DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/27/2025 and 08/26/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments see remarks, filed 08/26/2025, with respect to the claim 1-7, 10, and 12-20 have been fully considered but are moot because the arguments do not apply to the current combinations of references being used in the current rejection.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claim 1 recites limitation that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “an imaging device configured to…” [Line 2]
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1:
“an imaging device” (Fig. 1, #12 Page 5: Lines [0019-30] The imaging device12 is provided in a user device10 and is adapted for capturing images of one or more objects30 (e.g., building blocks). The imaging device12 is adapted to scan, capture and classify an image of one or more objects, and may be provided in the form of a camera on a personal computer, or in a handheld wireless device, such as a smartphone or smart watch. In one example, the imaging device12 operates together with a capture application that is adapted for providing a frame of reference for locating, scanning and imaging of objects. Fig. 1, illustrates the imaging device. (wherein the imaging device has sufficient structure wherein includes a camera on a personal or handheld device).).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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 1-7, 10, and 12-15 are rejected under 35 U.S.C 103 as being unpatentable over Velic (US 20170304732 A1) hereafter referenced as Velic in view of Rothschild (US 20170173486 A1) hereafter referenced as Rothschild and Sampara et al. (US 20170316489 A1) hereafter referenced as Sampara.
Regarding claim 1, Velic teaches a system for optical recognition of building blocks, comprising (Fig. 1, Paragraph [0043]- Velic discloses the system is configured to recognise a hierarchical toy object, i.e. a toy object including multiple toy construction elements that may be inter-connected with each other.):
an imaging device configured to capture images of building blocks (Fig. 1, Paragraph [0048]- Velic discloses the recognition system is configured to process the captured image so as to produce one or more processed versions of the captured image, and wherein the recognition system is configured to base the recognition on at least two images selected from the captured image and the one or more processed versions of the image, e.g. on the captured and one or more processed versions of the image or, alternatively, on two or more processed versions of the captured image.);
a memory configured to store captured images of building blocks (Fig. 1, Paragraph [0066]- Velic discloses an interactive game system including an image capturing device, a display adapted to show at least image data captured by the image capturing device, a data storage adapted to store captured image data, and a processor programmed to directly or indirectly interact with the image capturing device or act on the image data received directly or indirectly from the image capturing device and to perform processing steps of the above-mentioned method.);
and a processor configured to perform object recognition on images of building blocks, wherein the system is configured for the imaging device to communicate with the memory for conveying captured images of building blocks for storage in the memory (Fig. 1, Paragraph [0065]- Velic discloses a toy construction system including a plurality of toy construction elements, an image capturing device and a processor programmed to receive image data from the capturing device, the image data representing a digital image captured by the image capturing device. The processor is further configured to implement the steps of an embodiment of a method for recognizing real-world toy objects from the captured image as described herein, based on a trained deep convolutional classification model that is configured to recognize a plurality of toy objects, the plurality of toy objects including the plurality of toy construction elements.),
and for the processor to access and perform object recognition on captured images stored in the memory to identify individual building blocks in the captured images (Fig. 1, Paragraph [0151]- Velic discloses after classifying the image, information about the toy recognized toy construction element (e.g. an identifier identifying the type and color of the toy construction element) that is recognized is returned to the 3D detection module 520.),
and the processor is further configured to: generate a user inventory identifying a type of each building block identified in one or more captured images (Fig. 5, Paragraph [0151]- Velic discloses the recognition module 521 outputs a list of possible recognized toy construction elements along with respective confidence scores.)
Velic fails to explicitly teach and a quantity of each identified building block type, cross-reference the user inventory with a pre-stored library of block constructs to generate a list of block constructs that may be at least partially assembled with building blocks available in the user inventory.
However, Rothschild explicitly teaches and a quantity of each identified building block type (Fig.2 Paragraph [0045]- Rothschild discloses based on the determined number and type of the identified building block or the identified plurality of building blocks, the one or more instructions may further correspond to suggestions for assembling one or more other construction objects.),
cross-reference the user inventory with a pre-stored library of block constructs to generate a list of block constructs that may be at least partially assembled with building blocks available in the user inventory (Fig. 2, Paragraph [0040]- Rothschild discloses the received identification information may further comprise information regarding one or more construction objects that can be constructed by assembling the building block, or the plurality of building blocks constituting the assemblage.), Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic a system for optical recognition of building blocks with the teachings of Rothschild and a quantity of each identified building block type, cross-reference the user inventory with a pre-stored library of block constructs to generate a list of block constructs that may be at least partially assembled with building blocks available in the user inventory.
Wherein having Velic’s system for optical recognition of toys and a quantity of each identified building block type, cross-reference the user inventory with a pre-stored library of block constructs to generate a list of block constructs that may be at least partially assembled with building blocks available in the user inventory.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Velic and Rothschild are both systems for recognition of toys. Wherein Velic’s system wherein increased the accuracy of identification, while Rothschild’s system provides an increase in user experience and computational capability. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Rothschild et al. (US 20170173486 A1), Paragraph [0055].
Although Velic in view of Rothschild teaches block construct, building blocks, a build inventory, and compare the user inventory with a build inventory of building blocks required for assembling the construct Velic in view of Rothschild fails to explicitly teach for each listed construct, compare the user inventory with a build inventory of building blocks required for assembling the construct, and identify building blocks that are required by the build inventory and missing from the user inventory.
However, Sampara explicitly teaches for each listed construct, compare the user inventory with a build inventory of building blocks required for assembling the construct, and identify building blocks that are required by the build inventory and missing from the user inventory (Fig. 2, Paragraph [0043]- Sampara discloses the control circuit is configured to collect and update customer inventory information stored in the customer inventory information database, select one or more recommended recipes from the recipe database to recommend to a customer based on at least a customer inventory information associated with the customer, and for each recommended recipe comprising one or more missing ingredients not in a customer inventory according to the customer inventory information database: cause the user device to display a purchase suggestion of the one or more missing ingredients to the customer. (wherein the ingredients are the building blocks, the recipe is the build inventory, and the customer inventory is the user inventory)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild a system for optical recognition of building blocks with the teachings of Sampara for each listed construct, compare the user inventory with a build inventory of building blocks required for assembling the construct, and identify building blocks that are required by the build inventory and missing from the user inventory.
Wherein having Velic’s system for optical recognition of toys for each listed construct, compare the user inventory with a build inventory of building blocks required for assembling the construct, and identify building blocks that are required by the build inventory and missing from the user inventory.
The motivation behind the modification would have been to allow for more accurate selection of pieces and construct, since both Velic and Sampara are both systems for optical sensing for detecting items and determining steps for combining items to create a given output. Wherein Velic’s system wherein increased the accuracy of identification, while Sampara’s system provides an increase in user information about what is necessary to complete the construct. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Sampara et al. (US 20170316489 A1), Paragraph [0044].
Regarding claim 2, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Velic further teaches wherein the processor is configured, in performing object recognition, to perform object detection to identify individual building blocks in the captured image (Fig. 2 Paragraph [0042]- Velic discloses the detection module may be configured to perform image detection of a captured image so as to detect one or more toy objects in the captured digital image.).
Velic fails to explicitly teach and establish a count of each identified building block.
However, Rothschild explicitly teaches and establish a count of each identified building block (Fig. 2 Paragraph [0045]- Rothschild discloses based on the determined number and type of the identified building block or the identified plurality of building blocks, the one or more instructions may further correspond to suggestions for assembling one or more other construction objects.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Rothschild establish a count of each identified building block.
Wherein having Velic’s system for optical recognition of toys wherein establish a count of each identified building block.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Velic and Rothschild are both systems for recognition of toys. Wherein Velic’s system wherein increased the accuracy of identification, while Rothschild’s system provides an increase in user experience and computational capability. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Rothschild et al. (US 20170173486 A1), Paragraph [0055].
Regarding claim 3, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Velic further teaches wherein the processor is configured, in performing object recognition, to perform localization of detected building blocks to determine a location for individual building blocks in the captured image (Fig. 2, Paragraph [0050]- Velic discloses each candidate toy object may be an individual toy construction element or in itself an assembly of two or more interconnected toy construction elements. The process may then determine whether placement of a candidate toy object at the detected position within the scene is compatible with the constraints imposed by the toy construction system. The process may thus determine a most likely one of the candidate toy objects determined by the recognition module.).
Regarding claim 4, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Velic further teaches wherein the processor is configured, in performing object recognition, to perform bounding to identify edges defining perimeters for individual building blocks in the captured image (Fig. 2, Paragraph [0123]- Velic discloses all these objects are segmented and correctly classified which is shown on the right image of FIG. 2. Bounding boxes are placed around the objects and object labels are put on the top left corner of the recognized objects.).
Regarding claim 5 Velic in view of Rothschild and Sampara teaches the system according to claim 1, Velic further teaches wherein the processor is configured, in performing object recognition, to perform segmentation to isolate and extract a local image for individual building blocks in the captured image (Fig. 4C, Paragraph [0133]- Velic discloses FIG. 4C depicts a workflow in which an additional object detection step is performed before the actual recognition in order to segment the input image, e.g. if more than one object have to be recognized.).
Regarding claim 6, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Velic further teaches wherein the processor is configured, in performing object recognition, to perform matching between images of building blocks in the captured image and pre-stored images in the memory to match the extracted images with corresponding pre-stored images (Fig. 4B, Paragraph [0132]- Velic discloses in subsequent step S406, the process matches the information from a toy object database of known toy objects with the recognized toy element. For example, the information may include a 3D model of the object, connectivity information reflecting how the toy object can be interconnected with other toy objects, virtual attributes of the toy objects in a virtual environment, and/or the like.),
and, upon a successful matching of an extracted image with a pre-stored image, to designate the building block in the extracted image as a pre-defined building block type that is associated with the corresponding pre-stored image (Fig. 4B, Paragraph [0132]- Velic discloses in subsequent step S406, the process matches the information from a toy object database of known toy objects with the recognized toy element. For example, the information may include a 3D model of the object, connectivity information reflecting how the toy object can be interconnected with other toy objects, virtual attributes of the toy objects in a virtual environment, and/or the like. Other examples of information may include the price of a toy object, existing colours, construction sets where the element appears, buying locations and other information stored in the database or a web source. Matching information from a toy object database or a web source with a recognized real world toy objects may be conducted by querying a database or a web service.).
Regarding claim 7, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Velic further teaches wherein the processor is configured, in performing object recognition, to perform: object detection to identify individual building blocks in the captured image block (Fig. 2 Paragraph [0042]- Velic discloses the detection module may be configured to perform image detection of a captured image so as to detect one or more toy objects in the captured digital image.).
localization to determine a location for each of the individual building blocks identified in object detection of the captured image (Fig. 1, Paragraph [0050]- Velic discloses each candidate toy object may be an individual toy construction element or in itself an assembly of two or more interconnected toy construction elements. The process may then determine whether placement of a candidate toy object at the detected position within the scene is compatible with the constraints imposed by the toy construction system. The process may thus determine a most likely one of the candidate toy objects determined by the recognition module.);
bounding to identify edges defining perimeters for each of the individual building blocks detected and localized in the captured image (Fig. 1, Paragraph [0123]- Velic discloses all these objects are segmented and correctly classified which is shown on the right image of FIG. 2. Bounding boxes are placed around the objects and object labels are put on the top left corner of the recognized objects.);
segmentation to isolate and extract a local image for each of the individual building blocks detected, localized and bound in the captured image (Fig. 4C, Paragraph [0133]- Velic discloses FIG. 4C depicts a workflow in which an additional object detection step is performed before the actual recognition in order to segment the input image, e.g. if more than one object have to be recognized.);
and matching between the extracted images of the building blocks and pre-stored images in the memory to match the extracted images with corresponding pre-stored images (Fig. 4B, Paragraph [0132]- Velic discloses in subsequent step S406, the process matches the information from a toy object database of known toy objects with the recognized toy element. For example, the information may include a 3D model of the object, connectivity information reflecting how the toy object can be interconnected with other toy objects, virtual attributes of the toy objects in a virtual environment, and/or the like.),
and, upon a successful matching of an extracted image with a pre-stored image, to designate the building block in the extracted image as a pre-defined building block type that is associated with the corresponding pre-stored image (Fig. 4B, Paragraph [0132]- Velic discloses in subsequent step S406, the process matches the information from a toy object database of known toy objects with the recognized toy element. For example, the information may include a 3D model of the object, connectivity information reflecting how the toy object can be interconnected with other toy objects, virtual attributes of the toy objects in a virtual environment, and/or the like. Other examples of information may include the price of a toy object, existing colours, construction sets where the element appears, buying locations and other information stored in the database or a web source. Matching information from a toy object database or a web source with a recognized real world toy objects may be conducted by querying a database or a web service.).
Velic fails to explicitly teach and establish a count of each identified building block.
However, Rothschild teaches and establish a count of each identified building block (Fig. 2 Paragraph [0045]- Rothschild discloses based on the determined number and type of the identified building block or the identified plurality of building blocks, the one or more instructions may further correspond to suggestions for assembling one or more other construction objects.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Rothschild establish a count of each identified building block.
Wherein having Velic’s system for optical recognition of toys wherein establish a count of each identified building block.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Velic and Rothschild are both systems for recognition of toys. Wherein Velic’s system wherein increased the accuracy of identification, while Rothschild’s system provides an increase in user experience and computational capability. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Rothschild et al. (US 20170173486 A1), Paragraph [0055].
Regarding claim 10, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Although Velic in view of Rothschild block constructs and building blocks Velic in view of Rothschild fails to explicitly teach wherein the processor is further configured, for each listed block construct, to provide a user with a list of building blocks needed for assembling the block construct together with instructions for assembling the block construct.
However, Sampara explicitly teaches wherein the processor is further configured, for each listed construct, to provide a user with a list of building blocks needed for assembling the construct together with instructions for assembling the block construct (Fig. 2, paragraph [0035]- Sampara discloses the system determines whether each recommended recipe comprises one or more missing ingredients not in a customer inventory according to the customer inventory information database. If a substitute ingredient is available, the system may recommend the substitution for the missing ingredient in the recipe. In some embodiments, the system may first prompt the user to enter a planned portion size (e.g. for 2, for 4, etc.) for the recipe. (Wherein the ingredients are the building blocks needed to build the construct and the recipe is the instructions to do so)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Sampara wherein the processor is further configured, for each listed block construct, to provide a user with a list of building blocks needed for assembling the block construct together with instructions for assembling the block construct.
Wherein having Velic’s system for optical recognition of toys wherein the processor is further configured, for each listed block construct, to provide a user with a list of building blocks needed for assembling the block construct together with instructions for assembling the block construct.
The motivation behind the modification would have been to allow for more accurate selection of pieces and construct, since both Velic and Sampara are both systems for optical sensing for detecting items and determining steps for combining items to create a given output. Wherein Velic’s system wherein increased the accuracy of identification, while Sampara’s system provides an increase in user information about what is necessary to complete the construct. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Sampara et al. (US 20170316489 A1), Paragraph [0044].
Regarding claim 12, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Velic fails to explicitly teach wherein the processor is further configured, when identifying building blocks that are required by a build inventory and missing from the user inventory, to provide a means for a user to acquire the missing building blocks.
However, Rothschild teaches wherein the processor is further configured, when identifying building blocks that are required by a build inventory and missing from the user inventory, to provide a means for a user to acquire the missing building blocks (Fig. 2, Paragraph [0045]- Rothschild discloses based on the determined number and type of the identified building block or the identified plurality of building blocks, the one or more instructions may further correspond to a prompt to a user to buy a set of building blocks for assembling a desired construction object and include an e-commerce link to purchase the desired object.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Rothschild wherein the processor is further configured, when identifying building blocks that are required by a build inventory and missing from the user inventory, to provide a means for a user to acquire the missing building blocks.
Wherein having Velic’s system for optical recognition of toys wherein the processor is further configured, when identifying building blocks that are required by a build inventory and missing from the user inventory, to provide a means for a user to acquire the missing building blocks.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Velic and Rothschild are both systems for recognition of toys. Wherein Velic’s system wherein increased the accuracy of identification, while Rothschild’s system provides an increase in user experience and computational capability. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Rothschild et al. (US 20170173486 A1), Paragraph [0055].
Regarding claim 13, Velic in view of Rothschild and Sampara teaches the system according to claim 12, Velic fails to explicitly teach wherein the processor is configured to provide contact information for an entity that is offering the missing building blocks for sale.
However, Rothschild teaches wherein the processor is configured to provide contact information for an entity that is offering the missing building blocks for sale (Fig. 2, Paragraph [0045]- Rothschild discloses based on the determined number and type of the identified building block or the identified plurality of building blocks, the one or more instructions may further correspond to a prompt to a user to buy a set of building blocks for assembling a desired construction object and include an e-commerce link to purchase the desired object.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Rothschild wherein the processor is configured to provide contact information for an entity that is offering the missing building blocks for sale.
Wherein having Velic’s system for optical recognition of toys wherein the processor is configured to provide contact information for an entity that is offering the missing building blocks for sale.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Velic and Rothschild are both systems for recognition of toys. Wherein Velic’s system wherein increased the accuracy of identification, while Rothschild’s system provides an increase in user experience and computational capability. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Rothschild et al. (US 20170173486 A1), Paragraph [0055].
Regarding claim 14 Velic in view of Rothschild and Sampara teaches the system according to claim 12, Velic fails to explicitly teach wherein the processor is configured to provide a link to a website where the missing building blocks are available for purchase.
However, Rothschild teaches wherein the processor is configured to provide a link to a website where the missing building blocks are available for purchase (Fig. 2, Paragraph [0045]- Rothschild discloses based on the determined number and type of the identified building block or the identified plurality of building blocks, the one or more instructions may further correspond to a prompt to a user to buy a set of building blocks for assembling a desired construction object and include an e-commerce link to purchase the desired object.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Rothschild wherein the processor is configured to provide a link to a website where the missing building blocks are available for purchase.
Wherein having Velic’s system for optical recognition of toys wherein the processor is configured to provide a link to a website where the missing building blocks are available for purchase.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Velic and Rothschild are both systems for recognition of toys. Wherein Velic’s system wherein increased the accuracy of identification, while Rothschild’s system provides an increase in user experience and computational capability. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Rothschild et al. (US 20170173486 A1), Paragraph [0055].
Regarding claim 15, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Velic in view of Rothschild fails to explicitly teach wherein the processor is configured to identify, among the listed block constructs, recommended block constructs for the user based on pre-defined criteria.
However, Sampara explicitly teaches wherein the processor is configured to identify, among the listed block constructs, recommended block constructs for the user based on pre-defined criteria (Fig. 1, Paragraph [0023]- Sampara discloses the customer preference database may store a plurality of customer profiles comprising information such as one or more of: customer demographic, family size, dietary restrictions, dietary preferences, favorite recipes, favorite ingredients, recipe selection histories, planned recipes, planned events, purchase histories, etc. The central computer system 130 may select one or more recipes from the recipe database 122 and/or make purchase recommendations based on the information in the customer inventory information database 121 and/or the customer profile.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Sampara wherein the processor is configured to identify, among the listed block constructs, recommended block constructs for the user based on pre-defined criteria.
Wherein having Velic’s system for optical recognition of toys wherein the processor is configured to identify, among the listed block constructs, recommended block constructs for the user based on pre-defined criteria.
The motivation behind the modification would have been to allow for more accurate selection of pieces and construct, since both Velic and Sampara are both systems for optical sensing for detecting items and determining steps for combining items to create a given output. Wherein Velic’s system wherein increased the accuracy of identification, while Sampara’s system provides an increase in user information about what is necessary to complete the construct. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Sampara et al. (US 20170316489 A1), Paragraph [0044].
Claims 16-19 are rejected under 35 U.S.C 103 as being unpatentable over Velic (US 20170304732 A1) hereafter referenced as Velic in view of Rothschild (US 20170173486 A1) hereafter referenced as Rothschild, Sampara et al. (US 20170316489 A1) hereafter referenced as Sampara, and Hauser et al. (US 20100250341 A1) hereafter referenced as Hauser.
Regarding claim 16, Velic in view of Rothschild and Sampara teaches the system according to claim 15, Although Velic in view of Rothschild and Sampara teaches Block constructs Velic in view of Rothschild and Sampara fails to explicitly teach wherein the pre-defined criteria comprises popularity of the listed block construct.
However, Hauser explicitly teaches wherein the pre-defined criteria comprises popularity of the listed construct (Paragraph [0021]- Hauser discloses a content recommendation engine 108 that matches each user's interests from the profile built by the profile generator 104 with an available pool of content to find the closest matches to a user's interests factoring in relevance, recentness, popularity and the interest of other similar users.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Hauser wherein the pre-defined criteria comprises popularity of the listed construct.
Wherein having Velic’s system for optical recognition of toys wherein the pre-defined criteria comprises popularity of the listed construct.
The motivation behind the modification would have been to allow for better construct recommendations, since both Velic and Hauser are both systems that make recommendations to the user. Wherein Velic’s system wherein increased the accuracy of identification, while Hauser’s system provides an increase in efficiency of delivering user specific content. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Hauser et al. (US 20100250341 A1), Paragraph [0020].
Regarding claim 17, Velic in view of Rothschild and Sampara teaches the system according to claim 15, Although Velic in view of Rothschild and Sampara teaches Block constructs Velic in view of Rothschild and Sampara fails to explicitly wherein the pre-defined criteria comprises relevance of the listed block construct to another block construct engaged by the user.
However, Hauser explicitly teaches wherein the pre-defined criteria comprises relevance of the listed block construct to another construct engaged by the user (Paragraph [0021]- Hauser discloses a content recommendation engine 108 that matches each user's interests from the profile built by the profile generator 104 with an available pool of content to find the closest matches to a user's interests factoring in relevance, recentness, popularity and the interest of other similar users.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Hauser wherein the pre-defined criteria comprises relevance of the listed block construct to another block construct engaged by the user.
Wherein having Velic’s system for optical recognition of toys wherein the pre-defined criteria comprises relevance of the listed block construct to another block construct engaged by the user.
The motivation behind the modification would have been to allow for better construct recommendations, since both Velic and Hauser are both systems that make recommendations to the user. Wherein Velic’s system wherein increased the accuracy of identification, while Hauser’s system provides an increase in efficiency of delivering user specific content. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Hauser et al. (US 20100250341 A1), Paragraph [0020].
Regarding claim 18, Velic in view of Rothschild and Sampara teaches the system according to claim 15, Although Velic in view of Rothschild and Sampara teaches Block constructs Velic in view of Rothschild and Sampara fails to explicitly teach wherein the pre-defined criteria comprises similarity of the listed block construct to another block construct engaged by the user.
However, Hauser explicitly teaches wherein the pre-defined criteria comprises similarity of the listed block construct to another construct engaged by the user (Fig. 1, Paragraph [0021]- Hauser discloses the content recommendation engine 108 selects the content most likely to be of interest to a user 102 using a matching/scoring function that may be done based on a weighting of the following factors: relevance to the user based on similarity to the user's interests (using either a user profile based solely on one publisher's content or based on a profile across all publishers sharing profiles)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Hauser wherein the pre-defined criteria comprises similarity of the listed block construct to another block construct engaged by the user.
Wherein having Velic’s system for optical recognition of toys wherein the pre-defined criteria comprises similarity of the listed block construct to another block construct engaged by the user.
The motivation behind the modification would have been to allow for better construct recommendations, since both Velic and Hauser are both systems that make recommendations to the user. Wherein Velic’s system wherein increased the accuracy of identification, while Hauser’s system provides an increase in efficiency of delivering user specific content. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Hauser et al. (US 20100250341 A1), Paragraph [0020].
Regarding claim 19, Velic in view of Rothschild and Sampara teaches the system according to claim 15, Although Velic in view of Rothschild and Sampara teaches Block constructs Velic in view of Rothschild and Sampara fails to explicitly teach wherein the pre-defined criteria comprises block constructs engaged by one or more other users who are associated with the user.
However, Hauser explicitly teaches wherein the pre-defined criteria comprises constructs engaged by one or more other users who are associated with the user (Paragraph [0025]- Hauser discloses a content recommendation engine 108 that matches each user's interests from the profile built by the profile generator 104 with an available pool of content to find the closest matches to a user's interests factoring in relevance, recentness, popularity and the interest of other similar users.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Hauser wherein the pre-defined criteria comprises block constructs engaged by one or more other users who are associated with the user.
Wherein having Velic’s system for optical recognition of toys wherein the pre-defined criteria comprises block constructs engaged by one or more other users who are associated with the user.
The motivation behind the modification would have been to allow for better construct recommendations, since both Velic and Hauser are both systems that make recommendations to the user. Wherein Velic’s system wherein increased the accuracy of identification, while Hauser’s system provides an increase in efficiency of delivering user specific content. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Hauser et al. (US 20100250341 A1), Paragraph [0020].
Claim 20 is rejected under 35 U.S.C 103 as being unpatentable over Velic (US 20170304732 A1) hereafter referenced as Velic in view of Rothschild (US 20170173486 A1) hereafter referenced as Rothschild, Sampara et al. (US 20170316489 A1) hereafter referenced as Sampara, and Agarwal et al. (US 20190147521 A1) hereafter referenced as Agarwal.
Regarding claim 20, Velic in view of Rothschild and Sampara teaches the system according to claim 1, Although Velic in view of Rothschild explicitly teaches building blocks Velic in view of Rothschild fails to explicitly teach wherein the processor is configured to: generate a separate user inventory for multiple different users, each user inventory identifying a type of each building block identified in one or more captured images and a quantity of each identified building block type.
However, Sampara explicitly teaches wherein the processor is configured to: generate a separate user inventory for multiple different users (Fig. 1, Paragraph [0020]- Sampara discloses the customer inventory information database 121 may store estimated inventories of a plurality of customers. In some embodiments, the estimated customer inventories may include a list of item types and item quantities.),
each user inventory identifying a type of each building block identified in one or more captured images and a quantity of each identified building block type (Fig. 3, Paragraph [0038]- Sampara discloses images of customer storage spaces are analyzed with image recognition algorithms. For example, the system may look at barcodes, logos, texts, and shapes in the images of items to determine an item's type and/or size. Item identified in the images may be used to update the customer's estimated inventory information. In some embodiments, the system may ignore objects that are not recognized through image recognition.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Velic in view of Rothschild and Sampara a system for optical recognition of building blocks with the teachings of Sampara wherein the processor is configured to: generate a separate user inventory for multiple different users, each user inventory identifying a type of each building block identified in one or more captured images and a quantity of each identified building block type.
Wherein having Velic’s system for optical recognition of toys wherein the processor is configured to: generate a separate user inventory for multiple different users, each user inventory identifying a type of each building block identified in one or more captured images and a quantity of each identified building block type.
The motivation behind the modification would have been to allow for more accurate selection of pieces and construct, since both Velic and Sampara are both systems for optical sensing for detecting items and determining steps for combining items to create a given output. Wherein Velic’s system wherein increased the accuracy of identification, while Sampara’s system provides an increase in user information about what is necessary to complete the construct. Please see Velic et al. (US 20170304732 A1), Paragraph [0163] and Sampara et al. (US 20170316489 A1), Paragraph [0044].
Velic in view of Roths