DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s preliminary amendment filed on August 2, 2024 has been entered and made of record.
Claim Interpretation
Claims 1-18 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they are all method claims.
Claim 19 is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the recitations of “memory”, “processor” and “instructions provide” sufficient structure to perform all claimed limitations.
Claim 15 is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it is an article of manufacture claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1 as a presentative claim, the 101 analysis is presented below.
Step 1: It is noted that claim 1 recites a method which is a process. Thus, claim 1 is directed to one of statutory categories of invention.
Step 2A Prong 1: Limitations “determining an attribute for the received image using a machine learning based framework trained at least in part on training images comprising a model environment that models the prescribed environment” are interpreted as be practically performed in the human mind (since claim does not define what attribute is, in view of BRI, it is similar to an idea that a person looks at the image to determine/identify the image attributes such as color, type of object, foreground/background, lighting, contrast, etc.). Thus, these limitations also fall into the “mental process” grouping of abstract idea. Therefore, claim 13 recites an abstract idea.
Step 2A Prong 2: It is noted that claim includes any additional elements (i)“receiving an image comprising a prescribed environment” and “machine learning”. With regard to (i), it is nothing more than data gathering which is insignificant extrasolution activity. With regard to (ii), it is recited at a high level of generality such that it amounts to no more than mere instructions to implement the abstract idea on a conventional computer and do not point to a specific improvement in computer itself. It is also well understood, routine, conventional activity in the art (see Kerr below). Thus, these additional elements do not amount to an integration of the judicial exception into a practical application. Therefore, claim is directed to an abstract idea that does not amount to an integration of the judicial exception into a practical application. Therefore, claim is directed to an abstract idea.
Step 2B: These additional elements, as pointed out in Step 2A prong 2, are (i)nothing more than data gathering which is insignificant activity and (ii)recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer and do not point to a specific improvement in computer itself. These additional elements, taken individually and in combination, do not contribute to an inventive concept and do not amount to significantly more than the judicial exception. Therefore, claim is not a patent eligible. Therefore, claim is not a patent eligible.
Claim 19 recites an apparatus and claim 20 recites a manufacture so each of these claims falls within one of the statutory categories of invention. It is noted that each of these claims recites similar claim limitations called for in the counterpart claim 1. Thus, the advanced statements as applied to claim 1 above are incorporated herein. It is also noted that claim 19 recites addition elements “memory” and “processor” and claim 20 recites additional elements “medium” and “computer instructions”. The additional elements “memory”, “processor”, “medium” and “computer instructions” are recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer. The claims do not point to a specific improvement in computer itself. The additional elements, taken individually and in combination, do not contribute to an inventive concept. Therefore, claims 19 and 20 are also directed to an abstract idea without significantly more.
The advanced statements as applied to claims 1 are incorporated hereinafter.
Regarding claim 2, the additional limitations “wherein the received image comprises a rendering and the prescribed environment comprises the model environment” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 2 is also directed to an abstract idea without significantly more.
Regarding claim 3, the additional limitations “wherein the received image comprises a photograph and the prescribed environment comprises a physical environment” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 3 is also directed to an abstract idea without significantly more.
Regarding claim 4, the additional limitations “wherein the attribute of the received image determined by the machine learning based framework comprises a location in three-dimensional space of a pixel comprising the received image” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 4 is also directed to an abstract idea without significantly more.
Regarding claim 5, the additional limitations “wherein the attribute of the received image determined by the machine learning based framework comprises xyz coordinates of a pixel comprising the received image” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 5 is also directed to an abstract idea without significantly more.
Regarding claim 6, the additional limitations “wherein the attribute of the received image determined by the machine learning based framework comprises a depth value of a pixel comprising the received image” are interpreted as be practically performed in the human mind (since claim does not define what attribute is, in view of BRI, it is similar to an idea that a person looks at the image to determine/identify the image attributes such as deep value of a pixel (color of the background)). Thus, claim 6 is also directed to an abstract idea without significantly more.
Regarding claim 7, the additional limitations “wherein the attribute of the received image determined by the machine learning based framework comprises a surface normal vector of a pixel comprising the received image” are interpreted as be practically performed in the human mind (since claim does not define what attribute is, in view of BRI, it is similar to an idea that a person looks at the image to determine/identify the image attributes such as objects (vectors), foreground and background (vectors)). Thus, claim 7 is also directed to an abstract idea without significantly more.
Regarding claim 8, the additional limitations “wherein the prescribed environment is constrained and known” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 8 is also directed to an abstract idea without significantly more.
Regarding claim 9, the additional limitations “wherein known information about the prescribed environment comprises known structure and geometry of the prescribed environment” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 9 is also directed to an abstract idea without significantly more.
Regarding claim 10, the additional limitations “wherein the prescribed environment comprises an apparatus or rig for photographing objects or items” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 10 is also directed to an abstract idea without significantly more.
Regarding claim 11, the additional limitations “wherein known information about the prescribed environment comprises known camera information” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 11 is also directed to an abstract idea without significantly more.
Regarding claim 12, the additional limitations “wherein known information about the prescribed environment comprises known lighting information” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 12 is also directed to an abstract idea without significantly more.
Regarding claim 13, the additional limitations “wherein determining the attribute comprises determining a plurality of attributes for the received image using the machine learning based framework” are interpreted as be practically performed in the human mind. Thus, claim 13 is also directed to an abstract idea without significantly more..
Regarding claim 14, the additional limitations “wherein the training images are labeled or otherwise associated with relevant metadata” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 14 is also directed to an abstract idea without significantly more.
Regarding claim 15, the additional limitations “wherein various attributes learned by the machine learning based framework from the training images are derived or inferred from labels or metadata associated with the training images” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 15 is also directed to an abstract idea without significantly more.
Regarding claim 16, the additional limitations “wherein the machine learning based framework comprises one or more neural networks” are recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer. The claims do not point to a specific improvement in computer itself. The additional elements, taken individually and in combination, do not contribute to an inventive concept. Therefore, claim 16 is also directed to an abstract idea without significantly more.
Regarding claim 17, the additional limitations “wherein the machine learning based framework comprises one or more convolutional neural networks” are recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer. The claims do not point to a specific improvement in computer itself. The additional elements, taken individually and in combination, do not contribute to an inventive concept. Therefore, claim 17 is also directed to an abstract idea without significantly more.
Regarding claim 18, the additional limitations “wherein the received image comprises a still image or a frame of a video sequence” are interpreted as data gathering which is insignificant extrasolution activity. Thus, claim 18 is also directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kerr et al. (U.S. Pat. App. Pub. No. 2017/0097948 A1, referred as Kerr hereinafter).
Regarding claim 1 as a representative claim, Kerr teaches a method, comprising:
receiving an image comprising a prescribed environment (see paras. [0059] (image received), [0019] (for example, “image of fireworks behind the Eiffel tower” corresponds to the so-called prescribed environment) and [0045] (object, people, fireworks and Eiffel depicted in image)); and determining an attribute for the received image using a machine learning based framework trained at least in part on training images comprising a model environment that models the prescribed environment (see paras. [0007] (using machine learning to extract image attributes), [0059] (neural network extracts image attributes)).
Regarding claim 2, Kerr further teaches wherein the received image comprises a rendering and the prescribed environment comprises the model environment (see para. [0059] (model images)).
Regarding claim 3, Kerr further teaches wherein the received image comprises a photograph and the prescribed environment comprises a physical environment (see paras. [0019] (for example, “image of fireworks behind the Eiffel tower” corresponds to the so-called prescribed environment), [0045] (object, people, fireworks and Eiffel depicted in image) and [0057] (image attributes comprising location)).
Regarding claim 4, Kerr further teaches wherein the attribute of the received image determined by the machine learning based framework comprises a location in three-dimensional space of a pixel comprising the received image (see paras. [0057] (image attributes comprising location) and [0069] (depth cameras); combination of location and depth cameras inherently provides 3D space)).
Regarding claim 5, Kerr further teaches wherein the attribute of the received image determined by the machine learning based framework comprises xyz coordinates of a pixel comprising the received image (see paras. [0057] (image attributes comprising location) and [0069] (depth cameras); combination of location and depth cameras inherently provides xyz coordinates)).
Regarding claim 6, Kerr further teaches wherein the attribute of the received image determined by the machine learning based framework comprises a depth value of a pixel comprising the received image (see paras. [0069] (depth cameras) and [0025] (background and foreground (i.e., Eiffel tower); in this case, background represents the depth of the image)).
Regarding claim 7, Kerr further teaches wherein the attribute of the received image determined by the machine learning based framework comprises a surface normal vector of a pixel comprising the received image (see paras. [0054] (feature vectors) and [0057] (image attributes comprising vector)).
Regarding claim 8, Kerr further teaches wherein the prescribed environment is constrained and known (see paras. [0019] (for example, “image of fireworks behind the Eiffel tower” corresponds to the so-called prescribed environment), [0045] (object, people, fireworks and Eiffel depicted in image) and [0057] (image attributes comprising location); in this case, image scene is constrained and known i.e., people/object, Eiffel, fireworks)).
Regarding claim 9, Kerr further teaches wherein known information about the prescribed environment comprises known structure and geometry of the prescribed environment (see paras. [0019] (for example, “image of fireworks behind the Eiffel tower” corresponds to the so-called known structure) and [0057] (image attributes comprising location; in this case, such location corresponds to so-called geometry)).
Regarding claim 10, Kerr further teaches wherein the prescribed environment comprises an apparatus or rig for photographing objects or items (see paras. [0019] (for example, “image of fireworks behind the Eiffel tower”), [0041] (a camera that has captured the image) and [0069] (cameras)).
Regarding claim 11, Kerr further teaches wherein known information about the prescribed environment comprises known camera information (see paras. [0015] (tag, metadata, time, author, location; these refer to the so-called camera information), [0057] (image attributes comprising location) and [0069] (cameras; camera information is inherently included in the image captured)).
Regarding claim 12, Kerr further teaches wherein known information about the prescribed environment comprises known lighting information (see fig. 3, color image source A depicted at 312 and paras. [0041] (a camera that has captured the image) and [0069] (camera); thus, known light information is inherently included in order to generate a color image)).
Regarding claim 13, Kerr further teaches wherein determining the attribute comprises determining a plurality of attributes for the received image using the machine learning based framework (see paras. [0007] (using machine learning to extract image attributes), [0059] (neural network extracts image attributes)).
Regarding claim 14, Kerr further teaches wherein the training images are labeled or otherwise associated with relevant metadata (see paras. [0015] (tag, metadata, time, author, location; these refer to the so-called camera information), [0019] (tags), [0037] (image database 104 includes similar attributes to the selected attributes, wherein the selected attributes comprising textural query per para. [0045]; thus image in the database includes label/annotation associated with it as well)).
Regarding claim 15, Kerr further teaches wherein various attributes learned by the machine learning based framework from the training images are derived or inferred from labels or metadata associated with the training images metadata (see paras. [0015] (tag, metadata, time, author, location; these refer to the so-called camera information), [0019] (tags), [0037] (image database 104 includes similar attributes to the selected attributes, wherein the selected attributes comprising textural query per paras. [0015] & [0045]; thus image in the database includes label/annotation associated with it as well)).
Regarding claim 16, Kerr further teaches wherein the machine learning based framework comprises one or more neural networks environment (see paras. [0007] (using machine learning to extract image attributes), and [0059] (neural network extracts image attributes)).
Regarding claim 17, Kerr further teaches wherein the machine learning based framework comprises one or more convolutional neural networks (see para. [0018] (deep neural networks; CNN is a specific model of the deep neural networks;. Thus, it is included in the deep neural networks)).
Regarding claim 18, Kerr further teaches wherein the received image comprises a still image or a frame of a video sequence (see paras. [0041] (a camera that has captured the image), [0045] (photos as attributes of the image)m [0017] (video)).
Regarding claim 19, the advanced statements as applied to claim 1 above are incorporated hereinafter. Kerr further teaches a processor and memory with instructions (see para. [0053] (“processor executing instructions stored in memory”)).
Regarding claim 20, the advanced statements as applied to claim 1 above are incorporated hereinafter. Kerr further teaches a computer program product embodied in a non-transitory computer storage medium (see paras. [0026] (software), [0053] (“processor executing instructions stored in memory”; software) and [0065] (instructions such as program modules)).
Claim 20 is additional rejected because it does not fall within at least one of the four categories of patent eligible subject matter. In this case, it is noted that claim recites “a computer program product embodied in a non-transitory computer-readable medium storing instructions, comprising computer instructions”. However, it fails to recite “when executed by a computer or processor” to perform claim functions. Thus, there is no functional relationship between instructions and a computer or processor. Claim as a whole is nothing more than a memory stick or CD-ROM storing recorded music. See MPEP 2111.05(III).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
An obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but an examined application claim is not patentably distinct from the reference claim(s) because the examined claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985). Anticipation is “the ultimate or epitome of obviousness” (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-54 of U.S. Pat. No. 11,989,820 B2, referred as referred as ‘820 patent hereinafter).
Although the conflicting claims are not identical, they are not patentably distinct from each other because each limitation of the instant claims is fully defined by claims of the ‘820 patent. For example, as to the instant claim 1 as a representative claim, claim 1 of the ‘820 patent discloses a method comprising (see line 1):
receiving an image comprising a prescribed environment (see line 2); and determining an attribute for the received image using a machine learning based framework trained at least in part on training images comprising a model environment that models the prescribed environment (see lines 6-8).
While claim 1 of the ‘820 patent includes additional limitations (i.e., transforming) that are not set forth in the instant claim 1, the use of transitional term “comprising/comprises” in the instant claim 1 fails to preclude the possibility of additional elements. Therefore, instant claim 1 fails to define an invention that is patentably distinct from claim 1 of the ‘820 patent.
Furthermore, each of the limitations recited in instant claim 1 is anticipated by patented claim 1 and anticipation is “the ultimate or epitome of obviousness.”
Likewise, each of instant claims 2-20 is fully defined by patented claims 1-54. Therefore, each of these claims 1 fails to define an invention that is patentably distinct from claims 1-54 of the ‘820 patent.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-54 of U.S. Pat. No. 12,002,149 B2, referred as referred as ‘149 patent hereinafter).
Although the conflicting claims are not identical, they are not patentably distinct from each other because each limitation of the instant claims is fully defined by claims of the ‘149 patent. For example, as to the instant claim 1 as a representative claim, claim 1 of the ‘149 patent discloses a method comprising (see line 1):
receiving an image comprising a prescribed environment (see lines 2-4); and determining an attribute for the received image using a machine learning based framework trained at least in part on training images comprising a model environment that models the prescribed environment (see lines 5-10).
While claim 1 of the ‘820 patent includes additional limitations (i.e., model) that are not set forth in the instant claim 1, the use of transitional term “comprising/comprises” in the instant claim 1 fails to preclude the possibility of additional elements. Therefore, instant claim 1 fails to define an invention that is patentably distinct from claim 1 of the ‘149 patent.
Furthermore, each of the limitations recited in instant claim 1 is anticipated by patented claim 1 and anticipation is “the ultimate or epitome of obviousness.”
Likewise, each of instant claims 2-20 is fully defined by claims 1-54 of the ‘149 patent. Therefore, each of these claims 1 fails to define an invention that is patentably distinct from claims 1-54 of the ‘149 patent.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-51 of U.S. Pat. No. 10,902,559 B2, referred as referred as ‘149 patent hereinafter).
Although the conflicting claims are not identical, they are not patentably distinct from each other because each limitation of the instant claims is fully defined by claims of the ‘559 patent. For example, as to the instant claim 1 as a representative claim, claim 1 of the ‘559 patent discloses a method comprising (see line 1):
receiving an image comprising a prescribed environment (see line 3: input image); and determining an attribute for the received image using a machine learning based framework trained at least in part on training images comprising a model environment that models the prescribed environment (see lines 2-11).
While claim 1 of the ‘559 patent includes additional limitations (i.e., “modifying…” and “output…”) that are not set forth in the instant claim 1, the use of transitional term “comprising/comprises” in the instant claim 1 fails to preclude the possibility of additional elements. Therefore, instant claim 1 fails to define an invention that is patentably distinct from claim 1 of the ‘559 patent.
Furthermore, each of the limitations recited in instant claim 1 is anticipated by patented claim 1 and anticipation is “the ultimate or epitome of obviousness.”
Likewise, each of instant claims 2-20 is fully defined by claims 1-51 of the ‘559 patent. Therefore, each of these claims 1 fails to define an invention that is patentably distinct from claims 1-51 of the ‘559 patent.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hoguet (U.S. Pat. App. Pub. No. 2013/0147799 A1) teaches a system and method for home and landscape design comprising an image having prescribed environment (fig. 1; para.[0020] (images correspond to 3D models)) and neural networks for analyzing such images (para. [0020]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY M DANG whose telephone number is (571)272-7389. The examiner can normally be reached Monday to Friday from 7:00AM to 3:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached at 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DMD
4/2026
/DUY M DANG/Primary Examiner, Art Unit 2662
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