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 .
This communication is in response to Application No. 18/967678, filed on 12/4/2024. Claims 1-20 are currently pending and have been examined. Claims 1-20 have been rejected as follows.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Claims 1-20 recite “branch locations”, however the specification makes no such mention to branch locations when describing the physical locations of retailers or other service locations (in for examples [0049] of the instant application). The specification also does not reference a different term which under broadest reasonable interpretation would be the equivalent of the branch locations for the purposes of antecedent basis. Clarification is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12175521. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitation of the instant application are anticipated by the claim limitations of the patent. This is shown in the comparison table below.
Instant Application
US12175521
1. A computer-implemented method, comprising:
maintaining time-sensitive datasets associated with a plurality of branch locations associated with a third-party system, wherein each time-sensitive dataset tracks a plurality of dynamic item entries that vary in a course of an operation of a particular branch location;
(from claim 1) wherein the third-party system operates in connection with a plurality of physical locations, and an operation of each physical location is documented by a time-sensitive dataset which includes a plurality of dynamic item entries that vary in a course of the operation of the physical location
accessing a machine learning model trained to predict a metric value representing a size of dynamic item entries available, wherein the machine learning model was trained by:
accessing a machine learning model trained to predict a metric value representing a size of dynamic item entries available, wherein, the machine learning model was trained by
obtaining training datasets of historical transactions at a plurality of geographical locations, the training datasets including weighted values associated with items at the geographical locations;
obtaining training datasets of historical transactions at a plurality of geographical locations, the training datasets including weighted values associated with items at the geographical locations;
forward propagating data from the training datasets through the machine learning model to generate predicted metric values;
forward propagating data from the training datasets through the machine learning model to generate predicted metric values;
backpropagating the predicted metric values through the machine learning model to adjust weights of the machine learning; and
backpropagating the predicted metric values through the machine learning model to adjust weights of the machine learning; and
saving the adjusted item availability model;
saving the adjusted item availability model;
receiving a selection of the dynamic item entries from a user device, the selection being in association with a session in which the user device requests for a physical arrangement between an agent and the third-party system at a branch location;
receiving, by an online system from a user device, a request to view, at a graphical user interface, available entries of a third-party system, the request being in association with a session in which the user device requests information identifying a physical arrangement between an agent of the online system and the third-party system at a physical location of the third-party system,
wherein the third-party system operates in connection with a plurality of physical locations, and an operation of each physical location is documented by a time-sensitive dataset which includes a plurality of dynamic item entries that vary in a course of the operation of the physical location
retrieving a geographical location associated with the user device;
retrieving a geographical location associated with the user device;
determining a subset of the branch locations associated with the third-party system that are eligible for further selection based on distances of the branch locations from the geographical location associated with the user device;
determining a subset of physical locations operated by the third-party system that are eligible for further selection based on distances of the physical locations from the geographical location associated with the user device;
determining, using the trained machine learning model, for each branch location in the subset, a metric measuring a size of the dynamic item entries available in the time-sensitive dataset corresponding to the branch location; and
determining, using the trained machine learning model, for each physical location in the subset, a metric measuring a size of the dynamic item entries available in the time-sensitive dataset;
selecting one of the branch locations in the subset based on the metric for carrying out the physical arrangement.
selecting one of the physical locations in the subset based on the metric; and
causing the dynamic item entries in the time-sensitive dataset associated with the selected physical location to be displayed at the graphical user interface, wherein the display of the dynamic item entries allows the user device to select the available entries of the third-party system to be included in the physical arrangement.
2. The computer-implemented method of claim 1, wherein determining the subset of the branch locations associated with the third-party system that are eligible for further selection comprises:
determining a list of candidate agents;
determining geographical locations of the list of candidate agents; and
determining the subset of branch locations further based on predicted distances of the physical arrangement carried out by the candidate agents.
2. The computer-implemented method of claim 1, wherein determining a subset of physical locations that are eligible for further selection comprises: determining a list of candidate agents of the online system; determining geographical locations of the list of candidate agents; and determining the subset of physical locations further based on predicted distances of the physical arrangement carried out by the candidate agents.
3. The computer-implemented method of claim 1, further comprising causing a graphical user interface to display a single location front that represents plurality of branch locations of the third-party system.
3. The computer-implemented method of claim 1, wherein causing the dynamic item entries in the time-sensitive dataset associated with the selected physical location to be displayed at the graphical user interface comprises causing the graphical user interface to display a single location front that represents plurality of physical locations of the third-party system.
4. The computer-implemented method of claim 1, wherein a selected branch location has an associated metric value that indicates the selected branch location has a highest number of dynamic item entries among the subset of branch locations.
4. The computer-implemented method of claim 1, wherein the selected physical location has an associated metric value that indicates the selected physical location has a highest number of dynamic item entries among the subset of physical locations.
5. The computer-implemented method of claim 1, wherein a selected branch location has an associated metric value that indicates the selected branch location has a highest likelihood that the user device will select a highest number of entries to be included in the physical arrangement.
5. The computer-implemented method of claim 1, wherein the selected physical location has an associated metric value that indicates the selected physical location has a highest likelihood that the user device will select a highest number of entries to be included in the physical arrangement.
6. The computer-implemented method of claim 1, further comprising:
determining that the subset of branch locations have values of the metric below a threshold; and
expanding distance radius for selecting branch locations that are eligible for further selection.
6. The computer-implemented method of claim 1, further comprising: determining that the subset of physical locations have values of the metric below a threshold; and expanding distance radius for selecting physical locations that are eligible for further selection.
7. The computer-implemented method of claim 1, further comprising:
receiving a final list of available entries selected by the user device to be included in the physical arrangement;
determining, based on the final list, that the physical arrangement is fulfillable at another branch location other than the selected branch location, the other branch location closer to the geographical location associated with the user device; and
switching the physical arrangement to the other branch location.
7. The computer-implemented method of claim 1, further comprising: receiving a final list of available entries selected by the user device to be included in the physical arrangement; determining, based on the final list, that the physical arrangement is fulfillable at another physical location other than the selected physical location, the other physical location closer to the geographical location associated with the user device; and switching the physical arrangement to the other physical location.
8. The computer-implemented method of claim 1, wherein the metric measuring the size of the dynamic item entries available is based on item categories.
8. The computer-implemented method of claim 1, wherein the metric measuring the size of the dynamic item entries available is based on item categories.
9. The computer-implemented method of claim 1, wherein selecting one of the branch locations in the subset based on the metric comprises:
determining that a first candidate branch location has a first metric value lower than a second metric value of a second candidate branch location;
determining that a difference between the first metric value and the second metric value is within a threshold;
determining that the first candidate branch location is a closer location; and
selecting the first candidate branch location as the selected branch location.
9. The computer-implemented method of claim 1, wherein selecting one of the physical locations in the subset based on the metric comprises: determining that a first candidate physical location has a first metric value lower than a second metric value of a second candidate physical location; determining that a difference between the first metric value and the second metric value is within a threshold; determining that the first candidate physical location is a closer location; and selecting the first candidate physical location as the selected physical location.
Claims 10-20 of the instant application recite parallel claim language to that of claims 1-9. Claims 10-20 of US PAT 12175521 recite parallel claim language to that of claims 1-9. Therefore claims 10-20 of the US PAT 12175521 are also conflicting with claims 10-20 of the instant application.
For these reasons it is determined while the conflicting claims are not identical, at least one examined application claim is not patentable distinct from the reference claims before the examined application claim is anticipated by the reference claim.
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.
The claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims 1-9 are a method, claims 10-18 are a computer program product, and claims 19-20 are a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101.
Step 2A Prong 1: The independent claims (1, 10 and 19, taking claim 1 as a representative claim) recite:
A computer-implemented method, comprising:
maintaining time-sensitive datasets associated with a plurality of branch locations associated with a third-party system, wherein each time-sensitive dataset tracks a plurality of dynamic item entries that vary in a course of an operation of a particular branch location;
accessing a machine learning model trained to predict a metric value representing a size of dynamic item entries available, wherein the machine learning model was trained by:
obtaining training datasets of historical transactions at a plurality of geographical locations, the training datasets including weighted values associated with items at the geographical locations;
forward propagating data from the training datasets through the machine learning model to generate predicted metric values;
backpropagating the predicted metric values through the machine learning model to adjust weights of the machine learning; and
saving the adjusted item availability model;
receiving a selection of the dynamic item entries from a user device, the selection being in association with a session in which the user device requests for a physical arrangement between an agent and the third-party system at a branch location;
retrieving a geographical location associated with the user device;
determining a subset of the branch locations associated with the third-party system that are eligible for further selection based on distances of the branch locations from the geographical location associated with the user device;
determining, using the trained machine learning model, for each branch location in the subset, a metric measuring a size of the dynamic item entries available in the time-sensitive dataset corresponding to the branch location; and
selecting one of the branch locations in the subset based on the metric for carrying out the physical arrangement.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for entering into an arrangement with the third party of an online concierge system to carry out an order list from a shopper. Time- sensitive data, such as location and inventory levels, are processed in order to fulfill the physical arrangement through a physical location (instant application at least 0049-0051). The steps under its broadest reasonable interpretation specifically fall under sales activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
A computer-implemented method, comprising: (claim 1)
A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: (claim 10)
An system comprising: a processor; and memory configured to store code comprising instructions, the instructions, when executed by the processor, cause the processor to: (claim 19)
maintaining time-sensitive datasets associated with a plurality of branch locations associated with a third-party system, wherein each time-sensitive dataset tracks a plurality of dynamic item entries that vary in a course of an operation of a particular branch location;
accessing a machine learning model trained to predict a metric value representing a size of dynamic item entries available, wherein the machine learning model was trained by:
forward propagating data from the training datasets through the machine learning model to generate predicted metric values;
backpropagating the predicted metric values through the machine learning model to adjust weights of the machine learning; and
receiving a selection of the dynamic item entries from a user device, the selection being in association with a session in which the user device requests for a physical arrangement between an agent and the third-party system at a branch location;
retrieving a geographical location associated with the user device;
determining a subset of the branch locations associated with the third-party system that are eligible for further selection based on distances of the branch locations from the geographical location associated with the user device;
determining, using the trained machine learning model, for each branch location in the subset, a metric measuring a size of the dynamic item entries available in the time-sensitive dataset corresponding to the branch location; and
The additional elements emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Further, the recitation of accessing a machine learning model trained to predict a metric value representing a size of dynamic item entries available, wherein the machine learning model was trained by: forward propagating data from the training datasets through the machine learning model to generate predicted metric values; backpropagating the predicted metric values through the machine learning model to adjust weights of the machine learning; and merely indicates a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exception, which involves training a machine learning model through forward and back propagation to be used in the determining for each branch location a metric measuring a sized of the dynamic item entries available, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Even when considered as an ordered combination, the additional elements of claim 1, 10, and 19 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 10, and 19 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 10, and 19 are ineligible.
Dependent claims 2-9, 11-18, and 20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 10 and 19 without significantly more.
2. The computer-implemented method of claim 1, wherein determining the subset of the branch locations associated with the third-party system that are eligible for further selection comprises: determining a list of candidate agents; determining geographical locations of the list of candidate agents; and determining the subset of branch locations further based on predicted distances of the physical arrangement carried out by the candidate agents. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
3. The computer-implemented method of claim 1, further comprising causing a graphical user interface to display a single location front that represents plurality of branch locations of the third-party system. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application. The recitation of the user interface is recited at a high level of generality and does not integrate the judicial exception into a practical application.
4. The computer-implemented method of claim 1, wherein a selected branch location has an associated metric value that indicates the selected branch location has a highest number of dynamic item entries among the subset of branch locations. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
5. The computer-implemented method of claim 1, wherein a selected branch location has an associated metric value that indicates the selected branch location has a highest likelihood that the user device will select a highest number of entries to be included in the physical arrangement. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
6. The computer-implemented method of claim 1, further comprising: determining that the subset of branch locations have values of the metric below a threshold; and expanding distance radius for selecting branch locations that are eligible for further selection. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
7. The computer-implemented method of claim 1, further comprising: receiving a final list of available entries selected by the user device to be included in the physical arrangement; determining, based on the final list, that the physical arrangement is fulfillable at another branch location other than the selected branch location, the other branch location closer to the geographical location associated with the user device; and switching the physical arrangement to the other branch location. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
8. The computer-implemented method of claim 1, wherein the metric measuring the size of the dynamic item entries available is based on item categories. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
9. The computer-implemented method of claim 1, wherein selecting one of the branch locations in the subset based on the metric comprises: determining that a first candidate branch location has a first metric value lower than a second metric value of a second candidate branch location; determining that a difference between the first metric value and the second metric value is within a threshold; determining that the first candidate branch location is a closer location; and selecting the first candidate branch location as the selected branch location. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Dependent claims 11-18 and 20 recite parallel claim language to claims 2-9 and therefore are also rejected for the reasons set forth above.
For at least these reasons claims 1-20 are rejected under 35 USC 101.
Subject Matter Free of Prior Art
Claims 1, 10 and 19 are determined to be free of prior art, however the claims remain rejected under 35 USC 101 and Non-Statutory Double Patenting, as set forth above. All dependent claims are also free of prior art by virtue of dependency, but remain rejected under 35 USC 101 and Double Patenting.
The closest prior art was found to be as follows:
Agarwal (US Patent 10,242,336) teaches “receive a request or search from a customer for an item offered by an electronic marketplace or other online goods provider associated with the service provider. The service provider may query the customer for or determine the customer's location. Additionally, the service provider may determine an inventory level of merchants for the item included in the request or search based on a machine learning algorithm utilizing the information about the merchants. For example, the machine learning algorithm may determine which merchants should be queried for item fulfillment based at least in part on an indication of a seller's probability of successfully fulfilling the order. The probability of success may be based at least in part on previously fulfilled orders for similar items by the merchant (e.g., within a certain time frame). Based at least in part on the customer's location, the inventory level determination, and/or the probability of success, the service provider may query a subset of merchants that are local to the customer” (abstract). Agarwal, however, fails to teach the independent claims as a whole as the reference does not disclose the steps of at least forward propagating data from the training datasets through the machine learning model to generate predicted metric values; backpropagating the predicted metric values through the machine learning model to adjust weights of the machine learning; and saving the adjusted item availability model as required in the claimed invention; or determining, using the trained machine learning model, for each branch location in the subset, a metric measuring a size of the dynamic item entries available in the time-sensitive dataset corresponding to the branch location.
Montserrat (US PGPUB 20-230197204) teaches “The training operation can include a forward propagation operation and a backward propagation operation. As part of the forward propagation operation, the machine learning model can receive training data including sequences of SNPs of known ancestral origins to generate predictions of ancestral origins of the sequences. A comparison between the predicted and true ancestral origin category (or between the predicted and known geographical coordinates of ancestral origin locale) of each SNP segment can be made. Various parameters of the predictor sub-model and the smoothing sub-model, such as the weights of the fully-connected neural network model, the parameters of the kernel of the convolutional neural network model, the decision trees, the weights associated with the SNP segments in the smoothing operations, etc., can be adjusted in the training operation to maximize the matching between the predicted and true ancestral origins” (para 0044). However, the reference does not teach the maintaining time-sensitive datasets associated with a plurality of branch locations associated with a third-party system, wherein each time-sensitive dataset tracks a plurality of dynamic item entries that vary in a course of an operation of a particular branch location; receiving a selection of the dynamic item entries from a user device, the selection being in association with a session in which the user device requests for a physical arrangement between an agent and the third-party system at a branch location; retrieving a geographical location associated with the user device; determining a subset of the branch locations associated with the third-party system that are eligible for further selection based on distances of the branch locations from the geographical location associated with the user device; determining, using the trained machine learning model, for each branch location in the subset, a metric measuring a size of the dynamic item entries available in the time-sensitive dataset corresponding to the branch location; and selecting one of the branch locations in the subset based on the metric for carrying out the physical arrangement, as required by the claimed invention.
Closest NPL of Wang teaches “The increase of consumer income has resulted in the rapid development of the retail industry in China, which provides high market potential for retail companies worldwide. However, site selection for retail shops has been a confusing business issue in practical business decisions. In this study, a two-step hybrid model in site selection for small retail shops was proposed. The two steps were spatial accessibility evaluation and market potential estimation. The spatial accessibility of target regions was evaluated based on the improved gravity model to determine regions that lack retail shops. Then, a PCA (principal component analysis)–BP (backpropagation network) model was established to estimate the market potential in the target regions. The two-step model could determine sites with the most market potential and low competition. We conducted the experiment in Guiyang, China and considered 18 socioeconomic factors to make the site selection convincing. Through the experiment, 42 locations were determined with high business value; the locations were recommended to the new retail shops. The accuracy of the PCA–BP model was then proven satisfactory by comparing it with other regression methods. The proposed model could guide retail chains in enhancing business location planning and formulating regional development policies”, Wang, however, fails to teach the features of the instant claims as a whole.
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 6/17/2026
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