/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. Claims 1-20 filed 05/19/2025 are pending for examination.
2. Continuity: This application filed 05/19/2025 is a Continuation of 17823177, filed 08/30/2022 ,now U.S. Patent # 12333590.
Double Patenting
3. 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.
3.1. Claims 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12333590, hereinafter Patent’ 590. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application and the claims of the Patent’ 590 are directed to the same concept of sorting search results using machine learning models, as is evident from the comparison of claims below:
The highlighted limitations of claim 1 of the Patent’ 590 teaches and covers all the limitations [underlined] of claim 1 of the instant application, see
Claim 1 of Patent’590:
A computer-implemented method for rank ordering a set of search results, comprising:
receiving, by a server device, data related to a plurality of items;
providing the data as input to a trained grouped linear regression model, wherein the trained grouped linear regression model (i) groups the plurality of items into a plurality of groups of items according to one or more item variables associated with the plurality of items defined by the data, each of the plurality of groups of items including a subset of the plurality of items associated with a type of the one or more item variables, and (ii) generates a score associated with a target variable for each of the plurality of items using a grouped linear regression applied on a group by group basis for the plurality of groups of items, the score, within a given group of the plurality of groups of items, being further based on sub-divisions of the given group, and the score for each of the plurality of items being relative within each of the plurality of groups of items;
receiving, as output of the trained grouped linear regression model, the score for each of the plurality of items;
storing the output of the trained grouped linear regression model in a data store, each entry in the data store including at least an item identifier for an item of the plurality of items, a group name for the item associated with one of the plurality of groups of items into which the item was grouped, and the score for the item;
receiving, from a user device associated with a user, search criteria for a search of the plurality of items, the search criteria associated with one or more types of the one or more item variables;
identifying, from the data store, one or more groups of items from the plurality of groups of items as a set of search results based on the search criteria;
determining an order to present, within the set of search results, one or more items of the subset of the plurality of items included in the one or more groups of items identified based on the score for each of the one or more items;
outputting, for display via the user device, the set of search results in the order; and
monitoring user interactions with the set of search results displayed via the user device, the user interactions including one or more types of user interactions with one or more items included in the set of search results indicative of a relevance of the one or more items to the user, wherein the trained grouped linear regression model is retrained based on the one or more types of user interactions to cause an adjustment to the score of each of the one or more items output by the retrained grouped linear regression model and stored in the data store for use in future searches.
Claim 1 of the instant application:
A computer-implemented method for ordering search results, comprising:
receiving item data related to a plurality of items;
applying a trained machine learning model to the item data to determine a plurality of scores corresponding to the plurality of items, wherein the plurality of items are grouped into a plurality of groups of items, and the trained machine learning model is applied on a group by group basis to the plurality of groups of items;
storing the plurality of scores corresponding to the plurality of items in a data store, wherein each score of the plurality of scores is stored in association with an identifier of one of the plurality of groups that the corresponding item from the plurality of items is grouped in;
receiving, from a user device, criteria for a search of the plurality of items;
identifying, from the data store, a group of items from the plurality of groups of items as a set of search results based on the criteria;
determining an order to present, within the set of search results, a subset of the plurality of items that are included in the group of items identified based on the score corresponding to each item in the subset of the plurality of items;
causing display of the set of search results in the order on the user device; and
receiving an indication of a user interaction with an item within the set of search results displayed on the user device, the user interaction indicative of a relevance of the item,
wherein the trained machine learning model is retrained based on the user interaction to cause an adjustment to the score of the item determined using the retrained model and stored in the data store for use in future searches.
Examiner has reviewed and compared the dependent claims, and the limitations of the dependent claims 2, 3, 4, 5, 6,7, 8, 9, 10, 11, 12, and 13 are covered and disclosed by the limitations of
Claims 4, 1, 1, 5, 1, 1, 6, 2, 9, 9, 10, 11, and 12 respectively of the Patent’590. Similarly, the limitations of claims 14-19 are also covered by the limitations of claims 1, 8, 9 and 10 of the Patent’ 590.
Claim 20 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 20 of U.S. Patent No. 12333590, hereinafter Patent’ 590 in view of Greenfield et al. [US Patent# 20190086924 A1], hereinafter Greenfield.
The claim 20 of the instant application recites limitations which are covered in the limitations of claims1 and 20 of the Patent’590, except that the training data [dataset] also includes ground truth score. Greenfield, in the same field of endeavor of using trained data teaches applying ground truth [see para 0075, “ the data-based controller characterization system 302 can provide a model configured to receive tracking error 220, …….. Based at least in part on such variables, data-based controller characterization system 302 can be configured to generate expected controller performance data 310. Expected controller performance data 310 can correspond, …… In some implementations, the model provided by data-based controller characterization system 302 can be a machine-learned model that is trained using a set of ground truth data that includes training data examples of tracking error 220 and vehicle state 218 matched with corresponding labels of expected controller performance data 310. “. Therefore, in view of the teachings of Greenfield in the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patent’590 to incorporate the concept of including ground truth score in the training data, so that as disclosed in Greenfield, it would tracking an error.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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 1-20 are to a process comprising a series of steps, which are statutory (Step 1: Yes).
Step 2A Analysis:
Claim 1 recites:
1 A computer-implemented method for ordering search results, comprising:
(i) receiving item data related to a plurality of items;
(ii) applying a trained machine learning model to the item data to determine a plurality of scores corresponding to the plurality of items, wherein the plurality of items are grouped into a plurality of groups of items, and the trained machine learning model is applied on a group by group basis to the plurality of groups of items;
(iii) storing the plurality of scores corresponding to the plurality of items in a data store, wherein each score of the plurality of scores is stored in association with an identifier of one of the plurality of groups that the corresponding item from the plurality of items is grouped in;
(iv) receiving, from a user device, criteria for a search of the plurality of items;
(v) identifying, from the data store, a group of items from the plurality of groups of items as a set of search results based on the criteria;
(vi) determining an order to present, within the set of search results, a subset of the plurality of items that are included in the group of items identified based on the score corresponding to each item in the subset of the plurality of items;
(vii) causing display of the set of search results in the order on the user device; and
(viii) receiving an indication of a user interaction with an item within the set of search results displayed on the user device, the user interaction indicative of a relevance of the item,
(ix) wherein the trained machine learning model is retrained based on the user interaction to cause an adjustment to the score of the item determined using the retrained model and stored in the data store for use in future searches.
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 1-20 recite abstract idea.
The highlighted limitations comprising, “ applying a trained model to the item data to determine a plurality of scores corresponding to the plurality of items, wherein the plurality of items are grouped into a plurality of groups of items, and the trained model is applied on a group by group basis to the plurality of groups of items; identifying, from the data store, a group of items from the plurality of groups of items as a set of search results based on the criteria; determining an order to present, within the set of search results, a subset of the plurality of items that are included in the group of items identified based on the score corresponding to each item in the subset of the plurality of items; wherein the model is retrained based on the user interaction to cause an adjustment to the score of the item determined using the retrained model and stored in the data store for use in future searches. “, 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: 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); For example, , but for by a computer-implemented language, the claim encompasses a person observing and analyzing received item data for plurality of items, such as vehicles, and applying a trained model, which can be a mathematical model such as a trained linear regression model to the received data to group the items based on determined scores, identify a group of items from the determined groups a set of search results based on a criteria for received search results and then determine an order for the search results to be presented to the user. Further, the person can also update or retrain a model on receiving new data such as user’s interaction with the items.
Thus, claim 1 with its dependent claims 2-13 recite an abstract idea. Since the limitations of claims 14-19, and 20 are similar to those discussed for claim 1, they are analyzed on the same basis reciting 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 1-20: The judicial exception is not integrated into a practical application.
Claim 1 recites the additional limitations of a generic computer implementing the following steps:
(i)receiving item data related to a plurality of items; (ii)applying a trained machine learning model to the item data to determine a plurality of scores corresponding to the plurality of items, wherein the plurality of items are grouped into a plurality of groups of items, and the trained machine learning model is applied on a group by group basis to the plurality of groups of items; (iii) storing the plurality of scores corresponding to the plurality of items in a data store, wherein each score of the plurality of scores is stored in association with an identifier of one of the plurality of groups that the corresponding item from the plurality of items is grouped in; (iv) receiving, from a user device, criteria for a search of the plurality of items; (v) identifying, from the data store, a group of items from the plurality of groups of items as a set of search results based on the criteria; (vi) determining an order to present, within the set of search results, a subset of the plurality of items that are included in the group of items identified based on the score corresponding to each item in the subset of the plurality of items; (vii) causing display of the set of search results in the order on the user device; and (viii) receiving an indication of a user interaction with an item within the set of search results displayed on the user device, the user interaction indicative of a relevance of the item, (ix) wherein the trained machine learning model is retrained based on the user interaction to cause an adjustment to the score of the item determined using the retrained model and stored in the data store for use in future searches.
The limitations in steps, “(i)receiving item data related to a plurality of items; (iii) storing the plurality of scores corresponding to the plurality of items in a data store, wherein each score of the plurality of scores is stored in association with an identifier of one of the plurality of groups that the corresponding item from the plurality of items is grouped in; (iv) receiving, from a user device, criteria for a search of the plurality of items; (vii) causing display of the set of search results in the order on the user device; and (viii) receiving an indication of a user interaction with an item within the set of search results displayed on the user device, the user interaction indicative of a relevance of the item, “, are mere data gathering [related to items, criteria for search of items, and data on user interaction with items output/displaying order of search results, and storing data of the plurality of scores, recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”) and do not impose any meaningful limits on the claim. See MPEP 2106.05. Further, these limitations are recited as being performed by a computer recited at a high level of generality and the computer is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f).
The limitations of steps (ii), (v), and (vii) 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 steps (ii) and (ix) recite using a trained machine learning model [MLM] to determine a plurality of scores and retrain machine learning 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; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of “determine a plurality of scores “using the trained machine learning model is used to generally apply the abstract idea without placing any limits on how the trained MLL functions. See MPEP 2106.05(f).
Although the additional element “using a trained MLM” limits the identified judicial exception “determine a plurality of scores,” 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). The limitations in step (ix) recite retraining the MLM using interaction data. Is merely reciting a generic step of retraining the MLM based on new data, which does not provide an improvement in the functioning of the MLM and as such the limitations in steps (ii) and (ix) do not add any meaningful limits on practicing the abstract idea.
Accordingly, even in combination, these additional elements in claim 1 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 1 is directed to an abstract idea. Since the other two independent claims 14 and 20 recite similar limitations, they are analyzed on the same basis as directed to an abstract idea.
Dependent claims 2-13 have been fully reviewed. Claims 2-4, 9-10, and 13 recite using MLM in a nominal manner, as discussed for claim 1, and implementing the abstract idea. Limitations in claims 5-8, and 11-12 recite descriptive non-functional subject matter, and steps reciting mental processes as well insignificant extra-solution activity and merely extending the scope of the base claim1 of implementing an abstract idea. Limitations in the dependent claims 15-19 are similar to the limitations already discussed for claims 1, and 8-10. Therefore, all the dependent claims 2-13, and 15-19 do not integrate the abstract idea into a practical application as they do not impose any limit on practicing the abstract idea.
Thus, even when viewed individually and in combination, the additional elements in claims 1-20 do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Step 2A=Yes. Claims 1-20 are directed to abstract ideas.
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 1-20 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 1-20 amount to no more than mere instructions to apply the exception using 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 generic- computer components and generally linking the judicial exception to a particular technological environment or field of use using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The use of MLM in providing results in the claims, as analyzed in Step 2A, Prong Two, is recited in a nominal manner without providing details on improving the functioning of the machine learning model itself, citing reduced storage requirements, lowered system complexity, and the prevention of “catastrophic forgetting”.
As per MPEP 2106, a conclusion that an additional element or elements is/are extra-solution activity, or are well-understood, conventional and routine activity in step 2A should be re-evaluated in step 2B. Here the receiving, storing, transmitting, and output/displaying steps were considered extra-solution activity, or are well-understood, conventional and routine activity activities in step 2A and thus are re-evaluated in step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. 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 receiving, acquiring, transmitting, and displaying steps using a generic computer is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the receiving, acquiring, transmitting, and displaying 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). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Even when considered in combination, the additional elements in claims 1-20 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 1-20, as rafted, are not patent eligible.
5. Prior art discussion:
Reference independent claims 1, 14, and 20, the prior art of record, neither teaches nor render obvious at least the limitations, as a whole, comprising processing the received data related to a plurality of items using a machine learning model, the machine learning model having been trained to output a score for each of the plurality of items, the machine learning model having been trained to group the plurality of items into a plurality of groups of items, identifying, from the data store, a set of search results in a group of items based on the received search criteria from a user device for a search of the plurality of items, determining an order within the set of search results, one or more items of the subset of the plurality of items included in the one or more groups of items identified based on the score for each of the one or more items, and receiving an indication of a user interaction with an item within the set of search results displayed on the user device, the user interaction indicative of a relevance of the item, wherein the trained machine learning model is retrained based on the user interaction to cause an adjustment to the score of the item determined using the retrained model and stored in the data store for use in future searches. Claims 2-13 depend from claim 1, and claims 15-19, depend from claim 14.
6 Note: If the independent clams 1, 11, and 20 are amended to overcome the 101 rejection, then the application can be placed in condition for allowance.
7. Best Prior art of record:
The prior art references cited below may include some related limitations as using trained machine learning models for ranking search results, receiving search query from a user, ranking search results and providing them to the user interface, but none of them alone or combined teaches or renders obvious the limitations from the independent claims 1, 14, and 20, as a whole, cited in paragraph 3 above:
(i) (Wang et al. [US 20220245162 A1 cited in the parent application 17823177 and cited in the IDS filed 05/19/2025, now US Patent# 12333590; see claims 1 and 11, cited in the Non-Final Rejection mailed 06/05/2024] describes a system and method comprising obtaining, from a database, item attributes for each of the received plurality of items along with a query, generating features based on the first item attributes for each of the plurality of items and a score for each of the plurality of items by applying a machine learning model to the corresponding features and the query, determining matching attributes for each of the plurality of items based on a textual comparison between the corresponding first item attributes and the query and determining a number of the matching attributes corresponding to each of the plurality of items, adjusting the score of each of the plurality of items based on the number of the matching attributes corresponding to each of the plurality of items and generating ranking data based on the adjusted first score for each of the plurality of items, storing the ranking data in the database, and transmitting the ranking data.
(ii) Krishna et al. [US 20220245505 A1 [See paras 0034—0035] describes that a web server 104 applies one or more trained machine learning models to customer session data and/or customer purchase data to determine items to advertise to a customer, the web server may apply a machine learning model to the search terms received from the customer to determine one or more items to advertise to the customer (e.g., search results).
(iii) Price et al. [US 12346919 B2; see claim 1] describes a computer receiving customer information, determining , using a a machine learning model trained to approximate the pricing model based on historical output of the pricing model, a score for each vehicle within the set of vehicles based on the customer information and vehicle information for the set of vehicles, and wherein the score is determined based on an objective maximization function that maximizes both: a first probability that the customer will purchase the vehicle, and a second probability that a profitability of a sale price of the vehicle is agreeable to the vehicle dealership; and generating a list of vehicles based on the set of vehicles, sorted by the score for each vehicle.
(iv) Sernau et al. [US 20190205402 A1 cited in the parent application 17823177, now US Patent# 12333590 and cited in the IDS filed 05/19/2025; see para 0032, cited in the Non-Final Rejection mailed 06/05/2024] describes a computing system using a trained machine-learning model to process attributes of the known type, a first ranking score of the content item for a user based on the first set of attributes. As an example, certain attributes may have a one-to-one mapping with a known attribute type (e.g., a song title may have a one-to-one mapping with an item label). Other attributes of the content item may be transformed to conform to the known attribute type (e.g., a content item having a separate “depth,” “height,” and “width” attributes may be transformed to form a single known “dimension” attribute, which may have the format: height x width x depth). The ranking score represents the likely relative desirability of the content item relative to other content items. The machine-learning model may process input user data associated with a particular user for whom the content item is being ranked. The ranking score may represent a likely suitability of the content item for that particular user.
(v) Mishra et al. [US Patent 11,442, 999 B2’ cited in the parent application 17823177, now US Patent# 12333590 and cited in the IDS filed 05/19/2025, see claim 1, cited in the Non-Final Rejection mailed 06/05/2024] describes receiving, from a search engine, a set of ranked search results determined to be relevant to a search query associated with a user and determining an initial positioning score for a first search result in the set of ranked search results based upon output of a trained machine learning model.
Foreign reference:
(vi) WO 2019129520 A1, cited in the parent application 17823177, now US Patent# 12333590 and cited in the IDS filed 05/19/2025; see Abstract describes a computer implemented method comprising performing a plurality of individual searches on data objects stored in a database, combining the plurality of individual searches into a combined search, determining a weight for each of the individual searches, and obtaining a search result of the combined search, wherein the search result of the combined search comprises scores associated with a subset of the data objects.
NPL references:
(vii) D. T. Siriwardana, H. Ulpathakumbura, T. Wanniarachchi, l. Bhagya and S. Thelijjagoda, "Product Recommendation and Context Validation Based E-Commerce System With Currency Free Economy," 2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS), Kandy, Sri Lanka, 2021, pp. 68-73, retrieved from IP.COM on 08282026 describes that a website Ceylon Barter Bay’ will recommend related items with both collaborative and content-based recommendations performed using linear regression, respectively.
(viii) Dawei Yin’, Yuening Hu', Jiang Tang', Tim Daly Jr, Mianwel Zhou, Hua Quyang, Jianhul Chen, Changsung Kang, Hongbo Deng, Chikashi Nobata, Jean-Mare Langlois, Yi Chang; “Ranking Relevance in Yahoo Search”; Relevance Science, Yahoo! inc.; cited in the parent application 17823177, now US Patent# 12333590 and cited in the IDS filed 05/19/2025 describes that a Yahoo search engine retrieving most relevant documents from a “corpus of billions within a fraction of second” using Machine Learned Ranking.
Conclusion
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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