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 .
Acknowledgements
This Office Action is in response to Applicant’s response filed on January 2, 2026 (“January 2026 Response”). The January 2026 Response contained, inter alia, claim amendments (“January 2026 Claim Amendments”), amendments to the specification (“January 2026 Specification”) and “REMARKS” (“January 2026 Remarks”).
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 of the Subject Matter Eligibility Analysis for Products and Processes1 (“SME Analysis”):
Claims 1-20 are directed to at least one of the statutory categories. In particular,
Claims 1-7 are directed to a process.
Claims 8-14 are directed to an article of manufacture.
Claims 15-20 are directed to a system.
Step 2A- Prong One of the SME Analysis:
Claim 1 (representative of independent Claim 8 and independent Claim 15) recites/describes the following steps:
receiving information related to user online activities (“Step 1”);
generating a plurality of component graphs representing candidate households based on pairs of user and non-user entity and pairs of a non-user entity and another non-user entity, wherein all the pairs are determined based on the information related to user online activities (“Step 2”);
classifying the plurality of component graphs to identify component graphs corresponding to households (“Step 3”);
adapting the component graphs corresponding to households based on dynamics of the user online activities (“Step 4”).
These steps, under their broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) gathering and graphing user data to represent households, classifying, and modifying the graphs according to user activities (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the “mental processes” subject matter grouping of abstract ideas.
Additionally, Steps 2-4, under their broadest reasonable interpretation, describe or set-forth mathematical relationships between user online data and households in the form of graphs, which amounts to one or more mathematical relationships, one or more mathematical formulas or equations, one or more mathematical calculations. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
As such, the Examiner concludes that Claim 1 recites an abstract idea.
Independent Claims 8 and 15 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Each of the depending claims likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis. Any element(s) recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim.
Step 2A- Prong Two of the SME Analysis:
The claims recite the additional elements/limitations of:
“via a machine-trained multi-layer neural network”;
a machine readable and non-transitory medium having information recorded thereon, wherein the information, when read by the machine, causes the machine to perform the following steps: (see Claim 8);
a system (see Claim 15);
a user activity determiner implemented by a processor and configured for, a component graph generator implemented by a processor and configured for, a household-graph (H-graph) representation classifier implemented by a processor and configured for, an H-graph representation maintenance unit implemented by a processor and configured for (see Claim 15);
“based on a household classification model previously trained via machine learning” (see Claims 5, 12, and 19);
a household interest determiner implemented by a processor and configured for (see Claim 20); and
a household content service provider implemented by a processor and configured for (see Claim 20).
The requirement to execute the claimed steps/functions using “a machine readable and non-transitory medium having information recorded thereon,” (see Claim 8), “system” (see Claim 15), “a user activity determiner implemented by a processor and configured for,” (see Claim 15) “a component graph generator implemented by a processor and configured for,” (see Claim 15) “a household-graph (H-graph) representation classifier implemented by a processor and configured for,” (see Claim 15) “an H-graph representation maintenance unit implemented by a processor and configured for” (see Claim 15); “a household interest determiner implemented by a processor and configured for” (see Claim 20); and “a household content service provider implemented by a processor and configured for” (see Claim 20), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recited additional element of “via a machine-trained multi-layer neural network” simply appends insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional element is considered “extra-solution” because the claim uses (“classifying”) the neural network at a high level of generality. As such, this limitation does not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element of “based on a household classification model previously trained via machine learning” (see Claims 5, 12, and 19) simply appends insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional element is considered “extra-solution” because the claim does not recite training the model and the training occurs “previously” to the claim. As such, this limitation does not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Furthermore, although the claims recite a specific sequence of computer-implemented functions, and although the specification suggests certain functions may be advantageous for various reasons (e.g., business reasons), the Examiner has determined that the ordered combination of claim elements (i.e., the claims as a whole) are not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem.
The dependent claims fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
Therefore, the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B of the SME Analysis:
As discussed above in “Step 2A – Prong 2”, the requirement to execute the claimed steps/functions using “a machine readable and non-transitory medium having information recorded thereon,” “system,” “a user activity determiner implemented by a processor and configured for,” (see Claim 15) “a component graph generator implemented by a processor and configured for,” (see Claim 15) “a household-graph (H-graph) representation classifier implemented by a processor and configured for,” (see Claim 15) “an H-graph representation maintenance unit implemented by a processor and configured for” (see Claim 15); “a household interest determiner implemented by a processor and configured for” (see Claim 20); and “a household content service provider implemented by a processor and configured for” (see Claim 20) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recited additional elements of “via a machine-trained multi-layer neural network” and “based on a household classification model previously trained via machine learning” (see Claims 5, 12, and 19) simply appends insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). Therefore, these limitations do not qualify as “significantly more”. (see MPEP 2106.05(d)). This conclusion is based on a factual determination.
It is additionally noted that the determination that associating/storing data in a database is well-understood, routine, and conventional is supported by the Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93), and MPEP 2106.05(d)(II), which note the well-understood, routine, conventional nature of associating/storing data in a database.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity).
The dependent claims fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea identified by the Examiner to which each respective claim is directed).
As such, no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above.
For the reasons stated above, Claims 1-20 as whole do not amount to significantly more than the abstract idea itself.
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.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Atlas et al. (US 2017/0235846 A1)(“Atlas”) in view of Dasdan et al. (US 2016/0275545 A1)(“Dasdan”) and further in view of Park et al. (US 2023/0247103 A1)(“Park”).
Claims 1, 8, and 15
As to Claims 1, 8, and 15, Atlas discloses a method (“method,” [0018]) [a machine readable and non-transitory medium (“storage devices of a computer system configured using computer program code to perform the acts described...” [0053]) having information recorded thereon, wherein the information, when read by the machine, causes the machine to perform the following steps:][a system (“machine” [0053]), comprising:] comprising:
[a user activity determiner (pairing engine 202) implemented by a processor (“one or more processors,” [0053]) and configured for] receiving information related to user online activities (“anonymous Internet user data, namely ‘activity history’ associated with the Internet connectable devices,” [0053], “use raw or preprocessed data for associating devices with device attributes, for example device activity history; optionally, the raw or unprocessed data includes, for example, log files, bitstream data, and other network traffic containing either cookie or device identifiers.” [0054]);
[a component graph generator (module 1104) implemented by a processor (“one or more processors,” [0053]) and configured for] generating a plurality of component graphs representing candidate groups [households] (“each individual cluster of digital identities stored within a storage device acts as a digital identity group communication structure U.sub.1, U.sub.2, ... U.sub.N, comprising a group of devices that are associated with the same user and that can be used to communicate with the user who owns the grouped devices.” “Thus, a digital identity group communication structure identifies a group of devices associated with the same user so as to expand the available avenues of communication with that user, thereby enhancing opportunities for communication with the user.” [0060]) based on pairs of user and non-user entity and pairs of a non-user entity and another non-user entity (“As used herein, the term ‘device pair’ refers to an information structure that is stored in a storage device and that indicates a pairing of distinct digital identities to a single user.” [0062], “As used herein, the terms ‘device ID’ and ‘device identifier’ and ‘digital identity’ refer to an identifier for a user device, a user device program stored in a computer readable storage device, a user device information structure (for example, a software cookie) stored in a computer readable storage device or a device user identity, for example.” “Examples of deviceIDs include web browser cookies, cellular telephone device identifiers, MAC addresses, userids, and other identifiers that are linked to a specific client device, client program, or device user.” “As used herein, the term ‘device’ or ‘user device’ is used generally to refer to digital entities such as laptop computer systems, desktop computer systems, the cellular phones, tablet computer system, smart watches, smart devices such as internet connected appliances, and web browsers, for example. The teachings of the present disclosure may be used with a wide variety of different deviceIDs. In an example of digital identity pairings that will be disclosed, web browser cookies on laptop computer systems, desktop computers, and device identifiers on cellular phone devices are used as deviceIDs.” [0069]), wherein all the pairs are determined based on the information related to user online activities (“The clustering engine 206 produces a graph structure within a computer readable storage device that represents at least a portion of the identified candidate device pairs and the determined scores.” [0059], “an example graph produced using the cluster engine of FIG. 12 for the candidate device pairs of FIGS. 4A to 4B” [0172], “In the course of generating a graph, the module 1104 prunes some graph edges from the device graph. More particularly, in accordance with some embodiments, the module 1104 implements a local graph sparsification process to clean the graph before graph clustering is performed” [0173], note: the graph structure contains sub-graphs as can be seen in Fig.13);
[a household-graph (H-graph) representation classifier (module 1115) implemented by a processor (“one or more processors,” [0053]) and configured for] classifying the plurality of component graphs to identify component graphs corresponding to groups [households] (“In response to the decision module 1110 determining that the cluster fitness requirements have been achieved, a cluster accuracy filter module 1115 determines which device identifiers of a proposed user device cluster are to be associated with a final user device cluster and which devices of the user proposed device cluster are to be removed. A module 1116 outputs the cluster as a final cluster.” [0176]); and
[an H-graph representation maintenance unit (module 1118) implemented by a processor (“one or more processors,” [0053]) and configured for] adapting the component graphs corresponding to groups [households] based on dynamics of the user online activities (“Following the module 1114 or following the module 1116, depending upon control flow for a given potential user device cluster, control flows to a decision module 1118, which determines whether or not there are more potential device clusters in the graph to be evaluated. In response to a determination that that are more potential device clusters in the graph to be evaluated, control flows to back to the module 1106 and another potential user device cluster is identified. In response to a determination that there are no additional device clusters to be evaluated, control flows to the module 1120, which causes decision module to wait for new potential device clusters” [0176]).
Atlas does not directly disclose
that the groups represented by the graphs are households; and
the classifying is via a machine-trained multi-layer neural network.
Dasdan teaches households represented by graphs (“A device graph may initially be obtained in operation 402. A device graph that specifies relationships between devices (including apps, browsers, or the like) as belonging to the same user or a closely related set of users may be obtained in any suitable manner.” [0043], “For example, if several devices use the same IP address, such as provided for a home network, these devices may be related to an aggregated user, such as a household” [0043], “The separately maintained and/or merged user data may be used to anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising.” [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Atlas by the features of Dasdan, and in particular: to include in the groups represented by the graphs of Atlas, the households as taught by Dasdan. A person having ordinary skill in the art would have been motivated to combine these features because “[a]dvertisers may prefer to target a particular group of users when presenting an advertisement as part of an advertising campaign” (Dasdan, [0004]) and it would help to “anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising” (Dasdan, [0072]).
Park teaches classifying via a machine-trained multi-layer neural network (“The first neural network model 162 may be a model learned to acquire a probability that two external devices among a plurality of external devices belong to the same group.” “The first neural network model 162 and the second neural network model 163 may include a graph neural network (GNN).” [0064], “The AI model may be generated through learning. Here, being generated through learning means that a basic artificial intelligence model is learned using a plurality of learning data by a learning algorithm, such that a predefined operation rule or artificial intelligence model set to perform a desired characteristic (or purpose) is generated. The artificial intelligence model may be composed of a plurality of neural network layers.” [0090], [0091], “The electronic apparatus 100 may define at least one group based on the second graph. For example, if a probability corresponding to a first edge constituting the second graph is greater than a predetermined value, the electronic apparatus 100 may define that two external devices corresponding to two nodes directly connected to the first edge are in the same group.” [0117], see Fig.6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Atlas/Dasdan combination by the features of Park, and in particular: to include in the classifying of Atlas, classifying via a machine-trained multi-layer neural network, as taught by Park. A person having ordinary skill in the art would have been motivated to combine these features because it would provide “more accurate clustering” (Park, [0006]).
Claims 2, 9, and 16
Atlas further discloses wherein the user online activities include connections among entities (“anonymous Internet user data, namely ‘activity history’ associated with the Internet connectable devices,” [0053], “use raw or preprocessed data for associating devices with device attributes, for example device activity history; optionally, the raw or unprocessed data includes, for example, log files, bitstream data, and other network traffic containing either cookie or device identifiers.” [0054]), wherein the entities include:
Users (“Examples of deviceIDs include… userids” [0069]); and
the non-user entities the include devices capable of connecting to the Internet (“Examples of deviceIDs include…cellular telephone device identifiers, MAC addresses,…and other identifiers that are linked to a specific client device,..” [0069]), and
online information sources including browsers (“Examples of deviceIDs include web browser cookies, …client program”,” [0069]).
Claims 3, 10, and 17
Atlas further discloses wherein the step of generating a plurality of component graphs comprises:
identifying relevant connections among entities based on the information related to online user activities (“after collecting Internet usage data, a next step in digital identity pairing, in accordance with some embodiments of the present disclosure, is to determine a set of candidate digital identity pairs” [0093]);
constructing an overall graph based on the connections (“A module 1104 assembles a graph that incorporates the received device pairs. In some embodiments of the present disclosure, a module 1104 configures a computer system to act as a graph generator. FIG. 13 is an illustrative drawing representing an example graph produced using the cluster engine of FIG. 12 for the candidate device pairs of FIGS. 4A to 4B in accordance with some embodiments of the present disclosure.” [0172]), wherein the overall graph includes nodes corresponding to entities and edges corresponding to connections among entities represented by the nodes (“Graph nodes correspond to device identifiers. Graph edges that connect graph nodes indicate device pairs” [0172]);
extracting, from the overall graph, the plurality of component graphs based on a predetermined condition (“In the course of generating a graph, the module 1104 prunes some graph edges from the device graph. More particularly, in accordance with some embodiments, the module 1104 implements a local graph sparsification process to clean the graph before graph clustering is performed” [0173], note: the graph structure contains sub-graphs as can be seen in Fig.13), wherein
each of the plurality of component graphs includes edges representing connections of different entities represented by nodes included in the component graph (“Graph nodes correspond to device identifiers. Graph edges that connect graph nodes indicate device pairs” [0172]) and none of the nodes in the component graph is connected to a node outside of the component graph (“(i) the devices within the found clusters are maximally inter-connected,” [0190], note: instant specification at [0044] states that “a maximal component graph is incapable of reaching an external node.”).
Claims 4, 11, and 18
Atlas further discloses wherein the step of identifying relevant connections among entities comprises:
extracting, from the information related to online user activities, connections between two entities (“In embodiments of the present disclosure that use IP addresses as common source/destination identifiers, two different techniques are optionally used within the system 200 to select potential digital identity pairs for further analysis. A first strategy is to examine the number of different digital identities known to use the same IP address.” [0094]);
removing some of the connections based on one or more predetermined filtering criteria (“Candidate device pairs having precision pair scores that do not pass the score quality threshold are removed from use in the feature based scoring module 708.” [0145]);
providing remaining connections as the relevant connections (“Candidate device pairs having precision pair scores that pass a score quality threshold, namely aforementioned ‘one or more threshold criteria’, are passed to a module 710, that transmits the candidate device pairs and their precision pair scores to the cluster module 206.” [0145]).
Claims 5, 12, and 19
Atlas further discloses wherein the step of classifying the plurality of component graphs comprises:
with respect to each of the plurality of component graphs,
obtaining a node representation for each of nodes included in the component graph (“initially assigning each vertex in the pairing graph a unique label” [0178]),
aggregating node representations of the nodes included in the component graph to derive a representation for the component graph (“Each node will aggregate the score for all the labels that it receives, will pick the label with maximum score and assign that label to itself if the new score is greater than the score of the current label.” [0183]),
classifying (“In response to the decision module 1110 determining that the cluster fitness requirements have been achieved, a cluster accuracy filter module 1115 determines which device identifiers of a proposed user device cluster are to be associated with a final user device cluster and which devices of the user proposed device cluster are to be removed. A module 1116 outputs the cluster as a final cluster.” [0176]), based on a group [household] (wherein the combination of Atlas and Dasdan as stated previously teaches households as part of the groups of graphs) classification model previously trained via machine learning, the component graph based on the representation of the component graph with a binary label indicative of whether the component graph characterizes a household (“Labeled device pairs with the label=-1 are associated with feature vectors known to be indicative of a pair of devices that is not associated with the same user. Labeled device pairs with the label=+1 are associated with feature vectors known to be indicative of a pair of devices that is associated with the same user. The labeled device pairs are used in the system 200 to learn during a training stage to assess whether or not feature vectors of respective unlabeled candidate device pairs are indicative of the respective candidate device pairs being associated with the same user.” [0137]).
Claims 6 and 13
Atlas does not directly disclose but Dasdan teaches
analyzing user-content activity information (“user data” [0071]) related to members of a household represented by a component graph (“device graph,” “The merged user data (FIG. 6A) or separate device user data (FIG. 6B) may be obtained for each updated device graph. Any time a new device graph and updated user data are generated or updated, the previous device graph and updated user data may be maintained and stored so as to allow analysis of how the device relationships and corresponding user data have changed over time” [0071]), wherein the user-content activity information related to the members records content consumed by the members and activities of the members with respect to the content (“user profile and on-line behavior,” [0072]”);
estimating one or more interests of the household based on the user-content activity information (“The separately maintained and/or merged user data may be used to anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising. For example, a campaign may be set up and executed based on merged user profiles from their related devices.” [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Atlas/Dasdan/Park combination by the features of Dasdan and in particular: to include in the Atlas/Dasdan/Park combination, the features of: analyzing user-content activity information related to members of a household represented by a component graph, wherein the user-content activity information related to the members records content consumed by the members and activities of the members with respect to the content; estimating one or more interests of the household based on the user-content activity information, as taught by Dasdan. A person having ordinary skill in the art would have been motivated to combine these features because “[a]dvertisers may prefer to target a particular group of users when presenting an advertisement as part of an advertising campaign” (Dasdan, [0004]) and it would help to “anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising” (Dasdan, [0072]).
Claims 7 and 14
Atlas does not directly disclose but Dasdan teaches providing personalized content service by:
identifying a service household for personalized content service (“The separately maintained and/or merged user data may be used to anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising.” [0072]);
obtaining one or more interests estimated with respect to the service household (“some devices may be associated with user data indicating interest in baby products, while other related devices may be associated with professional data indicating a high income. The merged user data in this later example may be used to promote high-end baby products to such users across one or more of their related devices” [0095]);
identifying content in alignment with the one or more interests associated with the service household (“some devices may be associated with user data indicating interest in baby products, while other related devices may be associated with professional data indicating a high income. The merged user data in this later example may be used to promote high-end baby products to such users across one or more of their related devices” [0095]); and
recommending the identified content to the service household (“some devices may be associated with user data indicating interest in baby products, while other related devices may be associated with professional data indicating a high income. The merged user data in this later example may be used to promote high-end baby products to such users across one or more of their related devices” [0095]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Atlas/Dasdan/Park combination by the features of Dasdan and in particular: to include in the Atlas/Dasdan/Park combination, the features of: providing personalized content service by: identifying a service household for personalized content service; obtaining one or more interests estimated with respect to the service household; identifying content in alignment with the one or more interests associated with the service household; and recommending the identified content to the service household, as taught by Dasdan. A person having ordinary skill in the art would have been motivated to combine these features because “[a]dvertisers may prefer to target a particular group of users when presenting an advertisement as part of an advertising campaign” (Dasdan, [0004]) and it would help to “anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising” (Dasdan, [0072]).
Claim 20
Atlas does not directly disclose but Dasdan teaches
a household interest determiner (“program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.” [0118]) implemented by a processor (processor 801) and configured for recommending content to a household by:
analyzing user-content activity information (“user data” [0071]) related to members of a household represented by a component graph (“device graph,” “The merged user data (FIG. 6A) or separate device user data (FIG. 6B) may be obtained for each updated device graph. Any time a new device graph and updated user data are generated or updated, the previous device graph and updated user data may be maintained and stored so as to allow analysis of how the device relationships and corresponding user data have changed over time” [0071]), wherein the user-content activity information related to the members records content consumed by the members and activities of the members with respect to the content (“user profile and on-line behavior,” [0072]”), and estimating one or more interests of the household based on the user-content activity information (“The separately maintained and/or merged user data may be used to anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising. For example, a campaign may be set up and executed based on merged user profiles from their related devices.” [0072]); and
a household content service provider (“program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.” [0118]) implemented by a processor and configured for:
identifying a service household for personalized content service (“The separately maintained and/or merged user data may be used to anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising.” [0072]), obtaining one or more interests estimated with respect to the service household (“some devices may be associated with user data indicating interest in baby products, while other related devices may be associated with professional data indicating a high income. The merged user data in this later example may be used to promote high-end baby products to such users across one or more of their related devices” [0095]), identifying content in alignment with the one or more interests associated with the service household (“some devices may be associated with user data indicating interest in baby products, while other related devices may be associated with professional data indicating a high income. The merged user data in this later example may be used to promote high-end baby products to such users across one or more of their related devices” [0095]), and recommending the identified content to the service household (“some devices may be associated with user data indicating interest in baby products, while other related devices may be associated with professional data indicating a high income. The merged user data in this later example may be used to promote high-end baby products to such users across one or more of their related devices” [0095]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Atlas/Dasdan/Park combination by the features of Dasdan and in particular: to include in the Atlas/Dasdan/Park combination, the features of: a household interest determiner implemented by a processor and configured for recommending content to a household by: analyzing user-content activity information related to members of a household represented by a component graph, wherein the user-content activity information related to the members records content consumed by the members and activities of the members with respect to the content, and estimating one or more interests of the household based on the user-content activity information; a household content service provider implemented by a processor and configured for: identifying a service household for personalized content service, obtaining one or more interests estimated with respect to the service household, identifying content in alignment with the one or more interests associated with the service household, and recommending the identified content to the service household, as taught by Dasdan. A person having ordinary skill in the art would have been motivated to combine these features because “[a]dvertisers may prefer to target a particular group of users when presenting an advertisement as part of an advertising campaign” (Dasdan, [0004]) and it would help to “anonymously target and analyze user profile and on-line behavior data across multiple devices that are used by the same user or a set of closely related users, such as a household, to facilitate on-line targeted advertising” (Dasdan, [0072]).
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application does not include claim limitations that are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Response to Arguments
Applicant’s arguments filed in the January 2026 Remarks have been fully considered and addressed below.
101 Arguments
On pages 14-15, Applicant argues that the claims do not fall under “mental processes” because classifying via a machine learning model cannot by performed in the human mind. However, the machine learning model is not addressed under the identification of the abstract idea. Therefore, the argument is not persuasive.
On page 15, Applicant argues that the claims are not directed to mathematical concepts or organizing human activity. However, Applicant does not present reasons why it would not be considered in those categories. Therefore, the argument is not persuasive.
On page 16, Applicant argues that the claims are “tied to a practical application” because they “provide an improvement in the technical field.” Applicant appears to supports this idea by quoting portions of the specification directed to the classifying of graphs based on the machine trained models. However, the Examiner notes that while the claims state using a neural network, it is recited at a high level of generality. Merely using the neural network as a tool to perform a general function such as “classifying” without describing how the neural network performs the classifying does not amount to improving a technology or technical field.
On pages 17-18, Applicant argues that the ordered combination amounts to more than the judicial exception. However, Applicant does not explain why the ordered combination of their claims would amount to significantly more than the abstract idea. Therefore, the argument is not persuasive.
103 Arguments
On pages 19-20, Applicant argues that Atlas does not teach “generating graphs based on pairs of user and device capable of connecting to the Internet, pairs of user and browser, and pairs of device and browser.” However, it is noted that the claims do not specify that pairs are “pairs of user and browser, and pairs of device and browser.” Nonetheless, Atlas discloses based pairs of user and non-user entity and pairs of a non-user entity and another non-user entity (“As used herein, the term ‘device pair’ refers to an information structure that is stored in a storage device and that indicates a pairing of distinct digital identities to a single user.” [0062], “As used herein, the terms ‘device ID’ and ‘device identifier’ and ‘digital identity’ refer to an identifier for a user device, a user device program stored in a computer readable storage device, a user device information structure (for example, a software cookie) stored in a computer readable storage device or a device user identity, for example.” “Examples of deviceIDs include web browser cookies, cellular telephone device identifiers, MAC addresses, userids, and other identifiers that are linked to a specific client device, client program, or device user.” “As used herein, the term ‘device’ or ‘user device’ is used generally to refer to digital entities such as laptop computer systems, desktop computer systems, the cellular phones, tablet computer system, smart watches, smart devices such as internet connected appliances, and web browsers, for example. The teachings of the present disclosure may be used with a wide variety of different deviceIDs. In an example of digital identity pairings that will be disclosed, web browser cookies on laptop computer systems, desktop computers, and device identifiers on cellular phone devices are used as deviceIDs.” [0069]), wherein all the pairs are determined based on the information related to user online activities (“The clustering engine 206 produces a graph structure within a computer readable storage device that represents at least a portion of the identified candidate device pairs and the determined scores.” [0059], “an example graph produced using the cluster engine of FIG. 12 for the candidate device pairs of FIGS. 4A to 4B” [0172], “In the course of generating a graph, the module 1104 prunes some graph edges from the device graph. More particularly, in accordance with some embodiments, the module 1104 implements a local graph sparsification process to clean the graph before graph clustering is performed” [0173], note: the graph structure contains sub-graphs as can be seen in Fig.13). Therefore, the argument is not persuasive.
On pages 19-20, Applicant argues that Atlas does not teach “classifying…” However, Atlas is not relied upon for that feature. Park teaches classifying via a machine-trained multi-layer neural network (“The first neural network model 162 may be a model learned to acquire a probability that two external devices among a plurality of external devices belong to the same group.” “The first neural network model 162 and the second neural network model 163 may include a graph neural network (GNN).” [0064], “The AI model may be generated through learning. Here, being generated through learning means that a basic artificial intelligence model is learned using a plurality of learning data by a learning algorithm, such that a predefined operation rule or artificial intelligence model set to perform a desired characteristic (or purpose) is generated. The artificial intelligence model may be composed of a plurality of neural network layers.” [0090], [0091], “The electronic apparatus 100 may define at least one group based on the second graph. For example, if a probability corresponding to a first edge constituting the second graph is greater than a predetermined value, the electronic apparatus 100 may define that two external devices corresponding to two nodes directly connected to the first edge are in the same group.” [0117], see Fig.6). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Atlas/Dasdan combination by the features of Park, and in particular: to include in the classifying of Atlas, the classifying via a machine-trained multi-layer neural network, as taught by Park. A person having ordinary skill in the art would have been motivated to combine these features because it would provide “more accurate clustering” (Park, [0006]). Therefore, the argument is not persuasive.
Conclusion
Applicant’s amendment filed on January 2, 2026 necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONICA A MANDEL whose telephone number is (571)270-7046. The examiner can normally be reached Monday and Thursday 10:00 AM-6:00 PM.
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/M.A.M/Examiner, Art Unit 3622
/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
1 See Subject Matter Eligibility Analysis for Products and Processes in MPEP §2106 III.
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