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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/20/26 has been entered.
Notice to Applicant
The following is a Non-Final Office Action. In response to Examiner’s Final of 1/20/26, Applicant, on 4/20/26, amended claims. Claims 1-4, 6-12, and 14-20 are pending in the instant application and have been rejected below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “monitoring component” in claim 17; “content component” in claim 18; and “training component” in claim 20.
Claims 17-18, and 20 “components” are interpreted as having corresponding structure of “software stored in memory and executed by the processor” based on [0069-0070] as filed, and since claim 16 already recites “processor and memory.” Examiner notes that claim 16 has “memory component” but with no functional limitation; the processing device is then configured to perform operations. Accordingly, claims 17-18 are the claims interpreted as invoking 112(f).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-4, 6-12, and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
“A method for data processing, comprising:
obtaining, … attribute data for a plurality of users, wherein the attribute data comprises attributes based on interactions between the plurality of users and a digital platform; (Applicant’s [0054] as published states “Machine learning parameters, also known as model parameters or weights, are variables that provide a behavior and characteristics of a machine learning model. In some cases, machine learning parameters are learned or estimated from training data and are used to make predictions or perform tasks based on learned patterns and relationships in the data”; [0100] as published “attributes, include an amount of time between a user action on the digital platform and a transaction by the user on the digital platform (ProximityToTransaction), a number of products added to a shopping cart by the user (NumAddToCart), a number of products clicked on by the user (NumProductClick), a number of times the user visited the digital platform using a particular operating system (NumSessionsOS), a number of times the user clicked on a promotion; [0095] as filed - As used herein, “interaction data” refers to a data set including data relating to at least one interaction between a user and a digital platform. As used herein, a “digital platform” is a platform that displays digital content, such a website; Examples of user interactions include visiting the digital platform from a different digital platform, visiting specific sections of the digital platform, hyperlink clicks, viewing digital content on the digital platform.);
clustering the plurality of users to obtain a set of clusters;
generating,…, a directed graph based on the attribute data, wherein… to generate directed graphs representing user clusters using training data including user interaction data, wherein the directed graph includes a plurality of edges and wherein each edge of the plurality of edges includes an edge weight learned … based on the attributes and comprising a numerical indication of a causal effect of a first attribute of the attributes on a second attribute of the attributes;
updating, …, the set of clusters based on a probability function represented by the directed graph; and
providing, …, , content to the user… based on the updated set of clusters ([0116] as filed… causal relations between at least two interactions… information about how members of the cluster interact with digital platform, and provided information is used to more accurately inform actions (such as providing targeted content)… to further a goal of the digital platform (such as encouraging a purchase… or increasing time spent on the digital platform)).”
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “certain methods of organizing human activity” – commercial or legal interactions (advertising, marketing or sales activities), as here we have obtaining attribute data (e.g. number of products added to a cart, time until transition, clicked on a promotion, time spent in a session, etc [0100 as published) based on interactions (e.g. [0095] as published – visits to webpages), clustering the users (i.e. grouping them), then generating a directed graph based on the attribute data and user interactions (e.g. [0095] visits to webpages) where the graph includes edges (i.e. a series of website pages visited) and the edges have weights based on attributes comprising a numerical indication of causal effects of a first and second attribute, and causal relationships among the visits such as a series of website pages visited leading to another page, such as a purchase page or shopping cart by a user ([0116 as published – leading to a “goal” of encouraging a purchase of a product, or time spent on the website), then updating the clusters based on a probability function represented by the directed graph, and providing content based on the updated cluster, which is for purpose of advertising/marketing to improve a goal, such as purchase of a product or time spent on a website. Regarding the clustering, generating a directed graph based on attributes, and then update the user cluster based on causal relationships, this is considered part of the “advertising/marketing” of certain methods of organizing human activity - in view of specification here where causal relationships are sequence of website visits, where customized content (e.g. [0038] generate user cluster; [0053-0056] – model parameters or weights… learned or estimated… to make predictions… maximize a performance metric; [0084, 0116] as published – content is promotions or encouraging a purchase), interpreted as make clusters and maximize purchases/views of website pages). Accordingly, claim 1 is directed to an abstract idea because it is clustering users based on websites visited, forming a graph representing causal relationships leading to a purchase (or other goal), updating clusters based on the graph, and providing customized content (e.g. marketing).
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements that are:
A method for data processing, comprising:
obtaining, by a machine learning model comprising an artificial neural network that includes parameters stored in memory and that is executed by at least one user processor attribute data for a plurality of users, wherein the attribute data comprises attributes based on interactions between the plurality of users and a digital platform;
…
generating, by the machine learning model, a directed graph based on the attribute data, wherein the artificial neural network is trained to generate directed graphs representing user clusters using training data including user interaction data, wherein the directed graph includes a plurality of edges and wherein each edge of the plurality of edges includes an edge weight learned by the machine learning model based on the attributes and comprising a numerical indication of a causal effect of a first attribute of the attributes on a second attribute of the attributes;
updating, by the machine learning model, the set of clusters based on a probability function represented by the directed graph; and
providing, by a content component, stored in the memory and executed by the at least one processor, content to a user via the digital platform based on the updated set of clusters.
Individually or in combination - MPEP 2106.05f applies –the claim involves a computer in that the machine learning model and content components are stored in memory and executed by a processor. Having each step “executed by the processor” is considered “apply it [the abstract idea] on a computer”; merely uses a computer as a tool to perform an abstract idea; See July 2024 Subject Matter Eligibility Update, Example 47, claim 2; Example 48, claim 1; the “train at least one machine learning model” here is “mere instructions to implement abstract idea on a computer at MPEP 2106.05f); see also MPEP 2106.05h “field of use” for combination of computer and machine learning model which is an artificial neural network. The additional elements of machine learning and neural network are used to learn “edges and weights, the numerical values of the graph.”
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing system, is treated as MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235)); and combination of data, computer, and machine learning model comprising an artificial neural network is MPEP 2106.05h (Field of use). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim 9 is rejected for similar reasons as claim 1. Independent claim 9 is directed to a method at step 1, which is a statutory category. Claim 9 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. Claim 9 is directed to an abstract idea, similar to claim 1, because it is clustering users based on websites visited, where the model corresponds to forming a graph representing causal relationships leading to a purchase (or other goal), updating clusters based on attributes. Further, Claim 9 recites “training… a machine learning model,… where the model is corresponds to directed graph that includes edges comprising a numerical indication of a causal effect” and updating “by the training component, the parameters”. At step 2a, prong two and step 2B, the “training” as amended and executed by the computer is considered, like in claim 1, part of “apply it [abstract idea] on a computer” (MPEP 2106.05f) and “field of use” (MPEP 2106.05h). The “training” is for purposes of improving the model of causal relationships [leading to a purchase, or time spent visiting the website].
Independent claim 16 is directed to an apparatus at step 1, which is a statutory category. Claim 16 recites similar limitations as claim 1 and claim 9 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. Claim 16 explicitly recites processor, memory, instructions executed by processor. As above, even recited explicitly, this is considered MPEP 2106.05f (apply it [abstract idea] on a computer).
Claims 2, 7 and 10, 20; and 15 narrow the abstract idea by having plurality of clusters and graphs for updating; and/or updating parameters [claim 15, 20].
Claims 3 and 11 narrow the abstract idea by randomly assigning users to a cluster; Claims 4 and 12 are assigning users to cluster based on interaction data (e.g. website visits, to characterize their interest or profile).
Claims 6 and 14 narrow the abstract idea by having math of calculating a likelihood of assigning a user to a cluster.
Claim 8 narrows the abstract idea by narrowing the marketing aspects; by selecting a target interaction for the user (e.g. [0093, 0116] as filed – likely to cause occurrence of goal interaction, such as encouraging a purchase or increasing time spent on the website/platform).
Claim 17 narrows the abstract by collecting interaction data; similar to limitations addressed in claim 1 as “obtaining”; narrows abstract idea by collecting more data on users (e.g. visits, purchases, etc). The “monitoring component,” is interpreted as part of the computer, and is also considered part of additional elements at step 2a, prong two and step 2B for presumably a computer executing the operations.
Claim 18 narrows the abstract by customizing content; similar to limitations addressed in claim 1. The “content component,” is interpreted as part of the computer, and is also considered part of additional elements at step 2a, prong two and step 2B for a computer executing the operations.
Claim 19 has additional element of “user interface” to display the content; this is interpreted as part of the computer, and is also considered part of additional elements at step 2a, prong two and step 2B for a computer executing the operations; and “field of use” (MPEP 2106.05h).
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
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.
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 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 1-2, 4, 6-10, 12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthy (US 2019/0138917), and Hong (KR 20210028550).
Concerning claim 1, Krishnamurthy discloses:
A method for data processing (Krishnamurthy – see par 25 - From a data science perspective, distributions of visitors may be investigated across different clusters and audience subsets. Models can be developed to predict the behavior of a new visitor by classifying the new visitor as similar in behavior to a pre-existing group of visitors. The new visitor's properties or behavior can be inferred from the known properties or behaviors of the pre-existing group. This enables a marketer to gain insights about a new visitor and potential customer, perform operations for targeted advertising, and thereby lift revenue.), comprising:
obtaining, by a machine learning model (Krishnamurthy – see par 30 - A prediction model is generated based on the multiple graph feature vectors using a machine learning system.) comprising … that includes parameters stored in memory and that is executed by at least one user processor (Applicant’s specification [0054] as published states “Machine learning parameters, also known as model parameters or weights, are variables that provide a behavior and characteristics of a machine learning model. In some cases, machine learning parameters are learned or estimated from training data and are used to make predictions or perform tasks based on learned patterns and relationships in the data”; Applicant’s [0100] as published “attributes, include an amount of time between a user action on the digital platform and a transaction by the user on the digital platform (ProximityToTransaction), a number of products added to a shopping cart by the user (NumAddToCart), a number of products clicked on by the user (NumProductClick), a number of times the user visited the digital platform using a particular operating system (NumSessionsOS), a number of times the user clicked on a promotion; [0095] as filed - As used herein, “interaction data” refers to a data set including data relating to at least one interaction between a user and a digital platform. As used herein, a “digital platform” is a platform that displays digital content, such a website; Examples of user interactions include visiting the digital platform from a different digital platform, visiting specific sections of the digital platform, hyperlink clicks, viewing digital content on the digital platform”
Krishnamurthy discloses the limitations based on broadest reasonable interpretation in light of the specification– see par 40 - In a World Wide Web (WWW) environment with end-user interaction based on selecting links of web pages via a web browser, selectstream data may include so-called clickstream data. see par 64 - The directed graph construction module 204 may construct a directed graph to be representative of a path of travel of an end user as the end user traverses over multiple data objects. As illustrated, the example directed graph 300 includes multiple vertices 302 and multiple directed edges 304. see par 71 - performance of machine learning on a directed graph is enabled by computing one or more invariant features of the graph. In one or more example embodiments, computation of some invariant features results in a single vector of real-valued features for each directed graph (“features” disclose “parameters”). The computed invariant features enable consideration of at least one measure of similarity between two or more directed graphs. see par 151 - The example computing device 1202 as illustrated includes at least one processing system 1204, one or more computer-readable media 1206; see par 160 - Accordingly, software, hardware, or executable modules may be implemented as one or more instructions or logic embodied on some form of computer-readable storage media or by one or more hardware elements 1210. The computing device 1202 may be configured to implement particular instructions or functions corresponding to software or hardware modules;
Krishnamurthy discloses having machine learning to process multiple graph features corresponding to multiple end users, in conjunction with behavioral attributions; as giving an open-ended list of implementation approaches for machine learning systems (See par 45). Hong discloses “artificial neural network” as claimed:
a machine learning model comprising an “artificial neural network that includes parameters” (Hong – page 3, 3rd paragraph - Artificial Neural Networks (ANNs) are generally artificial intelligence models used for estimation or approximation functions that can rely on a large number of unknown inputs. These neural networks generally comprise a system of interconnected "neurons" that exchange information with each other. These connections have numerical weights that can be adjusted based on experience, so the neural network can adapt and learn from the input (“inputs” disclose parameters). see page 3, 5th paragraph - The apparatus 100 for inducing a user's behavior based on the result of analyzing the user's behavior data using an artificial intelligence learning model according to one disclosure will be abbreviated as the behavior analysis device 100 and described).
Krishnamurthy and Hong disclose:
attribute data for a plurality of users (Applicant’s [0100] as published “attributes, include an amount of time between a user action on the digital platform and a transaction by the user on the digital platform (ProximityToTransaction), a number of products added to a shopping cart by the user (NumAddToCart), a number of products clicked on by the user (NumProductClick), a number of times the user visited the digital platform using a particular operating system (NumSessionsOS), a number of times the user clicked on a promotion while using a particular smart phone (NumPromotionHitsSmartPhone), a number of times the user clicked on a promotion while using something other than the particular smart phone (NumPromotionHitsOthers), a number of times a user viewed a product having a price under a particular price threshold (NumCheapProductViewed), a number of times a user clicked on a particular page of the digital platform (NumPageHits), and an amount of time spent by the user on the digital platform during a session (TimePerSession).”
Krishnamurthy discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 29 - For instance, a transition from one data object to another data object may be incorporated into a directed graph as a directed edge. Data objects may include, for example, webpages accessed via a web browser or product descriptions accessed via a mobile retail app. In example WWW implementations, data objects of a collection of data objects may therefore include webpages of a web site, and a transition from one data object to another data object may include clicking on a link that takes a web browser from one webpage to another webpage. see par 38 - the term “behavioral attribute,” as used herein, refers to a categorization or label or description of a behavior exhibited by an end user. Examples of a behavioral attribute may include, but are not limited to, made a purchase, left a website, … purchased after a discount was offered, signed up to receive emails, abandoned a cart, selected a particular product from among several product options, returned to a website after a delay, or some combination thereof.), wherein the attribute data comprises attributes based on interactions between the plurality of users and a digital platform ([0095] as filed - As used herein, “interaction data” refers to a data set including data relating to at least one interaction between a user and a digital platform. As used herein, a “digital platform” is a platform that displays digital content, such a website, an app, an email, etc… Examples of user interactions include visiting the digital platform from a different digital platform, visiting specific sections of the digital platform, hyperlink clicks, viewing digital content on the digital platform, adding a product to a cart, purchasing a product, an amount of time spent on a section of the digital platform, etc.
Krishnamurthy discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 32 - In these manners, dynamic behavior of an end user may be captured in a vector of features computed from invariant graph features. A directed graph may capture natural information about an end user's activities (e.g., cycles and repeating visits) as well as more latent information that is otherwise difficult to acquire about an end user's interactive activities (e.g., the Eigen values of a graph that reflect a “density of connections” in a visitor's website browsing). Information from a directed graph that is derived based on an end users' dynamic interactions can be pertinent to machine learning tasks that lead to accurate targeted marketing. see par 40 - In a World Wide Web (WWW) environment with end-user interaction based on selecting links of web pages via a web browser, selectstream data may include so-called clickstream data. By way of example, “targeted selectstream data” may be obtained from one or more indications of data objects requested by a targeted end user);
clustering the plurality of users to obtain a set of clusters (Krishnamurthy – see par 101 - Two example types of machine learning systems 502 are illustrated: (i) a cluster-based machine learning system having a clustering operation 504 and (ii) a classification-based machine learning system having a training operation 506. With regard to the clustering operation 504, the multiple graph feature vectors 214 are separated into multiple clusters 510 based on one or more similarities between or among different ones of the multiple graph feature vectors 214. A k-means clustering algorithm, with k set to a value between three and seven, may be used, for example; see par 102-103 - The end users 114 may also be separated into different segments 508 in accordance with the corresponding behavioral attributes 516 or other known attributes, such as those from demographic information or marketing data.);
generating, by the machine learning model, a directed graph based on the attribute data (Krishnamurthy – see par 29 - a transition from one data object to another data object may be incorporated into a directed graph as a directed edge. Data objects may include, for example, webpages accessed via a web browser or product descriptions accessed via a mobile retail app), wherein the artificial neural network is trained to generate directed graphs representing user clusters using training data including user interaction data (Krishnamurthy – see par 115 - With regard to the training operation 506, a training set of graph feature vectors may be provided by associating respective ones of at least a portion of the multiple graph feature vectors 214 with a respective classification category 512. For example, a respective classification category 512 for a respective graph feature vector 214 may be determined using a corresponding behavioral attribute 516. For instance, two classification categories 512 may be “converts to a purchase” and “does not convert to a purchase.”
Hong, disclosing limitation with “artificial neural network” – see page 3, last paragraph - the behavior analysis apparatus 100 may calculate a probability that a “purchase”, which is the final purpose of the shopping site, occurs using an artificial intelligence model that has been previously learned on the user's behavior data up to now. The pre-learned artificial intelligence model at the beginning of the day is a learning probabilities of purchases using all behavioral data of users who have accessed the existing shopping site, and purchases (success) or non-purchases (failures) for each user's behavioral data (disclosing different clusters from all behavioral data of users). see page 4, 1st paragraph - the behavior analysis apparatus 100 may use a first artificial intelligence model that has been trained to derive a success probability using the user's behavior data, and a second artificial intelligence model that generates a customized event from the success probability. see page 4, 6th paragraph - the behavior analysis apparatus 100 may generate a time series flow of the user's behavior data as first graph data. see page 4, 8th-9th paragraphs - the graph data includes at least one vertex corresponding to each data object and an edge that connects each vertex and corresponds to an association relationship between the data objects, and each edge has a directionality; see page 5, 7th -8th paragraphs – behavior data of viewing a product page, shopping cart, other product pages, purchase process, etc. and “behavior path”), wherein the directed graph includes a plurality of edges (Kriahnamurthy – see par 29 - a transition from one data object to another data object may be incorporated into a directed graph as a directed edge. Data objects may include, for example, webpages accessed via a web browser or product descriptions accessed via a mobile retail app. In example WWW implementations, data objects of a collection of data objects may therefore include webpages of a web site, and a transition from one data object to another data object may include clicking on a link that takes a web browser from one webpage to another webpage. see par 41 - A directed edge may be defined to represent a dynamic transition by an end user from a source data object to a destination data object. For instance, a current web page may correspond to a source data object and may include a link to another web page, and the other web page may correspond to a destination data object if an end user clicks on the link.), and wherein each edge of the plurality of edges includes an edge … learned by the machine learning model based on the attributes and comprising a numerical indication of a causal effect of a first attribute of the attributes on a second attribute of the attributes (Applicant’s [0031] as published states “. As used herein, a “target interaction” refers to an interaction included in the interaction data that is identified to contribute to a causation of another interaction of the interaction data (such as a goal interaction). “ [0063] as published states “machine learning model 215 generates a directed graph based on the user cluster and the interaction data, where the directed graph represents causal relationships among the interactions.” [0116] as published states goal can be “encouraging a purchase on the digital platform, or increasing time spent on the digital platform” Krishnamurthy discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 61 - each respective directed graph 212 is constructed based on the selectstream data 210 corresponding to a respective end user 114 of the multiple end users 114. see par 64, FIG. 3 – directed graph construction based on path of travel over data objects; see par 65 - each vertex 302 corresponds to a data object 110 (of FIG. 1), and each directed edge 304 corresponds to a transition between two data objects 110. The transition 306 may represent a selection by an end user of the link on the first web page that points to the second web page so that the end user may traverse a portion of the WWW website from the first web page to the second web page; see par 66 - a mathematical description of one or more example embodiments in a WWW scenario, a path traversed by an end user visiting a website is modelled as a directed graph G(V,E) that is defined as follows: [0067] V: a set of the URLs visited by the end user; and [0068] E: set of all transitions (e.g., clicks) by the visiting end user. Each directed edge corresponds to one line of a set of clickstream data, with a source vertex being a referrer URL and a destination vertex being a current URL. The directed graph captures the dynamics of the behavior of an end user over time across multiple visits to a website. As shown in FIG. 3 at an arrow 314, multiple edges 304 may be included between two vertices 302 if an end user 114 traverses between two corresponding data objects more than once… may be included… to create or use a prediction model 216; see par 103 - As another example, if 80% of the end users 114 that are separated into a particular cluster 510 make a purchase (disclosing “causal effect” for second attribute) if offered free shipping via a code from a pop-up window (disclosing “causal effect of 1st attribute leading to 2nd attribute), then a new, targeted end user that corresponds to a targeted graph feature vector that is similar to those graph feature vectors 214 of the particular cluster 510 may be predicted to be convertible into a customer with a free shipping offer; see par 130 - a marketer may use a classifier based on dynamic webpage transitions to identify a website visitor having a propensity to convert; see par 132 - graph features as described herein may be utilized in other machine learning tasks that are designed to build models of visitor attrition, to forecast website traffic, to anticipate a propensity to buy a specific class of products, or some combination thereof, and so forth. the graph features represent the likely unique behavior of a website visitor, using these graph features as variables can improve the algorithm and hence the lift generated by automated personalization activity.
Hong discloses “artificial neural network” is trained to generate directed graph and wherein each edge of the plurality of edges includes an edge “weight” learned by the machine learning model based on the attributes and comprising a numerical indication of a causal effect of a first attribute of the attributes on a second attribute of the attributes
see also Hong – see page 3, 3rd paragraph – Artificial Neural Networks (ANNs)…These neural networks generally comprise a system of interconnected "neurons" that exchange information with each other. These connections have numerical weights that can be adjusted based on experience, so the neural network can adapt and learn from the input. see page 4, 9th paragraph - the graph data includes at least one vertex corresponding to each data object and an edge that connects each vertex and corresponds to an association relationship between the data objects, and each edge has a directionality; see page 8, 6th paragraph - The behavior analysis apparatus 100 may calculate a probability of occurrence of an edge that may be generated from graph data based on the number of edges connected to the vertices. Here, the edge is a virtual association that connects each vertex, and is an index that checks whether other behavioral data can be predicted from the behavioral data);
updating, by the machine learning model, the set of clusters based on a probability function represented by the directed graph (Krishnamurthy – see par 103 - As another example, if 80% of the end users 114 that are separated into a particular cluster 510 make a purchase if offered free shipping via a code from a pop-up window, then a new, targeted end user that corresponds to a targeted graph feature vector that is similar to those graph feature vectors 214 of the particular cluster 510 may be predicted to be convertible into a customer with a free shipping offer; see par 115 – With regard to the training operation 506, a training set of graph feature vectors may be provided by associating respective ones of at least a portion of the multiple graph feature vectors 214 with a respective classification category 512, a respective classification category 512 for a respective graph feature vector 214 may be determined using a corresponding behavioral attribute 516. see par 125 - The targeted end user is permitted to traverse a website by visiting multiple webpages. Each webpage selection by the targeted end user can cause a behavioral prediction engine to update a cluster to which the targeted end user is being mapped. The cluster updating may cease when the cluster index stabilizes. Hence, a behavioral prediction may be produced based on one or more behaviors assigned to the stabilized cluster;
see also Hong – see page 4, 1st paragraph - the behavior analysis apparatus 100 may use a first artificial intelligence model that has been trained to derive a success probability using the user's behavior data, and a second artificial intelligence model that generates a customized event from the success probability. page 5, 1st paragraph - A case in which the user's behavior data reaches the final purpose of the integrated platform is determined as success, and a criterion for determining the probability of success may be determined by an artificial intelligence model learned from behavior data of existing buyers. By initiation, the artificial intelligence model may predict the user's future behavior data based on the user's behavior data up to the present, and calculate a probability that the predicted future behavior data will lead to a purchase; page 8, 8th paragraph - the behavior analysis apparatus 100 may learn a probability of reaching the target data from the user's behavior data by using the association relationship. In order to automatically generate and learn the relationship by initiation, the vertices corresponding to the data objects that can be checked for the occurrence of the relationship on the graph data, edge information connecting the vertices, and the previously acquired data objects Vertex and edge information with properties similar to those of the fields are required. According to the start of the day, the behavior analysis apparatus 100 may continue learning by merging the behavior data acquired in real time with the existing data.); and
providing, by a content component stored in the memory and executed by the at least one processor, content to the user via the digital platform based on the updated set of clusters (0116] as filed… causal relations between at least two interactions… information about how members of the cluster interact with digital platform, and provided information is used to more accurately inform actions (such as providing targeted content)… to further a goal of the digital platform (such as encouraging a purchase… or increasing time spent on the digital platform)
Krishnamurthy – see par 124 - If a distribution of end users in a given cluster for a particular behavioral attribute is high, then that cluster may be used for behavioral targeting by associating an action to that cluster. In other words, a tailored opportunity, which may correspond to a web server taking some action in a WWW ecommerce scenario, may be associated with one or more of the clusters. Thus, an associated action may be taken responsive to a targeted end user being assigned to a given cluster. see par 127, FIG. 8 - The tailored opportunity issuance module 802 may be part of a behavioral prediction engine 122 (of FIG. 2 or 6). see par 130 - visitors with a medium likelihood to convert may be enticed with a discount opportunity or merely with one or more emails or ads. see par 131 - if many of the visitors in a given cluster belong to a particular segment of interest, the marketer can create an association/targeting rule based on this distribution of the particular segment within the given cluster. To implement such targeting, the graph features of a visitor and the distance of the corresponding graph feature vector to the center of the given cluster can be computed. If the distance is less than some selected threshold distance, then the targeting rule can be triggered to issue a tailored opportunity in an attempt to induce the visitor to perform some action;
see also Hong – see page 4, 3rd paragraph - behavior analysis apparatus 100 may calculate a probability that the user will finally make a purchase action, and may provide a customized event for inducing a purchase based on the calculated probability. For example, customized events include providing coupons, recommending products, recommending pages of interest, and inducing purchases using pop-up messages. By initiation, the behavior analysis apparatus 100 may determine a type of a customized event to be recommended to a user using an artificial intelligence model; see page 8, 2nd to last paragraph – FIG. 4 is diagram of providing a customized event for inducing a user’s behavior based on user’s behavior data; see page 9, 2nd paragraph - the behavior analysis apparatus 100 may predict a purchase probability in real time according to the accumulation of behavior data, and the purchase probability predicted through an artificial intelligence model may vary according to a user's behavior path. In addition, the behavior analysis apparatus 100 may predict a next behavior based on the user's behavior data up to now, and generate an event that may induce the user to purchase from the predicted behavior).
Both Krishnamurthy and Hong are analogous art as they are directed to analyzing website visits and giving different content (see Krishnamurthy Abstract, par 30; Hong Abstract, page 4, 3rd paragraph). Krishnamurthy discloses having machine learning to process multiple graph features corresponding to multiple end users, in conjunction with behavioral attributions; as giving an open-ended list of implementation approaches for machine learning systems used for clustering/classification (See par 45) where “tailored opportunities” are presented to a targeted end user responsive to a behavioral prediction. Krishnamurthy also discloses clustering users into segments (See par 101-103, FIG. 5) and having a “propensity” to convert related to dynamic webpage transitions (See par 130, 132). Hong improves upon Krishnamurthy by further including an “artificial neural network” as part of artificial intelligence models for analyzing user behavior on paths leading to purchases (See page 3, 3rd paragraph) and weights in neural network (See page 3, 3rd paragraph; page 8, 6th paragraph). One of ordinary skill in the art would be motivated to further include an “artificial neural network” and for weights/probabilities of visiting another webpage as represented by an edge in a graph to efficiently improve upon the analysis of end user behavior for providing tailored opportunities (See Abstract) and targeted advertising (See par 25), the features values used in machine learning on the directed graph, and the “propensity” related to webpage transitions in Krishnamurthy.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for using machine learning to produce an output/opportunity recommendation for websites and user clusters (see par 25, 130-132) in Krishnamurthy, to further include “artificial neural network” for analyzing users visiting websites and probabilities for occurrence of edges in Hong, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning independent claim 9, Krishnamurthy and Hong disclose:
A method for data processing (Krishnamurthy – see par 25 - From a data science perspective, distributions of visitors may be investigated across different clusters and audience subsets. Models can be developed to predict the behavior of a new visitor by classifying the new visitor as similar in behavior to a pre-existing group of visitors. The new visitor's properties or behavior can be inferred from the known properties or behaviors of the pre-existing group. This enables a marketer to gain insights about a new visitor and potential customer, perform operations for targeted advertising, and thereby lift revenue), comprising:
obtaining, by a training component (Krishnamurthy – see par 30 - A prediction model is generated based on the multiple graph feature vectors using a machine learning system; see par 115 - With regard to the training operation 506, a training set of graph feature vectors may be provided by associating respective ones of at least a portion of the multiple graph feature vectors 214 with a respective classification category 512. A classifier 514 is trained using the provided training set of graph feature vectors such that a new, targeted graph feature vector may be classified by the machine learning system 502 as part of the prediction model 216.) stored in memory and executed by at least one processor (Krishnamurthy – see par 151 - The example computing device 1202 as illustrated includes at least one processing system 1204, one or more computer-readable media 1206; see par 160 - Accordingly, software, hardware, or executable modules may be implemented as one or more instructions or logic embodied on some form of computer-readable storage media or by one or more hardware elements 1210. The computing device 1202 may be configured to implement particular instructions or functions corresponding to software or hardware modules) training data including a set of clusters and user interaction data including attribute data for a plurality of users (Krishnamurthy [similar to claim 1]– see par 38, 100 - end user 114 may be associated with at least one behavioral attribute 516. Examples of a behavioral attribute 516 include made a purchase, signed up for email communications, selected a particular product, bought a certain total amount of goods or services, … permanently left a website, returned to a website after some elapsed time, or some combination thereof. see par 101 - Two example types of machine learning systems 502 are illustrated: (i) a cluster-based machine learning system having a clustering operation 504 and (ii) a classification-based machine learning system having a training operation 506. With regard to the clustering operation 504, the multiple graph feature vectors 214 are separated into multiple clusters 510 based on one or more similarities between or among different ones of the multiple graph feature vectors 214. A k-means clustering algorithm, with k set to a value between three and seven, may be used, for example; see par 102-103 - The end users 114 may also be separated into different segments 508 in accordance with the corresponding behavioral attributes 516 or other known attributes, such as those from demographic information or marketing data), wherein the attribute data comprises attributes based on interactions between the plurality of users and a digital platform (Krishnamurthy [same as cl. 1] – see par 29, 38; see par 32 - In these manners, dynamic behavior of an end user may be captured in a vector of features computed from invariant graph features. A directed graph may capture natural information about an end user's activities (e.g., cycles and repeating visits) as well as more latent information that is otherwise difficult to acquire about an end user's interactive activities (e.g., the Eigen values of a graph that reflect a “density of connections” in a visitor's website browsing). Information from a directed graph that is derived based on an end users' dynamic interactions can be pertinent to machine learning tasks that lead to accurate targeted marketing. see par 40 - In a World Wide Web (WWW) environment with end-user interaction based on selecting links of web pages via a web browser, selectstream data may include so-called clickstream data. By way of example, “targeted selectstream data” may be obtained from one or more indications of data objects requested by a targeted end user);
training, by the training component, of a machine learning model (Krishnamurthy – see par 30 - A prediction model is generated based on the multiple graph feature vectors using a machine learning system) comprising…to generate directed graphs representing user clusters using the training data (Krishnamurthy [same as cl. 1] – see par 101; see par 102-103), wherein the machine learning model includes parameters stored in the memory and that is executed by at least one processor based on the attribute data (Krishnamurthy [see cl. 1] – see par 40, 64; see par 115 – training operation 506… a respective classification category 512 for a respective graph feature vector 214 may be determined using a corresponding behavioral attribute 516; see par 151 - The example computing device 1202 as illustrated includes at least one processing system 1204, one or more computer-readable media 1206; see par 160 - Accordingly, software, hardware, or executable modules may be implemented as one or more instructions or logic embodied on some form of computer-readable storage media or by one or more hardware elements 1210. The computing device 1202 may be configured to implement particular instructions or functions corresponding to software or hardware modules.)
Krishnamurthy discloses having machine learning to process multiple graph features corresponding to multiple end users, in conjunction with behavioral attributions; as giving an open-ended list of implementation approaches for machine learning systems (See par 45).
Hong discloses:
comprising “an artificial neural network” to generate directed graphs representing user clusters using the training data, wherein the machine learning model that includes parameters stored in memory and that is executed by at least one user processor (Hong – same as cl. 1 above - page 3, 3rd paragraph - Artificial Neural Networks (ANNs) are generally artificial intelligence models; see page 3, last paragraph - the behavior analysis apparatus 100 may calculate a probability that a “purchase”, which is the final purpose of the shopping site, occurs using an artificial intelligence model that has been previously learned on the user's behavior data up to now. The pre-learned artificial intelligence model at the beginning of the day is a learning probabilities of purchases using all behavioral data of users who have accessed the existing shopping site, and purchases (success) or non-purchases (failures) for each user's behavioral data (disclosing different clusters from all behavioral data of users); see page 3, 5th paragraph;); see page 9, last paragraph -the behavior analysis apparatus 100 may classify users who have a history of reaching target data into a first group based on the user's information on using the integrated platform. For example, the behavior analysis apparatus 100 may be classified into a one-time purchaser group, a two-time purchaser group, or the like according to the number of purchases by the user. In addition, the behavior analysis apparatus 100 may create a user group that does not have a previous purchase history).
Krishnamurthy and Hong disclose:
wherein the machine learning model corresponds to a directed graph that includes a plurality of edges wherein each edge of the plurality of edges includes an edge weight learned by the machine learning model based on the attributes and comprising a numerical indication of a causal effect of a first attribute of the attributes on a second attribute of the attributes ( [same as cl. 1 above] Krishnamurthy – see par 61, 64-68, 103, 132
Hong [same as cl. 1 above] - see page 3, 3rd paragraph; see page 4, 9th paragraph).
updating, by the machine learning model, the set of clusters based on a probability function represented by the directed graph (Krishnamurthy [same as cl. 1 above] – see par 103; see par 115 ; see par 125; see also Hong – see page 4, 1st paragraph; page 5, 1st paragraph); and
updating, by the training component, the parameters of the machine learning model based on the updated set of clusters (Krishnamurthy – see par 32 - Information from a directed graph that is derived based on an end users' dynamic interactions can be pertinent to machine learning tasks that lead to accurate targeted marketing. see par 45 - A machine learning system may be used to generate or operate a prediction model. Examples of implementation approaches for machine learning systems for unsupervised or supervised learning may include, but are not limited to, cluster analysis, association rule learning, classification, support vector machines (SVMs), Bayesian networks, regression; see par 71 - performance of machine learning on a directed graph is enabled by computing one or more invariant features of the graph. In one or more example embodiments, computation of some invariant features results in a single vector of real-valued features for each directed graph. The computed invariant features enable consideration of at least one measure of similarity between two or more directed graphs;
see also Hong – page 3, 3rd paragraph - Artificial Neural Networks (ANNs) are generally artificial intelligence models used for estimation or approximation functions that can rely on a large number of unknown inputs. These neural networks generally comprise a system of interconnected "neurons" that exchange information with each other. These connections have numerical weights that can be adjusted based on experience, so the neural network can adapt and learn from the input (“inputs” disclose parameters).)
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning independent claim 16, Krishnamurthy and Hong disclose:
A system (Krishnamurthy – see par 25 [as in claim 1, 9] - From a data science perspective, distributions of visitors may be investigated across different clusters and audience subsets… The new visitor's properties or behavior can be inferred from the known properties or behaviors of the pre-existing group. This enables a marketer to gain insights about a new visitor and potential customer, perform operations for targeted advertising, and thereby lift revenue; see par 160 The computing device 1202 ), comprising:
a memory component (Krishnamurthy – see par 151 - The example computing device 1202 as illustrated includes at least one processing system 1204, one or more computer-readable media 1206; see par 160 - Accordingly, software, hardware, or executable modules may be implemented as one or more instructions or logic embodied on some form of computer-readable storage media or by one or more hardware elements 1210. The computing device 1202 may be configured to implement particular instructions or functions corresponding to software or hardware modules);
a processing device coupled to the memory component, the processing device configured to perform operation comprising (Krishnamurthy -see par 151 [as in cl. 1 above] - computing device 1202 as illustrated includes at least one processing system 1204, one or more computer-readable media 1206; see par 160 - Accordingly, software, hardware, or executable modules may be implemented as one or more instructions or logic embodied on some form of computer-readable storage media or by one or more hardware elements 1210. The computing device 1202 may be configured to implement particular instructions or functions); and
The remaining limitations are similar to claim 1 above and are rejected for the same reasons.
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claims 2 and 10, Krishnamurthy and Hong disclose:
The method of claim 1, further comprising:
generating, by the machine learning model, a plurality of directed graphs corresponding to the set of clusters (Krishnamurthy – see par 99 - As illustrated for the prediction model generation scheme 500, multiple graph feature vectors 214 respectively correspond to multiple end users 114 and multiple behavioral attributes 516. The graph feature vectors 214 and the corresponding behavioral attributes 516 may be used in the machine learning system 502. see par 101 - With regard to the clustering operation 504, the multiple graph feature vectors 214 are separated into multiple clusters 510 based on one or more similarities between or among different ones of the multiple graph feature vectors 214. see par 124 - example implementations of behavioral targeting using clustering, the graph feature vectors 214 corresponding to end users 114 (of FIGS. 2 and 4) may be clustered to a fixed number of groups using e.g. a k-means clustering algorithm. Because the dimensions of the graph feature vectors capture dynamic user behavior, end users with similar behavior tend to cluster together); and
updating, by the machine learning model, the set of clusters based on the plurality of directed graphs (Krishnamurthy – see par 125 - The targeted end user is permitted to traverse a website by visiting multiple webpages. Each webpage selection by the targeted end user can cause a behavioral prediction engine to update a cluster to which the targeted end user is being mapped. The cluster updating may cease when the cluster index stabilizes. For instance, the updating may cease if the cluster index is unchanged after a certain number of consecutive iterations of dynamic user behavior—such as after two consecutive iterations. It may be inferred that the behavior of the targeted end user is or will be similar to previous end users assigned to the stabilized cluster. Hence, a behavioral prediction may be produced based on one or more behaviors assigned to the stabilized cluster; see par 126 - the graph features may be used to classify end users into classification categories corresponding to behavioral attributes. An SVM classifier, for instance, may be used to predict whether a targeted end user is likely to convert and make a purchase without the company taking an inducing action. Additionally or alternatively, classifiers may be built that predict other behavioral attributes, such as ‘is likely to abandon cart,’ ‘is likely to return’).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claim 4 and 12, Krishnamurthy and Hong discloses:
The method of claim 1, wherein obtaining the set of clusters comprises:
assigning, by the machine learning model, the users to the set of clusters based on the attribute data (Krishnamurthy – par 121 - The prediction model 216 includes, uses, is generated from, or is otherwise related to multiple graph feature vectors 214, multiple corresponding behavioral attributes 516 exhibited by multiple end users 114 (of FIGS. 2 and 5), and at least one similarity score 702; see par 124 - If a distribution of end users in a given cluster for a particular behavioral attribute is high, then that cluster may be used for behavioral targeting by associating an action to that cluster. In other words, a tailored opportunity, which may correspond to a web server taking some action in a WWW ecommerce scenario, may be associated with one or more of the clusters; par 126 - the graph features may be used to classify end users into classification categories corresponding to behavioral attributes;
see also Hong – see page 5, 2nd to last paragraph – when success probability of behavior data is less than a threshold, determine user as a “failure prospect.” see page 9, last paragraph - the behavior analysis apparatus 100 may classify users who have a history of reaching target data into a first group based on the user's information on using the integrated platform. For example, the behavior analysis apparatus 100 may be classified into a one-time purchaser group, a two-time purchaser group, or the like according to the number of purchases by the user).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claims 6 and 14, Krishnamurthy and Hong disclose:
The method of claim 1, wherein updating the set of clusters comprises:
calculating, by the machine learning model, a likelihood of a user being assigned to a cluster of the set of clusters based on the directed graph (Krishnamurthy – see par 103 - distribution of end users 114 belonging to different segments 508 across various clusters 510 may be ascertained. Hence, a percentage of end users 114 that belong to a given segment of the different segments 508 may be ascertained for each cluster of the various clusters 510. For instance, four of five clusters 510 may have between 75 and 100% of their clustered graph feature vectors 214 corresponding to end users 114 that belong to a segment 508 of engaged end users, with the fifth cluster 510 having less than 35% belonging to the engaged visitor segment;
see also Hong – see page 5, 2nd paragraph - the behavior analysis apparatus 100 based on an initiation may determine the user as a success predictor when it is determined that the success probability of the behavioral data is greater than or equal to the threshold value. In an embodiment, information on a prospective successor may be collected as information on a potential customer; page 5, 3rd paragraph - The behavior analysis apparatus 100 according to an initiation may determine a threshold value that will lead to success of behavioral data using an artificial intelligence model. According to another embodiment, the threshold value may be an appropriate value designated from a path of behavioral data leading to success calculated through an artificial intelligence model.).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claim 7 and 15, Krishnamurthy and Hong disclose:
The method of claim 1, further comprising:
iteratively updating, by the machine learning model, the set of clusters and the directed graph (Krishnamurthy – see par 125 - The targeted end user is permitted to traverse a website by visiting multiple webpages. Each webpage selection by the targeted end user can cause a behavioral prediction engine to update a cluster to which the targeted end user is being mapped. The cluster updating may cease when the cluster index stabilizes. For instance, the updating may cease if the cluster index is unchanged after a certain number of consecutive iterations of dynamic user behavior—such as after two consecutive iterations;
See also Hong, page 8, 8th paragraph - the behavior analysis apparatus 100 may learn a probability of reaching the target data from the user's behavior data by using the association relationship. In order to automatically generate and learn the relationship by initiation, the vertices corresponding to the data objects that can be checked for the occurrence of the relationship on the graph data, edge information connecting the vertices, and the previously acquired data objects Vertex and edge information with properties similar to those of the fields are required. According to the start of the day, the behavior analysis apparatus 100 may continue learning by merging the behavior data acquired in real time with the existing data).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claim 8, Krishnamurthy and Hong disclose:
The method of claim 1, further comprising:
selecting, by the content component, a target interaction for the user based on the set of clusters, wherein the content is provided based on the target interaction ([0093, 0116] as filed – at least one target interaction (e.g., an interaction included in the interaction data that is likely to cause occurrence of goal interaction), such as encouraging a purchase or increasing time spent on the website/platform.
Krishnamurthy – see par 46 - the term “tailored opportunity,” as used herein, refers to an opportunity that is presented to a targeted end user responsive to a behavioral prediction. Examples of an opportunity may include, but are not limited to, purchase a product with a discount; A tailored opportunity may be presented to a targeted end-user in real-time, at a later moment, with a pop-up window, with a banner announcement; see par 130 - a marketer may use a classifier based on dynamic webpage transitions to identify a website visitor having a propensity to convert. A propensity score may be incorporated into a visitor's profile and applied to targeting rules to increase a lifetime value of a customer and therefore generate higher lift. For example, a discount opportunity may be omitted for visitors with a high likelihood to convert, but visitors with a medium likelihood to convert may be enticed with a discount opportunity or merely with one or more emails or ads;
see also Hong - see page 4, 3rd paragraph - behavior analysis apparatus 100 may calculate a probability that the user will finally make a purchase action, and may provide a customized event for inducing a purchase based on the calculated probability. For example, customized events include providing coupons, recommending products, recommending pages of interest. see page 9, 2nd paragraph - the behavior analysis apparatus 100 may predict a purchase probability in real time according to the accumulation of behavior data, and the purchase probability predicted through an artificial intelligence model may vary according to a user's behavior path. In addition, the behavior analysis apparatus 100 may predict a next behavior based on the user's behavior data up to now, and generate an event that may induce the user to purchase from the predicted behavior).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claim 17, Krishnamurthy and Hong disclose:
The system of claim 16, further comprising:
a monitoring component configured to collect the attribute data for the digital platform (Krishnamurthy – see par 54 - For environment 100, the information management user 112 may be associated with the server device 102 or with the behavioral prediction engine 122 to facilitate an analysis of requested data objects 110, e.g., to facilitate an analysis of web traffic for a website. see par 65, FIG. 3 - each vertex 302 corresponds to a data object 110 (of FIG. 1), and each directed edge 304 corresponds to a transition between two data objects 110. In a WWW browser scenario, the pair of source and destination data objects may represent different web pages. see par 144 - The prediction model 216 may be generated, for instance, using a machine learning system 502 with a first set of inputs representative of dynamic transitions from one data object to another data object over a collection of data objects 110 and with a second set of inputs including or representing labels indicative of actions taken by associated ones of the multiple end users 114;
see also Hong page 7, last paragraph – page 8, 1st paragraph - the artificial intelligence model may be learned using user behavior data such as a user's inflow path, user identification information, page visit order, scroll information, content purchase history, and time spent on the page. In addition to this, the user's behavioral data can include all actions performed by the user by accessing the integrated platform. page 9, 2nd paragraph - the behavior analysis apparatus 100 may predict a purchase probability in real time according to the accumulation of behavior data, and the purchase probability predicted through an artificial intelligence model may vary according to a user's behavior path).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claim 18, Krishnamurthy and Hong disclose:
The system of claim 16, further comprising:
a content component configured to generate content based on the set of clusters (Krishnamurthy – see 124, 127 as in claim 1; par 130 - visitors with a medium likelihood to convert may be enticed with a discount opportunity or merely with one or more emails or ads; see par 131 - if many of the visitors in a given cluster belong to a particular segment of interest, the marketer can create an association/targeting rule based on this distribution of the particular segment within the given cluster. To implement such targeting, the graph features of a visitor and the distance of the corresponding graph feature vector to the center of the given cluster can be computed. If the distance is less than some selected threshold distance, then the targeting rule can be triggered to issue a tailored opportunity in an attempt to induce the visitor to perform some action;
see also Hong - see page 4, 3rd paragraph - behavior analysis apparatus 100 may calculate a probability that the user will finally make a purchase action, and may provide a customized event for inducing a purchase based on the calculated probability. For example, customized events include providing coupons, recommending products, recommending pages of interest. see page 9, 2nd paragraph - the behavior analysis apparatus 100 may predict a purchase probability in real time according to the accumulation of behavior data, and the purchase probability predicted through an artificial intelligence model may vary according to a user's behavior path. In addition, the behavior analysis apparatus 100 may predict a next behavior based on the user's behavior data up to now, and generate an event that may induce the user to purchase from the predicted behavior).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claim 19, Krishnamurthy and Hong disclose:
The system of claim 18, further comprising:
a user interface configured to display the content (Krishnamurthy – see par 46 – tailored opportunity presented to a targeted end-user… with a pop-up window, an email, visually using a display; see par 50 – end-user device 104 can be mobile device coupled to a separate screen; device with image output; see par 130 - visitors with a medium likelihood to convert may be enticed with a discount opportunity or merely with one or more emails or ads;
see also Hong – see page 6, 2nd paragraph - The customized event by initiation is an event preset by the administrator to reach the target data, and
may include a user interface according to the type of the customized event. see page 4, 3rd paragraph - behavior analysis apparatus 100 may calculate a probability that the user will finally make a purchase action, and may provide a customized event for inducing a purchase based on the calculated probability. For example, customized events include providing coupons, recommending products, recommending pages of interest. see page 9, 2nd paragraph-3rd paragraph - the behavior analysis apparatus 100 may predict a purchase probability in real time according to the accumulation of behavior data, and the purchase probability predicted through an artificial intelligence model may vary according to a user's behavior path. In addition, the behavior analysis apparatus 100 may predict a next behavior based on the user's behavior data up to now, and generate an event that may induce the user to purchase from the predicted behavior; a pop-up message is displayed to increase the probability of the user's purchase).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 above.
Concerning claim 20, Krishnamurthy and Hong disclose:
The system of claim 16, further comprising:
a training component (Krishnamurthy – see par 30 - A prediction model is generated based on the multiple graph feature vectors using a machine learning system; see par 115 - With regard to the training operation 506, a training set of graph feature vectors may be provided by associating respective ones of at least a portion of the multiple graph feature vectors 214 with a respective classification category 512. A classifier 514 is trained using the provided training set of graph feature vectors such that a new, targeted graph feature vector may be classified by the machine learning system 502 as part of the prediction model 216) configured to update the parameters of the machine learning model (Krishnamurthy – see par 32 - Information from a directed graph that is derived based on an end users' dynamic interactions can be pertinent to machine learning tasks that lead to accurate targeted marketing. see par 45 - A machine learning system may be used to generate or operate a prediction model. Examples of implementation approaches for machine learning systems for unsupervised or supervised learning may include, but are not limited to, cluster analysis, association rule learning, classification, support vector machines (SVMs), Bayesian networks, regression; see par 71 - performance of machine learning on a directed graph is enabled by computing one or more invariant features of the graph. In one or more example embodiments, computation of some invariant features results in a single vector of real-valued features for each directed graph. The computed invariant features enable consideration of at least one measure of similarity between two or more directed graphs);
also Hong – page 3, 3rd paragraph - Artificial Neural Networks (ANNs) are generally artificial intelligence models used for estimation or approximation functions that can rely on a large number of unknown inputs. These neural networks generally comprise a system of interconnected "neurons" that exchange information with each other. These connections have numerical weights that can be adjusted based on experience, so the neural network can adapt and learn from the input (“inputs” disclose parameters).
It would be obvious to combine Krishnamurthy and Hong for the same reasons as claim 1 and claim 9 above.
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthy (US 2019/0138917), and Hong (KR 20210028550), as applied to claims 1-2, 4, 6-10, 12, and 14-20 above, and further in view of Yang (US 2021/0406761).
Concerning claims 3 and 11, Krishnamurthy discloses that a percentage of end users belong to a given segment of the different segments (See par 103). Hong discloses the behavior analysis apparatus 100 may calculate a probability that a “purchase”, which is the final purpose of the shopping site based on learned user’s behavior data, and the pre-learned artificial intelligence model at the beginning of the day is a learning probabilities of purchases using all behavioral data of users who have accessed the existing shopping site, and purchases (success) or non-purchases (failures) for each user's behavioral data (see page 3, last paragraph).
Yang discloses:
The method of claim 1, wherein obtaining the set of clusters comprises:
randomly assigning, by the machine learning model, the plurality of users to the set of clusters (Yang – see par 43 - . In one example implementation, the co-cluster assignments and distribution of the items 102 and the users 104 may be randomly initialized. The co-cluster assignments (i.e., user and item distributions to the co-clusters 108) can be parameterized. Those parameters may be fine-tuned by an objective function. Thus, one of the goals of the DUICC model 200 may be to minimize the objective function by fine-tuning the parameters).
Krishnamurthy, Hong, and Yang are analogous art as they are directed to analyzing website visits and clustering (see Krishnamurthy Abstract, par 30; Hong Abstract; Yang Abstract, par 23 – preferences used to generate recommendations, advertisements). Krishnamurthy discloses that a percentage of end users belong to a given segment of the different segments (See par 103). Hong discloses the behavior analysis apparatus 100 may calculate a probability that a “purchase”, which is the final purpose of the shopping site based on learned user’s behavior data, and the pre-learned artificial intelligence model at the beginning of the day is a learning probabilities of purchases using all behavioral data of users who have accessed the existing shopping site, and purchases (success) or non-purchases (failures) for each user's behavioral data (see page 3, last paragraph). Yang improves upon Krishnamurthy and Hong by further including random assignment to clusters. One of ordinary skill in the art would be motivated to further include a “random assignments” to initialize clusters to efficiently improve upon the percentage of users belonging to a segment in Krishnamurthy and the probabilities of profiles being placed in groups of “leading to purchase” or not in Hong.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for using machine learning to produce an output/opportunity recommendation for websites and user clusters (see par 25, 131) in Krishnamurthy, to further include “artificial neural network” for analyzing users visiting websites and giving supplemental content, and probabilities of profiles being placed in groups of “leading to purchase” or not in Hong, and to randomly initialize assignments to clusters as disclosed in Yang, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Response to Arguments
Applicant’s arguments 4/20/26 have been considered but are not persuasive and/or moot in view of revised rejections in response to the amendments.
Applicant argues with regards to claim 1 that since the claim does not explicitly recite “purchase”, or “marketing,” it is not directed to an abstract idea. Remarks, pages 11. In response, Examiner respectfully disagrees. The claim still needs to be interpreted under broadest reasonable interpretation in light of the specification. See MPEP 2106(II) Patent Subject Matter Eligibility - “ESTABLISH BROADEST REASONABLE INTERPRETATION OF CLAIM AS A WHOLE.” The argument that “by removing reference to advertising or marketing, the claim becomes eligible” is not persuasive here, as the specification and disclosure as a whole is for customizing content, such as promotions and marketing, to help increase purchases by users. Applicants further cite to In re Van Geuns (Fed. Cir. 1993) to argue that that limitations from the specification are not read into the claims. The argument is not persuasive – Examiner is not narrowing the claim, but following proper Claim Interpretation as in MPEP 2111. See e.g. “During patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification."… The court explained that "reading a claim in light of the specification, to thereby interpret limitations explicitly recited in the claim, is a quite different thing from ‘reading limitations of the specification into a claim,’ to thereby narrow the scope of the claim by implicitly adding disclosed limitations which have no express basis in the claim."
Applicant then argues that “purchasing”, “encourage a purchase,” or “increasing time spent” on the digital platform are not claimed elements, and are just “one described goal” in the Specification, and therefore the claim does not recite commercial or marketing activities. Remarks, page 11. In response, Examiner respectfully disagrees with the interpretation. Applicant has carefully removed all references to purchase, advertising, or marketing from the claim. However, even when analyzing under 35 USC 101, as stated in MPEP 2100, one must interpret the claim based on broadest reasonable interpretation in light of the specification. The specification indicates in the background in [0001] as published clustering users to give them targeted content. “Target interaction” and resulting “content” is in claim 8. The claim has “interactions”, which are used to build the “directed graph.” The “interactions” are in [0095] as published where “Examples of user interactions include visiting the digital platform from a different digital platform, visiting specific sections of the digital platform, hyperlink clicks, viewing digital content on the digital platform, adding a product to a cart, purchasing a product, an amount of time spent on a section of the digital platform, etc.” Paragraph [0116] also connects multiple limitations from the claim – user clusters, causal relations between interactions; which is used for providing content to further a goal (such as encouraging a purchase on the digital platform or increasing time spent on the digital platform). Applicants arguments are not persuasive, as throughout the specification, the working examples for the claim terms are for providing content for the business/marketing/purchase purpose.
Applicant then argues with regards to claim 1 that the claim is a practical application because specification [022-027] demonstrates there is an improvement to machine learning technology. Remarks, pages 13-15. In response, Examiner respectfully disagrees. Examiner has reviewed these portions. Unfortunately, the machine learning itself is not improved here; rather, these portions are about how best to cluster the users, which is market segmentation; and it is improving the business process of clustering and providing content, which can be promotions, to users so they will be more likely to purchase items. Applicant further argues that “updating” the user cluster “more accurately informs targeted content for achieving a desired outcome for a user cluster” in [0027]. This passage also fits with [0116] as published explaining that the ”goal” is for “encouraging a purchase” or “increasing time spent.” The limitation of “updating the user cluster” is viewed as “updating… with more data on customers” and not as improving the computer, but rather, narrowing the abstract idea by updating the market segmentation of user clusters as more webpage visits are analyzed. The artificial neural network and “training” is just “apply it”; the learning or neural network itself is not improved.
Applicant then presents arguments on step 2B, (remarks pages 16-17). Examiner respectfully disagrees. The “updating” is also viewed as just gathering more data on the abstract idea, and updating clusters based on additional user website visitor data.
With regards to 103, Applicant’s arguments are moot in light of the new rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IVAN R GOLDBERG whose telephone number is (571)270-7949. The examiner can normally be reached 830AM - 430PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IVAN R GOLDBERG/ Primary Examiner, Art Unit 3619
Prosecution Insights