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 .
1. Applicant’s amendment filed 08/12/2026 is entered. Claims 1, 13, and 15-18 are currently amended. Claim 14 is a canceled claim. Claims 1-13, 15-20 are pending for examination.
2. Telephone Interview:
At the request of the Applicant a telephone interview was conducted on 08/10/2026 and the telephone summary issued as reproduced:
“ Issues Discussed:
35 U.S.C. 101
Suggested amendments to the independent claim were discussed, which, as per Examiner do not overcome 35 USC 101 rejection, because, when analyzed per Step 2A, Prong One and Step 2A, Prong Two the additional elements do not integrate the recited judicial exception into a practical application because (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).Subject matter from the other Specification paras including 0003, 0073-0075 , and other Office example were discussed. Currently, no agreement reached. Future amendments When submitted formally will be subject to reconsideration and search.”
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1—13, 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 1-12 are to a system, 13, 15-17 are to a process comprising a series of steps, and clams 18-20 to manufacture, which are statutory (Step 1: Yes).
Step 2A Analysis:
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 1-13, 15-20 recite abstract idea.
Claim 1 recites:
1. (Currently Amended) A system, comprising:
a processor; and a memory having instructions that when executed by the processor cause the processor to:
(i) receive a plurality of requests associated with a plurality of items corresponding to a plurality of item types, wherein each item type includes one or more items, wherein the plurality of requests are received from a third-party computer in response to user interactions with information items displayed on a user interface of a first application executed by the third-party computer;
(ii) select, from the plurality of item types, a subset of target item types that satisfy a predefined type selection criterion associated with the plurality of requests, wherein each item of the plurality of items is classified to a respective item type of the plurality of item types by a tree-based neural network of a trained boosting selection model, the tree-based neural network comprising a plurality of trained decision trees, and wherein the classifying comprises:
(ii a) traversing each trained decision tree of the plurality of trained decision trees to arrive at a respective final node of each trained decision tree; and
(ii b) applying an aggregation process to combine final nodes of the plurality of trained decision trees into a final output, the aggregation process comprising applying a majority-voting process to identify a classification selected by a majority of the plurality of trained decision trees;
(iii) for each item type of the subset of target item types, select, by the trained boosting selection model, a set of boosting items from a set of target items of a respective target item type based on an item score of each of the set of target items, wherein the trained boosting selection model further comprises a deep neural network and is trained using a training data set;
(iv) generate, by the trained boosting selection model, an ordered list of boosting information items for the subset of target item types by consolidating the sets of boosting items of the subset of target item types[[.]];
(v) generate, based at least on the ordered list of boosting information items, a plurality of candidate information items including at least a set of boosting information items corresponding to the sets of boosting items of the subset of target item types and
(vi) provide information of the plurality of candidate information items to a second application, distinct from the first application, executed by the third-party computer, a subset of the information of the plurality of candidate information items indicating an order of each boosting information item in the ordered list of boosting information items, wherein information of at least one of the plurality of candidate information items is displayed on a user interface of the second application.
The highlighted limitations comprising, “ ( ii) select, from the plurality of item types, a subset of target item types that satisfy a predefined type selection criterion associated with the plurality of requests, wherein each item of the plurality of items is classified to a respective item type of the plurality of item types ; (b) applying an aggregation process into a final output, the aggregation process comprising applying a majority-voting process to identify a classification; (iii) for each item type of the subset of target item types, select a set of boosting items from a set of target items of a respective target item type based on an item score of each of the set of target items; (iv) generate an ordered list of boosting information items for the subset of target item types by consolidating the sets of boosting items of the subset of target item types; (v) generate, based at least on the ordered list of boosting information items, a plurality of candidate information items including at least a set of boosting information items corresponding to the sets of boosting items of the subset of target item types “;
Under their broadest reasonable interpretation, they fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. MENTAL PROCESSES: claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); • a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011. These limitations, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of “by a processor and using tree based neural network ”. That is, other than reciting “by a processor and using tree based neural network” nothing in the claim elements precludes the step from practically being performed manually in the mind using a pen and paper. For example, but for the “by a processor and using tree based neural network” language, the claim encompasses a person looking at collected plurality of requests associated with a plurality of items with different types, selecting from these plurality of item types , a subset of item types as per predetermined criterion, and based on determined item ratings, such as number of stars based on number of reviews, and score by manual regression using profit margin and the volume of items sold in preset of time, and then selecting a set of boosting items from a set of target items based on an item ratings and score of each of the set of target items and based on the selection generating an ordered list of boosting items. The mere nominal recitation of by a processor does not take the claim limitations out of the mental process grouping. Thus, the claim 1 recites a mental process. Since the other two independent claims 13 and 18 recite similar limitations, they are analyzed on the same basis reciting “Mental Processes”.
Thus, claims 1 , 13, and 18 with their respective dependent claims 2-12, 15-17, and 19-20 recite an abstract idea (Step 2A, Prong One: YES).
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 1-13, 15-20: The judicial exception is not integrated into a practical application. Claim 1 recites the additional limitations of using generic computer implementing the steps of: (
(i) receive a plurality of requests associated with a plurality of items corresponding to a plurality of item types, wherein each item type includes one or more items, wherein the plurality of requests are received from a third-party computer in response to user interactions with information items displayed on a user interface of a first application executed by the third-party computer;
(ii) select, from the plurality of item types, a subset of target item types that satisfy a predefined type selection criterion associated with the plurality of requests, wherein each item of the plurality of items is classified to a respective item type of the plurality of item types by a tree-based neural network of a trained boosting selection model, the tree-based neural network comprising a plurality of trained decision trees, and wherein the classifying comprises: (ii a) traversing each trained decision tree of the plurality of trained decision trees to arrive at a respective final node of each trained decision tree; and (ii b) applying an aggregation process to combine final nodes of the plurality of trained decision trees into a final output, the aggregation process comprising applying a majority-voting process to identify a classification selected by a majority of the plurality of trained decision trees;
(iii) for each item type of the subset of target item types, select, by the trained boosting selection model, a set of boosting items from a set of target items of a respective target item type based on an item score of each of the set of target items, wherein the trained boosting selection model further comprises a deep neural network and is trained using a training data set;
(iv) generate, by the trained boosting selection model, an ordered list of boosting information items for the subset of target item types by consolidating the sets of boosting items of the subset of target item types;
(v) generate, based at least on the ordered list of boosting information items, a plurality of candidate information items including at least a set of boosting information items corresponding to the sets of boosting items of the subset of target item types and
(vi) provide information of the plurality of candidate information items to a second application, distinct from the first application, executed by the third-party computer, a subset of the information of the plurality of candidate information items indicating an order of each boosting information item in the ordered list of boosting information items, wherein information of at least one of the plurality of candidate information items is displayed on a user interface of the second application.
The limitations “(i) receive a plurality of requests associated with a plurality of items corresponding to a plurality of item types, wherein each item type includes one or more items, wherein the plurality of requests are received from a third-party computer in response to user interactions with information items displayed on a user interface of a first application executed by the third-party computer; and “(vi) provide information of the plurality of candidate information items to a second application, distinct from the first application, executed by the third-party computer, a subset of the information of the plurality of candidate information items indicating an order of each boosting information item in the ordered list of boosting information items, wherein information of at least one of the plurality of candidate information items is displayed on a user interface of the second application.”; are mere data gathering, output, transmitting and displaying recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). These limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, output, transmitting and displaying. See MPEP 2106.05, and are recited as being performed by a computer. The computer is recited at a high level of and is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f).
In limitations of steps (ii), (iii), (iv), and (v) the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The limitations in ((ii), (iii), and (iv) reciting “using a tree-based neural network of a trained boosting selection model” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished by improving the functioning of the a tree-based neural network of a trained boosting selection model itself, citing reduced storage requirements, lowered system complexity, and the prevention of “Catastrophic forgetting” “ or loss function”; see Exparte Desjardines.
The judicial exceptions of selecting and classifying the plurality of items to a respective item type and selecting a set of boosting items using a tree-based neural network of a trained boosting selection model merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “the tree-based neural network of a trained boosting selection model” limits the identified judicial exceptions in the steps (ii), (iii), and (iv), this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed individually and in combination, the additional elements in claim 1, as analyzed above, do not integrate the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Since the other two independent claims 13, and 18 recite similar limitations as claim 1, they are analyzed on the same basis directed to the judicial exception. (Step 2A: YES).
Dependent claims 2-3, 5-9, and 12 recite determining various properties for target item type based on collected data, and claims 10-11 recite comparing item scores, forming an ordered list, associating an ordered list and adjusting the item scores, which similar to the limitations of claim m1, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. See MPEP 2106.04(a)(2) Abstract Idea Groupings [R-07.2022] II. MENTAL PROCESSES. Limitations of claim 4 describe the boosting items which is non-functional descriptive subject matter. Accordingly, limitations of the dependent claims 2-12 are recited at a high level of generality and do not impose any meaningful limits on practicing the abstract idea.
Limitations of dependent claims, 15-17 merely recite non-functional descriptive subject matter describing the candidate information, determining new type of item type, and deciding to add or not the new item type to the boosting list of items, which similar to the limitations of claim 1, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. See MPEP 2106.04(a)(2) Abstract Idea Groupings [R-07.2022] II. MENTAL PROCESSES. Limitations of claim 15 describe the plurality of candidate boosting items which is non-functional descriptive subject matter. Accordingly, limitations of the dependent claims 14-17 are recited at a high level of generality and do not impose any meaningful limits on practicing the abstract idea.
Dependent claims 19 and 20 recite using a heuristic model and an item feeding model to determine the ordered list of boosting information items, which can be performed by humans as the heuristic model is a practical experience based approach, and it is a structured model comprised of survey questions. Thus, claims 19-20 recite mental processes.
Thus, dependent claims 2-12, 15-17, and 19-20 do not impose any meaningful limits on practicing the abstract idea, and are directed to an abstract idea.
Even when viewed individually and in combination, the additional elements of claims 1-13, 15-20 do not integrate the recited judicial exception into a practical application, because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claims 1-13, 15-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claims recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim.
As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 1-13, 15- amount to no more than mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use using a generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Using a tree-based neural network of a trained boosting selection model, as analyzed under Step 2A above, amounts to simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Additional elements comprising receiving requests, providing information and displaying information were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they have determined insignificant limitations as necessary data gathering/transmitting/ outputting/displaying data . However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). ). The background of the example does not provide any indication that the computer components are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere data gathering/ transmitting/ outputting /displaying/presenting/ data steps using a generic computer are well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the receiving requests and providing information steps are well-understood, routine conventional activities are supported under Berkheimer Option 2. See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Even when considered individually and in combination, the additional elements in claims 1-13, 15-20 represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Thus, claims 1-13, 15-20 are patent ineligible.
4. Prior art discussion:
Reference claim 1, the prior art, alone or combined, neither teaches nor renders obvious, at least the limitations comprising, as a whole, a processor implementing the steps select, from the plurality of item types, a subset of target item types that satisfy a predefined type selection criterion associated with the plurality of requests, wherein each item of the plurality of items is classified to a respective item type of the plurality of item types by a tree-based neural network of a trained boosting selection model, the tree-based neural network comprising a plurality of trained decision trees, and wherein the classifying comprises: traversing each trained decision tree of the plurality of trained decision trees to arrive at a respective final node of each trained decision tree; and applying an aggregation process to combine final nodes of the plurality of trained decision trees into a final output, the aggregation process comprising applying a majority-voting process to identify a classification selected by a majority of the plurality of trained decision trees; for each item type of the subset of target item types, select, by the trained boosting selection model, a set of boosting items from a set of target items of a respective target item type based on an item score of each of the set of target items, wherein the trained boosting selection model further comprises a deep neural network and is trained using a training data set; generate, by the trained boosting selection model, an ordered list of boosting information items for the subset of target item types by consolidating the sets of boosting items of the subset of target item types”. Claims 2-12 depend from claim 1. The other two independent claims 13 and 18 recite similar limitations as claim 1 and claims 15-17 depend from claim 13, and claims19-20 depend from claim 18.
5. Discussion of the most relevant prior art:
The following references have been identified as the most relevant prior art to the claimed invention.
(i) Gudla et al. [US 20240104622 A1; see para 0043] in the field of boosting scores for ranking items matching search query describes a boosting module 214 determining a boosting variable based on a set of parameters for a trained machine learning model. The machine learning training module 230 trains a machine learning model .such as the item selection model to predict a probability of conversion for a customer and an item, the boosting module 214 determines one or more boosting variables based on one or more parameters that the model uses to process an input. The boosting module 214 also alternatively may determine a boosting variable based on live order data.
(ii) Kim et al. [US 20240427833 A1 cited in the Non-Final Rejection mailed 05/12/2026, see para 0157, see Fig.4A] describes the control unit 130 selecting a content satisfying a desired (or alternatively, preset) ranking condition as a ranking-boosting content among the contents for which new episodes are serialized on a specific day of the week (e.g., Thursday), and allow ranking-boosting information 411 or 431 to be further included in the item (e.g., 410 or 430) corresponding to the selected ranking-boosting content, and ranking-boosting information is not included in the item 420 of the content that is not selected as a ranking-boosting content.
(iii) Prendki [US 20200043014 A1 cited in the Non-Final Rejection mailed 05/12/2026 ; see para 0115] describes that method 400 includes a block 440 displaying a list of item results to the first user, wherein the list is based on the updated ranking scores for the items in the category of items.
NPL references:
(iv) B. Vivek Arvind, J. Swaminathan and K. R. Viswanathan, "An improvised filtering based intelligent recommendation technique for web personalization," 2012 Annual IEEE India Conference (INDICON), Kochi, India, 2012, pp. 1194-1199, retrieved from IP.COM on 09142026 and cited in the Non-Final Rejection mailed 05/12/2026; describes an intelligent recommendation system utilizing Boosted item based collaborative filtering for the efficient rating of predicted items and Association rule mining technique for making a personalized recommender system for the target user, which can improve the overall web recommendation precision
(v) Manojkumar Rangasamy Kannadasa et al. “Addressing Purchase-Impression Gap through a Sequential Re-ranker”; ARXIV ID: 2010.14570; Publication Date: 2020-10-27 retrieved from IP. Com on 04/20/2026 and cited in the Non-Final Rejection mailed 05/12/2026 describes methods for distributing of items based on historic shopping patterns and presenting a sequential reranker that methodically reranks top search results produced by a conventional pointwise scoring ranker. The sequential reranker enables addressing purchase impression gap with respect to multiple item aspects.
(v) Athanasios N. Nikolakopoulos et al. “Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks”; ARXIV ID: 1909.03579; Publication Date: 2019-09-08; retrieved from IP. Com on 04/20/2026 and cited in the Non-Final Rejection mailed 05/12/2026 describes Item-based models are among the most popular collaborative filtering approaches for building recommender systems and comprehensive set of experiments on real-world datasets verify the theoretically predicted properties of the proposed approach and indicate that they are directly linked to significant improvements in top-n recommendation accuracy.
Foreign reference:
(vii) CN 110659940A cited in the IDS filed 01/27/2025 describes selecting advertising products for attracting traffic and promoting products.
6. Allowability: If the independent claims are amended to overcome 35 USC 101 rejection the claims can be considered for allowance. However, all amendments will be subject to reconsideration and search.
Response to Arguments
7. 35 USC 101 rejection:
Applicant's arguments filed 08/12/2026, see pages 11-15 have been fully considered but they are not persuasive.
Step 2A, Prong One:
Examiner has reviewed fully the Applicant’s arguments on pages 11-12 that claim 1 does not recite mental process. Examiner respectfully disagrees with the Applicant’s arguments. Step 2A Prong 1 analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations in claim 1 comprising, “ ( ii) select, from the plurality of item types, ….. wherein each item of the plurality of items is classified to a respective item type of the plurality of item types ; (b) applying an aggregation process into a final output, the aggregation process comprising applying a majority-voting process to identify a classification; (iii) for each item type of the subset of target item types, select a set of boosting items from a set of target items of a respective target item type based on an item score of each of the set of target items; (iv) generate an ordered list of boosting information items for the subset of target item types by consolidating the sets of boosting items of the subset of target item types; and (v) generate, based at least on the ordered list of boosting information items, a plurality of candidate information items including at least a set of boosting information items corresponding to the sets of boosting items of the subset of target item types “;, as analyzed above in paragraph 3 above, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. MENTAL PROCESSES: claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); • a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011.
Applicant’s arguments referring to using a tree-based neural network of trained boosting selection for classifying, aggregating steps, are considered as additional elements and are analyzed under Step 2A, Prong Two analysis and not under Step 2A, Prong One. These applicant’s arguments do not preclude the limitations from practically being performed manually in the mind using a pen and paper. And reciting a Mental Process under Step 2A, Prong One.
Applicant’s reference to Office Example 48 claim 3 has no relevance here, because in Example 48, steps (a) was considered reciting a mathematical concept, and steps (b) and (c) recite mental processes, groupings of abstract ideas. The limitations ins steps d) detecting a source address associated with the one or more malicious network packets,” “(e) dropping the one or more malicious network packets,” and “(f) blocking future traffic from the source address.” were considered additional elements and were analyzed under Step 2A, Part two.
In view of the foregoing, the limitations in the independent claims 1, 13, and 18 do “set forth” and “describe” abstract idea.
Step 2A, Prong Two:
Examiner has reviewed fully the Applicant’s arguments on pages 12-14 that claim 1 is not directed to an abstract idea. Examiner respectfully disagrees with the Applicant’s arguments.
In response to the Applicant’s arguments that the references fail to show certain features of the invention, “The specification explains that the item feed and ordering module "may monitor user engagement, content quality of the information items 808S, and/or item quality of the items 806 associated with the plurality of requests 804, and dynamically update the plurality of candidate information items 808C...thereby enhancing a relevance level of the candidate information items 808C provided to the third-party computer 802." As-Filed Specification, [0075]. This scoring and transmission of priority data selectively where it matters reflects a technical improvement in the information distribution system between computers.”, it is noted that the features upon which applicant relies (i.e.” item feed and ordering module "may monitor user engagement, content quality of the information items 808S, and/or item quality of the items 806 associated with the plurality of requests 804, and dynamically update the plurality of candidate information items 808C...thereby enhancing a relevance level of the candidate information items 808C provided to the third-party computer 802”]) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The applicant’s arguments, “ Claim 1, as amended, reflects this improvement. Claim 1, as amended, recites "generat[ing], based at least on the ordered list of boosting information items, a plurality of candidate information items including at least a set of boosting information items corresponding to the sets of boosting items of the subset of target item types," and "provid[ing] information of the plurality of candidate information items to a second application...a subset of the information of the plurality of candidate information items indicating an order of each boosting information item in the ordered list of boosting information items, wherein information of at least one of the plurality of candidate information items is displayed on a user interface of the second application." do not reflect technical improvement such as to hardware or software, or reduced storage requirements, lowered system complexity or lowering the latency, but instead merely amount to mental process of generating a plurality of candidate of items based on the ordered list of boosting items. The limitations reciting providing information to a second application and then displayed on user interface is mere data transmitting, output and displaying, using generic computers amount to insignificant extra-solution activity, wherein the computers are used as a mere tool to implement generic computer functions. See MPEP 2106.05 (f) 2.
Examiner disagrees with the he applicant’s arguments , “ Further, claim 1 does not invoke a generic processor to "select...a subset of target item types" but specifies the operations by which the item classification is performed: "traversing each trained decision tree of the plurality of trained decision trees to arrive at a respective final node of each trained decision tree" and "applying an aggregation process to combine final nodes of the plurality of trained decision trees into a final output, the aggregation process comprising applying a majority-voting process to identify a classification selected by a majority of the plurality of trained decision trees.". This further corresponds to Example 48's Claim 2, which integrates the exception into a practical application by reciting details of how the DNN aided the cluster assignments and what was then done with the result. In Example 48, the USPTO held that such steps were "not insignificant extra-solution activity, mere instructions to apply the exception, or mere field of use limitations," but that they rather reflected the disclosed improvement. Claim 1, as amended, similarly recites how the trained boosting selection model operates, how the tree-based classification feeds target item type selection, which feeds boosting item selection and the ordered list, which in turn drives generation of candidate information items and their provision with order-indicating information for display. Therefore, claim 1, as amended, integrates the cited concepts into a practical application under Step 2A Prong Two, and is therefore further eligible under 35 U.S.C. § 101.”. First , it seems the Applicant meant Example 48 and claim 3 and not claim 2, as claim 2 is patent ineligible. The limitations reciting “using a tree-based neural network of a trained boosting selection model” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished by improving the functioning of the a tree-based neural network of a trained boosting selection model itself, citing reduced storage requirements, lowered system complexity, and the prevention of “Catastrophic forgetting” “ or loss function”; see Exparte Desjardines. The judicial exceptions of selecting and classifying the plurality of items to a respective item type and selecting a set of boosting items using a tree-based neural network of a trained boosting selection model merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “the tree-based neural network of a trained boosting selection model” limits the identified judicial exceptions in the steps (ii), (iii), and (iv), this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Secondly, Example 48 claim 3 is not applicable here. The additional elements in steps (d)—(f) the system detects network intrusions and takes real-time remedial actions, including dropping suspicious packets and blocking traffic from suspicious source addresses and reflect improvement in the technical field of network intrusion detection and improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). Thus, the additional elements in steps (d)-(f), when considered in combination, integrate the abstract idea into a practical application because the claim improves the functioning of a computer or technical field. See MPEP 2106.04(d)(1) and 2106.05(a). Thus, the claim as a whole integrates the judicial exception into a practical application (Step 2A, Prong Two: YES), such that the claim is not directed to the judicial exception. (Step 2A: NO). The claim is eligible. Here, in the instant application, the additional elements, as analyzed in paragraph 3 above, when viewed in combination, do not integrate the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Since the other two independent claims 13, and 18 recite similar limitations as claim 1, they are analyzed on the same basis directed to the judicial exception. (Step 2A: YES).
In view of the foregoing, the Applicant’s arguments are not persuasive and Examiner’s interpretation that the claims are directed to an abstract idea is sustainable and maintained.
Step 2B:
Examiner has reviewed fully the Applicant’s arguments on page 14 that claim 1 is not directed to an abstract idea and recites “Significantly More”.. Examiner respectfully disagrees with the Applicant’s arguments. The additional elements comprising receiving data, transmitting data and displaying data have been considered as insignificant extra-solution activity, wherein the computer is recited at a high level of generality and used as a tool to execute generic computer functions, as discussed above and also see the detail analysis under Step 2B s in paragraph 3 above. Further, as discussed above in detail, the claims 3 of Example 48 is not applicable here, because the additional elements in claim 1, as analyzed in paragraph 3 above, when viewed in combination, do not integrate the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES) and the limitations do not amount to, “Significantly More”.
Applicant has not filed separate arguments against the rejection of the other pending claims.
In view of the foregoing, rejection of all pending claims 1-13, 15-20 under 35 USC 101 is sustainable and maintained.
Conclusion
8. Final Action:
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 YOGESH C GARG whose telephone number is (571)272-6756. The examiner can normally be reached Max-Flex.
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/YOGESH C GARG/Primary Examiner, Art Unit 3688
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