DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner Note
2. Joseph King is no longer continuing prosecution on application number 18/767,773. It has been transferred to Examiner Jessica Lemieux.
DETAILED ACTION
3. This Non-Final Office action is in response to the application filed on July 24th, 2024 and in response to Applicant’s Arguments/Remarks filed on January 23rd, 2026. Claims 1-9 are pending.
Priority
4. Application 18/767,773 was filed on July 9th, 2024 which is a continuation of 17/975,072 filed on October 27th, 2022 which is a continuation of PCT/JP2021/017839 filed on May 11th, 2021 which has foreign priority to JP2020-088630 filed on May 21st, 2020.
Examiner Request
5. The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Terminal Disclaimer
6. The terminal disclaimer filed on January 23rd, 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of 12,067,573 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Arguments
7. Applicant’s arguments, with respect to the double patenting rejection of claims 1-9 have been fully considered and are persuasive in view of the filing of the terminal disclaimer. The double patenting rejection of claims 1-9 has been withdrawn.
8. Applicant states that the prior art doesn’t disclose “the one or more values [input to a machine learning model] are based on the stroke data included in the artwork created by the artist.” Examiner notes that these arguments are made with respect to the amended claims. Examiner disagrees with the applicant’s conclusion that the pending claims as amended are in condition for allowance, as the amended claims have been considered but applicant’s arguments are moot in view of the new ground of rejection below.
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.
9. Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-9 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claims 1, 4, and 7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 8 and 15 the limitations of (Claim 1 being representative) recites:
input one or more values included in stroke data associated with an artwork of an artist to a […] model, wherein the […] model is generated based on […] the one or more values which indicate features of artwork of the artist; wherein the one or more values are based on the stroke data included in the artwork created by the artist and are related to at least one of a brush stroke speed, a pen pressure value, a pen angle data, or time allocation of a pen touch state and a pen hover state; and output an artist feature value associated with the artwork from the […] model.
These limitations as drafted are processes that, under the broadest reasonable interpretation, constitute encompasses collecting stroke characteristic information, evaluating that information using criteria/model, and producing a classification in the form of an artist feature value, which are an observation, evaluation, and judgement process that falls within the mental process grouping. The recited computer and machine learning model that is generated based on training an artificial intelligence (AI) program) (claims 1, 4, and 7) merely perform the claimed evaluation using generic computer technology. Accordingly, Claims 1, 4, and 7 recite an abstract idea.
This judicial exception is not integrated into a practical application. The additional elements that are recited are reciting a computer and a machine learning model that is generated based on training an artificial intelligence (AI) program) (claims 1, 4, and 7). These additional elements are not described by the applicant and are recited at a high-level of generality (i.e., generic computer components performing generic computer functions) such that they amounts no more than mere instructions to apply the exception using a generic computer components. The claims do not recite any improvement to machine learning technology itself, such as a new training technique, or improved computer functionality. Rather, the machine learning is invoked as a tool to analyze artist stroke information and output a result. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1, 4, and 7 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
Claims 1, 4, and 7 do not include additional elements that are sufficient to amount to significantly more (also known as an “inventive concept”) than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements recited above to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer components performing their ordinary functions of receiving data, executing instructions, processing information, and outputting results. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even when considered separately and as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Thus claims 1, 4, and 7 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent claims 2, 3, 5, 6, 8, and 9 are similarly rejected because they merely further narrow the same abstract idea of independent claims 1, 4, and 7 and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claims 2, 5, and 8 merely recite the additional use of pen touch and pen up coordinate information as additional input data, which further limits the data used to perform the abstract idea.
Claims 3, 6, and 9 merely recite embedding a watermark indicative of the artwork feature value into the artwork. These limitations merely specify an additional use of the result generated by the abstract analysis. The claim does not recite any improvement to watermarking technology or any particular technique for embedding the watermark.
Therefore claims 2, 3, 5, 6, 8, and 9 are patent ineligible. The dependent claims 2, 3, 5, 6, 8, and 9 are merely add further limitations directed to the information analyzed or the use of the resulting artist feature value and do not amount to anything that is significantly more than the abstract idea itself. In other words, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limitations that, in an ordered combination provide “significantly more” or providing any integration into a practical application. Rather, the dependent claims are merely further reciting features that are just as abstract as independent Claims 1, 4, and 7. Therefore, Claims 1-9 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §102 and §103 (or as subject to pre-AIA 35 U.S.C. §102 and §103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. §103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
10. Claims 1, 4 and 7 are rejected under 35 U.S.C. §103 as being unpatentable over Elgammal (US Patent Publication 2019/0385003A1, hereinafter Elgammal) in view of Humbert et al. (US Patent Publication 2021/0345913 A1, hereinafter Humbert).
Regarding claim 4, Elgammal discloses:
A method performed by a computer, the method comprising: inputting one or more values included in stroke data associated with an artwork of an artist to a machine learning model, wherein the machine learning model is generated based on training an artificial intelligence (AI) program with the one or more values which indicate features of artwork of the artist; wherein the one or more values are based on the stroke data included in the artwork created by the artist […]; (paragraph [0012]: a computer-implemented method of assessing a work of art that includes a plurality of artist's strokes. A plurality of digital images of works of art, for which the identity of the artist is known, are provided to a computer; each of such known works of art includes a plurality of artist's strokes. A computer is used to identify individual strokes within such known works of art, as well as to determine stroke characteristics for each identified individual stroke. Using the computer, one or more stroke signatures are established from such stroke characteristics associated with the artist of each such known work of art. These stroke signatures are stored in a memory associated with a computer, for example, within a computational model.); and
outputting an artist feature value associated with the artwork from the machine learning model (Figure 1, paragraphs [0019]: The determined corresponding stroke characteristics are compared to stroke characteristics derived from at least a first computational model that is based on authentic works of art by a first known artist. The computer-implemented method determines the statistical likelihood that each stroke being analyzed was created by such first known artist, and aggregate that set of determined statistical likelihoods to determine a statistical likelihood that the work of art being analyzed was created by such first known artist. A high statistical likelihood that the work of art being analyzed was created by such first known artist indicates that the work of art being analyzed is likely to be an authentic work of art by such first known artist and [0091]: Segmented image 704 is provided to block 706 to quantify the stroke shape and other characteristics. Segmented image 704 is also provided to block 708 to quantify the tone and local shape variations, partly with the help of recurrent neural network 710. The output results generated by blocks 706 and 708 are provided to stroke classifier block 712, and such results are combined to classify each segmented stroke. The results of block 712 are then provided to drawing classifier block 714 to create an overall classification of the drawing).
Thus, Elgammal teaches inputting values based on stroke data associated with an artwork of an artist to a machine learning model based on known works of art, and outputting an artist-related statistical likelihood classification associated with the artwork. However, Elgammal does not expressly disclose that the values used to train and/or be input to the machine learning model are related to at least one of a brush stroke speed, pen pressure value, pen angle data, or time allocation of a pen touch state and pen hover state as recited.
Humbert which teaches acquiring stroke-related data while a user creates handwritten or drawn material using a handwriting instrument and analyzing the acquired data using a trained artificial intelligence/neural network, discloses that the values used to train and/or be input to the machine learning model are related to at least one of a brush stroke speed, pen pressure value, pen angle data, or time allocation of a pen touch state and pen hover state (Figs 3 and 8 (including associated text) and (paragraphs [0021,-0023, 0042, 0055-0063 and 0090].)
Therefore it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have included, among the stroke characteristics utilized by Elgammal’s machine-learning artist-classification system, one or more dynamic stroke characteristics such as stroke velocity, pressure, and/or stroke and in-air duration as taught by Humbert. Elgammal expressly seeks characteristics of individual strokes that distinguish the manner in which different artists create strokes, while Humbert teaches that measurable characteristics generated during creation of a stroke, including velocity, pressure, and stroke/in-air duration, may be extracted and supplied to a trained neural-network classifier. One of ordinary skill in the art would therefore have recognized these known stroke characteristics as additional information suitable for characterizing the manner in which an individual produces strokes and thereby providing additional discriminating information to Elgammal’s artist-classification model.
Since claims 1 and 7 are substantially directed to the features and subject matter of claim 4, these claims are rejected for the grounds and rationale used to reject claim 4.
11. Claims 2, 5 and 8 are rejected under 35 U.S.C. §103 as being unpatentable over Elgammal in view of Humbert, in further view of Munich et al. (US Patent Publication 2002/0028017 A1, hereinafter Munich).
Regarding claims 2, 5 and 8, Elgammal in view of Humbert substantially teaches the subject matter of the respective independent claims as discussed above, and Elgammal further teaches the artwork is associated with stroke information derived from pen created drawings (paragraph [0045]: In the domain of drawing analysis it is very hard to obtain a dataset that uniformly samples artists and techniques. The collection used by Applicant is biased towards ink drawings, executed mostly with pen, or using brush in a few cases)
inputting [values] associated with an artwork of an artist to the machine learning model, wherein the machine learning model is generated based on training the AI program with values which indicate features of artwork of the artist; (paragraph [0012].); and
outputting an artist feature value associated with the artwork from the machine learning model (Figure 1, paragraphs [0019] and [0091]).
Elgammal in view of Humbert does not expressly disclose that the artwork is associated with a series of pen touch coordinates indicating positions of a pen touch and a series of pen up coordinates indicating positions of a pen up, and includes inputting the series of pen touch coordinates and the series of pen up coordinates associated with the artwork to the machine leaning model.
Munich discloses identifying a person using a tracked two-dimensional trajectory of a writing instrument, and more specifically a series of pen touch coordinates indicating positions of a pen touch and a series of pen up coordinates indicating positions of a pen up (paragraph [0028] where x(t), v(t) and a(t) are the two dimensional-components of the position, velocity and acceleration of the tracked point, and n.sub.a(t) is the additive zero-mean, Gaussian, white noise. The state of the filter X(t) includes three 2-dimensional variables, x(t), v(t) and a(t). The output of the model y(t) is the estimated position of the pen tip. and [0029] The trajectory obtained by the pen tip tracker and the filter, however, includes the total trajectory of the pen. This includes down portions (where the pen is touching the paper) and up portions (where the pen is not touching the paper) and [0034-0036]: The confidence measure is preferably used to decide whether a point likely corresponds to pen up or pen down… A further improvement is provided by modeling the probability of transition between these two states (pen up or pen down), given the current measurement and the previous state. 402 represents a Hidden Markov Model (HMM) with two states, one corresponding to pen up and the other corresponding to pen down. This is fed by the measurement at 399. The HMM learns the probabilities of moving from one state to the other and the probabilities of outputting a particular value of confidence from a set of examples….The HMM estimates the most likely state of the system, e.g., pen up or pen down, at any given point in the trajectory. The sate of a HMM is a hidden variable that can be estimated based on a model of the system and random clues or measurements that one obtains).
Therefore it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have included the pen-touch and pen-up positional coordinate information taught by Munich among the stroke information supplied to the machine-learning system of Elgammal as modified by Humbert. Elgammal analyzes how an individual creates strokes for purposes of artist attribution, and Humbert teaches incorporating dynamic stroke-creation information into a trained classifier. Munich further teaches that both pen-up and pen-down trajectory information may be used for identifying a person and expressly notes that pen-up strokes of an individual were as consistent as the pen-down strokes. One of ordinary skill therefore would have recognized that retaining the positional trajectory during both the pen-touch and pen-up portions would provide additional information regarding how the creator moves the writing instrument between and during strokes and thus provide additional stroke-behavior information for use by the classifier.
12. Claims 3, 6 and 9 are rejected under 35 U.S.C. §103 as being unpatentable over Elgammal in view of Humbert and Munich, and in further view of Tadano (US Patent Publication 2013/0024698 A1, hereinafter Tadano).
Regarding claims 3, 6 and 9, Elgammal in view of Humbert and Munich does not expressly disclose embedding a watermark indicative of the artwork feature value into the artwork.
However, Tadano discloses a digital content management system including a digital watermark embedding device that generates embedding information based on identification information, which may include identification information of a user or owner, and embeds a digital watermark into digital content based on the generated embedding information (Figs 1 and 6, abstract and paragraphs [0055-0058, 0123-0128]).
Therefore it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have embedded a digital watermark indicative of the artwork feature value generated by the machine-learning system of Elgammal as modified by Humbert and Munich into the corresponding digital artwork, as taught by Tadano. Elgammal generates artwork attribution information associated with a particular artwork, while Tadano teaches embedding identification information into corresponding digital content. One of ordinary skill therefore would have recognized that embedding information indicative of the generated artwork feature value into the artwork would maintain the artist-identification or attribution information in association with the artwork and facilitate subsequent identification or verification of the artwork.
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
C. R. Johnson et al., "Image processing for artist identification," in IEEE Signal Processing Magazine, vol. 25, no. 4, pp. 37-48, July 2008
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA LEMIEUX whose telephone number is (571)270-3445. The examiner can normally be reached Monday-Friday 7AM-3PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TARIQ HAFIZ can be reached at (571) 272-5350. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA LEMIEUX/ Supervisory Patent Examiner, Art Unit 3626
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