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 .
Response to Amendment
The amendment filed on November 4, 2025 cancelled claims 2, 13, 19. Claims 1, 3, 12, 14, 18, and 20 were amended and no new claims 21-23 were added. Thus, the currently pending claims addressed below are claims 1, 3-12, 14-18, and 20-23.
Claim Objections
Claims 1, 3-12, 14-18, and 20-23 objected to because of the following informalities:
Independent claims 1, 12, and 18 have been amended to recite “and transmit the additional item recommendation data to the server to display, to the user in real-time, each item in the fourth set of items in the corresponding slot of the user interface.”. The claimed “the corresponding slot of the user interface” lacks proper antecedent basis. Independent claims 1, 12, and 18 each recite two different instances of “a corresponding slot of the user interface”. As such, the claimed “the corresponding slot of the user interface” does not have proper antecedent basis to only a single recitation of “a corresponding slot of the user interface”. Given, that it appears the user is using the same interface on two different occasions, once when issuing the search request and a second time when they are issuing the additional search request, and the interface is displaying search results on both occasions, the claimed “a corresponding slot of the user interface” would be different corresponding slots. Thus, it appears that the applicant intends the claimed “the corresponding slot of the user interface” to have antecedent basis to the second claimed “a corresponding slot of the user interface”. This being the case, the issue does not raise to the level of a 35 USC rejection, since one or ordinary skill in the art could determine the applicant’s intent. However, the lack of proper antecedent basis issue must still be resolved. As such, the examiner has merely raised an objection.
Dependent claims 3-11, 14-17, and 20-23 fail to cure the deficiencies of the claims from which they depend and, as such, are objected to based on dependency.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The amendment filed on November 4, 2025 has overcome the 35 USC 112(a) rejections of claims 1-20 detailed in the Non-Final Rejection dated September 8, 2025. Thus, the rejection is hereby withdrawn.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-12, 14-18, and 20-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 12, and 18 have been amended to recite: “… and generates the first item output data based on the at least first sample value;”. However, according to the claims, “the first item output data” characterizes “a set of second items comprising at least a portion of the first set of items”. The claimed “set of second items” is broad enough to encompass two or more items and the claimed “at least first sample value” is broad enough to encompass a single first sample value. Based on paragraphs 76-81, when a sample value is selected, the MAB chooses an item for presentation in the search result display. According to paragraphs 76-81, which is the only example of using a sampled value, the Brand Diffusion (BD) engine determines “an item from a plurality of items to display to the customer”, and does so by generating a plurality of items that are similar to each other but are of different brands” to be diffused, wherein each of the plurality of items has a respective probability distribution indicating a distribution, a mean value and a sampled value. The selection of an item for a particular slot, by the BD engine, is performed using an exploration-exploitation mechanism of a MAB. Thus, the only way in which more than a single item of the set of second items can be selected is if there are more than a single advertisement slots, and a different sample value is used for the selection of each item. The examiner is unable to find support in the applicant’s disclosure for a single sample value, which is a value associated with a single item (e.g., the item being clicked or not clicked when it was displayed), being used to select more than one item. As such, the applicant’s disclosure does not support the limitation “… and generates the first item output data based on the at least first sample value;” recited in claims 1, 12, and 18 as currently amended. Thus, claims 1, 12, and 18 fail to comply with the written description requirement.
Dependent claims 3-11, 14-17, and 20-23 fail to cure the deficiencies of the claims from which they depend and, as such, are rejected by virtue of dependency.
The examiner suggests amending the claims to require either the output generated by the third machine learning model to be an item of the first set of items or to require there be a plurality of probability distributions, a plurality of density regions, a plurality sample values, and a plurality of advertising slots, wherein each of a plurality of different items are selected and output in association with each of the plurality of advertising slots based on the sample value associated with each of the plurality of different items.
Independent claims 1, 12, and 18 have been amended to recite: “input a first similarity score to the executed third machine learning model and generate second item output data characterizing at least a first additional item from the plurality of items in the database…;”. As indicated above, as currently amended, the executed third machine learning model must be the “BD engine” disclosed in paragraphs 76-81. However, there is no disclosure in the applicant’s specification of the “BD engine” receiving a “similarity score” as input and outputting an additional item from the plurality of items in the database. Instead, the applicant’s disclosure supports a diffusion exploration (DE) model, using the PSE score of an item or global data, identifying similar items from different brands by generating similarity scores of additional items in the database to the original item in paragraphs 45-47 and 49-51. Paragraph 51 does indicate that the DE model can use a MAB-based exploration-exploitation approach. Based on paragraph 68 of the applicant’s specification, it is reasonable to assume, that the applicant’s BD model may be able to perform the functions of the DE model and, as such, be able to generate similarity scores and select additional items based on the similarity score. However, this would appear to occur prior to the BD engine selecting an item and not after the BD engine selects the item. Additionally, such selection of additional items would be based on similarity scores generated by the BD model and not input into the BD engine.
According to at least paragraphs 64-65 of the applicant’s specification PAS model 392, PSE model 394, BD model 396, and DE model 397 are different models, one or more of which can generate item recommendations. Based on paragraphs 66-68 of the applicant’s specification, PAS model 392 generates PAS scores for a plurality of items for a customer based on engagement data; computing device 102 identifies items with PAS scores below a threshold and generates a personal assortment set (PAS); PSE model 394 generate PSE scores for items (presumably items in the PAS) based on the PAS score, user session data associated with a user ID included in a search request, and the search request; the BD model identifies substitute items to advertise to the customer for each original item determined by the search model and rank the original items and substitute items. Thus, the BD model in paragraphs 66-68 performs only brand diffusion based on the original items identified by the search model.
According to at least paragraphs 69-70 of the applicant’s specification, PSE engine 402, BD Fulfillment engine 404, BD engine 406, search engine 408, and customer facing service engine 410 are different engines. According to at least paragraphs 70-75 of the applicant’s specification, item advertisement determination computing device 102 receives a search request 310 provided by a customer 401; the search request is provided to search engine 408 and BD fulfillment engine 404; search engine 408 generates original item scores 409 identifying and characterizing items for advertisement based on search request 310; upon receiving the search request from item advertisement determination computing device 102 and the original items from the search engine, BD fulfillment engine 404 obtains user session data 320 associated with a plurality of items from database 116, and executes a PAS model; the PAS model generates a PAS score for each of the original items; the BD fulfillment engine executes a PSE engine; the PSE engine generates the PSE scores by executing a PSE model that operates on the user session data and the search query data; the BD fulfillment engine, based on the PAS scores and/or the PSE scores, determines a diffusible item score for each of the original items and compares the PAS score and/or PSE score of the original items to a diffusible item score threshold; the BD fulfillment engine provides the diffusible item scores to BD engine 406; the BD engine normalizes the diffusible item score to the original item scores, generates normalized diffusible item scores, and provide the normalized diffusible items scores to customer facing service engine 410; the customer facing service engine ranks the normalized diffusible items scores and the original item scores to obtain a ranked list of recommended items which it sends to server 312. Thus, the BD engine in paragraphs 70-75 just normalizes the diffusible items scores determined by the BD fulfillment engine, while the customer facing service engine selects the items to recommend based on the normalized scores.
According to at least paragraphs 76-81 of the applicant’s specification, BD fulfillment engine 404 performs diffusion to generate items that are similar to each other and provides them to BD engine 406; the BD engine selects which of the items to display in the advertising slot based on the items, their probability distribution, mean value and/or sampled value by executing an exploration-exploitation mechanism of MAB. Thus, the BD engine disclosed in paragraphs 76-81 is able to output data based on the received sample values, but it does not perform diffusion to select any additional items. The diffusion in paragraph 76-81 is performed by the BD fulfillment engine prior to providing the first scores, the seconds scores, and the probability distribution to the BD engine.
According to at least paragraphs 82-85 of the applicant’s specification, advertisement determination computing device 102 receives a search request 310 from web server 104, generates an item score for an item based on the search terms of the search request and determines a first item based on the item score; the item advertisement determination computing device executes PAS model 392 to determine a PAS score for the first item; the item advertisement determination computing device determines the first value/PAS score is beyond a threshold value; the item advertisement determination computing device executes PSE model 394 to determine a second value based on session data for the user and the search query; the item advertisement determination computing device executes a BD model to identify one or more items of varying brands that are of the same item type as the first item; the item advertisement determination computing device transmits item recommendations to web server 104, where the item recommendations identify at least the second item; and the web server displays item advertisements for the second item to the user based on receiving the recommendation data. Thus, the BD model of paragraphs 82-85 can perform diffusion (e.g., such as the function of the DE model) based on similarity scores it generates, but it does not output the first item output based on the first scores, the second scores and the probability distribution.
According to paragraphs 86-89 of the applicant’s specification, item advertisement determination computing device 102 executes a PAS model to determine a PAS score for each of a plurality of items based on item engagement data and user transaction data; the item advertisement determination computing device determines a subset of the plurality of items by comparing the PAS scores to a threshold; the item advertisement determination computing device determines, for each item in the subset of the plurality of items, initial engagement data; the item advertisement determination computing device executes PSE model 394 to determine a PSE score for each of the subset of items based on the initial engagement data; the item advertisement determination computing device 102 executes BD model 396 to identify similar items based on the PSE score for each of the subset of items. However, the BD model of paragraphs 86-89 only receive the PSE score based on the initial engagement data to identify similar items. Thus, the BD model of paragraphs 86-89 does not receive similarity values as input when performing diffusion.
According to paragraphs 90-92 of the applicant’s specification, item advertisement determination computing device 102 executes a search algorithm to determine a first value to determine a first value for each of a plurality of items based on a search request; the item advertisement determination computing device obtains a plurality of similar items with corresponding second values from a database; the item advertisement determination computing device executes BD model 396 to determine which items among the first plurality of items and the number of similar items to include in a set of recommended items to advertise to the user based on the first plurality of items and their corresponding first values and the number of similar items and their corresponding second values; the item advertisement determination computing device transmits the recommendation data to web server 104; and the web server displays the item advertisements to the user on a search results page. Thus, the BD model of paragraphs 90-92 selects a plurality of items to recommend based on the first plurality of items and their corresponding first values and the number of similar items and their corresponding second values, but does not output an additional item based on a similarity score input after its first execution.
As such, it is clear that there is no support in the applicant’s specification of “a third machine learning model” that receives the first scores, the second scores, and the at least one probability distribution and executes to generate the first item output data, and then receives a first similarity score as input to execute a second time to generate at least one additional item. Therefore, claims 1, 12, and 18 have been amended to recite subject matter not supported by the applicant’s disclosure and, as such, fail to comply with the written description requirement
Dependent claims 3-11, 14-17, and 20-23 fail to cure the deficiencies of the claims from which they depend and, as such, are rejected by virtue of dependency.
The examiner suggests amending the claims in a manner consistent with one of the above identified embodiments that are supported by the applicant’s disclosure.
Independent claims 1, 12, and 18 have been amended to recite: “input each of the additional first scores, the additional second scores, and the updated at least one probability distribution to the executed third machine learning model and generate, based at least on the altered at least one density region, a fourth set of items different from the third set of items for displaying the fourth set of items in a corresponding slot of the user interface, wherein the executed third machine learning model samples the altered at least one density region of the at least one probability distribution to generate at least a second sample value, and generates the fourth set of items based on the at least second sample value; generate additional item recommendation data identifying the fourth set of items; and transmit the additional item recommendation data to the server to display, to the user in real-time, each item in the fourth set of items in the corresponding slot of the user interface.”. The examiner cannot find support for this limitation in the applicant’s specification. While the applicant’s disclosure supports receiving a second search request from the user and performing the previous steps in regards to the second search request. The manner in which it has been claimed is not supported by the applicant’s disclosure.
First, the at least one probability distribution is item specific and, as such, must be associated with at least one of the items in the first set of items and based on interactions with the at least one of the items in the first set of items. Likewise, the item recommendation data identifies the second set of items, which based on the claim is a set of items comprising at least a portion of the first set of items and the at least a first additional item. Thus, the user feedback data for at least one item in the second set of items used to update the at least one probability function must be item specific. Additionally, given its antecedent basis to the previously claimed “at least one probability distribution”, it must be specific to the “at least a portion of the first set of items” part of “the second set of items”. Since the “first set of items” and the “at least a portion of the first set of items” are items specifically associated with the initial “search request”, the applicant’s disclosure would only support inputting each of the additional first scores, the additional second scores, and “the updated at least one probability distribution” to the executed third machine learning model to generate a fourth set of items if the specific item associated with the at least one probability is also an item that is required to be associated with the third set of items determined in response to the second search request. For example, if the claim required that the user submit a second search request identical to the first search request and the third set of items was required to be identical to the first set of items, then the updated at least one probability distribution could be an input for the previously executed third machine learning model. However, as currently claimed there is no association between the first search request and the second search request, and no association between the first set of items and the third set of items. Given that the applicant’s specification only supports inputting a probability distribution associated with an item in the first set of items that has a first score and a score, there is no support for inputting a random probability distribution associated with a previous search into the third machine learning model with additional first scores and additional second scores of items in a third set of items associated with a different search request.
Second, the applicant’s disclosure only supports the third machine learning model using the first scores, the second scores, an at least one probability distribution to generate output data characterizing a fourth set of items comprising at least a portion of the first set of items. Thus, the applicant’s disclosure does not support “generate, based at least on the altered at least one density region, a fourth set of items different from the third set of items for displaying the fourth set of items in a corresponding slot of the user interface”. The phrase “a fourth set of items different from the third set of items” is broad enough to encompass items outside the items in the third set of items and, as such, constitutes new matter not found in the applicant’s disclosure.
Finally, the generating a fourth set of items based on the altered at least one density region and sampling of the altered at least one density region to generate at least a second sample value, and generating the fourth set of items based on the at least second sample value is only supported by the applicant’s disclosure if the item in the subset of second items for which the updated probability distribution was determined based on user feedback is also required to be an item in the third set of items and the at least one probability function was for said item included in the subset of first items and included in the third set of items. Without such requirements being present in the claim, the limitations directed to these limitations constitutes new matter not found in the applicant’s disclosure.
As such, it is clear that independent claims 1, 12, and 18, as currently amended, failing to comply with the written description requirement.
Dependent claims 3-11, 14-17, and 20-23 fail to cure the deficiencies of the claims from which they depend and, as such, are rejected by virtue of dependency.
The examiner suggests amending the claims to either require that the subset of the first set items selected with regard search request and the third set of items selected in response to the additional search request are the same items or to have the probability distribution used in regards to the additional search request be a probability distribution associated with at least one item of the third set of items rather than an altered probability distribution.
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, 3-12, 14-18, and 20-23 are directed to a system, a method, and a computer program product which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, claims 1, 3-12, 14-18, and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim(s) 1, 12, and 18 recite(s) the following abstract idea: (Examiner note: The server, and user interface of the server have been included as part of the abstract idea because they outside the scope of the applicant’s invention and, as such, cannot be considered “additional elements” of the claimed invention.)
receive a search request associated with a user from a server;
determine a first set of items from a stored plurality of items based on the search request;
execute a first algorithmic model (e.g., personal assortment set (PAS) algorithm based on applicant’s specification) and input item engagement data identifying the user’s engagement with each item in the first set of items to the executed first algorithmic model, and generate a first score for each item in the first set of items based on a first portion of the item engagement, wherein the executed first algorithmic model generates the first score by applying a separate weight to each of at least two covariate slices of a vector representing the item engagement data and by applying a random vector of the at least two covariate slices;
execute a second algorithmic model (e.g., product searchability (PSE) algorithm based on the applicant’s specification) and for each item in the first set of items, input the corresponding first score and the search request to the executed second algorithmic model, and generate a second score;
determine at least one probability distribution corresponding to a density of interactions associated with the user, wherein the at least one probability distribution comprises at least one density region;
execute a third algorithmic model (e.g., brand diffusion (BD) algorithm based on the applicant’s specification) and input to the executed third algorithmic model each of the first scores, the second scores, and the at least one probability distribution, and generate first item output data characterizing a second set of items comprising at least a portion of the first set of items, wherein the executed third algorithmic model samples the at least one density region of the at least one probability distribution to generate at least a first sample value, and generates the first item output data based on the at least first sample value;
input a first similarity score to the executed third algorithmic model and generate second item output data characterizing at least a first additional item from the plurality of items stored, wherein the at least first additional item is included in the second set of items and is of a same item type as a corresponding first item of the first set of items;
generate and transmit, to the server, item recommendation data identifying the second set of items in response to the search request, wherein the item recommendation data causes the server to display of each item in the second set of items in a corresponding slot of a user interface being displayed to the user;
receive user feedback data for at least one item in the second set of items;
dynamically update, in real-time and in response to receiving the user feedback data, the at least one probability distribution to alter the at least one density region of the at least one probability distribution by applying at least one weight to the user feedback data;
receive an additional search request associated with the user from the server;
determine, in response to receiving the additional search request, a third set of items from the plurality of items stored based on the additional search request;
execute the first algorithmic model by inputting additional item engagement data identifying the user's engagement with each item in the third set of items and generating an additional first score for each item in the third set of items based on a first portion of the additional item engagement data;
execute the second algorithmic model by, for each item in the third set of items, inputting the corresponding additional first score and the additional search request, and generating an additional second score;
input each of the additional first scores, the additional second scores, and the updated at least one probability distribution into the executed third algorithmic model and generate, based on at least the altered at least one density region, a fourth set of items different from the third set of items for displaying the fourth set of items in a corresponding slot of the user interface, wherein the third algorithmic model samples the altered at least one density region of the at least one probability distribution to generate at least a second sample value, and generates the fourth set of items based on the at least second sample value;
generate additional item recommendation data identifying the fourth set of items; and
transmit the additional item recommendation data to the server to display, to the user in real-time, each item in the fourth set of items in the corresponding slot of the user interface.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing, or sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of:
a determination computing device with at least one processor, a non-transitory memory storing software instructions (e.g., a personal assortment engine, a product searchability engine, a brand diffusion engine, and a user facing service engine), a database, and machine learning models which are applied (a general-purpose computer with generic computer components); and
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receive a search request associated with a user from a server (receiving data);
transmit, to the server, item recommendation data identifying the second set of items in response to the search request, wherein the item recommendation data causes the server to display of each item in the second set of items in a corresponding slot of a user interface being displayed to the user (transmitting data);
receive user feedback data for at least one item in the second set of items (receiving data);
receive an additional search request associated with the user from the server (receiving data); and
transmit the additional item recommendation data to the server to display, to the user in real-time, each item in the fourth set of items in the corresponding slot of the user interface (transmitting data);
The additional technical elements above are recited at a high-level of generality (i.e. as one or more generic processors performing generic computer functions of processing, communicating (e.g. transmitting and receiving), and displaying) such that it amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and generic computer components. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes)
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a database; a determination computing device with at least one processor, a non-transitory memory storing software instructions; a database, and machine learning models that are merely applied to perform the claimed functions amounts to no more than mere instructions to apply the exception using one or more general-purpose computer with generic computer components such as a processor, a memory, a database and machine learning models.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone or in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
The Examiner notes simply implementing an abstract concept on one or more computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires one or more general-purpose computers with generic-computer components (as evidenced from at least paragraphs 24-27 and 69 of the applicant’s specification which discloses that the determination computing device comprising a processor and non-transitory memory storing software instructions is just general-purpose computers with generic computer components; the Affinity v. Direct TV decision which discloses that a database is just a generic computer component; and the Recentive Analytics decision which discloses that machine learning models are merely software executed by a computer and merely amount to applying machine learning models in a particular environment, as well as, at least page 7, lines 23-29 of Langley et al. (“Approaches to Machine Learning”, Journal of the American Society for Information Science, February 16, 1984, pgs. 1-28) that discloses that machine learning models were old and well known by at least 1984); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receive a search request associated with a user from a server (receiving data);
transmit, to the server, item recommendation data identifying the second set of items in response to the search request, wherein the item recommendation data causes the server to display of each item in the second set of items in a corresponding slot of a user interface being displayed to the user (transmitting data);
receive user feedback data for at least one item in the second set of items (receiving data);
receive an additional search request associated with the user from the server (receiving data); and
transmit the additional item recommendation data to the server to display, to the user in real-time, each item in the fourth set of items in the corresponding slot of the user interface (transmitting data);
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e. “PEG” Step 2B=No).
The dependent claims 3-11; 14-17; and 20-23 appear to merely further limit the abstract idea by further limiting the at least one additional item of the second set of items which is considered part of the abstract idea (Claims 3, 14, and 20); further limiting the determination of the first score for each item in the first set of items which is considered part of the abstract idea (Claims 4-5, 7, and 15); further limiting the first portion of the item engagement data which is considered part of the abstract idea (Claim 6); further limiting the determination of the second score which is considered part of the abstract idea (Claims 8-10, and 16); further limiting the determination of the second set of items which is considered part of the abstract idea (Claims 11 and 17); and adding the additional steps of inputting a second similarity score and generating third item output data which are both considered part of the abstract idea itself (Claims 21-23), and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No)..
Thus, based on the detailed analysis above, claims 1, 3-12, 14-18, and 20-23 are not patent eligible.
Allowable Subject Matter
Claims 1, 3-12, 14-18, and 20-23 would be allowable if the applicant were to be able to overcome the Claim Objections, the 35 USC 112(a) rejections, and the 35 USC 101 rejections above.
The following is a statement of reasons for the indication of allowable subject matter: The examiner has found prior art (see Yi et al. 2018/0011854; Shivaswamy et al.: 2015/0046281; and Roy et al.: 2010/0121624) that discloses a system, a method, and a non-transitory computer readable medium comprising:
a system, a method, and a non-transitory computer readable medium comprising:
a processor; a database storing data representative of a plurality of items; and a non-transitory memory storing instructions that when executed, cause the processor to:
receive a search request associated with a user from a computing device;
determine a first set of items from the plurality of items in the database based on the search request;
determine, by a first machine learning model, a first score for each item in the first set of items based on a first portion of item engagement data identifying the user's engagement with each item in the first set of items;
determine, by a second machine learning model, for each item in the first set of items, a second score based on a corresponding first score and the search request;
determine, by a third machine learning model, a second set of items comprising at least a portion of the first set of items based on each of the first scores and the second scores and at least one additional item from the plurality of items in the database, wherein the third machine learning model applies at least one probability distribution for a density of interactions; and
generate and transmit item recommendation data identifying the second set of items in response to the search request, wherein the item recommendation data causes display of each item in a second set of items in a corresponding slot of a user interface;
in response to receiving user feedback data for at least one item in the second set of items, dynamically update the at least one probability distribution to alter at least one density region by applying at least one weight to the user feedback; and
repeating the steps above with regards to receiving an additional search request.
However, the examiner has been unable to find prior art that would be obvious to combine with Yi, Shivaswamy, and Roy that discloses:
wherein the first machine learning model determines the first score by applying a separate weight to each of the at least to covariate slices of a vector representing use session data and by applying a random vector of the at least two covariate slices.
Thus, claims 1, 3-12, 14-18, and 20-23 would be allowable over the prior art if the applicant were to be able to overcome the Claim Objections, the 35 USC 112(a) rejections, and the 35 USC 101 rejections above.
Response to Arguments
Applicant's arguments filed November 4, 2025 have been fully considered but they are not persuasive.
The applicant’s arguments with regard to the 35 USC 112(a) rejection of claims 1-20 are moot as the claim amendment has overcome the rejection. However, the examiner notes that the claim amendment has resulted in a new 35 USC 112(a) rejection which has been identified above.
The applicant’s arguments with regards to the 35 USC 101 rejections are not convincing.
The applicant asserts that the examiner has failed to establish a prima facie case of unpatentability under 35 USC 101. The examiner disagrees. the examiner has laid out a clear and complete analysis of the claims with regards to 35 USC 101 which details the factual based analysis used by the examiner and the conclusions reached based on said factual based analysis. The fact-based conclusion that the claims fail to satisfy the requirements of 35 USC 101 follows proper USPTO procedures for analyzing claims under 35 USC 101.
The applicant asserts that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 1 based on the Memorandum dated August 4, 2025 by Charles Kim, Deputy Commissioner for Patents (hereinafter the “Memorandum”) which states that examiners should be careful to distinguish claims that recite an exception from claims that merely involve an exception. The examiner disagrees. The applicant appears to have misinterpreted said Memorandum. Additionally, the instant claims bear no resemblance to Abstract Idea Example 39. First, the Memorandum did not change any policies or procedures in place for determining whether a claim satisfies the requirements under 35 USC 101 and/or fails to satisfy said requirements. The memorandum was merely a reminder of the policies and/or procedures that are in place. In regards to the cited section of the Memorandum explaining Abstract Idea Example 39, it was a reminder that in order for a claim limitation to be considered a “Mathematical Concept” it must recite an actual mathematical relationship, mathematical formula or equation, or mathematical calculation. In Abstract Idea Example 39 the limitation of “training the neural network in a first stage using the first training set” is so broad that instead of reciting an actual mathematical relationship, mathematical formula, mathematical equation, or mathematical calculations, it merely recites a step that could involve a mathematical relationship, a mathematical formula, a mathematical equation, or a mathematical calculation. Since, the limitation does not actually recite a mathematical relationship, a mathematical formula, a mathematical equation, or a mathematical calculation, the limitation cannot be considered part of the abstract idea itself. Instead, should any other limitation recite an abstract idea under Step 2a, Prong 1, the training limitation must be considered an “additional element” of the claim that is analyzed under Step 2a, Prong 2 and Step 2b. If no other limitations recite an abstract idea under Step 2a, Prong 1, then the claim recited patent eligible subject matter under 35 USC 101. The Memorandum was a reminder to examiners are to be careful when dealing with such broad claim limitations to ensure that they actually recite an abstract idea instead of merely involve an abstract idea. Given the breadth of the limitations in Abstract Idea Example 39, the claim did not recite Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human activity and, as such, the claim recited patent eligible subject matter under 35 USC 101. However, should a limitation of a claim depending from the claim in Abstract Idea Example 39 recite an actual mathematical relationship, a mathematical formula, a mathematical equation, or a mathematical calculation, said limitation of the dependent claim would recite an abstract idea under Step 2a, Prong 1 and the remainder of the limitations, inherited from the parent and recited in the dependent claim, would need to be analyzed under Step 2a, Prong 2 and/or Step 2b. The instant claims, in contrast to the limitations of the claim in Abstract Idea Example 39, clearly recite “Certain Methods of Organizing Human Activity”. The Merriam-Webster Online dictionary defines advertising as “the action of calling something to the attention of the public especially by paid announcements”. The instant claims generate and transmit “additional item recommendation data”. Thus, they are calling the additional item recommendation data” to the attention of a user when they issue a search request. As such, the claims clearly fall within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing, or sales activities or behaviors. When dealing with claims that recite “Certain Methods of Organizing Human Activity”, the courts have found that limitations directed towards receiving data, analyzing data, determining results based on the analysis, generating tailored content, and transmitting data are all part of the abstract idea itself (see at least the Electric Power Group decision, the Digitech decision, the Berkheimer decision, the Intellectual Ventures v. Cap One Bank decision, and the Two-way Media decisions). Therefore, the identified limitations of the instant claims in the rejection actually recite steps of the abstract idea itself instead of merely involving an abstract idea and have been properly included as part of the abstract idea itself. As such, the rejection is proper and entirely consistent with the Memorandum. Thus, the rejections have been maintained.
The applicant asserts that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 1 because they recite a technological improvement that allows the claimed system to display more relevant items to each specific user in a more relevant manner and allows for a reduction in processing resources (e.g., compute time, power) to locate these more relevant items. The examiner notes that improvements to a technology are not a consideration under Step 2a, Prong 1. Instead, whether the “additional elements” of a claim recite an improvement to a technology or technological field is a consideration under Step 2a, Prong 2. Nonetheless, the argued improvements are solely rooted in the abstract idea itself and not in the “additional elements” that are merely used as a tool to apply the abstract idea. The argued improved relevance of the items to a user and the argued improved manner in which they are displayed is rooted solely in the receiving data, analyzing the data, making determination based on the analysis, generating the recommendations, and transmitting the recommendations for display. As such, these argued improvements are rooted solely in the abstract idea itself. The argued improvement regarding a reduction in processing resources are improvement obtained by merely applying the abstract idea using software executing on a general-purpose computer (e.g., as per the Recentive Analytics decision generic machine learning models are merely software). Thus, the purported reduction in processing resources is rooted solely in the abstract idea which is then merely being applied using a general-purpose computer executing software. Improvements of this nature, irrespective of how groundbreaking, innovative or even brilliant they might be, are improvements to an abstract idea which are improvements in ineligible subject matter (see SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.). Thus, the rejections have been maintained.
The applicant asserts that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 2 because the software of the instant claims make non-abstract improvements to computer technology in a manner similar to the claims in the Desjardin decision, the Enfish decision, and the Core Wireless decision . The examiner disagrees. While software can certainly make non-abstract improvements to a computer technology, such improvements must be rooted in software processes which are considered “additional elements” of the claim. In the Desjardin decision the “additional element” of the machine learning model was considered an improved machine learning model that was invented by the inventor and performed in a manner different from traditional machine learning models in that it is able to learn new tasks in succession whilst protecting knowledge about previous tasks. In the Enfish decision the “additional element” of the self-referential database was considered an improved database, invented by the inventor, that operated in a manner different from traditional databases. In the Core Wireless Decision, the “additional elements” of the user interface and its functions was an improved user interface, invented by the inventor, that performed a combination functions not found in traditional user interfaces. Thus, in each of these cases, the improvement to the computer technology was rooted in the “additional elements” of the claim, wherein the additional elements were implemented using software. In contrast, the purported improvements in a computer technology by practicing the claims of the instant invention are rooted solely in the abstract idea itself which is merely applied using a general-purpose computer with generic computer components executing software which is an improvement to an abstract idea and, as such, an improvement in ineligible subject matter (see the SAP v. Investpic decision and the Recentive Analytics decision). Thus, the rejections have been maintained.
The applicant asserts that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 2 because the software of the instant claims make non-abstract improvements to computer technology in a manner similar to the claims in the McRO decision. The examiner disagrees. First, the claims in the McRO decision overcame 35 USC 101 under Step 2a, Prong 1 because they did not recite an abstract idea. While this makes an analysis under Step 2a, Prong 2 unnecessary, the court did find that the claims recite an improvement to a computer-related technology which is a consideration under Step 2a, Prong 2. Since, the claims did not recite an abstract idea, each and every limitation of the claim would be an “additional element” of the claim. The execution of these “additional elements” resulted in an improvement to a computer-related technology and, as such, would also overcome the 35 USC 101 rejection under Step 2a, Prong 2. In contrast, the claims of the instant invention do recite an abstract idea under Step 2a, Prong 2. The only “additional elements” of the instant claims are a general-purpose computer with generic computer components executing software. Thus, the instant claims merely apply the abstract idea using the general-purpose computer with generic computer components which is insufficient to transform an abstract idea into a practical application. Hence, the purported improvement of the instant claims is an improvement to an abstract idea which is an improvement in ineligible subject matter (see the SAP v. Investpic decision and the Recentive Analytics decision). Thus, the rejections have been maintained.
The applicant asserts that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 2 because the examiner has not considered the claims in a manner consistent with Memorandum which notes “An important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome.13“ and “Examiners are cautioned not to oversimplify claim limitations and expand the application of the “apply it” consideration.15”. The examiner disagrees. It appears that the applicant is interpreting these sections in a manner inconsistent with the context in which they appear. The cited quotations are subsections of “B. Step 2a, Prong Two” which describes how “Examiners should use the considerations discussed in MPEP 2106.04(d), subsection I in accordance with the procedure described in MPEP 2106.04(d), subsection II to evaluate whether the additional elements integrate the judicial exception into a practical application”. The applicant also appears to have overlooked the note in the Memorandum that states “While an additional limitation (or combination) that merely applies the judicial exception on a generic computer may not render a claim eligible on its own, an additional limitation (or combination) that meaningfully limits the judicial exception can render it eligible.”. Furthermore, the citations to “13 and 15” reference MPEP 2106.05(a) which states “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”. Thus, it is clear that the cited applicant cited sections of the Memorandum are in reference to “additional elements” of the claim, including any inherent “additional elements” which have not been positively claimed but are inherently required to be present in the claims based on the applicant’s disclosure. In the instant case, the “additional elements” of the claim are merely a general-purpose computer with generic computer component executing software. The claim merely requires the abstract idea to be applied using these “additional elements” and, as such, are incapable of overcoming the 35 USC 101 rejection under Step 2a, Prong 2. The examiner has not simplified the “additional elements” of the claim in any way, much less over simplified them. All of the limitations of the abstract idea are applied using a single computer with generic computer components executing software. Thus, there is no consideration with regard to an arrangement of devices each performing at least one significant step of the invention that results in an improvement similar to the BASCOM decision. Since every step of the abstract idea is merely applied using the general-purpose computer with generic computer components, any purported improvement, irrespective of whether the applicant considers it an improvement to a technology, is an improvement rooted solely in the abstract idea itself which is merely applied using a general-purpose computer. Improvements to an abstract idea are improvement in ineligible subject matter (see the SAP v. Investpic decision). Thus, it is clear the examiners conclusion that the claim fail to overcome the 35 USC 101 decision is consistent with the Memorandum, the MPEP, and all of the argued case law. Thus, the rejections have been maintained.
The applicant asserts that the claims overcome the 35 USC 101 rejection under Step 2b because the claims recited significantly more than mere “advertising, marketing, or sale activities or behaviors” and instead recite specific limitation other than what is well-understood, routine, conventional activity in the field. The examiner disagrees. It appears that the applicant has misunderstood the term “additional elements” as used in MPEP 2016.05 and the Office’s current examination guidelines. “Additional elements” are defined as those elements outside of the identified abstract idea. Whether limitations found in the abstract idea itself are: not well-understood, routine, and conventional in the field; improve the functioning of the computer itself or any other technology or technical field; and/or provide a technological solution to a technological problem are immaterial under Step 2b. Under Step 2b, the question is whether the “additional elements” are “significantly more” than the abstract idea. Thus, the “additional elements” of the claim can overcome a 35 USC 101 rejection under Step 2b, if the “additional elements” are: not well-understood, routine, and conventional in the field; improve the functioning of the computer itself or any other technology or technical field; and/or provide a technological solution to a technological problem. The only additional elements in the claim are a general-purpose computer with generic computer components that execute software. Thus, the “additional elements” of the instant claims are: well-understood, routine, and conventional in the field, do not improve the functioning of the computer itself or any other technology or technical field; and do not provide a technological solution to a technological problem. The instant claims merely apply the abstract idea using the general-purpose computer with generic computer components executing software which is insufficient to be considered “significantly more” under Step 2b. Any purported improvement realized from practicing the claimed steps is rooted solely in the abstract idea itself and, as such, is an improvement in ineligible subject matter (see the SAP v. Investpic decision and the Recentive Analytics decision). Thus, the rejections have been maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lakshminarayanan et al. (PGPUB: 2014/0081701) which discloses using brand synonyms to expand identification of items matching a user's request or search for items and/or used to expand identification of items to recommend to a user, wherein in a cosign similarity is calculated between the branded item and another, and identifying an additional item to include in a search results based on it having a cosine similarity score that is equal to or greater than a pre-defined cosine similarity threshold
Shivaswamy et al. (PGPUB: 2021/0342915) which discloses machine learning models that perform ranking of items to use a binary-regression model such as gradient boosting trees and random forests.
Srinivasan et al. (US Patent 9,817,846) which discloses determining at least one additional item of the second set of items that is of a different brand as the corresponding first item of the first set of items.
Wong et al. (US Patent 10,007,693) discloses original search results include a plurality of items associated with a search query and ranking the items according to a degree of relevance to the user such as user purchase history, and user activity history.
Yoon et al. (US Patent 11,748,413) determining search results using user behavior data such as previous searches, item clicks and assigning values to items based on these attributes.
Jadhav et al. (US Patent 9,779,441) discloses determining relevancy rankings of products based on values for each item derived from both attributes for the item and engagements of the items.)
Afshar et al. (PGPUB: 2021/0224582) discloses a machine learning model that performs similarity ranking based on features of items arranged in a vector space, where in the similarity rankings are based on vector space distance.
Wu (PGPUB: 2019/0164082) discloses providing digital content to users by applying a machine learning model based on composite utility scores reflecting multiple events categories, wherein the events include user interactions such as add to cart events and click events as well as applying weights such events.
Wagner et al. (PGPUB: 2007/0266025) discloses generating personalized search results based on user engagement data which is used to improve the relevancy of the search results.
Wang et al. (PGPUB: 2011/0010323) discloses a system for determining the relevancy of search results based on a sequence of interactions that the user performs while interacting with the search results and associated landing pages.
Rapaka et al. (PGPUB: 2018/0218087) which disclose ranking items returned by a search query based in part on a purchase probability associated with an item, wherein the purchase probability determined based on previously obtained user engagement data.
Mcdonnell et al. (US Patent 8,972,391) discloses performing relevance scoring on search results based on user engagement data associated with prior searches and the recency of the user engagement data.
Van Zwol et al. (US Patent 9,020,244) discloses ranking and selecting items using a machine learning model to determine the relevancy value for each item, wherein the machine learning model comprises a binary-regression model such as a gradient boosted tree.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN W VAN BRAMER whose telephone number is (571)272-8198. The examiner can normally be reached Monday-Thursday 5:30 am - 4 pm EST.
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/John Van Bramer/Primary Examiner, Art Unit 3622