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 .
Response to Amendment
Applicant’s response and amendment dated 2/27/26 are acknowledged and entered. Claims 1-20 are pending.
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.
Claims 1-5, 10-17, 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ranat (US 2016/0298931, previously cited).1 Regarding claim 1, Ranat teaches a computer-implemented method of generating, in an aircraft in flight, a feasibility display indicative of a feasibility of a weapon carried on the aircraft successfully engaging a target and/or a feasibility of a weapon carried on the target successfully engaging the aircraft (abstract), the method comprising: providing a database describing a performance envelope of the weapon (paragraph 0040); creating coefficients characteristic of that performance envelope using a generic algorithm (paragraph 0080), wherein the generic algorithm has a form of a polynomial, the creating including: a) generating candidate polynomials, variables of the candidate polynomials being some or all of a group of weapon or aircraft firing condition parameters (paragraph 0081); b) for each candidate polynomial, computing coefficients for that candidate polynomial which best fit that candidate polynomial to a characteristic of the performance envelope of the weapon using a criterion of least square error (paragraph 0081); c) for each candidate polynomial, generating a candidate score according to the a quality of the fit of that candidate polynomial to the characteristic of the performance envelope of the weapon (paragraph 0081); d) applying a genetic algorithm to the candidate polynomials and scores including selecting the best scoring candidate polynomial and discarding the other candidate polynomials, thereby identifying a best candidate polynomial and coefficients thereof (paragraph 0082); and e) repeating said identifying process until each of the characteristics of the performance envelope have corresponding polynomial models (paragraph 0083); uploading, to the aircraft, the coefficients of the identified best candidate polynomial (paragraph 0030); and selecting, by a reconstructor on the aircraft containing the same generic algorithm, the coefficients for the generic algorithm according to conditions of the aircraft and the target (paragraph 0093); using the selected coefficients, generating, by the reconstructor, the feasibility display (paragraph 0033); wherein step d) applying the genetic algorithm to the candidate polynomials and scores comprises: i) defining a set of orders and/or types of the candidate polynomials and dividing the defined set of orders and/or types into a plurality of sub-sets thereof (paragraph 0082: the best and worst scoring serve as the subsets); ii) iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials, including iteratively applying the genetic algorithm over the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof and saving the resulting respective coefficients and scores thereof (paragraph 0083); and iii) selecting the best scoring candidate polynomial using the saved coefficients and scores and discarding the other candidate polynomials, thereby identifying the best candidate polynomial and coefficients thereof (paragraph 0083); and wherein the selecting (paragraph 0085), by the reconstructor on the aircraft containing the same generic algorithm, the coefficients for the generic algorithm according to conditions of the aircraft and the target comprises selecting, by the reconstructor on the aircraft containing the same generic algorithm, the coefficients for the generic algorithm, if the aircraft and the target are within the performance envelope of the weapon, according to the conditions of the aircraft and the target (paragraph 0093). Regarding claim 2, Ranat teaches the iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials comprises iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials (paragraph 0082) on respective processors (paragraph 0033). Regarding claims 3 and 17, Ranat teaches the iteratively applying the genetic algorithm over the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof comprises: selecting combinations of the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof (paragraph 0084); and iteratively applying the genetic algorithm over the selected combinations of the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof (paragraph 0084). Regarding claim 4, Ranat teaches the iteratively applying the genetic algorithm over the selected combinations of the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof comprises iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof (paragraph 0084). Regarding claim 5, Ranat teaches the iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof comprises iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof on respective threads (paragraph 0084). Regarding claim 10, Ranat teaches the types of the candidate polynomials of the set thereof include univariate polynomials, multivariate polynomials and modifications thereof (paragraph 0022: the polynomial). Regarding claims 11 and 20, Ranat teaches the generic polynomial is of the form:
y
n
=
∑
m
=
1
M
n
α
m
n
x
1
p
1
m
n
x
2
p
2
m
n
…
where:
α
m
n
represent the m coefficients required to compute n;
x
1
,
…
,
x
N
i
represent normalized inputs;
y
1
,
…
,
y
N
i
represent the outputs;
p
1
m
n
represents the exponent of the
x
1
variable of the
m
t
h
term of the
n
t
h
polynomial (paragraph 0022). Regarding claim 12, Ranat teaches a system for generating in an aircraft in flight, a feasibility display indicative of a feasibility of a weapon carried on the aircraft successfully engaging a target and/or a feasibility of a weapon carried on the target successfully engaging the aircraft, the computer, the system comprising; a first computer comprising a memory and a processor, the first computer being remote from the aircraft (paragraph 0020); and a second computer comprising a memory and a processor, the second computer being onboard the aircraft (paragraph 0033), wherein the first computer is configured to: provide a database describing a performance envelope of the weapon (paragraph 0040); create coefficients characteristic of that performance envelope using a generic algorithm, wherein the generic algorithm has a form of a polynomial (paragraph 0080), the creating including: a) generating candidate polynomials, the variables of the candidate polynomials being some or all of a group of weapon or aircraft firing condition parameters (paragraph 0081); b) for each candidate polynomial, computing coefficients for that candidate polynomial which best fit that candidate polynomial to a characteristic of the performance envelope of the weapon using a criterion of least square error (paragraph 0081); c) for each candidate polynomial, generating a candidate score according to the quality of the fit of that candidate polynomial to the characteristic of the performance envelope of the weapon (paragraph 0081); d) applying a genetic algorithm to the candidate polynomials and scores including selecting the best scoring candidate polynomial polynomial(s) and discarding the other candidate polynomials, thereby identifying a best candidate polynomial and coefficients thereof; (paragraph 0082) and e) repeating said identifying process until each of the of the performance envelope have corresponding polynomial models (paragraph 0083); upload, to the second computer, the coefficients of the identified best candidate polynomial (paragraph 0030); wherein the second computer is configured to: select, by a reconstructor containing the same generic algorithm, the coefficients for the generic algorithm according to conditions of the aircraft and the target (paragraph 0093); and using the selected coefficients, generate, by the reconstructor, the feasibility display; wherein step d) applying the genetic algorithm to the candidate polynomials and scores comprises: i) defining a set of orders and/or types of the candidate polynomials and dividing the defined set of orders and/or types into a plurality of sub-sets thereof (paragraph 0082: the best and worst scoring serve as the subsets); ii) iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials, including iteratively applying the genetic algorithm over the variables of the candidate polynomials for each order and/or type of the respective sub-set thereof and saving the resulting respective coefficients and scores thereof (paragraph 0083); and iii) selecting the best scoring polynomial using the saved coefficients and scores and discarding the other candidate polynomials, thereby identifying the best candidate polynomial and coefficients thereof (paragraph 0083); and wherein the second computer is configured to select, by the reconstructor containing the same generic algorithm, the coefficients for the generic algorithm according to conditions of the aircraft and the target, if the aircraft and the target are within the performance envelope of the weapon, according to the conditions of the aircraft and the target (paragraph 0093). Regarding claim 13, Ranat teaches a display for displaying the feasibility display (paragraph 0051). Regarding claim 14, Ranat teaches an aircraft (paragraph 0033) comprising the second computer according to claim 12 (see rejection of claim 12 above). Regarding claim 15, Ranat teaches a computer, comprising a processor and a memory (paragraph 0020), the computer configured to implement the[[a]] method according to claim 1 (see rejection of claim 1 above). Regarding claim 16, Ranat teaches a non-transitory computer-readable storage medium comprising instructions, which when executed by a processor, cause the processor to perform the method (paragraph 0020) according to claim 1 (see rejection of claim 1 above).
Allowable Subject Matter
Claims 6-9 and 18-19 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. Claims 6 and 18 recite subject matter regarding the iterative application of the algorithm that is not present in the cited art. Claims 7-9 and 19 depend on claims 6 or 18, so they are allowable for at least these reasons.
Response to Arguments
Applicant's arguments filed 2/27/26 have been fully considered but they are not persuasive. Applicant argues that the cited art fails to teach all limitations of the claims. Applicant’s Remarks, pages 2-5. In particular, Applicant argues that Ranat fails to disclose step d of claim 1: “applying a genetic algorithm to the candidate polynomials and scores including selecting the best scoring candidate polynomial and discarding the other candidate polynomials, thereby identifying a best candidate polynomial and coefficients thereof.” Id. at 2-3. However, Ranat teaches this limitation. Paragraph 0082 of Ranat states “[t]he Genetic Algorithm is then applied to the candidate polynomials and scores. In this embodiment, the best scoring polynomials are retained and the other (i.e. worst scoring) polynomials are rejected. New candidate polynomials that have similar features to the retained candidate polynomials are then created to replace the rejected ones (e.g. by “breeding” the retained candidate polynomials). A set of coefficients and score values are then calculated for this new generation of candidates, and so on.” Ranat discloses applying a genetic algorithm to the candidate polynomials and scores: “[t]he Genetic Algorithm is then applied to the candidate polynomials and scores.” Ranat discloses selecting the best scoring candidate polynomial and discarding the other candidate polynomials: “the best scoring polynomials are retained and the other (i.e. worst scoring) polynomials are rejected.” Ranat teaches identifying a best candidate polynomial and coefficients thereof: “[a] set of coefficients and score values are then calculated for this new generation of candidates.” Applicant characterizes Ranat as not applying the genetic algorithm to the worst scoring polynomial, i.e. “the genetic algorithm may be applied prior to being identified as the worst scoring polynomial…the candidate would not be divided into the subset.” Applicant’s Remarks, page 3. Step d of claim 1 recites “applying a genetic algorithm to the candidate polynomials and scores including selecting the best scoring candidate polynomial and discarding the other candidate polynomials, thereby identifying a best candidate polynomial and coefficients thereof.” This language first applies the genetic algorithm, then selects the best scoring candidate polynomial and discards the other candidate polynomials. Ranat, in paragraph 0082, does the same process. Applicant next argues that there is no teaching of “concurrently” applying the genetic algorithm over the sub-sets. Applicant’s Remarks, page 3. Paragraph 0082 discloses applying the genetic algorithm to all the candidate polynomials at the same time. The iterative process is repeating that genetic algorithm process again in order to refine the polynomials, but the genetic algorithm itself is done to all candidates concurrently. Applicant next argues that Ranat fails to disclose “wherein the selecting, by the reconstructor on the aircraft containing the same generic algorithm, the coefficients for the generic algorithm according to conditions of the aircraft and the target comprises selecting, by the reconstructor on the aircraft containing the same generic algorithm, the coefficients for the generic algorithm, if the aircraft and the target are within the performance envelope of the weapon, according to the conditions of the aircraft and the target.” Applicant’s Remarks, page 4. In particular, Applicant argues that there is no mention in Ranat of “within the performance envelope of the weapon.” Id.
Ranat teaches performing the selection if the aircraft and target within the performance envelope of the weapon. In addition to the disclosures of paragraphs 0085 and 0093, paragraph 0064 discloses the definition of the performance envelope, and paragraph 0086 discloses fitting the model for a particular performance envelope. Paragraph 0040 discusses “generating data indicative of the weapon performance for each weapon firing possibility from within the defined ranges.” As a result, Ranat discloses “wherein the selecting, by the reconstructor on the aircraft containing the same generic algorithm, the coefficients for the generic algorithm according to conditions of the aircraft and the target comprises selecting, by the reconstructor on the aircraft containing the same generic algorithm, the coefficients for the generic algorithm, if the aircraft and the target are within the performance envelope of the weapon, according to the conditions of the aircraft and the target.” Applicant has not argued any other limitation or dependent claim, so those limitations and dependent claims are rejected by Ranat for the reasons set forth above.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW MIKELS whose telephone number is (571)270-5470. The examiner can normally be reached Monday to Thursday 7:00 AM ET - 4:30 PM ET, Friday 7:00 AM ET - 11:00 AM ET, the Examiner is on central time.2
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/MATTHEW MIKELS/Primary Examiner, Art Unit 2876
1 In addition to the cited portions, please see also the associated figures.
2 The Examiner can also be reached at matthew.mikels@uspto.gov.