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 .
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 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.
Examiner’s Note
In response to the Pre-Appeal Brief Request filed on 7/29/2026, prosecution has been reopened. The previous rejection mailed on 3/31/2026 has been withdrawn.
Status of Claims
Claims 1-20 remain pending, and are rejected.
Response to Arguments
Applicant’s arguments filed on 7/29/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, and are not persuasive for at least the following rationale:
Applicant’s arguments filed on 7/29/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 1-3 of the Pre-Appeal Brief, the Applicant makes arguments that the claims are not directed to certain methods of organizing human activity or mathematical concepts, and overgeneralizing the limitations of the claims. Comparisons are drawn to Example 47 and 48 of the Subject Matter Eligibility Examples that showed a line by line consideration of the claim recitations. The Applicant argues that the Office Action fails to consider the recitation of “a list of candidate items corresponding to the reference item, wherein the first neural network receives as input a plurality of vector representations of items in an embedding space stored in an item vector data store, the plurality of vector representations including a vector representation of the reference item, and determines the list of candidate items based on the filtering criterion, the list of candidate items comprising a vector representation for each candidate item”. It is argued that the claims as a whole recite elements that operate together within a specified machine-learning architecture in which a first neural network generates a filtered list of candidate items and a second neural network generates a centroid based on historical user interactions to determine recommended items.
Examiner respectfully disagrees. The Office Action lists the limitations that are directed to the abstract idea in Step 2A (Prong 1), and then addresses the additional limitations in Step 2A (Prong 2), and why the additional limitations do not integrate the abstract idea into a practical application. Examples 47 and 48 are examples of eligibility, and are not the requirements of how the rejections under 35 U.S.C. 101 are analyzed. While it is not a line by line analysis, the previous Office Action identifies the abstract idea of the claims, each limitation of claims that is the abstract idea, and how the limitations amount to certain methods of organizing human activity and mathematical concepts. The Office Action did not address the particular limitation as recited because the elements of the limitation that were not addressed were amended after-final, and were not present at the time of the Office Action. Addressing the amended limitation, the neural network merely receive an input to determine a list of candidate items based on the filtering criterion, which is an output of the abstract idea. The claims are not directed to how the neural network functions at an underlying technical level, or any improvements or changes to the neural network. It merely receives an input vector representations of the items to determine a list of candidate items based on filtering criterion, which are elements of identifying products to display to a user, and is an abstract idea as sales and marketing activities under certain methods of organizing human activity. There is no recitation of any machine-learning architecture. The neural networks are simply applied to the abstract idea to provide an output of the abstract idea, and perform the mathematical calculations of generating a centroid, such that the abstract idea may be performed on a computer.
On pages 3, it is argued that the Step 2B analysis is insufficient in view of the Prong 1 analysis, arguing that claim 1 recites a specific architecture in which a first neural network generates a filtered list of candidate items and a second neural network that generates a centroid based on historical user interactions to determine recommended items. The Applicant argues that these elements operate together with a machine-learning recommendation system to generate personalized recommendations for a user.
Examiner respectfully disagrees. As discussed above, the neural networks are not recited with any meaningful limitation except to provide an output for the abstract idea. The claims do not recite any underlying technology of changes or improvements to how the neural networks or any other technical field functions. As such, these elements do not provide significantly more than the abstract idea. Generating a filtered list of candidate items, generating a centroid based on historical user interactions to determine recommended items, and generating personalized recommendations are all abstract concepts of sales activities and mathematics. The additional elements merely generally link the abstract idea to a computing environment, and do not result in significantly more than the abstract idea.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Applicant’s arguments filed on 7/29/2026 with respect to the rejection under 35 U.S.C. 103 have been fully considered, and are persuasive. The cited references do not disclose “as output of a second neural network”, and prosecution has been reopened.
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 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-7 are directed to a system, which is an apparatus. Claims 8-14 are directed to a method, which is a process. Claims 15-20 are directed to a non-transitory computer-readable media, which is an article of manufacture. Therefore, claims 1-20m are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitations reciting the abstract idea of determining items similar to historical items associated with a user to provide as recommendation:
identify a reference item selected by a user;
generate, a list of candidate items, wherein receives an input of a plurality of candidate items corresponding to the reference item the plurality of vector representations including a vector representation of the reference item, and determines the list of candidate items based on the filtering criterion the list of candidate items comprising a vector representation for each candidate item;
generate, a centroid corresponding to an average of vector representations in the embedding space of the reference item and one or more interactions by the user with a plurality of historical items, receiving as input a frequency of the one or more interactions and determines the centroid with a selection of one or more historical items from the plurality of historical items based on the frequency;
determine, using the centroid and the list of candidate items, at least one candidate item from the list of candidate items based at least in part on a distance between the vector representation for each candidate item and the centroid;
output, to the user, an indication of the at least one candidate item from the list of candidate items.
The recited limitations above set forth the process for determining items similar to historical items associated with a user to provide as recommendation. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to generating a list of candidate items to rank by the distance of the items and outputting the list to a user (see specification [0010] disclosing problems of low accuracy and items of little interest in recommending items to a user), which is a sales and marketing endeavor. These limitations also amount to mathematical concepts, including mathematical calculations. The claims are directed to plotting points and determining a centroid corresponding to an average of the locations in the space and determining a distance between the items and the centroid, which are calculations.
Such concepts have been identified by the courts as abstract ideas (see: 2106.04(a)(2)).
Step 2A (Prong 2):
Returning to representative claim 1, Examiner acknowledges that claim 1 recites additional elements, such as:
a computer-readable storage medium storing program instructions;
one or more processors;
as output of a first neural network trained at least in part on filtering criterion, receives as input a plurality of vector representations of items in an embedding space stored in an item vector data store;
output of a second neural network;
Taken individually and as a whole, claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than apply the judicial exception on a general purpose computer.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement 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) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a computer-readable storage medium and one or more processors, these elements are recited with a very high level of generality. Specification paragraph [0093] discloses the non-transitory computer-readable storage medium can be any type of non-transitory computer-readable medium or other computer storage device. Specification paragraph [0095] discloses the processor may be any of a processing unit, DSP, ASIC, FPGA, microprocessor, controller, microcontroller, etc. As such, it is evident that these additional elements are any generic computing component and only serve to implement the abstract idea on a computing device. This is further evidences as these elements are merely recited in the beginning of the claim as causing the system to perform the various steps of the abstract idea. The neural networks are also recited with a very high level of generality. The claims merely recite that neural networks are used to process the information of the abstract idea. Specification paragraph [0028] also discloses how the data store may store any algorithm, AI, machine learning model, deep learning model, neural network, etc. to be used by the system. As such, it is evident that the neural networks are any generic machine learning model that is merely applied to the abstract idea to perform calculations and spit out an output. The additional elements of the claim only serve to provide a general link to a computing environment, but the claims are directed to the abstract idea.
In view of the above, under Step 2A (Prong 2), representative claim 1 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 8 (method): Claim 8 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 8 is rejected under at least similar rationale as provided above regarding claim 1.
Regarding Claim 15 (non-transitory computer-readable media): Claim 15 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 15 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 2-7, 9-14, and 16-20 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm of determining items similar to historical items associated with a user to provide as recommendation. Thus, each of claims 2-7, 9-14, and 16-20 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-7, 9-14, and 16-20 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-7, 9-14, and 16-20 rely on at least similar elements as recited in claim 1. Further additional elements (e.g., a gradient boosting module (claim 6)) are also acknowledged; however, the additional elements of claims 2-7, 9-14, and 16-20 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement 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) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-7, 9-14, and 16-20 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-7, 9-14, and 16-20 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 2-7, 9-14, and 16-20 do not add “significantly more” to the abstract idea.
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, 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, 8-9, 11-12, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable by Pal (US 20220374474 A1) in view of Flanagan (US 20200342358 A1).
Regarding Claim 1: Pal discloses a system comprising:
a computer-readable storage medium storing program instructions; (Pal: [0076] – “an exemplary computer-readable medium bearing instructions for generating a plurality of representative embedding vectors for a subscriber based on the subscriber's behaviors and/or actions with content items, and in making recommendations of content items to the subscriber”).
one or more processors configured to execute the program instructions; (Pal: [0075] – “When the computer-executable instructions that are hosted or stored on the computer-readable storage devices are executed by a processor of a computing device”).
identify a reference item selected by a user; (Pal: [0046] – “a current context of the requesting subscriber is determined. This current context may include, by way of illustration and not limitation, information about the nature of the request (e.g., explicit or implicit), one or more current content items with which the subscriber is interacting, explicit and/or implicitly identified interests of the subscriber, the capabilities of the device from which the subscriber generated the request, and the like”).
generate a list of candidate items corresponding to the reference item, wherein the first neural network receives as input a plurality of vector representations of items in an embedding space stored in an item vector data store, the plurality of vector representations including a vector representation of the reference item, and determines the list of candidate items, the list of candidate items comprising a vector representation for each candidate item; (Pal: [0047] – “generate, for each of the one or more representative embedding vectors, a set of content items that represent candidate recommended content items for the subscriber”; Pal: [0050] – “the representative embedding vector is projected into the content item embedding space. At block 504 and according to various embodiments of the disclosed subject matter, a first set of k content items is identified based on the projection of the representative embedding vector into the content item embedding space. The k content items of this first set of content items are selected from the corpus of content items and identified or selected as a function of a distance measurement between the currently-iterated representative embedding vector and content items of the corpus of content items”; Pal: [0068]- “a data store storing a corpus of content items 614, and a data store storing a content item graph 616 showing relationships between content items of the corpus of content items”).
generate a centroid corresponding to an average vector representation in the embedding space of the reference item and one or more interactions by the user with a plurality of historical items and the reference item, wherein receives as input a frequency of the one or more interactions and determines the centroid with a selection of one or more historical items from the plurality of historical items based on the frequency; (Pal: [0025] – “the projections of content items with which the subscriber has interacted are clustered into a plurality of clusters. In various embodiments of the disclosed subject matter, a clustering process may generate at least a threshold minimum number of clusters. FIG. 1B illustrates that a clustering process has clustered the projections of content items into a plurality of clusters, including clusters 120-130. Each cluster is viewed as an interest cluster of the subscriber. Additionally, and according to further aspects of the disclosed subject matter, a representative embedding vector is generated for each cluster, as represented by stars 140-150. In some embodiments of the disclosed subject matter, a representative embedding vector is generated as a centroid or averaged embedding vector of the content items projected within the cluster. In other embodiments of the disclosed subject matter, centroids may be found for each cluster after which the embedding vector of the closest content item within the cluster to the centroid is adopted as the representative embedding vector for the interest cluster”; Pal: [0037] – “the number of content items represented in the interest cluster, the frequency that a content item within the interest cluster was repeatedly accessed in the most-recent time period through activity of the currently-iterated subscriber”).
determine, using the centroid and the list of candidate items, at least one candidate item from the list of candidate items based at least in part on a distance between the vector representation for each candidate item and the centroid; (Pal: [0056] – “each content item of the related content item list is associated with a score indicating its relevance to the representative embedding vector, either by distance within the content item embedding space or by relatedness in the content item graph, such that the content items of the list may be ordered. At block 512, the related content item list is returned”; Pal: [0051] – “the content item closest to the projected representative embedding vector in the content item space is identified. According to aspects of the disclosed subject matter, instead of the representative embedding vector simply pointing (as projected into the content item embedding space) to a centroid of a cluster where no content item is located, in various embodiments the representative embedding vector is updated to point to the nearest content item within the subscriber's interest cluster”).
output, to the user, an indication of the at least one candidate item from the list if candidate items. (Pal: [0056] – “each content item of the related content item list is associated with a score indicating its relevance to the representative embedding vector, either by distance within the content item embedding space or by relatedness in the content item graph, such that the content items of the list may be ordered. At block 512, the related content item list is returned”).
Pal does not explicitly disclose a system comprising:
as output of a first neural network trained at least in part on filtering criterion;
based on the filtering criterion;
as output of a second neural network;
Notably, however, Pal does disclose training and applying neural networks to manipulate large amounts of data (Pal: [0081]), and generating a centroid of a cluster (Pal: [0051]).
To that accord, Flanagan does teach a system comprising:
as output of a first neural network trained at least in part on filtering criterion; (Flanagan: [0067] – “The present disclosure relates, in other words, to training at least one model, e.g. a Collaborative Filtering model A1, without having to transfer user data from the client to the server, and at the same time using the model A1 to calculate estimates custom-character for the unspecified elements of the client-item matrix R, the estimates to be used for generating personalized recommendations”).
based on the filtering criterion; (Flanagan: [0045] – “a Collaborative Filtering model A1 may be used to generate a first set of item recommendations R1.sub.ij, a so-called candidate set comprising some, if not all, of the above-mentioned elements”).
as output of a second neural network; (Flanagan: [0045] – “a Collaborative Filtering model A1 may be used to generate a first set of item recommendations R1.sub.ij, a so-called candidate set comprising some, if not all, of the above-mentioned elements r.sub.ij and estimates custom-character, whereafter a Predictive Modeling model A2 may be used to create the final, usable recommendations R2.sub.ij by scoring the initially recommended items R1.sub.ij and sorting them by weight”). In summary, a second neural network is used to used the output of the first neural network to generate another output. While Flanagan does not disclose generating a centroid, Pal discloses the centroid, and Flanagan discloses the use of multiple neural models using output of the other model to perform various functions in the process to determine recommendations.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Pal disclosing the system of identifying recommended items based on distance to a centroid of a cluster of past items with the use of neural networks trained in part on filtering criterion and determining a list of candidate items based on the filtering criterion as taught by Menon. One of ordinary skill in the art would have been motivated to do so in order to build more efficient machine learning processes and have as much data available as possible (Flanagan: [0004-0005]).
Regarding Claim 2: Pal in view of Flanagan discloses the limitations of claim 1 above.
Pal further discloses wherein the plurality of historical items comprise items in the embedding space with which the user has previously interacted. (Pal: [0025] – “the projections of content items with which the subscriber has interacted are clustered into a plurality of clusters. In various embodiments of the disclosed subject matter, a clustering process may generate at least a threshold minimum number of clusters. FIG. 1B illustrates that a clustering process has clustered the projections of content items into a plurality of clusters”).
Regarding Claim 4: Pal in view of Flanagan discloses the limitations of claim 1 above.
Pal further discloses wherein the at least one candidate item from the list of candidate items is based at least partly on a distance between the reference item and the centroid. (Pal: [0056] – “each content item of the related content item list is associated with a score indicating its relevance to the representative embedding vector, either by distance within the content item embedding space or by relatedness in the content item graph, such that the content items of the list may be ordered”).
Regarding Claim 5: Pal in view of Flanagan discloses the limitations of claim 1 above.
Pal further discloses wherein the at least one candidate item from the list of candidate items is based at least partly on at least one of a user location, a device type, a query-related feature, or information regarding to the reference item. Examiner notes Applicant recites at least one of in the claim. (Pal: [0057] – “the relevance scores of the content items in the obtained set of content items for the currently-iterated representative embedding vector may be optionally weighted according to importance of the interest cluster of the currently-iterated representative embedding vector, and/or according to the current context of the subscriber”).
Regarding Claims 8 and 15: Claims 8 and 15 recite substantially similar limitations as claim 1. Therefore, claims 8 and 15 are rejected under the same rationale as claim 1 above.
Regarding Claims 9 and 17: Claims 9 and 17 recite substantially similar limitations as claim 2. Therefore, claims 9 and 17 are rejected under the same rationale as claim 2 above.
Regarding Claims 11 and 18: Claims 11 and 18 recite substantially similar limitations as claim 4. Therefore, claims 11 and 18 are rejected under the same rationale as claim 4 above.
Regarding Claims 12 and 19: Claims 12 and 19 recite substantially similar limitations as claim 5. Therefore, claims 12 and 19 are rejected under the same rationale as claim 5 above.
Regarding Claim 16: Pal in view of Flanagan discloses the limitations of claim 15 above.
Pal further discloses wherein the processor is further configured to output, to the user, an indication of the at least one candidate item from the list of candidate items. (Pal: [0056] – “such that the content items of the list may be ordered. At block 512, the related content item list is returned”).
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Pal (US 20220374474 A1) and Flanagan (US 20200342358 A1), in view of Rama (US 20210216923 A1).
Regarding Claim 3: The combination of Pal and Flanagan discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein a location of the plurality of historical items represents a combination of a click embedding, an amenity embedding, and a geographical embedding. Notably, however, Pal does disclose projections of content items that the subscriber has interacted with in the past, and clustering them together in various interest clusters (Pal: [0025]).
To that accord, Rama does teach wherein a location of the plurality of historical items represents a combination of a click embedding, an amenity embedding, and a geographical embedding. (Rama: [0031] – “The deep neural network 202 operates to generate a demand agent vector 212 using demand agent data 210, which comprises various features, such as location, size, past bookings, responses to campaigns, clickstream data features, recency, frequency, monetary features, etc.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Pal and Flanagan disclosing the system of identifying recommended items based on distance to a centroid of a cluster of past items with the location representing a click, amenity, and geographical embedding as taught by Rama. One of ordinary skill in the art would have been motivated to do so in order to identify candidate items that with strong likelihood of future transaction (Rama: [0004]).
Regarding Claim 10: Claim 10 recites substantially similar limitations as claim 3. Therefore, claim 10 is rejected under the same rationale as claim 3 above.
Claims 6-7, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Pal (US 20220374474 A1) and Flanagan (US 20200342358 A1), in view of Wang (US 20160034853 A1).
Regarding Claim 6: The combination of Pal and Flanagan discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the first neural network includes a gradient boosting model. Notably, however, Pal does disclose using machine learning models to process data (Pal: [0081]).
To that accord, Wang does teach wherein the first neural network includes a gradient boosting model. (Wang: [0016] – “A hybrid recommender system combines aspects of at least two of collaborative filtering, content-based filtering, and contextual modeling in providing a recommendation. Most existing hybrid (e.g., logistic regression, gradient boosting tree, etc.)”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Pal and Flanagan disclosing the system of identifying recommended items based on distance to a centroid of a cluster of past items with the use of a gradient boosting model as taught by Wang. One of ordinary skill in the art would have been motivated to do so in order to consider all characteristics with the recommender system (Wang: [0016]).
Regarding Claim 7: The combination of Pal and Flanagan discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the list of candidate items is generated by a collaborative filtering model. Notably, however, Pal does disclose using machine learning models to process data (Pal: [0081]).
To that accord, Wang does teach wherein the list of candidate items is generated by a collaborative filtering model. (Wang: [0016] – “A hybrid recommender system combines aspects of at least two of collaborative filtering, content-based filtering, and contextual modeling in providing a recommendation”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Pal and Flanagan disclosing the system of identifying recommended items based on distance to a centroid of a cluster of past items with the use of a collaborative filtering model as taught by Wang. One of ordinary skill in the art would have been motivated to do so in order to use various types of models to filter out a recommendation (Pal: [0051]).
Regarding Claims 13 and 20: Claims 13 and 20 recite substantially similar limitations as claim 6. Therefore, claims 13 and 20 are rejected under the same rationale as claim 6 above.
Regarding Claim 14: Claim 14 recites substantially similar limitations as claim 7. Therefore, claim 14 is rejected under the same rationale as claim 7 above.
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.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 9/15/2026
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