DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Style
In this action unitalicized bold is used for claim language, while italicized bold is used for emphasis.
Information Disclosure Statement
All information disclosure statements were submitted prior to the first action and are incompliance with the provisions of 37 C.F.R. § 1.97. Accordingly, they have been considered.
Applicant Reply
“The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Unless specifically indicated in this office action, claim limitations are not interpreted as means plus function under § 112(f).
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. “’The standard is whether the words of the claim are understood by persons of ordinary skill in the art to have a sufficiently definite meaning as the name for structure.’” Williamson, 792 F.3d at 1349, 115 USPQ2d at 1111; see also Greenberg v. Ethicon Endo-Surgery, Inc., 91 F.3d 1580, 1583, 39 USPQ2d 1783, 1786 (Fed. Cir. 1996).” MPEP § 2181(I). Such claim limitation(s) is/are: “a training graph generation module” and “a graph model training module” in claim 15, and claims depending therefrom.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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.
Claims 1-29 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Generally: separately listed claim elements are construed as distinct components, all claim terms must be given weight, and there is presumed to be a difference in meaning and scope when different words or phrases are used in separate claims. Since different term or phrases are presumed to differ in scope and each term or phrase in the claims must find clear support in the description, a description of a single element in the Specification may fail to support multiple claim terms. “[C]laims must ‘conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description.’ 37 C.F.R. § 1.75(d)(1).” Phillips v. AWH Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (as cited in MPEP § 2111). Further, a lack of lack of detail in the Specification describing how a claimed result is achieved can support a finding that the Applicant was not in possession of the claimed invention at the time of filing, notwithstanding verbatim support. “It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.” MPEP § 2161.01(I). “An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved[.] See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc). The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. ‘Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement.’” MPEP § 2163.03.
All independent claims substantially recite “a) receiving training instruction data from an interface identifying: a subset of nodes and edges, said edges interconnecting said nodes, from a network graph representing a plurality of relationships, and an output variable associated with the accounting task for the trained graph model to predict; b) retrieving the subset of nodes and edges from the network graph; c) retrieving one or more accounting data records associated with one or more of the subset of nodes and edges; d) generating a training graph comprising the retrieved subset of nodes and edges supplemented by the accounting data; e) training a graph model to predict the output variable using the training graph, and f) deploying the training graph to a system for performing the data processing operation associated with the accounting task.” The Specification states that accounting data is ordinarily stored in ledger or tabular format, and explains that there is no clear way to apply graph modeling for data in these formats, or to convert the ledger or tabular data into a format usable by graph models. Spec. P. 1, ll. 32-38. The specification then describes “a first database 104 on which is stored a network graph and a second database 105 on which is stored accounting data.” Spec. P. 8, ll. 14-15. The Specification does not explain how the first graph database is created or indicate the format of the second database (i.e. graph or ledger/tabular). The Specification then describes a “system 101 [that] is configured to facilitate a technique whereby data from the first database 104 and data from the second database 105 can be selected and then combined to form a training graph. . . . an AI specialist [who] can provide [a] training data instruction to configure and train a graph model to perform a particular accounting processing task.” Spec. P. 9, ll. 17-27. According to the Specification “At a first step, S201, this training instruction data is received via the user interface 103 and communicated to the training graph generation module 102. Typically, the training instruction data comprises training graph selection data and graph model configuration data.” Spec. P. 9, ll. 28-33. In other words, the solution to the inability to create a graph appears to be – via an undisclosed technique – 1. creating an appropriate graph and then 2. getting an expert to decide how to add details to the graph, thereby creating an even better graph. Without any explanation of how the graph in the first database is created and given Applicant’s explanation that “it is not immediately obvious how graph models could be applied to perform useful tasks in systems that use” accounting data in tabular or ledger format, there is no support for the data in the first database, and by extension no support for the “instruction . . . identifying . . . a subset of nodes and edges” associated with an accounting task recited in the independent claims. See also Spec. P. 3, Col. 35-36. Second, Drawing on an expert to provide the training data instruction used for “identifying: a subset of nodes and edges” to be retrieved, with some “associated” accounting data, absent some explanation, implies a lack of possession of any particular way of acquiring the instruction as of the effective filing date. Said otherwise, if there were a clear method for identifying the subset of nodes and edges, one would not need an expert to create one. Without any other explanation showing how the subset of nodes and edges are identified, the only reasonable determination is that applicant was not in possession of any way of accomplishing this claimed operation. Similarly, the independent claims recite “receiving training instruction data from an interface identifying: . . . an output variable associated with the accounting task for the trained graph model to predict . . . training a graph model to predict the output variable using the training graph[.]” As shown above, the Specification describes the “training instruction data” as being received from an AI specialist. See supra. Invoking an “AI specialist” used for identifying parameters used in training the graph is not sufficient to show support for identifying parameters used to train the graph. Also explained in the Specification: “During the graph model configuration phase, the training instruction data is used to configure the parameters of the graph model.” Spec. ¶10. Invoking an expert does not supplant the requirement that Applicant show possession by disclosing sufficient techniques for implementing the claimed invention. Since the Specification explains that there is no clear way to create this type of graph in the first place without providing any particular technique of creating the graph, based on a preponderance of the evidence, examiner determines that applicant was not in possession of the claimed subject matter as of the effective filing date.
Claim 15 recites “a training graph generation module” and “a graph model training module” without disclosing the algorithm used by either module. Since both modules are interpreted under 112f, the lack of structural support (in the form of an algorithm in this case) means that both terms lack support.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 15-28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Generally: separately listed claim elements are construed as distinct components, that all claim terms must be given weight, there is presumed to be a difference in meaning and scope when different words or phrases are used in separate claims, and repeated and consistent descriptions in the specification indicate the proper scope of a claimed term. “[C]laims must ‘conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description.’ 37 C.F.R. § 1.75(d)(1).” Phillips v. AWH Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (as cited in MPEP § 2111). Therefore, use of two different terms in the claims that both rely on the description of a single structure in the Specification may render at least one term indefinite because there is no way to determine which term should be construed in view of the description of the single structure.
Claim 15 recites “a training graph generation module” and “a graph model training module” without disclosing the algorithm used by either module. Since both modules are interpreted under 112f, the lack of any description of the algorithm that would provide structural support for the claimed modules results in a lack of clarity. Specifically, there is no way to determine what is being claimed because there is no way to determine what the structure of the module would refer to without any particular associated algorithm.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
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 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) and the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
All claims are found to be directed to one of the four statutory categories, unless otherwise indicated in this action.
Step 2A Prongs One and Two (Alice Step 1): According to Office guidance, claims that read on math do not recite an abstract idea at step 2A1, when the claims fail to refer to the math by name.1 The MPEP also equates “recit[ing] a judicial exception” with “state[ing]” or “describ[ing]” an abstract idea in the claims.2 Consistent with this guidance, an abstract idea may be first recited in a dependent claim even though the independent claims read on that abstract idea. Claim limitations which recite any of the abstract idea groupings set forth in the manual are found to be directed, as a whole, to an abstract idea unless otherwise indicated.3 The claims do not recite additional elements that integrate the abstract ideas into a practical application.4 To confer patent eligibility to an otherwise abstract idea, claims may recite a specific means or method of solving a specific problem in a technological field.5
Independent Claims
1. A computer implemented method of generating a graph model for performing a data processing operation associated with an accounting task, said method comprising the steps of: (Generating a graph “for” performing an operations associated with an accounting task reads on a mental/mathematical process. Further, the claimed “accounting task” reads on a fundamental economic practice, which have been found to be abstract.) a) receiving training instruction data from an interface (Receiving an instruction is mere extra-solution activity.) identifying: a subset of nodes and edges, said edges interconnecting said nodes, from a network graph representing a plurality of relationships, and an output variable associated with the accounting task for the trained graph model to predict; (The claimed identifying of various aspects of a graph reads on a mental process. See also Spec. P. 6, ll. 1-7 describing an “AI expert” carrying out this identification.) b) retrieving the subset of nodes and edges from the network graph; (Retrieving identified parts of the network graph is mere extra-solution activity.) c) retrieving one or more accounting data records associated with one or more of the subset of nodes and edges; (Retrieving identified parts of the network graph is mere extra-solution activity.) d) generating a training graph comprising the retrieved subset of nodes and edges supplemented by the accounting data; (Generating a training graph reads on both a mental process and on mathematical operations.) e) training a graph model to predict the output variable using the training graph, (The training of a generic model to carry out the mental process of predicting an output variable merely recites implementing a mental process on generic computer components.6) and f) deploying the training graph to a system for performing the data processing operation associated with the accounting task. (This reads on using a generic computer component to implement the mental process of an accounting task.)
For rejections of claims 15 and 29, see rejection of claim 1.
Independent claim 15 is rejected for the reasons given in the rejection of claim 1. The claim also recites “[a] computer system for generating a graph model for performing a data processing operation, associated with an accounting task, said system comprising a training graph generation module and a graph model training module, wherein said training graph generation module is configured to” implement the method of claim 1. This is merely an instruction to apply the judicial exception using generic computer components.
Independent claim 29 is rejected for the reasons given in the rejection of claim 1. The claim also recites “[a] computer program which when run on a computing system controls the computing system to perform.” This is merely an instruction to apply the judicial exception using generic computer components.
Step 2B (Alice Step 2): The rejected claims do not recite additional elements that amount to significantly more than the judicial exception.
All additional limitations that do not integrate the claimed judicial exception into a practical application also fail to amount to significantly more, for the reasons given at step 2A2. All limitations found to be extra-solution activity at step 2A2 are found to be WURC, including limitations that read on mere data gathering, data storage, and data input/output/transfer. The limitations of “a) receiving training instruction data from an interface” “b) retrieving the subset of nodes and edges from the network graph;” and “c) retrieving one or more accounting data records associated with one or more of the subset of nodes and edges;” read on merely transmitting data, which has been found to be WURC. Should any other claim limitations be rejected at step 2A1 as extra-solution activity but omitted in the section directly above, it should be understood that such limitations are also found to be WURC at this step. Generic data input/output, storage, repetitive processing operations, and generic display of information and have been found to be generic WURC operations that do not transform the abstract idea into patent eligible subject matter, at the Alice step two analysis.7 Other aspects of generic computing have also been found to be WURC.8 Further, the description itself may provide support for a finding that claim elements are WURC. The analysis under § 112(a) as to whether a claim element is “so well-known that it need not be described in detail in the patent specification” is the same as the analysis as to whether the claim element is widely prevalent or in common use.9 Similarly, generic descriptions in the Specification of claimed components and features has been found to support a conclusion that the claimed components were conventional.10 Improvements to the relevant technology may support a finding that the claims include a patent eligible inventive concept. But some mechanism that results in any asserted improvements must be recited in the claim, and the Specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement.11 This applies to the dependent claims below.
Dependent Claims:
2. A method according to claim 1, wherein each node is representative of an entity. (This merely limits to a more particular mental process.)
3. A method according to claim 1, wherein each edge is representative of a relationship or interaction between entities associated with nodes that the edge interconnects. (This merely limits to a more particular mental process.)
4. A method according to claim 3, wherein each node has a type, said type indicative of a characteristic associated with entity of which the node is representative. (This merely limits to a more particular mental process.)
5. A method according to claim 4, wherein each edge has a type, said type indicative of a characteristic associated with the relationship of interaction with which the edge is associated. (This merely limits to a more particular mental process.)
6. A method according to claim 5, wherein the retrieved accounting data records define further accounting properties associated with the entities to which the nodes relate. (This merely limits to a more particular mental process.)
7. A method according to claim 6, wherein the retrieved accounting data records define further accounting properties associated with the interactions or relationships between entities to which the edges relate. (This merely limits to a more particular mental process.)
8. A method according to claim 2, wherein the entity to which each node relates is one of a party or an accounting data object. (This merely limits to a more particular mental process.)
9. A method according to claim 1, wherein the training instruction data specifies parameters of the graph model and step e) comprises a preliminary graph model configuration step comprising: setting one or more parameters of the graph model in accordance with the graph model configuration data. (This merely limits to a more particular mental process, using generic computer components.)
10. A method according to claim 9, wherein the preliminary graph model configuration step further comprises: configuring an input of the graph model in accordance with the subset of nodes and edges defined in the training instruction data. (This merely limits to a more particular mental process, using generic computer components.)
11. A method according to claim 10, wherein the preliminary graph model configuration step further comprises: configuring an output of the graph model in accordance with the output variable defined in the training instruction data. (This merely limits to a more particular mental process, using generic computer components.)
12. A method according to claim 5, wherein the training instruction data identifies the subset of nodes and edges by specifying one or more node types and one or more edge types and the step of retrieving the subset of nodes and edges from the network graph comprises retrieving nodes and edges of the specified types from the network graph. (This merely limits to a more particular mental process, using generic computer components.)
13. A method according to claim 1, wherein the interface runs on a computing device configured to receive the training instruction data from an operative. (This merely recites implementing the claimed abstract ideas, using generic computer components. Receiving instruction data “from an operative” is mere extra solution activity and WURC.)
14. A method according to claim 1, wherein the graph model is one of: a graph neural network; graph convolutional network; graph attention network; graph autoencoder; graph recurrent neural network; graph generative adversarial network; Bayesian graph neural network; causal graph network; differentiable graph network; symbolic graph network; relational graph network. (This merely recites various generic computer components that can be used to implement the claimed abstract ideas. Further, the specification fails to describe details of any of the claimed graph model structures.12)
For rejections of claims 16-28, see rejections of claims 2-14.
All dependent claims are rejected as containing the material of the claims from which they depend.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-29 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng (Graph Neural Network for Fraud Detection via Spatial-Temporal Attention; Aug 2022) and Rozemberczki (Little Ball of Fur: A Python Library for Graph Sampling; 2020).
1. A computer implemented method of generating a graph model for performing a data processing operation associated with an accounting task, said method comprising the steps of: (Cheng teaches “Recently developed graph neural network and attention mechanisms have shown the benefit of automatic feature learning [13], [14].” Cheng Abstract. “During implementation, our hardware system includes two servers with the same configuration. Each server contains two CPUs with Intel Xeon E5-2680 v3, four pieces of GPU with Telsa P100 and 512G memory. The software system is hosted by CentOS 7.2, Java 8 and Python 3.6. For each component, we employ Kafka [45] as the distributed message queue, Redis [46] as the in-memory database and Drools [47] on Apache Flink as the rule and streaming engine.” Cheng P. 3811.) a) receiving training instruction data from an interface identifying: a subset of nodes and edges, said edges interconnecting said nodes, from a network graph representing a plurality of relationships, (“Please note that, in order to maintain user-level transaction patterns, we combine users who maintain multiple credit cards into one user ID, and then filtered out inactive users that have less than ten records within one month.” Cheng P. 3806. “[W]e represent user consumptions of its card in different merchants as transaction graph with edges directed from user to merchant.” Chen P. 3803. In the context of information so represented in a graph, filtering out the users attached to an edge based on low transactions or filtering out cards associated with users having multiple cards, removes nodes and their associated edges.
The previously cited art does not expressly teach receiving training instruction data that identifies the subset of nodes and edges.
Rozemberczki teaches “Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploration-based network sampling techniques accessible to a large number of professionals, researchers, and students in a single streamlined framework.” Rozemberczki Abstract. “graphs represent complex interrelations, so that naive sampling can destroy the salient features that constitute the value of the graph data. Graph sampling algorithms therefore need to be sensitive to the various features that are relevant to the downstream tasks. Such features include statistics such as diameter, clustering coefficient [6], transitivity or degree distribution.” Rozemberczki P. 1. “Our contributions. Specifically, the main contributions of our work can be summarized as: (1) We present Little Ball of Fur a Python graph sampling library which includes various node, edge and exploration based subgraph sampling techniques.” Rozemberczki P. 2. See also Rozemberczki P. 2, Sec. 2.1, Graph Sampling Techniques. “Using these libraries we create a Watts-Strogatz graph (line 4) and a random walk sampler with the default hyperparameter settings of the sampling procedure (line 6).” Rozemberczki P. 3-4. One of ordinary skill in the art would understand python code used to sample nodes and edges of a graph as including an instruction to identify, and by extension receive, the identified nodes an edges. “[I]n considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom. In re Preda, 401 F.2d 825, 826, 159 USPQ 342, 344 (CCPA 1968)” MPEP § 2144.01.
It would have been obvious to one of ordinary skill before the effective filing date to combine the teaching of Rozemberczki because an instruction to select a particular part of the graph can be used to select parts of the graph that include enough information so as to avoid destroying the salient features that make the information stored by the graph structure valuable in the first place.) and an output variable associated with the accounting task for the trained graph model to predict; (See Cheng, Algorithm 1 showing the outputs defined by the program in line 16. ) b) retrieving the subset of nodes and edges from the network graph; (“For each time window t, we construct the graph at its beginning time, denoted as Gt. For the brevity of notations, we shorten it as G in a given time window. Then, the transaction graph is represented as . . . where . . . the set of user nodes, . . . denotes the set of merchant nodes . . . and . . . denotes the edge sets. There are two types of nodes . . . user nodes and . . . merchant nodes. . . . A graph neural network (GNN) layer takes the transaction graph as inputs.” Cheng P. 3803. Note that inputting the transaction graph to the GNN layer would be understood by one of ordinary skill to include retrieving some part of the transaction graph.) c) retrieving one or more accounting data records associated with one or more of the subset of nodes and edges; (“Finally, given feature tensor X, we could extract the temporal slices . . . denotes the time window and spatial slices . . . denotes the location codes. Please note that the representation X is constructed for each transaction while it is extracted from the corresponding set of the global records.” Cheng P. 3803.) d) generating a training graph comprising the retrieved subset of nodes and edges supplemented by the accounting data; (“For each time window t, we construct the graph at its beginning time, denoted as Gt.” Cheng P. 3803.) e) training a graph model to predict the output variable using the training graph, (“The fraud detection task takes the transaction representation rep, which is the tensor flatted vector learned by attention and convolution networks, and aims to learn the probability of being a fraudulent trade. The loss function is the likelihood defined as follows: [equation 10] . . . where repi denotes the representation of the ith transaction record, which is the output of 3D ConvNet, and 3 indicates the sample weight according to the biased distribution of fraud and legitimate records; yi denotes the label of ith records, which is set to 1 if the record is fraud and 0 otherwise; detect(repi:t) is the detection function that maps repi to a real valued score, indicating the probability that whether the current transaction is fraudulent. We implement (repi:tTheta) with two-layer ReLU and one-layer sigmoid network. The proposed STAGN can be optimized through the standard SGD-based algorithms. In this paper, we used Adam Optimizer to learn the parameters.” Cheng PP. 3805-3806.) and f) deploying the training graph to a system for performing the data processing operation associated with the accounting task. (See Cheng P. 3811, Fig. 13 and accompanying description including section 6.)
2. A method according to claim 1, wherein each node is representative of an entity. (Cheng teaches “[W]e represent user consumptions of its card in different merchants as transaction graph with edges directed from user to merchant.” Chen P. 3803.)
3. A method according to claim 1, wherein each edge is representative of a relationship or interaction between entities associated with nodes that the edge interconnects. (Cheng teaches “[W]e represent user consumptions of its card in different merchants as transaction graph with edges directed from user to merchant.” Chen P. 3803.)
4. A method according to claim 3, wherein each node has a type, said type indicative of a characteristic associated with entity of which the node is representative. (Cheng teaches “[W]e represent user consumptions of its card in different merchants as transaction graph with edges directed from user to merchant.” Chen P. 3803.)
5. A method according to claim 4, wherein each edge has a type, said type indicative of a characteristic associated with the relationship of interaction with which the edge is associated. (Cheng teaches “[W]e represent user consumptions of its card in different merchants as transaction graph with edges directed from user to merchant.” Chen P. 3803. “If there is a transaction between user and merchant, we create an edge e between them. We introduce ve to denote the feature vector of edge, where edge vector ve = [a,vl] contains continuous transaction amount a and one-hot representation of transaction location l.” Cheng P. 3803.)
6. A method according to claim 5, wherein the retrieved accounting data records define further accounting properties associated with the entities to which the nodes relate. (See rejection of claim 5. Each edge includes the transaction amount and the transaction location.)
7. A method according to claim 6, wherein the retrieved accounting data records define further accounting properties associated with the interactions or relationships between entities to which the edges relate. (See rejection of claim 5. Note that each transaction results in addition of an additional edge including the amount and location of the transaction.)
8. A method according to claim 2, wherein the entity to which each node relates is one of a party or an accounting data object. (Cheng teaches “[W]e represent user consumptions of its card in different merchants as transaction graph with edges directed from user to merchant.” Chen P. 3803.)
9. A method according to claim 1, wherein the training instruction data specifies parameters of the graph model and step e) comprises a preliminary graph model configuration step comprising: setting one or more parameters of the graph model in accordance with the graph model configuration data. (“In this experiment, we apply the preferred parameters for each of the baseline methods as they were originally proposed. For STAGN, we employ two convolution layers; each of them is set to 4 4 4 convolution kernel, followed by a max-pooling layer. Two fully connected layers are added on the top of 3D ConvNet, each of them consisting of 32 neurons. We set the temporal and spatial parameters (1 and 2) by cross-validation. The sample weight 3 is set by the training distribution of the positive and negative samples. In GNN layer, NNv shares the same parameter setting with NNe, which includes two full connected layers, with 64 and 32 neurons respectively. NNg is another two hidden layers MLP and both of their neuron sizes are set to 32.” Cheng P. 3806.)
10. A method according to claim 9, wherein the preliminary graph model configuration step further comprises: configuring an input of the graph model in accordance with the subset of nodes and edges defined in the training instruction data. (“In feature engineering, we construct the representation of each transaction record into tensor format, denoted as X 2 RN1N2N3 , where N1, N2, and N3 mean the dimensions of temporal, spatial and feature slices. That is, we construct a feature vector (vf 2 RN3 ) for each spatial-temporal (time horizon, location) pair, which is extracted from transactions that fall into that pair space. . . . In our implementation, the feature vector is concatenated by: . . . 2) Location-based graph feature, which will be described in Section 3.3. . . . In order to preserve global information of card fraud events, we represent user consumptions of its card in different merchants as transaction graph with edges directed from user to merchant. For each time window t, we construct the graph at its beginning time, denoted as Gt. For the brevity of notations, we shorten it as G in a given time window.” Cheng P. 3803.)
11. A method according to claim 10, wherein the preliminary graph model configuration step further comprises: configuring an output of the graph model in accordance with the output variable defined in the training instruction data. (See rejection of claim 10. “A graph neural network (GNN) layer takes the transaction graph as inputs. Particularly, each transaction r ¼ fu; t; l; m; ag is constructed as an edge in graph G. Then, we initialize the node feature vu and vm with random value, the edge feature ve as the concatenation of transaction amount and one-hot representation of location codes, and global graph feature vg as statistical information of G.” Cheng P. 3803-3804.)
12. A method according to claim 5, wherein the training instruction data identifies the subset of nodes and edges by specifying one or more node types and one or more edge types and the step of retrieving the subset of nodes and edges from the network graph comprises retrieving nodes and edges of the specified types from the network graph. (Please note that, in order to maintain user-level transaction patterns, we combine users who maintain multiple credit cards into one user ID, and then filtered out inactive users that have less than ten records within one month.” Cheng P. 3806. Note that edge “types” here would be edges associated with multiple cards from the same user, or edges associated with user having a number of records below some threshold.)
13. A method according to claim 1, wherein the interface runs on a computing device configured to receive the training instruction data from an operative. (“During implementation, we encode categorical data, such as user ID and location code, into one-hot representations. We round the time record from the millisecond level to a standard DataTime format (yyyy-MM-dd HH:mm:ss). For the amount attribute, like many other financial signals, it performs the distribution of long tail. We first cut off the outliers by the three-sigma rule [35] and then perform a log transform on the amount value.” Cheng P. 3806. One of ordinary skill in the art would understand this as a teaching of entering data into a computing device having an interface, in the context of the reference.)
14. A method according to claim 1, wherein the graph model is one of: a graph neural network; graph convolutional network; graph attention network; graph autoencoder; graph recurrent neural network; graph generative adversarial network; Bayesian graph neural network; causal graph network; differentiable graph network; symbolic graph network; relational graph network. (See Cheng Title.)
For rejections of claims 15 and 29, see rejection of claim 1.
For rejections of claims 16-28, see rejections of claims 2-14.
Conclusion
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PAUL M. KNIGHT
/PAUL M KNIGHT/
Primary Examiner, Art Unit 2148
1 This distinction between claims which read on math and claims which recite an abstract idea is based on official USPTO Guidance. The 2019 Subject Matter Eligibility (SME) Examples instructs examiners that a claim reciting “training the neural network” where the background describes training as “using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network” “does not recite any mathematical relationships, formulas, or calculations.” See 2019 SME Example 39, PP. 8-9 (emphasis added). In this example, the plain meaning of “training the neural network” read in light of the disclosure reads on backpropagation using the gradient of a mathematical loss function. See MPEP § 2111.01. In contrast, the 2024 SME Examples instructs examiners that a claim reciting “training, by the computer, the ANN . . . wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite an abstract idea because “[t]he plain meaning of [backpropagation algorithm and gradient descent algorithm] are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.” 2024 PEG Example 47, PP. 4-6. The Memorandum of August 4, 2025; Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, P. 3 also directs examiners that “training the neural network” recited in Example 39 merely “involve[s] . . . mathematical concepts” and contrasts claim 2 of example 47 as “referring to [specific] mathematical calculations by name[.]” (Emphasis added.)
2 “For instance, the claims in Diehr . . . clearly stated a mathematical equation . . . and the claims in Mayo . . . clearly stated laws of nature . . . such that the claims ‘set forth’ an identifiable judicial exception. Alternatively, the claims in Alice Corp. . . . described the concept of intermediated settlement without ever explicitly using the words ‘intermediated’ or ‘settlement.’” MPEP § 2106.04(II)(A).
3 “By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. . . . If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” MPEP § 2106.04(a). See also MPEP 2104(a)(2).
4 Step 2A prongs one and two are evaluated individually, consistent with the framework in the MPEP. Evaluation of relationships between abstract ideas and additional elements in one location promotes clarity of the record.
5 “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” MPEP 2106.04(d)(1). See also Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150-1152 (Fed. Cir. 2019).
6 Using generic machine learning techniques has been found to be abstract, but a determination that a generic model is a generic computer component used to implement the abstract idea, is more consistent with current Office guidance. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1208 (Fed. Cir. 2025).
7 See MPEP § 2106.05(d)(II) listing operations including “receiving or transmitting data,” “storing and retrieving data in memory,” and “performing repetitive calculations” as WURC. “The claims at issue do not require any nonconventional computer, network, or display components, or even a non-conventional and non-generic arrangement of known, conventional pieces, but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (emphasis added, internal quotes omitted).
8 “But ‘[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry.’ Content Extraction, 776 F.3d at 1347-48 (quoting Alice, 134 S. Ct at 2359). Here, the server simply receives data, ‘extract[s] classification information . . . from the received data,’ and ‘stor[es] the digital images . . . taking into consideration the classification information.’ See ‘295 patent, col. 10 ll. 1-17 (Claim 17). . . . These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (‘Nearly every computer will include a 'communications controller' and a 'data storage unit' capable of performing the basic calculation, storage, and transmission functions required by the method claims.’); Content Extraction, 776 F.3d at 1345, 1348 (‘storing information’ into memory, and using a computer to ‘translate the shapes on a physical page into typeface characters,’ insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324-25 (generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’ fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a ‘database’ and ‘a communication medium’ ‘are all generic computer elements’); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (‘That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.’).” TLI Commc'ns LLC v. AV Auto., LLC, 823 F.3d 607, 614 (Fed. Cir. 2016), Emphasis Added.
9 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I).
10 “Similarly, claim elements or combinations of claim elements that are routine, conventional or well-understood cannot transform the claims. (Citing BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290-1291 (Fed. Cir. 2018)). When the patent's specification ‘describes the components and features listed in the claims generically,’ it ‘support[s] the conclusion that these components and features are conventional.’ Weisner v. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022); see also Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1357-58 (Fed. Cir. 2024).” Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359 (Fed. Cir. 2024)
11 “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” MPEP § 2106.05(a).
12 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I).
Prosecution Insights