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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
Priority
A claim of priority to IN202321083903, filed 12/8/23 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8, 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention. Evidence that claims 1-10 fail(s) to correspond in scope with that which the inventor or a joint inventor, or for pre-AIA applications the applicant regards as the invention can be found in the specification. In that paper at paragraphs 12 and 13, the inventor or a joint inventor, or for pre-AIA applications the applicant has stated “A color representation module then creates a two-dimensional color matrix for each product part, encapsulating the color percentages corresponding to each palette color, thus illustrating the color distribution across the product parts. Finally, a color matching module compares the extracted color data against an extensive color database, arming to identify the closest matching colors for the different parts of the product. A key aspect of the method is the two-dimensional color representation matrix, which is crucial for its precision in analysis and serves as an integral part of search and recommendation processes.”
This statement indicates that the invention is different from what is defined in the claim(s) because the claims do not recite the two-dimensional color representation matrix. Thus claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: the creation/use of the two-dimensional color representation matrix, which the applicant regards as crucial and integral part of the invention. Claim 10 is rejected under a similar rationale. Note that claim 9 appears to recite this matrix and thus is not subject to this rejection. The examiner suggests incorporating claim 9 into the independent claims to overcome this rejection.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 recites “one or more sources of a repository, an internal storage, or a database.” It is unclear how to understand this limitation. Does it mean one or more sources of a repository and leave two hanging clauses of an internal storage or a database? Does it mean one or more sources comprising a repository, an internal storage, or database? Does it mean something else? The examiner is unable to determine applicant’s intent with this claim and for purposes of examination will find a single source. Claim 10 is similarly analyzed. Claims 2-9 are rejected as inheriting the deficiencies of the independent claim.
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 as best understood are rejected under 35 U.S.C. 102 as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over US 9,569,700, Santos, et al.
1. A system for multi-color product representation via part-wise color extraction, the system (100) comprising:
a. an image retrieval module (101) configured to retrieve a product image and associated data from one or more sources of a repository, an internal storage, or a database; (Santos 7:42-57 discloses retrieving product images from a catalog database)
b. a product identification module (102) configured to extract the product pixels of the retrieved image received from the image retrieval module (101); (Santos 7:57-8:18 provides the product to the identification system; 4:16-29 identifies the product in the image, note that as images are made of pixels, this would necessarily extract the pixels for use in identification)
c. a part identification module (103) configured to distinguish different parts of the product by mapping each pixel to the respective product part of the extracted product pixels from the product identification module (102); (Santos 4:30-46 identifies subparts of the item)
d. a color extraction module (104) designed to extract each pixel of a plurality of product parts’ data received from the part identification module (103); (Santos 4:30-46 identifies color of the item or subparts of the item)
f. a color matching module (106) configured to compare the extracted color information against a comprehensive color database to determine the closest matching colors for the plurality of product parts utilizing the color representation data from the color representation module (105). (Santos 16:53-17:10 describes an example where it identifies the closest (high confidence) of a color)
Santos does not explicitly disclose a
e. a color representation module (105) configured to create a two-dimensional vector for each product part, representing the color percentages for each color in the palette, operatively connected to the color extraction module (104);
However, applicant describes the two-dimensional vector as a vector where one axis represents the product parts and the other axis corresponds to the predefined color palette. It is noted that while applicant uses the term “vector” it appears that applicant is using the term as a table or database entry where the part and the color have correspondence. Spec ¶29. Santos at 4:30-46 identifies color of the item or subparts of the item, which matches a color and thus would necessarily have a vector created matching the item subpart and color of the item. Alternatively, such functionality would have been obvious as one would need to have a way to have a correspondence between the identified color and the product part for the system to be able to provide the identification of the color of parts of an item as disclosed by Santos.
Claim 10 is rejected under a similar rationale.
2. The system (100) as claimed in claim 1, wherein the image retrieval module (101) is configured to retrieve text description data of the product, detailing the name, characteristics, and part definitions to guide separate color extraction for parts discernible by material or boundaries for human evaluators, further configured to covert images of the products to multiple format and dimension for subsequent color analysis. Santos (3:45-4:29 describe text descriptions of the data and characteristics of the product; note that the part of the claim following “to guide” is considered intended use and given little patentable weight.)
3. The system (100) as claimed in claim 1, wherein the image retrieval module (101) is further configured to covert images of the products to multiple format and dimension for subsequent color analysis. (Santos 5:54-67 converts uses swatches (small portion of the image) or converts images to greyscale)
4. The system (100) as claimed in claim 1, wherein the product identification module (102) is configured to mark each pixel within an image individually, to ascertain its affiliation with the product, using an output of a localization deep learning module, a product description, or a rectangular sampling method, thereby facilitating the exclusion of non-product pixels for the subsequent color analysis. (Santos 2:1-36 uses a deep learning module to identify and classify subparts of an image)
5. The system (100) as claimed in claim 4, wherein the rectangular sampling method involves defining a rectangle with a smaller dimension than the image dimension and using it as a prompt for a prompt-based model for the extraction of product pixels from the image. (The elements of this claim are met by the alternative deep learning module in claim 4)
Claims 6-10 as best understood are rejected under 35 USC 103 as being unpatentable over Santos in view of US 9,741,137, Dorner et al.
Santos does not disclose
6. The system (100) as claimed in claim 1, wherein the part identification module (103) is designed to map each pixel of the product to a specific part by identifying individual pixel colors, aggregating similar colors based on the number of pixels sharing the same color out of the total number of pixels thereby facilitating the grouping of pixels belonging to different parts for subsequent color extraction module (104) to process and segregate pixels pertaining to each distinct part, for color analysis.
Dorner discloses
wherein the part identification module (103) is designed to map each pixel of the product to a specific part by identifying individual pixel colors, aggregating similar colors based on the number of pixels sharing the same color out of the total number of pixels thereby facilitating the grouping of pixels belonging to different parts for subsequent color extraction module (104) to process and segregate pixels pertaining to each distinct part, for color analysis. (Dorner :10-23-11:3 mapping pixels, aggregating/grouping colors)
It would have been obvious to modify the system of Santos to include the use of aggregating similar colors using a distance for the purposes of quantifying color similarity as taught by Dorner (10:23-31)
Santos does not disclose
7. The system (100) as claimed in claim 1, wherein the color extraction module (104) identifies and matches the pixel colors by measuring the distance from the RGB value of the pixel to each of the RGB value of a predefined color palette from the image retrieval module (101),
analyzes pixel distribution for each product part, and
calculates color percentages relative to the total number of pixels for the part.
Dorner discloses
wherein the color extraction module (104) identifies and matches the pixel colors by measuring the distance from the RGB value of the pixel to each of the RGB value of a predefined color palette from the image retrieval module (101), (Dorner 10:23-37 matches colors using distance)
analyzes pixel distribution for each product part, and (Dorner 10:63-66 pixel distribution)
calculates color percentages relative to the total number of pixels for the part. (Dorner 10:67-11:3 color percentage)
It would have been obvious to modify the system of Santos to include the use of aggregating similar colors using a distance for the purposes of quantifying color similarity as taught by Dorner (10:23-31)
Santos does not disclose
8. The system (100) as claimed in claim 1, wherein the color extraction module (104) employs distance measurement techniques including a Euclidean or an absolute distance to match each pixel's color within a part to the closest corresponding color in the defined palette.
Dorner discloses
wherein the color extraction module (104) employs distance measurement techniques including a Euclidean or an absolute distance to match each pixel's color within a part to the closest corresponding color in the defined palette. (Dorner 10:23-37, Euclidean distance)
It would have been obvious to modify the system of Santos to include the use of aggregating similar colors using a distance for the purposes of quantifying color similarity as taught by Dorner (10:23-31)
Santos does not disclose
9. The system (100) as claimed in claim 1, wherein the color matching module (106) is configured to compare the colors of different products by assessing the vector distance between their respective 2-dimensional color representation matrices produced by the color representation module (105) wherein the 2-dimensional color representation matrix includes one dimension for the defined parts of the product and another dimension for the colors on the defined color palette for the product.
Dorner discloses
wherein the color matching module (106) is configured to compare the colors of different products by assessing the vector distance between their respective 2-dimensional color representation matrices produced by the color representation module (105) wherein the 2-dimensional color representation matrix includes one dimension for the defined parts of the product and another dimension for the colors on the defined color palette for the product. (Dorner 6:1-9 2D array pixels which correspond to parts of an image and therefore parts of a product in the image and colors)
It would have been obvious to modify the system of Santos to include the use of 2D color matrix for the purposes of processing the pixels as taught by Dorner (5:56-6:9)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ming Shui whose telephone number is (303)297-4247. The examiner can normally be reached on 7-5 Pacific Time, M-Th.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Greg Morse can be reached on 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ming Shui/
Primary Examiner, Art Unit 2663
Prosecution Insights