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 .
DETAILED ACTION
Status of Claims
This Non-Final Office Action is in response to Applicant’s Request for Continued Examination (RCE) filed 10/28/2025.
In accordance with Applicant’s amendment, claims 1, 8, and 13 are amended. Claims 1-2, 6-8, and 10-13 are currently pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submissions filed on 10/28/2025 have been entered.
Response to Amendment
The 35 U.S.C. §112(b) rejection of claims 8 and 10 is withdrawn in response to applicant’s amendment.
Response to Arguments
Response to §101 Arguments: Applicant's arguments (Remarks at pgs. 10-16) with respect to the §101 rejection of claims 1-2, 6-8, and 10-13 have been considered, but are either directed to the amendments (which are therefore addressed in the updated §101 rejection), or are not found persuasive.
Applicant’s arguments at pgs. 10-12 are primarily raised in support of the new limitations added to independent claims 1/13, which have been considered and fully addressed in the updated §101 rejection set forth below.
In response to Applicant’s reliance on Ex Parte Desjardins and the related citation to Enfish (Remarks at pg. 12), the Examiner first notes that the fact pattern and claimed subject matter under consideration in Desjardins shares virtually no substantive similarities to applicant’s claims in this instance. For example, in Desjardins, which was directed to a computer-implemented method of training a machine learning model, the claimed improvement (which was supported by the Specification) was deemed to allow artificial intelligence (AI) systems to use less storage capacity and reduce system complexity. Applicant’s claimed invention for constructing a relationship graph does not involve a sequence of limitations analogous to the sequence of steps for training a machine learning model to optimize an object function, as claimed in Desjardins, and no similar improvement/reduction to storage capacity has been shown to be achieved by applicant’s claims. In response to the citation to Enfish, the Examiner emphasizes that the CAFC’s Enfish decision noted that the claimed invention for configuring memory according to a logical table embodied the technological solution/improvement (i.e., the self-referential table), which resulted in faster search times and smaller memory requirements, whereas Applicant’s claims merely rely on generic computing elements in independent claim 1 (*independent claim 13 is notably devoid of a computer or other additional elements) to perform the steps, but do nothing to configure, reconfigure, manipulate, transform, or improve a machine learning model, a computer, a database, or any technological components at all, but instead results in extracting graph nodes, links, and relationships, all which fall under the scope of the abstract idea groupings, as discussed in the Step 2A Prong One analysis set forth in the §101 rejection below.
In response to applicant’s reliance on a “score prediction model” that is a “learning model that performs weak supervised learning, such as a learning model using a convolutional neural network (CNN)” (Remarks at pg. 13), this argument lacks merit because it relies on features not recited in the claims, and it would be improper to import these limitations into the claims. See Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). See also, CollegeNet, Inc. v. Apply Yourself Inc., 418 F.3d 1225, 1231 (Fed. Cir. 2005) (while the specification can be examined for proper context of a claim term, limitations from the specification will not be imported into the claims).
In response to applicant’s suggestion that “the computation processing amount can be reduced” (Remarks at pg. 13), applicant has not shown this result to necessarily be achieved within the scope of the claimed invention. Any alleged result in the form of computational efficiency (reduction in the amount of computation required) by constructing a single graph to represent a user node and corresponding nodes/links therein is nothing more than an incidental, natural, and inevitable result of reducing the volume of data to be processed (i.e., a single graph as compared to multiple graphs), which does not amount to a technological improvement. Applicant’s attention is directed to PTAB Appeal Decision 2023-002258, which is instructive in view of its rationale addressing a similar argument (Decision at pgs. 19-21).
Applicant’s remarks (Remarks at pgs. 13-16) concerning the USPTO Memorandum published Aug. 4, 2025 have been reviewed as well, but are not deemed persuasive at showing the claims are eligible. Applicant’s citation to Example 39 (Remarks at pg. 14), the Examiner emphasizes that the analysis of Example 39 under Step 2A Prong 1 found that the claim “does not recite any of the judicial exceptions….the claim does not recite any mathematical relationships, formulas or equations….the claim does not recite a mental process…the claim does not recite any method of organizing human activity…Thus, the claim is eligible because it does not recite a judicial exception.” In contrast, when evaluated under Step 2A Prong 1, Applicant’s claims plainly recite steps for falling under both the “Certain methods of organizing human activity” and the “Mental Processes” as well as the “Mathematical Concepts” abstract idea groupings, as explained in the Step 2A Prong One analysis of the §101 rejection. Therefore, in contrast to claim 1 of Example 39 that cited no abstract ideas, Applicant’s claims clearly recite one or more abstract ideas.
Furthermore, while claim 1 of Example 39 employs a computer-implemented neural network technique to perform the second stage training (i.e., an updated training set containing false positives), applicant’s claims, in contrast, do not recite or require multi-stage “training” to be performed, and certainly not in any discernible training or machine-learning that involves re-training, a feedback loop, or in a manner that otherwise causes an improvement to the computing device, to a neural network or machine-learning model or algorithm, to a machine or any technology, and in further contrast to Example 39, applicant’s claimed invention lacks any subsequent step(s) or result(s) for updating, retraining, or otherwise causing a change, improvement, transformation, or the like to the computing device, a particular machine or any technical process such as digital image facial detection, which is a critical distinction between the invention considered in Example 39 and applicant’s claimed invention.
Moreover, Applicant’s claims have not been shown to yield any discernible transformation, to train a neural network using transformed and created training data, or yield any other technical improvement comparable to the claimed digital facial image detection of Example 39. Accordingly, Applicant’s reliance on the eligibility rationale of Example 39 is not persuasive.
For the reasons above along with the reasons set forth below in the updated §101 rejection, Applicant’s amendments and supporting arguments are not sufficient to overcome the §101 rejection.
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 1-2, 6-8, and 10-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-2, 6-8, and 10-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the subject matter eligibility guidance set forth in MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106.03), it is first noted that the claimed apparatuses (claims 1-2, 6-8, and 10-12) and method (claim 13) are each directed to a potentially eligible category of subject matter (i.e., machine and process, respectively). Accordingly, claims 1-2, 6-8, and 10-13 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One of the eligibility inquiry (as explained in MPEP 2106.04), it is next noted that the claims recite an abstract idea that falls under the “Certain methods of organizing human activity” abstract idea grouping by reciting limitations that set forth activities for managing commercial interactions (advertising, marketing, or sales activities or behaviors; business relations) or managing personal behavior or interactions (i.e., user interactions, wherein the users may be customers, see, e.g., Spec. at par. 4), and steps that, but for the generic computer implementation, may be implemented as “Mental Processes” (e.g., observation, evaluation, judgment, or opinion), and also recite activities that fall into the “Mathematical Concepts” abstract idea grouping by setting forth activities that may be implemented as mathematical relationships (e.g., a graph represented as nodes, links, vector representations, scores, see, e.g., Spec. at pars. 43, 49-54, 60-63, and 73-76). The limitations reciting the abstract idea, as set forth in independent claim 1 are identified in bold text below, whereas the additional elements are presented in plain text and are separately evaluated under Step 2A Prong Two and Step 2B:
at least one memory configured to store computer program code; and one or more processors configured to operate according to the computer program code, the computer program code comprising (These are additional elements evaluated below under Step 2A Prong Two and Step 2B):
user acquisition code configured to cause one or more processors to acquire a factual feature of each of a plurality of users as user features (The “acquire” step covers activity for managing commercial interactions because the acquired factual feature directly pertains to sale/market activity (e.g., customer behavior such as purchase history, Spec. at pars. 4, 35, 66), and but for the generic computer implementation, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion. In addition, the “acquire” step is considered as insignificant extra-solution activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution data gathering activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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));
item acquisition code configured to cause the one or more processors to acquire features regarding a plurality of items as item features from a predetermined database (The “acquire” step covers activity for managing commercial interactions because the acquired features directly pertain to sale/market activity (e.g., products/services purchased, see Spec. at pars. 39, 66), and but for the generic computer implementation, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion. In addition, the “acquire” step is considered as insignificant extra-solution activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution data gathering activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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));
construction code configured to cause the one or more processors to, based on the user features and the item features, construct a graph including a plurality of user nodes representing the plurality of users, a plurality of item nodes representing the plurality of items, and links indicating mutual relationships between the plurality of user nodes and the plurality of item nodes (The “construct a graph” step covers activity for managing commercial interactions because the graph may directly pertain to sale/market activity (e.g., products/services purchased by consumers, see Spec. at pars. 39, 66), and but for the generic computer implementation, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, and furthermore the construction of the graph may be accomplished via mathematical relationships representing the user and item features, see, e.g., Fig. 9 depicting the graph of nodes and links via a vector space);
wherein the construction code is further configured to cause the one or more processors to construct a first relationship graph between the plurality of users based on explicit links created from shared factual features and implicit links predicted through embedding learning, a second relationship graph between the plurality of items, and a third relationship graph between the plurality of users and the plurality of items, and perform learning of vector representations of nodes and links while performing weighting on the vector representations for links based on the closeness scores between nodes (This limitation describes activity for managing commercial interactions because the graph may directly pertain to sale/market activity (e.g., products/services purchased by consumers, see Spec. at pars. 39, 66), and but for the generic computer implementation, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, and furthermore the construction of the first graph may be accomplished via mathematical relationships, see, e.g., pars. 49-55, 74-77 and Figs. 4A/B, Fig. 5, and Fig. 6A, describing/displaying examples of mathematical relations and calculations to construct the graph using the embedding learning, vector representations, and closeness scores),
wherein construction code is further configured to create explicit links between user nodes sharing common factual features including at least one of same IP address, same address, or same credit card number, and creates implicit links between user nodes by embedding link features in a common feature space and inferring relationships between nodes not connected by explicit links (This limitation describes activity for managing commercial interactions because the graph may directly pertain to sale/market activity (e.g., products/services purchased by consumers, see Spec. at pars. 39, 66), and but for the generic computer implementation, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, and furthermore the construction of the first graph may be accomplished via mathematical relationships, see, e.g., pars. 45-55, 66, and Figs. 4A/B, describing/displaying examples of mathematical relations and calculations to create the explicit links between nodes using common factual features [i.e. user features], e.g., Fig. 4A, and implicit links, e.g., Fig. 4B, using the embedding learning and feature space);
extraction code configured to cause the one or more processors to extract a node representation, in the graph, of any node of the plurality of user nodes and the plurality of item nodes from the graph (The “extract” step covers activity for managing commercial interactions because the extraction of a node representation may directly pertain to sale/market activity (e.g., target marketing, see Spec. at pars. 117, 126), and but for the generic computer implementation, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, and furthermore the construction of the graph may be accomplished via mathematical relationships representing the user and item features, see, e.g., pars. 74-76, describing mathematical algorithm for extracting a user node representation in the vector space of nodes and links);
wherein the extraction code is configured to cause the one or more processors to extract, as the node representation, relationships of any node of the plurality of user nodes and the plurality of item nodes with the plurality of user nodes and the plurality of item nodes, from the graph (The “extract” step covers activity for managing commercial interactions because the extraction of a node representation and relationships may directly pertain to sale/market activity (e.g., target marketing, see Spec. at pars. 117, 126), and but for the generic computer implementation, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, and furthermore the construction of the graph may be accomplished via mathematical relationships representing the user and item features, see, e.g., pars. 74-76, describing mathematical algorithm for extracting a user node representation in the vector space of nodes and links);
wherein the any node is any user node of the plurality of user nodes, and the extraction code is configured to cause the one or more processors to extract, as the node representation, a user representation representing relationships of the any user node with the plurality of user nodes and the plurality of item nodes (The any node being a user node and “extract” step cover activity for managing commercial interactions because the extraction of a node representation may directly pertain to sale/market activity (e.g., target marketing, see Spec. at pars. 117, 126), and but for the generic computer implementation, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, and furthermore the construction of the graph may be accomplished via mathematical relationships representing the user and item features, see, e.g., pars. 74-76, describing mathematical algorithm for extracting user node representations),
wherein, in the user representation, pieces of information regarding one or more users, items, genres, and shops that are connected to a first user by implicit and explicit links are reflected in the user representation, wherein a node representation includes representation of an item or of a genre, wherein closeness scores of nodes connected to the first user are included in the user representation, and wherein the user representation of the first user includes a closeness score with respect to a neighboring representation (The details in these “wherein” statements cover activity for managing commercial interactions because the information regarding users, genres, and shops, the closeness scores and neighboring representation related thereto may all directly pertain to sale/market activity (e.g., target marketing, see Spec. at pars. 117, 126), and but for the generic computer implementation, these details could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, and furthermore the construction of the graph may be accomplished via mathematical relationships representing the user and item features, see, e.g., pars. 73-76, describing mathematical algorithm for extracting and representing commercial/marketing information via the nodes and links of the knowledge graph).
Claim 13 is directed to a method and recites substantially similar limitations as those set forth in claim 1 and discussed above, and has therefore been determined to recite the same abstract idea as claim 1.
With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP 2106.04(d)), the judicial exception is not integrated into a practical application. Independent claim 1 recites the additional elements of an information processing apparatus, at least one memory configured to store computer program code; and one or more processors configured to operate according to the computer program code, the computer program code comprising, user acquisition code configured to cause one or more processors, item acquisition code configured to cause the one or more processors, construction code configured to cause the one or more processors, extraction code configured to cause the one or more processors, however independent claim 13 does not recite any additional elements. The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (code, software, program, etc.) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquire activities are interpreted as additional elements, these activities at most amount to insignificant extra-solution activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry (as explained in MPEP 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent claim 1 recites the additional elements of an information processing apparatus, at least one memory configured to store computer program code; and one or more processors configured to operate according to the computer program code, the computer program code comprising, user acquisition code configured to cause one or more processors, item acquisition code configured to cause the one or more processors, construction code configured to cause the one or more processors, extraction code configured to cause the one or more processors, however independent claim 13 does not recite any additional elements. The additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements or computer-executed instructions (code, software, program, etc) to perform the abstract idea. See, e.g., Spec. at par. [0028], noting that “the user apparatus 11 is not limited to an apparatus of the form shown in FIG. 1, and may also be an apparatus such as a desktop PC (Personal Computer) or a laptop PC.” which merely serves to tie the abstract idea to a particular technological environment (generic computing environment), similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment) and does not amount to significantly more than the abstract idea itself. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Even if the acquire activities are interpreted as additional elements, these activities nevertheless amount to insignificant extra-solution data gathering or output activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2, 6-8, and 10-12 recite the same abstract idea(s) as recited in the independent claims, and have been determined to recite further details for activities for managing commercial interactions (advertising, marketing, or sales activities or behaviors) or managing personal behavior or interactions (e.g., following rules or instructions) or and/or activities that can be performed mentally under the same “certain methods of organizing human activity” or “mental processes” or “mathematical concepts” abstract idea groupings as discussed above. With respect to the graph being implemented as a neural network (claims 6-8 and 12), it is noted that, even if the neural network does not fall within the realm of “Certain Methods of Organizing Human Activity” or “Mental Processes,” the neural network falls under mathematical relationships as understood by those skilled in the art. “Adding one abstract idea (math) to another abstract idea” (fundamental economic practice) “does not render the claim non-abstract.” See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1326-27, 122 USPQ2d 1377, 1379-80 (Fed. Cir. 2017) (claim reciting multiple abstract ideas, i.e., the manipulation of information through a series of mental steps and a mathematical calculation, was held directed to an abstract idea and thus subjected to further analysis in part two of the Alice/Mayo test)). Nevertheless, even if considered as an additional element, the use of a neural network to implement the graph is recited at a high level of generality and fails to provide an improvement to the functioning of a computer or to any other technology or technical field, and under Step 2B it is noted that the use of a neural network is well-understood, routine, and conventional activity in the art. See, e.g., Negishi, US Patent No. 5,444,819 (col. 13, lines 10-13), noting “predicting and analyzing system, using neural networks, according to the conventional art.” See also, Dailey et al., US Patent No. 6,917,952 (col. 10, lines 10-12), noting “The preferred embodiment uses neural networks, and conventional methods of training them as are known in the art.” The code and one or more processors recited in claims 7-8, 10, and 12 amounts to using generic computing elements or instructions (code, software, program, etc.) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment), and is thus insufficient to add a practical application or significantly more. See MPEP 2106.05(f) and 2106.05(h). See also, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
Allowable over the prior art
Claims 1-2, 6-8, and 10-13 are allowable over the prior art. The closest prior art references of record, Curtiss et al. (US 2014/0025702) and Sun et al. (US 2021/0248449), are directed to filtering structured search queries based on privacy settings and to a recommender system using Bayesian graph convolution networks, respectively. The prior art of record teaches several features recited in independent claims 1 and 13, including, for example, at least one memory configured to store computer program code; and one or more processors configured to operate according to the computer program code, the computer program code comprising: user acquisition code configured to cause the one or more processors to acquire a factual feature of each of a plurality of users as user features; item acquisition code configured to cause the one or more processors to acquire features regarding a plurality of items as item features from a predetermined database; construction code configured to cause the one or more processors to, based on the user features and the item features, construct a graph including a plurality of user nodes representing the plurality of users, a plurality of item nodes representing the plurality of items, and links indicating mutual relationships between the plurality of user nodes and the plurality of item nodes; and extraction code configured to cause the one or more processors to extract a node representation, in the graph, of any node of the plurality of user nodes and the plurality of item nodes from the graph; and wherein the any node is any user node of the plurality of user nodes, and the extraction code is configured to cause the one or more processors to extract, as the node representation, a user representation representing relationships of the any user node with the plurality of user nodes and the plurality of item nodes, as recited in claim 1 and as similarly encompassed by independent claim 13 (See Non-Final OA mailed 02/26/2025 for prior art citations pertinent to the above-noted claim features). However, Curtiss et al. and the other prior art references of record do not teach or render obvious the claimed information processing apparatus comprising: at least one memory configured to store computer program code; and one or more processors configured to operate according to the computer program code, the computer program code comprising: user acquisition code configured to cause the one or more processors to acquire a factual feature of each of a plurality of users as user features; item acquisition code configured to cause the one or more processors to acquire features regarding a plurality of items as item features from a predetermined database; construction code configured to cause the one or more processors to, based on the user features and the item features, construct a graph including a plurality of user nodes representing the plurality of users, a plurality of item nodes representing the plurality of items, and links indicating mutual relationships between the plurality of user nodes and the plurality of item nodes; wherein the construction code is further configured to cause the one or more processors to construct a first relationship graph between the plurality of users based on explicit links created from shared factual features and implicit links predicted through embedding learning, a second relationship graph between the plurality of items, and a third relationship graph between the plurality of users and the plurality of items, and perform learning of vector representations of nodes and links while performing weighting on the vector representations for links based on the closeness scores between nodes; wherein construction code is further configured to create explicit links between user nodes sharing common factual features including at least one of same IP address, same address, or same credit card number, and creates implicit links between user nodes by embedding link features in a common feature space and inferring relationships between nodes not connected by explicit links; extraction code configured to cause the one or more processors to extract a node representation, in the graph, of any node of the plurality of user nodes and the plurality of item nodes from the graph, wherein the extraction code is configured to cause the one or more processors to extract, as the node representation, relationships of any node of the plurality of user nodes and the plurality of item nodes with the plurality of user nodes and the plurality of item nodes, from the graph, wherein the any node is any user node of the plurality of user nodes, and the extraction code is configured to cause the one or more processors to extract, as the node representation, a user representation representing relationships of the any user node with the plurality of user nodes and the plurality of item nodes, wherein, in the user representation, pieces of information regarding one or more users, items, genres, and shops that are connected to a first user by implicit and explicit links are reflected in the user representation, wherein a node representation includes representation of an item or of a genre, wherein closeness scores of nodes connected to the first user are included in the user representation, and wherein the user representation of the first user includes a closeness score with respect to a neighboring representation (as per independent claim 1) and information processing method comprising: acquiring a factual feature of each of a plurality of users as user features; acquiring features regarding a plurality of items as item features from a predetermined database; constructing, based on the user features and the item features, a graph including a plurality of user nodes representing the plurality of users, a plurality of item nodes representing the plurality of items, and links indicating mutual relationships between the plurality of user nodes and the plurality of item nodes; constructing a first relationship graph between the plurality of users based on explicit links created from shared factual features and implicit links predicted through embedding learning, a second relationship graph between the plurality of items, and a third relationship graph between the plurality of users and the plurality of items, and perform learning of vector representations of nodes and links while performing weighting on the vector representations for links based on the closeness scores between nodes; creating explicit links between user nodes sharing common factual features including at least one of same IP address, same address, or same credit card number, and creates implicit links between user nodes by embedding link features in a common feature space and inferring relationships between nodes not connected by explicit links; extracting, as the node representation, relationships of any node of the plurality of user nodes and the plurality of item nodes with the plurality of user nodes and the plurality of item nodes, from the graph, wherein the any node is any user node of the plurality of user nodes, and the extracting comprises extracting, as the node representation, a user representation representing relationships of the any user node with the plurality of user nodes and the plurality of item nodes, wherein, in the user representation, pieces of information regarding one or more users, items, genres, and shops that are connected to a first user by implicit and explicit links are reflected in the user representation, wherein a node representation includes representation of an item or of a genre, wherein closeness scores of nodes connected to the first user are included in the user representation, and wherein the user representation of the first user includes a closeness score with respect to a neighboring representation (as per independent claim 13), thereby rendering independent claims 1/13 and dependent claims 2, 6-8, and 10-12 as allowable over the prior art. Claims 1-2, 6-8, and 10-13 are not allowed, however, because they stand rejected under 35 USC §101, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Organization Mining Using Online Social Networks. Fire, Michael; Puzis, Rami. Networks and Spatial Economics 16.2: 545-578. Springer Nature. B.V. (Jun 2016): discloses the application or machine learning to identify organizational relationships, including organizational graphs and social media profile mining related thereto.
Knowledge-Enhanced Graph Neural Networks for Sequential Recommendation. Wang, Baocheng; Cai, Wentao. Information11.8: 388. MDPI AG. (2020): discloses the application of graph neural networks (GNNs) top improve the quality and efficiency of recommendations.
Dai et al. (US 2023/0089148): discloses features for interactive image scene graph pattern and search using a graph neural network.
Darr (US 2007/0226248): discloses techniques for social network pattern detection, including representing users, events, activities, etc. in graphs via nodes and connections based on interactions, degree of separation, and the like (See, e.g., Figs. 2-8).
Hoque et al. (US 2018/0005121): discloses features for providing enhanced relationship graph signals, including identifying collaboration metrics based on user interaction data of users of an application from an enterprise (pars. 14, 35, 46, 54-56, 84, and Fig. 4); accessing enterprise organizational data of the enterprise (paragraphs 81-82); identifying topic data from the user interaction data and the enterprise organizational data (pars. 13, 23, 32, and 81); and training a machine learning model (pars. 14, 35, and 83: relationship graph may use machine learning techniques to connect people to the relevant content).
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
11/25/2025