DETAILED ACTION
1. Claims 1-20 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
PRIORITY
3. Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to TAIWAN 111144590 filed 11/22/2022.
Response to Arguments
4. Applicant's arguments filed 6/28/26 have been fully considered but they are not persuasive.
i) Applicants argue that Chen does not disclose “performing finite domain representation planning by using the constrained causal graph to generate action data and configure a plurality of antenna elements of the network using the action data.” Specifically, Applicants argue that Chen is directed to “client-side dynamic” rather than the claimed “network-side dynamic.” First, the Examiner notes that Applicants appear to be reading limitations into the prior art that are not recited. Chen clearly recites “network-side dynamic” or “transmitter design” as defined by Applicants in at least the section in the page preceding and following previously cited Figure 3.5. Specifically, pages 56 with Figure 3.4, and 59 which recite transmitter side calculations. Page 56 recites “Fig. 3.4. Graphical representation of hierarchical model for transmitted symbols” and page 59 also recites “transmit antennae” and “transmit antenna ports.” The Examiner also notes that in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., limiting their claims to transmitter side dynamics since the claims clearly recite “transmit signals to, or receive signals from”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, the prior art rejection is MAINTAINED.
ii) With respect to Applicants arguments that “Chen fails to disclose dynamically switching among transmit diversity, spatial multiplexing, single-user beamforming, and MU-MIMO” in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., dynamically switching among transmit diversity, spatial multiplexing, single-user beamforming, and MU-MIMO) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, the prior art rejection is MAINTAINED.
iii) In response to applicant's argument that Chen does not read on the claims, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. This specifically refers to the multiple intended use statements of now amended claims 1 and 10 as “is to be configured to…” and “to transmit signals to, or receive signals from, the communication devices.” As such the prior art rejection is MAINTAINED.
iv) Applicants argue that amended claims 1 and 11 “cannot be performed in the human mind.” Applicants have provided no arguments to support their conclusory statement. The Examiner notes, for example Figure 3.4, and 3.5 of the prior art which could be designed mentally or with pencil and paper. As to the practical improvement argued by Applicants first the Examiner notes the multiple intended use statements or lack of explicit recitation above, and further the claims conclude with either transmitting or receiving data which are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. As such the 101 rejection is MAINTAINED.
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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
i) In view of Step 1 of the analysis, claim(s) 1 is directed to a statutory category as a process, and claim 11 is directed to a statutory category as a machine, which each represent a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
ii) In view of Step 2A, Prong One, claims 1 and 11 recite the abstract idea of generating visual diagrams or graphs in order to determine the configuration of an antenna elements which constitutes an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper as well as and alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
As to claims 1 and 11, the limitation of “generating a constrained causal graph according to observation data of a plurality of communication devices, wherein a plurality of causal variables of the constrained causal graph and a causal structure of the constrained causal graph are determined together;” would be analogous to a person generating visual diagrams or graphs and thus fall under Mental Processes.
As to claims 1 and 11, the limitation of “performing finite domain representation planning by using the constrained causal graph … configure a plurality of antenna elements of the network using the action data, wherein the plurality of antenna elements are divided into a plurality of groups according to the action data, and the action data indicates that one of the plurality of groups is to be configured to adopt spatial diversity, single-user multiplexing, multi-user multiplexing, single-user beamforming, or multi-user beamforming…” would be analogous to a person generating visual diagrams or graphs and thus fall under Mental Processes and/or as Mathematical Concepts including mathematical formulas or equations as well as calculations representing the finite domain representation aspect of the claim.
As to claim 11, other than reciting “a processing circuit,” nothing in the claim element precludes the step from practically being performed in the mind.
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims.
iii) In view of Step 2A, Prong Two, the judicial exception is not integrated into a practical application. In Claim 11, the additional element of “a processing circuit” merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claim 11 of “generate action data” and “to transmit signals to, or receive signals from, the communication devices;” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “generate action data” and “to transmit signals to, or receive signals from, the communication devices;” in claims 1 and 11, alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims and do not introduce further additional elements for consideration beyond those addressed above.
iv) In view of Step 2B, claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 11 recites, the additional element of “a processing circuit” merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claim 11 of “generate action data” and “to transmit signals to, or receive signals from, the communication devices;” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “generate action data” and “to transmit signals to, or receive signals from, the communication devices;” in claims 1 and 11, alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2 and 12 further define the types of data analysis which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 3 and 13 further define the types of data calculations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 4 and 14 further define the types of data analysis which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 5 and 15 further define the types of data conversion and mathematical determinations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 6 and 16 further define the types of data conversion and mathematical determinations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 7 and 17 further define the types of data which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 8 and 18 further define the types of data conversion, optimization, and mathematical determinations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 9 and 19 further define the types of data conversion and mathematical determinations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
Dependent claims 10 and 20 further define the types of data conversion and mathematical determinations which merely narrows the abstract idea identified as a mental process and/or mathematical concepts including mathematical formulas or equations as well as calculations.
v) Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
6. Claims 1-4, 8-14, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Chen, Chulong. Probabilistic graphical models and variational Bayesian inference in receiver design for MIMO-OFDM systems. Diss. Purdue University, 2013, hereafter Chen.
Regarding Claim 1: The reference discloses A planning method, for a network, comprising:
generating a constrained causal graph according to observation data of a plurality of communication devices, wherein a plurality of causal variables of the constrained causal graph and a causal structure of the constrained causal graph are determined together; and (Figures 3.5)
performing finite domain representation planning by using the constrained causal graph to generate action data and configure a plurality of antenna elements of the network using the action data, wherein the plurality of antenna elements are divided into a plurality of groups according to the action data, and the action data indicates that one of the plurality of groups is to be configured to adopt spatial diversity, single-user multiplexing, multi-user multiplexing, single-user beamforming, or multi-user beamforming to transmit signals to, or receive signals from, the communication devices. (Page 2, “Multiple-antenna techniques are another important piece in communication systems today. The multiplexing gain, the diversity gain and/or antenna gains are exploited to achieve high data rate and/or reliable transmission. Popular multi-antenna techniques include transmit diversity, spatial multiplexing, single-user beamforming, multi-user multiple-input multiple-output (MU-MIMO) and etc. The transmit diversity scheme makes use of all antennas at both transmitter and receiver to mitigate the effect of multipath fading.”)
Regarding Claim 2: The reference discloses The planning method of claim 1, wherein the step of generating the constrained causal graph according to the observation data of the plurality of communication devices comprises: converting the observation data into grounding data; (Page 7, Section 3.4.1, “…denote the set of all observed variables and unknown variables, respectively, in the graphical model for a SISO-OFDM signals over M consecutive symbols indexed by n.”)
and generating the constrained causal graph from the grounding data based on maximum a posteriori and point estimation. (Figure 3.5. Page 35, Section 2.4, 1st paragraph, “In the estimation context, with the posterior distribution, for instance, maximum a posteriori (MAP) estimator and minimum mean square error (MMSE) estimators may be derived by taking the maximum and expectation, respectively, of the posterior distribution.”)
Regarding Claim 3: The reference discloses The planning method of claim 2, wherein the step of generating the constrained causal graph from the grounding data based on maximum a posteriori and point estimation comprises: mapping a plurality of subdata in the grounding data to a plurality of causal variables of the constrained causal graph by using a plurality of observation functions. (Bottom of page 26 to top of page 27, “In mathematics a graph is a representation of a set of nodes where some pairs of them are connected by edges. Typically a graph is depicted in a diagrammatic form as a set of circles for nodes, joined by lines or arrows for the edges. In probabilistic graphical model, each node represent a random variable (or vectors), and the edges express probabilistic relationship between these variables. The graph then captures the way that the complete probability distribution decompose into a product of factors each depending only on a subset of the variables.”) (Page 7, Section 3.4.1, “…denote the set of all observed variables and unknown variables, respectively, in the graphical model for a SISO-OFDM signals over M consecutive symbols indexed by n.”)
Regarding Claim 4: The reference discloses The planning method of claim 3, wherein the plurality of observation functions are obtained based on a causal semantic generative model. (Page 31, Section 2.3.2, “Mathematically speaking, Bayesian networks provides a general framework to express the factorization in terms of conditional distributions of any joint probability distribution. This factorization in many practical scenarios captures the causal relation by which the data are generated according to the distribution of the underlying random variables. Such models are often called generative models. In this section, two important such Bayesian networks relevant to this work will be introduced. The discussion will start with the linear regression model and followed by the hidden Markov model (HMM).”)
Regarding Claim 8: The reference discloses The planning method of claim 1, wherein the step of performing the finite domain representation planning by using the constrained causal graph comprises: determining a solution of the finite domain representation planning by using a planning tree corresponding to the constrained causal graph according to Bayesian optimization, Causal Bayesian optimization, or Dynamic Causal Bayesian Optimization. (Page 36, Section 2.4.1, “In this section, an deterministic approximation algorithm based on variational method for Bayesian learning will be introduced. Variational Bayesian method provides an analytical approximation to the posterior distribution with necessary simplifying assumptions. It has its root in calculus of variations which aims to find derivatives of functions. Analogous to the derivative of a function with respect to input values, functional derivative expresses how the value of the functional changes in response to infinitesimal changes to the input function [38]. Thus many problem involving integrations may be recast as optimization problems over functionals. Variational methods have a long history of application in physics, statistics control theory and etc. To obtain good approximation solutions under the variational framework, one need first cast the problem in hand as optimization problem. This step alone will in general not lead to any approximation since the exact solution is naturally the optimal to a well defined cost function. Instead of searching the entire functional space, some restrictions is placed on the range of functions over which the optimization is performed. In this section, the particular form of factorization assumptions is applied and lead to the mean-field Bayesian variational method [18, 39].”)
Regarding Claim 9: The reference discloses The planning method of claim 1, wherein the step of performing the finite domain representation planning using the constrained causal graph comprises: performing the finite domain representation planning by using an initial state and the constrained causal graph. (Page 53, “For variational learning, the initial values of w and _l may be set to some values obtained by matching the parameters to the autocorrelation function (3.23) by solving Yule-Walker equation [56, 57]. Fig.3.3 shows the Bayesian network for (a) the sparse multipath channel in twolayer hierarchical model (3.19) (3.19) and (b) slow-fading multipath channel modeled as linear dynamic system (3.25) where the Markov chain representing correlated time series fh(t) l gK t=1 is shown compactly using plate.”)
Regarding Claim 10: The reference discloses The planning method of claim 9, wherein the initial state is generated by using another causal graph according to a structural causal model or a Bayesian network. (Page 53, “For variational learning, the initial values of w and _l may be set to some values obtained by matching the parameters to the autocorrelation function (3.23) by solving Yule-Walker equation [56, 57]. Fig.3.3 shows the Bayesian network for (a) the sparse multipath channel in twolayer hierarchical model (3.19) (3.19) and (b) slow-fading multipath channel modeled as linear dynamic system (3.25) where the Markov chain representing correlated time series fh(t) l gK t=1 is shown compactly using plate.”)
Regarding Claim 11: The reference discloses A communication device, comprising: a storage circuit, configured to store instructions of:
generating a constrained causal graph according to observation data of a plurality of communication devices, wherein a plurality of causal variables of the constrained causal graph and a causal structure of the constrained causal graph are determined together; and
performing finite domain representation planning by using the constrained causal graph to generate action data and configure a plurality of antenna elements of a network using the action data, wherein the plurality of antenna elements are divided into a plurality of groups according to the action data, and the action data indicates that one of the plurality of groups is to be configured to adopt spatial diversity, single-user multiplexing, multi-user multiplexing, single-user beamforming, or multi-user beamforming to transmit signals to, or receive signals from, the communication devices; and
a processing circuit, coupled to the storage device, configured to execute the instructions stored in the storage circuit. (See rejection for claim 1)
Regarding Claim 12: The reference discloses The communication device of claim 11, wherein the step of generating the constrained causal graph according to the observation data of the plurality of communication devices comprises: converting the observation data into grounding data; and
generating the constrained causal graph from the grounding data based on maximum a posteriori and point estimation. (See rejection for claim 2)
Regarding Claim 13: The reference discloses The communication device of claim 12, wherein the step of generating the constrained causal graph from the grounding data based on maximum a posteriori and point estimation comprises: mapping a plurality of subdata in the grounding data to a plurality of causal variables of the constrained causal graph by using a plurality of observation functions. (See rejection for claim 3)
Regarding Claim 14: The reference discloses The communication device of claim 13, wherein the plurality of observation functions are obtained based on a causal semantic generative model. (See rejection for claim 4)
Regarding Claim 18: The reference discloses The communication device of claim 11, wherein the step of performing the finite domain representation planning by using the constrained causal graph comprises: determining a solution of the finite domain representation planning by using a planning tree corresponding to the constrained causal graph according to Bayesian optimization, Causal Bayesian optimization, or Dynamic Causal Bayesian Optimization. (See rejection for claim 8)
Regarding Claim 19: The reference discloses The communication device of claim 11, wherein the step of performing the finite domain representation planning using the constrained causal graph comprises: performing the finite domain representation planning by using an initial state and the constrained causal graph. (See rejection for claim 9)
Regarding Claim 20: The reference discloses The communication device of claim 19, wherein the initial state is generated by using another causal graph according to a structural causal model or a Bayesian network. (See rejection for claim 10)
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
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.
7. Claim(s) 6-7 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Kiesbye, Jonis, et al. "Planning via model checking with decision-tree controllers." 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, hereafter Kiesbye.
Regarding Claim 6: Chen does not explicitly recite The planning method of claim 1, wherein the step of performing the finite domain representation planning by using the constrained causal graph comprises: converting the constrained causal graph into a domain file of a planning domain description library to perform the finite domain representation planning.
However Kiesbye discloses The planning method of claim 1, wherein the step of performing the finite domain representation planning by using the constrained causal graph comprises: converting the constrained causal graph into a domain file of a planning domain description library to perform the finite domain representation planning. (Figure 2 showing PRISM model/goal corresponding to the claimed constrained causal graph which is converted to a probabilistic PDDL)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the PDDL aspect of Kiesbye for the probabilistic aspect of Chen in order to allow for control of a process/system by running the planner. (Page 4351, Section VI (B), first paragraph, “As shown in Fig. 2, one can control the process/system by running a planner on a manually written PDDL domain, or one generated from the PRISM model, or by synthesizing a decision tree from the PRISM generated strategy.”)
Regarding Claim 7: Chen does not explicitly recite The planning method of claim 6, wherein a cause in the constrained causal graph corresponds to a precondition of an action in the domain file, and an effect instigated by the cause corresponds to an effect of the action in the domain file.
However Kiesbye discloses The planning method of claim 6, wherein a cause in the constrained causal graph corresponds to a precondition of an action in the domain file, and an effect instigated by the cause corresponds to an effect of the action in the domain file. (Figure 2, PDDL code on left reciting both the preconditions and effects)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the PDDL aspect of Kiesbye for the probabilistic aspect of Chen in order to allow for control of a process/system by running the planner. (Page 4351, Section VI (B), first paragraph, “As shown in Fig. 2, one can control the process/system by running a planner on a manually written PDDL domain, or one generated from the PRISM model, or by synthesizing a decision tree from the PRISM generated strategy.”)
Regarding Claim 16: The reference discloses The communication device of claim 11, wherein the step of performing the finite domain representation planning by using the constrained causal graph comprises: converting the constrained causal graph into a domain file of a planning domain description library to perform the finite domain representation planning. (See rejection for claim 6)
Regarding Claim 17: The reference discloses The communication device of claim 16, wherein a cause in the constrained causal graph corresponds to a precondition of an action in the domain file, and an effect instigated by the cause corresponds to an effect of the action in the domain file. (See rejection for claim 7)
Allowable Subject Matter
8. Claims 5 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims as well as resolving all intervening issues such as the 101 rejection above.
Claim 5 recites: The planning method of claim 1, wherein the step of performing the finite domain representation planning using the constrained causal graph comprises: converting a plurality of causal subgraphs into a plurality of two-dimensional matrices by using graph convolutional network;
converting the plurality of two-dimensional matrices into a plurality of first one-dimensional vectors; and
searching for a plurality of second one-dimensional vectors to make the constrained causal graph comprise at least one alternative branch, wherein each of the plurality of second one-dimensional vectors has smallest cosine similarity to one of the plurality of first one-dimensional vectors to serve as an alternative to the first one-dimensional vector.
Claim 15 recites: The communication device of claim 11, wherein the step of performing the finite domain representation planning using the constrained causal graph comprises: converting a plurality of causal subgraphs into a plurality of two-dimensional matrices by using graph convolutional network;
converting the plurality of two-dimensional matrices into a plurality of first one-dimensional vectors; and
searching for a plurality of second one-dimensional vectors to make the constrained causal graph comprise at least one alternative branch, wherein each of the plurality of second one-dimensional vectors has smallest cosine similarity to one of the plurality of first one-dimensional vectors to serve as an alternative to the first one-dimensional vector.
The closest prior art of record includes:
Thomas, Christo Kurisummoottil. Sparse Bayesian learning, beamforming techniques and asymptotic analysis for massive MIMO. Diss. Sorbonne Université, 2020.
Helmert, Malte. "Concise finite-domain representations for PDDL planning tasks." Artificial Intelligence 173.5-6 (2009): 503-535.
However, the closest prior art of record does not explicitly teach or render obvious the limitations above,
particularly in combination with the other limitations within the claims. The dependent claims are allowable for at least the same reasons as their respective independent claims.
Conclusion
9. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
10. All Claims are rejected.
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Thomas, Christo Kurisummoottil. Sparse Bayesian learning, beamforming techniques and asymptotic analysis for massive MIMO. Diss. Sorbonne Université, 2020.
Helmert, Malte. "Concise finite-domain representations for PDDL planning tasks." Artificial Intelligence 173.5-6 (2009): 503-535.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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