DETAILED ACTION
This action is in reply to the Amendments filed on 07/24/2026.
Claims 8-13 are cancelled.
Claims 1-7 and 14-20 are rejected.
Claims 1-7 and 14-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 07/24/2026, has been entered. Claim 1 has been amended.
Claim Objections
The claim objections from the prior Office Action have been withdrawn pursuant Applicant’s amendments.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/24/2026 has been entered.
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 .
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 1-7 and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or 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 (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-obtaining training data comprising a plurality of examples, each example comprising:
-a set of input features for the conversion model, wherein the set of input features comprises features for a warehouse, a term corresponding to a category of items within a taxonomy of items, a prefix received in a prior search, and a feature describing a number of orders for items from the warehouse that included an item within the category of the taxonomy of items corresponding to the term, wherein a category of items within the taxonomy of items represents a set of items that are related within the taxonomy of items, and
-a label indicating whether an item within the category corresponding to the term was included in a prior order received by an online concierge system corresponding to the prior search,
-wherein obtaining the training data comprises generating the plurality of examples, wherein at least one example of the plurality of examples is generated by:
-receiving a prefix for a search query from a client device associated with a user;
-generating a candidate term for each category of a set of categories within the taxonomy of items;
-transmitting the generated candidate terms to the client device for display to the user through a graphical user interface;
-receiving, from the client device, a selection by the user of a candidate term of the generated candidate terms;
-responsive to receiving the selection of the candidate term, selecting a set of candidate items based on the selected candidate term;
-transmitting the selected set of candidate items to the client device for display to the user;
-receiving an order from the client device, wherein the order comprises a set of items selected by the user;
-determining that no item in the set of items of the received order is associated with the category associated with the selected candidate term; and
-generating the example for the selected candidate term, wherein the example comprises a label indicating that no item of the category corresponding to the selected candidate term was included in the order;
-initializing a neural network for the conversion model that comprises a plurality of layers, where the neural network is configured to receive, as an input a set of features of a received prefix from a search, a candidate term corresponding to a candidate category within the taxonomy of items, and a warehouse, and is configured to generate a predicted probability of an item within the candidate category of the taxonomy of items corresponding to the candidate term being included in an order received by the online concierge system for the warehouse based on the prefix; and
-training the conversion model to, for a set of candidate terms input to the conversion model, generat[ing] a predicted probability for each candidate term that an item within a candidate category within the taxonomy corresponding to each candidate term would be included in an order if displayed as a candidate suggestion for a prefix to a search input by a user by, for each of the plurality of the examples of the training data:
-applying the [conversion model] neural network to the combination of the warehouse, the candidate term, the prefix received in the prior search, and the corresponding set of features of the combination to generate a predicted probability of an item within the candidate category corresponding to the candidate term being included in an order received by the online concierge system, wherein the candidate category comprises a plurality of items that are related in a taxonomy;
-backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the neural network, the backpropagating performed through the neural network and the one or more of the error terms based on a difference between the label applied to an example and the generated predicted probability;
-stopping the backpropagation after the one or more loss functions satisfy one or more criteria; and
-storing the set of parameters of the layers of the network on the computer-readable medium as parameters of the conversion model
The above limitations recite the concept of determining and providing candidate term suggestions for searching an item. The above limitations fall within the “Certain Methods of Organizing Human Activity” and the "mathematical concepts" groupings of abstract ideas, enumerated in MPEP 2106.04(a)(2)(11).
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)
Mathematical concepts include:
mathematical relationships
mathematical formulas or equations
mathematical calculations
The limitations of obtaining training data comprising a plurality of examples, each example comprising: a set of input features for the conversion model, wherein the set of input features comprises features for a warehouse, a term corresponding to a category of items within a taxonomy of items, a prefix received in a prior search, and a feature describing a number of orders for items from the warehouse that included an item within the category of the taxonomy of items corresponding to the term, wherein a category of items within the taxonomy of items represents a set of items that are related within the taxonomy of items, and wherein obtaining the training data comprises generating the plurality of examples, wherein at least one example of the plurality of examples is generated by: generating a candidate term for each category of a set of categories within the taxonomy of items; responsive to receiving the selection of the candidate term, selecting a set of candidate items based on the selected candidate term; determining that no item in the set of items of the received order is associated with the category associated with the selected candidate term; and generating the example for the selected candidate term, wherein the example comprises a label indicating that no item of the category corresponding to the selected candidate term was included in the order are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “obtaining,” “obtaining,” “generating,” “generating,” “selecting,” “determining,” and “generating” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of a label indicating whether an item within the category corresponding to the term was included in a prior order received by an online concierge system corresponding to the prior search, receiving a prefix for a search query from a client device associated with a user; transmitting the generated candidate terms to the client device for display to the user through a graphical user interface; receiving, from the client device, a selection by the user of a candidate term of the generated candidate terms; transmitting the selected set of candidate items to the client device for display to the user; receiving an order from the client device, wherein the order comprises a set of items selected by the user; initializing a neural network for the conversion model that comprises a plurality of layers, where the neural network is configured to receive, as an input a set of features of a received prefix from a search, a candidate term corresponding to a candidate category within the taxonomy of items, and a warehouse, and is configured to generate a predicted probability of an item within the candidate category of the taxonomy of items corresponding to the candidate term being included in an order received by the online concierge system for the warehouse based on the prefix; and training the conversion model to, for a set of candidate terms input to the conversion model, generat[ing] a predicted probability for each candidate term that an item within a candidate category within the taxonomy corresponding to each candidate term would be included in an order if displayed as a candidate suggestion for a prefix to a search input by a user by, for each of the plurality of the examples of the training data: applying the [conversion model] neural network to the combination of the warehouse, the candidate term, the prefix received in the prior search, and the corresponding set of features of the combination to generate a predicted probability of an item within the candidate category corresponding to the candidate term being included in an order received by the online concierge system, wherein the candidate category comprises a plurality of items that are related in a taxonomy; backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the neural network, the backpropagating performed through the neural network and the one or more of the error terms based on a difference between the label applied to an example and the generated predicted probability; stopping the backpropagation after the one or more loss functions satisfy one or more criteria; and storing the set of parameters of the layers of the network on the computer-readable medium as parameters of the conversion model are processes that, under their broadest reasonable interpretation, cover a commercial interaction, as well as, mathematical relationships, formulas, and calculations. That is, other than reciting that the concierge system is an online concierge system, that the prefix is received from a client device associated with a user, that the transmitting is to the client device and displayed through a graphical user interface, that the receiving is from the client device, that the transmitting is to the client device, that the receiving is from the client device, initializing a neural network for the conversion model that comprises a plurality of layers, that the input is received by the neural network, that the conversion model is trained, that the conversion model is a neural network, that the one or more error terms are backpropagated, that the set of the parameters are of the neural network, that the backpropagating performed through the neural network, that the backpropagation is stopped after the one or more loss functions satisfy one or more criteria, and that the set of parameters are of the layers of the network on the computer readable storage medium, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “an online concierge system,” “a client device associated with a user,” “a graphical user interface,” “initializing a neural network for the conversion model that comprises a plurality of layers,” “the neural network,” “training,” “backpropagating,” “the backpropagating performed through the neural network,” “stopping the backpropagation,” “layers of the network,” and “the computer readable storage medium” language, “receiving,” “transmitting,” “receiving,” “transmitting,” “receiving,” “received,” “initializing,” “receive,” “training,” “applying,” “update,” “stopping,” and “storing” in the context of this claim encompass advertising, and marketing or sales activities and mathematical relationships, formulas, and calculations.
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).
-obtaining training data comprising a plurality of examples, each example comprising:
-a set of input features for the conversion model, wherein the set of input features comprises features for a warehouse, a term corresponding to a category of items within a taxonomy of items, a prefix received in a prior search, and a feature describing a number of orders for items from the warehouse that included an item within the category of the taxonomy of items corresponding to the term, wherein a category of items within the taxonomy of items represents a set of items that are related within the taxonomy of items, and
-a label indicating whether an item within the category corresponding to the term was included in a prior order received by an online concierge system corresponding to the prior search,
-wherein obtaining the training data comprises generating the plurality of examples, wherein at least one example of the plurality of examples is generated by:
-receiving a prefix for a search query from a client device associated with a user;
-generating a candidate term for each category of a set of categories within the taxonomy of items;
-transmitting the generated candidate terms to the client device for display to the user through a graphical user interface;
-receiving, from the client device, a selection by the user of a candidate term of the generated candidate terms;
-responsive to receiving the selection of the candidate term, selecting a set of candidate items based on the selected candidate term;
-transmitting the selected set of candidate items to the client device for display to the user;
-receiving an order from the client device, wherein the order comprises a set of items selected by the user;
-determining that no item in the set of items of the received order is associated with the category associated with the selected candidate term; and
-generating the example for the selected candidate term, wherein the example comprises a label indicating that no item of the category corresponding to the selected candidate term was included in the order;
-initializing a neural network for the conversion model that comprises a plurality of layers, where the neural network is configured to receive, as an input a set of features of a received prefix from a search, a candidate term corresponding to a candidate category within the taxonomy of items, and a warehouse, and is configured to generate a predicted probability of an item within the candidate category of the taxonomy of items corresponding to the candidate term being included in an order received by the online concierge system for the warehouse based on the prefix; and
-training the conversion model to, for a set of candidate terms input to the conversion model, generate a predicted probability for each candidate term that an item within a candidate category within the taxonomy corresponding to each candidate term would be included in an order if displayed as a candidate suggestion for a prefix to a search input by a user by, for each of the plurality of the examples of the training data:
-applying the neural network to the combination of the warehouse, the candidate term, the prefix received in the prior search, and the corresponding set of features of the combination to generate a predicted probability of an item within the candidate category corresponding to the candidate term being included in an order received by the online concierge system, wherein the candidate category comprises a plurality of items that are related in a taxonomy;
-backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the neural network, the backpropagating performed through the neural network and the one or more of the error terms based on a difference between the label applied to an example and the generated predicted probability;
-stopping the backpropagation after the one or more loss functions satisfy one or more criteria; and
-storing the set of parameters of the layers of the network on the computer-readable medium as parameters of the conversion model
The additional elements of claim 1 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) as supported by paragraph [0071] of Applicant’s specification – “Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer.” Specifically, the additional elements of a non-transitory computer readable storage medium, an online concierge system, a client device associated with a user, a graphical user interface, initializing a neural network for the conversion model that comprises a plurality of layers, the neural network, training, backpropagating, the backpropagating performed through the neural network, stopping the backpropagation, and layers of the network, are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of obtaining data, receiving data, generating data, transmitting data, selecting data, determining data, training data, applying a model to generate data, updating data, and storing data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do 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). 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 claim 1, taken individually or as a whole, the additional elements of claim 9 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.
Claim 14 is a computer program product reciting similar functions as claim 1. Examiner notes that claim 14 recites the additional elements of a non-transitory computer readable storage medium, a processor, an online concierge system, a client device associated with a user, a graphical user interface, initializing a neural network for the conversion model that comprises a plurality of layers, the neural network, training, backpropagating, the backpropagating performed through the neural network, stopping the backpropagation, layers of the network, and a user interface element, however, claim 14 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Therefore, claims 1 and 14 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-7 and 15-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-7 and 15-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 5-7, 15-16, and 20 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 2-4 and 17-19 recite the additional elements of the online concierge system, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-7 and 15-20 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-7 and 15-20 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1 and 14, dependent claims 2-7 and 15-20 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. determining and providing candidate term suggestions for searching an item) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Subject Matter Allowable Over the Prior Art
In the present application, claims 1-7 and 14-20 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance:
Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 1-7 and 14-20 are allowable over the prior art as follows:
Claims 1-7 and 14-20 are allowable over 35 U.S.C. §103 as follows:
Claims 1-7 and 14-20 are allowable for the reasons detailed in the “Allowable Subject Matter” section of the Final Office Action dated 02/24/2026.
The most relevant prior art made of record includes previously cited Sen et al. (US 11,126,660 B1), previously cited Hinegardner et al. (US 11,016,964 B1), previously cited Cheng et al. (US 10,929,392 B1), and previously cited Achan et al. (US 2021/0034687 A1).
The most relevant NPL are:
Cited NPL reference U (cited 02/20/2026 and 07/31/2026 on PTO-892) teaches a convolutional neural networks adapted for hierarchical text classification task, but does not teach or suggest the recited limitations.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that, in the filed Declaration, Mr. Prasad explains that "[t]he process by which training data is collected and labeled for a machine-learning model was a recognized technical challenge in the field of machine-learning model development as of the priority date of this application" (see Declaration at paragraphs 6-7; Ex.Bat 1, 8, 10) and explains how the claimed invention addresses this problem (see Declaration at paragraphs 11, 13, 14) (Remarks, pages 11-13).
Examiner respectfully disagrees. The Declaration describes that “A model trained on insufficient or improperly labeled data could not exceed the ceiling imposed by that data, regardless of the sophistication of its architecture or training procedure. For a supervised machine-learning model, the method used to generate labeled training examples was not separable from the quality of the resulting model” and that the “claimed invention provides a particular process for generating training data.” This supports Examiner’s assertion that it is not the machine learning model technology itself that is improved (e.g. the machine learning architecture or training procedure), rather, the claims merely improve the data; improving data quality does not reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. Accordingly, the claims are ineligible.
Applicant further argues that Mr. Prasad also explains how "the arrangement of content in user-interface systems for autocomplete suggestions was also a recognized technical challenge in the field of user-interface design as of the priority date of this application" (see Declaration at paragraphs 8-9; Ex. C at 1), further identifies a portion of the specification that describes this problem [see Declaration at paragraphs (citing Specification at [0004])], and explains how the claimed invention in claim 14 addresses this problem (see Declaration at paragraphs 15, 17, 18) (Remarks, pages 13015).
Examiner respectfully disagrees. The amount of cognitive effort required by a user is not a technical problem, it is a business problem. Additionally, providing more accurate query completion suggestions does not provide a technical improve to the interface technology, it merely changes what data is displayed to a user. Additionally, MPEP 2106.05(a) describes that “the judicial exception alone cannot provide the improvement.” Providing more accurate query completion suggestions (i.e. producing a display arrangement in which the terms most likely to lead to an order appear at the most prominent positions in the interface) is an advertising, and marketing or sales activity and does not provide a technical improvement to the interface technology, rather, the interface technology is recited at a high level of generality such that it amounts 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). Accordingly, the claims are ineligible.
Applicant further argues that the MPEP and Ex parte Desjardins set forth a two-part test for determining whether a technical improvement is recited in the claims. First, does the disclosure "provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement"? MPEP 2106.05(a), see also Ex parte Desjardins et al., Appeal No. 2024-000567 (P.T.A.B. Appeals Review Panel, Sept. 26, 2025) at 8-9. Second, does the claim itself "reflect[] the disclosed improvement in technology"? MPEP 2106.05(a), see also Ex parte Desjardins et al., Appeal No. 2024-000567 (P.T.A.B. Appeals Review Panel, Sept. 26, 2025) at 8-9. These authorities contain important reminders to examiners evaluating applications for technical improvements. For example, the MPEP explains that the examiner's role is not to second-guess the technical rationale, but to confirm that the claims are supported by a coherent explanation of an asserted technical improvement. See MPEP § 2106.05(a) ("Generally, examiners are not expected to make a qualitative judgement on the merits of the asserted improvement.") Additionally, the specification "need not explicitly set forth the improvement"; instead, it simply must "provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement." Id Finally, Desjardins cautions examiners not to evaluate claims at "a high level of generality" and that"§§ 102, 103 and 112 are the traditional and appropriate tools to limit patent protection to its proper scope." Desjardins at 9-10. With these rules in mind, the claimed invention clearly meets the two-part test for reciting an improvement to a technical field. Mr. Prasad identifies the disclosed method as a technical improvement that addresses problems with collecting and labeling training data for a machine-learning model and the arrangement of content in user-interface designs. Accordingly, the disclosure provides "sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement." Mr. Prasad confirms that the cited steps in claim 14 are ones that produce "a labeled training dataset tied to actual serving events" and a neural network whose parameters "reflect actual conversion probabilities measured during live operation." Furthermore, Mr. Prasad confirms that the steps recited in claim 14 are ones that produce "a display arrangement in which terms most likely to lead to an order appear in the most prominent positions in the interface," which "reduc[ es] the number of interface interactions required." (see Declaration at paragraph 17). Therefore, the claims "reflect the disclosed improvement in technology." (see Declaration at paragraph 13). Thus, the claims meet the two-part test for reciting a technical improvement. Accordingly, the claims recite a practical application of a judicial exception and, as such, claim patentable subject matter under Step 2A, Prong Two of the Alice test. Therefore, the§ 101 rejection is improper and should be withdrawn. (Remarks, pages 15-17).
Examiner respectfully disagrees. MPEP 2106.05(a) describes “the examiner should analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. Generally, examiners are not expected to make a qualitative judgement on the merits of the asserted improvement. If the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 CFR 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification.” Neither the cited portions of Applicant’s specification nor the current claims set forth sufficient details that the such that one of ordinary skill in the art would recognize the claimed invention as providing a technical improvement. The improvements set forth do not reflect improvements in the functioning of a computer or an improvement to another technology or technical field. For instance, and as detailed in response to the arguments above, improving the data used to train the machine learning model does not improve the machine learning technology itself and providing more accurate query completion suggestions is an advertising, and marketing or sales activity and does not provide a technical improvement to the interface technology.
Furthermore, in Parte Desjardins the claims were not found eligible because they merely trained machine learning models, rather, the claims recited in Ex Parte Desjardins train the machine learning model in such a way that it “allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" (see Ex Parte Desjardins). The machine learning itself was improved. Accordingly, the claims are ineligible.
Examiner has considered the Declaration submitted by the Applicant, however, even taking the Declaration into account, the claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Newman et al. (US 12,072,918 B1) teaches searching items using a taxonomy.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIELLE E WEINER whose telephone number is (571)272-9007. The examiner can normally be reached M-F 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maria-Teresa (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.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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