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 .
Status of Claims
This action is in reply to the communications filed on June 17, 2026. The Applicants’ Amendment and Request for Reconsideration has been received and entered.
Claims 1, 4-9, and 12-16 are currently pending and have been examined. Claims 1, 4, 6, 9, 12, and 14 have been amended. Claims 2-3, 10-11 have been canceled.
The previous rejection of claims 2-4 and 10-12 under 35 USC 112(b) has been withdrawn.
Response to Arguments
Applicants’ amendments necessitated any new grounds of rejection.
The previous rejection of claims 2-4 and 10-12 under 35 USC 112(b) has been withdrawn in view of the cancellation of claims 2 and 10.
The Examiner notes that the previous rejection of claims 6 and 9-16 has not been addressed.
Applicants’ arguments regarding the rejections under 35 USC 101 have been fully considered but they are not persuasive. Applicants argue at page 9 of Applicants’ Reply dated June 17, 2026 (hereinafter “Applicants’ Reply”) that “Applicant’s claimed arrangement is similar to Example 39 of the U.S. Patent Office’s Subject Matter Eligibility Examples that relates to an improved facial detection technology.” Applicants argue at page 9 that “Similarly, Applicant’s claimed arrangement trains a deep learning (DL) model using customer-defined data toward a recommendation objective.” Applicants further argue at pages 9-10 of Applicants’ Reply that “Recommendations provided by current system are based on a generic model that is not tailored to specific customer data. Rather, current systems use generative artificial intelligence (AI) with no customer-specific data knowledge or use a generic recommendation machine learning (ML) model that does not include any customer data. The claimed arrangement improves upon current systems by receiving customer data, training a data model based on the customer data and a recommendation objective, and generating recommendations based on the trained data model and the recommendation objective to be displayed. See, e.g., paragraph 9 of the application. The combination of the user data and the recommendation objective increases the relevancy of the recommendations to the user. By being provided relevant recommendations quickly, the user does not get frustrated in searching for information, and may rely on the recommended results. These recommendations may be dynamic and/or adaptive to new data to continuously offer relevant recommendations. That is, the training of the data model may differ based on the recommendation objective and the user interactions, and this training may improve recommendations and/or provide the user with more relevant information over current systems. See, e.g., paragraph 10 of the application.” The Examiner respectfully disagrees with Applicants’ interpretation of Example 39.
The Examiner notes that Example 39 is directed to training a neural network for facial detection. The portion of the disclosure that is reproduced in Example 39 indicates that the invention uses “an expanded training set of facial images to train the neural network. This expanded training set is developed by applying mathematical transformation functions on an acquired set of facial images. These transformations can include affine transformations, for example, rotating, shifting, or mirroring or filtering transformations, for example, smoothing or contrast reduction. The neural networks are then trained with this expanded training set using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network.” Thus, Example 39 details both the type of model used and the specific training set and mathematical transformations used to train the model. Example 39 further states “Unfortunately, the introduction of an expanded training set increases false positives when classifying non-facial images. Accordingly, the second feature of applicant’s invention is the minimization of these false positives by performing an iterative training algorithm, in which the system is retrained with an updated training set containing the false positives produced after face detection has been performed on a set of non-facial images.” Thus, Example 39 is directed to both the creation of the problem (fast positives when classifying images) and the minimization of the problem.
In contrast, Applicants’ disclosure does not reference or define any specific learning model and provides no details about how the model is actually trained/retrained. Further, the problem solved by Applicants’ claims is not a problem having to do with training a machine learning model. Instead, the issue is improving the performance of the machine learning model, i.e., making more accurate recommendations for a user. This is not a technical problem. Instead, this is an abstract idea performed on a computer and using a computer as a tool.
Per MPEP 2106.04(a)(2)(III)(A), examples of claims that do not recite mental processes because they cannot be practically performed in the human mind include: a claim to a method for calculating an absolute position of a GPS receiver and an absolute time of reception of satellite signals, where the claimed GPS receiver calculated pseudoranges that estimated the distance from the GPS receiver to a plurality of satellites; a claim to detecting suspicious activity by using network monitors and analyzing network packets; a claim to a specific data encryption method for computer communication involving a several-step manipulation of data; and a claim to a method for rendering a halftone image of a digital image by comparing, pixel by pixel, the digital image against a blue noise mask, where the method required the manipulation of computer data structures (e.g., the pixels of a digital image and a two-dimensional array known as a mask) and the output of a modified computer data structure (a halftoned digital image).
In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind; claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind; a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind; and a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind).
Further, per MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer.” Thus, merely reciting the use of a computer is not sufficient to recite a technological improvement. MPEP 2106.04(a)(2)(III)(C) further indicates “In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.”
With this guidance in mind, the Examiner respectfully asserts that the instant claims recite a mental process that can be done with pen and paper or in the human mind or by using a computer as a tool to perform the method. Gathering user data and applying a model to make a prediction as to user behavior or the outcome of user behavior include observations, evaluations, judgments, and opinions, i.e., a mental process.
Thus, the rejection under 35 USC 101 is maintained.
Applicants’ arguments regarding the rejections under 35 USC 102 and 35 USC 103 have been fully considered but they are not persuasive. Applicants argue at page 11 of Applicants’ Reply that “Schlerf appears to disclose generating multiple feature vectors for multiple web pages using input data (session context, web page vectors, and user profile data), where the feature vector are input into a web page prediction model to determine a next web page 330 that the user is likely to view, but Schlerf does not disclose or suggest the claimed arrangement of: extracting customer-defined data for a user from a data warehouse, including context entity data, context entity metadata, context engagement data, and profile data; training a deep learning (DL) model using the customer-defined data toward a recommendation objective for the user; and generating one or more recommendations for the user based on the customer-defined data, the recommendation objective for the user, and generated event chains that represent previous interaction activities of the user.”
Similarly, Applicants argue at page 12 of Applicants’ Reply that “Schlerf combines all collected information based on the user’s interactions into a ‘feature vector’ and applies a web page prediction model to the combined feature vector to determine a predicted next web page, but Schlerf does not disclose or suggest the claimed arrangement of: extracting customer-defined data for a user from a data warehouse, including context entity data, context entity metadata, context engagement data, and profile data; training a deep learning (DL) model using the customer-defined data toward a recommendation objective for the user; and generating one or more recommendations for the user based on the customer-defined data, the recommendation objective for the user, and generated event chains that represent previous interaction activities of the user.”
The Examiner respectfully disagrees and notes that Applicants’ arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Applicants are summarizing alleged features in Schlerf and then saying that Schlerf does not disclose the claim limitations without addressing any patentable distinctions between Schlerf and the recited claims.
As discussed further below, the Examiner has cited Schlerf as disclosing generating, at the server, event chains that represent previous interaction activities of a user to make a prediction for a next activity using the trained DL model, wherein the event chains include one or more user interactions with one or more data items in the data warehouse (See Schlerf, at least col. 16, lines 40-60, central database system tracks web page addresses viewed by the user and session context including dates web pages were viewed and user’s device type, and previously rejected in now-canceled claims 2 and 3). The Examiner has also cited Schlerf as disclosing receiving, at the server, a request to provide personalized recommendations based on an interaction by the user with at least one selected from a group consisting of: a web site, an application, and an email (See Schlerf, at least col. 13, lines 39-50, web page curator provides recommendations for a web page to which a user can navigate; web page curator identifies data for recommending a next web page (such as previous web pages visited by a user, user characteristics, session context) and provides the data for input into the vector generator; col. 14, line 55 to col. 15, line 1, web page curator determines when to assist a user with navigating a website in response to receiving a user request to provide web navigation assistance; user input element (a button to request help) is generated for display at one or more web pages of a domain; in response to receiving an indication that the user has selected the user input element, the web page curator provides data to the vector generator).
Applicants remaining arguments have been fully considered but they have either been addressed above or they are moot in view of the new grounds of rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 9 and 12-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6 and 14: Claim 6 recites “generating, at the DL model, recommendations based on the customer-defined data and the recommendation objective for the user.” It is unclear if these are intended to be the same recommendations that were previously recited in claim 1 or if these are intended to be a second set of recommendations. For purposes of examination, the Examiner is interpreting this portion of claim 6 as referencing the same recommendations that were already generated in claim 1. The Examiner is also interpreting that the transmitting step in claim 6 is also transmitting the same recommendations as in claim 1.
Claim 14 is rejected for similar reasons.
Claims 9-16: Claim 9 recites “a server communicatively coupled to the data warehouse, the configured to.” The phrase “the configured to” is unclear. For purposes of examination, the Examiner is interpreting this phrase as “the server configured to.”
Claims 10-16 inherit the deficiencies of claim 9.
Claim Rejections - 35 USC § 101
Claims 1, 4-9, and 12-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent claims 1 and 9 are directed to a method and a system directed to generating a recommendation. With respect to claim 1, claim elements generating a customer-defined data model, extracting customer-defined data, training a model, generating event chains, and generating one or more recommendations, as drafted, illustrate steps that, under their broadest reasonable interpretation, cover a mental process. That is, other than reciting (in claim 9) that a server performs the method, nothing in the claim precludes the steps from practically being performed in the mind.
Claim 9 recites similar limitations.
The judicial exception is not integrated into a practical application. In particular, claims 1 and 9 recite receiving information and transmitting information. These limitations are considered to be insignificant extra-solution activity. Further, claim 1 recites a server and claim 9 recites a storage device and a server. These elements are recited at a high level of generality, i.e., as generic computer components performing generic computer functions. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, claims 1 and 9 recite receiving information and transmitting information. Per MPEP 2106.05(d)(II), elements such as receiving or transmitting data over a network, using the Internet to gather data, and storing and retrieving information in memory are considered to be computer functions that are well-understood, routine, and conventional functions. See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPG2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Further, as discussed above, claim 1 recites a server and claim 9 recites a storage device and a server. These elements are recited at a high level of generality (i.e., as generic computer components performing generic computer functions). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Thus, claims 1 and 9 are directed to the abstract idea.
Claims 4-8 and 12-16 depend from claims 1 and 9. Claims 4 and 12 are directed to transforming event chains into embeddings and are further directed to the abstract idea. Claims 4 and 12 are further directed to storing data which, as discussed above, is a function that is considered to be well-understood, routine, and conventional. Claims 5 and 13 are directed to generating the recommendations and are further directed to the abstract idea. Claims 5 and 13 are further directed to receiving data which, as discussed above, is a function that is considered to be well-understood, routine, and conventional. Claims 6 and 14 are directed to generating recommendations and are further directed to the abstract idea. Claims 6 and 14 are further directed to transmitting data which, as discussed above, is a function that is considered to be well-understood, routine, and conventional. Claims 7 and 15 are directed to extracting data, analyzing data, and performing attribution and are further directed to the abstract idea. Claims 7 and 15 are further directed to storing data which, as discussed above, is a function that is considered to be well-understood, routine, and conventional. Claims 8 and 16 are directed to performing the method in a multi-tenant system and are further directed to the abstract idea.
Thus, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4, 6, 8-9, 12, 14, and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 11,863,641 B1 to Schlerf et al. (hereinafter “Schlerf”).
Claims 1 and 9: Schlerf discloses a central database system that “provides predictive web navigation using machine learning and clustering to guide a user to a web page. After tracking a number of web pages viewed by various users on one or more web domains and the orders in which these web pages are viewed, the central database system can train a model to predict which web page a user is likely to view next.” (See Schlerf, at least Abstract). Schlerf further discloses a data warehouse comprising at least one storage device (See Schlerf, at least col. 5, line 53-67, central database system includes a database); and a server communicatively coupled to the data warehouse (See Schlerf, at least col. 5, line 53-67, central database system includes a model training engine, one or more models, a vector generator, a web page monitor, a database, and a web page curator; col. 25, lines 1-5, computer processor). Schlerf further discloses:
generating, at a server, a customer-defined data model storing the defined data model in a data warehouse that includes at least one storage device that is communicatively coupled to the server (See Schlerf, at least col. 5, line 53-67, central database system includes a model training engine, one or more models, a vector generator, a web page monitor, a database, and a web page curator; col. 6, line 42 to col. 7, line 11, model training engine trains a model in multiple stages; first stage uses generalized data collected across various domains, websites, web sessions, users, etc. to determine a web page that a user is likely to navigate next; in the second stage, the model training engine tailors the web page determination to a particular characteristic of web page navigation such as a specific user, user characteristic, entity, or web domain);
receiving, at the server, a recommendation objective for a user for the customer-defined data model (See Schlerf, at least col. 14, lines 25-30, web page curator queries the data structure using a given web page to determine one or more actions that the user may be intending to perform by using the given web page);
extracting, at the server, customer-defined data for the user from the data warehouse, including context entity data, context entity metadata, context engagement data, and profile data (See Schlerf, at least col. 15, line 59 to col. 16, line 40, web page prediction model receives input data such as session context data, web page vectors, and a user profile; col. 14, lines 55-67, web page curator receives user data from web page monitor (part of database) and database (user characteristics from a user profile or session context); col. 15, line 65 to col. 16, line 18, session context includes data and time user beings a session, an IP address, device type, web browser type; web pages may include a number of web pages viewed, order of the web pages viewed, web page addresses or data representing the content of the web pages viewed; user profile includes biographical attributes, demographic attributes, web browsing preferences, entity data such as employer data);
training, at the server, a deep learning (DL) model using the customer-defined data toward the recommendation objective for the user (See Schlerf, at least col. 6, line 42 to col. 7, line 11, model training engine trains a model in multiple stages; first stage uses generalized data collected across various domains, websites, web sessions, users, etc. to determine a web page that a user is likely to navigate next; in the second stage, the model training engine tailors the web page determination to a particular characteristic of web page navigation such as a specific user, user characteristic, entity, or web domain);
generating, at the server, event chains that represent previous interaction activities of a user to make a prediction for a next activity using the trained DL model, wherein the event chains include one or more user interactions with one or more data items in the data warehouse (See Schlerf, at least col. 16, lines 40-60, central database system tracks web page addresses viewed by the user and session context including dates web pages were viewed and user’s device type);
receiving, at the server, a request to provide personalized recommendations based on an interaction by the user with at least one selected from a group consisting of: a web site, an application, and an email (See Schlerf, at least col. 13, lines 39-50, web page curator provides recommendations for a web page to which a user can navigate; web page curator identifies data for recommending a next web page (such as previous web pages visited by a user, user characteristics, session context) and provides the data for input into the vector generator; col. 14, line 55 to col. 15, line 1, web page curator determines when to assist a user with navigating a website in response to receiving a user request to provide web navigation assistance; user input element (a button to request help) is generated for display at one or more web pages of a domain; in response to receiving an indication that the user has selected the user input element, the web page curator provides data to the vector generator);
generating, at the server, one or more recommendations for the user based on the user interaction, wherein the one or more recommendations are based on the customer-defined data, the recommendation objective for the user, and the generated event chains (See Schlerf, at least col. 15, line 42 to col. 16, line 40, web page prediction model receives input data such as session context data, web page vectors, and a user profile; web page prediction model outputs a predicted next web page; in one example, central database system tracks web page addresses viewed by the user; central database system generates a vector incorporating the web pages viewed by the user in April of the finance department, to recommend a web page including hyperlinks to where the user can find their tax documents); and
transmitting, at the server, the generated one or more recommendations to a device of the user for display (See Schlerf, at least FIG. 5B and associated text; col. 14, lines 35-50, web page curator modifies an interface to display a web element to direct a user to the predicted next web page; text with hyperlink such as “Are you trying to find your tax documents? A copy of your W-2 can be found at the following page”; col. 16, lines 35-40, web page curator accesses the predicted next web page to present on a client device).
Claim 9 is rejected for similar reasons.
Claims 4 and 12: Schlerf further discloses:
transforming, at the server, one or more of the event chains into embeddings for the user (See Schlerf, at least col. 16, lines 40-60, central database system tracks web page addresses viewed by the user and session context including dates web pages were viewed and user’s device type; central database system generates a feature vector that represents the web page addresses, the dates, and the device type); and
storing the embeddings in a model encoding (See Schlerf, at least col. 6, lines 15-40, model training engine uses vectors generated by the vector generator).
Claim 12 is rejected for similar reasons.
Claims 6 and 14: Schlerf further discloses:
transmitting, at the server, a request to the DL model based on at least one selected from the group consisting of: a profile of the user, and ambient data (See Schlerf, at least col. 13, lines 54-65, web page curator instructs model training engine to retrain a model in response to identifying that the user is performing a new action, i.e., an action that is different from actions that have been previously identified by models); and
generating, at the DL model, recommendations based on the customer-defined data and the recommendation objective for the user (See Schlerf, at least col. 15, line 59 to col. 16, line 40, web page prediction model receives input data such as session context data, web page vectors, and a user profile; web page prediction model outputs a predicted next web page); and
transmitting the generated recommendations to the device of the user for display (See Schlerf, at least FIG. 5B and associated text; col. 14, lines 35-50, web page curator modifies an interface to display a web element to direct a user to the predicted next web page; text with hyperlink such as “Are you trying to find your tax documents? A copy of your W-2 can be found at the following page”; col. 16, lines 35-40, web page curator accesses the predicted next web page to present on a client device).
Claim 14 is rejected for similar reasons.
Claims 8 and 16: Schlerf further discloses wherein the generating the customer-defined data model, the receiving the recommendation objective, the extracting the customer-defined data, the training the deep learning model, and the generating the one or more recommendations, and the transmitting the one or more recommendations is performed by the server for one or more different customers in a multi-tenant system of the server (See Schlerf, at least col. 1, lines 50-60, central database system tracks, for each of a set of users, a number and order of web pages within a domain viewed by the user; central database system generates a training data set using, for each of the users, the tracked number and order of web pages within the domain viewed by the user and one or more characteristics of the user; machine-learned model is then trained using the generated training data set to predict a next web page to be viewed by a viewing user based on web pages previously viewed by the viewing user and characteristics of the viewing user; col. 5, lines 10-15, central database system 240 can assist users in navigating various domains or web pages by recommending a next web page the user is likely to view based on their previously viewed web pages).
Claim 16 is rejected for similar reasons.
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Schlerf as applied to claims 1 and 9 above, and further in view of US . B2 to Lahav et al. (hereinafter “Lahav”).
Schlerf discloses all the limitations of claims 1 and 9 discussed above.
Schlerf further discloses receiving, at the server, a request for personalized content…, wherein the generating the one or more recommendations and the transmission of the generated one or more recommendations is based on the received request for the personalized content (See Schlerf, at least col. 14, lines 25-30, web page curator queries the data structure using a given web page to determine one or more actions that the user may be intending to perform by using the given web page; col. 15, line 59 to col. 16, line 40, web page prediction model receives input data such as session context data, web page vectors, and a user profile).
Schlerf does not expressly disclose that the request is based on an identifier for the user.
However, Lahav discloses “methods and systems for predicting the online behavior of one or more web users. More specifically, web users can be divided into groups based on a characteristic (e.g., observed online behavior, past interaction, demographic, etc.). Observed behavior from each group can be used to generate a corresponding model for predicting user behavior (e.g., whether the user will make a purchase).” (See Lahav, at least col. 1, lines 10-20). Lahav further discloses that the request is based on an identifier for the user (See Lahav, at least col. 13, lines 40-67, interface engine receives a webpage request that includes a user identifier; user information is retrieved using the user identifier).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the web page prediction system and method of Schlerf the ability that the request is based on an identifier for the user as disclosed by Lahav since the claimed invention is merely a combination of old elements, and in the 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. One of ordinary skill in the art would have been motivated to do so in order for a “merchant and/or web-page designer…to reliably identify users' intentions and/or preferences in order to improve the experience and facilitate sales.” (See Lahav, at least col. 1, lines 30-35).
Claim 13 is rejected for similar reasons.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Schlerf as applied to claims 1 and 9 above, and further in view of US 2022/0101402 A1 to Colombier et al. (hereinafter “Colombier”).
Schlerf discloses all the limitations of claims 1 and 9 discussed above.
Schlerf further discloses:
extracting, at an attribution engine of the server, customer data from the data warehouse (See Schlerf, at least col. 15, line 59 to col. 16, line 40, web page prediction model receives input data such as session context data, web page vectors, and a user profile; col. 14, lines 55-67, web page curator receives user data from web page monitor (part of database) and database (user characteristics from a user profile or session context);
extracting, at the attribution engine of the server, a customer-defined attribution and engagement signal configuration (See Schlerf, at least col. 15, line 59 to col. 16, line 40, web page prediction model receives input data such as session context data, web page vectors, and a user profile; col. 14, lines 55-67, web page curator receives user data from web page monitor (part of database) and database (user characteristics from a user profile or session context; col. 16, lines 40-60, central database system tracks web page addresses viewed by the user and session context including dates web pages were viewed and user’s device type).
Schlerf does not expressly disclose analyzing, at the attribution engine of the server, the extracted customer data for context engagement data based on the extracted customer-defined attribution and engagement signal configuration; performing, at the attribution engine of the server, attribution of at least one performance indicator to one or more of the context engagement data based on at least one attribution model; and storing the attribution at the data warehouse.
However, Colombier discloses a system and method “for determining an attractiveness value of a product displayed on a website. The method includes receiving sales data on at least one product displayed in a zone included in the webpage, determining at least one key performing indicators (KPI) on each of the at least one product from the received sales data, comparing the KPI of the at least one product displayed in the zone, determining the attractiveness value for each of the at least one product displayed in the zone, and an insight based on the comparison and the determined attractiveness value, and displaying an image of the at least one product, the KPI, and the insight on the display.” (See Colombier, at least Abstract). Colombier further discloses:
analyzing, at the attribution engine of the server, the extracted customer data for context engagement data based on the extracted customer-defined attribution and engagement signal configuration (See Colombier, at least para. [0053], method for determining product placement effectiveness is performed by analytic server; para. [0054], sales data including Key Performing Indicators (KPI) on a plurality of products displayed in a zone of a webpage are received; KPIs include page views, click or selection rate/number of clicks, attractiveness rate, conversion rate, product revenue, the name of the product displayed);
performing, at the attribution engine of the server, attribution of at least one performance indicator to one or more of the context engagement data based on at least one attribution model (See Colombier, at least para. [0051], shoes A-C are displayed on interface 210. For each of the shoes A-C the number of visits or selections 710 made by the user to the zone 230 within the website 210 is shown, as well as the conversion rate of the shoes, that is, the percentage of the visits to the individual website that results in a sale; for example, Shoe A may have a low number of visits to the website, but may have a high conversion rate of 58% when users of the advertisement website do select the advertisement. Based on the information, it may be ascertained that the discrepancy of low website visitation number, but high conversion rate may be due to the placement of the advertisement for Shoe A. Once a customer sees shoe A on the website 210, the customer is likely to visit; para. [0053], method for determining product placement effectiveness is performed by analytic server; para. [0055], at S830, the KPIs among the plurality of products displayed in the zone are compared. For example, the selection or the conversion rate of the plurality of the products may be compared as a factor on which image of the products is generating more value when it is placed in the zone of the interface of the display. In an embodiment, the KPI among the plurality of products may also be compared to an average for each KPI category. Afterwards, at S840, an attractiveness value is determined for each of the products, and an insight including a recommendation is created, based on the comparison. For example, when it is determined that a particular product has a high selection rate but a low conversion rate, a relatively high attractiveness value that also takes into account the lower conversion rate may be assigned for that particular product, and an insight may be made that the price of the product is too high, or that the product is out of stock); and
storing the attribution at the data warehouse (See Colombier, at least para. [0029], results of the analysis are stored in the database).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the web page prediction system and method of Schlerf the ability of analyzing, at the attribution engine of the server, the extracted customer data for context engagement data based on the extracted customer-defined attribution and engagement signal configuration; performing, at the attribution engine of the server, attribution of at least one performance indicator to one or more of the context engagement data based on at least one attribution model; and storing the attribution at the data warehouse as disclosed by Colombier since the claimed invention is merely a combination of old elements, and in the 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. One of ordinary skill in the art would have been motivated to do so in order to “determine the effectiveness of product placement locations or the value of the images themselves within websites in generating activity from the users.” (See Colombier, at least para. [0004]).
Claim 15 is rejected for similar reasons.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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/Anne M Georgalas/
Primary Examiner, Art Unit 3689
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