended item comprises: characterizing the similarity between the plurality of explanatory items and the recommended item by a counterfactual proximity. ([p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
wherein the method further comprises: before calculating the similarity between the plurality of explanatory items and the recommended item, removing at least a portion of the plurality of explanatory items from a training set, and training the recommendation model by remaining items in the training set; calculating a loss function value of the recommendation model during each training process; and determining a recommendation model with a smallest loss function value as a trained recommendation model. ([p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 4, NICE in view of WU teaches the method of claim 1.
WU teaches wherein the characterizing of the similarity between the plurality of explanatory items and the recommended item by the counterfactual proximity comprises: calculating the counterfactual proximity Pc between the plurality of explanatory items and the recommended item by the trained recommendation model:
PNG
media_image1.png
41
232
media_image1.png
Greyscale
wherein i is the recommended item, I is a set of all items in a system where the recommendation model is located, I\{i} is a set of remaining items in the set I of all items except the recommended item i, f(j; 0') is a predicted recommendation score for an item j calculated by a recommendation model 0', f(i; 0') is a predicted recommendation score for an item i calculated by the recommendation model 0', and the trained recommendation model. ([p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 5, NICE in view of WU teaches the method of claim 4.
WU teaches wherein the obtaining of the predetermined number of explanatory items from the plurality of explanatory items comprises: in response to the predetermined number |Eu,i| of the explanatory items being fixed, traversing
PNG
media_image2.png
46
50
media_image2.png
Greyscale
combinations of explanatory items, calculating the counterfactual proximity corresponding to each combination of explanatory items, and obtaining a combination of explanatory items with a largest counterfactual proximity, wherein Iu is the set of all items that an user u interacts with, and Eu, is a set of a part of the items that the user interacts with. ([p.1] the MIND model uses a capsule network to convert historical interaction behaviors into multiple interest vectors. [p.2] xu,t+ 1 indicates that the user u clicks all t items before the target item Yt+ 1, which is expressed as the user behavior sequence {Yl, Y2, ... , Yt}, where Yt Represents the t-th item that the user has clicked on, Rtxd represents a matrix composed of t item vectors with dimension d; [p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 6, NICE in view of WU teaches the method of claim 1.
WU teaches wherein the calculating of the similarity between the plurality of explanatory items and the recommended item comprises: obtaining a feature vector of each of the plurality of explanatory items and a feature vector of the recommended item from the recommendation model; calculating a Euclidean distance between the feature vector of each of the plurality of explanatory items and the feature vector of the recommended item; selecting a fixed number of explanatory items from the plurality of explanatory items as a candidate explanatory item set, wherein a Euclidean distance between a feature vector of each of explanatory items in the candidate explanatory item set and the feature vector of the recommended item is less than a Euclidean distance between a feature vector of each of explanation items other than the candidate explanatory item set in the plurality of explanatory items and the feature vector of the recommended item; and calculating the counterfactual proximity between explanatory items in the candidate explanatory item set and the recommended item by the trained recommendation model.. ([p.3] the vector dot product is used as the similarity function to calculate the similarity between the user feature representation x and the representation of each item, [p.4] Further, the similarity evaluation function cp0 is realized by calculating the dot product of the vector. Further, the distance function d is set to Euclidean distance. [p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 7, NICE in view of WU teaches the method of claim 6.
WU teaches wherein the obtaining of the predetermined number of explanatory items from the plurality of explanatory items comprises: in response to the predetermined number |Eu,i| of the explanatory items being fixed, traversing
PNG
media_image2.png
46
50
media_image2.png
Greyscale
combinations of explanatory items, calculating the counterfactual proximity corresponding to each combination of explanatory items, and obtaining a combination of explanatory items with a largest counterfactual proximity, wherein Ic is the candidate explanatory item set, and Eu, is a set of a part of the items that the user interacts with. ([p.3] the vector dot product is used as the similarity function to calculate the similarity between the user feature representation x and the representation of each item, [p.4] Further, the similarity evaluation function cp0 is realized by calculating the dot product of the vector. Further, the distance function d is set to Euclidean distance. [p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 8, NICE in view of WU teaches the method of claim 5.
WU teaches further comprising: outputting a maximum value of the counterfactual proximity. ([p.3] calculate the SampledSoftmax loss [p.3] the vector dot product is used as the similarity function to calculate the similarity between the user feature representation x and the representation of each item, [p.4] Further, the similarity evaluation function cp0 is realized by calculating the dot product of the vector. Further, the distance function d is set to Euclidean distance. [p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 9, NICE in view of WU teaches the method of claim 1.
NICE discloses further comprising: outputting the similarity between the predetermined number of explanatory items and the recommended item.. ([0003] By locating user vectors within the latent space, recommendations that are near to the vector can be provided to the users along with explanations (e.g., a recommendation is given because of an item's proximity to a particular feature).)
Regarding Claim 10, NICE in view of WU teaches the method of claim 9.
WU teaches wherein the calculating of the similarity between the plurality of explanatory items and the recommended item comprises: obtaining a feature vector of each of the plurality of explanatory items and a feature vector of the recommended item from the recommendation model; and calculating a Euclidean distance between the feature vector of each of the plurality of explanatory items and the feature vector of the recommended item, the similarity between the each of the plurality of explanatory items and the recommended item being characterized by the Euclidean distance, wherein the less the Euclidean distance, the greater the similarity. ([p.3] the vector dot product is used as the similarity function to calculate the similarity between the user feature representation x and the representation of each item, [p.4] Further, the similarity evaluation function cp0 is realized by calculating the dot product of the vector. Further, the distance function d is set to Euclidean distance. [p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 11, NICE in view of WU teaches the method of claim 10.
WU teaches wherein the obtaining of the predetermined number of explanatory items from the plurality of explanatory items comprises: selecting the predetermined number of explanatory items from the plurality of explanatory items, wherein a Euclidean distance between a feature vector of each of the predetermined number of explanatory items and the feature vector of the recommended item is less than a Euclidean distance between a feature vector of each of other explanatory items and the feature vector of the recommended item.. ([p.3] the vector dot product is used as the similarity function to calculate the similarity between the user feature representation x and the representation of each item, [p.4] Further, the similarity evaluation function cp0 is realized by calculating the dot product of the vector. Further, the distance function d is set to Euclidean distance. [p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 12, NICE in view of WU teaches the method of claim 11.
WU teaches wherein the outputting of the similarity between the predetermined number of explanatory items and the recommended item comprises: calculating an average of Euclidean distances between feature vectors of the predetermined number of explanatory items and the feature vector of the recommended item; and outputting the average of Euclidean distances. ([p.3] the vector dot product is used as the similarity function to calculate the similarity between the user feature representation x and the representation of each item, [p.4] Further, the similarity evaluation function cp0 is realized by calculating the dot product of the vector. Further, the distance function d is set to Euclidean distance. [p.2] Use the contrast learning method to compare the counterfactual positive and negative user representations in the vector space, calculate the user-user counterfactual comparison loss, and compare the counterfactual positive and negative user representations with the target item representation in the vector space, and calculate the useritem Counterfactual comparison loss; by optimizing the total loss function including user-user counterfactual comparison loss and user-item counterfactual comparison loss, complete the training of the sequence recommendation model;)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of NICE to include the features as taught by WU. One of ordinary skill in the art at the time the invention was filed would have been motivated to modify NICE in order to enhance the reliability and accuracy of recommendations (WU, [p.1]).
Regarding Claim 13, NICE in view of WU teaches the method of claim 1.
NICE discloses wherein the similarity between the plurality of explanatory items and the recommended item comprises: a similarity between tags of the plurality of explanatory items and a tag of the recommended item, a similarity between features of the plurality of explanatory items and a feature of the recommended item, a similarity between reviews of the plurality of explanatory items and a review of the recommended item, or a similarity of user feedback information of the plurality of explanatory items and user feedback information of the recommended item.. ([0003] By locating user vectors within the latent space, recommendations that are near to the vector can be provided to the users along with explanations (e.g., a recommendation is given because of an item's proximity to a particular feature).)
Claim 15 is an electronic device claim comprising: a memory; and a processor coupled to the memory having stored therein instructions that is rejected with the same basis as claim 1.
Claim 16 is a non-transitory computer readable storage medium claim that is rejected with the same basis as claim 1.
Claim 20 is an electronic device claim that is rejected with the same basis as claim 4.
Response to Arguments
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues that the steps are implemented via an electronic device and do not involve commercial interactions, business relations or the management of personal interactions.
Examiner disagrees. Generation of a recommended item, calculation of similarity between a plurality of explanatory items and a recommended item, obtaining a predetermined number of explanatory items as a recommended explanation fo the recommended item, and outputting the identification information of the predetermined number of explanatory items are all methods of organizing human activity. Implementation of these steps by an electronic device only generally links these abstract ideas to a particular technological environment and does not integrate them into a practical application.
Applicant further argues that the accuracy of explaining a recommended item using an explanatory item can be improved, enabling the recommendation system to explain the recommended item to a user more accurately. In other words, the accuracy of pushing information (information recommendation) by the electronic device is improved.
However, an improved, more efficient or more effective method of providing a recommendation is still, at its core, making a recommendation, which is a sales activity implemented by a computer.
Applicant further argues that the claims address a low accuracy of explaining a recommended item by a recommendation system, and is a non-abstract technical improvement directed to a practical application of improving the accuracy of information recommendation by the electronic device. However, improving accuracy of recommendations is not a technical problem, but rather an improved method of organizing human activity.
Applicant’s arguments with respect to rejection of the claim under 35 USC 103 have been considered but are moot in view of new grounds of rejection, necessitated by Applicant’s amendment.
Applicant argues that Wu does not disclose removing a portion of user sequences, which likely refers to the claim language removing at least a portion of the plurality of explanatory items from a training set.
Examiner disagrees. As applicant notes, concepts are replaced in the user sequence to achieve a ratio of counterfactual sequences. Further, Wu discloses, at [0131] sets of transformed data in which a subset of the set of data features of the input data set are modified to counterfactual features such that the set of transformed data achieves a predetermined target. This replacement and transformation of data sets is interpreted as removing at least a portion of the plurality of explanatory items from a training set. Examiner notes that the broadest reasonable interpretation of the claim language is being applied.
Conclusion
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 Michelle T. Kringen whose telephone number is (571)270-0159. The examiner can normally be reached M-F: 11am-7pm.
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, Marissa Thein can be reached at (571)272-6764. 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.
/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689
Prosecution Insights