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 .
Priority
Applicant claims priority to provisional U.S. Patent Application No. 63/041,150, filed June 19, 2020.
Information Disclosure Statement
The IDS submitted on 10/21/2021 was previously considered.
Status of Claims
Applicant’s claim amendments, filed 1/29/2025, have been entered. Claims 1, 2, 7-10, 15-17, and 19 have been amended. Claims 1-20 are currently pending in this application and have been examined.
Allowable Subject Matter
As noted for reasons in the “Indication of Allowable Subject Matter” section in the Office Action mailed 12/05/2024, claims 1-20 would be allowable if rewritten to overcome the claim rejection(s) under 35 U.S.C. 101 set forth in this Office Action.
Claim Objections
Claims 1-16 are objected to because of the following informalities:
Claim 1 recites the limitation “calculating… and the at least one determinative characteristic;” in lines 19-24 and should recite “calculating… and the at least one determinative characteristic; and” Claims 2-8 inherit the objections of claim 1.
Claim 9 recites the limitation “receiving a plurality of items accessible…” in lines 4-5 and should recite “receive a plurality of items accessible…’ Claims 10-16 inherit the objections of claim 9.
Claim 9 recites the limitation “organizing a plurality of items accessible…” in line 24 and should recite “organize a plurality of items accessible…’ Claims 10-16 inherit the objections of claim 9.
Appropriate correction is required.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Alice/Mayo test the claims are directed to statutory categories. Specifically, the methods, as claimed in claims 1-8 and 17-20, are directed to processes. Additionally, the system, as claimed in claims 9-16, is directed to an apparatus (see MPEP 2106.03).
Under Step 2A (prong 1), claim 1 recites at least the following limitations (emphasis added) that recite an abstract idea:
receiving a plurality of items for a query;
extracting a plurality of features associated with the plurality of items from one or more documents,
wherein the plurality of features comprises at least one of numerical features and textual features;
determining correlations between each of the plurality of features and at least one determinative characteristic for each of the plurality of items associated with a user behavior based on applying the plurality of features, in parallel, to a plurality of models, wherein each of the plurality of models includes a different model algorithm relative to each other, each model trained to apply a respective correlation strategy to determine a respective correlation between each feature of the plurality of features for each item and the at least one determinative characteristic, where determining the correlations comprises:
calculating based on applying the plurality of features for the plurality of items to each of the plurality of models, a respective Shapley value characterizing the respective correlation between each feature of each item and the at least one determinative characteristic;
determining one or more features for each of the plurality of items that are most strongly correlated with the at least one determinative characteristic according to the Shapley values; and
organizing the plurality of items to display the plurality of items in an order according to a ranking of the most strongly correlated one or more features of each of the plurality of items,
wherein, organizing the plurality of items comprises displaying, for each of the ordered plurality of items, the most strongly corrected one or more features and corresponding values for the most strongly correlated one or more features.
Claim 9 recites at least the following limitations (emphasis added) that recite an abstract idea:
receive a plurality of items accessible for a user query;
determine a plurality of features of the plurality of items from one or more documents;
determine correlations between each feature of the plurality of features and at least one determinative characteristic of each item associated with a user action based on applying the plurality of features, in parallel, to a plurality of models, each of the plurality of models including a different model algorithm relative to each other to apply a respective correlation strategy for determining a respective correlation between each feature of each item and the at least one determinative characteristic;
calculate a respective Shapley value characterizing the respective correlation between each feature and the at least one determinative characteristic using each of the plurality of models;
determine one or more features for each of the plurality of items that are most strongly correlated with the at least one determinative characteristic according to the Shapley values; and
organize the plurality of items to display the plurality of items in an order according to the most strongly correlated one or more features of each of the plurality of items; and
display the plurality of items in response to the user query, according to the most strongly correlated one or more features associated with each of the plurality of items;
wherein organizing the plurality of items comprises displaying, for each of the ordered plurality of items, the most strongly corrected one or more features and corresponding values for the most strongly correlated one or more features.
Claim 17 recites at least the following limitations (emphasis added) that recite an abstract idea:
receiving a user query related to a plurality of items for a query;
determining a plurality of features of the plurality of items based on one or more documents associated with the plurality of items, each item associated with a respective page,
wherein the plurality of features comprises at least one of numerical features and textual features;
determining correlations between each feature of the plurality of features and a determinative characteristic of an item associated with a certain user behavior based on applying the plurality of features, in parallel, to a plurality of models, wherein each of the plurality of models includes a different model algorithm relative to each other to apply a respective correlation strategy to determine a respective correlation between each feature of each item and the determinative characteristic;
calculating a respective Shapley value characterizing the respective correlation between each feature of each item and the determinative characteristic using the plurality of models;
determining an average value of each feature of the plurality of features based on the Shapley values associated with each feature of the plurality of features;
determining one or more of the features of the plurality of features for each of the plurality items that are most strongly correlated with the determinative characteristic according to the determined average values;
organizing the plurality of items to display a set of results according to a ranking of the most strongly correlated one or more features of each of the plurality of items; and
generating, in response to the user query, the set of results including at least two items of the plurality of items organized at the page based on the ranking of the most strongly correlated one or more features of each of the at least two items;
wherein, at the page displayed to a user, the at least two items of the set of results are organized according to the ranking of the most strongly corrected one or more features associated with each of the at least two items, and wherein, for each of the at least two items, displays the most strongly correlated one or more features and corresponding values.
These limitations recite certain methods of organizing human activity, such as performing commercial interactions (see MPEP 2106.04(a)(2)(II)). Certain methods of organizing human activity are defined by MPEP 2106.04 as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions In this case, the abstract ideas recited in claims 1, 9, and 17 are certain methods of organizing human activity because correlating features associated with a plurality of items and a characteristic determinative of user behavior in order to provide results according to the most strongly correlated features (i.e., a recommendation) is a commercial or legal interaction because it is a sales activity and/or relates to business relations.
Thus, under Prong 1 of Step 2A, claims 1, 9, and 17 recite an abstract idea.
Under Step 2A (prong 2), if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception (see MPEP 2106.04). As stated in the MPEP, when “an additional element merely recites the words ‘apply it (or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea,” the judicial exception has not been integrated into a practical application.
In this case, claim 1 includes additional elements such as (additional elements are bolded):
receiving, by a computing device, a plurality of items accessible through an electronic user interface for a query;
extracting, by the computing device, a plurality of features associated with the plurality of items from one or more electronic documents,
wherein the plurality of features comprises at least one of numerical features and textual features;
determining, by the computing device, correlations between each of the plurality of features and at least one determinative characteristic for each of the plurality of items associated with a user behavior based on applying the plurality of features, in parallel, to a plurality of machine learning models, wherein each of the plurality of machine learning models includes a different model algorithm relative to each other, each machine learning model trained to apply a respective correlation strategy to determine a respective correlation between each feature of the plurality of features for each item and the at least one determinative characteristic, where determining the correlations comprises:
calculating, by the computing device based on applying the plurality of features for the plurality of items to each of the plurality of machine learning models, a respective Shapley value characterizing the respective correlation between each feature of each item and the at least one determinative characteristic; and
determining, by the computing device, one or more features for each of the plurality of items that are most strongly correlated with the at least one determinative characteristic according to the Shapley values; and
organizing the plurality of items accessible through the electronic user interface to display the plurality of items in an order according to a ranking of the most strongly correlated one or more features of each of the plurality of items,
wherein, organizing the plurality of items accessible though the electronic user interface comprises displaying, for each of the ordered plurality of items, the most strongly corrected one or more features and corresponding values for the most strongly correlated one or more features.
In this case, claim 9 includes additional elements such as (additional elements are bolded):
a backend computing system comprising a non-transitory computer-readable memory storing instructions and a processor configured to execute the instructions to:
receive a plurality of items accessible though an electronic user interface for a user query;
determine a plurality of features of the plurality of items from one or more electronic documents;
determine correlations between each feature of the plurality of features and at least one determinative characteristic of each item associated with a user action based on applying the plurality of features, in parallel, to a plurality of machine learning models, each of the plurality of machine learning models including a different model algorithm relative to each other trained to apply a respective correlation strategy for determining a respective correlation between each feature of each item and the at least one determinative characteristic;
calculate a respective Shapley value characterizing the respective correlation between each feature and the at least one determinative characteristic using each of the plurality of machine learning models;
determine one or more features for each of the plurality of items that are most strongly correlated with the at least one determinative characteristic according to the Shapley values; and
organize the plurality of items accessible through the electronic user interface to display the plurality of items in an order according to the most strongly correlated one or more features of each of the plurality of items; and
a server in electronic communication with the blackened computing system, the server configured to host the electronic user interface and to display the plurality of items, at the electronic user interface in response to the user query, according to the most strongly correlated one or more features associated with each of the plurality of items;
wherein organizing the plurality of items accessible though the electronic user interface comprises displaying, for each of the ordered plurality of items, the most strongly corrected one or more features and corresponding values for the most strongly correlated one or more features.
In this case, claim 17 includes additional elements such as (additional elements are bolded):
receiving a user query related to a plurality of items accessible through an electronic user interface for a query;
determining a plurality of features of the plurality of items based on one or more electronic documents associated with the plurality of items, each item associated with a respective page of an electronic user interface,
wherein the plurality of features comprises at least one of numerical features and textual features;
determining correlations between each feature of the plurality of features and a determinative characteristic of an item associated with a certain user behavior based on applying the plurality of features, in parallel, to a plurality of machine learning models, wherein each of the plurality of machine learning models includes a different model algorithm relative to each other trained to apply a respective correlation strategy to determine a respective correlation between each feature of each item and the determinative characteristic;
calculating a respective Shapley value characterizing the respective correlation between each feature of each item and the determinative characteristic using the plurality of machine learning models;
determining an average value of each feature of the plurality of features based on the Shapley values associated with each feature of the plurality of features;
determining one or more of the features of the plurality of features for each of the plurality items that are most strongly correlated with the determinative characteristic according to the determined average values;
organizing the plurality of items accessible through the electronic user interface to display a set of results according to a ranking of the most strongly correlated one or more features of each of the plurality of items; and
generating, in response to the user query, the set of results including at least two items of the plurality of items organized at the page of the electronic user interface based on the ranking of the most strongly correlated one or more features of each of the at least two items;
wherein, at the page of the electronic user interface displayed to a user, the at least two items of the set of results are organized according to the ranking of the most strongly corrected one or more features associated with each of the at least two items, and wherein, for each of the at least two items, the electronic user interface displays the most strongly correlated one or more features and corresponding values.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. These additional elements merely amount to the general application of the abstract idea to a technical environment (“by a computing device”, “through an electronic user interface to display”, “to a plurality of trained machine learning models”, etc.) and insignificant pre-and-post solution activity (receiving information, transmitting information, and displaying information). The specification makes clear the general-purpose nature of the technological environment. This is because the additional elements of claims 1, 9, and 17 are recited at a high level of generality (i.e., as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform the abstract idea) (see Fig. 6; ¶¶0042-0043, ¶0065, ¶¶0068-0074). The specification indicates that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. The description demonstrates that these additional elements are merely generic devices such as a generic computer. Further, the additional elements do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks).
Therefore, considered both individually and as an ordered pair, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional elements are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not transform or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technology environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
In addition to the above, the recited receiving, transmitting, and displaying steps (even assuming arguendo they do not form part of the abstract idea, which the Examiner does not acquiesce), are at best little more than extra-solution activity (e.g., data gathering, presentation of data) that contributes nominally or insignificantly to the execution of the claimed system (see MPEP 2106.05(g)).
In view of the above, under Step 2A (prong 2), claims 1, 9, and 17 do not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to claims 1, 9, and 17, taken individually or as a whole the additional elements of claims 1, 9, and 17 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claims 1, 9, and 17 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
presenting offers
Even considered as an ordered combination (as a whole), the additional elements of claims 1, 9, and 17 do not add anything further than when they are considered individually.
In view of the above, claims 1, 9, and 17 do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
As such, claims 1, 9, and 17 are ineligible.
Regarding claims 2-8, 10-16, and 18-20
Dependent claim(s) 2-8, 10-16, and 18-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claim(s) 2-8, 10-16, and 18-20 merely further define the abstract limitations of claim(s) 1, 9, and 17 or provide further embellishments of the limitations recited in independent claim claim(s) 1, 9, and 17.
Claims 2-8, 10-16, 18-20 set forth:
wherein causing the electronic user interface to be organized according to the determined most strongly correlated item features comprises one or more of: causing a filter to be provided for each of the determined most strongly correlated item features in the electronic user interface; or causing a respective value for each most strongly correlated item feature for each of the plurality of items to be displayed when the items are displayed on the electronic user interface.
wherein the plurality of machine learning models comprises: a first model that outputs whether a correlation of each feature to the determinative characteristics is positive or negative; and a second model that provides calculations of the correlation of each feature based on the respective Shapley values.
wherein the plurality of machine learning models comprises one or more of: the first model comprises a linear regression model; or the second model comprises a tree-based algorithm.
wherein the plurality of machine learning models comprises a first model and a second model, wherein: the first model provides correlation calculations with respect to the numerical features identified based on performing a first category-specific pattern recognition on the plurality of items; and the second model provides correlation calculations with respect to the textual features identified based on performing a second category-specific pattern recognition performed on the plurality of items.
wherein determining the plurality of features of the plurality of items comprises: determining the numerical features of each of the plurality of items; and determining the textual features of each of the plurality of items.
wherein determining the textual features of each of the plurality of items comprises: identifying, for each item of the plurality of items, in a document associated with the item, zero or more textual strings, each textual string including text indicative of a respective feature; one or more of: discarding textual strings that are greater than a threshold length; discarding textual strings containing features with greater than a threshold quantity of possible values; or discarding textual strings containing features occurring less than a threshold quantity of times in the plurality of items; and designating features in the identified, non-discarded strings as the textual features.
receiving an interface navigation request from the user of the electronic user interface; determining one or more of the plurality of items to be displayed in response to the navigation request; and displaying, in response to the navigation request, the determined one or more items of the plurality of items to be displayed, including respective feature values for the determined most strongly correlated item features.
wherein the electronic user interface is a website or an application.
wherein determining the one or more features that are most strongly correlated with the determinative characteristic is determined using the plurality of machine learning models based on the average value of the Shapley values of each feature relative the other features of the one or more features.
Such recitations merely embellish the abstract idea of correlating features associated with a plurality of items and a characteristic determinative of user behavior in order to provide results according to the most strongly correlated features (i.e., a recommendation). The claims do not set forth any further additional limitations, and therefore such abstract embellishments are applied to the additional limitations recited in claim(s) 1, 9, and 17, which do no more than generally link the use of the abstract idea to a particular technological environment, do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim(s) 1, 9, and 17.
Thus, dependent 2-8, 10-16, and 18-20 are ineligible.
Response to Arguments
Applicant’s arguments, on page 16 of the Remarks filed 1/29/2026, with respect to the previous 35 USC §112 rejections have been fully considered and are persuasive in view of the currently amended claims. Accordingly the previous 35 USC §112 rejection of the claims are withdrawn.
Applicant’s arguments, on pages 11-16 of the Remarks filed 1/29/2026, with respect to the previous 35 USC §101 rejections have been fully considered but they are not persuasive. Applicant argues the amended claims do not recite the judicial exception of certain methods of organizing human activity. Examiner respectfully disagrees.
Specifically, Applicant argues on pages 12-13 that the claims are eligible over Step 2A, prong 1 as the claims, as a whole, does not merely recite certain methods of organizing human activity and/or commercial interactions or marketing or sales activities. Examiner respectfully disagrees. Applicant is reminded that in Prong One examiner evaluate whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Despite Applicant’s assertion to the contrary, the Examiner maintains that the claims clearly set forth or describe abstract idea(s) for those reasons set forth previously. Further, Applicant’s own assertion that the claims are directed towards “identifying one or more features of each of a plurality of items that most strongly correlate to the determinative characteristic(s)” is itself an abstract idea and underscores the Examiner’s findings under Prong One (see page 12 of the Remarks). Moreover, the argued “determine the one or more features from the plurality of features, the plurality of features is applied to the plurality of models, each model being trained to apply a respective correlation strategy to identify the one or more features of each item that most strongly correlate to the user action/behavior relative to the other features of each item. Additionally, the plurality of items, or a subset thereof, that are retrieved for the user query… the items being organized so as to be displayed in a certain order based on the ranking of the most strongly correlated one or more features of each of the plurality of items” and “discovering the product features of an item that are most strongly correlated to the determinative characteristic and presenting…the plurality of items ranked according to the most strongly correlated one or more features and providing the one or more features and their corresponding values in response to a user query to help simplify user navigation…” are also an abstract idea and further underscores the Examiner’s findings under Prong One. Providing search results (i.e., recommendations) to a user based on identifying one or more features of each item that most strongly correlates to the user action/behavior relative to the other features of each item is considered a method of organizing activity (see MPEP 2106.04(a)(2)(II)).
Examiner notes the arguments directed to practical applications and computer improvements is analyzed under Step 2A, Prong Two and not within Step 2A, Prong One.
Accordingly, Examiner maintains the claims recite an abstract idea.
Applicant argues on pages 13-16 that the amended claim integrates any abstract idea into a practical application. Examiner respectfully disagrees. While the Examiner agrees that the amended limitations including computing devices, a backend computing system comprising a non-transitory computer-readable memory storing instructions and a processor configured to execute the instructions, server in electronic communication with the backend computing system, the server configured to host the electronic user interface, electronic user interfaces, and trained machine learning models do not fall within the abstract idea, the Examiner disagrees that these elements impose meaningful limits on the judicial exception. As claimed, these elements represent the mere use of generic computing components to facility the abstract idea. Notably, the specification provides only a brief description of computing devices/backend computing system/server/processor executing software, electronic user interfaces receiving input and displaying output, and trained machine learning models processing and outputting information (see Fig. 6; ¶¶0042-0043, ¶0065, ¶¶0068-0074).
If it is asserted that the invention improves upon conventional function of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Although the specification need not explicitly set forth the improvement, it must describe the invention such that eh improvement would be apparent to one of ordinary sill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology (see MPEP 2106.05(a); MPEP 2106.04(d)(1)).
There is no indication from either the claims or the specification that the invention seeks to modify conventional operation of any such technology. Here again, the Examiner emphasizes the failure of the disclosure to set forth or describe the amended features, or any improvements that are achieved from or made relative to another technology or technical field. Contrary to Applicant’s assertion, the improvements manifested by the claimed invention are improvements to the abstract idea itself, not the computer or another technology or technical field. Applicant’s own disclosure reveal the impetus is improving the commercial process, no in technology:
[0030] Item feature recommendations enable users to select the correct items, such as products and services, when searching or browsing an electronic interface, such as a website or mobile application. For a customer, selecting the item that has the best trade-off between a characteristic determinative of user action (referred to herein as a "determinative characteristic"), such as price, and feature set can be time-consuming. Users can be overwhelmed by available choices. The features that most differentiate a particular item that is, most strongly linked with the determinative characteristic-are typically not information determined by, or provided by, the interface for the user's convenience. This disclosure includes use of interpretable machine learning methods to tackle this problem. The problem may be formulated as a determinative characteristic-driven supervised learning problem to discover the product features that best explain the determinative characteristic value of an item in a given item category.
[0031] The teachings of the instant disclosure may be applied to improve the functionality of a server hosting a website by more accurately determining the relevant features of an individual item or many items within a given category. Relevant features can be arranged more prominently for the user's review, can be placed more prominently in a filter list, or can be given greater weight when organizing search result rankings, or can be otherwise used to arrange the interface for the user, thereby simplifying user navigation and reducing the server's load of searches and page loads.
Applicant argues the claims are patent eligible because “like the claim in Example 37, the claims…integrate the proposed judicial exception into a practical application.” (Remarks pages 14-15). The examiner disagrees. The subject matter eligibility examples are hypothetical and only intended to be illustrative of the claim analysis under the MPEP. These examples are to be interpreted based on the fact patterns set forth in each example, as other fact patterns may have different eligibility outcomes. Example 37 provided a technical improvement to the technical problem of users only being able to manually create non-traditional arrangements of icons, by providing a method for rearranging icons on a graphical user interface (GUI), wherein the method moves the most used icons to a position on the GUI, specifically, closest to the ‘start’ icon of the computer system, based on a determined amount of use. The present claims provide no analogous technical solution. While the examiner acknowledges that an interface is generated, the claims provide no similar automatic arrangement of icons in response to an automatic determination by a processor that tracks the number of times each icon is selected or how much memory has been allocated to the individual processes associated with each icon over a period of time. Furthermore, unlike Example 37, Applicant’s specification provides no explanation of an improvement to the functioning of a computer or other technology. While the Examiner agrees that the specification addresses shortcomings in the field of providing relevant search results, the discussions present in the specification do not go as far as to address shortcomings in a technical field. Rather, the specification focuses on problems related to the business aspects of providing relevant search results rather than problems related to the technical field. The improvements argued by Applicant are merely alleged after the fact and in a conclusory manner, representing nothing more than a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art (see MPEP 2106.05 (d)(1)). Accordingly, there is no evidence, short of attorney argument, that a technological improvement is provided. Thus, the claims are not analogous to Example 37.
Applicant additionally argues that the Specification in paragraphs [0030]-[0031] and [0040]-[0046] teaches that the invention reduces the server’s load of searches and page loads, Applicant’s specification does not provide the requisite detail necessary such that one of ordinary skill in the art could recognize the claimed invention as providing an improvement. Applicant’s specification does not provide sufficient detail with respect to computing devices/backend computing system/server/processor executing software, electronic user interfaces receiving input and displaying output, and trained machine learning models processing and outputting information, and is specific only in their use in facilitating the abstract idea of correlating features associated with a plurality of items and a characteristic determinative of user behavior in order to provide results according to the most strongly correlated features (i.e., a recommendation).
The manner in which the currently pending claims are written is akin to ineligible decisions such as Affinity Labs of Texas v. DirecTV, LLC (Fed. Cir. 2016) (the court relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible), or, Internet Patents Corp. v. Active Network, Inc. (Fed. Cir. 2015) (claims contained no restriction on the manner in which the additional elements perform these claimed functions). The alleged improvement by Applicant is at best a bare assertion of an improvement sans sufficient detail to demonstrate that Applicant has provided the alleged improvement to the technical field.
The character of the claims as a whole is not directed to improving computer performance and do not recite any such benefit. The claims of the instant application, however, merely represent the use of generic computing technology used as a tool to perform the abstract idea in an online environment. The claims lack any restriction on the manner in which the computing operations are to be performed. The manner in which the currently pending claims are written is much more akin to the myriad of ineligible court decisions that employed generic computer components at a high-level to achieve improvements in commercial processes.
In review of the claimed invention, and in consideration of the specification as originally filed, the Examiner asserts that:
(i) the claimed invention does not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, but instead improves an abstract, commercial process, and,
(ii) the specification, as originally filed, does not provide sufficient discloser or technical explanation such that one of ordinary skill in the art would have determined that the disclosed invention provided an improvement to the functioning of a computer or another technology or technical field.
The problem of correlating features associated with a plurality of items and a characteristic determinative of user behavior in order to provide results according to the most strongly correlated features (i.e., a recommendation) is one that arises squarely in the commercial realm, and does not rise to improving the functioning of the computer or another technology or technical field. As understood from the specification, the intention of Applicant’s invention is to provide users with more relevant search results (¶¶0030-0031). The improvement manifested by the claimed invention is an improvement to the abstract idea itself, rather than the functioning of the computer or another technology or technical field, and is achieved leveraging generic computing hardware and software set forth at a high level of generality.
Even assuming a relationship of the claimed invention to another technology or technical field, if it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological process, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure most provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement (see MPEP 2106.05(a)). Even when a specification explicitly asserts an improvement, examiner should not determine a claim improves technology when only a bare assertion of an improvement is present without the detail necessary to be apparent to a person of ordinary skill in the art (see MPEP 2106.04(d)(1)).
Therefore the Examiner maintains the claims do not recite additional elements that integrate the judicial exception into a practical application of that exception and maintains the rejection Step 2A, Prong Two.
Applicant argues on page 16 that the amended claims provides an inventive concept under Step 2B. Examiner respectfully disagrees. As noted above in the full rejection of the claims, the claimed additional elements were evaluated individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Taken individually or as a whole the additional elements of the claims do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements of the claims do not add anything further than when they are considered individually and do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Accordingly, the Examiner maintains the 101 rejection of the claims.
Conclusion
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 LINDSEY B SMITH whose telephone number is (571)272-0519. The examiner can normally be reached Monday - Friday 9-6 EST.
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LINDSEY B. SMITH
Examiner
Art Unit 3688
/LINDSEY B SMITH/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688