DETAILED ACTION
Response to Arguments
The amendments filed 11/26/2025 have been entered and made of record.
Applicant's amendments and corresponding arguments filed 11/26/2025 have been fully considered, but are moot in view of the new ground(s) of rejection because the Applicant has substantially amended at least independent claim 1 with the newly added limitations, and made arguments and remarks based on the newly added limitations:
Re Claim 1, the newly added limitation “labeling the one or more images of the object with one or more object properties being configured in identification, evaluation, tracking, or verification of the object” has been considered, however, which is rejected by Duerksen as modified by STANIMIROVIC, and further in view of a new reference REYNAUD (US 20200400586 A1), because:
REYNAUD discloses labeling the one or more images of the object with one or more object properties being configured in identification, evaluation, tracking, or verification of the object (see REYNAUD: e.g., -- [0073] Each of the reference images stored in one or more databases can be labeled with one or more attributes that are identified or verified using an independent method. The attributes can be qualitative or quantitative, or a combination of both. The methods used to verify attributes can include a wide range of methods and processes. Scientific methods for attribute verification can include morphological, microscopic, chemical, and genetic analyses, among others. The methods can also include identification or measurement of specific chemical constituents or mineral compounds, pH, density, weight, and salinity. The attributes of tangible objects can be verified with organoleptic and sensory analysis to examine the taste, aroma, texture, or pattern of materials. Attributes that cannot be directly measured from an image of sample material, such as origin and growing conditions, can be verified by other means such as by reviewing written paper or electronic, verbal, or photographic records. In some cases, an attribute is a product name, container, batch, sample, or lot number.--, in [0073]; {herein “attributes of the object(s)” read on claimed “one or more object properties being configured in identification, evaluation, tracking, or verification of the object”});
Duerksen (as modified by STANIMIROVIC) and REYNAUD are combinable as they are in the same field of endeavor: identification of object and their characteristics/attributes based on image processing. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Duerksen (as modified by STANIMIROVIC)’s method using REYNAUD’s teachings by including labeling the one or more images of the object with one or more object properties to Duerksen (as modified by STANIMIROVIC)’s marking and object labels {see “Marked object” block 140 in Duerksen’s Fig. 1} and labeling images in order to verify attributes of the objects (see REYNAUD: e.g., in [0073]);
In response to Applicant’s Arguments and Remarks with regarding to above newly added limitation such as in page 10/16 of Applicant’s Arguments/Remarks of 11/26/2025, as reproduced below:
--Thus, the claimed labeling step adds object-level information to the image that is specifically intended to enable later identification, evaluation, tracking, or verification of the physical object itself. The labeling therefore operates at the object level and contributes functional information affecting downstream processing, not merely pixel level reliability classifications--
It is pointed out that this independent or other following claims do not further define or limit how “object-level information” added to the image, or the labels that labeled to the image has ever been applied in anywhere to solve any particular problems that Applicant thinks as the benefit, or as an advantage, because the step of “labeling the image” is not connected to any other steps;
In addition, in dependent claim 12, claim 12 limits that “labeling the image” occurs manually, that means that the action, or the step of “labeling the image” is carried out by a human/person; in other words, “labeling” is interpreted as: “giving a label, name, or a mark… etc., so that, Applicant’s above mentioned “object-level information” added to the image can be given broadest reasonable interpretation, such as including Marks of the “Marked object” block 140 in Duerksen’s Fig. 1, in that senses, Marks of the “Marked object” block 140 in Duerksen’s Fig. 1 can be portion of the physical object, and as the properties of the object; while in STANIMIROVIC’s disclosures, the thermal properties of the object which are in the representations of pixel values are considered as “object-level information” added to the image;
Furthermore, it is noticed that in Applicant’s Arguments/Remarks of 11/26/2025, in the last page, the “Registration No.” is empty.
Therefore, amended claims 1-24 are still not patentably distinguishable over the prior art reference(s). Further discussions are addressed in the prior art rejection section below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-5, 8-11, 15-24 are rejected under 35 U.S.C. 103 as being unpatentable over Duerksen (US 20190251349 A1), in view of STANIMIROVIC (US 20200279121 A1), and further in view of REYNAUD (US 20200400586 A1).
Re Claim 1, Duerksen discloses a method of image processing (see Duerksen: e. g., --A method for sorting objects includes capturing characteristic data from a plurality of objects and creating a plurality of feature points in a feature space based on the characteristic data. --, in abstract, and, --a method for determining authenticity is provided. The method may include providing an object of authentication. The method may include capturing characteristic data from the object of authentication. The method may include deriving authentication data from the characteristic data of the object of authentication. The method may include comparing the authentication data with an electronic database including reference authentication data to provide an authenticity score for the object of authentication. The reference authentication data may correspond to one or more reference objects of authentication other than the object of authentication. [0017] …. a method for creating an electronic database is provided. The method may include providing at least one plurality of reference objects of authentication collectively belonging to a class.--, in [0016]-[0017], and, --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image, deriving authentication data from the optical character recognition by extracting font information from the optical character recognition, comparing the authentication data with reference authentication data derived from statistical testing, including hypothesis testing of authentication data from a plurality of other marked objects, and determining the authenticity of the marked object from the comparing. [0023] In an embodiment, a method for determining the provenance of a marked object includes providing the marked object, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0024]) comprising the steps of:
generating one or more images of an object utilizing a first technology (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization…..TABLE 1 Characterization Type Sub-types Light imaging Color imaging, grayscale imaging, Spectroscopic imaging, IR/UV imaging, polarimetric imaging Interferometric 1D and 2D optical profilometry, confocal microscopy imaging interferometry Fluorescence Phosphorescence intensity imaging, phosphorescence imaging lifetime imaging, fluorescence lifetime imaging X-ray X-ray diffraction, X-ray scattering Terahertz Scatterometry Mechanical Atomic force microscopy profilometry SEM Electron contrast microscopy, SEM imaging, EDX Non-linear optical 2.sup.nd-order NLO characterization, 3.sup.rd order characterization NLO characterization Ultrasonic/ Spatial contrast ultrasound, measurement of speed of acoustic imaging sound in the object Thermographic Thermal conductivity, heat capacity imaging--, in [0072]-[0073], and Table 1);
transferring the one or more images of the object to a processor (see Duerksen: e. g., Fig. 1, and, -- authentication system 100 which includes analysis function 110, data storage 120, characterization function 130 and marked object 140. Analysis function 110 may be connected with data storage 120 and with characterization function 130. Analysis function 110 may be electrically or otherwise connected to data storage locally such as memory or disk storage integrated with analysis function 110 or may be network connected storage. Analysis function 110 may be connected with characterization function 130 locally as integrated functionality of may use a communication interface such as Ethernet or USB. Analysis function 110 may be, for example, a fixed or portable computer system such as a laptop, personal computer (“PC”), smart phone or programmable logic controller (“PLC”). Analysis function 110 may actively control characterization function 130 or may passively receive characteristic data from characterization function 130. [0072] Characterization function 130 may be any type of characterization suitable for the derivation of authentication and/or identifying information for marked object 140. For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization.--, in [0071]-[0072]; and, --FIG. 14 is a flow chart of process 1400 for authenticating a marked object associated with a marking system using an authentication application such as shown in FIG. 13. Process 1400 initiates with step 1410 wherein any necessary or optional setup and preparation steps may be performed. Setup and preparation operations may include the proper identification of a marked object or staging of the item to permit characteristic data capture via imaging as described herein. Process 1400 may include step 1420 wherein the authentication application may be loaded into the processor and memory of a device such as the smart phone of FIG. 13. Once loaded, the application may display a user interface such as in FIG. 13 with an image and button 1320. Process 1400 may include step 1430 where an image may be captured and displayed such as shown.--, in [0173]);
utilizing the processor to capture the one or more images of the object (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, --[0072] Characterization function 130 may be any type of characterization suitable for the derivation of authentication and/or identifying information for marked object 140. For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization.--, in [0071]-[0072]; and, --FIG. 14 is a flow chart of process 1400 for authenticating a marked object associated with a marking system using an authentication application such as shown in FIG. 13. Process 1400 initiates with step 1410 wherein any necessary or optional setup and preparation steps may be performed. Setup and preparation operations may include the proper identification of a marked object or staging of the item to permit characteristic data capture via imaging as described herein. Process 1400 may include step 1420 wherein the authentication application may be loaded into the processor and memory of a device such as the smart phone of FIG. 13. Once loaded, the application may display a user interface such as in FIG. 13 with an image and button 1320. Process 1400 may include step 1430 where an image may be captured and displayed such as shown.--, in [0173]);
although Duerksen discloses that object being labeled, and marking the object (see Duerksen: e. g., -- Objects transferred under this method are often tracked using printed text and barcode labels or RFID tags. As long as the transfer of objects is as prescribed by the quality system, the objects are assumed to be securely transferred and therefore genuine. However, supply chains may be stressed by events such as natural disasters, may be breached by unscrupulous parties, and may be difficult to maintain for many lower volume manufactured objects;--, in [0005], and, -- The internal evaluation may be performed by applying the classifier to the (digitized) laser training markings, and using the statistical distribution of points in the vector space generated by the classifier the characterize the resolution. A standard measure for characterizing the resolving power of a classifier such as the Davies-Bouldin index may be used. If this value is sufficiently high, it may be concluded that the classifier has high descriptive validity. [0098] The external evaluation may be performed by first digitizing the laser markings on the second family or collection of chips using the procedure described above. …[0099] A similar procedure may be applied to what may be described above for distinguishing between characters produced by different marking systems to the problem of distinguishing the formatting characteristics of each marking system. The formatting data—kerning, line spacing, in-line glyph placement—also may be captured and extracted using commercial OCR software. The relative positioning of a glyph in a character box may be contained in the metadata provided by ClearScan as “hints,” but rather than working with the ClearScan metadata to analyze the global layout of the markings on a chip, the labeled fonts may be exported for each marking system to other OCR platforms, e.g., FineReader (ABBYY, Milpitas, Calif.--, in [0099]);
Duerksen however does not explicitly disclose labeling the one or more images of the object with one or more object properties;
STANIMIROVIC discloses labeling the one or more images of the object with one or more object properties (see STANIMIROVIC: e.g., Fig. 3, Fig.7, and, -- The set of input images (5.0) comprises, for instance, one or more images of different modalities. Block 6.0 performs an unreliable object detection and segmentation based on given inputs. The given input includes the thermal image 4.0 and the description of unreliable object classes 1.0, and optionally includes images captured by additional imaging devices 3.0. Block 7.0 represents a set of labelled input images, which is created by labelling segmented unreliable objects (6.0), in the set of input images denoted in block 5.0. The labels thereby are indicative of at least one probability that at least one pixel or image region belongs to the at least one class of unreliable objects according to the at least one description of at least one class of unreliable objects. Finally, a computer vision algorithm, denoted in block 8.0, takes a set of labelled input images 7.0 (described in the following) as an input for further processing.--, in [0088], and, --Detection and segmentation of unreliable objects is performed based on their thermal properties, utilizing processing devices of the smartphones equipped with thermal cameras. Once the segmented images are obtained according to thermal images captured by the thermal cameras, these are transferred to devices that do not have thermal imaging capabilities, and registered with visible light images captured using cameras incorporated in these devices. Here, registration between segmented images allows for mapping of unreliable image regions from coordinate systems of segmented images, into coordinate systems of visible light images, captured by the smartphones which do not have thermal imaging capabilities. In this manner, image regions of visible light images are labelled as either reliable or unreliable, and only reliable image regions are used as an input of the tracking, localization and mapping algorithm. In this example, the at least one probability associated with at least one image region in the thermal images or visible light images is binary.--, in [0104], and [0121]-[0122]);
Duerksen and STANIMIROVIC are combinable as they are in the same field of endeavor: identification of object and their characteristics based on image processing. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Duerksen’s method using STANIMIROVIC’s teachings by including labeling the one or more images of the object with one or more object properties to Duerksen’s marking and object labels {see “Marked object” block 140 in Duerksen’s Fig. 1} in order to at least one probability that at least one pixel or image region belongs to indicate image regions that contains certain properties, or belongs to the at least one class (see STANIMIROVIC: e.g. Fig. 3, Fig. 7, and in [0088], and [0121]-[0122]); and
Duerksen as modified by STANIMIROVIC however still do not explicitly disclose labeling the one or more images of the object with one or more object properties being configured in identification, evaluation, tracking, or verification of the object;
REYNAUD discloses labeling the one or more images of the object with one or more object properties being configured in identification, evaluation, tracking, or verification of the object (see REYNAUD: e.g., -- [0073] Each of the reference images stored in one or more databases can be labeled with one or more attributes that are identified or verified using an independent method. The attributes can be qualitative or quantitative, or a combination of both. The methods used to verify attributes can include a wide range of methods and processes. Scientific methods for attribute verification can include morphological, microscopic, chemical, and genetic analyses, among others. The methods can also include identification or measurement of specific chemical constituents or mineral compounds, pH, density, weight, and salinity. The attributes of tangible objects can be verified with organoleptic and sensory analysis to examine the taste, aroma, texture, or pattern of materials. Attributes that cannot be directly measured from an image of sample material, such as origin and growing conditions, can be verified by other means such as by reviewing written paper or electronic, verbal, or photographic records. In some cases, an attribute is a product name, container, batch, sample, or lot number.--, in [0073]; {herein “attributes of the object(s)” read on claimed “one or more object properties being configured in identification, evaluation, tracking, or verification of the object”});
Duerksen (as modified by STANIMIROVIC) and REYNAUD are combinable as they are in the same field of endeavor: identification of object and their characteristics/attributes based on image processing. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Duerksen (as modified by STANIMIROVIC)’s method using REYNAUD’s teachings by including labeling the one or more images of the object with one or more object properties to Duerksen (as modified by STANIMIROVIC)’s marking and object labels {see “Marked object” block 140 in Duerksen’s Fig. 1} and labeling images in order to verify attributes of the objects (see REYNAUD: e.g., in [0073])
Duerksen as modified by STANIMIROVIC and REYNAUD further disclose saving the one or more images of the object to a database (see Duerksen: e. g., Fig. 1, saving to “Data Storage” block 120, and, -- The system may include a characterization module configured for capturing characteristic data from an object of authentication. The system may include a data storage module configured to store an electronic database including reference authentication data….. a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image, deriving authentication data from the optical character recognition by extracting font information from the optical character recognition, comparing the authentication data with reference authentication data derived from statistical testing, including hypothesis testing of authentication data from a plurality of other marked objects, and determining the authenticity of the marked object from the comparing.
[0023] In an embodiment, a method for determining the provenance of a marked object includes providing the marked object, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image, --, in [0019]-[0023], and [0065]-[0069]).
Re Claim 4, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose wherein the captured one or more images saved to the database comprises a two-dimensional image (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image, deriving authentication data from the optical character recognition by extracting font information from the optical character recognition, comparing the authentication data with reference authentication data derived from statistical testing, including hypothesis testing of authentication data from a plurality of other marked objects, and determining the authenticity of the marked object from the comparing. [0023] In an embodiment, a method for determining the provenance of a marked object includes providing the marked object, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0024]).
Re Claim 5, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose wherein the one or more images saved to the database comprises a partial two-dimensional image (see Duerksen: e. g., Fig. 8, Fig. 10, Fig. 11, --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image, deriving authentication data from the optical character recognition by extracting font information from the optical character recognition, comparing the authentication data with reference authentication data derived from statistical testing, including hypothesis testing of authentication data from a plurality of other marked objects, and determining the authenticity of the marked object from the comparing. [0023] In an embodiment, a method for determining the provenance of a marked object includes providing the marked object, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0024]).
Re Claim 8, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose wherein the first technology utilized for generating one or more images of the object is selected from a group comprising microscopy, x-ray diffraction, or 3D x- ray diffraction (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization…..TABLE 1 Characterization Type Sub-types Light imaging Color imaging, grayscale imaging, Spectroscopic imaging, IR/UV imaging, polarimetric imaging Interferometric 1D and 2D optical profilometry, confocal microscopy imaging interferometry Fluorescence Phosphorescence intensity imaging, phosphorescence imaging lifetime imaging, fluorescence lifetime imaging X-ray X-ray diffraction, X-ray scattering Terahertz Scatterometry Mechanical Atomic force microscopy profilometry SEM Electron contrast microscopy, SEM imaging, EDX Non-linear optical 2.sup.nd-order NLO characterization, 3.sup.rd order characterization NLO characterization Ultrasonic/ Spatial contrast ultrasound, measurement of speed of acoustic imaging sound in the object Thermographic Thermal conductivity, heat capacity imaging--, in [0072]-[0073], and Table 1).
Re Claim 9, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose wherein the one or more images are associated with an identification number (see Duerksen: e. g., -- The method of secure supply chain management controls the flow of objects from factory to end user and is regimented by a quality system. Objects transferred under this method are often tracked using printed text and barcode labels or RFID tags. As long as the transfer of objects is as prescribed by the quality system, the objects are assumed to be securely transferred and therefore genuine. However, supply chains may be stressed by events such as natural disasters, may be breached by unscrupulous parties, and may be difficult to maintain for many lower volume manufactured objects;--, in [0005]; and,
--Marking system identification step 220 may include determination and recording of the marking method, e.g., laser scribing; the determination and recording of the make, model, serial number, manufacturer and owner of the marking system,. e.g., LaserMark V12, S/N 123456789, produced in 2012 from Company XYZ, owned by Company ABC; or otherwise determining and recording of the source of the marked objects which may also derive from a known and trusted 3rd party rather than a manufacturer.--, in [0084], and [0078]-[0081]; and, -- enable anti-counterfeiting, authenticity and provenance determination techniques so that authentication tracking and control can be performed throughout the supply chain of the objects. In particular, the authentication data derived from the characteristic data of the object of authentication is compared to the reference authentication data to determine the authenticity. In some embodiments, sorting of the objects may be performed prior to the authentication process. For example, in some embodiments, the reference authentication data may not be readily available and have to be created based on unverified objects before the authentication process may be performed. The unverified objects may be sorted into one or more distinct groups. One or more reference objects may represent the one or more distinct groups, and reference authentication data may be derived from the one or more reference objects and used for the authentication process.--, in [0201]).
Re Claim 10, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose wherein the one or more images are associated with one or more object properties of the object (see Duerksen: e. g., -- Objects transferred under this method are often tracked using printed text and barcode labels or RFID tags. As long as the transfer of objects is as prescribed by the quality system, the objects are assumed to be securely transferred and therefore genuine. However, supply chains may be stressed by events such as natural disasters, may be breached by unscrupulous parties, and may be difficult to maintain for many lower volume manufactured objects;--, in [0005], -- The method of secure supply chain management controls the flow of objects from factory to end user and is regimented by a quality system. Objects transferred under this method are often tracked using printed text and barcode labels or RFID tags. As long as the transfer of objects is as prescribed by the quality system, the objects are assumed to be securely transferred and therefore genuine. However, supply chains may be stressed by events such as natural disasters, may be breached by unscrupulous parties, and may be difficult to maintain for many lower volume manufactured objects;--, in [0005]; and,
--Marking system identification step 220 may include determination and recording of the marking method, e.g., laser scribing; the determination and recording of the make, model, serial number, manufacturer and owner of the marking system,. e.g., LaserMark V12, S/N 123456789, produced in 2012 from Company XYZ, owned by Company ABC; or otherwise determining and recording of the source of the marked objects which may also derive from a known and trusted 3rd party rather than a manufacturer.--, in [0084], and [0078]-[0081]; and, -- enable anti-counterfeiting, authenticity and provenance determination techniques so that authentication tracking and control can be performed throughout the supply chain of the objects. In particular, the authentication data derived from the characteristic data of the object of authentication is compared to the reference authentication data to determine the authenticity. In some embodiments, sorting of the objects may be performed prior to the authentication process. For example, in some embodiments, the reference authentication data may not be readily available and have to be created based on unverified objects before the authentication process may be performed. The unverified objects may be sorted into one or more distinct groups. One or more reference objects may represent the one or more distinct groups, and reference authentication data may be derived from the one or more reference objects and used for the authentication process.--, in [0201]; and, -- The internal evaluation may be performed by applying the classifier to the (digitized) laser training markings, and using the statistical distribution of points in the vector space generated by the classifier the characterize the resolution. A standard measure for characterizing the resolving power of a classifier such as the Davies-Bouldin index may be used. If this value is sufficiently high, it may be concluded that the classifier has high descriptive validity. [0098] The external evaluation may be performed by first digitizing the laser markings on the second family or collection of chips using the procedure described above. …[0099] A similar procedure may be applied to what may be described above for distinguishing between characters produced by different marking systems to the problem of distinguishing the formatting characteristics of each marking system. The formatting data—kerning, line spacing, in-line glyph placement—also may be captured and extracted using commercial OCR software. The relative positioning of a glyph in a character box may be contained in the metadata provided by ClearScan as “hints,” but rather than working with the ClearScan metadata to analyze the global layout of the markings on a chip, the labeled fonts may be exported for each marking system to other OCR platforms, e.g., FineReader (ABBYY, Milpitas, Calif.--, in [0099]).
Re Claim 11, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose wherein the one or more object properties is selected from the group including an identification number, a manufacturer of the object, a manufacturing date of the object, an alloy property of the object, or an owner of the object (see Duerksen: e. g., -- The method of secure supply chain management controls the flow of objects from factory to end user and is regimented by a quality system. Objects transferred under this method are often tracked using printed text and barcode labels or RFID tags. As long as the transfer of objects is as prescribed by the quality system, the objects are assumed to be securely transferred and therefore genuine. However, supply chains may be stressed by events such as natural disasters, may be breached by unscrupulous parties, and may be difficult to maintain for many lower volume manufactured objects;--, in [0005]; and,
--Marking system identification step 220 may include determination and recording of the marking method, e.g., laser scribing; the determination and recording of the make, model, serial number, manufacturer and owner of the marking system,. e.g., LaserMark V12, S/N 123456789, produced in 2012 from Company XYZ, owned by Company ABC; or otherwise determining and recording of the source of the marked objects which may also derive from a known and trusted 3rd party rather than a manufacturer.--, in [0084], and [0078]-[0081]; and, -- enable anti-counterfeiting, authenticity and provenance determination techniques so that authentication tracking and control can be performed throughout the supply chain of the objects. In particular, the authentication data derived from the characteristic data of the object of authentication is compared to the reference authentication data to determine the authenticity. In some embodiments, sorting of the objects may be performed prior to the authentication process. For example, in some embodiments, the reference authentication data may not be readily available and have to be created based on unverified objects before the authentication process may be performed. The unverified objects may be sorted into one or more distinct groups. One or more reference objects may represent the one or more distinct groups, and reference authentication data may be derived from the one or more reference objects and used for the authentication process.--, in [0201]).
Re Claim 15, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose wherein the database comprises one or more object entries selected from the group comprising an object unique identification, object original imaging, at least one object feature, a date of addition to the database, an object manufacturing date, an object manufacturer identification, an object specification or an object model number (see Duerksen: e. g., Fig. 1, saving to “Data Storage” block 120, and, -- The system may include a characterization module configured for capturing characteristic data from an object of authentication. The system may include a data storage module configured to store an electronic database including reference authentication data….. a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image, deriving authentication data from the optical character recognition by extracting font information from the optical character recognition, comparing the authentication data with reference authentication data derived from statistical testing, including hypothesis testing of authentication data from a plurality of other marked objects, and determining the authenticity of the marked object from the comparing.
[0023] In an embodiment, a method for determining the provenance of a marked object includes providing the marked object, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image, --, in [0019]-[0023], and [0065]-[0069], and, --Marking system identification step 220 may include determination and recording of the marking method, e.g., laser scribing; the determination and recording of the make, model, serial number, manufacturer and owner of the marking system,. e.g., LaserMark V12, S/N 123456789, produced in 2012 from Company XYZ, owned by Company ABC; or otherwise determining and recording of the source of the marked objects which may also derive from a known and trusted 3rd party rather than a manufacturer.--, in [0084], and [0078]-[0081]; and, -- enable anti-counterfeiting, authenticity and provenance determination techniques so that authentication tracking and control can be performed throughout the supply chain of the objects. In particular, the authentication data derived from the characteristic data of the object of authentication is compared to the reference authentication data to determine the authenticity. In some embodiments, sorting of the objects may be performed prior to the authentication process. For example, in some embodiments, the reference authentication data may not be readily available and have to be created based on unverified objects before the authentication process may be performed. The unverified objects may be sorted into one or more distinct groups. One or more reference objects may represent the one or more distinct groups, and reference authentication data may be derived from the one or more reference objects and used for the authentication process.--, in [0201]).
Re Claim 16, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose the step of performing imaging the object utilizing a second technology (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization…..TABLE 1 Characterization Type Sub-types Light imaging Color imaging, grayscale imaging, Spectroscopic imaging, IR/UV imaging, polarimetric imaging Interferometric 1D and 2D optical profilometry, confocal microscopy imaging interferometry Fluorescence Phosphorescence intensity imaging, phosphorescence imaging lifetime imaging, fluorescence lifetime imaging X-ray X-ray diffraction, X-ray scattering Terahertz Scatterometry Mechanical Atomic force microscopy profilometry SEM Electron contrast microscopy, SEM imaging, EDX Non-linear optical 2.sup.nd-order NLO characterization, 3.sup.rd order characterization NLO characterization Ultrasonic/ Spatial contrast ultrasound, measurement of speed of acoustic imaging sound in the object Thermographic Thermal conductivity, heat capacity imaging--, in [0072]-[0073], and Table 1); and
capturing one or more second imaging features of the object (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, --[0072] Characterization function 130 may be any type of characterization suitable for the derivation of authentication and/or identifying information for marked object 140. For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization.--, in [0071]-[0072]; and, --FIG. 14 is a flow chart of process 1400 for authenticating a marked object associated with a marking system using an authentication application such as shown in FIG. 13. Process 1400 initiates with step 1410 wherein any necessary or optional setup and preparation steps may be performed. Setup and preparation operations may include the proper identification of a marked object or staging of the item to permit characteristic data capture via imaging as described herein. Process 1400 may include step 1420 wherein the authentication application may be loaded into the processor and memory of a device such as the smart phone of FIG. 13. Once loaded, the application may display a user interface such as in FIG. 13 with an image and button 1320. Process 1400 may include step 1430 where an image may be captured and displayed such as shown.--, in [0173]).
Re Claim 17, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose evaluating the captured images of the object during imaging of the object utilizing the second technology with the first set of images captured during the first imaging using the first technology (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization…..TABLE 1 Characterization Type Sub-types Light imaging Color imaging, grayscale imaging, Spectroscopic imaging, IR/UV imaging, polarimetric imaging Interferometric 1D and 2D optical profilometry, confocal microscopy imaging interferometry Fluorescence Phosphorescence intensity imaging, phosphorescence imaging lifetime imaging, fluorescence lifetime imaging X-ray X-ray diffraction, X-ray scattering Terahertz Scatterometry Mechanical Atomic force microscopy profilometry SEM Electron contrast microscopy, SEM imaging, EDX Non-linear optical 2.sup.nd-order NLO characterization, 3.sup.rd order characterization NLO characterization Ultrasonic/ Spatial contrast ultrasound, measurement of speed of acoustic imaging sound in the object Thermographic Thermal conductivity, heat capacity imaging--, in [0072]-[0073], and Table 1); and
capturing one or more second imaging features of the object (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, --[0072] Characterization function 130 may be any type of characterization suitable for the derivation of authentication and/or identifying information for marked object 140. For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization.--, in [0071]-[0072]; and, --FIG. 14 is a flow chart of process 1400 for authenticating a marked object associated with a marking system using an authentication application such as shown in FIG. 13. Process 1400 initiates with step 1410 wherein any necessary or optional setup and preparation steps may be performed. Setup and preparation operations may include the proper identification of a marked object or staging of the item to permit characteristic data capture via imaging as described herein. Process 1400 may include step 1420 wherein the authentication application may be loaded into the processor and memory of a device such as the smart phone of FIG. 13. Once loaded, the application may display a user interface such as in FIG. 13 with an image and button 1320. Process 1400 may include step 1430 where an image may be captured and displayed such as shown.--, in [0173]).
Re Claim 18, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose wherein the first technology is substantially similar to the second technology (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization…..TABLE 1 Characterization Type Sub-types Light imaging Color imaging, grayscale imaging, Spectroscopic imaging, IR/UV imaging, polarimetric imaging Interferometric 1D and 2D optical profilometry, confocal microscopy imaging interferometry Fluorescence Phosphorescence intensity imaging, phosphorescence imaging lifetime imaging, fluorescence lifetime imaging X-ray X-ray diffraction, X-ray scattering Terahertz Scatterometry Mechanical Atomic force microscopy profilometry SEM Electron contrast microscopy, SEM imaging, EDX Non-linear optical 2.sup.nd-order NLO characterization, 3.sup.rd order characterization NLO characterization Ultrasonic/ Spatial contrast ultrasound, measurement of speed of acoustic imaging sound in the object Thermographic Thermal conductivity, heat capacity imaging--, in [0072]-[0073], and Table 1); and
capturing one or more second imaging features of the object (see Duerksen: e. g., --a method for determining the authenticity of a marked object includes providing a marked object for authentication, capturing an optical image of the marked object, performing optical characterization recognition of the captured optical image,--, in [0022]-[0023]; and, --[0072] Characterization function 130 may be any type of characterization suitable for the derivation of authentication and/or identifying information for marked object 140. For conventional light imaging technologies, characterization function 130 may be or be integrated with a camera, microscope, smart phone, scanner, Google Glass or other device. Other types of characterization may include fixed or portable instrumentation designed to provide such characterization.--, in [0071]-[0072]; and, --FIG. 14 is a flow chart of process 1400 for authenticating a marked object associated with a marking system using an authentication application such as shown in FIG. 13. Process 1400 initiates with step 1410 wherein any necessary or optional setup and preparation steps may be performed. Setup and preparation operations may include the proper identification of a marked object or staging of the item to permit characteristic data capture via imaging as described herein. Process 1400 may include step 1420 wherein the authentication application may be loaded into the processor and memory of a device such as the smart phone of FIG. 13. Once loaded, the application may display a user interface such as in FIG. 13 with an image and button 1320. Process 1400 may include step 1430 where an image may be captured and displayed such as shown.--, in [0173]).
Re Claim 19, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose the method further comprises the step of utilizing AI to evaluate the captured images of the object by utilizing the second technology (see Duerksen: e. g., --TABLE 2 Analysis Methods statistical analysis image deconvolution principle component analysis image correlation roughness spectral analysis edge extraction reduced basis sets - wavelets character recognition neural network analysis--, in [0074]-[0075], --a representation has been derived of the sub-font of glyphs present in the marking system on the chips as imprinted by each of the marking systems, plus a sub-set of features that provide the greatest discrimination between the characters created by different marking systems. This may be a standard multi-objective optimization problem, which may be treated by a number of algorithms such as a distribution based cluster algorithm, for example, based on the feature distribution functions derived from the chip statistics, or a k-mean clustering algorithm that presumes a known number of clusters. Alternatively, a neural network may be used to train the classifier to identify the most relevant features for discrimination. An optimized classifier may be constructed from a plurality of characters by adaptively combining the separate classification processes of a plurality of weakly discriminating characters to “boost” the classifier discrimination, e.g., using “AdaBoost” method by Freund and Schapire.--, in [0096]).
Re Claim 20, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose identifying similar images and/or features collected in the database during the first imaging process with the one or more second imaging features of the object (see Duerksen: e. g., -- A PUF is a representation of one or more physical features of an object that are easy to evaluate but impossible to control, even with knowledge of the exact manufacturing process that produced the object. An ideal PUF may be fabricated by a manufacturing process that creates features so difficult to control that they serve the function of a random number generator, where the number is permanently associated with each individual object and no other. Each object is thus assigned an effective serialization without the need for an exogenous taggant. After inspection, at the time of manufacture, the unique feature of the inspected object is stored in a database for comparison by subsequent inspection at a later time.--, in [0009], and [0016]-[0019]; and,
--[0140] For the semiconductor example, raw wafer vendor and foundry chip manufacturer, wafer segmentation and chip packaging, semiconductor contract manufacturers (CMs) who assemble devices onto circuit boards may be included within the authentication trail and use the defined authentication systems with low cost method of authentication that enables validation of both current production and legacy parts, absent direct participation by the semiconductor manufacturers themselves. Furthermore, the system and methods herein may provide the capability of non-contact validation of parts even after they installation on a circuit board, thereby enabling provenance identification, for example, for systems integrators.
[0141] Authentication and identification capability may be provided as a service by maintaining a database of known genuine objects, and charging a low cost subscription fee to contract manufacturers and upstream integrators to access it. In the field, end users may capture images of suspect parts using conventional USB-enabled microscopes, already installed in their facilities, and cloud-based software may be automatically perform identification calculations and feed back to the user confirmation of point of origin.--, in [0140]-[0141]).
Re Claim 21, Duerksen as modified by STANIMIROVIC and REYNAUD further discloseidentifying images and/or features that will be used to pull the corresponding object information from the database collected during the first technology (see Duerksen: e. g., -- A PUF is a representation of one or more physical features of an object that are easy to evaluate but impossible to control, even with knowledge of the exact manufacturing process that produced the object. An ideal PUF may be fabricated by a manufacturing process that creates features so difficult to control that they serve the function of a random number generator, where the number is permanently associated with each individual object and no other. Each object is thus assigned an effective serialization without the need for an exogenous taggant. After inspection, at the time of manufacture, the unique feature of the inspected object is stored in a database for comparison by subsequent inspection at a later time.--, in [0009], and [0016]-[0019]; and,
--[0140] For the semiconductor example, raw wafer vendor and foundry chip manufacturer, wafer segmentation and chip packaging, semiconductor contract manufacturers (CMs) who assemble devices onto circuit boards may be included within the authentication trail and use the defined authentication systems with low cost method of authentication that enables validation of both current production and legacy parts, absent direct participation by the semiconductor manufacturers themselves. Furthermore, the system and methods herein may provide the capability of non-contact validation of parts even after they installation on a circuit board, thereby enabling provenance identification, for example, for systems integrators.
[0141] Authentication and identification capability may be provided as a service by maintaining a database of known genuine objects, and charging a low cost subscription fee to contract manufacturers and upstream integrators to access it. In the field, end users may capture images of suspect parts using conventional USB-enabled microscopes, already installed in their facilities, and cloud-based software may be automatically perform identification calculations and feed back to the user confirmation of point of origin.--, in [0140]-[0141]).
Re Claim 22, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose the step of validating that the object is matching a specific object from the database object collected during the first technology (see Duerksen: e. g., -- A PUF is a representation of one or more physical features of an object that are easy to evaluate but impossible to control, even with knowledge of the exact manufacturing process that produced the object. An ideal PUF may be fabricated by a manufacturing process that creates features so difficult to control that they serve the function of a random number generator, where the number is permanently associated with each individual object and no other. Each object is thus assigned an effective serialization without the need for an exogenous taggant. After inspection, at the time of manufacture, the unique feature of the inspected object is stored in a database for comparison by subsequent inspection at a later time.--, in [0009], and, --a system for determining authenticity is provided. The system may include a characterization module configured for capturing characteristic data from an object of authentication. The system may include a data storage module configured to store an electronic database including reference authentication data. The system may include a processor operatively coupled to the characterization module and the data storage module. The processor may be programmed to operate the characterization module to capture the characteristic data from the object of authentication. The processor may be programmed to derive authentication data from the characteristic data of the object of authentication. The processor may be programmed to compare the authentication data with the electronic database including the reference authentication data to provide an authenticity score for the object of authentication. The processor may be programmed to access the data storage module to store and retrieve one or more of: the authentication data, the electronic database including the reference authentication data, the characteristic data from the object of authentication, and the authenticity score.--, in [0016]-[0019]; and,
--[0140] For the semiconductor example, raw wafer vendor and foundry chip manufacturer, wafer segmentation and chip packaging, semiconductor contract manufacturers (CMs) who assemble devices onto circuit boards may be included within the authentication trail and use the defined authentication systems with low cost method of authentication that enables validation of both current production and legacy parts, absent direct participation by the semiconductor manufacturers themselves. Furthermore, the system and methods herein may provide the capability of non-contact validation of parts even after they installation on a circuit board, thereby enabling provenance identification, for example, for systems integrators.
[0141] Authentication and identification capability may be provided as a service by maintaining a database of known genuine objects, and charging a low cost subscription fee to contract manufacturers and upstream integrators to access it. In the field, end users may capture images of suspect parts using conventional USB-enabled microscopes, already installed in their facilities, and cloud-based software may be automatically perform identification calculations and feed back to the user confirmation of point of origin.--, in [0140]-[0141]).
Re Claim 23, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose the database comprises an SQL database (see Duerksen: e. g., --[0075] Authentication and/or identifying information produced by analysis function 110 may be stored into data storage 120 for future access. Data storage 120 may be a structured database such as a SQL database residing upon a local or networked server or may be “Cloud” or peer-based data storage. An authentication configuration associated with a marked object may include, but is not limited to, fields such as listed in TABLE 3. Certain data fields may be associated with object authentication and others may be associated with object provenance. The authentication data fields may be associated with the ability to relate an object to an object family during an authentication process such as process 300. The provenance data fields may be associated with the ability to relate the object family to a marking system and/or fabricator of origin.
TABLE-US-00003 TABLE 3 Data Field Name Description ObjectName Name, part number, serial number, and the like of the marked object ObjectManufacturer The manufacturer of the marked object Date The date of manufacture or authentication of the marked object MarkingSystemID The type, brand name, manufacturer, serial number, and the like of the marking system used to mark the object AuthenticationFeatures Authentication data derived from characterization of the marked object AuthenticationAccuracy A figure of merit determined from the repeated characterization and analysis of authentication features derived from characterization of one or more marked objects--, in [0075]).
Re Claim 24, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose tracking one or more historical changes to the object (see Duerksen: e. g., --when authenticating marked objects associated with multiple marking systems, the variability in features created by different marking systems may be high, but the variability of features of a single marking system may be low. Thus, the process of characterizing the object may indirectly characterize the marking system that made the object. The marked object may be tracked back to this marking system even though the object may have never been characterized. This contrasts with authentication using a PUF where ideally variability may be high at the object level, and objects are not traceable back to their marking systems of origin. The low degree of variability of features within a family of marked objects permits discrimination of these features amongst all marking systems. The predictability of and limited access to the marking systems giving rise to the features may enable secure authentication. Laser marking and other marking technologies discussed herein may provide this appropriate degree of variability and sufficient common features.--, in [0079]).
Claims 2-3, 6-7, 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Duerksen as modified by STANIMIROVIC and REYNAUD, and further in view of BENEDETTO (US 20240020354 A1, Date Filed: 2022-07-15).
Re Claim 2, Duerksen as modified by STANIMIROVIC and REYNAUD further disclose comprising the step of evaluating the one or more images; and tracking an ownership of the object which can be current or historical ownership; wherein the step of tracking the ownership of the object (see Duerksen: e. g., -- The method of secure supply chain management controls the flow of objects from factory to end user and is regimented by a quality system. Objects transferred under this method are often tracked using printed text and barcode labels or RFID tags. As long as the transfer of objects is as prescribed by the quality system, the objects are assumed to be securely transferred and therefore genuine. However, supply chains may be stressed by events such as natural disasters, may be breached by unscrupulous parties, and may be difficult to maintain for many lower volume manufactured objects;--, in [0005]; and,
--Marking system identification step 220 may include determination and recording of the marking method, e.g., laser scribing; the determination and recording of the make, model, serial number, manufacturer and owner of the marking system,. e.g., LaserMark V12, S/N 123456789, produced in 2012 from Company XYZ, owned by Company ABC; or otherwise determining and recording of the source of the marked objects which may also derive from a known and trusted 3rd party rather than a manufacturer.--, in [0084], and [0078]-[0081]; and, -- enable anti-counterfeiting, authenticity and provenance determination techniques so that authentication tracking and control can be performed throughout the supply chain of the objects. In particular, the authentication data derived from the characteristic data of the object of authentication is compared to the reference authentication data to determine the authenticity. In some embodiments, sorting of the objects may be performed prior to the authentication process. For example, in some embodiments, the reference authentication data may not be readily available and have to be created based on unverified objects before the authentication process may be performed. The unverified objects may be sorted into one or more distinct groups. One or more reference objects may represent the one or more distinct groups, and reference authentication data may be derived from the one or more reference objects and used for the authentication process.--, in [0201]);
Duerksen as modified by STANIMIROVIC and REYNAUD however do not explicitly disclose tracking the ownership of the object utilizes blockchain technology;
BENEDETTO discloses tracking the ownership of the object utilizes blockchain technology (see BENEDETTO: e.g., -- [0032] In addition to the identification of the real-world object, the ownership of the real-world object is also determined using user data of the user obtained from user profile datastore (not shown), in some implementations. In some other implementations, the unique fingerprint of the specific instance of the real-world object is used to generate a non-fungible token (NFT) (operation 132) within a blockchain, which acts as a digital ledger to keep track of the use of the specific instance of the real-world object. The blockchain can be a proprietary or a generic blockchain. In some implementations, the real-world objects and the virtual objects are tracked using NFTs maintained in separate blockchains. In other implementations, the real-world objects and the virtual objects are tracked via NFTs maintained in a single blockchain. In yet other implementations, the blockchain can be maintained separately for each interactive application, wherein the real-world objects and the virtual objects used in the interactive application are maintained and tracked using distinct NFTs. In alternate implementations, the blockchain can be maintained separately for each type of interactive applications. For example, a first blockchain can be used to generate NFTs for tracking the real-world objects and virtual objects used in video games, a second blockchain can be used to generate NFTs for tracking the real-world objects and/or virtual objects used in social media applications, etc.
[0033] The blockchain works by registering each transaction representing use of a real-world or virtual object as a separate entry within the blockchain. The data included in the NFT within the blockchain provides sufficient details that can be used to verify the authenticity of the real-world object, past use history, past and present ownership, characteristics of the real-world object or virtual object, physical attributes of the real-world object, virtual attributes of the virtual object, etc. The blockchain maintaining the history of use of the real-world and virtual objects is stored on multiple servers and used to further authenticate the real-world or virtual objects by ensuring that the data contained in the blockchain for a real-world or a virtual object on one server is same as the blockchain data contained for the real-world or virtual object on majority of servers. When a real-world or virtual object is gifted or transferred or acquired by another user, the ownership of the real-world or virtual object is automatically updated to the corresponding NFT in the blockchain using a new block to reflect the current owner. The real-world object identified in operation 124 can be further validated (operation 126) using the details included in the corresponding NFT within the blockchain, wherein the validation includes validating the identity of the real-world object and validating the ownership of the real-world object.--, in [0032]-[0033]);
Duerksen (as modified by STANIMIROVIC and REYNAUD) and BENEDETTO are combinable as they are in the same field of endeavor: identification of object and their characteristics based on image processing. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Duerksen’s method using BENEDETTO’s teachings by including tracking the ownership of the object utilizes blockchain technology to Duerksen’s tracking objects in order to provide sufficient details that can be used to verify the authenticity of the real-world object, past use history, past and present ownership, characteristics of the real-world object or virtual object, physical attributes of the real-world object, virtual attributes of the virtual object, etc. with using blockchain technology (see BENEDETTO: e.g. in [0032]-[0033]).
Re Claim 3, Duerksen as modified by STANIMIROVIC and REYNAUD and BENEDETTO further disclose the step of utilizing the blockchain technology to encrypt information related to the ownership of the object (see BENEDETTO: e.g., -- [0032] In addition to the identification of the real-world object, the ownership of the real-world object is also determined using user data of the user obtained from user profile datastore (not shown), in some implementations. In some other implementations, the unique fingerprint of the specific instance of the real-world object is used to generate a non-fungible token (NFT) (operation 132) within a blockchain, which acts as a digital ledger to keep track of the use of the specific instance of the real-world object. The blockchain can be a proprietary or a generic blockchain. In some implementations, the real-world objects and the virtual objects are tracked using NFTs maintained in separate blockchains. In other implementations, the real-world objects and the virtual objects are tracked via NFTs maintained in a single blockchain. In yet other implementations, the blockchain can be maintained separately for each interactive application, wherein the real-world objects and the virtual objects used in the interactive application are maintained and tracked using distinct NFTs. In alternate implementations, the blockchain can be maintained separately for each type of interactive applications. For example, a first blockchain can be used to generate NFTs for tracking the real-world objects and virtual objects used in video games, a second blockchain can be used to generate NFTs for tracking the real-world objects and/or virtual objects used in social media applications, etc.
[0033] The blockchain works by registering each transaction representing use of a real-world or virtual object as a separate entry within the blockchain. The data included in the NFT within the blockchain provides sufficient details that can be used to verify the authenticity of the real-world object, past use history, past and present ownership, characteristics of the real-world object or virtual object, physical attributes of the real-world object, virtual attributes of the virtual object, etc. The blockchain maintaining the history of use of the real-world and virtual objects is stored on multiple servers and used to further authenticate the real-world or virtual objects by ensuring that the data contained in the blockchain for a real-world or a virtual object on one server is same as the blockchain data contained for the real-world or virtual object on majority of servers. When a real-world or virtual object is gifted or transferred or acquired by another user, the ownership of the real-world or virtual object is automatically updated to the corresponding NFT in the blockchain using a new block to reflect the current owner. The real-world object identified in operation 124 can be further validated (operation 126) using the details included in the corresponding NFT within the blockchain, wherein the validation includes validating the identity of the real-world object and validating the ownership of the real-world object.--, in [0032]-[0033]).
Re Claim 6, Duerksen as modified by STANIMIROVIC and REYNAUD and BENEDETTO further disclose wherein the one or more images saved to the database comprises a three-dimensional image (see STANIMIROVIC: e.g., --multi-dimensional probability density functions, additional input variables could be, but are not limited to: pixel temperature, x-y position of the pixels in the image, X-Y-Z position of the pixels in the 3D space etc. Further, for each object, a probability thresholding function (t) can be provided, which is utilized to threshold pixels, or image regions, in detection and segmentation block (6.0). E.g. the probability thresholding function can be a binary function, according to which pixels, or image regions, which have probability higher than a threshold are labeled as parts of a respective unreliable object.--, in [0170]-[0174]; also see and BENEDETTO: e.g., --first identifies a real-world object in the possession of a user (operation 124). Data related to the real-world object is captured using a plurality of sensors, scanners, image capturing devices, audio devices, etc., that are available within or coupled to the computing device 102 and/or disposed in a real-world space (i.e., user's physical location) 108 where the user 100 is interacting with an interactive application, such as a video game. The various sensors, devices, scanners capture the data in real-time upon detecting the user having the real-world object while they are interacting with the video game. In some implementations, the image data captured by one or more image capturing devices are used to identify the visual data of the real-world object by leveraging a technique like photogrammetry. The images of the real-world object are captured from various angles so as to be able to construct a virtual three-dimensional replica of the real-world object capturing all the visual attributes.--, in [0029], [0053], and [0064]-[0066]). See the similar obviousness and motivation statements for the combination of the disclosures of the cited references as addressed above for claim 2.
Re Claim 7, Duerksen as modified by STANIMIROVIC, REYNAUD and BENEDETTO further disclose wherein the one or more images saved to the database comprises a partial three-dimensional image (see STANIMIROVIC: e.g., --multi-dimensional probability density functions, additional input variables could be, but are not limited to: pixel temperature, x-y position of the pixels in the image, X-Y-Z position of the pixels in the 3D space etc. Further, for each object, a probability thresholding function (t) can be provided, which is utilized to threshold pixels, or image regions, in detection and segmentation block (6.0). E.g. the probability thresholding function can be a binary function, according to which pixels, or image regions, which have probability higher than a threshold are labeled as parts of a respective unreliable object.--, in [0170]-[0174]; also see and BENEDETTO: e.g., --first identifies a real-world object in the possession of a user (operation 124). Data related to the real-world object is captured using a plurality of sensors, scanners, image capturing devices, audio devices, etc., that are available within or coupled to the computing device 102 and/or disposed in a real-world space (i.e., user's physical location) 108 where the user 100 is interacting with an interactive application, such as a video game. The various sensors, devices, scanners capture the data in real-time upon detecting the user having the real-world object while they are interacting with the video game. In some implementations, the image data captured by one or more image capturing devices are used to identify the visual data of the real-world object by leveraging a technique like photogrammetry. The images of the real-world object are captured from various angles so as to be able to construct a virtual three-dimensional replica of the real-world object capturing all the visual attributes.--, in [0029], [0053], and [0064]-[0066]).
Re Claim 12, Duerksen as modified by STANIMIROVIC, REYNAUD and BENEDETTO further disclose wherein the step of labeling the one or more images of the object with one or more object properties occurs manually (see STANIMIROVIC: e.g., Fig. 3, Fig.7, and, -- The set of input images (5.0) comprises, for instance, one or more images of different modalities. Block 6.0 performs an unreliable object detection and segmentation based on given inputs. The given input includes the thermal image 4.0 and the description of unreliable object classes 1.0, and optionally includes images captured by additional imaging devices 3.0. Block 7.0 represents a set of labelled input images, which is created by labelling segmented unreliable objects (6.0), in the set of input images denoted in block 5.0. The labels thereby are indicative of at least one probability that at least one pixel or image region belongs to the at least one class of unreliable objects according to the at least one description of at least one class of unreliable objects. Finally, a computer vision algorithm, denoted in block 8.0, takes a set of labelled input images 7.0 (described in the following) as an input for further processing.--, in [0088], and, --Detection and segmentation of unreliable objects is performed based on their thermal properties, utilizing processing devices of the smartphones equipped with thermal cameras. Once the segmented images are obtained according to thermal images captured by the thermal cameras, these are transferred to devices that do not have thermal imaging capabilities, and registered with visible light images captured using cameras incorporated in these devices. Here, registration between segmented images allows for mapping of unreliable image regions from coordinate systems of segmented images, into coordinate systems of visible light images, captured by the smartphones which do not have thermal imaging capabilities. In this manner, image regions of visible light images are labelled as either reliable or unreliable, and only reliable image regions are used as an input of the tracking, localization and mapping algorithm. In this example, the at least one probability associated with at least one image region in the thermal images or visible light images is binary.--, in [0104], and [0121]-[0122]; and see see Duerksen: e. g., -- Objects transferred under this method are often tracked using printed text and barcode labels or RFID tags. As long as the transfer of objects is as prescribed by the quality system, the objects are assumed to be securely transferred and therefore genuine. However, supply chains may be stressed by events such as natural disasters, may be breached by unscrupulous parties, and may be difficult to maintain for many lower volume manufactured objects;--, in [0005], and, -- The internal evaluation may be performed by applying the classifier to the (digitized) laser training markings, and using the statistical distribution of points in the vector space generated by the classifier the characterize the resolution. A standard measure for characterizing the resolving power of a classifier such as the Davies-Bouldin index may be used. If this value is sufficiently high, it may be concluded that the classifier has high descriptive validity. [0098] The external evaluation may be performed by first digitizing the laser markings on the second family or collection of chips using the procedure described above. …[0099] A similar procedure may be applied to what may be described above for distinguishing between characters produced by different marking systems to the problem of distinguishing the formatting characteristics of each marking system. The formatting data—kerning, line spacing, in-line glyph placement—also may be captured and extracted using commercial OCR software. The relative positioning of a glyph in a character box may be contained in the metadata provided by ClearScan as “hints,” but rather than working with the ClearScan metadata to analyze the global layout of the markings on a chip, the labeled fonts may be exported for each marking system to other OCR platforms, e.g., FineReader (ABBYY, Milpitas, Calif.--, in [0099]; also see BENEDETTO: e.g., --In some implementations, machine learning algorithm 140 with an object recognition algorithm embedded therein is used to identify the specific instance of the real-world object. The machine learning algorithm 140 creates and updates an artificial intelligence (AI) model 120 that is trained using data captured for various virtual objects. The data is used to identify the unique characteristics of each real-world object. For example, if the real-world object is a red ball, then the unique characteristics can include visible characteristics of the red ball, such as the dimensions (e.g., diameter, surface area, etc.), color (e.g., hue, saturation, luminance, etc.), texture, etc. The visible characteristics of the specific instance of the red ball can also include user customization, distinct markings, visible imperfections, etc., that can further distinguish the specific instance of the real-world object. In addition to the visible characteristics, the other attributes can be used to identify other innate characteristics (e.g., weight, volume, etc.) defining physical, kinetic, chemical properties, for example. These characteristics can be used in the metaverse to apply to a digital character or perform certain task or to generate a desirable outcome. The object recognition algorithm is configured to identify the specific instance of the real-world object using the unique characteristics identified by the machine learning algorithm 140 from analyzing the physical attributes of the real-world objects captured by the plurality of sensors/devices.--, in [0031]). See the similar obviousness and motivation statements for the combination of the disclosures of the cited references as addressed above for claim 2.
Re Claim 13, Duerksen as modified by STANIMIROVIC and REYNAUD and BENEDETTO further disclose wherein the step of labeling the one or more images of the object with one or more object properties occurs automatically (see STANIMIROVIC: e.g., Fig. 3, Fig.7, and, -- The set of input images (5.0) comprises, for instance, one or more images of different modalities. Block 6.0 performs an unreliable object detection and segmentation based on given inputs. The given input includes the thermal image 4.0 and the description of unreliable object classes 1.0, and optionally includes images captured by additional imaging devices 3.0. Block 7.0 represents a set of labelled input images, which is created by labelling segmented unreliable objects (6.0), in the set of input images denoted in block 5.0. The labels thereby are indicative of at least one probability that at least one pixel or image region belongs to the at least one class of unreliable objects according to the at least one description of at least one class of unreliable objects. Finally, a computer vision algorithm, denoted in block 8.0, takes a set of labelled input images 7.0 (described in the following) as an input for further processing.--, in [0088], and, --Detection and segmentation of unreliable objects is performed based on their thermal properties, utilizing processing devices of the smartphones equipped with thermal cameras. Once the segmented images are obtained according to thermal images captured by the thermal cameras, these are transferred to devices that do not have thermal imaging capabilities, and registered with visible light images captured using cameras incorporated in these devices. Here, registration between segmented images allows for mapping of unreliable image regions from coordinate systems of segmented images, into coordinate systems of visible light images, captured by the smartphones which do not have thermal imaging capabilities. In this manner, image regions of visible light images are labelled as either reliable or unreliable, and only reliable image regions are used as an input of the tracking, localization and mapping algorithm. In this example, the at least one probability associated with at least one image region in the thermal images or visible light images is binary.--, in [0104], and [0121]-[0122]; and see Duerksen: e. g., -- Objects transferred under this method are often tracked using printed text and barcode labels or RFID tags. As long as the transfer of objects is as prescribed by the quality system, the objects are assumed to be securely transferred and therefore genuine. However, supply chains may be stressed by events such as natural disasters, may be breached by unscrupulous parties, and may be difficult to maintain for many lower volume manufactured objects;--, in [0005], and, -- The internal evaluation may be performed by applying the classifier to the (digitized) laser training markings, and using the statistical distribution of points in the vector space generated by the classifier the characterize the resolution. A standard measure for characterizing the resolving power of a classifier such as the Davies-Bouldin index may be used. If this value is sufficiently high, it may be concluded that the classifier has high descriptive validity. [0098] The external evaluation may be performed by first digitizing the laser markings on the second family or collection of chips using the procedure described above. …[0099] A similar procedure may be applied to what may be described above for distinguishing between characters produced by different marking systems to the problem of distinguishing the formatting characteristics of each marking system. The formatting data—kerning, line spacing, in-line glyph placement—also may be captured and extracted using commercial OCR software. The relative positioning of a glyph in a character box may be contained in the metadata provided by ClearScan as “hints,” but rather than working with the ClearScan metadata to analyze the global layout of the markings on a chip, the labeled fonts may be exported for each marking system to other OCR platforms, e.g., FineReader (ABBYY, Milpitas, Calif.--, in [0099]; also see BENEDETTO: e.g., --In some implementations, machine learning algorithm 140 with an object recognition algorithm embedded therein is used to identify the specific instance of the real-world object. The machine learning algorithm 140 creates and updates an artificial intelligence (AI) model 120 that is trained using data captured for various virtual objects. The data is used to identify the unique characteristics of each real-world object. For example, if the real-world object is a red ball, then the unique characteristics can include visible characteristics of the red ball, such as the dimensions (e.g., diameter, surface area, etc.), color (e.g., hue, saturation, luminance, etc.), texture, etc. The visible characteristics of the specific instance of the red ball can also include user customization, distinct markings, visible imperfections, etc., that can further distinguish the specific instance of the real-world object. In addition to the visible characteristics, the other attributes can be used to identify other innate characteristics (e.g., weight, volume, etc.) defining physical, kinetic, chemical properties, for example. These characteristics can be used in the metaverse to apply to a digital character or perform certain task or to generate a desirable outcome. The object recognition algorithm is configured to identify the specific instance of the real-world object using the unique characteristics identified by the machine learning algorithm 140 from analyzing the physical attributes of the real-world objects captured by the plurality of sensors/devices.--, in [0031]). See the similar obviousness and motivation statements for the combination of the disclosures of the cited references as addressed above for claim 2.
Re Claim 14, Duerksen as modified by STANIMIROVIC and REYNAUD and BENEDETTO further disclose comprising the step of utilizing a cloud computing environment to perform one or more steps of the image processing (see BENEDETTO: e.g., -- hat access services, such as providing access to games of the current embodiments, delivered over a wide geographical area often use cloud computing. Cloud computing is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users do not need to be an expert in the technology infrastructure in the “cloud” that supports them. Cloud computing can be divided into different services, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Cloud computing services often provide common applications, such as video games, online that are accessed from a web browser, while the software and data are stored on the servers in the cloud. The term cloud is used as a metaphor for the Internet, based on how the Internet is depicted in computer network diagrams and is an abstraction for the complex infrastructure it conceals.--, in [0055]). See the similar obviousness and motivation statements for the combination of the disclosures of the cited references as addressed above for claim 2.
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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/WEI WEN YANG/Primary Examiner, Art Unit 2662