DETAILED ACTION
This action is in response to the claims filed 07/19/2022 for Application number 17/867,751. Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/19/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 12, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2, 12, and 18 recites the limitation "the multiple nodes" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
computing a value for a first decision item associated with a top level dimension of the directed acyclic graph, wherein the value for the first decision item is computed based on the input dataset and using a reinforcement learning agent for the top level dimension can be considered to be a mathematical calculation;
computing a value for a second decision item associated with a non-top level dimension from the directed acyclic graph, can be considered to be a mathematical calculation
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers mathematical calculations which falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “a computing machine”. Thus, this element in the claim is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f).
Additionally, the claim recites the additional elements – “a configuration for a directed acyclic graph comprising nodes representing dimensions, each dimension being associated with a decision item of the action”, “using a reinforcement learning agent for the top level dimension”, “wherein the value for the second decision item is computed based on the input dataset and a specified subset of outputs of reinforcement learning engines for dimensions upstream from the non-top level dimension in the directed acyclic graph and using a reinforcement learning agent for the non-top level dimension”. These elements that are recited are only generally linked to the judicial exception. Please see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: accessing, at a computing machine, an input dataset for computing an action including multiple decision items and a configuration for a directed acyclic graph comprising nodes representing dimensions, each dimension being associated with a decision item of the action. This limitation is a mere data gathering step and thus is an insignificant extra-solution activity.
providing, via the computing machine, the computed action including at least the value for the first decision item and the value for the second decision item. This limitation is a mere data gathering step and thus is an insignificant extra-solution activity.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a computing machine to perform the steps of the claimed process amount 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.
Additionally, the additional elements of “a configuration for a directed acyclic graph comprising nodes representing dimensions, each dimension being associated with a decision item of the action”, “using a reinforcement learning agent for the top level dimension”, “wherein the value for the second decision item is computed based on the input dataset and a specified subset of outputs of reinforcement learning engines for dimensions upstream from the non-top level dimension in the directed acyclic graph and using a reinforcement learning agent for the non-top level dimension” amount to generally linking the additional elements to the judicial exception.
Furthermore, the limitations of accessing, at a computing machine, an input dataset for computing an action including multiple decision items and a configuration for a directed acyclic graph comprising nodes representing dimensions, each dimension being associated with a decision item of the action and providing, via the computing machine, the computed action including at least the value for the first decision item and the value for the second decision item are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components, generally linking the additional elements to the judicial exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the directed acyclic graph comprises unidirectional edges between the multiple nodes, wherein an upstream dimension being upstream from the non-top level dimension comprises an edge existing from a node of the upstream dimension to a node of the non-top level dimension. This limitation amounts to generally linking the additional elements to the judicial exception. Please see MPEP §2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: generating an ordered list of the dimensions of the directed acyclic graph based on a structure of the directed acyclic graph, wherein the top level dimension occupies a first position in the ordered list, wherein the dimensions upstream from the non-top level dimension appear prior to the non-top level dimension in the ordered list. This limitation amounts to generally linking the additional elements to the judicial exception. Please see MPEP §2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the configuration for the direct acyclic graph is accessed via one or more configuration files. This limitation is a mere data gathering step and thus is an insignificant extra-solution activity.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the configuration for the direct acyclic graph is generated via a graphical user interface. This limitation is a mere data gathering step and thus is an insignificant extra-solution activity.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the top level dimension lacks upstream dimensions in the direct acyclic graph. This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1 above.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the top level dimension is upstream from the non-top level dimension in the direct acyclic graph, wherein the specified subset of outputs of reinforcement learning agents associated with the dimensions upstream from the non-top level dimension comprises an output of the reinforcement learning agent for the top level dimension. This limitation amounts to generally linking the additional elements to the judicial exception. Please see MPEP §2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: causing, via the computing machine, performance of the computed action; This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP §2106.05(f)
determining a result of the computed action; This limitation amounts to an additional mental step in addition to the judicial exception identified in the rejection of claim 1 and
iteratively training the reinforcement learning agent for the top level dimension or the reinforcement learning agent for the non-top level dimension based on the determined result. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP §2106.05(f)
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: computing a value for at least one decision item associated with a Nth dimension of the directed acyclic graph, wherein N is a positive integer corresponding to a position of the Nth dimension in a linearization of the directed acyclic graph comprising at least N dimensions, wherein the value of the decision item is computed based on the input dataset and a specified subset of outputs of reinforcement learning engines for dimensions upstream from the Nth dimension in the directed acyclic graph and using a reinforcement learning agent for the Nth dimension. This limitation amounts to additional mathematical calculations in addition to the judicial exception identified in the rejection of claim 1.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the action comprises transmitting an offer message to a user, wherein the multiple decision items comprise a channel, a day, a time, and an offer message identifier. This limitation is a mere data gathering step and thus is an insignificant extra-solution activity.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Claim 11 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 11 additionally requires analysis for “A non-transitory machine-readable medium storing instructions which, when executed by one or more computing machines, cause the one or more computing machines to perform operations…” however this additional element amounts to mere instructions to apply the judicial exception using a generic computer component.
Regarding Claims 12-16, they recite features similar to claims 2, 4, 3, 5 and 6 are rejected for at least the same reasons therein.
Claim 17 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 17 additionally requires analysis for “A system comprising: processing circuitry; and a memory storing instructions which, when executed by the processing circuitry, cause the processing circuitry to perform operations…” however these additional elements (processing circuitry and memory) amount to mere instructions to apply the judicial exception using a generic computer component.
Regarding Claims 18-20, they recite features similar to claims 2-4 are rejected for at least the same reasons therein.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-8, 11, 13, 15-17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Song et al. ("US 20210192358 A1", hereinafter "Song").
Regarding claim 1, Song teaches A method comprising:
accessing, at a computing machine, an input dataset for computing an action including multiple decision items (“Thus in one aspect a relational forward model (RFM) neural network system for predicting or explaining the actions of multiple agents in a shared environment comprises an input to receive agent data representing agent actions for each of multiple agents, and one or more processors” [¶0005]) and a configuration for a directed acyclic graph comprising nodes representing dimensions, each dimension being associated with a decision item of the action (“The graph data may comprise data representing at least nodes and edges of a graph; the edges may be directed (corresponds to a “directed acyclic graph”) or undirected. Each of the agents may be represented by a node. Non-agent entities in the environment may also each be represented by a node. The nodes have node attributes, for example for determining the actions of each agent” [¶0005]);
computing a value for a first decision item associated with a top level dimension of the directed acyclic graph, wherein the value for the first decision item is computed based on the input dataset and using a reinforcement learning agent for the top level dimension (“FIG. 2a shows a graph neural network subsystem 110… For the input graph the vector of node features may be derived from the node attributes, such as agent position and velocity; where attributes are not applicable e.g. the state of a non-agent entity, they may be given a zero value. For the input graph the edges may have no attributes, and the vector of global features may represent the global score or reward where applicable.” [¶0045])
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” [Figure 2a]);
computing a value for a second decision item associated with a non-top level dimension from the directed acyclic graph (See Figure 2a, any node that has an edge going into another node and an incoming edge from another node would be considered to be a “non-top” level dimension), wherein the value for the second decision item is computed based on the input dataset (See ¶0045, discloses node attribute values) and a specified subset of outputs of reinforcement learning engines for dimensions upstream from the non-top level dimension in the directed acyclic graph and using a reinforcement learning agent for the non-top level dimension (“Outputting the representation may comprise processing the node attributes for a node of the decoded graph data to determine a predicted action of the agent represented by the node. (corresponds to “specified subset of outputs of reinforcement learning engines”)” [¶0022]); and
providing, via the computing machine, the computed action including at least the value for the first decision item and the value for the second decision item. (“Outputting the representation may then comprise processing the edge attributes of an edge of the decoded graph data connecting an influencing (origin) node, which may be an agent or non-agent node, to an agent node to determine data representing the importance of the influencing node to the agent node.” [¶0023; note: Origin node would include the value from the top-level and non-top level dimension(nodes))])
Regarding claim 4, Song teaches The method of claim 1, wherein the configuration for the direct acyclic graph is accessed via one or more configuration files. (“A program can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g., files that store one or more modules, sub programs, or portions of code.” [¶0085])
Regarding claim 5, Song teaches The method of claim 1, wherein the configuration for the direct acyclic graph is generated via a graphical user interface. (“Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components.” [0091])
Regarding claim 6, Song teaches The method of claim 1, wherein the top level dimension lacks upstream dimensions in the direct acyclic graph. (“
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” [Figure 2a; Top-level node has no incoming edges thus “lacks upstream dimensions”])
Regarding claim 7, Song teaches The method of claim 1, wherein the top level dimension is upstream from the non-top level dimension in the direct acyclic graph (“
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” [Figure 2a; top level node edges is connected to non top-level nodes thus would be “upstream” from the non-top level nodes]), wherein the specified subset of outputs of reinforcement learning agents associated with the dimensions upstream from the non-top level dimension comprises an output of the reinforcement learning agent for the top level dimension. (“Outputting the representation may comprise processing the node attributes for a node of the decoded graph data to determine a predicted action of the agent represented by the node. (corresponds to “specified subset of outputs of reinforcement learning engines”)” [¶0022])
Regarding claim 8, Song teaches The method of claim 1, further comprising:
causing, via the computing machine, performance of the computed action (“The reinforcement learning system may be configured to select actions to be performed by one of the agents interacting with the shared environment.” [¶0016]);
determining a result of the computed action (“Significantly, because the described systems for predicting/explaining the actions of multiple agents require less experience to learn they can achieve good results when trained jointly with a reinforcement learning system, even whilst the other agents are still learning and their behavior is changing.” [¶0029]); and
iteratively training the reinforcement learning agent for the top level dimension or the reinforcement learning agent for the non-top level dimension based on the determined result. (“In some cases, the RL system can select the action to be performed by the agent in accordance with an exploration policy. For example, the exploration policy may be an ϵ-greedy exploration policy, where the RL system selects the action to be performed by the agent in accordance with the action selection output with probability 1−ϵ, and selects the action to be performed by the agent randomly with probability ϵ. In this example, ϵ is a scalar value between 0 and 1.” [¶0068; e-greedy exploration corresponds to “iteratively training”])
Regarding claim 11, it is substantially similar to claim 1 respectively, and is rejected in the same manner, the same art, and reasoning applying. Claim 11 additionally requires A non-transitory machine-readable medium storing instructions which, when executed by one or more computing machines, cause the one or more computing machines to perform operations… (Song, [¶0083] “one or more modules of computer program instructions encoded on a tangible non transitory program carrier for execution by, or to control the operation of, data processing apparatus.”)
Regarding claims 13, 15, and 16, it is substantially similar to claims 4, 5 and 6 respectively, and are rejected in the same manner, the same art, and reasoning applying
Regarding claim 17, it is substantially similar to claim 1 respectively, and is rejected in the same manner, the same art, and reasoning applying. Claim 17 additionally requires A system comprising: processing circuitry; and a memory storing instructions which, when executed by the processing circuitry, cause the processing circuitry to perform operations… (Song, [¶0083], “Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them.”)
Regarding claim 20, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 3, 12, 14, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Song in view of Stanton et al. ("US 20250265494 A1" which claims priority to US Provisional Application #63336953 filed 04/29/2022, hereinafter "Stanton").
Regarding claim 2, Song teaches The method of claim 1, however fails to explicitly teach wherein the directed acyclic graph comprises unidirectional edges between the multiple nodes, wherein an upstream dimension being upstream from the non-top level dimension comprises an edge existing from a node of the upstream dimension to a node of the non-top level dimension.
Stanton teaches wherein the directed acyclic graph comprises unidirectional edges between the multiple nodes, wherein an upstream dimension being upstream from the non-top level dimension comprises an edge existing from a node of the upstream dimension to a node of the non-top level dimension. (“Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that receives data representing a computational graph including multiple nodes and directional edges unidirectional connecting two neighboring nodes and receives training data including a reward function for a reinforcement learning model.” [Abstract])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Song in order to use a computational graph (i.e. DAG) with unidirectional edges between nodes as taught by Stanton. One would have been motivated to make this modification so the system can train or perform an inference operation using the described techniques for an input user query in a fraction of conventional techniques such as a graphical neural network, and is further scalable and efficient for computational graphs of billions of nodes and tens of billions of edges. [¶0008, Stanton]
Regarding claim 3, Song teaches The method of claim 1, however fails to explicitly teach further comprising:
generating an ordered list of the dimensions of the directed acyclic graph based on a structure of the directed acyclic graph, wherein the top level dimension occupies a first position in the ordered list, wherein the dimensions upstream from the non-top level dimension appear prior to the non-top level dimension in the ordered list.
Stanton teaches generating an ordered list of the dimensions of the directed acyclic graph based on a structure of the directed acyclic graph, wherein the top level dimension occupies a first position in the ordered list, wherein the dimensions upstream from the non-top level dimension appear prior to the non-top level dimension in the ordered list. (“The example algorithm receives as input a starting node n, a predetermined number of walks c, a predetermined walk length k, a set of edges E, weight values W in a computational graph determined based on historical data, and a multiplier m… The system implementing the example algorithm first selects an i.sup.th node (interpreted as the top level dimension) of a sequence of nodes sorted in a predetermined order (“ordered list of the dimensions”). To achieve it, the system selects a particular edge E.sub.n,* from the edge set E according to a uniform distribution, and search for the node using binary search.” [¶0105-¶0106])
Same motivation to combine the teachings of Song/Stanton as claim 2.
Regarding claims 12 and 14, they are substantially similar to claims 2 and 3 respectively, and is rejected in the same manner, the same art, and reasoning applying
Regarding claims 18 and 19, they are substantially similar to claims 2 and 3 respectively, and is rejected in the same manner, the same art, and reasoning applying
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Song in view of Khare et al. ("Linearize, Predict and Place: Minimizing the Makespan for Edge-based Stream Processing of Directed Acyclic Graphs", hereinafter "Khare").
Regarding claim 9, Song teaches The method of claim 1, further comprising: computing a value for at least one decision item associated with a Nth dimension of the directed acyclic graph (See Figure 2a), wherein the value of the decision item is computed based on the input dataset (See ¶0045; discloses node attribute values) and a specified subset of outputs of reinforcement learning engines for dimensions upstream from the Nth dimension in the directed acyclic graph and using a reinforcement learning agent for the Nth dimension (“Outputting the representation may comprise processing the node attributes for a node of the decoded graph data to determine a predicted action of the agent represented by the node. (corresponds to “specified subset of outputs of reinforcement learning engines”)” [¶0022]).
However fails to explicitly teach wherein N is a positive integer corresponding to a position of the Nth dimension in a linearization of the directed acyclic graph comprising at least N dimensions
Khare teaches wherein N is a positive integer corresponding to a position of the Nth dimension in a linearization of the directed acyclic graph comprising at least N dimensions (“DAG Linearization: We present an algorithm that transforms any given DAG into an approximate set of linear chains in order to approximate the latency of a path in the DAG. This set includes the target path of the DAG, whose latency we are interested in approximating, in addition to other mapped linear chains. Upon the execution of this set of linear chains, the latency of the path we are interested in is observed to be very close to the measured latency along that path when the original DAG structure is executed. The transformation algorithm considers both the split (or fork), and join (or merge) points in DAGs” [pg. 2, right col, first bullet])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Song’s teachings in order to implement the DAG linearization method of Khare. One would have been motivated to make this modification to approximate path latencies in the original DAG structure so as to guide the operator placement decisions. [pg. 2, top right col, Khare]
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Song in view of He ("US 11210690 B2", hereinafter "He").
Regarding claim 10, Song teaches The method of claim 1, however fails to explicitly teach wherein the action comprises transmitting an offer message to a user, wherein the multiple decision items comprise a channel, a day, a time, and an offer message identifier.
He teaches wherein the action comprises transmitting an offer message to a user, wherein the multiple decision items comprise a channel, a day, a time, and an offer message identifier. (The marketing behavior is determined by comprehensively considering multiple factors, including the selection of the marketing channel, the selection of marketing content, and the determination of the marketing time period. The marketing behavior is determined based at least on the combination of the channel, the marketing content, and the marketing time period. As such, a marketing behavior determined by an agent simultaneously considers multiple factors in the marketing process; to be specific, the deep reinforcement learning system simultaneously learns multiple activities and factors in the marketing process, so as to comprehensively learn the entire service process and service target of referral marketing, and better promote service execution results.” [col 2, lines 60-67 – col 3, lines 1-7])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Song’s teachings with the marketing actions utilized by the reinforcement learning system of He. One would have been motivated to make this modification in order to implement machine learning to improve service execution results. [col 1, background, lines 24-47, He]
Conclusion
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