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 .
This communication is responsive to the amendment filed 3/24/2026.
Claims 1, 3, 13, and 15 have been amended, claim 7 has been previously canceled, and no claims have been added.
Claims 1-6 and 8-20 are pending with claims 1 and 13 as independent claims.
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 –
Claims 1-5, 8-16, and 18-20 are rejected under 35 U.S.C. 102(a)(1) based upon being anticipated by Page (US 2015/0324490, published 11/12/2015).
Claim 1. A product configuration design system, comprising:
a) a product configuration design server, comprising:
a configuration generation model; and a machine learner, which is configured to process a machine learning algorithm for training and executing the configuration generation model; Page discloses in [0081-0084] “a customization system may determine qualitative data using machine learning based on user habits, e.g., products the user purchases, views, or rates.” And in [0085-0095] “The customization system may use machine learning from previous customer ratings of results or other feedback data to continuously refine its decision making process and styles for a particular user or group of users, e.g., as qualitative input.” And in [0180-0181] “style data may reside in a style portal, e.g., that includes the computer, so that the style portal may modify learning-enabled styles as needed… the computer may use learning to modify a style. For instance, the computer may use context learning to create context sensitive rules as users (e.g., the crowd) create styles, customize products with their styles, accept or reject customized products, and modify their styles or context rules.” And in [0182] “for the context “product=jacket” and a region of purpose with tags “sleeve” and “inner surface”, over time the computer, e.g., style portal, may learn that textures and finishes for that context should be soft, not aggressive, and prevent a texture with associated qualitative modifiers “spiky” or “aggressive” from being used in that context for styles where users have enabled context based rules, e.g., based on crowd learning.” And [0254] “a customizable shoe design 1600 which includes design geometry plus customization vectors and associated data.” (emphasis added) Examiner Note: The configuration generation model may be the “product customization system”, which “stores customizable product designs, styles, and uses a customization algorithm to apply particular styles for particular users to a particular customizable product design”, such as “customizable shoe design 1600” such that the customization system may use the machine learning module , trained using feedback data, to customize a product such as shoe design 1600, and
a product storage configured to store a customizable tagging system, comprising a hierarchy of tags with each of the tags associated with at least one three-dimensional object representation; Page discloses in [0058-0060, 0065, 0180-0182, 0211-0212, and 0241] “Each customizable product design may include digital or other representations of faces (surfaces), facets, edges, curves, vertices, volumes, voxels, unique identifiers (ID tags) for each face, edge and vertex, or combinations of two or more of these… The computer may allow style creation via a cloud-based style portal. In these implementations, style data may reside in a style portal, e.g., that includes the computer, so that the style portal may modify learning-enabled styles as needed… Surfaces, vertices, edges, or combinations of two or more of these can be tracked with ID tags or other types of identification or memory location information so that the surfaces, vertices, edges, or combinations of two or more of these can be uniquely and reliably retrieved and distinguished… A product design includes customization vectors that define attributes for a product which may be customized, e.g., using a style… A customization vector may define a set of rules about which attributes of a product may be customized, in what ways the attributes may be customized, in what contexts the attributes may be customized, and appropriate ranges for the attributes… the computer may learn context rules by automatic analysis of many users' style adaptations and choices over time. For example, for the context “product=jacket” and a region of purpose with tags “sleeve” and “inner surface”, over time the computer, e.g., style portal, may learn that textures and finishes for that context should be soft, not aggressive, and prevent a texture with associated qualitative modifiers “spiky” or “aggressive” from being used in that context for styles where users have enabled context based rules, e.g., based on crowd learning… The computer determines a feature hierarchy for a customized product design using priority information from the product design and, optionally, from the style (908). In some examples, the computer may create a product feature hierarchy, e.g., that describes an order in which to perform feature customization for the product design, by assessing attribute priorities from the product design… the computer may match the most important style attributes with the most important features of the product to be customized and include those attributes higher in the refined feature hierarchy than less important attributes, e.g., from the style, the product design, or both… the computer may evaluate each potential match of style data, e.g., style attributes, to product customization vectors, e.g., product attributes, to determine if each attribute to be applied from the style conflicts with directives or rules from the customizable product design… The customization system may handle the conflict in texture unit size by scaling the preferred texture from the user's style so that it may meet the requirements specified in the customization vectors for this product. Unit texture elements have been scaled down and distributed over the primary aesthetic surface.” (emphasis added) Examiner Note: tags such as “sleeve” and “inner surface” in product “jacket” may be customized based on feature hierarchy using priority information, and
b) a product configuration design device connected to the product configuration design server; Page discloses in [0089-0094] "a user device 102 may send a request to a computer 106 via a communication link 104, e.g., a network, for information about a particular product. .. the user device 102 receives data indicating user input. The user input may indicate selection or modification of a style, responses to questions, e.g., received from the computer 1 06, selection of a product or product category to be customized, a request that a product be customized with a style, or a combination of two or more of these." (emphasis added) Examiner Note: The product configuration design device may be user device 102, which is in communication with computer 106, design server, via link 104 connection,
wherein the machine learner generates a plurality of product configurations as an output from a machine learning calculation on the configuration generation model in response to receiving a three-dimensional object representation, a collection, and an inspiration source as input to the configuration generation model by placing materials from the collection and the input from the inspiration source on individual regions of the three-dimensional object based on material properties of the collection and tags associated with the three-dimensional object representation; Page discloses in [0066] “the system receives a product design from a product designer, e.g., a company, that includes a set of customization vectors and associated controls such that possible variations made to the product design using the customization vectors, and associated attribute ranges defined by the customization vectors, yield results that the product designer deems useful and acceptable.” And in [0081-0085] “The customization system may use machine learning from previous customer ratings of results or other feedback data to continuously refine its decision making process and styles for a particular user or group of users, e.g., as qualitative input… a computer representation of a product design is by nature quantitative, but a customization system may customize the design of a shoe sole, with inherent dimensional information, with a knurled tread pattern using qualitative inputs “bold” and “angular” instead of using only specific user choices, e.g. color, or numerical or quantitative input… a customization system may determine qualitative data using machine learning based on user habits, e.g., products the user purchases, views, or rates… the customization system may apply personal styles with contextual intelligence, e.g., to determine how specific attributes can be applied under different circumstances. For example, contextual intelligence may include: a) types of contexts; b) attributes to apply; and c) rules within a context that affect how attributes are applied.” And in [0102-0106] “A computer receives data indicating creation of a product design (202). For instance, the computer receives data indicating creation of customization vectors for the product design (204). The product design may include a product design geometry and customization vectors. The computer may receive data indicating the product design geometry and customization vectors from one or more product designers, each using an input device such as the user device 102. The customization vectors refer to specific features and attributes of the product design and specify how those features and attributes may be changed… The computer receives data indicating creation of a style that is not specific to a particular product design (206).” And in [0107] “The computer receives data indicating selection of the product design by a user (208). The selection may be a single mouse click or one tap on a touch screen to select an image of the product design.” And in [0111] “The computer modifies the product design by applying attributes from the style to the customization vectors for the product design (212). For instance, the computer parses the customizable product design and style data for the style to create a customized product design.” And in [0112] “The computer may selectively apply attributes from the style corresponding to the user according to instructions for the customization vector. The term “selectively” may mean finding a best fit between available style data in the style and customization vectors for the product design. The computer may use logic, reasoning, heuristics, prioritization, best fit ranking and other appropriate methods to create the customized product design.” And in [0113] “The computer provides instructions for generation of a presentation of the modified product design to the user (214).” And in [0116] “The computer receives data indicating changes to the modified product design or selection of another product design by the user (218). The data indicating the changes to the modified product design or the selection of another product design may indicate that the user does not accept the modified product design. After the computer receives the data indicating the changes to the modified product design, the computer may perform step 212, e.g., and continue with the process 200.” And in [0119] “The computer provides data to a manufacturing system indicating the modified product design (222). For instance, the data causes the manufacturing system to create a customized product using the modified product design.” And in [0167, 0188, and 0248] “a pre-defined style may be useful for brands to realize revenue via third-party manufactured products, e.g., typically 3D printed products, while maintaining brand integrity and consistency… The computer prompts the user to answer questions related to products, categories, brands, lifestyle, or preferences (706). For example, the computer may determine questions about the products types, product brands, or both, the user likes to determine styles typically associated with those products or brands or style attributes typically associated with those products or brands. The computer may use these styles typically associated with those products or attributes from these styles as elements of one or more styles when creating a style(s) for the user… FIG. 14A shows a block diagram of an example of a customizable computer mouse design 1400 which includes generic design geometry and customization vectors and associated data. One customization vector describes an area 1402 which is defined as an area where a logo may be applied. Another customization vector describes a region of purpose 1404 which is tagged in customization vector data as a primary aesthetic surface. Another customization vector may describe a region of purpose 1406 which is tagged in customization vector data both as a secondary aesthetic surface and as a grip surface. The overall size and dimensions of customizable computer mouse design 1400 may be customizable. Many other customization vectors are possible (and likely) for this product and other products. A small subset has been described here for brevity.” And in [0249] “FIG. 14B shows a block diagram of a customized computer mouse design 1400b which is a modified version of the customizable computer mouse design 1400 of FIG. 14A.” And in [0250] “FIG. 14C shows a block diagram of a second version of a customized computer mouse design 1400c which is a modified version of the customizable computer mouse design 1400 of FIG. 14A.” (emphasis added) Examiner Note: at least a 3D product design, style information as collection of vectors, e.g. colors, materials, textures, themes, etc. and context information, e.g. fashion, lifestyle designations, casual, formal , etc. as inspiration or influencing factors, may be selected by a user, via input to a machine leaning used by the customization system, in order to output possible variations of the product design. Figs. 14b-c may represent possible variations of generic product design 1400 illustrated in fig. 14a. steps 212-218 may be a loop that the user may create a plurality of variations of the 3D product. The inspired source may be a brand of a third-party manufactured product.
wherein the input from the inspiration source comprises colors or color combinations derived from the inspiration source which comprises a plurality of images that are parsed by the machine learner, Page discloses in [0156, 0167, 0180-0188, 0190-0195 and 0235] “The computer receives feedback from the user regarding products the user may like including images, scenarios, products, and brands (710). For example, the computer may provide the user device with instructions for presentation of an image to prompt the user for responses that indicate whether or not the user likes the image, e.g., approves or disapproves of the image, to create or modify a style for the user. The computer may use the user's responses to determine styles or style data for other users who have the same preferences as the user, e.g., like the same images, dislike the same images, or both… a pre-defined style may be useful for brands to realize revenue via third-party manufactured products, e.g., typically 3D printed products, while maintaining brand integrity and consistency… The computer prompts the user to answer questions related to products, categories, brands, lifestyle, or preferences… the computer may use brand preference data, e.g., data about brands the user likes. For example, if the user likes a particular brand, the computer may create a style with materials, colors, and geometric treatments similar to the products of that particular brand… the computer may determine that an image of a logo for the user best applies to an icon or a stamp. In some examples, an icon may be a visual representation of a shape that may be made in a variety of ways, for example as a graphic with distinct color. In some examples, a stamp may be a shape that is presented as relief, e.g., surface deviation, and may or may not have color distinct from the item it is applied to.” (emphasis added) Examiner Note: input from the user indicating style preferences may be used, by parsing or identifying user desired styles, by the customization system to provide customized product, wherein the inspiration source may be product brand by a third-party manufactured product,
wherein the product configuration design server further comprises: wherein the tags indicate material properties and material usage on regions of the three-dimensional object representation; Page discloses in [0058] “Each customizable product design may include digital or other representations of faces (surfaces), facets, edges, curves, vertices, volumes, voxels, unique identifiers (ID tags) for each face, edge and vertex, or combinations of two or more of these. Shape data for a product may be in the form of surface-based data, facet-based data, voxel-based data, parametric instructions based data, or combinations of two or more of these.” And in [0069] “A customization vector may include a region of purpose. Regions of purpose can be areas, groups of surfaces, subsets of the product volume, or combinations of two or more of these that can be defined as having a common purpose or that can be treated as a unit with respect to customization. For example, the grip on a tool may include one or more surfaces in the product design that can define a region of purpose.” And in [0079-0080] “A customization system may optimize the resulting design of the customizable product using customization vectors specified by the product designer and style data which may be general or user-specific. The customization system may create quantitative output data, e.g., the specific geometry, materials, colors and manufacturing methods of a customized design. The customization system may use either or both qualitative and quantitative inputs selected from style data and may use them to create typically unique customized designs… The customization system may use non-geometric inputs, e.g., qualitative inputs or other inputs that may not be geometrically represented such as colors, color palettes, and materials.” And in [0081] “a computer representation of a product design is by nature quantitative, but a customization system may customize the design of a shoe sole, with inherent dimensional information, with a knurled tread pattern using qualitative inputs “bold” and “angular” instead of using only specific user choices, e.g. color, or numerical or quantitative input. In some implementations, a customization system may determine qualitative data using machine learning based on user habits, e.g., products the user purchases, views, or rates.” (emphasis added) Examiner Note: areas of surfaces on the product design may be tags that facilitate customizing the product design by applying colors, materials, textures, and qualitative data as part of a style such as “bold”, “angular”, “organic”, or “soft”. The term “material properties” may be interpreted as property, e.g. color, material, and/or texture, for a surface area as indicated in [0006] “The product design may include product design data of at least one of a surface, a facet, an edge, a vertex, a volume, a voxel, a feature, a region of purpose, or a property for a surface or an edge. The property may include data representing at least one of a color, a material, or a texture for the surface or the edge.” And the term “material usage” may be interpreted as an elastic property, e.g. mechanical spring constant of 200 pounds-force per inch, for a surface region, e.g. a sole area, as indicated in [0254] “Another customization vector describes a set of areas or volumes 1604 which are designated as “sole” and “grip areas” and requiring elastic properties such as a mechanical spring constant of 200 pounds-force per inch, per square inch of sole area. A sole design of any shape or material may be added within the defined volumes of 1604 provided it meets all the specified criteria from the customization vectors.”, and
Claims 2 and 14. The rejection of the product configuration design system of claim 1 is incorporated, wherein the collection comprises a plurality of materials and a plurality of colors; Page discloses in [0228-0229] “The style data 1100 may include content 1102 and modifiers 1104. The content 1102 may include 2D graphic data 1106, 3D geometric data 1116 and other content 1130... The other content 1130 may include color palettes 1132, colors 1134, text 1136, materials 1138, and other customer data 1140.” (emphasis added).
Claims 3 and 15. The rejection of the product configuration design system of claim 1 is incorporated, wherein the product configuration design device is configured to receive a user input to determine which regions of the three-dimensional object representation are being automatically configured; and wherein the configuration generation model determines, for each product configuration, a combination of material and color for each of the determined regions of the three-dimensional object representation. Page discloses in [0063, 0082-0084, 0091 and 0231] “a surface may include a surface, a facet, a voxel, a triangle, an edge, a curve, a vertex, a parametric instruction, or other data that defines a layer of a product… The computer 106 processes the user input, e.g., the selection of a product or product category, a style for the user and product design data to create a customized product design which is customized for the user… The customization system may produce a customized product design from a product design and customization vectors plus style data inputs by selectively modifying geometry, features or attributes of the product design, e.g., specified by the customization vectors. “Selectively modifying” may mean modifying only those geometries, features, regions of purpose, described in more detail below, materials, colors, sizes, manufacturing methods or attributes of a design which are useful in creating a customized design tailored to contexts specified via the product design and customization vectors… a customization system may modify a product design before storing the product design in a database, e.g., by selecting some or all of the product design data to be used for a customization vector and applying style data to the customization vector. The customization system may mark or tag the product design data, e.g., some aspect of the design, for use as a customization vector in a customization process. In some examples, the “design data” might be some edges in the product design. The system would then tag these edges as a customization vector that could have different edge treatments applied, e.g. beveled, rounded, etc., during the customization process and using a user's style.”.(emphasis added) Examiner Note: the user may indicate one or more regions to be customized via the input “design data”. the customization system may tag the user indicated region and apply user indicated colors and materials preferred by the user for the purpose of customizing the user design product.
Claims 4 and 16. The rejection of the product configuration design system of claim 1 is incorporated, wherein each product configuration in the plurality of product configurations comprises the three-dimensional object representation, which comprises a plurality of regions, each applied with a corresponding material representation with a corresponding color combination; Page discloses in [0248-0250] “FIG. 14A shows a block diagram of an example of a customizable computer mouse design 1400 which includes generic design geometry and customization vectors and associated data. One customization vector describes an area 1402 which is defined as an area where a logo may be applied. Another customization vector describes a region of purpose 1404 which is tagged in customization vector data as a primary aesthetic surface. Another customization vector may describe a region of purpose 1406 which is tagged in customization vector data both as a secondary aesthetic surface and as a grip surface.” (emphasis added) Examiner Note: product design 1400 may be a 3D object representation which comprise plurality of regions, e.g. regions 1402, 1404, and 1406, for customization, wherein at least one region may be customized by combination of specific material and certain color and/or style using customization vectors as illustrated in figs. 1400A-C).
Claim 5. The rejection of the product configuration design system of claim 1 is incorporated, wherein the product configuration design server further comprises:
a) a processor; b) a non-transitory memory; c) an input/output component; and d) a data bus connecting the processor, the non-transitory memory, and the input/output component; Page discloses in [0277] “Computing device 2000 includes a processor 2002, memory 2004, a storage device 2006, a high speed interface 2008 connecting to memory 2004 and high speed expansion ports 2010, and a low speed interface 2012 connecting to low speed bus 2014 and storage device 2006. Each of the components 2002, 2004, 2006, 2008, 2010, and 2012, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 2002 can process instructions for execution within the computing device 2000, including instructions stored in the memory 2004 or on the storage device 2006 to display graphical information for a GUI on an external input/output device, such as display 2016 coupled to high speed interface 2008.” (emphasis added).
Claim 8. The rejection of the product configuration design system of claim 1 is incorporated, wherein the product configuration design device is configured to enable a user to select a tag from the product storage; Page discloses in [0005] “the system may allow a user to select a style, defined by the user or another user, which the system will apply to products viewed by the user. The system may include multiple styles, e.g., which include style data, each of which may be collections, e.g., databases, of information that can include attribute values, rules, personal data, biometric data, context data, patterns, textures, images, logos, icons, motifs, selective space structures, or any two or more of these that form a design language that the system can apply to products. A product style may be unique to a particular user. A product style may be promoted by a particular brand. A product style might not be product-specific.” And in [0069] “A customization vector may include a region of purpose. Regions of purpose can be areas, groups of surfaces, subsets of the product volume, or combinations of two or more of these that can be defined as having a common purpose or that can be treated as a unit with respect to customization. For example, the grip on a tool may include one or more surfaces in the product design that can define a region of purpose. The surfaces of the grip can be grouped together and manipulated together during a customization process as a single region of purpose.” (emphasis added) Examiner Note: a user may select style data from a database to applied to a surface or region on the 3D object representation. Computer 106 (server) may determine the requested product design and style (selected tag in the design) for the requesting user, wherein the user may provide input for modifying the style selected by the server or computer 106. In the example given in [0083], the customization system may select style color such as “black”, because higher in the ranking (parent tag), and the user may change the style selection of the customization system by modifying the style “black” to select “purple”.
Claim 9. The rejection of the product configuration design system of claim 8 is incorporated, wherein the three-dimensional object representation is associated with a child tag of the selected tag; Page discloses in [0083] “The customization system may find a “best fit” between available style data and customization vectors and context data for the product to be customized. A best fit is not necessarily an exact match. For instance, an available attribute in style data might be purple which the style rates at nine for a particular context and black which the style rates at six, but the customization vector may rank black at ten and purple at two. In this example, the customization system may select black because of the higher combined score, e.g., of fifteen, even though purple is dominant in the style.” (emphasis added) Examiner Note: the style color “black” may be ranked higher based on score in one metric. However, based on the dominant ranking, another metric, style color “purple” may be ranked higher. Based on the score metric, the style color “black” may be a parent tag, whereas the style color “purple” may be a child tag.
Claims 10 and 18. The rejection of the product configuration design system of claim 1 is incorporated, wherein the product configuration design device is configured to display the plurality of product configurations, to be accepted or rejected by a user; Page discloses in [0115] “The computer determines whether the user accepts the modified product design (216). For instance, the user device may receive data indicating that the user selects or rejects the modified product design.” (emphasis added) Examiner Note: at least one customized product design may be displayed to a user such that the user may accept or reject the customized product design.
Claims 11 and 19. The rejection of the product configuration design system of claim 10 is incorporated, wherein the configuration generation model is trained with the plurality of accepted configurations and the plurality of rejected configurations, based on an input of the three-dimensional object representation, the collection, and the inspiration source; Page discloses in [0079, 0081-0082, 0084- 0085, and 0180-0182] "the computer may use context learning to create context sensitive rules as users (e.g., the crowd) create styles, customize products with their styles, accept or reject customized products, and modify their styles or context rules... the computer may learn context rules by automatic analysis of many users' style adaptations and choices over time. For example, for the context " product=jacket" and a region of purpose with tags "sleeve" and "inner surface", over time the computer, e.g., style portal, may learn that textures and finishes for that context should be soft, not aggressive, and prevent a texture with associated qualitative modifiers "spiky" or "aggressive" from being used in that context for styles where users have enabled context based rules, e.g., based on crowd learning.” (emphasis added) Examiner Note: the customization system may learn (trained) as users create styles, customize, using inputs, products with their styles, accept or reject customized products. The customization system may be optimized by selectively modifies features and/or attributes of particular product design but not another. See [0082].
Claims 12 and 20. The rejection of the product configuration design system of claim 11 is incorporated, wherein at least one accepted configuration in the plurality of accepted configurations comprises the three-dimensional object representation, comprising a plurality of regions, with each of the regions applied a corresponding material representation with a corresponding color combination, wherein the plurality of regions comprises at least one locked region applied with a locked material representation with a locked color combination to fix the at least one locked region with the locked material representation; Page discloses in [0213] “The computer determines, for each of the highest priority attributes, whether a value for the highest priority attribute conflicts with a rule for the product design (912). For instance, the computer determines whether the value indicates that the customized product should be blue but a rule for the product design indicates that the product is made from copper and cannot be painted or that the product should be red.” And in [0215] “In response to determining that the style includes another attribute for the respective product attribute, the computer determines another attribute for the respective product attribute using the style and the updated feature hierarchy (916).” And in [0216] “In response to determining that the style does not include another attribute for the respective product attribute, the computer uses a default attribute for the respective product attribute (918).” (emphasis added) Examiner Note: at least one region of a product design should not be painted based on a rule or the product design should only be in red color. This would indicate the product design may be locked to specific style that may be defined by a rule.
Claim 13. The claim is directed towards a method of product configuration design, comprising:
a) receiving selection of a three-dimensional object representation from a product configuration design server by using a product configuration design device; b) receiving selection of a collection from the product configuration design device; c) receiving selection of an inspiration source from the product configuration design device; Page discloses in [0011, 0017-0018, 0048-0074, 0090-0091, and 0107-0108] "receiving data indicating a selection of a product design by a user for creation of a three-dimensional product that includes a plurality of attributes, ... The computer receives data indicating selection of the product design by a user (208). The selection may be a single mouse click or one tap on a touch screen to select an image of the product design ... the system may allow a user to select a style, defined by the user or another user, which the system will apply to products viewed by the user ... One aspect of styles is data, which may include but is not limited to data representing colors, color palettes (e.g., groups of colors to be used together), images, textures, textures created from images, embossed or debossed features, biometric data, features derived from images, logos, motif, personal icons, graphics, graphics derived from images, sketches, features derived from sketches, ... a style, a product design, or both may include preferences and rules about how style data should be used. For example, a style may include the rules. In some examples, a product design or a customization process may include rules. Rules may include which attributes or data (such as colors, textures, materials, graphics, embossed icons, etc.) are applied to what categories of products." (emphasis added) Examiner Note: the user may select the product design, such as shoe design 1600 and the system may create a three-dimensional product design for the product design. Furthermore, the user may select style data representing color palettes such as groups of colors to be used together for customizing the product design. The user may, also, select style features derived from images, logos, motif, graphics derived from images, etc., and
d) generating a plurality of product configurations as an output from a machine learning calculation on a configuration generation model, in response to receiving the three-dimensional object representation, the collection, and the inspiration source by placing materials from the collection and an input from the inspiration source on individual regions of the three-dimensional object based on material properties of the collection and tags associated with the three-dimensional object representation; Page discloses in [0066] “the system receives a product design from a product designer, e.g., a company, that includes a set of customization vectors and associated controls such that possible variations made to the product design using the customization vectors, and associated attribute ranges defined by the customization vectors, yield results that the product designer deems useful and acceptable.” And in [0081-0085] “The customization system may use machine learning from previous customer ratings of results or other feedback data to continuously refine its decision making process and styles for a particular user or group of users, e.g., as qualitative input… a computer representation of a product design is by nature quantitative, but a customization system may customize the design of a shoe sole, with inherent dimensional information, with a knurled tread pattern using qualitative inputs “bold” and “angular” instead of using only specific user choices, e.g. color, or numerical or quantitative input… a customization system may determine qualitative data using machine learning based on user habits, e.g., products the user purchases, views, or rates… the customization system may apply personal styles with contextual intelligence, e.g., to determine how specific attributes can be applied under different circumstances. For example, contextual intelligence may include: a) types of contexts; b) attributes to apply; and c) rules within a context that affect how attributes are applied.” And in [0102-0106] “A computer receives data indicating creation of a product design (202). For instance, the computer receives data indicating creation of customization vectors for the product design (204). The product design may include a product design geometry and customization vectors. The computer may receive data indicating the product design geometry and customization vectors from one or more product designers, each using an input device such as the user device 102. The customization vectors refer to specific features and attributes of the product design and specify how those features and attributes may be changed… The computer receives data indicating creation of a style that is not specific to a particular product design (206).” And in [0107] “The computer receives data indicating selection of the product design by a user (208). The selection may be a single mouse click or one tap on a touch screen to select an image of the product design.” And in [0111] “The computer modifies the product design by applying attributes from the style to the customization vectors for the product design (212). For instance, the computer parses the customizable product design and style data for the style to create a customized product design.” And in [0112] “The computer may selectively apply attributes from the style corresponding to the user according to instructions for the customization vector. The term “selectively” may mean finding a best fit between available style data in the style and customization vectors for the product design. The computer may use logic, reasoning, heuristics, prioritization, best fit ranking and other appropriate methods to create the customized product design.” And in [0113] “The computer provides instructions for generation of a presentation of the modified product design to the user (214).” And in [0116] “The computer receives data indicating changes to the modified product design or selection of another product design by the user (218). The data indicating the changes to the modified product design or the selection of another product design may indicate that the user does not accept the modified product design. After the computer receives the data indicating the changes to the modified product design, the computer may perform step 212, e.g., and continue with the process 200.” And in [0119] “The computer provides data to a manufacturing system indicating the modified product design (222). For instance, the data causes the manufacturing system to create a customized product using the modified product design.” And in [0167, 0188, and 0248] “a pre-defined style may be useful for brands to realize revenue via third-party manufactured products, e.g., typically 3D printed products, while maintaining brand integrity and consistency… The computer prompts the user to answer questions related to products, categories, brands, lifestyle, or preferences (706). For example, the computer may determine questions about the products types, product brands, or both, the user likes to determine styles typically associated with those products or brands or style attributes typically associated with those products or brands. The computer may use these styles typically associated with those products or attributes from these styles as elements of one or more styles when creating a style(s) for the user… FIG. 14A shows a block diagram of an example of a customizable computer mouse design 1400 which includes generic design geometry and customization vectors and associated data. One customization vector describes an area 1402 which is defined as an area where a logo may be applied. Another customization vector describes a region of purpose 1404 which is tagged in customization vector data as a primary aesthetic surface. Another customization vector may describe a region of purpose 1406 which is tagged in customization vector data both as a secondary aesthetic surface and as a grip surface. The overall size and dimensions of customizable computer mouse design 1400 may be customizable. Many other customization vectors are possible (and likely) for this product and other products. A small subset has been described here for brevity.” And in [0249] “FIG. 14B shows a block diagram of a customized computer mouse design 1400b which is a modified version of the customizable computer mouse design 1400 of FIG. 14A.” And in [0250] “FIG. 14C shows a block diagram of a second version of a customized computer mouse design 1400c which is a modified version of the customizable computer mouse design 1400 of FIG. 14A.” (emphasis added) Examiner Note: at least a 3D product design, style information as collection of vectors, e.g. colors, materials, textures, themes, etc. and context information, e.g. fashion, lifestyle designations, casual, formal , etc. as inspiration or influencing factors, may be selected by a user, via input to a machine leaning used by the customization system, in order to output possible variations of the product design. Figs. 14b-c may represent possible variations of generic product design 1400 illustrated in fig. 14a. steps 212-218 may be a loop that the user may create a plurality of variations of the 3D product. The inspired source may be a brand of a third-party manufactured product,
wherein the input from the inspiration source comprises colors or color combinations derived from the inspiration source which comprises a plurality of images that are parsed by a machine learner; Page discloses in [0156, 0167, 0180-0188, 0190-0195 and 0235] “The computer receives feedback from the user regarding products the user may like including images, scenarios, products, and brands (710). For example, the computer may provide the user device with instructions for presentation of an image to prompt the user for responses that indicate whether or not the user likes the image, e.g., approves or disapproves of the image, to create or modify a style for the user. The computer may use the user's responses to determine styles or style data for other users who have the same preferences as the user, e.g., like the same images, dislike the same images, or both… a pre-defined style may be useful for brands to realize revenue via third-party manufactured products, e.g., typically 3D printed products, while maintaining brand integrity and consistency… The computer prompts the user to answer questions related to products, categories, brands, lifestyle, or preferences… the computer may use brand preference data, e.g., data about brands the user likes. For example, if the user likes a particular brand, the computer may create a style with materials, colors, and geometric treatments similar to the products of that particular brand… the computer may determine that an image of a logo for the user best applies to an icon or a stamp. In some examples, an icon may be a visual representation of a shape that may be made in a variety of ways, for example as a graphic with distinct color. In some examples, a stamp may be a shape that is presented as relief, e.g., surface deviation, and may or may not have color distinct from the item it is applied to.” (emphasis added) Examiner Note: input from the user indicating style preferences may be used, by parsing or identifying user desired styles, by the customization system to provide customized product, wherein the inspiration source may be product brand by a third-party manufactured product,
wherein the product configuration design server further comprises: wherein the tags indicate material properties and material usage on regions of the three-dimensional object representation; Page discloses in [0058] “Each customizable product design may include digital or other representations of faces (surfaces), facets, edges, curves, vertices, volumes, voxels, unique identifiers (ID tags) for each face, edge and vertex, or combinations of two or more of these. Shape data for a product may be in the form of surface-based data, facet-based data, voxel-based data, parametric instructions based data, or combinations of two or more of these.” And in [0069] “A customization vector may include a region of purpose. Regions of purpose can be areas, groups of surfaces, subsets of the product volume, or combinations of two or more of these that can be defined as having a common purpose or that can be treated as a unit with respect to customization. For example, the grip on a tool may include one or more surfaces in the product design that can define a region of purpose.” And in [0079-0080] “A customization system may optimize the resulting design of the customizable product using customization vectors specified by the product designer and style data which may be general or user-specific. The customization system may create quantitative output data, e.g., the specific geometry, materials, colors and manufacturing methods of a customized design. The customization system may use either or both qualitative and quantitative inputs selected from style data and may use them to create typically unique customized designs… The customization system may use non-geometric inputs, e.g., qualitative inputs or other inputs that may not be geometrically represented such as colors, color palettes, and materials.” And in [0081] “a computer representation of a product design is by nature quantitative, but a customization system may customize the design of a shoe sole, with inherent dimensional information, with a knurled tread pattern using qualitative inputs “bold” and “angular” instead of using only specific user choices, e.g. color, or numerical or quantitative input. In some implementations, a customization system may determine qualitative data using machine learning based on user habits, e.g., products the user purchases, views, or rates.” (emphasis added) Examiner Note: areas of surfaces on the product design may be tags that facilitate customizing the product design by applying colors, materials, textures, and qualitative data as part of a style such as “bold”, “angular”, “organic”, or “soft”. The term “material properties” may be interpreted as property, e.g. color, material, and/or texture, for a surface area as indicated in [0006] “The product design may include product design data of at least one of a surface, a facet, an edge, a vertex, a volume, a voxel, a feature, a region of purpose, or a property for a surface or an edge. The property may include data representing at least one of a color, a material, or a texture for the surface or the edge.” And the term “material usage” may be interpreted as an elastic property, e.g. mechanical spring constant of 200 pounds-force per inch, for a surface region, e.g. a sole area, as indicated in [0254] “Another customization vector describes a set of areas or volumes 1604 which are designated as “sole” and “grip areas” and requiring elastic properties such as a mechanical spring constant of 200 pounds-force per inch, per square inch of sole area. A sole design of any shape or material may be added within the defined volumes of 1604 provided it meets all the specified criteria from the customization vectors.”, and
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Page as applied to claims 1 and 13 above and further in view of Dubey et al. (US 2019/0325628, filed 04/23/2018, hereinafter as Dubey).
As per claims 6 and 17, the rejection of the product configuration design system of claim 1 is incorporated and further Page discloses in [0085] “The customization system may use machine learning from previous customer ratings of results or other feedback data to continuously refine its decision making process and styles for a particular user or group of users, e.g., as qualitative input.” (emphasis added).
Page does not explicitly disclose wherein the configuration generation model is a convolutional artificial neural network with at least two hidden layers. However, Dubey, in an analogous art, discloses in ([0021, 0050, 0070-0071 and 0086] "artificial intelligence (Al)-based design platform for assisting in design phases of products ... style transfer includes pre-processing of the style image 302 and the content image 304... the convolution layer 652 includes a convolution neural network (CNN) ... CNNs can include an input layer, a set of hidden layers, and an output layer... the set of hidden layers can include convolutional layers” the hidden layers may provide feature/style image input for content image customization).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Page with the teaching of Dubey for providing hidden layers for providing style/feature image input for customizing content image by transferring styles/features from input style image to content image such that the design process would elevate a significant demand on resources, such as processors and memory, as the designer iterates over multiple designs. Dubey Background.
Response to Arguments
Applicant's arguments filed 3/24/2026 have been fully considered but they are not persuasive.
Argument: Applicant argues “Page does not disclose the features of “the input from the inspiration source comprises colors or color combinations derived from the inspiration source which comprises a plurality of images that are parsed by the machine learner,” as recited in amended claim 1.”
Response: Page teaches the limitation in [0153-0155] “the computer may receive data indicating the user selected to modify a style (508). For instance, the user device may receive data indicating the user selected an option to modify a pre-existing style and provides the data to the computer… The computer receives data indicating a selection of multiple textures and a usage context for each texture (510). For instance, the user device may receive the data indicating the user selection of one or more textures for the pre-existing style and usage contexts for each texture and provides the data to the computer… The computer receives data indicating a selection of multiple materials or multiple colors and a usage context for each material or color (512). For example, the user device receives the data indicating the user selection of one or more materials, one or more colors, or both, and associated usage contexts for each of the materials and for each of the colors, and provides the data to the computer.” And in [0189] “One aspect of styles is data, which may include but is not limited to data representing colors, color palettes (e.g., groups of colors to be used together), images, textures, textures created from images, embossed or debossed features, biometric data, features derived from images, logos, motif, personal icons, graphics, graphics derived from images, sketches, features derived from sketches, application specific data that may or may not be related to biometric data, collections of preferences, likes or dislikes, materials, other personal data, data about past purchases, data about a person's environment or home, and any other appropriate data. Some or all style data may be derived from external, cultural or brand sources.” (emphasis added). The input for colors may inspired from style data from external, cultural or brand sources.
Argument: Applicant argues “Page does not disclose the feature of "placing materials from the collection and the input from the inspiration source on individual regions of the three-dimensional object based on material properties of the collection and tags associated with the three-dimensional object representation," as recited in amended claim 1.”
Response: Page teaches in [0188] “the computer may use brand preference data, e.g., data about brands the user likes. For example, if the user likes a particular brand, the computer may create a style with materials, colors, and geometric treatments similar to the products of that particular brand.” And in [0081] “a computer representation of a product design is by nature quantitative, but a customization system may customize the design of a shoe sole, with inherent dimensional information, with a knurled tread pattern using qualitative inputs “bold” and “angular” instead of using only specific user choices, e.g. color, or numerical or quantitative input. In some implementations, a customization system may determine qualitative data using machine learning based on user habits, e.g., products the user purchases, views, or rates.” (emphasis added). The customization system may use the style data to customize the design product using selected colors, materials, etc.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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.
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/AHAMED I NAZAR/Examiner, Art Unit 2178 6/4/2026
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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