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 .
Status of Claims
Claims 1-12 were previously pending and subject to a non-final rejection dated May 21, 2025. In Response, submitted August 19, 2025, claims 1-5 and 8-12 were amended, and claims 6 and 7 were cancelled. Therefore, claims 1-5 and 8-12 are currently pending and subject to the following final rejection.
Response to Arguments
Applicant’s remarks on Page 8 of the Response regarding alleged “Allowable Subject Matter”, have been fully considered.
Examiner notes, that the cited non-final office action does not indicated claims 7-9 as “Allowable Subject Matter” nor are these claims objected to, rather they stand rejected. In Para. 82 of the previously cited non-final under the heading of “Novel and Non-Obvious Over the Prior Art”, Examiner makes clear that these claims stand rejected under 101 as demonstrated earlier in the Office Action (See Para. 31, previously cited non-final), and in no way indicates allowability if rewritten in independent form including all limitations of the base claim and intervening claims.
Applicant’s remarks on Page 8 of the Response regarding the previous objection of the claims, have been fully considered. These objections are withdrawn in light of the amended claims.
Applicant’s remarks on Page 8 of the Response regarding the previous interpretation of the claims under 35 U.S.C. 112(f), have been fully considered and are found to be persuasive in view of the amended claims, and these interpretations have been removed from the detailed rejection below.
Applicant’s remarks on Pages 8-14 of the Response, regarding the previous rejection of the claims under 35 U.S.C. 101, have been fully considered and are not found persuasive.
On Page 9 of the Response, Applicant argues “Applicant respectfully submits that all of the current claims are clearly directed to statutory categories of subject matter. Claims 1-10 are directed to a process and claims11 and 12 are directed to a machine. Thus, the first step of the analysis is satisfied.”
Examiner notes, as discussed further in the detailed rejection below, Examiner is in agreeance on this point.
On Pages 8-9 of the Response, Applicant argues “Applicant respectfully submits that claim 1 is not directed to an abstract idea. … Applicant respectfully submits that the claims in the present invention are not directed to a mental process as indicated in the Office Action for at least three reasons, 1). The Claims are necessarily rooted in computer technology. 2) The claimed invention is directed to an improvement. 3) The claims amount to significantly more than the Judicial exception under Step 2B”
Examiner notes, neither the detailed rejection below or the cited non-final office action mailed May 21, 2025 indicate that the claims are directed a mental process. Para. 25 of the previously cited non-final details that the limitations recite “certain methods of organizing human activity”, and more specifically the commercial interaction of recommending a type of a box to package products, as supported by Para. 2 of the Applicant’s PG Publication. Therefore, this aspect of the argument is moot however the “three reasons” will be discussed individually below as the arguments detail these reasons.
On Pages 9-10 of the Response, Applicant argues “1) Necessarily rooted in computer technology[:] First, the claimed invention is not directed to certain methods of organizing human activity because the claims are necessarily rooted in computer technology. As indicated in the technical field, the present invention relates to ‘a method for learning which box can most efficiently load one or more products and recommending it using artificial intelligence’ (Specification, pg. 1, ln. 19-21). … Additionally, Claim 1 recites in part, ‘creating, based on the searching for the box, an initial solution set that generates a plurality of first sample objects having randomly generating loading information and rotation direction information’ and “generating an arrangement information by selecting a pair of sample objects of the first sample objects, slicing sections within a range of a number of products of the pair of sample objects and copy information within the slicing sections and non-overlapped information in the non-slicing sections to generate a plurality of second sample objects.’ It is unclear how such elements could be considered related to certain methods of organizing human activity. Rather, these claimed elements are necessarily rooted in computer technology. Further, the Specification mentions a problem, e.g., namely that ‘[d]epending on the box or container box and the type of products contained in the box or container box, damage may occur or quality may change during the process of storing or delivering the products, so it is necessary to prevent such losses when storing or delivering the same products in the future.’ (Id. at pg.2 ln 21-25). The claimed invention provides a solution in computer-technology, by optimizing the recommendation for box size, improving the packaging process, reducing costs and increasing efficiency. For example, the Specification provides that an ‘objective of the present disclosure is to solve the above problems and to provide a method for recommending the best box to pack products using volume information of the products, and a system for executing the method. (See Id. pg. 3 ln. 1-5)” That is, the claimed invention is directed to an improvement.)
Examiner notes, the mere presence of additional elements such as “artificial intelligence” (“machine learning model” in the claim language) does not preclude the claims from accurately being determined to recite an abstract idea, nor does it inherently mean that the invention is “rooted in computer technology”. “Learning which box can most efficiently load one or more products and recommending it” is merely a recitation of the abstract idea. The additional element of “a machine learning model” (represented as artificial intelligence within this argument) must be analyzed within Step 2A, Prong Two and Step 2B. As discussed further in the detailed rejection below, the machine learning model is used simply as a tool to perform the abstract idea and to generally link the abstract idea to the technical field of machine learning, and therefore fails to integrate the abstract idea into a practical application at Step 2A Prong Two or amount to significantly more at Step 2B.
Examiner additionally notes, that limitations such as “creating, based on the searching for the box, an initial solution set that generates a plurality of first sample objects having randomly generating loading information and rotation direction information" and "generating an arrangement information by selecting a pair of sample objects of the first sample objects, slicing sections within a range of a number of products of the pair of sample objects and copy information within the slicing sections and non-overlapped information in the non-slicing sections to generate a plurality of second sample objects” may be accurately categorizes within more than one abstract idea grouping. These limitations are accurately categorized within the abstract idea grouping of “certain methods of organizing human activity” in that they illustrate how the invention makes its determinations for the commercial interaction of “recommending a type of a box to package products”. It is also noted, that certain aspects of these limitations rely heavily on mathematical concepts, and could also be accurately categorized as such, however Examiner provides only the abstract idea category which best fits the entirety of the recited abstract idea in the detailed rejection below in order to prevent confusion, as most limitations would not be considered “Mathematical Concepts”.
Examiner further notes, “[d]epending on the box or container box and the type of products contained in the box or container box, damage may occur or quality may change during the process of storing or delivering the products, so it is necessary to prevent such losses when storing or delivering the same products in the future” does not represent a technical but rather potential issues with business processes related to box/container selection. The invention solves this abstract problem by performing abstract ideas such as “optimizing the recommendation for box size, improving the packaging process, reducing costs and increasing efficiency” to “recommend[] the best box to pack products using volume information of the products”. These are not technical improvements (e.g., to the machine learning model), but rather improvements to the abstract idea of “recommending the best box to pack a product”. As noted in MPEP 2106.05(a)(II), an improvement in the abstract idea itself is not an improvement in technology.
On Pages 10-12 of the Response, Applicant argues “2) Claims are directed to an improvement … the Applicant's claims are directed to a specific improvement in the field of recommending a type of box. In order to address the problems described above, the claimed invention includes a machine learning model and "trains the machine learning model using training data that specifies the volume information of products as input variables and specifies the packaging score for each box as target.’ (See Id. pg. 13, ln. 9-12). Specifically, the ‘box recommendation module 120 extracts the product size information i22 from the product specification data i2 newly entered into the system 100, and then inputs it into the machine learning model 30, and the machine learning model 30 calculates and outputs the packaging score for each box r’ (Id. at pg. 36 ln. 6-13). As described in the Specification, ‘the single box with the highest packaging score is selected and the specification information for that box is output. However, the present disclosure is not limited to his, and multiple boxes are selected in the order of the highest packaging score, and the specification information for each box is output.’ Id. pg. 36, ln. 21-25, pg. 37, ln. 1. Additionally, to get the box recommendation, the Specification describes that ‘the system 100 generates loading order information of product and rotation direction information for each product again by using the sample object data si, and calculates suitability of generated values. The system 100 may generate a plurality of child sample objects by crossing a pair of parent sample objects with each other, vary the child sample objects, and then select a plurality of parent and child sample objects according to priority so as to be reset as parent sample objects.’ Specification, pg. 40 ln. 2-10. As illustrated with reference to FIGs. 13 and 14, the ‘system 100 randomly determines slicing sections within a range of the number of products and copies information included in the slicing sections.’ Id., pg. 46, In. 4-10. This enables varying of the ‘product number information included in the loading information and the product rotation direction information included in the rotation direction information.’ Id., pg. 49, In. 9-13. The Specification goes on to mention that the ‘system 100 calculates a ranking according to one or more objective function values corresponding to the parent objects pil and pi2 and the child objects cil and ci2.’ Id., pg. 49, In. 16-20. Accordingly, the ‘system 100 uses the one or more objective function values corresponding to the parent objects pil and pi2 and child the number np of other sample objects that are Pareto superior to a certain sample object and a set Sp of other sample objects for which the sample object is Pareto superior, and calculate Preto Frontal levels.’ Id., pg. 51, In. 12-18. Therefore, ‘system 100 may select a sample object having the highest Pareto front level and the largest crowding distance value among the plurality of sample objects selected . . . so as to generate and output loading result data Ir to the outside of the system 100.’ Id., pg. 57, In. 8-12. Accordingly, by utilizing the sampling and slicing described herein, more accurate boxing recommendations are provided. These features are present in at least independent claim 1. For example, independent claim 1 recites in part, ‘creating, based on the searching for the box, an initial solution set that generates a plurality of first sample objects having randomly generating loading information and rotation direction information from second product specification data comprising product size information and product rotation direction count information’ and ‘generating an arrangement information by selecting a pair of sample objects of the first sample objects, slicing sections within a range of a number of products of the pair of sample objects and copy information within the slicing sections and non-overlapped information in the non-slicing sections to generate a plurality of second sample objects, determining a variation index of at least one second sample object, randomly varying a rotation direction information of a product associated with the variation index of the one second sample object, and selecting the plurality of. first or second sample objects in order of priority according to one or more objective function values so as to be reset as the first sample objects for generating the arrangement information.’
Examiner notes, “recommending a type of box” is an abstract idea, and not a technical field. This further supports the discussion above that the alleged improvements are improvements to the abstract idea and not to the technology.
Examiner further notes, as discussed further in the detailed rejection below, “creating, based on the searching for the box, an initial solution set that generates a plurality of first sample objects having randomly generating loading information and rotation direction information from second product specification data comprising product size information and product rotation direction count information” and “generating an arrangement information by selecting a pair of sample objects of the first sample objects, slicing sections within a range of a number of products of the pair of sample objects and copy information within the slicing sections and non-overlapped information in the non-slicing sections to generate a plurality of second sample objects, determining a variation index of at least one second sample object, randomly varying a rotation direction information of a product associated with the variation index of the one second sample object, and selecting the plurality of. first or second sample objects in order of priority according to one or more objective function values so as to be reset as the first sample objects for generating the arrangement information” are entirely recitations of the abstract idea, and therefore unhelpful in bringing the claims to eligibility over 101.
Examiner further notes, while the claims are read in light of the specification, the specification is not read into the claims. Therefore, insofar as they reflect the claim language of the invention, the specification language of "train[ing] … using training data that specifies the volume information of products as input variables and specifies the packaging score for each box as target.” “extracting the product size information i22 from the [newly entered] product specification data i2 …, and then input[ting] it …, and … calculate[ing] and output[ting] the packaging score for each box r” “[selecting] the single box with the highest packaging score … and [outputting] the specification information for that box …, and [selecting] multiple boxes … in the order of the highest packaging score, and [outputting] the specification information for each box” “generat[ing] loading order information of product and rotation direction information for each product again by using the sample object data si, and calculate[ing] suitability of generated values. … generat[ing] a plurality of child sample objects by crossing a pair of parent sample objects with each other, vary the child sample objects, and then select[ing] a plurality of parent and child sample objects according to priority so as to be reset as parent sample objects.” “randomly determin[ing] slicing sections within a range of the number of products and cop[ying] information included in the slicing sections’ varying the “product number information included in the loading information and the product rotation direction information included in the rotation direction information” “calculate[ing] a ranking according to one or more objective function values corresponding to the parent objects pil and pi2 and the child objects cil and ci2” “us[ing] the one or more objective function values corresponding to the parent objects pil and pi2 and child the number np of other sample objects that are Pareto superior to a certain sample object and a set Sp of other sample objects for which the sample object is Pareto superior, and calculate[ing] Preto Frontal levels” “select[ing] a sample object having the highest Pareto front level and the largest crowding distance value among the plurality of sample objects selected . . . so as to generate and output loading result data Ir” and “utilizing the sampling and slicing described herein, [to provide] more accurate boxing recommendations” are all recitations of the abstract idea, and unhelpful in bringing the claims to eligibility.
The additional elements of the machine learning model and the box recommendation circuitry are recited only as tools to perform the abstract idea (i.e., apply it) or to generally link the invention to the field of machine learning, and consist with courts findings, fail to integrate the abstract idea into a practical application or amount to significantly more.
On Pages 12-14 of the Response, Applicant argues “3) The Ordered combination amounts to significantly more … Like the claims in McRo, claim 1 of the present application ‘uses an ordered combination of specific rules that renders information into a specific format that is then used and applied to create desired results.’ (McRo, page 25; emphasis added.) ‘We hold that the ordered combination of claimed steps, using unconventional rules that relate sub- sequences of phonemes, timings, and morph weight sets, is not directed to an abstract idea and is therefore patent eligible subject matter under§ 101.’ (McRo at 1302-1303.) … Similarly, even if the present invention is considered organizing human activity, the claimed invention uses an ordered combination of specific rules that amount to more than the judicial exception. The claimed invention provides an automated solution that is an improvement upon previous boxing recommendation methods. Specifically, the method and device is capable of storing information related to millions of products, packaging materials, and relationships therein. A human could not possibly be able to handle the information designated for the method and device. Accordingly, because the claimed invention is 1.) necessarily rooted in computer technology, 2.) the claimed invention is directed to an improvement, and 3.) the claimed invention amounts to significantly more than the judicial exception, Applicant respectfully submits the rejection under 101 be withdrawn.”
Examiner notes, “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. … an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea.” (MPEP 2106.05(a), emphasis added). As noted previously, the problems and solutions put forth in the specification are not technical problems or technical solutions, but merely assert improvements that are found entirely in the abstract idea. The specification further puts forth no technical explanations or problems regarding the inabilities of computers to perform the tasks accomplished by the claimed invention, analogous to McRO.
Examiner additionally notes, the alleged improvements of “storing information related to millions of products, packaging materials, and relationships therein” is analogous to Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) where the courts found “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. That is, these alleged improvements arise simply because the invention uses a computer as a tool to perform the abstract idea (i.e., “apply it”) which fails to integrate the abstract idea into a practical application or amount to significantly more. Therefore, after full and proper analysis of the claims, they remain ineligible over 101.
Applicant’s remarks on Pages 14-16 of the Response, regarding the previous rejection of the claims under 35 U.S.C. 103, have been fully considered and are found persuasive in light of the amended claims.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities: the claims recites “as input variable” in limitations 6 and 4 respectively, and “randomly generating loading information” in limitations 4 and 2 respectively. These limitations should recite “as an input variable” and “randomly generated loading information”. Appropriate correction is required.
Claim 11 is objected to because of the following informalities: the claim recites “generating an arrangement information”, “determining a variation index”, “randomly varying a rotation”, and “selecting the plurality” in limitation 3; “pre-learning and training the machine learning model” and “specifying a second packaging score” in limitation 4; and “calculating the second packaging score” in limitation 5. These limitations should recite “generate an arrangement information”, “determine determining a variation index”, “randomly vary a rotation”, “select the plurality”, “perform pre-learning and training of the machine learning model”, “specify a second packaging score”, and “calculate the second packaging score”. Appropriate correction is required.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-5 and 8-10 are directed to a method (i.e., a process); claims 11-12 are directed to a system (i.e., a machine). Therefore, claims 1-5 and 8-12 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claim 1 substantially recites receiving first product specification data having product size information and extracting the product size information from the first product specification data;
inputting the extracted product size information and outputting a first packaging score for each box;
searching for a box with the highest priority packaging score among the first packaging scores for each box, and obtaining and outputting box specification information corresponding to the searched box;
creating, based on the searching for the box, an initial solution set that generates a plurality of first sample objects having randomly generating loading information and rotation direction information from second product specification data comprising product size information and product rotation direction count information; and
generating an arrangement information by selecting a pair of sample objects of the first sample objects, slicing sections within a range of a number of products of the pair of sample objects and copy information within the slicing sections and non-overlapped information in the non-slicing sections to generate a plurality of second sample objects, determining a variation index of at least one second sample object, randomly varying a rotation direction information of a product associated with the variation index of the one second sample object, and selecting the plurality of first or second sample objects in order of priority according to one or more objective function values so as to be reset as the first sample objects for generating the arrangement information, wherein
before the receiving the first product, pre-learning and training by specifying size information for each product as input variable and specifying a second packaging score for each box as a target,
before the pre-learning and training, calculating the second packaging score for each box from packaging status information in which scores are assigned for each evaluation item of storage status of box and delivery status, and
the outputting box specification comprises generating and displaying, as a graph, objective function values according to an evolution generation count that is an iteration count of generating the arrangement information.
Independent claim 11 substantially recites extracting product size information from first product specification data, and inputting the extracted product size information, and searching for a box with the highest priority packaging score among first packaging scores for each box output, and obtaining and outputs box specification information corresponding to the searched box;
creating, based on the searching for the box, an initial solution set that generates a plurality of first sample objects having randomly generating loading information and rotation direction information from second product specification data comprising product size information and product rotation direction count information; and
generating an arrangement information by selecting a pair of sample objects of the first sample objects, slicing sections within a range of a number of products of the pair of sample objects and copy information within the slicing sections and non-overlapped information in the non-slicing sections to generate a plurality of second sample objects, determining a variation index of at least one second sample object, randomly varying a rotation direction information of a product associated with the variation index of the one second sample object, and selecting the plurality of first or second sample objects in order of priority according to one or more objective function values so as to be reset as the first sample objects for generating the arrangement information, wherein
before the receiving the first product, pre-learning and training by specifying size information for each product as input variable and specifying a second packaging score for each box as a target,
before the pre-learning and training, calculating the second packaging score for each box from packaging status information in which scores are assigned for each evaluation item of storage status of box and delivery status, and
the outputting box specification comprises generating and displaying, as a graph, objective function values according to an evolution generation count that is an iteration count of generating the arrangement information.
The limitations stated above are processes/functions that under broadest reasonable interpretation covers “certain methods of organizing human activity” (commercial or legal interactions) of recommending a type of a box to package products (specification, pg. 1, ln. 16-18). Therefore, the claim recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1 and 11 as a whole amount to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent), and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of: (i) a machine learning model (claims 1, 11), (ii) a box recommendation circuitry (claim 11).
The additional element of (i) a machine learning model are recited at a high level of generality (See Para. 11 of the Applicant’s PG Publication discussing the machine learning model) such that when viewed as whole/ordered combination, do no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. machine learning technology) (See MPEP 2106.05(h)).
The additional elements of (ii) a box recommendation circuitry are recited at a high level of generality (see Para. 54 of the Applicant’s PG Publication discussing the box recommendation circuitry) such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
Accordingly, these additional elements, when viewed as a whole/ordered combination [See Figures 1 showing all the additional elements of (i) a machine learning model, (ii) a box recommendation circuitry in combination], do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent), and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)); and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims 1 and 11 are ineligible.
Dependent Claims 2 and 4-10 merely narrow the previously recited abstract idea limitations. For reasons described above with respect to claim 1 these judicial exceptions are not meaningfully integrated into a practical application or significantly more than the abstract idea. Thus, claims 2 and 4-10 are also ineligible.
Step 2A, Prong Two
Dependent Claim 3 further narrows the previously recited abstract idea limitations. Claim 3 also recites the additional elements of an artificial neural network, which is recited at a high-level of generality (See Para. 96 of the Applicants PG Publication disclosing the artificial neural network) such that when viewed as whole/ordered combination, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. ANN technology) (See MPEP 2106.05(h)).
Accordingly, the additional elements, when viewed individually and as a whole/ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional element amounts to no more than: generally linking the use of a judicial exception to a particular technological environment or field of use, and is not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional element of an artificial neural network does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, claim 3 is ineligible.
Step 2A, Prong Two
Dependent Claim 12 further narrows the previously recited abstract idea limitations. Claim 12 also recites the additional elements of a packaging score calculation circuitry, which is recited at a high-level of generality (See Para. 53 of the Applicants PG Publication disclosing the packaging score calculation circuitry) such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
Accordingly, the additional elements, when viewed individually and as a whole/ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional element amounts to no more than: “apply it” (or an equivalent), and is not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional element of a packaging score calculation module does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, claim 12 is ineligible.
Novel and Non-Obvious Over the Prior Art
Claims 1-5 and 8-12 are novel and non-obvious over the prior art; however, these claims are subject to the above rejections.
The closest prior art is U.S. Patent Application No. 2017/0083864 to Saito et al (hereafter Saito). Saito discloses using product specification data in a machine learning environment to determine prioritization of a plurality of possible boxes for packaging.
The next closest prior art is U.S. Patent Application No. 2023/0191615 to Creusot et al (hereafter Creusot). Creusot discloses outputting packaging scores using a machine learning model.
The next closest prior art is U.S. Patent Application No. 2023/0278219 to Kuck et al (hereafter Kuck). Kuck discloses creating initial solution sets using randomly generated loading information and rotation direction from product size information.
The next closest prior art is U.S. Patent Application No. 2022/0122031 to Powers et al (hereafter Powers). Powers discloses using randomly generated object samples in order to determine variation and objective function values and exporting graphical interpretation to user interfaces.
The next closest prior art is U.S. Patent Application No. 2024/0346791 to Moon et al (hereafter Moon). Moon discloses generating loading and rotation direction information comprising size information and product rotation direction count information
The next closest prior art is U.S. Patent Application No. 2016/0358074 to Latapie et al (hereafter Latapie). Latapie discloses use of pre-learning step of training a machine learning model through specification of inputs and outputs.
The next closest prior art is U.S. Patent Application No. 2019/0236740 to Rao et al (hereafter Rao). Rao discloses using data regarding evaluation of an item’s storage status and delivery status in training a machine learning model.
The next closest prior art is U.S. Patent Application No. 2023/0129665 to Kumar et al (hereafter Kumar). Kumar discloses outputting objective function values in graph form.
While the closest prior art above teaches the various aspects of the claimed invention individually, the combination of these references are not obvious in such a way that they would have been obvious to one of ordinary skill in the art at the time of invention. Further, these references fail to explicitly teach slicing sections within a range of a number of products, and non-slicing sections. Therefore, the claims are rendered novel and non-obvious over the prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 DAVID G GODBOLD whose telephone number is (571)272-5036. The examiner can normally be reached M-F 8-5.
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/DAVID G. GODBOLD/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628