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 .
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanuka et al. US-Patent No. 11,210,450 (hereinafter Dhanuka) in view of Moreno et al. US-PGPUB No. 2024/0143943 (hereinafter Moreno).
Re Claim 1:
Dhanuka teaches a method comprising:
receiving, by a processing device, a query referencing an input font for performing a visual similarity search (Dhanuka teaches at FIG. 1 and column 5, lines 19-67 that the input data 114 includes an input font 116 for performing visual similarity search using Similarity Module 110);
generating, by the processing device, a search result specifying at least one variable font that is visually similar to the input font by searching a plurality of variable fonts based on the query, the generating including (Dhanuka teaches at FIG. 1 and column 5, lines 19-67 generating variable font 112 and identifies a design axis of a variable font such that values of the design axis are proportional to an attribute of glyphs of the variable font.
Dhanuka teaches at FIG. 1 and column 6, lines 1-37 the similarity module 110 leverages the relationship between design axis values and the attribute values of the glyphs of the variable font to identify a design axis value that corresponds to an attribute value of the input font 116 and applies the identified design axis value to the variable font and generate an instance of the variable font that is visually similar to the input font 116 and the similarity module 110 determines a similarity score between the generated instance of the variable font and the input font 116):
forming a plurality of instances for the at least one variable font, respectively, by adjusting one or more axes usable to change an appearance of the at least one variable font (
Dhanuka teaches at column 9, lines 59-67 and column 10, lines 1-45 that the modification module 206 receives the custom instance data 210, the variable font data 112 and the input data 114 as inputs and processes the custom instance data 210, the variable font data 112 and the input data 114 and generates an instance of the variable font as a visually similar font 322. The modification module 206 uses this similarity score to identify additional design axes of the variable font which are usable to maximize the similarity score
Dhanuka teaches at column 5, lines 54-67 and column 6, lines 1-37 forming a plurality of instances of variant font using the weight axis values and values of weights of glyphs of instances of the variable font and the similarity module 110 leverages the relationship between design axis values and the attribute values of the glyphs of the variable font to identify a design axis value that corresponds to an attribute value of the input font 116 and applies the identified design axis value to the variable font and generate an instance of the variable font that is visually similar to the input font 116 and the similarity module 110 determines a similarity score between the generated instance of the variable font and the input font 116.
Dhanuka further teaches at column 6, lines 55-67 that the similarity module 110 also determines a relationship between values of the Width axis and widths of the glyphs of instances of the variable font. The similarity module 110 is implemented leveraging the relationship between the values of the Width axis and corresponding widths of glyphs of instance of the variable font to identify a value of the Width axis corresponding to the particular width of the glyphs of the input font 116.
Dhanuka teaches at column 7, lines 15-40 that the similarity module 110 also derives a relationship between values of the Slant axis and slants of glyphs of instances of the variable font and uses the identified value of the Slant axis to generate an instance of the variable font have the particular slant of the glyphs of the input font 116….the glyphs of the generated instance of the variable font have the particular weight, the particular width and the particular slant of the glyphs of the input font 116); and
identifying the at least one variable font by comparing the plurality of instances with the input font using a machine-learning model (Dhanuka teaches at column 6, lines 38-54 that the similarity module 110 generates or receives a latent representation of the input font 116 and a latent representation of the generated instance of the variable font using one or more convolutional neural networks and extracts a feature vector describing a visual appearance of the input font 116 and a feature vector describing a visual appearance of the generated instance of the variable font. The similarity module 110 determines a similarity score by calculating a Euclidean distance between the feature vectors and a relatively small Euclidean distance corresponds to a relatively high similarity score.
Dhanuka further teaches at column 6, lines 55-67 that the similarity module 110 also determines a relationship between values of the Width axis and widths of the glyphs of instances of the variable font. The similarity module 110 is implemented leveraging the relationship between the values of the Width axis and corresponding widths of glyphs of instance of the variable font to identify a value of the Width axis corresponding to the particular width of the glyphs of the input font 116); and
presenting, by the processing device, the search result for display in a user interface (Dhanuk teaches at column 8, lines 8-19 that glyphs rendered using the similar font 118 are displayed in a user interface 120 of the display device 106 and the similarity module 110 is capable of generating instances of a single variable font which are visually similar to multiple different fonts described by the input data 114).
Moreover, Moreno teaches receiving, by a processing device, a query referencing an input font for performing a visual similarity search (Moreno teaches at FIG. 1 and at Paragraph 0032 a request 120 referencing an input font name and at Paragraph 0055 that the client request 120 may include a string specification. In one implementation, the string specification can contain a display text, a font identifier, a font size, a display resolution, and an available display screen area at a vehicle display. After receiving operation 504, control passes to authenticating operation 506.);
generating, by the processing device, a search result specifying at least one variable font that is visually similar to the input font by searching a plurality of variable fonts based on the query (
Moreno teaches at FIG. 1 and Paragraph 0039-0040 that the database 118 stores target font specified in the client request 120 containing a listing of all the font names, font locations and font versions of the target font and when a font version in the database response 126 matches a font version specified in the client request 120, the server instructions may cause the server 104 to select a font location associated with the font version in the database response 126.
Moreno teaches at Paragraph [0053] Turning back to FIG. 2, once the server response is received by the client, the client application 202 extracts the string size from the server response, matching the string size value to the correct translation variant text. The string size is then entered into the cell corresponding to the translation variant text. For example, the English variant text size in cell 230 is populated with a value of 15 pixels for corresponding English translation variant text 218. This value was extracted from the server response and entered into cell 230 by the client application 202. Similarly, cell 232 is populated with a value of 14 for corresponding Spanish translation variant text 220 and cell 234 is populated with a value of 21 for corresponding French translation variant text 222).
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have incorporated Moreno’s various features including retrieving a font version (instance) of a variable font from the database based on the client request specifying an input font into Dhanuka’s font retrieval operations for presenting a variable font based on the user input font. One of the ordinary skill in the art would have been motivated to have matched a variable font in the font library to the input font.
Re Claim 2:
The claim 2 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the search result includes values of at least one said axes of the at least one variable font.
However, Dhanuk further teaches the claim limitation that the search result includes values of at least one said axes of the at least one variable font (Dhanuka further teaches at column 6, lines 55-67 that the similarity module 110 also determines a relationship between values of the Width axis and widths of the glyphs of instances of the variable font. The similarity module 110 is implemented leveraging the relationship between the values of the Width axis and corresponding widths of glyphs of instance of the variable font to identify a value of the Width axis corresponding to the particular width of the glyphs of the input font 116.
Dhanuka teaches at column 7, lines 15-40 that the similarity module 110 also derives a relationship between values of the Slant axis and slants of glyphs of instances of the variable font and uses the identified value of the Slant axis to generate an instance of the variable font have the particular slant of the glyphs of the input font 116….the glyphs of the generated instance of the variable font have the particular weight, the particular width and the particular slant of the glyphs of the input font 116.
Dhanuk teaches at column 8, lines 8-19 that glyphs rendered using the similar font 118 are displayed in a user interface 120 of the display device 106 and the similarity module 110 is capable of generating instances of a single variable font which are visually similar to multiple different fonts described by the input data 114).
Re Claim 3:
The claim 3 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the at least one variable font is configured using a single font file configured to define the plurality of instances.
However, Dhanuka further teaches the claim limitation that the at least one variable font is configured using a single font file configured to define the plurality of instances (Dhanuka teaches at column 5, lines 12-20 that the variable font data 112 describes a plurality of variable fonts and includes a font file for each of the plurality of variable fonts and these font files define design axes used by the variable fonts, adjustable ranges of the design axes, default or named instances of the variable fonts).
Re Claim 4:
The claim 4 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the one or more axes includes weight, width, slant, and optical size.
However, Dhnauka further teaches the claim limitation that the one or more axes includes weight, width, slant, and optical size (Dhanuka teaches at column 8, lines 56-67 that the axis module 204 processes the variable font data 112 to derive relationship between values of attributes of glyphs of a variable font and registered design axis values of the variable font and determines relationship between values of the attributes of glyphs of the variable font and the values of an Italic axis, an Optical Size axis, a Slant axis, a Width axis and a Weight axis).
Re Claim 5:
The claim 5 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the forming includes:
producing a variable font representation of a respective said instance; and generating a rasterized font representation by rasterizing the variable font representation.
Dhanuka further teaches the claim limitation that the forming includes:
producing a variable font representation of a respective said instance; and generating a rasterized font representation by rasterizing the variable font representation (Dhanuk teaches at column 8, lines 8-19 that glyphs rendered using the similar font 118 are displayed in a user interface 120 of the display device 106 and the similarity module 110 is capable of generating instances of a single variable font which are visually similar to multiple different fonts described by the input data 114.
Dhanuka teaches at column 14, lines 49-59 that FIG. 6 is a flow diagram depicting a procedure 600 in an example implementation in which a digital image depicting glyphs rendered using an input font is received and an instance of a variable font is generated that is visually similar to the input font. A digital image depicting glyphs rendered using an input font is received (block 602). The computing device 102 implements the similarity module 110 to receive the digital image in one example. Attribute values of the glyphs depicted in the digital image are determined (block 604). The similarity module 110 determines the attribute values of the glyphs depicted in the digital image).
Re Claim 6:
The claim 6 encompasses the same scope of invention as that of the claim 5 except additional claim limitation that the variable font representation is configured as a vector graphic.
However, Dhanuka teaches the claim limitation that the variable font representation is configured as a vector graphic (Dhanuka teaches at column 5, lines 12-20 that the variable font data 112 describes a plurality of variable fonts and includes a font file for each of the plurality of variable fonts and these font files define design axes used by the variable fonts, adjustable ranges of the design axes, default or named instances of the variable fonts.
It is known that the variable font is inherently represented as vector graphic glyphs allowing for a glyph in an OpenTyle font to define a desired weight or width---see Paragraph 0005 of Patel US-PGPUB No. 2019/0155872).
Re Claim 7:
The claim 7 encompasses the same scope of invention as that of the claim 5 except additional claim limitation that the generating of the rasterized font representation is performed using differentiable rasterization.
Dhanuka further teaches the claim limitation that the generating of the rasterized font representation is performed using differentiable rasterization (Dhanuk teaches at column 8, lines 8-19 that glyphs rendered using the similar font 118 are displayed in a user interface 120 of the display device 106 and the similarity module 110 is capable of generating instances of a single variable font which are visually similar to multiple different fonts described by the input data 114.
Dhanuka teaches at column 14, lines 49-59 that FIG. 6 is a flow diagram depicting a procedure 600 in an example implementation in which a digital image depicting glyphs rendered using an input font is received and an instance of a variable font is generated that is visually similar to the input font. A digital image depicting glyphs rendered using an input font is received (block 602). The computing device 102 implements the similarity module 110 to receive the digital image in one example. Attribute values of the glyphs depicted in the digital image are determined (block 604). The similarity module 110 determines the attribute values of the glyphs depicted in the digital image).
Re Claim 8:
The claim 8 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that the identifying includes comparing latent encoded features generated by the machine-learning model based on the query with latent encoded features generated by the machine-learning model from the plurality of instances of the at least one variable font.
Dhanuka further teaches the claim limitation that the identifying includes comparing latent encoded features generated by the machine-learning model based on the query with latent encoded features generated by the machine-learning model from the plurality of instances of the at least one variable font (Dhanuka teaches at column 6, lines 38-54 that the similarity module 110 generates or receives a latent representation of the input font 116 and a latent representation of the generated instance of the variable font using one or more convolutional neural networks and extracts a feature vector describing a visual appearance of the input font 116 and a feature vector describing a visual appearance of the generated instance of the variable font. The similarity module 110 determines a similarity score by calculating a Euclidean distance between the feature vectors and a relatively small Euclidean distance corresponds to a relatively high similarity score).
Re Claim 9:
The claim 9 encompasses the same scope of invention as that of the claim 1 except additional claim limitation that locating a subset of the plurality of variable fonts that includes the at least one variable font and wherein the generating of the search result is based on the subset.
Dhanuka further teaches the claim limitation that locating a subset of the plurality of variable fonts that includes the at least one variable font and wherein the generating of the search result is based on the subset (
Dhanuka teaches at column 9, lines 59-67 and column 10, lines 1-45 that the modification module 206 receives the custom instance data 210, the variable font data 112 and the input data 114 as inputs and processes the custom instance data 210, the variable font data 112 and the input data 114 and generates an instance of the variable font as a visually similar font 322. The modification module 206 uses this similarity score to identify additional design axes of the variable font which are usable to maximize the similarity score
Dhanuka teaches at column 5, lines 54-67 and column 6, lines 1-37 forming a plurality of instances of variant font using the weight axis values and values of weights of glyphs of instances of the variable font and the similarity module 110 leverages the relationship between design axis values and the attribute values of the glyphs of the variable font to identify a design axis value that corresponds to an attribute value of the input font 116 and applies the identified design axis value to the variable font and generate an instance of the variable font that is visually similar to the input font 116 and the similarity module 110 determines a similarity score between the generated instance of the variable font and the input font 116.
Dhanuka further teaches at column 6, lines 55-67 that the similarity module 110 also determines a relationship between values of the Width axis and widths of the glyphs of instances of the variable font. The similarity module 110 is implemented leveraging the relationship between the values of the Width axis and corresponding widths of glyphs of instance of the variable font to identify a value of the Width axis corresponding to the particular width of the glyphs of the input font 116.
Dhanuka further narrows down the search results (the instances of the variable font) using the Slant axis and/or Optical axis.
Dhanuka teaches at column 7, lines 15-40 that the similarity module 110 also derives a relationship between values of the Slant axis and slants of glyphs of instances of the variable font and uses the identified value of the Slant axis to generate an instance of the variable font have the particular slant of the glyphs of the input font 116….the glyphs of the generated instance of the variable font have the particular weight, the particular width and the particular slant of the glyphs of the input font 116).
Re Claim 10:
The claim 10 encompasses the same scope of invention as that of the claim 9 except additional claim limitation that the locating is performed using a plurality of font embeddings that are maintained in a cache and generated using machine learning from the plurality of variable fonts, respectively.
Dhanuka further teaches the claim limitation that the locating is performed using a plurality of font embeddings that are maintained in a cache and generated using machine learning from the plurality of variable fonts, respectively (Dhanuka teaches at column 6, lines 38-54 that the similarity module 110 generates or receives a latent representation of the input font 116 and a latent representation of the generated instance of the variable font using one or more convolutional neural networks and extracts a feature vector describing a visual appearance of the input font 116 and a feature vector describing a visual appearance of the generated instance of the variable font. The similarity module 110 determines a similarity score by calculating a Euclidean distance between the feature vectors and a relatively small Euclidean distance corresponds to a relatively high similarity score).
Re Claim 11:
The claim 11 recites a computing device comprising:
a processing device; and
a computer-readable storage medium storing instructions that, responsive to execution by the processing device, causes the processing device to perform operations including
generating a search result specifying at least one variable font by searching, as part of a visual similarity search, a plurality of variable fonts based on a query referencing an input font, the generating including:
forming a plurality of instances for the at least one variable font from a single font file, respectively, by adjusting one or more axes usable to change an appearance of the at least one variable font; and
identifying the at least one variable font as visually similar to the input font by comparing the plurality of instances with the input font using a machine-learning model.
The claim 11 is in parallel with the claim 1 in the form of an apparatus claim. The claim 11 is subject to the same rationale of rejection as the claim 1.
Moreover, Dhanuka further teaches the claim limitation of a computing device comprising:
a processing device; and
a computer-readable storage medium storing instructions that, responsive to execution by the processing device, causes the processing device to perform operations [of the claim 1] (Dhanuka teaches at FIG. 8 and column 16, lines 1-67 that the processors-executable instructions may be electronically executable instruction and the computer-readable media 806 includes memory storage 812 and computer readable storage media includes a storage device implemented in a method or technology suitable for storage of computer readable instructions).
Re Claim 12:
The claim 12 encompasses the same scope of invention as that of the claim 11 except additional claim limitation that the query includes a digital image depicting the input font.
However, Dhanuka further teaches the claim limitation that the query includes a digital image depicting the input font (Dhanuka teaches at column 14, lines 49-59 that FIG. 6 is a flow diagram depicting a procedure 600 in an example implementation in which a digital image depicting glyphs rendered using an input font is received and an instance of a variable font is generated that is visually similar to the input font. A digital image depicting glyphs rendered using an input font is received (block 602). The computing device 102 implements the similarity module 110 to receive the digital image in one example. Attribute values of the glyphs depicted in the digital image are determined (block 604). The similarity module 110 determines the attribute values of the glyphs depicted in the digital image).
Re Claim 13:
The claim 13 encompasses the same scope of invention as that of the claim 11 except additional claim limitation that the forming includes:
producing a variable font representation of a respective said instance; and
generating a rasterized font representation by rasterizing the variable font representation.
The claim 13 is in parallel with the claim 5 in the form of an apparatus claim. The claim 13 is subject to the same rationale of rejection as the claim 5.
Re Claim 14:
The claim 14 encompasses the same scope of invention as that of the claim 13 except additional claim limitation that the variable font representation is configured as a vector graphic and the generating of the rasterized font representation is performed using differentiable rasterization.
The claim 14 is in parallel with the claims 6 and 7 in the form of an apparatus claim. The claim 14 is subject to the same rationale of rejection as the claim 6 and 7.
Re Claim 15:
The claim 15 encompasses the same scope of invention as that of the claim 11 except additional claim limitation that the identifying includes comparing latent encoded features generated by the machine-learning model based on the query with latent encoded features generated by the machine-learning model from the plurality of instances of the at least one variable font.
The claim 15 is in parallel with the claim 8 in the form of an apparatus claim. The claim 15 is subject to the same rationale of rejection as the claim 8.
Re Claim 16:
The claim 16 recites one or more computer-readable storage media storing instructions that, responsive to execution by a processing device, causes the processing device to perform operations including:
receiving a query referencing an input font; and
presenting a search result for display in a user interface, the search result specifying at least one variable font and a corresponding axis value located by searching a plurality of variable fonts based on the query referencing the input font.
The claim 16 is in parallel with the claim 1 in the form of a computer program product. The claim 16 is subject to the same rationale of rejection as the claim 1.
Moreover, Dhanuka further teaches the claim limitation of one or more computer-readable storage media storing instructions that, responsive to execution by a processing device, causes the processing device to perform operations [of the claim 1] (Dhanuka teaches at FIG. 8 and column 16, lines 1-67 that the processors-executable instructions may be electronically executable instruction and the computer-readable media 806 includes memory storage 812 and computer readable storage media includes a storage device implemented in a method or technology suitable for storage of computer readable instructions).
Moreover, Moreno teaches one or more computer-readable storage media storing instructions that, responsive to execution by a processing device, causes the processing device to perform operations including:
receiving a query referencing an input font (Moreno teaches at FIG. 1 and at Paragraph 0032 a request 120 referencing an input font name and at Paragraph 0055 that the client request 120 may include a string specification. In one implementation, the string specification can contain a display text, a font identifier, a font size, a display resolution, and an available display screen area at a vehicle display. After receiving operation 504, control passes to authenticating operation 506.); and
presenting a search result for display in a user interface, the search result specifying at least one variable font and a corresponding axis value located by searching a plurality of variable fonts based on the query referencing the input font (
Moreno teaches at FIG. 1 and Paragraph 0039-0040 that the database 118 stores target font specified in the client request 120 containing a listing of all the font names, font locations and font versions of the target font and when a font version in the database response 126 matches a font version specified in the client request 120, the server instructions may cause the server 104 to select a font location associated with the font version in the database response 126.
Moreno teaches at Paragraph [0053] Turning back to FIG. 2, once the server response is received by the client, the client application 202 extracts the string size from the server response, matching the string size value to the correct translation variant text. The string size is then entered into the cell corresponding to the translation variant text. For example, the English variant text size in cell 230 is populated with a value of 15 pixels for corresponding English translation variant text 218. This value was extracted from the server response and entered into cell 230 by the client application 202. Similarly, cell 232 is populated with a value of 14 for corresponding Spanish translation variant text 220 and cell 234 is populated with a value of 21 for corresponding French translation variant text 222).
It would have been obvious to one of the ordinary skill in the art before the filing date of the instant application to have incorporated Moreno’s various features including retrieving a font version (instance) of a variable font from the database based on the client request specifying an input font into Dhanuka’s font retrieval operations for presenting a variable font based on the user input font. One of the ordinary skill in the art would have been motivated to have matched a variable font in the font library to the input font.
Re Claim 17:
The claim 17 encompasses the same scope of invention as that of the claim 16 except additional claim limitation that the search result is generated by: forming a plurality of instances for the at least one variable font, respectively, by adjusting a plurality of axes usable to change an appearance of the at least one variable font; and
identifying the at least one variable font by comparing latent encoded features the plurality of instances with latent coded features of the input font using a machine-learning model.
The claim 17 is in parallel with the claim 8 in the form of computer program product. The claim 17 is subject to the same rationale of rejection as the claim 8.
Re Claim 18:
The claim 18 encompasses the same scope of invention as that of the claim 17 except additional claim limitation that the forming includes: producing a variable font representation of a respective said instance as a vector graphic; and generating a rasterized font representation by rasterizing the variable font representation.
The claim 18 is in parallel with the claim 5 in the form of computer program product. The claim 18 is subject to the same rationale of rejection as the claim 5.
Re Claim 19:
The claim 19 encompasses the same scope of invention as that of the claim 18 except additional claim limitation that the variable font representation is configured as a vector graphic
and the generating of the rasterized font representation is performed using differentiable rasterization.
The claim 19 is in parallel with the claims 6 and 7 in the form of computer program product. The claim 19 is subject to the same rationale of rejection as the claims 6-7.
Re Claim 20:
The claim 20 encompasses the same scope of invention as that of the claim 16 except additional claim limitation that the query includes a digital image depicting the input font and the plurality of axes includes weight, width, slant, and optical size.
The claim 20 is in parallel with the claim 4 in the form of computer program product. The claim 19 is subject to the same rationale of rejection as the claim 4.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIN CHENG WANG whose telephone number is (571)272-7665. The examiner can normally be reached Mon-Fri 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, King Poon can be reached at 571-270-0728. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JIN CHENG WANG/Primary Examiner, Art Unit 2617