Detailed Action
This Final Rejection is responsive to the amendment to the claims filed on 8/6/2025. Claims 1-15 are pending. Claims 1, 10 and 15 are independent claims.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The rejections of claims 8 and 14 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement, have been withdrawn as necessitated by the amendment.
Claim 18 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 18, the specification lacks support for based on the affinity graph comprising the edge indicating the cannibalization relation type between the two nodes, performing optimization-based automatic decision making to automatically place an order indicating a first quantity of products for the first product and a second quantity of products for a second product. The specification discloses the “Finally, an optimization problem may be defined and solved for the automatic ordering of products.” (42). However, there is no mention of automatically “indicating a first quantity of products for the first product and a second quantity of products for a second product” as claimed.
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 1-15 remain, and 16-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a “method” and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites:
affinity graph extraction, the method comprising: (A mental process extracting information from an affinity graph).
a) building an affinity graph based on data (A mental process of creating a graphical representation of relationships based on data received).
learn node and relation representations in the affinity graph (a mental process of learning what the graph details entail). and d) adjusting the affinity graph based on the scoring function and iteratively repeating operations b ), c ), and d) one or more times to determine new relations between nodes representing the undetermined relationships. (A mental process of adjusting graph based on another factor such as the scoring function, determining new relations).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements:
A computer-implemented method; This limitation merely recites a computer to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
the data including multiple elements with undetermined relationships, wherein each element is represented as a node in the affinity graph and relations between nodes are represented as edges in the affinity graph; This limitation merely further specifies a particular technological environment which the abstract ideas are to take place, i.e., a field of use (See MPEP 2106.05(h)).
applying a machine learning algorithm; This limitation merely recites a computer/machine learning model to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
wherein each edge has a relation type selected from a set of two or more relation types; This limitation merely further specifies a particular technological environment which the abstract ideas are to take place, i.e., a field of use (See MPEP 2106.05(h)).
c) learning a machine learning scoring function for each relation type; This limitation merely recites a computer/machine learning model to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, there are additional elements that only amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above with respect to integration of the abstract ideas into practical application, the remaining additional elements only specify technological environment to perform the method. Limiting the abstract idea to a particular field of use or technological environment cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 1:
A process as recited by Claim 1.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 does not recite any abstract ideas
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional element:
The method according to claim 1, wherein the multiple elements represent multiple
products and the two or more relation types include a product cannibalization relation type
and a product binding relation type. This limitation merely further specifies a particular technological environment which the abstract ideas are to take place, i.e., a field of use (See MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract ideas into practical application, the additional elements only specify technological environment to perform the method. Limiting the abstract idea to a particular field of use or technological environment cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 1:
A process as recited by Claim 1.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites:
The method according to claim 1, wherein the building the affinity graph includes
extracting cannibalizing and binding elements from the data. (A mental process of building a visual representation with specific information from data received).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 does not further recite any additional elements.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 1:
A process as recited by Claim 1.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 does not recite any abstract ideas
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional element:
The method according to claim 1, the method comprising receiving the data from a
distributed network of sensors prior to building the affinity graph. This limitation merely recites insignificant extra-solution activity of data gathering (See MPEP 2106.05(g)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. The additional element of “The method according to claim 1, the method comprising receiving the data from a distributed network of sensors prior to building the affinity graph.” is well understood, routine, and conventional activity of “Receiving or transmitting data over a network, e.g., using the internet to gather data” (See MPEP 2106.05(d)). Limiting the abstract idea to a well understood, routine and conventional activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
A process as recited by Claim 1.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites:
The method according to claim 1, wherein the building the affinity graph is performed
using hypothesis testing. (A mental process and a mathematical process of building a visual representation while calculating a statistical test to build it)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 does not further recite any additional elements.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
A process as recited by Claim 1.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites:
The method according to claim 1, further comprising performing optimization-based… decision making based on the affinity graph. (A mental process of making decisions based on a graph).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 does further recites additional element:
automatic; This limitation merely recites a computer to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. As
discussed above with respect to integration of the abstract idea into practical application, the additional element only amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 1:
A process as recited by Claim 1.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites:
The method according to claim 1, wherein the adjusting includes adding and/or
removing an edge between nodes in the affinity graph. (A mental process of adding or removing elements of a visual representation).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 does not further recite any additional elements.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
A process as recited by Claim 1.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites:
The method according to claim 1, wherein the adjusting includes removing an edge
between two nodes from the affinity graph (A mental process of removing elements of a visual representation).
if the scoring function for a relation between the two nodes is greater than a threshold value (A mathematical process of calculating a scoring function then using an inequality to determine the next action).
and adding an edge in the affinity graph between the two nodes (A mental process of adding elements of a visual representation).
if the scoring function for the relation between the two nodes is smaller than the threshold value. (A mathematical process of calculating a scoring function then using an inequality to determine the next action).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 does not further recite any additional elements.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 1:
A process as recited by Claim 1.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites:
learns cardinalities of each of the multiple elements. (A mental process of learning details of elements).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 does further recites additional element:
The method according to claim 1, wherein the applying a machine learning algorithm further; This limitation merely recites a computer/machine learning model to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element only amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 1:
Claim 10 recites a “system” and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites:
affinity graph extraction, the method comprising: (A mental process extracting information from an affinity graph).
a) building an affinity graph based on data (A mental process of creating a graphical representation of relationships based on data received).
learn node and relation representations in the affinity graph (a mental process of learning what the graph details entail).
and d) adjusting the affinity graph based on the scoring function and iteratively repeating
operations b ), c ), and d) one or more times to determine new relations between nodes
representing the undetermined relationships. (A mental process of adjusting graph based on another factor such as the scoring function, determining new relations).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 further recites additional elements:
A system comprising one or more hardware processors which, alone or in
combination, are configured to provide for execution of a method of affinity graph extraction comprising: This limitation merely recites a computer to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
the data including multiple elements with undetermined relationships, wherein each element is represented as a node in the affinity graph and relations between nodes are represented as edges in the affinity graph; This limitation merely further specifies a particular technological environment which the abstract ideas are to take place, i.e., a field of use (See MPEP 2106.05(h)).
applying a machine learning algorithm; This limitation merely recites a computer/machine learning model to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
wherein each edge has a relation type selected from a set of two or more relation types; This limitation merely further specifies a particular technological environment which the abstract ideas are to take place, i.e., a field of use (See MPEP 2106.05(h)).
c) learning a machine learning scoring function for each relation type; This limitation merely recites a computer/machine learning model to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, there are additional elements that only amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above with respect to integration of the abstract ideas into practical application, the remaining additional elements only specify technological environment to perform the method. Limiting the abstract idea to a particular field of use or technological environment cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 1:
A machine as recited by Claim 10.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 11 does not recite any abstract ideas
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 11 further recites additional element:
The system of claim 10, wherein the multiple elements represent multiple products and the two or more relation types include a product cannibalization relation type
and a product binding relation type. This limitation merely further specifies a particular technological environment which the abstract ideas are to take place, i.e., a field of use (See MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract ideas into practical application, the additional elements only specify technological environment to perform the method. Limiting the abstract idea to a particular field of use or technological environment cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 1:
A machine as recited by Claim 10.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 12 recites:
The system of claim 10, wherein the method further comprises performing optimization-based… decision making based on the affinity graph. (A mental process of making decisions based on a graph)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 12 does further recites additional element:
automatic; This limitation merely recites a computer to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element only amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 1:
A machine as recited by Claim 10.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 13 recites:
The system of claim 10, wherein the adjusting includes adding and/or
removing an edge between nodes in the affinity graph. (A mental process of adding or removing elements of a visual representation).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 13 does not further recite any additional elements.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 1:
A machine as recited by Claim 10.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 14 recites:
The system of claim 10, wherein the adjusting includes removing an edge between two nodes from the affinity graph (A mental process of removing elements of a visual representation).
if the scoring function for a relation between the two nodes is greater than a threshold value (A mathematical process of calculating a scoring function then using an inequality to determine the next action).
and adding an edge in the affinity graph between the two nodes (A mental process of adding elements of a visual representation).
if the scoring function for the relation between the two nodes is smaller than the threshold value. (A mathematical process of calculating a scoring function then using an inequality to determine the next action).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 14 does not further recite any additional elements.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 1:
Claim 15 recites a “tangible, non-transitory computer-readable medium” and is thus a manufacture, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites:
affinity graph extraction, the method comprising: (A mental process extracting information from an affinity graph).
a) building an affinity graph based on data (A mental process of creating a graphical representation of relationships based on data received).
learn node and relation representations in the affinity graph (a mental process of learning what the graph details entail).
and d) adjusting the affinity graph based on the scoring function and iteratively repeating
operations b ), c ), and d) one or more times to determine new relations between nodes
representing the undetermined relationships. (A mental process of adjusting graph based on another factor such as the scoring function, determining new relations).
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 further recites additional elements:
A tangible, non-transitory computer-readable medium having instructions thereon
which, upon being executed by one or more hardware processors, alone or in combination,
provide for execution of a method of affinity graph extraction comprising: This limitation merely recites a generic computer component to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
the data including multiple elements with undetermined relationships, wherein each element is represented as a node in the affinity graph and relations between nodes are
represented as edges in the affinity graph; This limitation merely further specifies a particular technological environment which the abstract ideas are to take place, i.e., a field of use (See MPEP 2106.05(h)).
applying a machine learning algorithm; This limitation merely recites a computer/machine learning model to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
wherein each edge has a relation type selected from a set of two or more relation types; This limitation merely further specifies a particular technological environment which the abstract ideas are to take place, i.e., a field of use (See MPEP 2106.05(h)).
c) learning a machine learning scoring function for each relation type; This limitation merely recites a computer/machine learning model to perform the abstract ideas of the claims e.g., “apply it to a device” (See MPEP 2106.05(f)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, there are additional elements that only amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above with respect to integration of the abstract ideas into practical application, the remaining additional elements only specify technological environment to perform the method. Limiting the abstract idea to a particular field of use or technological environment cannot provide an inventive concept. The claim is not patent eligible.
Claim 16 is directed towards an abstract idea without significantly more. Abstract idea in Step 2A, prong 1: “wherein learning the machine learning scoring function for each relation type comprises using the machine learning scoring function to determine a score for two nodes from the affinity graph, and wherein adjusting the affinity graph comprises: based on comparing the score for the two nodes from the affinity graph with a threshold, adding an edge to connect the two nodes, wherein the edge indicates the cannibalization relation type between the two nodes.” The determination of a score and comparison of the score, and the adding of an edge to a graph are activities which can be performed mentally, with math and/or pen and paper.
Claim 17 is directed towards an abstract idea without significantly more. Abstract idea in Step 2A, prong 1: “wherein a first node of the two nodes is associated with a first product and a second node of the two nodes is associated with a second product, and wherein adding the edge indicating the cannibalization relation type between the two nodes indicates that the first product causes fewer sold items of the second product” The determination of nodes in a graph, and the adding of an edge to a graph are activities which can be performed mentally, with math and/or pen and paper.
Claim 18 is directed towards an abstract idea without significantly more. Abstract idea in Step 2A, prong 1: “based on the affinity graph comprising the edge indicating the cannibalization relation type between the two nodes, performing optimization-based decision making to automatically place an order indicating a first quantity of products for the first product and a second quantity of products for a second product.” The determination a decision to automatically place an order of products can be performed mentally.
Additional elements in Step 2A, prong 2: “performing optimization-based automatic decision making to automatically place an order indicating a first quantity of products for the first product and a second quantity of products for a second product.”. This limitation, recited at a high level of generality, only amounts to “apply it” using a generic computer component (MPEP 2106.05(f)).
Step 2B: The limitation of “performing optimization-based automatic decision making to automatically place an order indicating a first quantity of products for the first product and a second quantity of products for a second product.” does not generate a practical application, which amounts to significantly more than the abstract idea. This limitation, recited at a high level of generality, only amounts to “apply it” using a generic computer component (MPEP 2106.05(f)).
Claim 19 recites a similar decision-making step to claim 18 and is likewise rejected.
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.
Claims 1-3, 10-11, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al., US PG PUB 2016/0246901 in view of Resheff et al. US PG PUB 2021/0065245, in view of Kim et al. US PG PUB 20040103018, further in view of Ain et al. "Machine-learning Scoring Functions to Improve Structure-Based Binding Affinity Prediction and Virtual Screening" 2015.
Regarding Claim 1, Kashyap et al. teaches
A computer-implemented method for affinity graph extraction, the method comprising: (Kashyap et al. Par. 21, “Described herein are methods, computer systems and Software for calculating net affinity between entities in an affinity graph”, signifies extracting data from an affinity graph to calculate net affinity).
The direct affinity between two entities is at least one of non-directed, directed and temporal affinity. An affinity graph is then created with entities as nodes and corresponding direct affinities between them as edges”, signifies an affinity graph created from raw data that will then have relationships map once the graph is built, the affinity graph has elements as nodes and relationships as edges).
wherein each edge has a relation type selected from a set of two or more relation types; (Kashyap et al., Par. 60, “After receiving the raw data from memory 420, the processing device 430 determines three types of direct affinities between the entities. Such as non-directed, directed and temporal affinities, using the raw data. The processing device 430 further creates an affinity graph with entities as nodes and edges between them representing the respective non-directed affinity, directed affinity or temporal affinities.”, signifies three relation types for the edges to be).
function for each relation type; (Kashyap et al. Par. 30 “Following is an example of formula for calculating non-directed affinity MAB between two entities A and B…”, Par. 38 “Following is an example of formula for calculating directed affinity DAB between two entities A and B…” and Par. 46 “Following is an example of formula for calculating temporal affinity TAB between two entities A and B…”, signifies scoring functions for each of relation types taught).
Kashyap et al. does not teach the following limitation, however Resheff et al. does.
b.) applying a machine learning algorithm to learn node and relation representations in the affinity graph, (Resheff et al. Par. 2 “The method also includes clustering groups of nodes within the plurality of nodes to form a plurality of clusters among the plurality of nodes. The method also includes labeling the plurality of edges as a plurality of relationships types. Labeling is performed by receiving, as input to a machine learning model, a vector comprising attributes representing the plurality of clusters, the plurality of nodes, and the plurality of edges. Labeling is also performed by outputting, from the machine learning model, a plurality of probabilities. Each of the plurality of probabilities corresponds to a corresponding probability that an edge in the plurality of edges represents a relationship type between two nodes in the plurality of nodes.”, signifies a machine learning model that labels and predicts nodes and their relationships).
In addition, Kashyap et al. teach in Figs. 1, 3A-3D, Par. 33 “104 represents directed affinity between the entities A and B. Directed affinity between two entities indicates the additional likelihood of selecting an entity provided the other entity has been selected. For example, in a retail scenario, directed affinity 104 from product A to B is the likelihood of purchasing B, provided A has been purchased.”, signifies different elements to different nodes and describes product binding type as an example where describes likelihood of buying one item and then another) --and a binding relation type.
Kashyap fails to explicitly teach wherein the two or more relation types include a cannibalization relation type. However, Kim et al. teach in Par. 20 “Products with negative relationships to the promoted product are referred to as cannibalized products.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”, signifies product cannibalization in a graph depicting relationship). Kim et al., Kashyap et al., Resheff et al., Ain et al., and Kim et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the cannibalizing data and relationship types taught by Kim et al. The motivation to do so is to analyze product relationships in sales as either products increasing or reducing another product’s sales (Kim et al., Par. 38, “As previously presented, affinity relationships produce coefficients that are greater than 1, while cannibalized relationships produce coefficients that are less than 1.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”).
d) adjusting the affinity graph based on the scoring function and iteratively repeating operations b), c), and d) one or more times to determine new relations between nodes representing the undetermined relationships. (Kashyap et al. Par. 55 “Thus, the three types of net affinities between any two nodes in the affinity graph can be calculated using the effective electrical circuit between the two nodes. It should be noted that the affinity graph could consist of more number of nodes and the electrical circuit topology may be extended to affinity graph with higher number of nodes in a similar manner.”, and Par. 64, “Further, affinity graph is created with restaurants as nodes and respective affinities between them as edges. The edges could be of three types such as non-directed, directed and temporal affinities. Net affinity algorithm in accordance with the present invention is used to determine net affinity scores between all pairs of restaurants. Further, customer input is received in form of restaurants he likes. The restaurants input by the customer may be located at customer's home city or other places not in the vicinity of the hotel. Further, customer could input his/her preferences or desired attributes for the restaurants…”, signifies adjusting the graph by extending more edges and nodes based on affinity which repeats the steps to create a graph and calculate affinity scores, Par. 64 shows steps to create the graph) Kashyap et al. and Resheff et al. both teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., with the machine learning algorithm that learns nodes and relations of Resheff et al. The motivation to do so is to be able to predict relationships, some that may be hidden in underlying data, as well as make predictions and take actions based on those relationships (Specifically, a machine learning algorithm predicts relationship types based on relationship behavior inferred or predicted from the underlying financial transaction data. Automatic computer actions, such as taking a computerized security action or transmitting an electronically actionable message, can then be taken based on the automatically discerned relationship labels according to pre-defined rules or policies. Thus, the one or more embodiments provide for a technical ability to automatically discern hidden relationships in underlying data and then act on those relationships.”).
Kashyap et al., nor Resheff et al. teaches the following limitation however Ain et al. does.
c) learning a machine learning scoring (Ain et al., Fig. 3 and Pg. 407, Col. 2, Par. 2 “The training set is used for model training and selection, with the selected model after training becoming the SF. The SF can now be used to predict binding of test set complexes from their features.”, signifies training a machine learning scoring function to determine a relation in binding affinity). Kashyap et al., Resheff et al., and Ain et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al. with the machine learning scoring functions of Ain et al. The motivation to do is to use machine learning to create and updating a more optimized scoring function for the specific task rather than a predetermined one (Ain et al., Page. 406, Col. 2, Par. 3, “namely machine-learning SFs where machine learning is used to replace a predetermined functional form. Different uses of machine learning in docking have been reviewed, including related applications such as iterative rescoring of poses or building optimal consensus scores.”).
Regarding Claim 2, Kashyap et al., teaches:
The method according to claim 1, and the binding relation type is a product binding relation type products -- (Kashyap et al. Figs. 1, 3A-3D, Par. 33 “104 represents directed affinity between the entities A and B. Directed affinity between two entities indicates the additional likelihood of selecting an entity provided the other entity has been selected. For example, in a retail scenario, directed affinity 104 from product A to B is the likelihood of purchasing B, provided A has been purchased.”, which signifies different elements to different nodes and describes product binding type as an example where describes likelihood of buying one item and then another);
Kashyap et al., Resheff et al. nor Ain et al. teaches the following limitation however Kim et al. does. wherein the cannibalization relation type is a product cannibalization relation type.
(Kim et al., Par. 20 “Products with negative relationships to the promoted product are referred to as cannibalized products.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”, signifies product cannibalization in a graph depicting relationship). Kashyap et al., Resheff et al., Ain et al., and Kim et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the cannibalizing data and relationship types taught by Kim et al. The motivation to do so is to analyze product relationships in sales as either products increasing or reducing another product’s sales (Kim et al., Par. 38, “As previously presented, affinity relationships produce coefficients that are greater than 1, while cannibalized relationships produce coefficients that are less than 1.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”).
Regarding Claim 3, Kashyap et al., teaches:
The method according to claim 1, wherein the building the affinity graph (Kashyap et al. Par. 16, “The raw data is used to determine direct affinities between the entities. The direct affinity between two entities is at least one of non-directed, directed and temporal affinity. An affinity graph is then created with entities as nodes and corresponding direct affinities between them as edges”, signifies an affinity graph created from raw data that will then have relationships map once the graph is built, the affinity graph has elements as nodes and relationships as edges).
and binding elements (Kashyap et al. Figs. 1, 3A-3D, Par. 33 “104 represents directed affinity between the entities A and B. Directed affinity between two entities indicates the additional likelihood of selecting an entity provided the other entity has been selected. For example, in a retail scenario, directed affinity 104 from product A to B is the likelihood of purchasing B, provided A has been purchased.”, signifies different elements to different nodes and describes product binding type as an example where describes likelihood of buying one item and then another)
from the data. (Kashyap et al., Par. 49 “At step 202, raw data regarding entities is collected… At step 204, the raw data is used to determine direct affinities between the entities. The direct affinities determined at the step 204 are at least one of directed affinity, non-directed affinity and temporal affinity between any two entities.”, signifies elements and relationships from raw data.
Kashyap et al., Resheff et al. nor Ain et al. teaches the following limitation however Kim et al. does.
includes extracting cannibalizing (Kim et al., Fig. 1, Par. 20 “Products with negative relationships to the promoted product are referred to as cannibalized products.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”, signifies product cannibalization in a graph depicting relationship with product elements in the figure with coke, Doritos and Pepsi and extracting data from viewing the graph).
Claims 10-11 recite a system claim that corresponds directly with the method claims 1-2 respectively and therefore rejected for the same reasons given.
Claim 15 is the non-transitory computer-readable medium claim that corresponds directly with the method claim 1 and therefore rejected for the same reasons given in the rejection of claim 1.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al., US PG PUB 2016/0246901 in view of Resheff et al. US PG PUB 2021/0065245, in view of Kim et al. US PG PUB 20040103018, further in view of Ain et al. "Machine-learning Scoring Functions to Improve Structure-Based Binding Affinity Prediction and Virtual Screening" 2015, as applied to claims 1, 10, 15 above, and further in view of Costabello et al. US PG PUB 2019/0287006.
Regarding Claim 4, Kashyap et al., Resheff et al. nor Ain et al. teaches the following limitation however Costabello et al. does.
The method according to claim 1, the method comprising receiving the data from a distributed network of sensors prior to building the affinity graph. (Costabello et al., Figs. 1, 3, 5, Par. 26, “the machine and sensor data 105 may be another source of data and information. In an IoT environment, many systems and products are equipped with numerous sensors or diagnostic equipment that may provide a plethora of machine and sensor data 105. There may be a number of physical devices, vehicles, appliances, systems, or products that are equipped with electronics, software, and sensors, where most, if not all, of these items may be connected to a network and share some measure of connectivity with each other. This may enable these and other pieces of equipment to communicate and exchange data”, and Par. 6, “FIG. 5 illustrates a data flow diagram 500 for knowledge graph generation. As depicted, the knowledge graph generator 303 may generate the knowledge graph 302 using a variety of data sources. These may include components from the data source layer 101, analytics layer 111, applications layer 121, or other data source 630 not depicted in FIG. 1. In most cases, data may largely be acquired from machines and sensor equipment as shown in FIG. 1”, signifies receiving data from multiple connected sensors and then building the graph using the data).
Kashyap et al., Resheff et al., Ain et al., and Costabello et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the sensors of Costabello et al. The motivation to do so is to detect data remotely and give analytics (Costabello et al., Par. 26 “This may also allow various systems, objects, and items to be detected, sensed, or remotely controlled over one or more networks, creating a vast array of asset management functionalities. These may include abilities to provide data analytics on equipment”).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al., US PG PUB 2016/0246901 in view of Resheff et al. US PG PUB 2021/0065245, in view of Kim et al. US PG PUB 20040103018, further in view of Ain et al. "Machine-learning Scoring Functions to Improve Structure-Based Binding Affinity Prediction and Virtual Screening" 2015, as applied to claims 1, 10, 15 above, and further in view of Aili et al. US PG PUB 2022/0223146.
Regarding Claim 5, Kashyap et al., Resheff et al., nor Ain et al. teaches the following limitation however Aili et al. does.
The method according to claim 1, wherein the building the affinity graph is performed using hypothesis testing. (Aili et al. Fig. 5 and Par. 50, “Once an initial knowledge graph 501 is established, a probability analyzer 504 determines the most likely intention 507 and sends it to an alternate intent hypothesis tester 509 which confirms or rejects the intent based on feedback from the client/speaker 508. If the intent is not accepted 506, an incongruity detector 503 analyzes the knowledge graph and generates new nodes or edges in the knowledge graph 501.”, signifies using a hypothesis test to build an accurate graph).
Kashyap et al., Resheff et al., Ain et al., and Aili et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the hypothesis tester of Aili et al. The motivation to do so is the create a knowledge graph with the correct and accurate intentions and relation (Aili et al., Par. 50, “The process starting with the alternate intent hypothesis generator begins anew and this is repeated until the most accurate intentions are determined and the conversation continues 510.”)
Claims 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable Kashyap et al., US PG PUB 2016/0246901 in view of Resheff et al. US PG PUB 2021/0065245, in view of Kim et al. US PG PUB 20040103018, further in view of Ain et al. "Machine-learning Scoring Functions to Improve Structure-Based Binding Affinity Prediction and Virtual Screening" 2015, to claims 1, 10, 15 above, and further in view of Zhu et al. US PG PUB 2022/0269936.
Regarding Claim 6, Kashyap et al., Resheff et al. nor Ain et al. teaches the following limitation however Shu et al. does.
The method according to claim 1, further comprising performing optimization-based automatic decision making based on the affinity graph. (Zhu et al., Figs. 1-2, Par. 36, “Additional information with knowledge graphs can potentially allow for improved prediction and decision making. The system and/or method disclose herein can implement a framework for jointly learning forecasting models and correlation structures that exploit graph connectivity from knowledge graphs… An algorithm is provided for exploiting parameters' sparsity in gradient calculations for correlation parameters, leading to optimization gains for networks such as large financial networks. Experimental evaluation of modelling and algorithmic methods in real-world financial markets with two types of knowledge graphs demonstrate sparser connectivity structures, runtime improvements and high-quality predictions”, signifies optimization-based automatic decision making through a system and algorithm using a knowledge graph).
Kashyap et al., Resheff et al., Ain et al., and Zhu et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the decision and prediction system of Zhu et al. The motivation to so is to provide accurate predictions on the data from a knowledge graph (Zhu et al., Par. 36 “Experimental evaluation of modelling and algorithmic methods in real-world financial markets with two types of knowledge
graphs demonstrate sparser connectivity structures, runtime improvements and high-quality predictions.”).
Claim 12 is the system claim that corresponds directly with the method claim 6 and therefore rejected for the same reasons given in the rejection of claim 6.
Claim 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al., US PG PUB 2016/0246901 in view of Resheff et al. US PG PUB 2021/0065245, in view of Kim et al. US PG PUB 20040103018, further in view of Ain et al. "Machine-learning Scoring Functions to Improve Structure-Based Binding Affinity Prediction and Virtual Screening" 2015, 1, 10, 15 above, and further in view of Del Villar et al. US PG PUB 2023/0112763.
Regarding Claim 7, Kashyap et al. teaches the following limitation.
The method according to claim 1, wherein the adjusting includes adding…an edge between nodes in the affinity graph. (Kashyap et al. Par. 55 “Thus, the three types of net affinities between any two nodes in the affinity graph can be calculated using the effective electrical circuit between the two nodes. It should be noted that the affinity graph could consist of more number of nodes and the electrical circuit topology may be extended to affinity graph with higher number of nodes in a similar manner.”, and Par. 64, “Further, affinity graph is created with restaurants as nodes and respective affinities between them as edges. The edges could be of three types such as non-directed, directed and temporal affinities. Net affinity algorithm in accordance with the present invention is used to determine net affinity scores between all pairs of restaurants. Further, customer input is received in form of restaurants he likes. The restaurants input by the customer may be located at customer's home city or other places not in the vicinity of the hotel. Further, customer could input his/her preferences or desired attributes for the restaurants…”, signifies adjusting the graph by extending which adds more edges and nodes based on affinity which repeats the steps to create a graph and calculate affinity scores, Par. 64 shows steps to create the graph)
Kashyap et al., Resheff et al., nor Ain et al. teaches the following limitation however Del Villar et al. does.
…and/or removing… (Del Villar et al., Par. 81, “the graph presentation manager 132 may remove one or more nodes or edges based on correlation values exceeding a maximum threshold value”, signifies a removal of an edge based on a scoring calculation of correlation greater than a threshold).
Kashyap et al., Resheff et al., Ain et al., and Del Villar et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the graph presentation manager that can remove nodes of Del Villar et al. The motivation to do so is have an accurate knowledge graph without nodes and edges that do not provide any value to the graph (Del Villar et al., Par. 81, “In this way, the graph presentation manager 132 may exclude those nodes and edges that are rare and provide minimum utility. In addition, the graph presentation manager 132 may exclude those nodes and edges that have almost universal co-occurrence within the digital content items and similarly provide minimal utility.”);
Claim 13 is the system claim that corresponds directly with the method claim 7 and therefore rejected for the same reasons given in the rejection of claim 7.
Claim 8, 14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al., US PG PUB 2016/0246901 in view of Resheff et al. US PG PUB 2021/0065245, in view of Kim et al. US PG PUB 20040103018, further in view of Ain et al. "Machine-learning Scoring Functions to Improve Structure-Based Binding Affinity Prediction and Virtual Screening" 2015, 1, 10, 15 above, and further in view of Bellinger et al, hereinafter Bellinger US 20220284362 A1.
Regarding Claim 8, Kashyap et al., Resheff et al., nor Ain et al., teaches the following limitations wherein the adjusting includes removing an edge between two nodes from the affinity graph if the scoring function for a relation between the two nodes is less than a threshold value and adding an edge in the affinity graph between the two nodes if the scoring function for the relation between the two nodes is greater than the threshold value. However, Bellinger discloses deleting edges to corresponding nodes when organizational relationships no longer exist, or have substantially declined(32)-- wherein the adjusting includes removing an edge between two nodes from the affinity graph if the scoring function for a relation between the two nodes is less than a threshold value.
Additionally, Bellinger discloses adding edges to corresponding nodes when organizational relationships have a high enough score, which is greater than a threshold (32)-- and adding an edge in the affinity graph between the two nodes if the scoring function for the relation between the two nodes is greater than the threshold value.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the references, including the method of creating an affinity graph of Kashyap et al., and Bellinger. The motivation to do so is to quickly and efficiently identify organization relationships (1-2).
Claim 14 is the system claim that corresponds directly with the method claim 8 and therefore rejected for the same reasons given in the rejection of claim 8.
Regarding Claim 16, Kashyap et al. teaches the following limitation.
adding an edge to connect the two nodes. (Kashyap et al. Par. 55 “Thus, the three types of net affinities between any two nodes in the affinity graph can be calculated using the effective electrical circuit between the two nodes. It should be noted that the affinity graph could consist of more number of nodes and the electrical circuit topology may be extended to affinity graph with higher number of nodes in a similar manner.”, and Par. 64, “Further, affinity graph is created with restaurants as nodes and respective affinities between them as edges. The edges could be of three types such as non-directed, directed and temporal affinities. Net affinity algorithm in accordance with the present invention is used to determine net affinity scores between all pairs of restaurants. Further, customer input is received in form of restaurants he likes. The restaurants input by the customer may be located at customer's home city or other places not in the vicinity of the hotel. Further, customer could input his/her preferences or desired attributes for the restaurants…”, signifies adjusting the graph by extending which adds more edges and nodes based on affinity which repeats the steps to create a graph and calculate affinity scores, Par. 64 shows steps to create the graph)
Kashyap et al fail to explicitly teach wherein learning the machine learning scoring function for each relation type comprises using the machine learning scoring function to determine a score for two nodes from the affinity graph. However, Resheff et al. teach in Par. 2 “The method also includes clustering groups of nodes within the plurality of nodes to form a plurality of clusters among the plurality of nodes. The method also includes labeling the plurality of edges as a plurality of relationships types. Labeling is performed by receiving, as input to a machine learning model, a vector comprising attributes representing the plurality of clusters, the plurality of nodes, and the plurality of edges. Labeling is also performed by outputting, from the machine learning model, a plurality of probabilities. Each of the plurality of probabilities corresponds to a corresponding probability that an edge in the plurality of edges represents a relationship type between two nodes in the plurality of nodes.”, signifies a machine learning model that labels and predicts nodes and their relationships). Kashyap et al. fail to explicitly teach, and wherein adjusting the affinity graph comprises: based on comparing the score for the two nodes from the affinity graph with a threshold, adding an edge to connect the two nodes, wherein the edge indicates the cannibalization relation type between the two nodes. However, Bellinger discloses adding edges to corresponding nodes when organizational relationships have a high enough score, which is greater than a threshold (32)--.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the references, including the method of creating an affinity graph of Kashyap et al., and Bellinger. The motivation to do so is to quickly and efficiently identify organization relationships (1-2).
Furthermore, Kim et al. teach in Par. 20 “Products with negative relationships to the promoted product are referred to as cannibalized products.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”, signifies product cannibalization in a graph depicting relationship). It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Kashyap et al,the references, and the cannibalizing data and relationship types taught by Kim et al. The motivation to do so is to analyze product relationships in sales as either products increasing or reducing another product’s sales (Kim et al., Par. 38, “As previously presented, affinity relationships produce coefficients that are greater than 1, while cannibalized relationships produce coefficients that are less than 1.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”).
Regarding Claim 17, Kashyap et al. teaches the following limitation.
Kashyap et al., teach: wherein a first node of the two nodes is associated with a first product and a second node of the two nodes is associated with a second product-- (Kashyap et al. Figs. 1, 3A-3D, Par. 33 “104 represents directed affinity between the entities A and B. Directed affinity between two entities indicates the additional likelihood of selecting an entity provided the other entity has been selected. For example, in a retail scenario, directed affinity 104 from product A to B is the likelihood of purchasing B, provided A has been purchased.”, which signifies different elements to different nodes and describes product binding type as an example where describes likelihood of buying one item and then another);
Furthermore, Kashyap et al fails to teach the following limitation: and wherein adding the edge indicating the cannibalization relation type between the two nodes indicates that the first product causes fewer sold items of the second product. However, Kim et al. shows (Kim et al., Par. 20 “Products with negative relationships to the promoted product are referred to as cannibalized products.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”, signifies product cannibalization in a graph depicting relationship). --and wherein adding the edge indicating the cannibalization relation type between the two nodes indicates that the first product causes fewer sold items of the second product. Kashyap et al., Resheff et al., Ain et al., and Kim et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the cannibalizing data and relationship types taught by Kim et al. The motivation to do so is to analyze product relationships in sales as either products increasing or reducing another product’s sales (Kim et al., Par. 38, “As previously presented, affinity relationships produce coefficients that are greater than 1, while cannibalized relationships produce coefficients that are less than 1.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”).
Regarding Claim 18, Kashyap et al. teaches the following limitation.
Kashyap et al fails to teach the following limitation: based on the affinity graph comprising the edge indicating the cannibalization relation type between the two nodes. However, Kim et al. shows calculating coefficients that depict cannibalized relationship between product, which can then be used to automatically adjust purchasing systems (45-46)-- performing optimization-based automatic decision making to automatically place an order indicating a first quantity of products for the first product and a second quantity of products for a second product.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the cannibalizing data and relationship types taught by Kim et al. The motivation to do so is to analyze product relationships in sales as either products increasing or reducing another product’s sales (Kim et al., Par. 38, “As previously presented, affinity relationships produce coefficients that are greater than 1, while cannibalized relationships produce coefficients that are less than 1.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al., US PG PUB 2016/0246901 in view of Resheff et al. US PG PUB 2021/0065245, in view of Kim et al. US PG PUB 20040103018, further in view of Ain et al. "Machine-learning Scoring Functions to Improve Structure-Based Binding Affinity Prediction and Virtual Screening" 2015, 1, 10, 15 above, and further in view of Hodos et al. US PG PUB 2023/0316128.
Regarding Claim 9, Kashyap et al., Resheff et al., nor Ain et al. teaches the following limitation however Hodos et al. does.
The method according to claim 1, wherein the applying a machine learning algorithm further learns cardinalities of each of the multiple elements. (Hodos et al., Par. 65, "Likelihood score 306 based on the extracted graph-based statistics 302 of a likely relationship may be computed using, for example, a score function or one or more rule-based and/or ML models described herein. For example, the graph-based statistics or the underlying metrics
include, but are not limited to the total number of node counts, the count of unique nodes mediating each target's connection to the disease/query node, and the total number of edge counts, the number of unique edges along paths from the query node to each target.", signifies a count of all nodes of the graphs which is the cardinalities of the elements of the graph);
Kashyap et al., Resheff et al., Ain et al., and Hodos et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the metrics of Hodos et al. The motivation to so is to use the metrics to calculate a score for the edge on a graph (Hodos et al., Par. 72, “The graph-based statistics are assessed to determine predicted relationships or the likelihood of predicated relationships between the one or more target nodes and the query node. Graph-based statistics are inputted to an analysis component, where the scoring of the graph-based statistics based on a set of metrics begins to compute, in this case, likelihood score resulting from the graph-based statistics.”).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Kashyap et al., US PG PUB 2016/0246901 in view of Resheff et al. US PG PUB 2021/0065245, in view of Kim et al. US PG PUB 20040103018, further in view of Ain et al. "Machine-learning Scoring Functions to Improve Structure-Based Binding Affinity Prediction and Virtual Screening" 2015, in view of Bellinger, as applied to claim 1 above, and further in view of Campbell US 20120245969 A1.
Regarding Claim 19, Kashyap et al. teaches the following limitation.
Kashyap et al fails to teach the following limitation: based on the affinity graph comprising the edge indicating the cannibalization relation type between the two nodes performing optimization-based automatic decision making to determine a first shelf location in a storefront for the first product and a second shelf location in the storefront for the second product. However, Kim et al. shows (Kim et al., Par. 20 “Products with negative relationships to the promoted product are referred to as cannibalized products.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”, signifies product cannibalization in a graph depicting relationship). -- based on the affinity graph comprising the edge indicating the cannibalization relation type between the two nodes. Kashyap et al., and Kim et al. all teach analogous art to the present invention because they are reasonably pertinent to determine relations of data.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., the machine learning algorithm that learns nodes and relations of Resheff et al., the machine learning scoring functions of Ain et al., and the cannibalizing data and relationship types taught by Kim et al. The motivation to do so is to analyze product relationships in sales as either products increasing or reducing another product’s sales (Kim et al., Par. 38, “As previously presented, affinity relationships produce coefficients that are greater than 1, while cannibalized relationships produce coefficients that are less than 1.”, and Par. 40 “Moreover, in some embodiments at 340, the resulting coefficients can be applied to projected demand modules for the related product to produce graphs, tables, and other demand relationships. These visual aids assist an organization in visualizing and analyzing the demand affects associated with affinity and cannibalization of related organizational products when promoted products are promoted”).
Moreover, Campbell teaches creating additional product sales based on a specific shelf location within a store (43, 78)-- performing optimization-based automatic decision making to determine a first shelf location in a storefront for the first product and a second shelf location in the storefront for the second product. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the method of creating an affinity graph of Kashyap et al., and product placement teachings of Campbell since Campbell teaches locating, and eliminating poor performing or selling items in order to create more space for better performing items (78), which would lead to increasing sales and profits.
Response to Arguments
Applicant's arguments filed 8/6/2025 have been fully considered but they are not persuasive. Regarding the 35 USC 101 abstract idea rejection, the Applicant indicates that “the Office has not met its burden of establishing that the claims are directed to an abstract idea or that the claims do not recite significantly more. In any case, it is respectfully submitted that claims 1-15 ("the claims") are not directed to an abstract idea, have a practical application….”(page 7). The examiner disagrees since the claims recite steps that can be performed mentally, math and/or pen and paper, where the additional elements are generic in nature and do not amount to significantly more than the abstract idea, or produce a practical application of the abstract idea.
Additionally, the Applicant states that “as explained in Example 39 of the USPTO Subject Matter Eligibility Examples ("Example 39"), applying transformations to digital facial images to create a first training set and using the first training set to train a neural network would not recite a mental process because these steps cannot be practically performed in the human mind. See Example 39. Similarly, the claims also describe applying transformations to data to prior using the machine learning algorithm to process the data.”(page 8). The examiner disagrees with this comparison, since adjusting an affinity graph as recited in the claims is directed towards steps that can be performed in the mind. Whereas, the image transformation steps and the limitations recited in example 39 cannot be performed in the human mind. Additionally, the affinity graph adjustment is basically data manipulation and not transformation such as the ones found in example 39.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The Applicant indicates that “notably, while Kim does describe products with positive relationships to the promoted product are referred to as affinity products and products with negative relationships to the promoted product are referred to as cannibalized products, Kim fails to disclose or suggest applying a machine learning algorithm to learn a relation type including a cannibalization relation type and a binding relation type….”(page 13). Kim does teach the cannibalization of products to produce graphs to assist in visualization of these relationships (20, 40). However, Kim is not relied on to teach the use of a machine learning algorithm.
Applicant’s arguments with respect to claim(s) 16-19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
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/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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