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 .
Claims 1-19 are pending for examination. Claims 1, 18, and 19 are independent.
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.
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. Such claim limitation(s) is/are:
“acquisition unit” in claims 1 and 14
“selection unit” in claims 1, and 5-7
“optimization machine communication unit” in claims 7-12
“extraction unit” in claims 12-13
“output unit” in claims 15-17
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 § 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 1-19 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-17 are directed to a device, claims 18 are directed to a method, and claim 19 is directed to a program. Thus, only claims 1-18 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 19
Claim 19 the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim recites "An information processing program" without expressly reciting any hardware component or physical structure. Therefore, it appears that the claim is directed to software per se, which is not patent eligible subject matter under 35 USC 101.
Regarding Claim 1:
2A Prong 1:
(This step for selecting samples is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
An information processing device comprising: (The information processing device is understood to be a generic computer element - See MPEP 2106.05(f).)
an acquisition unit that acquires a data supply method, a model to be trained, and designation information related to a size and a category of a sample set to be used for training of the model; (The acquisition unit is understood to be a generic computer element - See MPEP 2106.05(f). This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
a selection unit (The selection unit is understood to be a generic computer element - See MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
An information processing device comprising: (The information processing device is understood to be a generic computer element - See MPEP 2106.05(f).)
an acquisition unit that acquires a data supply method, a model to be trained, and designation information related to a size and a category of a sample set to be used for training of the model; (The acquisition unit is understood to be a generic computer element - See MPEP 2106.05(f). This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
a selection unit (The selection unit is understood to be a generic computer element - See MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
well, understood, routine and conventional activity as disclosed in combination
of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 18: see the rejection of claim 1 above. Same rationale applies.
Regarding Claim 19: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “An information processing program that enables processing to be executed, the processing comprising:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 2
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the data supply method is data supply from the dataset, and the sample set is a subset of the dataset. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the data supply - See MPEP 2106.05(h).)
Regarding Claim 3
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the model to be trained is a prediction model with a learning parameter (Training a machine learning model is understood as mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f).)), and the task of the model is a type classification of an output corresponding to an input. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the task- See MPEP 2106.05(h).)
Regarding Claim 4
2A Prong 1:
wherein information entropy provided to the model is information entropy calculated by using Kullback-Leibler divergence or Fisher information. (This step is understood to be a recitation of a mental process (i.e., evaluation) or mathematical calculation.)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 5
2A Prong 1:
wherein the selection unit selects a sample set so as to optimize an objective function indicating information entropy provided to the model. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 6
2A Prong 1:
wherein the selection unit selects the sample set based on the objective function expressed in a quadratic unconstrained binary optimization (QUBO) format. (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 7
2A Prong 1:
wherein the selection unit selects the sample set based on the calculation result. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2:
an optimization machine communication unit that transmits a coefficient matrix corresponding to the objective function to an optimization machine configured to perform combinatorial optimization calculation and that receives a calculation result of the combinatorial optimization calculation from the optimization machine, (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
an optimization machine communication unit that transmits a coefficient matrix corresponding to the objective function to an optimization machine configured to perform combinatorial optimization calculation and that receives a calculation result of the combinatorial optimization calculation from the optimization machine, (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 8
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2:
wherein the optimization machine communication unit receives, from the optimization machine, a calculation result indicating a variable after the combinatorial optimization calculation. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
wherein the optimization machine communication unit receives, from the optimization machine, a calculation result indicating a variable after the combinatorial optimization calculation. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 9
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2:
wherein the optimization machine communication unit receives, from the optimization machine, the calculation result related to binary variables each corresponding to data. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
wherein the optimization machine communication unit receives, from the optimization machine, the calculation result related to binary variables each corresponding to data. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 10
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2:
wherein the optimization machine communication unit transmits the coefficient matrix to a quantum computer or a combinatorial optimization accelerator. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
wherein the optimization machine communication unit transmits the coefficient matrix to a quantum computer or a combinatorial optimization accelerator. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 11
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2:
wherein the optimization machine communication unit transmits the coefficient matrix to an optimization machine selected by the user among a plurality of the optimization machines. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
wherein the optimization machine communication unit transmits the coefficient matrix to an optimization machine selected by the user among a plurality of the optimization machines. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 12
2A Prong 1:
(This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2:
an extraction unit (The unit is understood to be a generic computer element - See MPEP 2106.05(f).)
wherein the optimization machine communication unit transmits the coefficient matrix extracted by the extraction unit to the optimization machine. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
an extraction unit (The unit is understood to be a generic computer element - See MPEP 2106.05(f).)
wherein the optimization machine communication unit transmits the coefficient matrix extracted by the extraction unit to the optimization machine. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 13
2A Prong 1:
wherein the extraction unit extracts the coefficient matrix corresponding to an input of the optimization machine from the objective function. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 14
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2:
wherein the acquisition unit acquires a model that is a prediction model that the user desires to train. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
wherein the acquisition unit acquires a model that is a prediction model that the user desires to train. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 15
2A Prong 1: The claim does not recite any Abstract idea.
further comprising an output unit that outputs information related to the sample set selected by the selection unit. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
further comprising an output unit that outputs information related to the sample set selected by the selection unit. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 16
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2:
wherein the output unit transmits the sample set to a terminal device used by a user. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
wherein the output unit transmits the sample set to a terminal device used by a user. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claim 17
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2:
wherein the output unit transmits a trained model, which has been trained using the sample set, to a terminal device used by a user. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
wherein the output unit transmits a trained model, which has been trained using the sample set, to a terminal device used by a user. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
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.
Claim(s) 1-3, 14-16, and 18-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al. (US 20230087292 A1, hereinafter "Wang").
Regarding Claim 1
Wang discloses: An information processing device ([Para 0217, 0233, Fig 10, and Fig 12-13] describes a fine-grained recognition apparatus.) comprising: an acquisition unit that acquires a data supply method, a model to be trained, and designation information ([Para 0217-0222, 0233-0236, Fig 10, and Fig 12-13] describes image obtaining module (i.e. data supply method), training module (i.e. model to be training), and input/obtaining module (i.e. designation information).) related to a size and a category of a sample set to be used for training of the model ([Para 0134, 0137, 0143, 0157, 0160 and Fig 5-6] describes using a target/source dataset (i.e. designation information) having a quantity (i.e. size) and category label to be used for training a model (e.g. CNN).); and
a selection unit that selects a sample set to be used for the training of the model from a dataset based on information entropy determined according to the model and based on the designation information. ([Para 0140-0144, 0164, 0174, and Fig 5-6] describes calculating information entropy and selecting a specified amount of data for training.)
Regarding Claim 18
Wang discloses: An information processing method comprising: (Claim 18 is a method claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 19
Wang discloses: An information processing program that enables processing to be executed ([Para 0080-0082, 0261, and 0277]), the processing comprising: (Claim 19 is a program claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 2
Wang discloses: The information processing device according to claim 1, wherein the data supply method is data supply from the dataset, and the sample set is a subset of the dataset. ([Para 140-0144, 0164, 0174, 0217-0222, 0233-0236, Fig 5-6, Fig 10, and Fig 12-13], Wang describes image obtaining module (i.e. data supply method) and the selecting input data is a subset.)
Regarding Claim 3
Wang discloses: The information processing device according to claim 1, wherein the model to be trained is a prediction model with a learning parameter, and the task of the model is a type classification of an output corresponding to an input. ([Para 0124, 0157-0158, 0160 and Fig 5-6] describes a neural network with parameters for a recognition task that outputs corresponding to the input. )
Regarding Claim 14
Wang discloses: The information processing device according to claim 1, wherein the acquisition unit acquires a model that is a prediction model that the user desires to train. ([Para 0137-0154, 0219-0222, Fig 5-6, Fig 10, and Fig 12-13] describes a classification model to perform training on.)
Regarding Claim 15
Wang discloses: The information processing device according to claim 1, further comprising an output unit that outputs information related to the sample set selected by the selection unit. ([Para 0140-0144, 0164, 0174, and Fig 5-6] describes selecting a specified amount of data for training (i.e. outputted and provided to model).)
Regarding Claim 16
Wang discloses: The information processing device according to claim 15, wherein the output unit transmits the sample set to a terminal device used by a user. ([Para 0272-0277 Fig 5-6, Fig 10, and Fig 12-13] describes implementing functions on a user computer)
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.
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Hoi et al. ("Batch Mode Active Learning and Its Application to Medical Image Classification", hereinafter "Hoi").
Regarding Claim 4
Wang discloses: The information processing device according to claim 1,
Wang does not explicitly disclose: wherein information entropy provided to the model is information entropy calculated by using Kullback-Leibler divergence or Fisher information.
However, Hoi discloses in the same field of endeavor: wherein information entropy provided to the model is information entropy calculated by using Kullback-Leibler divergence or Fisher information. ([Section 3 and Section 6] describes applying Fisher information matrix to measure the overall informativeness for a set of unlabeled examples.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Batch Mode Active learning disclosed by Hoi into the method of Data annotation disclosed by Wang to calculate information entropy using Fisher information. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Batch Mode Active learning disclosed by Hoi as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to select a number of informative examples for a model.
Regarding Claim 5
Wang in view of Hoi discloses: The information processing device according to claim 1, wherein the selection unit selects a sample set so as to optimize an objective function indicating information entropy provided to the model. ([Section 3 and Equation 4], Hoi describes selecting a subset based on optimization function such as equation 4 (i.e. objective function). Also describes optimizing objective function by a submodular function.)
Claim(s) 6-10, and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Hoi and Israel (US 20150193692 A1, hereinafter "Israel").
Regarding Claim 6
Wang in view of Hoi discloses: The information processing device according to claim 5,
Wang in view of Hoi does not explicitly disclose: wherein the selection unit selects the sample set based on the objective function expressed in a quadratic unconstrained binary optimization (QUBO) format.
However, Israel discloses in the same field of endeavor: wherein the selection unit selects the sample set based on the objective function expressed in a quadratic unconstrained binary optimization (QUBO) format. ([Para 0043-0044, 0048, and Fig 2-7] describes QUBO and minimizing a quadrative objective over binary variables xi using matrix Q.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Quantum Binary Optimization disclosed by Israel into the method of Wang in view of Hoi to perform an objective function expressed in a quadratic unconstrained binary optimization (QUBO) format. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Quantum Binary Optimization disclosed by Israel as all the references are in the field of optimization functions. A person of ordinary skill of the art would have been motivated to perform the combination for being able to find the best solution amongst a set of possible solutions for an optimization problem.
Regarding Claim 7
Wang in view of Hoi and Israel discloses: The information processing device according to claim 5, further comprising
an optimization machine communication unit that transmits a coefficient matrix corresponding to the objective function to an optimization machine configured to perform combinatorial optimization calculation and that receives a calculation result of the combinatorial optimization calculation from the optimization machine, wherein the selection unit selects the sample set based on the calculation result. ([Para 0043-0048, 0054, 0024, Fig 2-7 and Fig 9], Israel describes solving QUBO represented by matrix Q and hybrid digital/quantum systems.)
Regarding Claim 8
Wang in view of Hoi and Israel discloses: The information processing device according to claim 7, wherein the optimization machine communication unit receives, from the optimization machine, a calculation result indicating a variable after the combinatorial optimization calculation. ([Para 0032-0033, 0043-0048, 0054, 0024, and Fig 2-7], Israel describes assigning optimization variables and associated gap values.)
Regarding Claim 9
Wang in view of Hoi and Israel discloses: The information processing device according to claim 8, wherein the optimization machine communication unit receives, from the optimization machine, the calculation result related to binary variables each corresponding to data. ([Para 0031-0033, 0055-0056, and Fig 2-7], Israel describes QUBO variables are binary decision variables .)
Regarding Claim 10
Wang in view of Hoi and Israel discloses: The information processing device according to claim 7, wherein the optimization machine communication unit transmits the coefficient matrix to a quantum computer or a combinatorial optimization accelerator. ([Para 0012-0014, 0031, 0062-0064, 0067-0072 and Fig 9-10], Israel describes solving QUBO problem on a digital computer or quantum and shows hybrid system.)
Regarding Claim 12
Wang in view of Hoi and Israel discloses: The information processing device according to claim 7, further comprising an extraction unit that extracts the coefficient matrix, wherein the optimization machine communication unit transmits the coefficient matrix extracted by the extraction unit to the optimization machine. ([Para 0033, 0054, 0043-0048, Fig 2-7 and Fig 9] describes generating QUBO coefficients, constructing matrix Q and submitting Q for optimization.)
Regarding Claim 13
Wang in view of Hoi and Israel discloses: The information processing device according to claim 12, wherein the extraction unit extracts the coefficient matrix corresponding to an input of the optimization machine from the objective function. ([Para 0002-0005, 0036-0039, Fig 2-7 and Fig 9], Israel describes an optimization objective with coefficient matrix Q and binary variable x (e.g. input) )
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Hoi, Israel, and Dadashikelayeh (US 20190019103 A1, hereinafter " Dadashikelayeh").
Regarding Claim 11
Wang in view of Hoi and Israel discloses: The information processing device according to claim 7,
Wang in view of Hoi and Israel does not explicitly disclose:
However, Dadashikelayeh discloses in the same field of endeavor: wherein the optimization machine communication unit transmits the coefficient matrix to an optimization machine selected by the user among a plurality of the optimization machines. ([Abstract, Para 0004-0010, 0062-0074, Fig 1, and Fig 5] describes accepting user input from an application at an application interface, QUBO formulation (e.g. coefficient matrix Q), and selecting which application/solver is executed on a digital computer.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Quantum Computing disclosed by Dadashikelayeh into the method of Wang in view of Hoi and Israel to transmit coefficient matrix to an optimization machine selected by a user. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Quantum Computing disclosed by Dadashikelayeh as all the references are in the field of executing optimization algorithms. A person of ordinary skill of the art would have been motivated to perform the combination for being able to allow users to incorporate their input in the optimization process.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Alemi (US 20190258937 A1, hereinafter "Alemi").
Regarding Claim 17
Wang discloses: The information processing device according to claim 15,
Wang does not explicitly disclose: wherein the output unit transmits a trained model, which has been trained using the sample set, to a terminal device used by a user.
However, Alemi discloses in the same field of endeavor: wherein the output unit transmits a trained model, which has been trained using the sample set, to a terminal device used by a user. ([Para 0041 and Fig 2] describes outputting trained neural network to a user device.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Training Neural Networks disclosed by Alemi into the method of Data annotation disclosed by Wang to output a trained model. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Training Neural Networks disclosed by Alemi as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to provide users with optimized and trained machine learning models.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sharma et al. (US 20220383203 A1) describes feature selecting based on opti.
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/TEWODROS E MENGISTU/Examiner, Art Unit 2127
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