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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Examiner notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 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 1, 2, and 4-9 are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al., US 2023/0315943 A1 (Yin) in view of Grossi et al., FPGA implementation of a stochastic neural network for monotonic pseudo-Boolean optimization (Grossi).
Claim 1.
Yin teaches A data processing device comprising: one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to: store values of a plurality of state variables included in an Ising-type evaluation function that evaluates a solution to a combinatorial optimization problem, (Yin 44) “The storage unit 11 stores H(x), a plurality of (hereinafter N) values of state variables (x;), a plurality of (hereinafter M) values of auxiliary variables (xk), a first weight value (W ij described above) between each of the N xj s, and a second weight value (Wkj) between any one of the N xjs and each of the M xk' s.” (0060) “Note that the processing unit 12 may also search for a state where the value of the evaluation function is maximized by changing the signs of the evaluation function indicated in Expression (1) and the constraint term indicated in Expression (7) (in this case, the state where the maximum value is reached is to be the optimum solution).”
values of a plurality of auxiliary variables that represent whether there is violation of each of a plurality of constraint conditions of the combinatorial optimization problem, (Yin 0046) “The M xj s represent whether or not each of the M constraint conditions is violated. In the following description, description will be made assuming that xk has a value of 1 in the case of violating a constraint condition with the identification number=k and has a value of O in the case of satisfying the constraint condition, but the present disclosure is not limited to this. A spin variable having a value of -1 or + 1 may also be used as xk. Furthermore, the auxiliary variable may have a plurality of values other than O in the case of a constraint condition violation.”
a total value of values of a plurality of constraint terms weighted by a coefficient that represents a weight of each of the plurality of constraint conditions and a value of the evaluation function, (Yin 0049) “Ak is a proportional coefficient related to a constraint term with the identification number=k and represents a weight of the constraint term. Ak may be a different value for each constraint term. Uk represents an upper limit that a resource amount (Rk(x)) needs to satisfy in the inequality constraint. Rk(x) may be represented by the following Expression (8).”
a first local field that represents a change amount of the total value when each of the values of the plurality of state variables changes, (Yin 0065) “Then, the processing unit 12 calculates ΔH in a case where a value of the selected state variable changes (Step S2). For example, in a case where X; is selected, ΔH may be calculated by an expression ΔH=-h; ΔX; based on h; indicated in Expression (11).”
a second local field used to specify a constraint violation amount for each of the plurality of constraint conditions, (Yin 0057) “The M second local fields (hk) stored in the storage unit 11 may be represented by the following Expression (12).”
and a value of the coefficient, (Yin 0058) “The storage unit 11 may further store a bias coefficient (bj), the proportional coefficient (λ k), and the upper limit (Uk). Furthermore, the storage unit 11 may store various types of data such as calculation conditions when the processing unit 12 executes the data processing method to be described later.”
repeat, at a time of searching for the solution, a search process that includes determining whether to permit a change in a value of a first state variable among the plurality of state variables based on the first local field, (Yin 0070-0071) “A times, the processing unit 12 repeats the processing from Step S1.” (0064-0065) “The processing unit 12 selects a state variable of a candidate (hereinafter referred to as a flip candidate) whose value is to be changed from the N state variables (Step Sl). The processing unit 12 selects the state variable of the flip candidate at random or in a predetermined order, for example. Then, the processing unit 12 calculates Δ H in a case where a value of the selected state variable changes (Step S2). For example, in a case where X; is selected, Δ H may be calculated by an expression ΔH=-hiΔ Xi based on hi indicated in Expression (11).”
updating the value of the first state variable, the first local field, the second local field, and the total value when the change in the value of the first state variable is determined to be permitted, (Yin 0086) “In a case where it is determined that flip is permissible, the processing unit 12 updates hi, hk, H(x), and Xi (state variables for which it is determined that flip is permissible) (Step S4). Note that the processing unit 12 does not update hi, hk, H(x), and X; unless it is determined that flip is permissible.” (0069) “The processing unit 12 updates H(x) by adding Δ H to the original H(x).”
determining whether to permit a change in a value of a first auxiliary variable among the plurality of auxiliary variables based on the second local field, (Yin 0071-74) “(Step S6). The processing unit 12 selects the auxiliary variable of the flip candidate at random or in a predetermined order, for example. . . (Step S7). For example, in a case where xk is selected, ΔH may be calculated by an expression ΔH= λk Δxk by using hk indicated in Expression (12).”
and updating the value of the first auxiliary variable and the first local field when the change in the value of the first auxiliary variable is determined to be permitted, (Yin 0075-0076) “In a case where it is determined that flip is permissible for xk of the flip candidate, the processing unit 12 updates hi H(x), and xk (auxiliary variables for which it is determined that flip is permissible) (Step S9). Note that the processing unit 12 does not update hi H(x), and xk unless it is determined that flip is permissible. The processing unit 12 updates H(x) by adding ΔH to the original H(x). Furthermore, for example, in a case where it is determined that the flip is permissible for xk, the processing unit 12 updates hi by adding Δh,=- λk Wkt Δxk to the original h, for each of the N state variables.”
Yin does not explicitly teach, but Grossi teaches adjust the value of the coefficient based on one selected from the total value and whether there is the violation. (Grossi Pg. 875 Section 3.2) “we update only those connections that cause violations in the current state.” (See also Algorithm 1)
are analogous to the claimed invention because they are from the same field of endeavor of
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of before him or her, to modify the storage and constraints of Yin with coefficient adjustments of Grossi to gradually strengthen the constraints with a penalty mechanism as Grossi suggests. (Grossi Pg. 872 Section 1)
Claim 2.
Modified Yin with Grossi teaches The data processing device according to claim 1, wherein the adjusting the value of the coefficient is executed each time the search process is performed a certain number of times. (Grossi Algorithm 1) “for all i = 1 to n do . . . if (gk not satisfied) then Ɣk= Ɣk+1” {EXAMINERS NOTE: Each outer loop iteration places the constraint weight adjustment pass after the state variable sweep, the outer loop repeats while violations remain.}
Claim 4.
Modified Yin with Grossi teaches The data processing device according to claim 1, wherein the one or more processors are further configured to when the total value at the time of adjustment of the value of the coefficient is smaller than a minimum value of the total value obtained before the adjustment and in a state where none of the plurality of constraint conditions is violated (Yin 0044) “The storage unit 11 stores H(x), a plurality of (hereinafter N) values of state variables (xi), a plurality of (hereinafter M) values of auxiliary variables (xk), a first weight value (Wij described above) between each of the N xi’s, and a second weight value (Wkj) between any one of the N xi’s and each of the M xk' s.” (0087) “Note that the processing unit 12 may cause the storage unit 11 to hold total energy and a state in a case where the energy becomes the minimum until then.” (0088) “Then, the processing unit 12 outputs, as a calculation result, a state corresponding to the minimum energy in all the replicas, among the minimum energy stored after the flip determination processing described above is repeated the predetermined number of times in each replica.”
and there is a constraint condition in which the violation occurs among the plurality of constraint conditions, increase the value of the coefficient of the constraint condition. (Grossi Pg. 875 Section 3.2) “here we update only those connections that cause violations in the current state.” (Algorithm 1) “for all i = 1 to n do . . . if (gk not satisfied) then Ɣk= Ɣk+1”
Claim 5.
Modified Yin teaches The data processing device according to claim 1, wherein the one or more processors are further configured to correct the first local field and the total value based on an adjustment amount of the value of the coefficient. (Yin 0056)
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(0076) “the processing unit 12 updates h, by adding Δh,=- λk Wk Δxk to the original hi for each of the N state variables” {EXAMINERS NOTE: Applying the increment corrects the first stored variable in the local field. The same coefficient change produces Δh,=- λk Wk Δxk, adding that amount corrects the stored total value.}
Claim 6.
Modified Yin with Grossi teaches The data processing device according to claim 5, wherein the one or more processors are further configured to correct the first local field by subtracting a product of a weight value between the first state variable and the first auxiliary variable, (Yin 0056)
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(0076) “the processing unit 12 updates h, by adding Δh,=- λk Wk Δxk to the original hi for each of the N state variables” (0055) “on the auxiliary variable xk may be represented by the weight value Wki and an influence of a change in a value of the auxiliary variable xk on the state variable Xi may be represented by -λkWki” (See also fig 2)
the adjustment amount, (Grossi Algorithm 1) “if (gk not satisfied) then”
and the value of the first auxiliary variable from the first local field that represents the change amount when the value of the first state variable changes. (Yin 0044) “The storage unit 11 stores H(x), a plurality of (hereinafter N) values of state variables (xi), a plurality of (hereinafter M) values of auxiliary variables (xk), a first weight value (Wij described above) between each of the N xi’s, and a second weight value (Wkj) between any one of the N xi’s and each of the M xk' s.” (0047) “the storage unit 11 stores a first local field (hi) that represents a change amount of H(x) in a case where each of the values of the N xi’ s changes, and a second local field (hk) that is a value proportional to a change amount of H(x) in a case where each of the values of the M xk's changes.”
Claim 7.
Modified Yin teaches The data processing device according to claim 5, wherein the one or more processors are further configured to correct the total value by adding a product of the adjustment amount, the second local field, (Yin 0050) See expressions 9 and 12
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(69) “The processing unit 12 updates H(x) by adding Δ H to the original H(x).” {EXAMINERS NOTE: Holding state variables fixed, changing λk makes Hnew. Implementing that addition supplies the claimed correction. hk supplies the second factor.}
and the first auxiliary variable to the total value. (Yin 0046) “The M x/ s represent whether or not each of the M constraint conditions is violated” (0050) “Expression (4) may be represented by the following Expression (9) by using the auxiliary variable (xk).”
Claim 8.
Yin teaches A non-transitory computer-readable storage medium storing a data processing program that causes at least one computer to execute a process, the process comprising: storing values of a plurality of state variables included in an Ising-type evaluation function that evaluates a solution to a combinatorial optimization problem, (Yin 44) “The storage unit 11 stores H(x), a plurality of (hereinafter N) values of state variables (x;), a plurality of (hereinafter M) values of auxiliary variables (xk), a first weight value (W ij described above) between each of the N xj s, and a second weight value (Wkj) between any one of the N xjs and each of the M xk' s.” (0060) “Note that the processing unit 12 may also search for a state where the value of the evaluation function is maximized by changing the signs of the evaluation function indicated in Expression (1) and the constraint term indicated in Expression (7) (in this case, the state where the maximum value is reached is to be the optimum solution).” (Claim 6) “A non-transitory computer-readable storage medium . . .”
values of a plurality of auxiliary variables that represent whether there is violation of each of a plurality of constraint conditions of the combinatorial optimization problem, (Yin 0046) “The M xj s represent whether or not each of the M constraint conditions is violated. In the following description, description will be made assuming that xk has a value of 1 in the case of violating a constraint condition with the identification number=k and has a value of O in the case of satisfying the constraint condition, but the present disclosure is not limited to this. A spin variable having a value of -1 or + 1 may also be used as xk. Furthermore, the auxiliary variable may have a plurality of values other than O in the case of a constraint condition violation.”
a total value of values of a plurality of constraint terms weighted by a coefficient that represents a weight of each of the plurality of constraint conditions and a value of the evaluation function, (Yin 0049) “Ak is a proportional coefficient related to a constraint term with the identification number=k and represents a weight of the constraint term. Ak may be a different value for each constraint term. Uk represents an upper limit that a resource amount (Rk(x)) needs to satisfy in the inequality constraint. Rk(x) may be represented by the following Expression (8).”
a first local field that represents a change amount of the total value when each of the values of the plurality of state variables changes, (Yin 0065) “Then, the processing unit 12 calculates ΔH in a case where a value of the selected state variable changes (Step S2). For example, in a case where X; is selected, ΔH may be calculated by an expression ΔH=-h; ΔX; based on h; indicated in Expression (11).”
a second local field used to specify a constraint violation amount for each of the plurality of constraint conditions, (Yin 0057) “The M second local fields (hk) stored in the storage unit 11 may be represented by the following Expression (12).”
and a value of the coefficient; (Yin 0058) “The storage unit 11 may further store a bias coefficient (bj), the proportional coefficient (λ k), and the upper limit (Uk). Furthermore, the storage unit 11 may store various types of data such as calculation conditions when the processing unit 12 executes the data processing method to be described later.”
repeating, at a time of searching for the solution, a search process that includes determining whether to permit a change in a value of a first state variable among the plurality of state variables based on the first local field, (Yin 0070-0071) “A times, the processing unit 12 repeats the processing from Step S1.” (0064-0065) “The processing unit 12 selects a state variable of a candidate (hereinafter referred to as a flip candidate) whose value is to be changed from the N state variables (Step Sl). The processing unit 12 selects the state variable of the flip candidate at random or in a predetermined order, for example. Then, the processing unit 12 calculates Δ H in a case where a value of the selected state variable changes (Step S2). For example, in a case where X; is selected, Δ H may be calculated by an expression ΔH=-hiΔ Xi based on hi indicated in Expression (11).”
updating the value of the first state variable, the first local field, the second local field, and the total value when the change in the value of the first state variable is determined to be permitted, (Yin 0086) “In a case where it is determined that flip is permissible, the processing unit 12 updates hi, hk, H(x), and Xi (state variables for which it is determined that flip is permissible) (Step S4). Note that the processing unit 12 does not update hi, hk, H(x), and X; unless it is determined that flip is permissible.” (0069) “The processing unit 12 updates H(x) by adding Δ H to the original H(x).”
determining whether to permit a change in a value of a first auxiliary variable among the plurality of auxiliary variables based on the second local field, (Yin 0071-74) “(Step S6). The processing unit 12 selects the auxiliary variable of the flip candidate at random or in a predetermined order, for example. . . (Step S7). For example, in a case where xk is selected, ΔH may be calculated by an expression ΔH= λk Δxk by using hk indicated in Expression (12).”
and updating the value of the first auxiliary variable and the first local field when the change in the value of the first auxiliary variable is determined to be permitted; and adjusting the value of the coefficient based on one selected from the total value and whether there is the violation. (Yin 0075-0076) “In a case where it is determined that flip is permissible for xk of the flip candidate, the processing unit 12 updates hi H(x), and xk (auxiliary variables for which it is determined that flip is permissible) (Step S9). Note that the processing unit 12 does not update hi H(x), and xk unless it is determined that flip is permissible. The processing unit 12 updates H(x) by adding ΔH to the original H(x). Furthermore, for example, in a case where it is determined that the flip is permissible for xk, the processing unit 12 updates hi by adding Δh,=- λk Wkt Δxk to the original h, for each of the N state variables.”
Yin does not explicitly teach, but Grossi teaches adjust the value of the coefficient based on one selected from the total value and whether there is the violation. (Grossi Pg. 875 Section 3.2) “we update only those connections that cause violations in the current state.” (See also Algorithm 1)
are analogous to the claimed invention because they are from the same field of endeavor of
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of before him or her, to modify the storage and constraints of Yin with coefficient adjustments of Grossi to gradually strengthen the constraints with a penalty mechanism as Grossi suggests. (Grossi Pg. 872 Section 1)
Claim 9.
Yin teaches A data processing method for a computer to execute a process comprising: storing values of a plurality of state variables included in an Ising-type evaluation function that evaluates a solution to a combinatorial optimization problem, (Yin 44) “The storage unit 11 stores H(x), a plurality of (hereinafter N) values of state variables (x;), a plurality of (hereinafter M) values of auxiliary variables (xk), a first weight value (W ij described above) between each of the N xj s, and a second weight value (Wkj) between any one of the N xjs and each of the M xk' s.” (0060) “Note that the processing unit 12 may also search for a state where the value of the evaluation function is maximized by changing the signs of the evaluation function indicated in Expression (1) and the constraint term indicated in Expression (7) (in this case, the state where the maximum value is reached is to be the optimum solution).”
values of a plurality of auxiliary variables that represent whether there is violation of each of a plurality of constraint conditions of the combinatorial optimization problem, (Yin 0046) “The M xj s represent whether or not each of the M constraint conditions is violated. In the following description, description will be made assuming that xk has a value of 1 in the case of violating a constraint condition with the identification number=k and has a value of O in the case of satisfying the constraint condition, but the present disclosure is not limited to this. A spin variable having a value of -1 or + 1 may also be used as xk. Furthermore, the auxiliary variable may have a plurality of values other than O in the case of a constraint condition violation.”
a total value of values of a plurality of constraint terms weighted by a coefficient that represents a weight of each of the plurality of constraint conditions and a value of the evaluation function, (Yin 0049) “Ak is a proportional coefficient related to a constraint term with the identification number=k and represents a weight of the constraint term. Ak may be a different value for each constraint term. Uk represents an upper limit that a resource amount (Rk(x)) needs to satisfy in the inequality constraint. Rk(x) may be represented by the following Expression (8).”
a first local field that represents a change amount of the total value when each of the values of the plurality of state variables changes, (Yin 0065) “Then, the processing unit 12 calculates ΔH in a case where a value of the selected state variable changes (Step S2). For example, in a case where X; is selected, ΔH may be calculated by an expression ΔH=-h; ΔX; based on h; indicated in Expression (11).”
a second local field used to specify a constraint violation amount for each of the plurality of constraint conditions, (Yin 0057) “The M second local fields (hk) stored in the storage unit 11 may be represented by the following Expression (12).”
and a value of the coefficient; (Yin 0058) “The storage unit 11 may further store a bias coefficient (bj), the proportional coefficient (λ k), and the upper limit (Uk). Furthermore, the storage unit 11 may store various types of data such as calculation conditions when the processing unit 12 executes the data processing method to be described later.”
repeating, at a time of searching for the solution, a search process that includes determining whether to permit a change in a value of a first state variable among the plurality of state variables based on the first local field, (Yin 0070-0071) “A times, the processing unit 12 repeats the processing from Step S1.” (0064-0065) “The processing unit 12 selects a state variable of a candidate (hereinafter referred to as a flip candidate) whose value is to be changed from the N state variables (Step Sl). The processing unit 12 selects the state variable of the flip candidate at random or in a predetermined order, for example. Then, the processing unit 12 calculates Δ H in a case where a value of the selected state variable changes (Step S2). For example, in a case where X; is selected, Δ H may be calculated by an expression ΔH=-hiΔ Xi based on hi indicated in Expression (11).”
updating the value of the first state variable, the first local field, the second local field, and the total value when the change in the value of the first state variable is determined to be permitted, (Yin 0086) “In a case where it is determined that flip is permissible, the processing unit 12 updates hi, hk, H(x), and Xi (state variables for which it is determined that flip is permissible) (Step S4). Note that the processing unit 12 does not update hi, hk, H(x), and X; unless it is determined that flip is permissible.” (0069) “The processing unit 12 updates H(x) by adding Δ H to the original H(x).”
determining whether to permit a change in a value of a first auxiliary variable among the plurality of auxiliary variables based on the second local field, (Yin 0071-74) “(Step S6). The processing unit 12 selects the auxiliary variable of the flip candidate at random or in a predetermined order, for example. . . (Step S7). For example, in a case where xk is selected, ΔH may be calculated by an expression ΔH= λk Δxk by using hk indicated in Expression (12).”
and updating the value of the first auxiliary variable and the first local field when the change in the value of the first auxiliary variable is determined to be permitted; and adjusting the value of the coefficient based on one selected from the total value and whether there is the violation. (Yin 0075-0076) “In a case where it is determined that flip is permissible for xk of the flip candidate, the processing unit 12 updates hi H(x), and xk (auxiliary variables for which it is determined that flip is permissible) (Step S9). Note that the processing unit 12 does not update hi H(x), and xk unless it is determined that flip is permissible. The processing unit 12 updates H(x) by adding ΔH to the original H(x). Furthermore, for example, in a case where it is determined that the flip is permissible for xk, the processing unit 12 updates hi by adding Δh,=- λk Wkt Δxk to the original h, for each of the N state variables.”
Yin does not explicitly teach, but Grossi teaches adjust the value of the coefficient based on one selected from the total value and whether there is the violation. (Grossi Pg. 875 Section 3.2) “we update only those connections that cause violations in the current state.” (See also Algorithm 1)
are analogous to the claimed invention because they are from the same field of endeavor of
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of before him or her, to modify the storage and constraints of Yin with coefficient adjustments of Grossi to gradually strengthen the constraints with a penalty mechanism as Grossi suggests. (Grossi Pg. 872 Section 1)
Allowable Subject Matter
Claim 3 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.
When reading the claim 3 in light of the specification, none of the references alone or in combination disclose or suggest the combination of limitations specified in claim 3. Specifically, Yin data processing to search for a combination of values, and Grossi teaches neural network optimization. Tin modified by Grossi however does not anticipate or render obvious, either alone or in combination “The data processing device according to claim 1, wherein the one or more processors are further configured to decrease the value of the coefficient of each of the plurality of constraint conditions when the total value at the time of adjustment of the value of the coefficient is equal to or greater than a minimum value of the total value obtained before the adjustment and in a state where none of the plurality of constraint conditions is violated.” As claimed in claim 3.
Conclusion
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/JOHN DAVID HAGLER/ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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