DETAILED ACTION
Status of Claims
• The following is an office action in response to the communications filed 07/25/2025.
• Claims 1-3 , 6-8, and 11-13 have been amended.
• Claims 4-5, 9-10, and 14-15 have been canceled.
• Claims 1-3, 6-8, and 11-13 are currently pending and have been examined.
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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of Application No. CN 202111583155.9, filed on 12/22/2021 has been received.
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, 6-8, and 11-13 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. The 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.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-3 are directed to a method, claims 6-8 are directed to a machine, and claims 11-13 are directed to a manufacture. Therefore, claims 1-3, 6-8, and 11-13 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
extracting, from historical log data, object attribute information and historical behavior data and historical display data corresponding to the object attribute information;
acquiring, from a historical recommendation information base, historical recommendation data corresponding to the object attribute information, wherein the historical recommendation data comprises the historical display data;
searching the historical recommendation data for first historical recommendation data which is the same as the historical display data;
obtaining second historical recommendation data according to the historical display data, the historical behavior data and the first historical recommendation data;
inputting the second historical recommendation data and third historical recommendation data into a preset recommendation model, to obtain a model, wherein the third historical recommendation data is historical recommendation data other than the first historical recommendation data among the historical recommendation data; and
upon reception of first identity attribute information, searching a target for to-be-recommended data corresponding to the first identity attribute information;
inputting the to-be-recommended data into the preset recommendation model, to obtain a recommendation display click rate, a recommendation display non-click rate and a recommendation non-display rate corresponding to each piece of the to-be-recommended data;
for each piece of the to-be-recommended data, determining a recommendation index according to the recommendation display click rate, the recommendation display non-click rate and the recommendation non-display rate;
ranking the to-be-recommended data according to an order of recommendation indexes of all pieces of the to-be-recommended data from high to low to obtain ranked to-be-recommended data; and
selecting a preset number of pieces of the to-be-recommended data from the ranked to-be-recommended data, and determining the preset number of pieces of the to-be-recommended data as recommendation data corresponding to the first identity attribute information, and displaying the recommendation data.
The above limitations recite the concept of providing recommendation based on historic data. These limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. The claims are similar to the Mental Process of collecting information, analyzing it, and displaying certain results of the collection and analysis. These limitations, under their broadest reasonable interpretation, further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, providing recommendations based on historic data is a sales activity. This is further illustrated in paragraph [0003] of the Specification, describing providing recommendations for commodities to customers. Independent claims 6 and 11 recites similar limitations as claim 1 and accordingly, these concepts are similarly encompassed by Certain Methods of Organizing Human Activity and Mental Processes. Accordingly, independent claims 6 and 11 fall within the same identified groupings of abstract ideas as claim 1. Accordingly, under Prong One of Step 2A of the MPEP, claims 1, 6, and 11 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 6, and 11 recite additional elements, such as a computer, to train the preset recommendation model, a trained preset recommendation model, a target database, an interface of the computer, a data processing device, comprising: a processor, a memory and a communication bus, wherein the processor, when executing a running program stored in the memory, and a non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, enables the processor to implement a data processing. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1, 6, and 11 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1, 6, and 11 merely recite a commonplace business method (i.e., recommendations based on historical data) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 6, and 11 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 6, and 11 specifying that the abstract idea of providing recommendations based on historic data is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 6, and 11 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 6, and 11 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 6, and 11 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1, 6, and 11, these claims recites additional elements, such as a computer, to train the preset recommendation model, a trained preset recommendation model, a target database, an interface of the computer, a data processing device, comprising: a processor, a memory and a communication bus, wherein the processor, when executing a running program stored in the memory, and a non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, enables the processor to implement a data processing. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1, 6, and 11 are manual processes, e.g., receiving information, analyzing information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1, 6, and 11 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims 1, 6, and 11 specifying that the abstract idea of providing recommendations based on historic data is executed in a computer merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 6, and 11 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 6, and 11 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-3, 7-8, and 12-13, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-3, 7-8, and 12-13 further fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. The claims also fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, in that they recite marketing or sales activities. Dependent claims 2-3, 7-8, and 12-13 fail to identify additional elements and as such, are not indicative of integration into a practical application. As such, under Step 2A, dependent claims 2-3, 7-8, and 12-13are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1, 11, and 16, dependent claims 2-3, 7-8, and 12-13, analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e., providing recommendations based on historic data) being applied on a general purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Further, these limitations generally link the use of the abstract idea to a particular technological environment or field of use. Accordingly, under the Alice/Mayo test, claims 1-3, 6-8, and 11-13 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6-8, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Melamed et al. (US 20220019912 A1), hereinafter Melamed, in view of Severinghaus et al. (US 20140279190 A1), hereinafter Severinghaus, in view of Eletreby et al. (US 20220351239 A1), hereinafter Eletreby.
In regards to claim 1, Melamed discloses a data processing method, comprising (Melamed: [abstract]; [0001]):
extracting, from historical log data, object attribute information and historical behavior data and historical display data corresponding to the object attribute information (Melamed: [0006] – “historical data time interval is used to identify historic user distribution data and historic content distribution data that occurred within the historical data time interval (e.g., information related to users, content recommended to the users, and whether users viewed content recommendations or not and/or interacted with the content recommendations or not, etc.)”; [0047] – “In order to identify what historical training data 506…entries of events that occurred during prior serve times of the model 508 serving content recommendations to client devices are generated and evaluated….When an event occurs, an entry for the event may be generated. The entry may comprise an event type of the event (e.g., an impression event, a click event, a purchase event, etc.)”; [0057] – “the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data 506. In this way, the historical training data 506 may comprise content attributes of content items that were available to serve and/or were served as content recommendations during the historic data time interval, event data indicating whether users viewed and/or interacted with the content recommendations, user attributes of the users whom were served with the content recommendations, and/or other historic user distribution data and historic content distribution data”; examiner notes that consistent with Specification [0034], object attribute information is interpreted to be user attribute information, though it could additionally be interpreted as information regarding recommendation served);
acquiring, from a historical recommendation information base, historical recommendation data corresponding to the object attribute information, wherein the historical recommendation data comprises the historical display data (Melamed: [0057] – “the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data 506. In this way, the historical training data 506 may comprise content attributes of content items that were available to serve and/or were served as content recommendations during the historic data time interval, event data indicating whether users viewed and/or interacted with the content recommendations, user attributes of the users whom were served with the content recommendations, and/or other historic user distribution data and historic content distribution data”; [0047] – “In order to identify what historical training data 506…entries of events that occurred during prior serve times of the model 508 serving content recommendations to client devices are generated and evaluated….When an event occurs, an entry for the event may be generated. The entry may comprise an event type of the event (e.g., an impression event, a click event, a purchase event, etc.)”; examiner notes that the entries of historical data is interpreted to be a historical recommendation base and all data relating to recommendations to a user is interpreted to be historical recommendation data);
first historical recommendation data which is the same as the historical display data (Melamed: [0057] – “the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data 506. In this way, the historical training data 506 may comprise content attributes of content items that were available to serve and/or were served as content recommendations during the historic data time interval, event data indicating whether users viewed and/or interacted with the content recommendations, user attributes of the users whom were served with the content recommendations, and/or other historic user distribution data and historic content distribution data”; [0047] – “In order to identify what historical training data 506…entries of events that occurred during prior serve times of the model 508 serving content recommendations to client devices are generated and evaluated….When an event occurs, an entry for the event may be generated. The entry may comprise an event type of the event (e.g., an impression event, a click event, a purchase event, etc.)”; the examiner notes the data regarding click events is interpreted to be first data and this is the same as display data because it pertains to displayed recommendations);
obtaining second historical recommendation data according to the historical display data, historical behavior data and the first historical recommendation data (Melamed: [0037] – “model may be trained based upon various events, such as impression data of impression events (e.g., a content recommendation being viewed by a user) and click data of click events (e.g., whether a particular user interacted with a content recommendation or did not interact with the content recommendation)”; [0057] – “the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data 506. In this way, the historical training data 506 may comprise…event data indicating whether users viewed and/or interacted with the content recommendations”; the examiner notes that whether a user clicked or did not click is interpreted to be second historical recommendation data);
inputting the second historical recommendation data and third historical recommendation data into a preset recommendation model to train the preset recommendation model, to obtain a trained preset recommendation model, wherein the third historical recommendation data is historical recommendation data other than the first historical recommendation data among the historical recommendation data (Melamed: [0058] – “the model 508 is trained using…the historical training data 506 in order to predict user content preferences of users”; [0037] – “the model may be trained based upon various events, such as impression data of impression events (e.g., a content recommendation being viewed by a user) and click data of click events (e.g., whether a particular user interacted with a content recommendation or did not interact with the content recommendation). In this way, various training data may be input into the model for training the model to predict user content preferences”; [0057] – “the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data 506. In this way, the historical training data 506 may comprise content attributes of content items that were available to serve and/or were served as content recommendations during the historic data time interval, event data indicating whether users viewed and/or interacted with the content recommendations”; [0050] – “historic user distribution data of users being served content recommendations during the historical data time interval may be candidates for inclusion within the historical training data 506 for training the model 508 (e.g., …whether the users were displayed a content recommendation…)”; the examiner notes that whether the recommendations are displayed interpreted to be third recommendation data, which is different that clicking behavior on the recommendations (first data)); and
upon reception of first identity attribute information (Melamed: [0044] – “The request may request a content recommendation from the recommendation system 502 to display through the user interface of the email application. The request may comprise user attributes of the user, such as age, location, gender, browsing history, purchase history, and/or a wide variety of information about the user that may be utilized by the recommendation system 502 to tailor the content recommendation to the interests of the user”; [0059] and Fig. 4 – “At 408, once the model 508 has been trained using the current training data 504 and the historical training data, the model 408 is used to actively serve requests for content recommendations”; [0035] – “an application hosted by a user device may transmit a request to the recommendation system for a content recommendation to display to the user through the application. In another example, a content provider of a website may transmit a request to the recommendation system for a content recommendation to display to the user through the website”;),
searching a target database for to-be-recommended data corresponding to the first identity attribute information (Melamed: [0040] – “serving data (e.g., user distribution of users for which content recommendations are being actively requested and content distribution of content items that are currently available to recommend)”; [0045] – “system 502 may utilize a model 508 to predict user content preferences of the user based upon the user attributes and content attributes of available content to recommend to the user. In an example, the model 508 may assign ranks to the available content based upon predicted likelihoods that the user will interact with content recommendations of the available content”; the examiner interprets the available content to be to-be-recommended data);
inputting the to-be-recommended data into the trained preset recommendation model, to obtain a recommendation display click information corresponding to each piece of the to-be-recommended data (Melamed: [0045] – “system 502 may utilize a model 508 to predict user content preferences of the user based upon the user attributes and content attributes of available content to recommend to the user. In an example, the model 508 may assign ranks to the available content based upon predicted likelihoods that the user will interact with content recommendations of the available content”); and
for each piece of the to-be-recommended data, determining a recommendation index according to the recommendation display click information (Melamed: [0045] – “system 502 may utilize a model 508 to predict user content preferences of the user based upon the user attributes and content attributes of available content to recommend to the user. In an example, the model 508 may assign ranks to the available content based upon predicted likelihoods that the user will interact with content recommendations of the available content”);
ranking the to-be-recommended data according to an order of recommendation indexes of all pieces of the to-be-recommended data from high to low to obtain ranked to-be- recommended data (Melamed: [0045] – “system 502 may utilize a model 508 to predict user content preferences of the user based upon the user attributes and content attributes of available content to recommend to the user. In an example, the model 508 may assign ranks to the available content based upon predicted likelihoods that the user will interact with content recommendations of the available content… A content recommendation 512 may be generated to recommend content having a highest rank indicative of a relatively high likelihood that the user will engage with the content recommendation 512 because the user has an interest in the content”); and
selecting a preset number of pieces of the to-be-recommended data from the ranked to- be-recommended data, and determining the preset number of pieces of the to-be- recommended data as the recommendation data corresponding to the first identity attribute information, and displaying the recommendation data on an interface of the computer (Melamed: [0045] – “system 502 may utilize a model 508 to predict user content preferences of the user based upon the user attributes and content attributes of available content to recommend to the user. In an example, the model 508 may assign ranks to the available content based upon predicted likelihoods that the user will interact with content recommendations of the available content… A content recommendation 512 may be generated to recommend content having a highest rank indicative of a relatively high likelihood that the user will engage with the content recommendation 512 because the user has an interest in the content… transmits the content recommendation 512 over the network to the client device 514 to display through the user interface”; [0006] – “generate and provide content recommendations to client devices for display to users”).
Melamed further discloses identifying first recommendation data (Melamed: [0057]). This is identified from recorded events regarding prior recommendations (Melamed: [0047]). Thus, it is strongly inferred that this data is searched when identifying a type of recommendation event data. Melamed additionally discloses recommendation information such as recommendation display click, recommendation display non-click, and recommendation non-display (Melamed: [0057] – “the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data 506. In this way, the historical training data 506 may comprise…event data indicating whether users viewed and/or interacted with the content recommendations”; [0037] – “the model may be trained based upon various events, such as…click data of click events (e.g., whether a particular user interacted with a content recommendation or did not interact with the content recommendation)”). However Melamed does not explicitly disclose searching the historical recommendation data for first data; and rates pertaining to recommendation information.
However, Severinghaus teaches a similar recommendation method (Severinghaus: [0002]), including
searching the historical recommendation data for data (Severinghaus: [0046] – “recommendation engine 314 can query…recommendation history 310…to select content for inclusion in generated content 212”; [0042] – “server 202 can store recommendations made to users as part of creating generated content 212. The stored recommendation history 310 can be queried by the server 202 when generating recommendations”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the searching of Severinghaus in the method of Melamed because Melamed already discloses determining data and Severinghaus is merely demonstrating that data may be determined by searching historical recommendations. Additionally, it would have been obvious to have included searching the historical recommendation data for data as taught by Severinghaus because searching data stores is well-known and the use of it in a recommendation setting would have allowed for recommendations based on user recommendation history context (Severinghaus: [0042]).
Additionally, Eletreby teaches a similar recommendation method (Eletreby: [0026]), including
rates pertaining to recommendation information (Eletreby: [0005] – “determine, for each of a plurality of the search queries, one or more item engagement metrics (e.g., stacked item engagement metrics), such as an order-through rate (OTR), an add-to-cart rate (ATR), or a click-through rate (CTR), based on the historical user session data”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the rates of Eletreby in the method of Melamed/Severinghaus because Melamed/Severinghaus already discloses data and Eletreby is merely demonstrating that data may be rates. Additionally, it would have been obvious to have included rates pertaining to recommendation information as taught by Eletreby because searching rates are well-known and the use of it in a recommendation setting would have allowed for improved search results (Eletreby: [0002]).
In regards to claim 2, Melamed/Severinghaus/Eletreby teaches the method of claim 1. Melamed further discloses wherein obtaining the second historical recommendation data according to the historical display data, the historical behavior data and the first historical recommendation data comprises: performing data splicing on the historical display data and the first historical recommendation data to obtain recommendation display data (Melamed: [0047] – “In order to identify what historical training data 506…entries of events that occurred during prior serve times of the model 508 serving content recommendations to client devices are generated and evaluated….When an event occurs, an entry for the event may be generated. The entry may comprise an event type of the event (e.g., an impression event, a click event, a purchase event, etc.)”; the examiner interprets the entries being generated to be splicing the data to obtain the recommendation display data (i.e., the historical data));
classifying the recommendation display data into recommendation display click data and recommendation display non-click data according to the historical behavior data (Melamed: [0057] – “the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data 506. In this way, the historical training data 506 may comprise…event data indicating whether users viewed and/or interacted with the content recommendations”; [0037] – “the model may be trained based upon various events, such as…click data of click events (e.g., whether a particular user interacted with a content recommendation or did not interact with the content recommendation)”; [0047] – “When an event occurs, an entry for the event may be generated. The entry may comprise an event type of the event (e.g., an impression event, a click event, a purchase event, etc.)”); and
determining the recommendation display click data and the recommendation display non-click data as the second historical recommendation data, wherein the third historical recommendation data is recommendation non-display data (Melamed: [0050] – “historic content distribution data of content recommendations being served and/or available to be served to users during the historical data time interval may be candidates for inclusion within the historical training data 506 for training the model 508 (e.g., …whether a user was displayed a content recommendation, whether the user clicked the content recommendation…)”; [0037] – “the model may be trained based upon various events, such as…click data of click events (e.g., whether a particular user interacted with a content recommendation or did not interact with the content recommendation)”; the examiner notes that a determination of whether a user was displayed content means nondisplay data is determined);
wherein training the preset recommendation model by using the second historical recommendation data and the third historical recommendation data, to obtain the trained preset recommendation model, comprises: training the preset recommendation model by using the recommendation display click data, the recommendation display non-click data and the recommendation non-display data, to obtain the trained preset recommendation model (Melamed: [0050] – “determine a historical data time interval that is used to select what historic user distribution data and historic content distribution data to include within the historical training data 506 for training the model 508…historic content distribution data of content recommendations being served and/or available to be served to users during the historical data time interval may be candidates for inclusion within the historical training data 506 for training the model 508 (e.g., … whether a user was displayed a content recommendation, whether the user clicked the content recommendation, …)”).
In regards to claim 3, Melamed/Severinghaus/Eletreby teaches the method of claim 2. Melamed further discloses wherein training the preset recommendation model by using the recommendation display click data, the recommendation display non-click data and the recommendation non-display data comprises: sequentially inputting each piece of data in the recommendation display click data, the recommendation display non-click data and the recommendation non-display data into a preset recommendation model to obtain a predicted recommendation display click information corresponding to the each piece of data (Melamed: [0037] – “the model may be trained based upon various events, such as impression data of impression events (e.g., a content recommendation being viewed by a user) and click data of click events (e.g., whether a particular user interacted with a content recommendation or did not interact with the content recommendation). In this way, various training data may be input into the model for training the model to predict user content preferences”; [0046-0047] – “the model 508 may be trained (e.g., periodically trained) using training data to understand what types of content will be engaging to certain types of users…entries of events that occurred during prior serve times of the model 508 serving content recommendations to client devices are generated and evaluated. In an example, an event may correspond to a content recommendation of a content item being displayed to a user (an impression). In another example, an event may correspond to a content recommendation of a content item being interacted with, such as clicked, by a user…When an event occurs, an entry for the event may be generated. The entry may comprise an event type of the event (e.g., an impression event, a click event, a purchase event, etc.), a serve time of the day and time and which the model 508 was used to generate and serve the content recommendation to a client device…entries are created for events associated with the model 508 serving content recommendations to client devices.”; [0058] – “the model will…predict a click probability”; [0045] – “model 508 may assign ranks to the available content based upon predicted likelihoods that the user will interact with content recommendations of the available content. For example, a woman's dress may be ranked relatively lower than a football based upon the user being a 15 year old male having an interest in sports. A content recommendation 512 may be generated to recommend content having a highest rank indicative of a relatively high likelihood that the user will engage with the content recommendation 512 because the user has an interest in the content”); and
training the preset recommendation model (Melamed: [0002] – “During a training phase, the model is trained on training data, such user distribution data and content distribution data indicating what types of users (e.g., features of users) interacted with particular content recommendations (e.g., features of content being recommended by the content recommendations) and what types of users did not interact with (ignored) content recommendations”).
Melamed further discloses recommendation information such as recommendation display click, recommendation display non-click, and recommendation non-display (Melamed: [0057] – “the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data 506. In this way, the historical training data 506 may comprise…event data indicating whether users viewed and/or interacted with the content recommendations”; [0037] – “the model may be trained based upon various events, such as…click data of click events (e.g., whether a particular user interacted with a content recommendation or did not interact with the content recommendation)”). Yet Melamed does not explicitly disclose predicted rates pertaining to recommendation information; where the model is trained based on the predicted rates pertaining to recommendation information.
However, Eletreby teaches a similar recommendation method (Eletreby: [0026]), including
predicted rates pertaining to recommendation information (Eletreby: [0005] – “determine, for each of a plurality of the search queries, one or more item engagement metrics (e.g., stacked item engagement metrics), such as an order-through rate (OTR), an add-to-cart rate (ATR), or a click-through rate (CTR), based on the historical user session data”; the examiner interprets the rates determined based on historical data to be the predicted rates); and
where the model is trained based on the predicted rates pertaining to recommendation information (Eletreby: [0038] – “To train a machine learning model, item ranking computing device 102 may obtain, from database 116, historical user session data that identifies and characterizes item engagements and corresponding search queries…item ranking computing device 102 may determine, based on the user engagement data, an order-through rate (OTR), and add-to-cart rate (ATR), or a click-through rate (CTR) for each item-query pair”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the rates of Eletreby in the method of Melamed because Melamed already discloses data and Eletreby is merely demonstrating that data may be rates. Additionally, it would have been obvious to have included predicted rates pertaining to recommendation information; where the model is trained based on the predicted rates pertaining to recommendation information as taught by Eletreby because searching rates are well-known and the use of it in a recommendation setting would have allowed for improved search results (Eletreby: [0002]).
In regards to claim 6, claim 6 is directed to an apparatus. Claim 6 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The combined method of Melamed/Severinghaus/Eletreby teaches the limitations of claim 1 as noted above. Melamed further discloses a data processing device, comprising: a processor, a memory and a communication bus, wherein the processor, when executing a running program stored in the memory, is configured to (Melamed: [0021]; [0028]). Claim 6 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claim 7, all the limitations in apparatus claim 7 are closely parallel to the limitations of method claim 2 analyzed above and rejected on the same bases.
In regards to claim 8, all the limitations in apparatus claim 8 are closely parallel to the limitations of method claim 3 analyzed above and rejected on the same bases.
In regards to claim 11, claim 11 is directed to a medium. Claim 11 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The combined method of Melamed/Severinghaus/Eletreby teaches the limitations of claim 1 as noted above. Melamed further discloses a non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a data processing method, the method comprising (Melamed: [0060]). Claim 11 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claim 12, all the limitations in medium claim 12 are closely parallel to the limitations of method claim 2 analyzed above and rejected on the same bases.
In regards to claim 13, all the limitations in medium claim 13 are closely parallel to the limitations of method claim 3 analyzed above and rejected on the same bases.
Response to Arguments
Applicant’s arguments, filed 07/25/2025, have been fully considered.
35 U.S.C. § 101
Applicant argues the claims do not recite a judicial exception because the claims are “not limited to advertising and marketing” and “the claimed invention cannot be purely performed in human mind” (Remarks pages 9-11). The examiner disagrees. Initially, the examiner notes that a computer, to train the preset recommendation model, a trained preset recommendation model, a target database, an interface of the computer, a data processing device, comprising: a processor, a memory and a communication bus, wherein the processor, when executing a running program stored in the memory, and a non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, enables the processor to implement a data processing have been analyzed as additional elements and accordingly are not analyzed as part of the abstract idea. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. The claims recite acquiring and searching data in order to rank and display data. This represents mental processes such as observations and evaluations. These limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. The claims are similar to the Mental Process of collecting information, analyzing it, and displaying certain results of the collection and analysis. These limitations, under their broadest reasonable interpretation, further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, providing recommendations based on historic data is a sales activity. This is further illustrated in paragraph [0003] of the Specification, describing providing recommendations for commodities to customers. Accordingly, the claims are directed to a mental process and to certain methods of organizing human activity.
Applicant argues the claims are patent eligible because the claims amount to significantly more and provide an inventive step because “effectiveness of data provided to the user is improved” Remarks page 17. The examiner disagrees. The MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” The MPEP further states that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art,” and that, “conversely, if the specification explicitly sets forth an improvement but in a conclusory manner…the examiner should not determine the claim improves technology” (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of providing recommendation based on historic data. This is illustrated in specification paragraph [0027] which discusses that the invention is related to more accurate recommendations. With respect to Applicant’s argument regarding more effective data, the examiner notes that providing more effective data is not a technical improvement but merely an improvement to the abstract idea.
Although the claims include computer technology such a computer, to train the preset recommendation model, a trained preset recommendation model, a target database, an interface of the computer, a data processing device, comprising: a processor, a memory and a communication bus, wherein the processor, when executing a running program stored in the memory, and a non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, enables the processor to implement a data processing, such elements are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of providing recommendation based on historic data in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to technological improvements but are directed to improving the business method of providing recommendation based on historic data. The claimed process, while arguably resulting in a more accurate recommendation, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the server and/or computer components that operate the system. Rather, the claimed process is utilizing data sets related to items and users while still employing the same server and/or computer components used in conventional systems to improve providing recommendation based on historic data, e.g. a business method, and therefore is merely applying the abstract idea using generic computing components. As such, the claims are not integrated into practical application and do not provide significantly more.
35 U.S.C. § 103
Applicant argues the claims are allowable over the cited art because the cited art does not teach or disclose “a recommendation display click rate, a recommendation display non-click rate and a recommendation non-display rate” (Remarks pages 11-14). The examiner disagrees. The cited art teaches this limitation. Initially, Melamed discloses information pertaining to Melamed recommendation display click, recommendation display non-click, and recommendation non-display. Melamed discloses this in [0057], disclosing the historic user distribution data and the historic content distribution data occurring within the historic data time interval is identified as the historical training data. This may comprise event data indicating whether users viewed and/or interacted with the content recommendations. Melamed further discloses this in [0037], disclosing the model may be trained based upon various events, such as click data of click events (e.g., whether a particular user interacted with a content recommendation or did not interact with the content recommendation). The examiner notes that determining whether a user clicks or views something is determining display click, display non-click, and non-display information. Furthermore, Eletreby teaches rates pertaining to recommendation information. Eletreby teaches this in [0005], teaching determining, for each of a plurality of the search queries, one or more item engagement metrics (e.g., stacked item engagement metrics), such as an order-through rate (OTR), an add-to-cart rate (ATR), or a click-through rate (CTR), based on the historical user session data. Thus, the cited art teaches this limitation in this claim.
Conclusion
NPL reference U, initially cited in the Office action dated 03/28/2025, teaches using machine learning to determine recommendations. Historical data of a user may be collected and a machine learning model trained. Personalized recommendations can be provided based on the trained model.
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 ANNA MAE MITROS whose telephone number is (571)272-3969. The examiner can normally be reached Monday-Friday from 9:30-6.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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