DETAILED ACTION
The following is a Non-Final, First Office Action on the Merits in response to communications filed July 6, 2026. Claims 1–20 are currently pending.
Election/Restrictions
Applicant’s election with traverse of Group I, claims 1–6 and 14–20 in the reply filed on July 6, 2026 is acknowledged. The traversal is on the grounds that the Requirement for Restriction fails to identify different search queries, explain why prior art applicable to one invention would not be applicable to another invention, or explain why the inventions are likely to raise different non-prior art issues. Examiner disagrees.
MPEP 803(II) states that “a serious examination burden, for example, may be prima facie shown by appropriate explanation of non-prior art issues under 35 U.S.C. 101, pre-AIA 35 U.S.C. 112, first paragraph, and/or 35 U.S.C. 112(a) relevant to one invention that are not relevant to the other invention.” Here, the Restriction identified different conceptual elements within the different groups, which explains both why prior art applicable to one invention would not be applicable to another invention and why the inventions are likely to raise different non-prior art issues. Further, Examiner notes that MPEP 803 does not require identifying different search queries for each grouping as a basis for an “appropriate explanation”. As a result, Applicant’s remarks are not persuasive because the remarks are neither commensurate with the issued Requirement for Restriction nor the requirements set forth under MPEP 803.
However, upon reconsideration of the claims in view of the included search history, the Requirement for Restriction is withdrawn.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites “one or more successful product configurations” in lines 4–5. The term “successful” is a relative term which renders the claim indefinite. The term “successful” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
In view of the above, claim 13 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
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 1–20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements to “preprocess product configuration details with item-level information to create a preprocessed dataset associated with product configurations”; “generate a prediction associated with a proposed product configuration, wherein the prediction includes a binary output and a numerical output”; “calculate the SHAP values for individual features within a set of features for the proposed product configuration to determine a contribution of each feature in the set of features to the prediction”; and “output an indication of the prediction with the SHAP values.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity related to commercial sales activities or behaviors because the elements describe a process for evaluating features of a product configuration. The elements further recite mental processes because the elements embody observations or evaluations that can be practically performed in the human mind or by a human using pen and paper. Finally, the element to “calculate” recites mathematical concepts because the element recites a mathematical calculation. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 2–6 further describe the process for evaluating features of a product configuration and further recite certain methods of organizing human activity, mental processes, and/or mathematical concepts for the same reasons as stated above. As a result, claims 2–6 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include one or more processors, a plurality of databases, a machine learning model that includes a classifier and a regression model, and an element to train the machine learning model using preprocessed data and a gradient boosting algorithm. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2 and 4 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include an element to “encode” (claim 2) and an element to “output instructions for an interface” (claim 4). When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 2 and 4 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 3 and 5–6 do not include any additional elements beyond those included with respect to the claims from which claims 3 and 5–6 depend. As a result, claims 3 and 5–6 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include one or more processors, a plurality of databases, a machine learning model that includes a classifier and a regression model, and an element to train the machine learning model using preprocessed data and a gradient boosting algorithm. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2 and 4 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include an element to “encode” (claim 2) and an element to “output instructions for an interface” (claim 4). The additional elements do not amount to significantly more than the recited abstract idea because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 2 and 4 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 3 and 5–6 do not include any additional elements beyond those included with respect to the claims from which claims 3 and 5–6 depend. As a result, claims 3 and 5–6 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
With respect to Step 2A Prong One of the framework, claim 7 recites an abstract idea. Claim 7 includes elements for “receiving item-level data from a first data source and feature-level data from a second data source”; “preprocessing the item-level data and the feature-level data to correlate historical item data to specific components and features”; “receiving input indicating a proposed product configuration”; “predicting an outcome associated with the proposed product configuration”; “generating a visualization of the outcome, wherein the visualization further indicates a plurality of contributions associated with the specific components and features”; and “outputting the visualization.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity related to commercial sales activities or behaviors because the elements describe a process for evaluating and visualizing an outcome associated with a product configuration. The elements further recite mental processes because the elements embody observations or evaluations that can be practically performed in the human mind or by a human using pen and paper. As a result, claim 7 recites an abstract idea under Step 2A Prong One.
Claims 8–13 further describe the process for evaluating and visualizing an outcome associated with a product configuration and further recite certain methods of organizing human activity and/or mental processes for the same reasons as stated above. As a result, claims 8–13 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 7 does not include additional elements that integrate the abstract idea into a practical application. Claim 7 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a model system, a user device, a step for “training a machine learning model”, and steps for “generating” and “outputting” instructions. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the user device is a generic computing component that is merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 7 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 8–13 do not include any additional elements beyond those included with respect to the claims from which claims 8–13 depend. As a result, claims 8–13 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 7 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 7 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a model system, a user device, a step for “training a machine learning model”, and steps for “generating” and “outputting” instructions. The additional elements do not amount to significantly more than the recited abstract idea because the user device is a generic computing component that is merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 7 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 8–13 do not include any additional elements beyond those included with respect to the claims from which claims 8–13 depend. As a result, claims 8–13 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
With respect to Step 2A Prong One of the framework, claim 14 recites an abstract idea. Claim 14 includes elements to “receive input indicating a proposed product configuration”; “provide the input to generate a prediction associated with the proposed product configuration”; “determine a set of contribution values associated with portions of the proposed product configuration”; and “generate text indicating the prediction and a graph indicating the set of contribution values”.
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity related to commercial sales activities or behaviors because the elements describe a process for evaluating and visualizing prediction contributions associated with a product configuration. The elements further recite mental processes because the elements embody observations or evaluations that can be practically performed in the human mind or by a human using pen and paper. As a result, claim 14 recites an abstract idea under Step 2A Prong One.
Claims 15–20 further describe the process for evaluating and visualizing prediction contributions associated with a product configuration and further recite certain methods of organizing human activity and/or mental processes for the same reasons as stated above. As a result, claims 15–20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 14 does not include additional elements that integrate the abstract idea into a practical application. Claim 14 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a computer-readable medium, one or more processors, a device, a machine learning model, a user interface, and elements to “generate” and “output” instructions. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 14 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 16 and 18 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a classifier and a regression model (claim 16) and an element indicating that the instructions “cause the device to provide the input to the classifier or to the regression model” (claim 18). When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 16 and 18 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 15, 17, and 19–20 do not include any additional elements beyond those included with respect to the claims from which claims 15, 17, and 19–20 depend. As a result, claims 15, 17, and 19–20 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 14 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 14 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a computer-readable medium, one or more processors, a device, a machine learning model, a user interface, and elements to “generate” and “output” instructions. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 14 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 16 and 18 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a classifier and a regression model (claim 16) and an element indicating that the instructions “cause the device to provide the input to the classifier or to the regression model” (claim 18). The additional elements do not amount to significantly more than the recited abstract idea because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 16 and 18 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 15, 17, and 19–20 do not include any additional elements beyond those included with respect to the claims from which claims 15, 17, and 19–20 depend. As a result, claims 15, 17, and 19–20 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102(a)(1)
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 7, 10–16, and 19–20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by KÜHN et al. (U.S. 2023/0244837).
Claim 7: Kühn discloses a method, comprising:
receiving, by a model system (See FIG. 1), item-level data from a first data source and feature-level data from a second data source (See paragraph 27, in view of paragraphs 3 and 37, wherein an internal source provides information associated with product research, product manufacturing, product assembly, and product marketing, and wherein product characteristics/attributes include properties of the product; and see paragraph 27, wherein external sources provide product sales and survey information);
preprocessing, by the model system, the item-level data and the feature-level data to correlate historical item data to specific components and features (See FIG. 1 and paragraphs 27–29, in view of paragraph 37, wherein the data pre-processor generates an input dataset based on the internal and external data, and wherein master data includes product attribute data “pertaining to quantity of product vending/sales in a historical timeline, product hierarchy, product characteristics and other aspects pertaining to the product”);
training, by the model system, a machine learning model on the historical item data correlated to the specific components and features (See paragraph 31, in view of paragraphs 27–29 and 37, wherein a machine learning model is trained based on the ingested data; see also paragraphs 51 and 54);
receiving, by the model system and from a user device, input indicating a proposed product configuration (See paragraphs 40 and 55, wherein what-if scenarios and new product/attribute simulations are received from a user; see also paragraph 59, wherein the system may recite user inputs from another device);
predicting, by the model system, an outcome associated with the proposed product configuration using the machine learning model (See paragraphs 55 and 32, in view of paragraphs 52–54, wherein the system generates predictions associated with a given product attribute set; see also FIG. 3I–3J);
generating, by the model system, instructions for a visualization of the outcome, wherein the visualization further indicates a plurality of contributions associated with the specific components and features (See FIG. 3I–3J and paragraphs 52 and 55, wherein output visualizations are generated; see also paragraph 59); and
outputting, by the model system and to the user device, the instructions for the visualization (See FIG. 3I–3J and paragraphs 52 and 55, wherein output visualizations are generated; see also paragraph 59).
Claim 10: Kühn discloses the method of claim 7, wherein the specific components and features comprise one or more of: a form factor; a weight; a battery life; a build material; a connectivity option; a graphics capability; a memory capacity; a processor speed; a security feature; a hard disk configuration; or a quantity of expansion slots (See paragraph 37, wherein product characteristics include physical characteristics, and wherein Examiner notes that the listed components and features are afforded limited patentable weight because the components and features do not patentably limit the method steps of claim 7).
Claim 11: Kühn discloses the method of claim 7, further comprising: ranking, by the model system, the specific components and features using exploratory data analysis for training the machine learning model (See paragraphs 32–33 and 36, in view of paragraph 31, wherein attribute importance is quantified using a machine learning model trained on ingested product/attribute data; see also FIG. 3I and paragraphs 52 and 55).
Claim 12: Kühn discloses the method of claim 7, wherein generating the instructions for the visualization comprises: generating, by the model system, shapley additive explanation (SHAP) values to indicate the plurality of contributions associated with the specific components and features (See FIG. 3I and paragraphs 32 and 52, wherein SHAP values are generated).
Claim 13: Kühn discloses the method of claim 7, wherein preprocessing the item-level data and the feature-level data comprises: joining, by the model system, the specific components and features to corresponding entries in the historical item data to enable the machine learning model to identify one or more successful product configurations (See paragraphs 31–32, in view of paragraph 37, wherein the machine learning system is trained on ingested data, and wherein master data includes product attribute data “pertaining to quantity of product vending/sales in a historical timeline, product hierarchy, product characteristics and other aspects pertaining to the product”).
Claim 14: Kühn discloses a non-transitory computer-readable medium storing a set of instructions (See paragraphs 57–58), the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device (See paragraphs 57–58), cause the device to:
receive input indicating a proposed product configuration (See paragraphs 40 and 55, wherein what-if scenarios and new product/attribute simulations are received from a user; see also paragraph 59, wherein the system may recite user inputs from another device);
provide the input to a machine learning model to generate a prediction associated with the proposed product configuration (See paragraphs 55 and 32, in view of paragraphs 52–54, wherein the system generates predictions associated with a given product attribute set; see also FIG. 3I–3J);
determine a set of contribution values associated with portions of the proposed product configuration (See FIG. 3I and paragraphs 32 and 52, wherein SHAP values are generated with respect to attributes of a product);
generate instructions for a user interface (UI) that includes text indicating the prediction and a graph indicating the set of contribution values (See FIG. 3I–3J and paragraphs 52 and 55, wherein output visualizations are generated; see also paragraph 59); and
output the instructions for the UI (See FIG. 3I–3J and paragraphs 52 and 55, wherein output visualizations are generated; see also paragraph 59).
Claim 15: Kühn discloses the non-transitory computer-readable medium of claim 14, wherein the set of contribution values comprises a set of shapley additive explanation (SHAP) values (See FIG. 3I and paragraphs 32 and 52, wherein SHAP values are generated).
Claim 16: Kühn discloses the non-transitory computer-readable medium of claim 14, wherein the machine learning model includes a classifier and a regression model (See paragraph 51, wherein the system utilizes an ensemble of classification and regression models); see also paragraphs 31 and 54).
Claim 19: Kühn discloses the non-transitory computer-readable medium of claim 14, wherein the graph comprises a bar graph (See FIG. 3I–3J).
Claim 20: Kühn discloses the non-transitory computer-readable medium of claim 14, wherein the set of contribution values are represented as differentials relative to a base value (See FIG. 3I and paragraph 52, wherein SHAP values are generated using summation to delta properties to map differences between base and specific input values).
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.
Claims 1, 3–6, 9, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over KÜHN et al. (U.S. 2023/0244837) in view of Kondapaneni (U.S. 2025/0285146).
Claim 1: Kühn discloses a device for predicting contributions of feature sets to an outcome using shapley additive explanation (SHAP) values, comprising:
one or more processors (See paragraphs 57–58) configured to:
preprocess product configuration details with item-level information from a plurality of databases to create a preprocessed dataset associated with product configurations (See FIG. 1 and paragraphs 27–29, in view of paragraph 37, wherein data from internal and external sources is ingested, and wherein the data pre-processor generates an input dataset based on the internal and external data, and wherein master data includes product attribute data “pertaining to quantity of product vending/sales in a historical timeline, product hierarchy, product characteristics and other aspects pertaining to the product”);
train a machine learning model using the preprocessed dataset and a gradient boosting algorithm, wherein the machine learning model includes a classifier and a regression model (See paragraphs 31 and 51, in view of paragraph 54, wherein the trained model includes an ensemble model that utilizes both classifiers and regression models, and wherein the machine learning model utilizes a gradient boosting approach);
generate a prediction associated with a proposed product configuration using the machine learning model, wherein the prediction includes a numerical output from the regression model (See paragraphs 55 and 32, in view of paragraphs 52–54, wherein the system generates predictions associated with a given product attribute set, and FIG. 3I, in view of paragraphs 51–52, wherein feature contribution is quantified and displayed using regression models; see also FIG. 3J);
calculate the SHAP values for individual features within a set of features for the proposed product configuration to determine a contribution of each feature in the set of features to the prediction (See FIG. 3I and paragraphs 32 and 52, wherein SHAP values are generated); and
output an indication of the prediction with the SHAP values (See FIG. 3I and paragraphs, wherein SHAP values are output; see also paragraph 59). Kühn does not expressly disclose the remaining claim elements.
Kondapaneni discloses wherein the prediction includes a binary output from the classifier (See FIG. 4 and paragraph 71, in view of paragraph 59, wherein the interface identifies the binary output of the classifier indicating the performance classification of the product).
Kühn discloses a system directed to modeling and evaluating product attributes. Kondapaneni discloses a system directed to planning assortments based on product attribute analysis. Each reference discloses a system directed to evaluating product attributes. The technique of utilizing binary classifier outputs is applicable to the system of Kühn as they each share characteristics and capabilities; namely, they are directed to evaluating metrics related to evaluating product attributes.
One of ordinary skill in the art would have recognized that applying the known technique of Kondapaneni would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Kondapaneni to the teachings of Kühn would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate product attribute evaluations into similar systems. Further, applying binary classifier outputs to Kühn would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 3: Kühn discloses the device of claim 1, wherein the one or more processors are further configured to: exclude records with missing attributes from the preprocessed dataset (See paragraphs 29 and 49, wherein missing data is replaced, such that records having missing attribute values are not utilized).
Claim 4: Kühn discloses the device of claim 1, wherein, to output the indication of the prediction with the SHAP values, the one or more processors are configured to: output instructions for an interface that displays the prediction in text along with a graph showing the SHAP values relative to a base value (See FIG. 3I and paragraph 52, wherein SHAP values are generated and displayed using summation to delta properties to map differences between base and specific input values; see also FIG. 3J and paragraph 55).
Claim 5: Kühn does not expressly disclose the elements of claim 5.
Kondapaneni discloses wherein the text comprises the binary output from the classifier (See FIG. 4 and paragraph 71, in view of paragraph 59, wherein the interface identifies the binary output of the classifier indicating the performance classification of the product).
One of ordinary skill in the art would have recognized that applying the known technique of Kondapaneni would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 6: Kühn discloses the device of claim 4, wherein the text comprises the numerical output from the regression model (See FIG. 3I, in view of paragraphs 51–52, wherein feature contribution is quantified and displayed using regression models).
Claim 9: Kühn discloses the method of claim 7, wherein the outcome associated with the proposed product configuration comprises: a predicted quantity of units within a time period (See paragraphs 55, 33, and 36, in view of paragraph 37, wherein product volume predictions are generated in the context of timelines data). Kühn does not expressly disclose the remaining claim elements.
Kondapaneni discloses a classification indicating whether the proposed product configuration will be a best seller (See FIG. 4 and paragraph 71, in view of paragraph 59, wherein products are classified into high- and low-performing categories, and wherein Examiner notes that the “best seller” terminology is afforded limited patentable weight as a nonfunctional label that does not patentably limit the predicted outcome).
One of ordinary skill in the art would have recognized that applying the known technique of Kondapaneni would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 17: Kühn discloses the non-transitory computer-readable medium of claim 16, wherein the prediction includes a numerical output from the regression model or a combination thereof (See FIG. 3I, in view of paragraphs 51–52, wherein feature contribution is quantified and displayed). Kühn does not expressly disclose the remaining claim elements.
Kondapaneni discloses wherein the prediction includes a binary output from the classifier (See FIG. 4 and paragraph 71, in view of paragraph 59, wherein the interface identifies the binary output of the classifier indicating the performance classification of the product).
One of ordinary skill in the art would have recognized that applying the known technique of Kondapaneni would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over KÜHN et al. (U.S. 2023/0244837) in view of Kondapaneni (U.S. 2025/0285146), and in further view of Hall et al. (U.S. 2023/0376981).
Claim 2: As detailed above, Kühn and Kondapaneni disclose the elements of claim 1. Although Kühn discloses product configuration details (See citations above), Kühn and Kondapaneni do not disclose the remaining claim elements.
Hall discloses functionality to encode the product configuration details into a numerical format for the preprocessed dataset (See paragraphs 38 and 80, wherein product features and product data features are encoded).
As disclosed above, Kühn discloses a system directed to modeling and evaluating product attributes, and Kondapaneni discloses a system directed to planning assortments based on product attribute analysis. Hall discloses a system directed to product attribute research and development. Each reference discloses a system directed to evaluating product attributes. The technique of encoding product details is applicable to the systems of Kühn and Kondapaneni as they each share characteristics and capabilities; namely, they are directed to evaluating metrics related to evaluating product attributes.
One of ordinary skill in the art would have recognized that applying the known technique of Hall would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Hall to the teachings of Kühn and Kondapaneni would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate product attribute evaluations into similar systems. Further, applying product detail encoding to Kühn and Kondapaneni would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over KÜHN et al. (U.S. 2023/0244837) in view of Nichols et al. (U.S. 2025/0378412).
Claim 8: As detailed above, Kühn discloses the elements of claim 7.
Kühn discloses the method of claim 7, further comprising: standardizing, by the model system, the historical item data and standardizing the specific components and features (See paragraphs 27–29 and 49, in view of paragraph 37, wherein raw, unstructured data is standardized using ingestible templates). Kühn does not expressly disclose the remaining claim elements.
Nichols discloses standardizing the historical data across multiple currencies; and standardizing across multiple measurement units (See paragraphs 61–65, wherein units of measurement are standardized, and wherein currency values are normalized to a common scale).
Kühn discloses a system directed to modeling and evaluating product attributes. Nichols discloses a system directed to evaluating software development metrics. Each reference discloses a system directed to evaluating metrics related to product development. The technique of standardizing measurement units and currency values is applicable to the system of Kühn as they each share characteristics and capabilities; namely, they are directed to evaluating metrics related to product development.
One of ordinary skill in the art would have recognized that applying the known technique of Nichols would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Nichols to the teachings of Kühn would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate product development evaluations into similar systems. Further, applying measurement unit and currency value standardization to Kühn would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Further, although Nichols does not directly pertain to the field of product configurations, a reference in a field different from that of applicant's endeavor may be reasonably pertinent if it is one which, because of the matter with which it deals, logically would have commended itself to an inventor's attention in considering his or her invention as a whole (MPEP 2141.01). The prior art of record provides common essential elements, even though the reference to Nichols is not directed to product configuration analysis but rather based evaluating work performance associated with product development; furthermore, it solves the pertinent problem.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over KÜHN et al. (U.S. 2023/0244837) in view of Hall et al. (U.S. 2023/0376981).
Claim 18: As detailed above, Kühn discloses the elements of independent claim 14 and dependent claim 16.
Kühn discloses the non-transitory computer-readable medium of claim 16, wherein the input indicates an output preference, and wherein the one or more instructions, that cause the device to provide the input to the machine learning model, cause the device to provide the input to the model based on the output preference (See paragraph 52, in view of paragraphs 53–55, wherein the user selects an algorithm, thereby indicating an output preference, and wherein the selected algorithm performs the associated analysis). Kühn does not expressly disclose the remaining claim elements.
Hall discloses wherein the one or more instructions, that cause the device to provide the input to the machine learning model, cause the device to provide the input to the classifier or to the regression model (See paragraph 107, in view of paragraph 40, wherein the system selects the predictive model based on the product attribute, and wherein the predictive models include classification and regression models).
Kühn discloses a system directed to modeling and evaluating product attributes. Hall discloses a system directed to product attribute research and development. Each reference discloses a system directed to evaluating product attributes. The technique of selecting a classifier or regression model is applicable to the system of Kühn as they each share characteristics and capabilities; namely, they are directed to evaluating metrics related to evaluating product attributes.
One of ordinary skill in the art would have recognized that applying the known technique of Hall would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Hall to the teachings of Kühn would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate product attribute evaluations into similar systems. Further, applying classifier or regression model selection to Kühn would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
ABE (U.S. 2025/0384194) discloses a system directed to evaluating the cost and design of integrated circuit packaging using SHAP analysis; and
Sureshkumar et al. (U.S. 2022/0092651) discloses a system directed to generating product insights by analyzing configuration, calibration, and feature information in view of product reviews.
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/WILLIAM S BROCKINGTON III/ Primary Examiner, Art Unit 3623
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